WorldWideScience

Sample records for hurricane forecast improvement

  1. Hurricane feedback research may improve intensity forecasts

    Science.gov (United States)

    Schultz, Colin

    2012-06-01

    Forecasts of a hurricane's intensity are generally much less accurate than forecasts of its most likely path. Large-scale atmospheric patterns dictate where a hurricane will go and how quickly it will get there. The storm's intensity, however, depends on small-scale shifts in atmospheric stratification, upwelling rates, and other transient dynamics that are difficult to predict. Properly understanding the risk posed by an impending storm depends on having a firm grasp of all three properties: translational speed, intensity, and path. Drawing on 40 years of hurricane records representing 3090 different storms, Mei et al. propose that a hurricane's translational speed and intensity may be closely linked.

  2. Extreme Wind, Rain, Storm Surge, and Flooding: Why Hurricane Impacts are Difficult to Forecast?

    Science.gov (United States)

    Chen, S. S.

    2017-12-01

    The 2017 hurricane season is estimated as one of the costliest in the U.S. history. The damage and devastation caused by Hurricane Harvey in Houston, Irma in Florida, and Maria in Puerto Rico are distinctly different in nature. The complexity of hurricane impacts from extreme wind, rain, storm surge, and flooding presents a major challenge in hurricane forecasting. A detailed comparison of the storm impacts from Harvey, Irma, and Maria will be presented using observations and state-of-the-art new generation coupled atmosphere-wave-ocean hurricane forecast model. The author will also provide an overview on what we can expect in terms of advancement in science and technology that can help improve hurricane impact forecast in the near future.

  3. Data Assimilation within the Advanced Circulation (ADCIRC) Modeling Framework for Hurricane Storm Surge Forecasting

    KAUST Repository

    Butler, T.

    2012-07-01

    Accurate, real-time forecasting of coastal inundation due to hurricanes and tropical storms is a challenging computational problem requiring high-fidelity forward models of currents and water levels driven by hurricane-force winds. Despite best efforts in computational modeling there will always be uncertainty in storm surge forecasts. In recent years, there has been significant instrumentation located along the coastal United States for the purpose of collecting data—specifically wind, water levels, and wave heights—during these extreme events. This type of data, if available in real time, could be used in a data assimilation framework to improve hurricane storm surge forecasts. In this paper a data assimilation methodology for storm surge forecasting based on the use of ensemble Kalman filters and the advanced circulation (ADCIRC) storm surge model is described. The singular evolutive interpolated Kalman (SEIK) filter has been shown to be effective at producing accurate results for ocean models using small ensemble sizes initialized by an empirical orthogonal function analysis. The SEIK filter is applied to the ADCIRC model to improve storm surge forecasting, particularly in capturing maximum water levels (high water marks) and the timing of the surge. Two test cases of data obtained from hindcast studies of Hurricanes Ike and Katrina are presented. It is shown that a modified SEIK filter with an inflation factor improves the accuracy of coarse-resolution forecasts of storm surge resulting from hurricanes. Furthermore, the SEIK filter requires only modest computational resources to obtain more accurate forecasts of storm surge in a constrained time window where forecasters must interact with emergency responders.

  4. The impact of underwater glider observations in the forecast of Hurricane Gonzalo (2014)

    Science.gov (United States)

    Goni, G. J.; Domingues, R. M.; Kim, H. S.; Domingues, R. M.; Halliwell, G. R., Jr.; Bringas, F.; Morell, J. M.; Pomales, L.; Baltes, R.

    2017-12-01

    The tropical Atlantic basin is one of seven global regions where tropical cyclones (TC) are commonly observed to originate and intensify from June to November. On average, approximately 12 TCs travel through the region every year, frequently affecting coastal, and highly populated areas. In an average year, 2 to 3 of them are categorized as intense hurricanes. Given the appropriate atmospheric conditions, TC intensification has been linked to ocean conditions, such as increased ocean heat content and enhanced salinity stratification near the surface. While errors in hurricane track forecasts have been reduced during the last years, errors in intensity forecasts remain mostly unchanged. Several studies have indicated that the use of in situ observations has the potential to improve the representation of the ocean to correctly initialize coupled hurricane intensity forecast models. However, a sustained in situ ocean observing system in the tropical North Atlantic Ocean and Caribbean Sea dedicated to measuring subsurface thermal and salinity fields in support of TC intensity studies and forecasts has yet to be implemented. Autonomous technologies offer new and cost-effective opportunities to accomplish this objective. We highlight here a partnership effort that utilize underwater gliders to better understand air-sea processes during high wind events, and are particularly geared towards improving hurricane intensity forecasts. Results are presented for Hurricane Gonzalo (2014), where glider observations obtained in the tropical Atlantic: Helped to provide an accurate description of the upper ocean conditions, that included the presence of a low salinity barrier layer; Allowed a detailed analysis of the upper ocean response to hurricane force winds of Gonzalo; Improved the initialization of the ocean in a coupled ocean-atmosphere numerical model; and together with observations from other ocean observing platforms, substantially reduced the error in intensity forecast

  5. Forecasting Hurricane Tracks Using a Complex Adaptive System

    National Research Council Canada - National Science Library

    Lear, Matthew R

    2005-01-01

    Forecast hurricane tracks using a multi-model ensemble that consists of linearly combining the individual model forecasts have greatly reduced the average forecast errors when compared to individual...

  6. A Complex Adaptive System Approach to Forecasting Hurricane Tracks

    National Research Council Canada - National Science Library

    Lear, Matthew R

    2005-01-01

    Forecast hurricane tracks using a multi-model ensemble that consists of linearly combining the individual model forecasts have greatly reduced the average forecast errors when compared to individual...

  7. Improved Satellite Techniques for Monitoring and Forecasting the Transition of Hurricanes to Extratropical Storms

    Science.gov (United States)

    Folmer, Michael; Halverson, Jeffrey; Berndt, Emily; Dunion, Jason; Goodman, Steve; Goldberg, Mitch

    2014-01-01

    The Geostationary Operational Environmental Satellites R-Series (GOES-R) and Joint Polar Satellite System (JPSS) Satellite Proving Grounds have introduced multiple proxy and operational products into operations over the last few years. Some of these products have proven to be useful in current operations at various National Weather Service (NWS) offices and national centers as a first look at future satellite capabilities. Forecasters at the National Hurricane Center (NHC), Ocean Prediction Center (OPC), NESDIS Satellite Analysis Branch (SAB) and the NASA Hurricane and Severe Storms Sentinel (HS3) field campaign have had access to a few of these products to assist in monitoring extratropical transitions of hurricanes. The red, green, blue (RGB) Air Mass product provides forecasters with an enhanced view of various air masses in one complete image to help differentiate between possible stratospheric/tropospheric interactions, moist tropical air masses, and cool, continental/maritime air masses. As a compliment to this product, a new Atmospheric Infrared Sounder (AIRS) and Cross-track Infrared Sounder (CrIS) Ozone product was introduced in the past year to assist in diagnosing the dry air intrusions seen in the RGB Air Mass product. Finally, a lightning density product was introduced to forecasters as a precursor to the new Geostationary Lightning Mapper (GLM) that will be housed on GOES-R, to monitor the most active regions of convection, which might indicate a disruption in the tropical environment and even signal the onset of extratropical transition. This presentation will focus on a few case studies that exhibit extratropical transition and point out the usefulness of these new satellite techniques in aiding forecasters forecast these challenging events.

  8. Diagnostics comparing sea surface temperature feedbacks from operational hurricane forecasts to observations

    Directory of Open Access Journals (Sweden)

    Ian D. Lloyd

    2011-11-01

    Full Text Available This paper examines the ability of recent versions of the Geophysical Fluid Dynamics Laboratory Operational Hurricane Forecast Model (GHM to reproduce the observed relationship between hurricane intensity and hurricane-induced Sea Surface Temperature (SST cooling. The analysis was performed by taking a Lagrangian composite of all hurricanes in the North Atlantic from 1998–2009 in observations and 2005–2009 for the GHM. A marked improvement in the intensity-SST relationship for the GHM compared to observations was found between the years 2005 and 2006–2009 due to the introduction of warm-core eddies, a representation of the loop current, and changes to the drag coefficient parameterization for bulk turbulent flux computation. A Conceptual Hurricane Intensity Model illustrates the essential steady-state characteristics of the intensity-SST relationship and is explained by two coupled equations for the atmosphere and ocean. The conceptual model qualitatively matches observations and the 2006–2009 period in the GHM, and presents supporting evidence for the conclusion that weaker upper oceanic thermal stratification in the Gulf of Mexico, caused by the introduction of the loop current and warm core eddies, is crucial to explaining the observed SST-intensity pattern. The diagnostics proposed by the conceptual model offer an independent set of metrics for comparing operational hurricane forecast models to observations.

  9. Data Assimilation within the Advanced Circulation (ADCIRC) Modeling Framework for Hurricane Storm Surge Forecasting

    KAUST Repository

    Butler, T.; Altaf, Muhammad; Dawson, C.; Hoteit, Ibrahim; Luo, X.; Mayo, T.

    2012-01-01

    levels, and wave heights—during these extreme events. This type of data, if available in real time, could be used in a data assimilation framework to improve hurricane storm surge forecasts. In this paper a data assimilation methodology for storm surge

  10. Medium range forecasting of Hurricane Harvey flash flooding using ECMWF and social vulnerability data

    Science.gov (United States)

    Pillosu, F. M.; Jurlina, T.; Baugh, C.; Tsonevsky, I.; Hewson, T.; Prates, F.; Pappenberger, F.; Prudhomme, C.

    2017-12-01

    During hurricane Harvey the greater east Texas area was affected by extensive flash flooding. Their localised nature meant they were too small for conventional large scale flood forecasting systems to capture. We are testing the use of two real time forecast products from the European Centre for Medium-range Weather Forecasts (ECMWF) in combination with local vulnerability information to provide flash flood forecasting tools at the medium range (up to 7 days ahead). Meteorological forecasts are the total precipitation extreme forecast index (EFI), a measure of how the ensemble forecast probability distribution differs from the model-climate distribution for the chosen location, time of year and forecast lead time; and the shift of tails (SOT) which complements the EFI by quantifying how extreme an event could potentially be. Both products give the likelihood of flash flood generating precipitation. For hurricane Harvey, 3-day EFI and SOT products for the period 26th - 29th August 2017 were used, generated from the twice daily, 18 km, 51 ensemble member ECMWF Integrated Forecast System. After regridding to 1 km resolution the forecasts were combined with vulnerable area data to produce a flash flood hazard risk area. The vulnerability data were floodplains (EU Joint Research Centre), road networks (Texas Department of Transport) and urban areas (Census Bureau geographic database), together reflecting the susceptibility to flash floods from the landscape. The flash flood hazard risk area forecasts were verified using a traditional approach against observed National Weather Service flash flood reports, a total of 153 reported flash floods have been detected in that period. Forecasts performed best for SOT = 5 (hit ratio = 65%, false alarm ratio = 44%) and EFI = 0.7 (hit ratio = 74%, false alarm ratio = 45%) at 72 h lead time. By including the vulnerable areas data, our verification results improved by 5-15%, demonstrating the value of vulnerability information within

  11. The Impact of Cross-track Infrared Sounder (CrIS) Cloud-Cleared Radiances on Hurricane Joaquin (2015) and Matthew (2016) Forecasts

    Science.gov (United States)

    Wang, Pei; Li, Jun; Li, Zhenglong; Lim, Agnes H. N.; Li, Jinlong; Schmit, Timothy J.; Goldberg, Mitchell D.

    2017-12-01

    Hyperspectral infrared (IR) sounders provide high vertical resolution atmospheric sounding information that can improve the forecast skill in numerical weather prediction. Commonly, only clear radiances are assimilated, because IR sounder observations are highly affected by clouds. A cloud-clearing (CC) technique, which removes the cloud effects from an IR cloudy field of view (FOV) and derives the cloud-cleared radiances (CCRs) or clear-sky equivalent radiances, can be an alternative yet effective way to take advantage of the thermodynamic information from cloudy skies in data assimilation. This study develops a Visible Infrared Imaging Radiometer Suite (VIIRS)-based CC method for deriving Cross-track Infrared Sounder (CrIS) CCRs under partially cloudy conditions. Due to the lack of absorption bands on VIIRS, two important quality control steps are implemented in the CC process. Validation using VIIRS clear radiances indicates that the CC method can effectively obtain the CrIS CCRs for FOVs with partial cloud cover. To compare the impacts from assimilation of CrIS original radiances and CCRs, three experiments are carried out on two storm cases, Hurricane Joaquin (2015) and Hurricane Matthew (2016), using Gridpoint Statistical Interpolation assimilation system and Weather Research and Forecasting-Advanced Research Version models. At the analysis time, more CrIS observations are assimilated when using CrIS CCRs than with CrIS original radiances. Comparing temperature, specific humidity, and U/V winds with radiosondes indicates that the data impacts are growing larger with longer time forecasts (beyond 72 h forecast). Hurricane track forecasts also show improvements from the assimilation of CrIS CCRs due to better weather system forecasts. The impacts of CCRs on intensity are basically neutral with mixed positive and negative results.

  12. A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes

    Science.gov (United States)

    Krishnamurti, T. N.; Kumar, V.; Simon, A.; Bhardwaj, A.; Ghosh, T.; Ross, R.

    2016-06-01

    This review provides a summary of work in the area of ensemble forecasts for weather, climate, oceans, and hurricanes. This includes a combination of multiple forecast model results that does not dwell on the ensemble mean but uses a unique collective bias reduction procedure. A theoretical framework for this procedure is provided, utilizing a suite of models that is constructed from the well-known Lorenz low-order nonlinear system. A tutorial that includes a walk-through table and illustrates the inner workings of the multimodel superensemble's principle is provided. Systematic errors in a single deterministic model arise from a host of features that range from the model's initial state (data assimilation), resolution, representation of physics, dynamics, and ocean processes, local aspects of orography, water bodies, and details of the land surface. Models, in their diversity of representation of such features, end up leaving unique signatures of systematic errors. The multimodel superensemble utilizes as many as 10 million weights to take into account the bias errors arising from these diverse features of multimodels. The design of a single deterministic forecast models that utilizes multiple features from the use of the large volume of weights is provided here. This has led to a better understanding of the error growths and the collective bias reductions for several of the physical parameterizations within diverse models, such as cumulus convection, planetary boundary layer physics, and radiative transfer. A number of examples for weather, seasonal climate, hurricanes and sub surface oceanic forecast skills of member models, the ensemble mean, and the superensemble are provided.

  13. The Importance of Hurricane Research to Life, Property, the Economy, and National Security.

    Science.gov (United States)

    Busalacchi, A. J.

    2017-12-01

    The devastating 2017 Atlantic hurricane season has brought into stark relief how much hurricane forecasts have improved - and how important it is to make them even better. Whereas the error in 48-hour track forecasts has been reduced by more than half, according to the National Hurricane Center, intensity forecasts remain challenging, especially with storms such as Harvey that strengthened from a tropical depression to a Category 4 hurricane in less than three days. The unusually active season, with Hurricane Irma sustaining 185-mph winds for a record 36 hours and two Atlantic hurricanes reaching 150-mph winds simultaneously for the first time, also highlighted what we do, and do not, know about how tropical cyclones will change as the climate warms. The extraordinary toll of Hurricanes Harvey, Irma, and Maria - which may ultimately be responsible for hundreds of deaths and an estimated $200 billion or more in damages - underscores why investments into improved forecasting must be a national priority. At NCAR and UCAR, scientists are working with their colleagues at federal agencies, the private sector, and the university community to advance our understanding of these deadly storms. Among their many projects, NCAR researchers are making experimental tropical cyclone forecasts using an innovative Earth system model that allows for variable resolution. We are working with NOAA to issue flooding, inundation, and streamflow forecasts for areas hit by hurricanes, and we have used extremely high-resolution regional models to simulate successfully the rapid hurricane intensification that has proved so difficult to predict. We are assessing ways to better predict the damage potential of tropical cyclones by looking beyond wind speed to consider such important factors as the size and forward motion of the storm. On the important question of climate change, scientists have experimented with running coupled climate models at a high enough resolution to spin up a hurricane

  14. NOAA HRD's HEDAS Data Assimilation System's performance for the 2010 Atlantic Hurricane Season

    Science.gov (United States)

    Sellwood, K.; Aksoy, A.; Vukicevic, T.; Lorsolo, S.

    2010-12-01

    The Hurricane Ensemble Data Assimilation System (HEDAS) was developed at the Hurricane Research Division (HRD) of NOAA, in conjunction with an experimental version of the Hurricane Weather and Research Forecast model (HWRFx), in an effort to improve the initial representation of the hurricane vortex by utilizing high resolution in-situ data collected during NOAA’s Hurricane Field Program. HEDAS implements the “ensemble square root “ filter of Whitaker and Hamill (2002) using a 30 member ensemble obtained from NOAA/ESRL’s ensemble Kalman filter (EnKF) system and the assimilation is performed on a 3-km nest centered on the hurricane vortex. As part of NOAA’s Hurricane Forecast Improvement Program (HFIP), HEDAS will be run in a semi-operational mode for the first time during the 2010 Atlantic hurricane season and will assimilate airborne Doppler radar winds, dropwindsonde and flight level wind, temperature, pressure and relative humidity, and Stepped Frequency Microwave Radiometer surface wind observations as they become available. HEDAS has been implemented in an experimental mode for the cases of Hurricane Bill, 2009 and Paloma, 2008 to confirm functionality and determine the optimal configuration of the system. This test case demonstrates the importance of assimilating thermodynamic data in addition to wind observations and the benefit of increasing the quantity and distribution of observations. Applying HEDAS to a larger sample of storm forecasts would provide further insight into the behavior of the model when inner core aircraft observations are assimilated. The main focus of this talk will be to present a summary of HEDAS performance in the HWRFx model for the inaugural season. The HEDAS analyses and the resulting HWRFx forecasts will be compared with HWRFx analyses and forecasts produced concurrently using the HRD modeling group’s vortex initialization which does not employ data assimilation. The initial vortex and subsequent forecasts will be

  15. Assessing the Effectiveness of the Cone of Probability as a Visual Means of Communicating Scientific Forecasts

    Science.gov (United States)

    Orlove, B. S.; Broad, K.; Meyer, R.

    2010-12-01

    We review the evolution, communication, and differing interpretations of the National Hurricane Center (NHC)'s "cone of uncertainty" hurricane forecast graphic, drawing on several related disciplines—cognitive psychology, visual anthropology, and risk communication theory. We examine the 2004 hurricane season, two specific hurricanes (Katrina 2005 and Ike 2008) and the 2010 hurricane season, still in progress. During the 2004 hurricane season, five named storms struck Florida. Our analysis of that season draws upon interviews with key government officials and media figures, archival research of Florida newspapers, analysis of public comments on the NHC cone of uncertainty graphic and a multiagency study of 2004 hurricane behavior. At that time, the hurricane forecast graphic was subject to misinterpretation by many members of the public. We identify several characteristics of this graphic that contributed to public misinterpretation. Residents overemphasized the specific track of the eye, failed to grasp the width of hurricanes, and generally did not recognize the timing of the passage of the hurricane. Little training was provided to emergency response managers in the interpretation of forecasts. In the following year, Katrina became a national scandal, further demonstrating the limitations of the cone as a means of leading to appropriate responses to forecasts. In the second half of the first decade of the 21st century, three major changes occurred in hurricane forecast communication: the forecasts themselves improved in terms of accuracy and lead time, the NHC made minor changes in the graphics and expanded the explanatory material that accompanies the graphics, and some efforts were made to reach out to emergency response planners and municipal officials to enhance their understanding of the forecasts and graphics. There were some improvements in the responses to Ike, though a number of deaths were due to inadequate evacuations, and property damage probably

  16. Targeted observations to improve tropical cyclone track forecasts in the Atlantic and eastern Pacific basins

    Science.gov (United States)

    Aberson, Sim David

    In 1997, the National Hurricane Center and the Hurricane Research Division began conducting operational synoptic surveillance missions with the Gulfstream IV-SP jet aircraft to improve operational forecast models. During the first two years, twenty-four missions were conducted around tropical cyclones threatening the continental United States, Puerto Rico, and the Virgin Islands. Global Positioning System dropwindsondes were released from the aircraft at 150--200 km intervals along the flight track in the tropical cyclone environment to obtain wind, temperature, and humidity profiles from flight level (around 150 hPa) to the surface. The observations were processed and formatted aboard the aircraft and transmitted to the National Centers for Environmental Prediction (NCEP). There, they were ingested into the Global Data Assimilation System that subsequently provides initial and time-dependent boundary conditions for numerical models that forecast tropical cyclone track and intensity. Three dynamical models were employed in testing the targeting and sampling strategies. With the assimilation into the numerical guidance of all the observations gathered during the surveillance missions, only the 12-h Geophysical Fluid Dynamics Laboratory Hurricane Model forecast showed statistically significant improvement. Neither the forecasts from the Aviation run of the Global Spectral Model nor the shallow-water VICBAR model were improved with the assimilation of the dropwindsonde data. This mediocre result is found to be due mainly to the difficulty in operationally quantifying the storm-motion vector used to create accurate synthetic data to represent the tropical cyclone vortex in the models. A secondary limit on forecast improvements from the surveillance missions is the limited amount of data provided by the one surveillance aircraft in regular missions. The inability of some surveillance missions to surround the tropical cyclone with dropwindsonde observations is a possible

  17. Application of a regional hurricane wind risk forecasting model for wood-frame houses.

    Science.gov (United States)

    Jain, Vineet Kumar; Davidson, Rachel Ann

    2007-02-01

    Hurricane wind risk in a region changes over time due to changes in the number, type, locations, vulnerability, and value of buildings. A model was developed to quantitatively estimate changes over time in hurricane wind risk to wood-frame houses (defined in terms of potential for direct economic loss), and to estimate how different factors, such as building code changes and population growth, contribute to that change. The model, which is implemented in a simulation, produces a probability distribution of direct economic losses for each census tract in the study region at each time step in the specified time horizon. By changing parameter values and rerunning the analysis, the effects of different changes in the built environment on the hurricane risk trends can be estimated and the relative effectiveness of hypothetical mitigation strategies can be evaluated. Using a case study application for wood-frame houses in selected counties in North Carolina from 2000 to 2020, this article demonstrates how the hurricane wind risk forecasting model can be used: (1) to provide insight into the dynamics of regional hurricane wind risk-the total change in risk over time and the relative contribution of different factors to that change, and (2) to support mitigation planning. Insights from the case study include, for example, that the many factors contributing to hurricane wind risk for wood-frame houses interact in a way that is difficult to predict a priori, and that in the case study, the reduction in hurricane losses due to vulnerability changes (e.g., building code changes) is approximately equal to the increase in losses due to building inventory growth. The potential for the model to support risk communication is also discussed.

  18. Forecasted Flood Depth Grids Providing Early Situational Awareness to FEMA during the 2017 Atlantic Hurricane Season

    Science.gov (United States)

    Jones, M.; Longenecker, H. E., III

    2017-12-01

    The 2017 hurricane season brought the unprecedented landfall of three Category 4 hurricanes (Harvey, Irma and Maria). FEMA is responsible for coordinating the federal response and recovery efforts for large disasters such as these. FEMA depends on timely and accurate depth grids to estimate hazard exposure, model damage assessments, plan flight paths for imagery acquisition, and prioritize response efforts. In order to produce riverine or coastal depth grids based on observed flooding, the methodology requires peak crest water levels at stream gauges, tide gauges, high water marks, and best-available elevation data. Because peak crest data isn't available until the apex of a flooding event and high water marks may take up to several weeks for field teams to collect for a large-scale flooding event, final observed depth grids are not available to FEMA until several days after a flood has begun to subside. Within the last decade NOAA's National Weather Service (NWS) has implemented the Advanced Hydrologic Prediction Service (AHPS), a web-based suite of accurate forecast products that provide hydrograph forecasts at over 3,500 stream gauge locations across the United States. These forecasts have been newly implemented into an automated depth grid script tool, using predicted instead of observed water levels, allowing FEMA access to flood hazard information up to 3 days prior to a flooding event. Water depths are calculated from the AHPS predicted flood stages and are interpolated at 100m spacing along NHD hydrolines within the basin of interest. A water surface elevation raster is generated from these water depths using an Inverse Distance Weighted interpolation. Then, elevation (USGS NED 30m) is subtracted from the water surface elevation raster so that the remaining values represent the depth of predicted flooding above the ground surface. This automated process requires minimal user input and produced forecasted depth grids that were comparable to post

  19. Understanding Variability in Beach Slope to Improve Forecasts of Storm-induced Water Levels

    Science.gov (United States)

    Doran, K. S.; Stockdon, H. F.; Long, J.

    2014-12-01

    The National Assessment of Hurricane-Induced Coastal Erosion Hazards combines measurements of beach morphology with storm hydrodynamics to produce forecasts of coastal change during storms for the Gulf of Mexico and Atlantic coastlines of the United States. Wave-induced water levels are estimated using modeled offshore wave height and period and measured beach slope (from dune toe to shoreline) through the empirical parameterization of Stockdon et al. (2006). Spatial and temporal variability in beach slope leads to corresponding variability in predicted wave setup and swash. Seasonal and storm-induced changes in beach slope can lead to differences on the order of a meter in wave runup elevation, making accurate specification of this parameter essential to skillful forecasts of coastal change. Spatial variation in beach slope is accounted for through alongshore averaging, but temporal variability in beach slope is not included in the final computation of the likelihood of coastal change. Additionally, input morphology may be years old and potentially very different than the conditions present during forecast storm. In order to improve our forecasts of hurricane-induced coastal erosion hazards, the temporal variability of beach slope must be included in the final uncertainty of modeled wave-induced water levels. Frequently collected field measurements of lidar-based beach morphology are examined for study sites in Duck, North Carolina, Treasure Island, Florida, Assateague Island, Virginia, and Dauphin Island, Alabama, with some records extending over a period of 15 years. Understanding the variability of slopes at these sites will help provide estimates of associated water level uncertainty which can then be applied to other areas where lidar observations are infrequent, and improve the overall skill of future forecasts of storm-induced coastal change. Stockdon, H. F., Holman, R. A., Howd, P. A., and Sallenger Jr, A. H. (2006). Empirical parameterization of setup

  20. Multi-hazard risk analysis related to hurricanes

    Science.gov (United States)

    Lin, Ning

    Hurricanes present major hazards to the United States. Associated with extreme winds, heavy rainfall, and storm surge, landfalling hurricanes often cause enormous structural damage to coastal regions. Hurricane damage risk assessment provides the basis for loss mitigation and related policy-making. Current hurricane risk models, however, often oversimplify the complex processes of hurricane damage. This dissertation aims to improve existing hurricane risk assessment methodology by coherently modeling the spatial-temporal processes of storm landfall, hazards, and damage. Numerical modeling technologies are used to investigate the multiplicity of hazards associated with landfalling hurricanes. The application and effectiveness of current weather forecasting technologies to predict hurricane hazards is investigated. In particular, the Weather Research and Forecasting model (WRF), with Geophysical Fluid Dynamics Laboratory (GFDL)'s hurricane initialization scheme, is applied to the simulation of the wind and rainfall environment during hurricane landfall. The WRF model is further coupled with the Advanced Circulation (AD-CIRC) model to simulate storm surge in coastal regions. A case study examines the multiple hazards associated with Hurricane Isabel (2003). Also, a risk assessment methodology is developed to estimate the probability distribution of hurricane storm surge heights along the coast, particularly for data-scarce regions, such as New York City. This methodology makes use of relatively simple models, specifically a statistical/deterministic hurricane model and the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, to simulate large numbers of synthetic surge events, and conducts statistical analysis. The estimation of hurricane landfall probability and hazards are combined with structural vulnerability models to estimate hurricane damage risk. Wind-induced damage mechanisms are extensively studied. An innovative windborne debris risk model is

  1. Effects of track and threat information on judgments of hurricane strike probability.

    Science.gov (United States)

    Wu, Hao-Che; Lindell, Michael K; Prater, Carla S; Samuelson, Charles D

    2014-06-01

    Although evacuation is one of the best strategies for protecting citizens from hurricane threat, the ways that local elected officials use hurricane data in deciding whether to issue hurricane evacuation orders is not well understood. To begin to address this problem, we examined the effects of hurricane track and intensity information in a laboratory setting where participants judged the probability that hypothetical hurricanes with a constant bearing (i.e., straight line forecast track) would make landfall in each of eight 45 degree sectors around the Gulf of Mexico. The results from 162 participants in a student sample showed that the judged strike probability distributions over the eight sectors within each scenario were, unsurprisingly, unimodal and centered on the sector toward which the forecast track pointed. More significantly, although strike probability judgments for the sector in the direction of the forecast track were generally higher than the corresponding judgments for the other sectors, the latter were not zero. Most significantly, there were no appreciable differences in the patterns of strike probability judgments for hurricane tracks represented by a forecast track only, an uncertainty cone only, or forecast track with an uncertainty cone-a result consistent with a recent survey of coastal residents threatened by Hurricane Charley. The study results suggest that people are able to correctly process basic information about hurricane tracks but they do make some errors. More research is needed to understand the sources of these errors and to identify better methods of displaying uncertainty about hurricane parameters. © 2013 Society for Risk Analysis.

  2. Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew.

    Science.gov (United States)

    Pasetto, Damiano; Finger, Flavio; Camacho, Anton; Grandesso, Francesco; Cohuet, Sandra; Lemaitre, Joseph C; Azman, Andrew S; Luquero, Francisco J; Bertuzzo, Enrico; Rinaldo, Andrea

    2018-05-01

    Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi

  3. Adapting National Water Model Forecast Data to Local Hyper-Resolution H&H Models During Hurricane Irma

    Science.gov (United States)

    Singhofen, P.

    2017-12-01

    The National Water Model (NWM) is a remarkable undertaking. The foundation of the NWM is a 1 square kilometer grid which is used for near real-time modeling and flood forecasting of most rivers and streams in the contiguous United States. However, the NWM falls short in highly urbanized areas with complex drainage infrastructure. To overcome these shortcomings, the presenter proposes to leverage existing local hyper-resolution H&H models and adapt the NWM forcing data to them. Gridded near real-time rainfall, short range forecasts (18-hour) and medium range forecasts (10-day) during Hurricane Irma are applied to numerous detailed H&H models in highly urbanized areas of the State of Florida. Coastal and inland models are evaluated. Comparisons of near real-time rainfall data are made with observed gaged data and the ability to predict flooding in advance based on forecast data is evaluated. Preliminary findings indicate that the near real-time rainfall data is consistently and significantly lower than observed data. The forecast data is more promising. For example, the medium range forecast data provides 2 - 3 days advanced notice of peak flood conditions to a reasonable level of accuracy in most cases relative to both timing and magnitude. Short range forecast data provides about 12 - 14 hours advanced notice. Since these are hyper-resolution models, flood forecasts can be made at the street level, providing emergency response teams with valuable information for coordinating and dispatching limited resources.

  4. The measurement of winds over the ocean from Skylab with application to measuring and forecasting typhoons and hurricanes

    Science.gov (United States)

    Cardone, V. J.; Pierson, W. J.

    1975-01-01

    On Skylab, a combination microwave radar-radiometer (S193) made measurements in a tropical hurricane (AVA), a tropical storm, and various extratropical wind systems. The winds at each cell scanned by the instrument were determined by objective numerical analysis techniques. The measured radar backscatter is compared to the analyzed winds and shown to provide an accurate method for measuring winds from space. An operational version of the instrument on an orbiting satellite will be able to provide the kind of measurements in tropical cyclones available today only by expensive and dangerous aircraft reconnaissance. Additionally, the specifications of the wind field in the tropical boundary layer should contribute to improved accuracy of tropical cyclone forecasts made with numerical weather predictions models currently being applied to the tropical atmosphere.

  5. A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

    Science.gov (United States)

    Saleh, Firas; Ramaswamy, Venkatsundar; Georgas, Nickitas; Blumberg, Alan F.; Pullen, Julie

    2016-07-01

    This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ˜ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

  6. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L

    2004-01-01

    .... The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved advisories...

  7. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L; Cardone, Vincent J; Cox, Andrew T; Augustus, Ellsworth H; Colonnese, Christopher P

    2003-01-01

    .... The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved advisories...

  8. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L

    2005-01-01

    .... The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved advisories...

  9. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L; Cardone, Vincent J; Cox, Andrew T

    2006-01-01

    ... of tropical cyclones The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved...

  10. A look into hurricane Maria rapid intensification using Meteo-France's Arome-Antilles model.

    Science.gov (United States)

    Pilon, R.; Faure, G.; Dupont, T.; Chauvin, F.

    2017-12-01

    Category 5 Hurricane Maria created a string of humanitarian crises. It caused billions of dollars of damage over the Caribbean but is also one of the worst natural disaster in Dominica.The hurricane took approximately 29 hours to strengthen from a tropical storm to a major category 5 hurricane. Here we present real-time forecasts of high resolution (2.5 km) Arome-Antilles regional model forced by real-time ECMWF's Integrated Forecasting System. The model was able to relatively represent well the rapid intensification of the hurricane whether it was in timing or in location of the eye and strength of its eye wall.We will present an outline of results.

  11. Decision Science Perspectives on Hurricane Vulnerability: Evidence from the 2010–2012 Atlantic Hurricane Seasons

    Directory of Open Access Journals (Sweden)

    Kerry Milch

    2018-01-01

    Full Text Available Although the field has seen great advances in hurricane prediction and response, the economic toll from hurricanes on U.S. communities continues to rise. We present data from Hurricanes Earl (2010, Irene (2011, Isaac (2012, and Sandy (2012 to show that individual and household decisions contribute to this vulnerability. From phone surveys of residents in communities threatened by impending hurricanes, we identify five decision biases or obstacles that interfere with residents’ ability to protect themselves and minimize property damage: (1 temporal and spatial myopia, (2 poor mental models of storm risk, (3 gaps between objective and subjective probability estimates, (4 prior storm experience, and (5 social factors. We then discuss ways to encourage better decision making and reduce the economic and emotional impacts of hurricanes, using tools such as decision defaults (requiring residents to opt out of precautions rather than opt in and tailoring internet-based forecast information so that it is local, specific, and emphasizes impacts rather than probability.

  12. The 2017 Hurricane Season: A Revolution in Geostationary Weather Satellite Imaging and Data Processing

    Science.gov (United States)

    Weiner, A. M.; Gundy, J.; Brown-Bertold, B.; Yates, H.; Dobler, J. T.

    2017-12-01

    Since their introduction, geostationary weather satellites have enabled us to track hurricane life-cycle movement from development to dissipation. During the 2017 hurricane season, the new GOES-16 geostationary satellite demonstrated just how far we have progressed technologically in geostationary satellite imaging, with hurricane imagery showing never-before-seen detail of the hurricane eye and eyewall structure and life cycle. In addition, new ground system technology, leveraging high-performance computing, delivered imagery and data to forecasters with unprecedented speed—and with updates as often as every 30 seconds. As additional satellites and new products become operational, forecasters will be able to track hurricanes with even greater accuracy and assist in aftermath evaluations. This presentation will present glimpses into the past, a look at the present, and a prediction for the future utilization of geostationary satellites with respect to all facets of hurricane support.

  13. Development of an Adaptable Display and Diagnostic System for the Evaluation of Tropical Cyclone Forecasts

    Science.gov (United States)

    Kucera, P. A.; Burek, T.; Halley-Gotway, J.

    2015-12-01

    NCAR's Joint Numerical Testbed Program (JNTP) focuses on the evaluation of experimental forecasts of tropical cyclones (TCs) with the goal of developing new research tools and diagnostic evaluation methods that can be transitioned to operations. Recent activities include the development of new TC forecast verification methods and the development of an adaptable TC display and diagnostic system. The next generation display and diagnostic system is being developed to support evaluation needs of the U.S. National Hurricane Center (NHC) and broader TC research community. The new hurricane display and diagnostic capabilities allow forecasters and research scientists to more deeply examine the performance of operational and experimental models. The system is built upon modern and flexible technology that includes OpenLayers Mapping tools that are platform independent. The forecast track and intensity along with associated observed track information are stored in an efficient MySQL database. The system provides easy-to-use interactive display system, and provides diagnostic tools to examine forecast track stratified by intensity. Consensus forecasts can be computed and displayed interactively. The system is designed to display information for both real-time and for historical TC cyclones. The display configurations are easily adaptable to meet the needs of the end-user preferences. Ongoing enhancements include improving capabilities for stratification and evaluation of historical best tracks, development and implementation of additional methods to stratify and compute consensus hurricane track and intensity forecasts, and improved graphical display tools. The display is also being enhanced to incorporate gridded forecast, satellite, and sea surface temperature fields. The presentation will provide an overview of the display and diagnostic system development and demonstration of the current capabilities.

  14. The Impact of Microphysical Schemes on Intensity and Track of Hurricane

    Science.gov (United States)

    Tao, W. K.; Shi, J. J.; Chen, S. S.; Lang, S.; Lin, P.; Hong, S. Y.; Peters-Lidard, C.; Hou, A.

    2010-01-01

    During the past decade, both research and operational numerical weather prediction models [e.g. Weather Research and Forecasting Model (WRF)] have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with a 1-2 km or less horizontal resolutions. The WRF is a next-generation meso-scale forecast model and assimilation system that has incorporated a modern software framework, advanced dynamics, numeric and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. The WRF model can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options. At Goddard, four different cloud microphysics schemes (warm rain only, two-class of ice, two three-class of ice with either graupel or hail) are implemented into the WRF. The performances of these schemes have been compared to those from other WRF microphysics scheme options for an Atlantic hurricane case. In addition, a brief review and comparison on the previous modeling studies on the impact of microphysics schemes and microphysical processes on intensity and track of hurricane will be presented. Generally, almost all modeling studies found that the microphysics schemes did not have major impacts on track forecast, but did have more effect on the intensity. All modeling studies found that the simulated hurricane has rapid deepening and/or intensification for the warm rain-only case. It is because all hydrometeors were very large raindrops, and they fell out quickly at and near the eye-wall region. This would hydrostatically produce the lowest pressure. In addition, these modeling studies suggested that the simulated hurricane becomes unrealistically strong by removing the evaporative cooling of cloud droplets and melting of ice particles. This is due to the

  15. The Impact of Microphysics on Intensity and Structure of Hurricanes

    Science.gov (United States)

    Tao, Wei-Kuo; Shi, Jainn; Lang, Steve; Peters-Lidard, Christa

    2006-01-01

    During the past decade, both research and operational numerical weather prediction models, e.g. Weather Research and Forecast (WRF) model, have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with a 1-2 km or less horizontal resolutions. WFW is a next-generation mesoscale forecast model and assimilation system that has incorporated modern software framework, advanced dynamics, numeric and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WFW model can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options such as Lin et al. (1983), WSM 6-class and Thompson microphysics schemes. We have recently implemented three sophisticated cloud microphysics schemes into WRF. The cloud microphysics schemes have been extensively tested and applied for different mesoscale systems in different geographical locations. The performances of these schemes have been compared to those from other WRF microphysics options. We are performing sensitivity tests in using WW to examine the impact of six different cloud microphysical schemes on hurricane track, intensity and rainfall forecast. We are also performing the inline tracer calculation to comprehend the physical processes @e., boundary layer and each quadrant in the boundary layer) related to the development and structure of hurricanes.

  16. AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system

    Directory of Open Access Journals (Sweden)

    Chun Yang

    2016-06-01

    Full Text Available A method to assimilate all-sky radiances from the Advanced Microwave Scanning Radiometer 2 (AMSR2 was developed within the Weather Research and Forecasting (WRF model's data assimilation (WRFDA system. The four essential elements are: (1 extending the community radiative transform model's (CRTM interface to include hydrometeor profiles; (2 using total water Qt as the moisture control variable; (3 using a warm-rain physics scheme for partitioning the Qt increment into individual increments of water vapour, cloud liquid water and rain; and (4 adopting a symmetric observation error model for all-sky radiance assimilation.Compared to a benchmark experiment with no AMSR2 data, the impact of assimilating clear-sky or all-sky AMSR2 radiances on the analysis and forecast of Hurricane Sandy (2012 was assessed through analysis/forecast cycling experiments using WRF and WRFDA's three-dimensional variational (3DVAR data assimilation scheme. With more cloud/precipitation-affected data being assimilated around tropical cyclone (TC core areas in the all-sky AMSR2 assimilation experiment, better analyses were obtained in terms of the TC's central sea level pressure (CSLP, warm-core structure and cloud distribution. Substantial (>20 % error reduction in track and CSLP forecasts was achieved from both clear-sky and all-sky AMSR2 assimilation experiments, and this improvement was consistent from the analysis time to 72-h forecasts. Moreover, the all-sky assimilation experiment consistently yielded better track and CSLP forecasts than the clear-sky did for all forecast lead times, due to a better analysis in the TC core areas. Positive forecast impact from assimilating AMSR2 radiances is also seen when verified against the European Center for Medium-Range Weather Forecasts (ECMWF analysis and the Stage IV precipitation analysis, with an overall larger positive impact from the all-sky assimilation experiment.

  17. Evolution of Subjective Hurricane Risk Perceptions: A Bayesian Approach

    OpenAIRE

    David Kelly; David Letson; Forest Nelson; David S. Nolan; Daniel Solis

    2009-01-01

    This paper studies how individuals update subjective risk perceptions in response to hurricane track forecast information, using a unique data set from an event market, the Hurricane Futures Market (HFM). We derive a theoretical Bayesian framework which predicts how traders update their perceptions of the probability of a hurricane making landfall in a certain range of coastline. Our results suggest that traders behave in a way consistent with Bayesian updating but this behavior is based on t...

  18. It Takes Two: NASA and NOAA's Shared Path of Hurricane Science Flights with the Global Hawk. Time for the Research To Operations (R2O) Transition?

    Science.gov (United States)

    Emory, A. E.; Wick, G. A.; Dunion, J. P.; McLinden, M.; Schreier, M. M.; Black, P.; Hood, R. E.; Sippel, J.; Tallapragada, V.

    2017-12-01

    The impacts of Harvey, Irma, and Maria during the 2017 Atlantic hurricane season re-emphasized the critical need for accurate operational forecasts. The combined NASA East Pacific Origins and Characteristics of Hurricanes (EPOCH) and NOAA UAS field campaign during August 2017 was the fourth campaign in a series of dual agency partnerships between NASA and NOAA to improve forecasting accuracy in tropical cyclogenesis and rapid intensification. A brief history of Global Hawk (GH) hurricane field campaigns, including GRIP (2010), HS3 (2012-2014), NOAA-SHOUT (2015-2016) and EPOCH (2017), will show the incremental steps taken over the last eight years to bring the GH from a research platform to a candidate for operational hurricane reconnaissance. GH dropsondes were assimilated into the ECMWF and HWRF forecast models during the 2015-2016 NOAA SHOUT campaigns. EPOCH marked the first time that GH dropsondes were assimilated in real-time into NOAA's GFS forecast model. Early results show that assimilating dropsonde data significantly increases skill in predicting intensity change, which is game changing since the National Hurricane Center intensity error trend has remained virtually unchanged, particularly at 24 hours, over the last 25 years. The results from the past few years suggest that a paradigm shift of sampling the environment with a high-altitude, long-duration UAS like the GH that is capable of deploying up to 90 dropsondes ahead of and over the top of a developing or strengthening tropical cyclone could produce the best return on hurricane forecast predictions in subsequent years. Recommendations for the future, including lessons learned and the potential for R2O transition will be discussed.

  19. Nature Run for the North Atlantic Ocean Hurricane Region: System Evaluation and Regional Applications

    Science.gov (United States)

    Kourafalou, V.; Androulidakis, I.; Halliwell, G. R., Jr.; Kang, H.; Mehari, M. F.; Atlas, R. M.

    2016-02-01

    A prototype ocean Observing System Simulation Experiments (OSSE) system, first developed and data validated in the Gulf of Mexico, has been applied on the extended North Atlantic Ocean hurricane region. The main objectives of this study are: a) to contribute toward a fully relocatable ocean OSSE system by expanding the Gulf of Mexico OSSE to the North Atlantic Ocean; b) demonstrate and quantify improvements in hurricane forecasting when the ocean component of coupled hurricane models is advanced through targeted observations and assimilation. The system is based on the Hybrid Coordinate Ocean Model (HYCOM) and has been applied on a 1/250 Mercator mesh for the free-running Nature Run (NR) and on a 1/120 Mercator mesh for the data assimilative forecast model (FM). A "fraternal twin" system is employed, using two different realizations for NR and FM, each configured to produce substantially different physics and truncation errors. The NR has been evaluated using a variety of available observations, such as from AVISO, GDEM climatology and GHRSST observations, plus specific regional products (upper ocean profiles from air-borne instruments, surface velocity maps derived from the historical drifter data set and tropical cyclone heat potential maps derived from altimetry observations). The utility of the OSSE system to advance the knowledge of regional air-sea interaction processes related to hurricane activity is demonstrated in the Amazon region (salinity induced surface barrier layer) and the Gulf Stream region (hurricane impact on the Gulf Stream extension).

  20. Calculations of the hurricane eye motion based on singularity propagation theory

    Directory of Open Access Journals (Sweden)

    Vladimir Danilov

    2002-02-01

    Full Text Available We discuss the possibility of using calculating singularities to forecast the dynamics of hurricanes. Our basic model is the shallow-water system. By treating the hurricane eye as a vortex type singularity and truncating the corresponding sequence of Hugoniot type conditions, we carry out many numerical experiments. The comparison of our results with the tracks of three actual hurricanes shows that our approach is rather fruitful.

  1. Low-wave number analysis of observations and ensemble forecasts to develop metrics for the selection of most realistic members to study multi-scale interactions between the environment and the convective organization of hurricanes: Focus on Rapid Intensification

    Science.gov (United States)

    Hristova-Veleva, S. M.; Chen, H.; Gopalakrishnan, S.; Haddad, Z. S.

    2017-12-01

    Tropical cyclones (TCs) are the product of complex multi-scale processes and interactions. The role of the environment has long been recognized. However, recent research has shown that convective-scale processes in the hurricane core might also play a crucial role in determining TCs intensity and size. Several studies have linked Rapid Intensification to the characteristics of the convective clouds (shallow versus deep), their organization (isolated versus wide-spread) and their location with respect to dynamical controls (the vertical shear, the radius of maximum wind). Yet a third set of controls signifies the interaction between the storm-scale and large-scale processes. Our goal is to use observations and models to advance the still-lacking understanding of these processes. Recently, hurricane models have improved significantly. However, deterministic forecasts have limitations due to the uncertainty in the representation of the physical processes and initial conditions. A crucial step forward is the use of high-resolution ensembles. We adopt the following approach: i) generate a high resolution ensemble forecast using HWRF; ii) produce synthetic data (e.g. brightness temperature) from the model fields for direct comparison to satellite observations; iii) develop metrics to allow us to sub-select the realistic members of the ensemble, based on objective measures of the similarity between observed and forecasted structures; iv) for these most-realistic members, determine the skill in forecasting TCs to provide"guidance on guidance"; v) use the members with the best predictive skill to untangle the complex multi-scale interactions. We will report on the first three goals of our research, using forecasts and observations of hurricane Edouard (2014), focusing on RI. We will focus on describing the metrics for the selection of the most appropriate ensemble members, based on applying low-wave number analysis (WNA - Hristova-Veleva et al., 2016) to the observed and

  2. Examining Hurricane Track Length and Stage Duration Since 1980

    Science.gov (United States)

    Fandrich, K. M.; Pennington, D.

    2017-12-01

    positive increase though time. This compliments the results of the track length analysis indicating that as storms intensify faster, they are doing so over a shorter distance. It is expected that this research could be used to improve hurricane track forecasting and provide information about the effects of climate change on tropical systems and the tropical environment.

  3. Sub-Ensemble Coastal Flood Forecasting: A Case Study of Hurricane Sandy

    Directory of Open Access Journals (Sweden)

    Justin A. Schulte

    2017-12-01

    Full Text Available In this paper, it is proposed that coastal flood ensemble forecasts be partitioned into sub-ensemble forecasts using cluster analysis in order to produce representative statistics and to measure forecast uncertainty arising from the presence of clusters. After clustering the ensemble members, the ability to predict the cluster into which the observation will fall can be measured using a cluster skill score. Additional sub-ensemble and composite skill scores are proposed for assessing the forecast skill of a clustered ensemble forecast. A recently proposed method for statistically increasing the number of ensemble members is used to improve sub-ensemble probabilistic estimates. Through the application of the proposed methodology to Sandy coastal flood reforecasts, it is demonstrated that statistics computed using only ensemble members belonging to a specific cluster are more representative than those computed using all ensemble members simultaneously. A cluster skill-cluster uncertainty index relationship is identified, which is the cluster analog of the documented spread-skill relationship. Two sub-ensemble skill scores are shown to be positively correlated with cluster forecast skill, suggesting that skillfully forecasting the cluster into which the observation will fall is important to overall forecast skill. The identified relationships also suggest that the number of ensemble members within in each cluster can be used as guidance for assessing the potential for forecast error. The inevitable existence of ensemble member clusters in tidally dominated total water level prediction systems suggests that clustering is a necessary post-processing step for producing representative and skillful total water level forecasts.

  4. The Impact of Microphysical Schemes on Hurricane Intensity and Track

    Science.gov (United States)

    Tao, Wei-Kuo; Shi, Jainn Jong; Chen, Shuyi S.; Lang, Stephen; Lin, Pay-Liam; Hong, Song-You; Peters-Lidard, Christa; Hou, Arthur

    2011-01-01

    During the past decade, both research and operational numerical weather prediction models [e.g. the Weather Research and Forecasting Model (WRF)] have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with 1-2 km or less horizontal resolutions. WRF is a next-generation meso-scale forecast model and assimilation system. It incorporates a modern software framework, advanced dynamics, numerics and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WRF can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options. At NASA Goddard, four different cloud microphysics options have been implemented into WRF. The performance of these schemes is compared to those of the other microphysics schemes available in WRF for an Atlantic hurricane case (Katrina). In addition, a brief review of previous modeling studies on the impact of microphysics schemes and processes on the intensity and track of hurricanes is presented and compared against the current Katrina study. In general, all of the studies show that microphysics schemes do not have a major impact on track forecasts but do have more of an effect on the simulated intensity. Also, nearly all of the previous studies found that simulated hurricanes had the strongest deepening or intensification when using only warm rain physics. This is because all of the simulated precipitating hydrometeors are large raindrops that quickly fall out near the eye-wall region, which would hydrostatically produce the lowest pressure. In addition, these studies suggested that intensities become unrealistically strong when evaporative cooling from cloud droplets and melting from ice particles are removed as this results in much weaker downdrafts in the simulated

  5. Extreme Hurricane-Generated Waves in Gulf of Mexico

    National Research Council Canada - National Science Library

    Alberto, Carlos; Fernandes, Santos

    2005-01-01

    .... Although WaveWatchIII (WW3) is used by many operational forecasting centers around the world, there is a lack of field studies to evaluate its accuracy in regional applications and under extreme conditions, such as Hurricanes...

  6. Impact of Moist Physics Complexity on Tropical Cyclone Simulations from the Hurricane Weather Research and Forecast System

    Science.gov (United States)

    Kalina, E. A.; Biswas, M.; Newman, K.; Grell, E. D.; Bernardet, L.; Frimel, J.; Carson, L.

    2017-12-01

    The parameterization of moist physics in numerical weather prediction models plays an important role in modulating tropical cyclone structure, intensity, and evolution. The Hurricane Weather Research and Forecast system (HWRF), the National Oceanic and Atmospheric Administration's operational model for tropical cyclone prediction, uses the Scale-Aware Simplified Arakawa-Schubert (SASAS) cumulus scheme and a modified version of the Ferrier-Aligo (FA) microphysics scheme to parameterize moist physics. The FA scheme contains a number of simplifications that allow it to run efficiently in an operational setting, which includes prescribing values for hydrometeor number concentrations (i.e., single-moment microphysics) and advecting the total condensate rather than the individual hydrometeor species. To investigate the impact of these simplifying assumptions on the HWRF forecast, the FA scheme was replaced with the more complex double-moment Thompson microphysics scheme, which individually advects cloud ice, cloud water, rain, snow, and graupel. Retrospective HWRF forecasts of tropical cyclones that occurred in the Atlantic and eastern Pacific ocean basins from 2015-2017 were then simulated and compared to those produced by the operational HWRF configuration. Both traditional model verification metrics (i.e., tropical cyclone track and intensity) and process-oriented metrics (e.g., storm size, precipitation structure, and heating rates from the microphysics scheme) will be presented and compared. The sensitivity of these results to the cumulus scheme used (i.e., the operational SASAS versus the Grell-Freitas scheme) also will be examined. Finally, the merits of replacing the moist physics schemes that are used operationally with the alternatives tested here will be discussed from a standpoint of forecast accuracy versus computational resources.

  7. Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

    KAUST Repository

    Altaf, Muhammad

    2013-08-01

    This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.

  8. Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

    KAUST Repository

    Altaf, Muhammad; Butler, T.; Luo, X.; Dawson, C.; Mayo, T.; Hoteit, Ibrahim

    2013-01-01

    This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.

  9. The Impact of Microphysics on Intensity and Structure of Hurricanes and Mesoscale Convective Systems

    Science.gov (United States)

    Tao, Wei-Kuo; Shi, Jainn J.; Jou, Ben Jong-Dao; Lee, Wen-Chau; Lin, Pay-Liam; Chang, Mei-Yu

    2007-01-01

    During the past decade, both research and operational numerical weather prediction models, e.g. Weather Research and Forecast (WRF) model, have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with a 1-2 km or less horizontal resolutions. WRF is a next-generation mesoscale forecast model and assimilation system that has incorporated modern software framework, advanced dynamics, numeric and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WRF model can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options such as Purdue Lin et al. (1983), WSM 6-class and Thompson microphysics schemes. We have recently implemented three sophisticated cloud microphysics schemes into WRF. The cloud microphysics schemes have been extensively tested and applied for different mesoscale systems in different geographical locations. The performances of these schemes have been compared to those from other WRF microphysics options. We are performing sensitivity tests in using WRF to examine the impact of six different cloud microphysical schemes on precipitation processes associated hurricanes and mesoscale convective systems developed at different geographic locations [Oklahoma (IHOP), Louisiana (Hurricane Katrina), Canada (C3VP - snow events), Washington (fire storm), India (Monsoon), Taiwan (TiMREX - terrain)]. We will determine the microphysical schemes for good simulated convective systems in these geographic locations. We are also performing the inline tracer calculation to comprehend the physical processes (i.e., boundary layer and each quadrant in the boundary layer) related to the development and structure of hurricanes and mesoscale convective systems.

  10. Isentropic Analysis of a Simulated Hurricane

    Science.gov (United States)

    Mrowiec, Agnieszka A.; Pauluis, Olivier; Zhang, Fuqing

    2016-01-01

    Hurricanes, like many other atmospheric flows, are associated with turbulent motions over a wide range of scales. Here the authors adapt a new technique based on the isentropic analysis of convective motions to study the thermodynamic structure of the overturning circulation in hurricane simulations. This approach separates the vertical mass transport in terms of the equivalent potential temperature of air parcels. In doing so, one separates the rising air parcels at high entropy from the subsiding air at low entropy. This technique filters out oscillatory motions associated with gravity waves and separates convective overturning from the secondary circulation. This approach is applied here to study the flow of an idealized hurricane simulation with the Weather Research and Forecasting (WRF) Model. The isentropic circulation for a hurricane exhibits similar characteristics to that of moist convection, with a maximum mass transport near the surface associated with a shallow convection and entrainment. There are also important differences. For instance, ascent in the eyewall can be readily identified in the isentropic analysis as an upward mass flux of air with unusually high equivalent potential temperature. The isentropic circulation is further compared here to the Eulerian secondary circulation of the simulated hurricane to show that the mass transport in the isentropic circulation is much larger than the one in secondary circulation. This difference can be directly attributed to the mass transport by convection in the outer rainband and confirms that, even for a strongly organized flow like a hurricane, most of the atmospheric overturning is tied to the smaller scales.

  11. A Space-Based Perspective of the 2017 Hurricane Season from the Global Precipitation Measurement (GPM) Mission

    Science.gov (United States)

    Skofronick Jackson, G.; Petersen, W. A.; Huffman, G. J.; Kirschbaum, D.; Wolff, D. B.; Tan, J.; Zavodsky, B.

    2017-12-01

    The Global Precipitation Measurement (GPM) mission collected unique, near real time 3-D satellite-based views of hurricanes in 2017 together with estimated precipitation accumulation using merged satellite data for scientific studies and societal applications. Central to GPM is the NASA-JAXA GPM Core Observatory (CO). The GPM-CO carries an advanced dual-frequency precipitation radar (DPR) and a well-calibrated, multi-frequency passive microwave radiometer that together serve as an on orbit reference for precipitation measurements made by the international GPM satellite constellation. GPM-CO overpasses of major Hurricanes such as Harvey, Irma, Maria, and Ophelia revealed intense convective structures in DPR radar reflectivity together with deep ice-phase microphysics in both the eyewalls and outer rain bands. Of considerable scientific interest, and yet to be determined, will be DPR-diagnosed characteristics of the rain drop size distribution as a function of convective structure, intensity and microphysics. The GPM-CO active/passive suite also provided important decision support information. For example, the National Hurricane Center used GPM-CO observations as a tool to inform track and intensity estimates in their forecast briefings. Near-real-time rainfall accumulation from the Integrated Multi-satellitE Retrievals for GPM (IMERG) was also provided via the NASA SPoRT team to Puerto Rico following Hurricane Maria when ground-based radar systems on the island failed. Comparisons between IMERG, NOAA Multi-Radar Multi-Sensor data, and rain gauge rainfall accumulations near Houston, Texas during Hurricane Harvey revealed spatial biases between ground and IMERG satellite estimates, and a general underestimation of IMERG rain accumulations associated with infrared observations, collectively illustrating the difficulty of measuring rainfall in hurricanes.GPM data continue to advance scientific research on tropical cyclone intensification and structure, and contribute to

  12. Using Temperature Forecasts to Improve Seasonal Streamflow Forecasts in the Colorado and Rio Grande Basins

    Science.gov (United States)

    Lehner, F.; Wood, A.; Llewellyn, D.; Blatchford, D. B.; Goodbody, A. G.; Pappenberger, F.

    2017-12-01

    Recent studies have documented the influence of increasing temperature on streamflow across the American West, including snow-melt driven rivers such as the Colorado or Rio Grande. At the same time, some basins are reporting decreasing skill in seasonal streamflow forecasts, termed water supply forecasts (WSFs), over the recent decade. While the skill in seasonal precipitation forecasts from dynamical models remains low, their skill in predicting seasonal temperature variations could potentially be harvested for WSFs to account for non-stationarity in regional temperatures. Here, we investigate whether WSF skill can be improved by incorporating seasonal temperature forecasts from dynamical forecasting models (from the North American Multi Model Ensemble and the European Centre for Medium-Range Weather Forecast System 4) into traditional statistical forecast models. We find improved streamflow forecast skill relative to traditional WSF approaches in a majority of headwater locations in the Colorado and Rio Grande basins. Incorporation of temperature into WSFs thus provides a promising avenue to increase the robustness of current forecasting techniques in the face of continued regional warming.

  13. An Integrated Ensemble-Based Operational Framework to Predict Urban Flooding: A Case Study of Hurricane Sandy in the Passaic and Hackensack River Basins

    Science.gov (United States)

    Saleh, F.; Ramaswamy, V.; Georgas, N.; Blumberg, A. F.; Wang, Y.

    2016-12-01

    Advances in computational resources and modeling techniques are opening the path to effectively integrate existing complex models. In the context of flood prediction, recent extreme events have demonstrated the importance of integrating components of the hydrosystem to better represent the interactions amongst different physical processes and phenomena. As such, there is a pressing need to develop holistic and cross-disciplinary modeling frameworks that effectively integrate existing models and better represent the operative dynamics. This work presents a novel Hydrologic-Hydraulic-Hydrodynamic Ensemble (H3E) flood prediction framework that operationally integrates existing predictive models representing coastal (New York Harbor Observing and Prediction System, NYHOPS), hydrologic (US Army Corps of Engineers Hydrologic Modeling System, HEC-HMS) and hydraulic (2-dimensional River Analysis System, HEC-RAS) components. The state-of-the-art framework is forced with 125 ensemble meteorological inputs from numerical weather prediction models including the Global Ensemble Forecast System, the European Centre for Medium-Range Weather Forecasts (ECMWF), the Canadian Meteorological Centre (CMC), the Short Range Ensemble Forecast (SREF) and the North American Mesoscale Forecast System (NAM). The framework produces, within a 96-hour forecast horizon, on-the-fly Google Earth flood maps that provide critical information for decision makers and emergency preparedness managers. The utility of the framework was demonstrated by retrospectively forecasting an extreme flood event, hurricane Sandy in the Passaic and Hackensack watersheds (New Jersey, USA). Hurricane Sandy caused significant damage to a number of critical facilities in this area including the New Jersey Transit's main storage and maintenance facility. The results of this work demonstrate that ensemble based frameworks provide improved flood predictions and useful information about associated uncertainties, thus

  14. Case Study of Hurricane Felix (2007) Rapid Intensification

    Science.gov (United States)

    Colon-Pagan, I. C.; Davis, C. A.; Holland, G. J.

    2010-12-01

    The forecasting of tropical cyclones (TC) rapid intensification (RI) is one of the most challenging problems that the operational community experiences. Research advances leading to improvements in predicting this phenomenon would help government agencies make decisions that could reduce the impact on communities that are so often affected by these weather-related events. It has been proposed that TC RI is associated to various factors, including high sea-surface temperatures, weak vertical wind shear, and the ratio of inertial to static stability, which improves the conversion of diabatic heating into circulation. While a cyclone develops, the size of the region of high inertial stability (IS) decreases whereas the magnitude of IS increases. However, it’s unknown whether this is a favorable condition or a result of RI occurrences. The purpose of this research, therefore, is to determine if the IS follows, leads or changes in sync with the intensity change by studying Hurricane Felix (2007) RI phase. Results show a trend of increasing IS before the RI stage, followed by an expansion of the region of high IS. This episode is eventually followed by a decrease in both the intensity and region of positive IS, while the maximum wind speed intensity of the TC diminished. Therefore, we propose that monitoring the IS may provide a forecast tool to determine RI periods. Other parameters, such as static stability, tangential wind, and water vapor mixing ratio may help identify other features of the storm, such as circulation and eyewall formation. The inertial stability (IS) trend during the period of rapid intensification, which occurred between 00Z and 06Z of September 3rd. Maximum values of IS were calculated before and during this period of RI within a region located 30-45 km from the center. In fact, this region could represent the eye-wall of Hurricane Felix.

  15. Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting

    Science.gov (United States)

    Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.

    2014-12-01

    Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.

  16. Rapid shelf-wide cooling response of a stratified coastal ocean to hurricanes.

    Science.gov (United States)

    Seroka, Greg; Miles, Travis; Xu, Yi; Kohut, Josh; Schofield, Oscar; Glenn, Scott

    2017-06-01

    Large uncertainty in the predicted intensity of tropical cyclones (TCs) persists compared to the steadily improving skill in the predicted TC tracks. This intensity uncertainty has its most significant implications in the coastal zone, where TC impacts to populated shorelines are greatest. Recent studies have demonstrated that rapid ahead-of-eye-center cooling of a stratified coastal ocean can have a significant impact on hurricane intensity forecasts. Using observation-validated, high-resolution ocean modeling, the stratified coastal ocean cooling processes observed in two U.S. Mid-Atlantic hurricanes were investigated: Hurricane Irene (2011)-with an inshore Mid-Atlantic Bight (MAB) track during the late summer stratified coastal ocean season-and Tropical Storm Barry (2007)-with an offshore track during early summer. For both storms, the critical ahead-of-eye-center depth-averaged force balance across the entire MAB shelf included an onshore wind stress balanced by an offshore pressure gradient. This resulted in onshore surface currents opposing offshore bottom currents that enhanced surface to bottom current shear and turbulent mixing across the thermocline, resulting in the rapid cooling of the surface layer ahead-of-eye-center. Because the same baroclinic and mixing processes occurred for two storms on opposite ends of the track and seasonal stratification envelope, the response appears robust. It will be critical to forecast these processes and their implications for a wide range of future storms using realistic 3-D coupled atmosphere-ocean models to lower the uncertainty in predictions of TC intensities and impacts and enable coastal populations to better respond to increasing rapid intensification threats in an era of rising sea levels.

  17. Rapid shelf‐wide cooling response of a stratified coastal ocean to hurricanes

    Science.gov (United States)

    Miles, Travis; Xu, Yi; Kohut, Josh; Schofield, Oscar; Glenn, Scott

    2017-01-01

    Abstract Large uncertainty in the predicted intensity of tropical cyclones (TCs) persists compared to the steadily improving skill in the predicted TC tracks. This intensity uncertainty has its most significant implications in the coastal zone, where TC impacts to populated shorelines are greatest. Recent studies have demonstrated that rapid ahead‐of‐eye‐center cooling of a stratified coastal ocean can have a significant impact on hurricane intensity forecasts. Using observation‐validated, high‐resolution ocean modeling, the stratified coastal ocean cooling processes observed in two U.S. Mid‐Atlantic hurricanes were investigated: Hurricane Irene (2011)—with an inshore Mid‐Atlantic Bight (MAB) track during the late summer stratified coastal ocean season—and Tropical Storm Barry (2007)—with an offshore track during early summer. For both storms, the critical ahead‐of‐eye‐center depth‐averaged force balance across the entire MAB shelf included an onshore wind stress balanced by an offshore pressure gradient. This resulted in onshore surface currents opposing offshore bottom currents that enhanced surface to bottom current shear and turbulent mixing across the thermocline, resulting in the rapid cooling of the surface layer ahead‐of‐eye‐center. Because the same baroclinic and mixing processes occurred for two storms on opposite ends of the track and seasonal stratification envelope, the response appears robust. It will be critical to forecast these processes and their implications for a wide range of future storms using realistic 3‐D coupled atmosphere‐ocean models to lower the uncertainty in predictions of TC intensities and impacts and enable coastal populations to better respond to increasing rapid intensification threats in an era of rising sea levels. PMID:28944132

  18. Assessing the Effects of Climate Variability on Orange Yield in Florida to Reduce Production Forecast Errors

    Science.gov (United States)

    Concha Larrauri, P.

    2015-12-01

    Orange production in Florida has experienced a decline over the past decade. Hurricanes in 2004 and 2005 greatly affected production, almost to the same degree as strong freezes that occurred in the 1980's. The spread of the citrus greening disease after the hurricanes has also contributed to a reduction in orange production in Florida. The occurrence of hurricanes and diseases cannot easily be predicted but the additional effects of climate on orange yield can be studied and incorporated into existing production forecasts that are based on physical surveys, such as the October Citrus forecast issued every year by the USDA. Specific climate variables ocurring before and after the October forecast is issued can have impacts on flowering, orange drop rates, growth, and maturation, and can contribute to the forecast error. Here we present a methodology to incorporate local climate variables to predict the USDA's orange production forecast error, and we study the local effects of climate on yield in different counties in Florida. This information can aid farmers to gain an insight on what is to be expected during the orange production cycle, and can help supply chain managers to better plan their strategy.

  19. Hurricane Imaging Radiometer Wind Speed and Rain Rate Retrievals during the 2010 GRIP Flight Experiment

    Science.gov (United States)

    Sahawneh, Saleem; Farrar, Spencer; Johnson, James; Jones, W. Linwood; Roberts, Jason; Biswas, Sayak; Cecil, Daniel

    2014-01-01

    Microwave remote sensing observations of hurricanes, from NOAA and USAF hurricane surveillance aircraft, provide vital data for hurricane research and operations, for forecasting the intensity and track of tropical storms. The current operational standard for hurricane wind speed and rain rate measurements is the Stepped Frequency Microwave Radiometer (SFMR), which is a nadir viewing passive microwave airborne remote sensor. The Hurricane Imaging Radiometer, HIRAD, will extend the nadir viewing SFMR capability to provide wide swath images of wind speed and rain rate, while flying on a high altitude aircraft. HIRAD was first flown in the Genesis and Rapid Intensification Processes, GRIP, NASA hurricane field experiment in 2010. This paper reports on geophysical retrieval results and provides hurricane images from GRIP flights. An overview of the HIRAD instrument and the radiative transfer theory based, wind speed/rain rate retrieval algorithm is included. Results are presented for hurricane wind speed and rain rate for Earl and Karl, with comparison to collocated SFMR retrievals and WP3D Fuselage Radar images for validation purposes.

  20. An Exploration of Wind Stress Calculation Techniques in Hurricane Storm Surge Modeling

    Directory of Open Access Journals (Sweden)

    Kyra M. Bryant

    2016-09-01

    Full Text Available As hurricanes continue to threaten coastal communities, accurate storm surge forecasting remains a global priority. Achieving a reliable storm surge prediction necessitates accurate hurricane intensity and wind field information. The wind field must be converted to wind stress, which represents the air-sea momentum flux component required in storm surge and other oceanic models. This conversion requires a multiplicative drag coefficient for the air density and wind speed to represent the air-sea momentum exchange at a given location. Air density is a known parameter and wind speed is a forecasted variable, whereas the drag coefficient is calculated using an empirical correlation. The correlation’s accuracy has brewed a controversy of its own for more than half a century. This review paper examines the lineage of drag coefficient correlations and their acceptance among scientists.

  1. Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm

    International Nuclear Information System (INIS)

    Xiao, Liye; Qian, Feng; Shao, Wei

    2017-01-01

    Highlights: • Propose a hybrid architecture based on a modified bat algorithm for multi-step wind speed forecasting. • Improve the accuracy of multi-step wind speed forecasting. • Modify bat algorithm with CG to improve optimized performance. - Abstract: As one of the most promising sustainable energy sources, wind energy plays an important role in energy development because of its cleanliness without causing pollution. Generally, wind speed forecasting, which has an essential influence on wind power systems, is regarded as a challenging task. Analyses based on single-step wind speed forecasting have been widely used, but their results are insufficient in ensuring the reliability and controllability of wind power systems. In this paper, a new forecasting architecture based on decomposing algorithms and modified neural networks is successfully developed for multi-step wind speed forecasting. Four different hybrid models are contained in this architecture, and to further improve the forecasting performance, a modified bat algorithm (BA) with the conjugate gradient (CG) method is developed to optimize the initial weights between layers and thresholds of the hidden layer of neural networks. To investigate the forecasting abilities of the four models, the wind speed data collected from four different wind power stations in Penglai, China, were used as a case study. The numerical experiments showed that the hybrid model including the singular spectrum analysis and general regression neural network with CG-BA (SSA-CG-BA-GRNN) achieved the most accurate forecasting results in one-step to three-step wind speed forecasting.

  2. Female hurricanes are deadlier than male hurricanes.

    Science.gov (United States)

    Jung, Kiju; Shavitt, Sharon; Viswanathan, Madhu; Hilbe, Joseph M

    2014-06-17

    Do people judge hurricane risks in the context of gender-based expectations? We use more than six decades of death rates from US hurricanes to show that feminine-named hurricanes cause significantly more deaths than do masculine-named hurricanes. Laboratory experiments indicate that this is because hurricane names lead to gender-based expectations about severity and this, in turn, guides respondents' preparedness to take protective action. This finding indicates an unfortunate and unintended consequence of the gendered naming of hurricanes, with important implications for policymakers, media practitioners, and the general public concerning hurricane communication and preparedness.

  3. A Coordinated USGS Science Response to Hurricane Sandy

    Science.gov (United States)

    Jones, S.; Buxton, H. T.; Andersen, M.; Dean, T.; Focazio, M. J.; Haines, J.; Hainly, R. A.

    2013-12-01

    In late October 2012, Hurricane Sandy came ashore during a spring high tide on the New Jersey coastline, delivering hurricane-force winds, storm tides exceeding 19 feet, driving rain, and plummeting temperatures. Hurricane Sandy resulted in 72 direct fatalities in the mid-Atlantic and northeastern United States, and widespread and substantial physical, environmental, ecological, social, and economic impacts estimated at near $50 billion. Before the landfall of Hurricane Sandy, the USGS provided forecasts of potential coastal change; collected oblique aerial photography of pre-storm coastal morphology; deployed storm-surge sensors, rapid-deployment streamgages, wave sensors, and barometric pressure sensors; conducted Light Detection and Ranging (lidar) aerial topographic surveys of coastal areas; and issued a landslide alert for landslide prone areas. During the storm, Tidal Telemetry Networks provided real-time water-level information along the coast. Long-term networks and rapid-deployment real-time streamgages and water-quality monitors tracked river levels and changes in water quality. Immediately after the storm, the USGS serviced real-time instrumentation, retrieved data from over 140 storm-surge sensors, and collected other essential environmental data, including more than 830 high-water marks mapping the extent and elevation of the storm surge. Post-storm lidar surveys documented storm impacts to coastal barriers informing response and recovery and providing a new baseline to assess vulnerability of the reconfigured coast. The USGS Hazard Data Distribution System served storm-related information from many agencies on the Internet on a daily basis. Immediately following Hurricane Sandy the USGS developed a science plan, 'Meeting the Science Needs of the Nation in the Wake of Hurricane Sandy-A U.S. Geological Survey Science Plan for Support of Restoration and Recovery'. The plan will ensure continuing coordination of internal USGS activities as well as

  4. The Use of Ambient Humidity Conditions to Improve Influenza Forecast

    Science.gov (United States)

    Shaman, J. L.; Kandula, S.; Yang, W.; Karspeck, A. R.

    2017-12-01

    Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing. These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast and provide further evidence that humidity modulates rates of influenza transmission.

  5. The use of ambient humidity conditions to improve influenza forecast.

    Science.gov (United States)

    Shaman, Jeffrey; Kandula, Sasikiran; Yang, Wan; Karspeck, Alicia

    2017-11-01

    Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1-4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing) and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively). These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast.

  6. The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

    Energy Technology Data Exchange (ETDEWEB)

    Finley, Cathy [WindLogics, St. Paul, MN (United States)

    2014-04-30

    This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the

  7. Estimating cellular network performance during hurricanes

    International Nuclear Information System (INIS)

    Booker, Graham; Torres, Jacob; Guikema, Seth; Sprintson, Alex; Brumbelow, Kelly

    2010-01-01

    Cellular networks serve a critical role during and immediately after a hurricane, allowing citizens to contact emergency services when land-line communication is lost and serving as a backup communication channel for emergency responders. However, due to their ubiquitous deployment and limited design for extreme loading events, basic network elements, such as cellular towers and antennas are prone to failures during adverse weather conditions such as hurricanes. Accordingly, a systematic and computationally feasible approach is required for assessing and improving the reliability of cellular networks during hurricanes. In this paper we develop a new multi-disciplinary approach to efficiently and accurately assess cellular network reliability during hurricanes. We show how the performance of a cellular network during and immediately after future hurricanes can be estimated based on a combination of hurricane wind field models, structural reliability analysis, Monte Carlo simulation, and cellular network models and simulation tools. We then demonstrate the use of this approach for assessing the improvement in system reliability that can be achieved with discrete topological changes in the system. Our results suggest that adding redundancy, particularly through a mesh topology or through the addition of an optical fiber ring around the perimeter of the system can be an effective way to significantly increase the reliability of some cellular systems during hurricanes.

  8. The use of ambient humidity conditions to improve influenza forecast.

    Directory of Open Access Journals (Sweden)

    Jeffrey Shaman

    2017-11-01

    Full Text Available Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1-4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively. These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast.

  9. Using Instrument Simulators and a Satellite Database to Evaluate Microphysical Assumptions in High-Resolution Simulations of Hurricane Rita

    Science.gov (United States)

    Hristova-Veleva, S. M.; Chao, Y.; Chau, A. H.; Haddad, Z. S.; Knosp, B.; Lambrigtsen, B.; Li, P.; Martin, J. M.; Poulsen, W. L.; Rodriguez, E.; Stiles, B. W.; Turk, J.; Vu, Q.

    2009-12-01

    Improving forecasting of hurricane intensity remains a significant challenge for the research and operational communities. Many factors determine a tropical cyclone’s intensity. Ultimately, though, intensity is dependent on the magnitude and distribution of the latent heating that accompanies the hydrometeor production during the convective process. Hence, the microphysical processes and their representation in hurricane models are of crucial importance for accurately simulating hurricane intensity and evolution. The accurate modeling of the microphysical processes becomes increasingly important when running high-resolution models that should properly reflect the convective processes in the hurricane eyewall. There are many microphysical parameterizations available today. However, evaluating their performance and selecting the most representative ones remains a challenge. Several field campaigns were focused on collecting in situ microphysical observations to help distinguish between different modeling approaches and improve on the most promising ones. However, these point measurements cannot adequately reflect the space and time correlations characteristic of the convective processes. An alternative approach to evaluating microphysical assumptions is to use multi-parameter remote sensing observations of the 3D storm structure and evolution. In doing so, we could compare modeled to retrieved geophysical parameters. The satellite retrievals, however, carry their own uncertainty. To increase the fidelity of the microphysical evaluation results, we can use instrument simulators to produce satellite observables from the model fields and compare to the observed. This presentation will illustrate how instrument simulators can be used to discriminate between different microphysical assumptions. We will compare and contrast the members of high-resolution ensemble WRF model simulations of Hurricane Rita (2005), each member reflecting different microphysical assumptions

  10. Hurricane Katrina as a "teachable moment"

    Directory of Open Access Journals (Sweden)

    M. H. Glantz

    2008-04-01

    Full Text Available By American standards, New Orleans is a very old, very popular city in the southern part of the United States. It is located in Louisiana at the mouth of the Mississippi River, a river which drains about 40% of the Continental United States, making New Orleans a major port city. It is also located in an area of major oil reserves onshore, as well as offshore, in the Gulf of Mexico. Most people know New Orleans as a tourist hotspot; especially well-known is the Mardi Gras season at the beginning of Lent. People refer to the city as the "Big Easy". A recent biography of the city refers to it as the place where the emergence of modern tourism began. A multicultural city with a heavy French influence, it was part of the Louisiana Purchase from France in early 1803, when the United States bought it, doubling the size of the United States at that time.

    Today, in the year 2007, New Orleans is now known for the devastating impacts it withstood during the onslaught of Hurricane Katrina in late August 2005. Eighty percent of the city was submerged under flood waters. Almost two years have passed, and many individuals and government agencies are still coping with the hurricane's consequences. And insurance companies have been withdrawing their coverage for the region.

    The 2005 hurricane season set a record, in the sense that there were 28 named storms that calendar year. For the first time in hurricane forecast history, hurricane forecasters had to resort to the use of Greek letters to name tropical storms in the Atlantic and Gulf (Fig.~1.

    Hurricane Katrina was a Category 5 hurricane when it was in the middle of the Gulf of Mexico, after having passed across southern Florida. At landfall, Katrina's winds decreased in speed and it was relabeled as a Category 4. It devolved into a Category 3 hurricane as it passed inland when it did most of its damage. Large expanses of the city were inundated, many parts under water on

  11. Hurricane Katrina as a "teachable moment"

    Science.gov (United States)

    Glantz, M. H.

    2008-04-01

    By American standards, New Orleans is a very old, very popular city in the southern part of the United States. It is located in Louisiana at the mouth of the Mississippi River, a river which drains about 40% of the Continental United States, making New Orleans a major port city. It is also located in an area of major oil reserves onshore, as well as offshore, in the Gulf of Mexico. Most people know New Orleans as a tourist hotspot; especially well-known is the Mardi Gras season at the beginning of Lent. People refer to the city as the "Big Easy". A recent biography of the city refers to it as the place where the emergence of modern tourism began. A multicultural city with a heavy French influence, it was part of the Louisiana Purchase from France in early 1803, when the United States bought it, doubling the size of the United States at that time. Today, in the year 2007, New Orleans is now known for the devastating impacts it withstood during the onslaught of Hurricane Katrina in late August 2005. Eighty percent of the city was submerged under flood waters. Almost two years have passed, and many individuals and government agencies are still coping with the hurricane's consequences. And insurance companies have been withdrawing their coverage for the region. The 2005 hurricane season set a record, in the sense that there were 28 named storms that calendar year. For the first time in hurricane forecast history, hurricane forecasters had to resort to the use of Greek letters to name tropical storms in the Atlantic and Gulf (Fig.~1). Hurricane Katrina was a Category 5 hurricane when it was in the middle of the Gulf of Mexico, after having passed across southern Florida. At landfall, Katrina's winds decreased in speed and it was relabeled as a Category 4. It devolved into a Category 3 hurricane as it passed inland when it did most of its damage. Large expanses of the city were inundated, many parts under water on the order of 20 feet or so. The Ninth Ward, heavily

  12. Improving Post-Hurricane Katrina Forest Management with MODIS Time Series Products

    Science.gov (United States)

    Lewis, Mark David; Spruce, Joseph; Evans, David; Anderson, Daniel

    2012-01-01

    Hurricane damage to forests can be severe, causing millions of dollars of timber damage and loss. To help mitigate loss, state agencies require information on location, intensity, and extent of damaged forests. NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data products offers a potential means for state agencies to monitor hurricane-induced forest damage and recovery across a broad region. In response, a project was conducted to produce and assess 250 meter forest disturbance and recovery maps for areas in southern Mississippi impacted by Hurricane Katrina. The products and capabilities from the project were compiled to aid work of the Mississippi Institute for Forest Inventory (MIFI). A series of NDVI change detection products were computed to assess hurricane induced damage and recovery. Hurricane-induced forest damage maps were derived by computing percent change between MODIS MOD13 16-day composited NDVI pre-hurricane "baseline" products (2003 and 2004) and post-hurricane NDVI products (2005). Recovery products were then computed in which post storm 2006, 2007, 2008 and 2009 NDVI data was each singularly compared to the historical baseline NDVI. All percent NDVI change considered the 16-day composite period of August 29 to September 13 for each year in the study. This provided percent change in the maximum NDVI for the 2 week period just after the hurricane event and for each subsequent anniversary through 2009, resulting in forest disturbance products for 2005 and recovery products for the following 4 years. These disturbance and recovery products were produced for the Mississippi Institute for Forest Inventory's (MIFI) Southeast Inventory District and also for the entire hurricane impact zone. MIFI forest inventory products were used as ground truth information for the project. Each NDVI percent change product was classified into 6 categories of forest disturbance intensity. Stand age

  13. Hurricane Loss Estimation Models: Opportunities for Improving the State of the Art.

    Science.gov (United States)

    Watson, Charles C., Jr.; Johnson, Mark E.

    2004-11-01

    The results of hurricane loss models are used regularly for multibillion dollar decisions in the insurance and financial services industries. These models are proprietary, and this “black box” nature hinders analysis. The proprietary models produce a wide range of results, often producing loss costs that differ by a ratio of three to one or more. In a study for the state of North Carolina, 324 combinations of loss models were analyzed, based on a combination of nine wind models, four surface friction models, and nine damage models drawn from the published literature in insurance, engineering, and meteorology. These combinations were tested against reported losses from Hurricanes Hugo and Andrew as reported by a major insurance company, as well as storm total losses for additional storms. Annual loss costs were then computed using these 324 combinations of models for both North Carolina and Florida, and compared with publicly available proprietary model results in Florida. The wide range of resulting loss costs for open, scientifically defensible models that perform well against observed losses mirrors the wide range of loss costs computed by the proprietary models currently in use. This outcome may be discouraging for governmental and corporate decision makers relying on this data for policy and investment guidance (due to the high variability across model results), but it also provides guidance for the efforts of future investigations to improve loss models. Although hurricane loss models are true multidisciplinary efforts, involving meteorology, engineering, statistics, and actuarial sciences, the field of meteorology offers the most promising opportunities for improvement of the state of the art.

  14. Ensemble Streamflow Forecast Improvements in NYC's Operations Support Tool

    Science.gov (United States)

    Wang, L.; Weiss, W. J.; Porter, J.; Schaake, J. C.; Day, G. N.; Sheer, D. P.

    2013-12-01

    Like most other water supply utilities, New York City's Department of Environmental Protection (DEP) has operational challenges associated with drought and wet weather events. During drought conditions, DEP must maintain water supply reliability to 9 million customers as well as meet environmental release requirements downstream of its reservoirs. During and after wet weather events, DEP must maintain turbidity compliance in its unfiltered Catskill and Delaware reservoir systems and minimize spills to mitigate downstream flooding. Proactive reservoir management - such as release restrictions to prepare for a drought or preventative drawdown in advance of a large storm - can alleviate negative impacts associated with extreme events. It is important for water managers to understand the risks associated with proactive operations so unintended consequences such as endangering water supply reliability with excessive drawdown prior to a storm event are minimized. Probabilistic hydrologic forecasts are a critical tool in quantifying these risks and allow water managers to make more informed operational decisions. DEP has recently completed development of an Operations Support Tool (OST) that integrates ensemble streamflow forecasts, real-time observations, and a reservoir system operations model into a user-friendly graphical interface that allows its water managers to take robust and defensible proactive measures in the face of challenging system conditions. Since initial development of OST was first presented at the 2011 AGU Fall Meeting, significant improvements have been made to the forecast system. First, the monthly AR1 forecasts ('Hirsch method') were upgraded with a generalized linear model (GLM) utilizing historical daily correlations ('Extended Hirsch method' or 'eHirsch'). The development of eHirsch forecasts improved predictive skill over the Hirsch method in the first week to a month from the forecast date and produced more realistic hydrographs on the tail

  15. The effect of proximity to hurricanes Katrina and Rita on subsequent hurricane outlook and optimistic bias.

    Science.gov (United States)

    Trumbo, Craig; Lueck, Michelle; Marlatt, Holly; Peek, Lori

    2011-12-01

    This study evaluated how individuals living on the Gulf Coast perceived hurricane risk after Hurricanes Katrina and Rita. It was hypothesized that hurricane outlook and optimistic bias for hurricane risk would be associated positively with distance from the Katrina-Rita landfall (more optimism at greater distance), controlling for historically based hurricane risk and county population density, demographics, individual hurricane experience, and dispositional optimism. Data were collected in January 2006 through a mail survey sent to 1,375 households in 41 counties on the coast (n = 824, 60% response). The analysis used hierarchal regression to test hypotheses. Hurricane history and population density had no effect on outlook; individuals who were male, older, and with higher household incomes were associated with lower risk perception; individual hurricane experience and personal impacts from Katrina and Rita predicted greater risk perception; greater dispositional optimism predicted more optimistic outlook; distance had a small effect but predicted less optimistic outlook at greater distance (model R(2) = 0.21). The model for optimistic bias had fewer effects: age and community tenure were significant; dispositional optimism had a positive effect on optimistic bias; distance variables were not significant (model R(2) = 0.05). The study shows that an existing measure of hurricane outlook has utility, hurricane outlook appears to be a unique concept from hurricane optimistic bias, and proximity has at most small effects. Future extension of this research will include improved conceptualization and measurement of hurricane risk perception and will bring to focus several concepts involving risk communication. © 2011 Society for Risk Analysis.

  16. Improving weather forecasts for wind energy applications

    Science.gov (United States)

    Kay, Merlinde; MacGill, Iain

    2010-08-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms-1 and around 25 ms-1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  17. Improving weather forecasts for wind energy applications

    International Nuclear Information System (INIS)

    Kay, Merlinde; MacGill, Iain

    2010-01-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms -1 and around 25 ms -1 . A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  18. Use of temperature to improve West Nile virus forecasts.

    Directory of Open Access Journals (Sweden)

    Nicholas B DeFelice

    2018-03-01

    Full Text Available Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV transmission dynamics and spillover infection to humans. Here we explore whether inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that on average increased absolute forecast accuracy 5%, 10%, 12%, and 6%, respectively, over the non-temperature forced baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperature influences rates of WNV transmission. The findings provide a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs.

  19. THE ACCURACY AND BIAS EVALUATION OF THE USA UNEMPLOYMENT RATE FORECASTS. METHODS TO IMPROVE THE FORECASTS ACCURACY

    Directory of Open Access Journals (Sweden)

    MIHAELA BRATU (SIMIONESCU

    2012-12-01

    Full Text Available In this study some alternative forecasts for the unemployment rate of USA made by four institutions (International Monetary Fund (IMF, Organization for Economic Co-operation and Development (OECD, Congressional Budget Office (CBO and Blue Chips (BC are evaluated regarding the accuracy and the biasness. The most accurate predictions on the forecasting horizon 201-2011 were provided by IMF, followed by OECD, CBO and BC.. These results were gotten using U1 Theil’s statistic and a new method that has not been used before in literature in this context. The multi-criteria ranking was applied to make a hierarchy of the institutions regarding the accuracy and five important accuracy measures were taken into account at the same time: mean errors, mean squared error, root mean squared error, U1 and U2 statistics of Theil. The IMF, OECD and CBO predictions are unbiased. The combined forecasts of institutions’ predictions are a suitable strategy to improve the forecasts accuracy of IMF and OECD forecasts when all combination schemes are used, but INV one is the best. The filtered and smoothed original predictions based on Hodrick-Prescott filter, respectively Holt-Winters technique are a good strategy of improving only the BC expectations. The proposed strategies to improve the accuracy do not solve the problem of biasness. The assessment and improvement of forecasts accuracy have an important contribution in growing the quality of decisional process.

  20. Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

    2015-08-05

    Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

  1. Hurricane Loss Analysis Based on the Population-Weighted Index

    Directory of Open Access Journals (Sweden)

    Grzegorz Kakareko

    2017-08-01

    Full Text Available This paper discusses different measures for quantifying regional hurricane loss. The main measures used in the past are normalized percentage loss and dollar value loss. In this research, we show that these measures are useful but may not properly reflect the size of the population influenced by hurricanes. A new loss measure is proposed that reflects the hurricane impact on people occupying the structure. For demonstrating the differences among these metrics, regional loss analysis was conducted for Florida. The regional analysis was composed of three modules: the hazard module stochastically modeled the wind occurrence in the region; the vulnerability module utilized vulnerability functions developed in this research to calculate the loss; and the financial module quantified the hurricane loss. In the financial module, we calculated three loss metrics for certain region. The first metric is the average annual loss (AAL which represents the expected loss per year in percentage. The second is the average annual dollar loss which represents the expected dollar amount loss per year. The third is the average annual population-weighted loss (AAPL—a new measure proposed in this research. Compared with the AAL, the AAPL reflects the number of people influenced by the hurricane. The advantages of the AAPL are illustrated using three different analysis examples: (1 conventional regional loss analysis, (2 mitigation potential analysis, and (3 forecasted future loss analysis due to the change in population.

  2. The impact of Saharan Dust on the genesis and evolution of Hurricane Earl (2010)

    Science.gov (United States)

    Pan, B.; Wang, Y.; Hsieh, J. S.; Lin, Y.; Hu, J.; Zhang, R.

    2017-12-01

    Dust, one of the most abundant natural aerosols, can exert substantial radiative and microphysical effects on the regional climate and has potential impacts on the genesis and intensification of tropical cyclones (TCs). A Weather Research and Forecasting Model and the Regional Oceanic Modeling System coupled model (WRF-ROMS) is used to simulate the evolution of Hurricane Earl (2010), of which Earl was interfered by Saharan dust at the TC genesis stage. A new dust module has been implemented to the TAMU two-moment microphysics scheme in the WRF model. It accounts for both dust as Cloud Condensation Nuclei (CCN) and Ice Nuclei (IN). The hurricane track, intensity and precipitation have been compared to the best track data and TRMM precipitation, respectively. The influences of Saharan dust on Hurricane Earl are investigated with dust-CCN, dust-IN, and dust-free scenarios. The analysis shows that Saharan dust changes the latent heat and moisture distribution, invigorates the convections in the hurricane's eyewall, and suppresses the development of Earl. This finding addresses the importance of accounting dust microphysics effect on hurricane predictions.

  3. Value of Improved Wind Power Forecasting in the Western Interconnection (Poster)

    Energy Technology Data Exchange (ETDEWEB)

    Hodge, B.

    2013-12-01

    Wind power forecasting is a necessary and important technology for incorporating wind power into the unit commitment and dispatch process. It is expected to become increasingly important with higher renewable energy penetration rates and progress toward the smart grid. There is consensus that wind power forecasting can help utility operations with increasing wind power penetration; however, there is far from a consensus about the economic value of improved forecasts. This work explores the value of improved wind power forecasting in the Western Interconnection of the United States.

  4. Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

    Science.gov (United States)

    Zhu, Jiang; Stevens, E.; Zavodsky, B. T.; Zhang, X.; Heinrichs, T.; Broderson, D.

    2014-01-01

    Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.

  5. Improving Artificial Neural Network Forecasts with Kalman Filtering ...

    African Journals Online (AJOL)

    In this paper, we examine the use of the artificial neural network method as a forecasting technique in financial time series and the application of a Kalman filter algorithm to improve the accuracy of the model. Forecasting accuracy criteria are used to compare the two models over different set of data from different companies ...

  6. Enhanced short-term wind power forecasting and value to grid operations. The wind forecasting improvement project

    Energy Technology Data Exchange (ETDEWEB)

    Orwig, Kirsten D. [National Renewable Energy Laboratory (NREL), Golden, CO (United States). Transmission Grid Integration; Benjamin, Stan; Wilczak, James; Marquis, Melinda [National Oceanic and Atmospheric Administration, Boulder, CO (United States). Earth System Research Lab.; Stern, Andrew [National Oceanic and Atmospheric Administration, Silver Spring, MD (United States); Clark, Charlton; Cline, Joel [U.S. Department of Energy, Washington, DC (United States). Wind and Water Power Program; Finley, Catherine [WindLogics, Grand Rapids, MN (United States); Freedman, Jeffrey [AWS Truepower, Albany, NY (United States)

    2012-07-01

    The current state-of-the-art wind power forecasting in the 0- to 6-h timeframe has levels of uncertainty that are adding increased costs and risks to the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: (1) a one-year field measurement campaign within two regions; (2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and (3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provide an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis. (orig.)

  7. A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

    Science.gov (United States)

    Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

    2017-04-01

    Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of

  8. The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Freedman, Jeffrey M. [AWS Truepower, LLC, Albany, NY (United States); Manobianco, John [MESO, Inc., Troy, NY (United States); Schroeder, John [Texas Tech Univ., Lubbock, TX (United States). National Wind Inst.; Ancell, Brian [Texas Tech Univ., Lubbock, TX (United States). Atmospheric Science Group; Brewster, Keith [Univ. of Oklahoma, Norman, OK (United States). Center for Analysis and Prediction of Storms; Basu, Sukanta [North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth, and Atmospheric Sciences; Banunarayanan, Venkat [ICF International (United States); Hodge, Bri-Mathias [National Renewable Energy Lab. (NREL), Golden, CO (United States); Flores, Isabel [Electricity Reliability Council of Texas (United States)

    2014-04-30

    This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10 - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.

  9. Use of Temperature to Improve West Nile Virus Forecasts

    Science.gov (United States)

    Shaman, J. L.; DeFelice, N.; Schneider, Z.; Little, E.; Barker, C.; Caillouet, K.; Campbell, S.; Damian, D.; Irwin, P.; Jones, H.; Townsend, J.

    2017-12-01

    Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether the inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that were on average 5%, 10%, 12%, and 6% more accurate, respectively, than the baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperatures influence rates of WNV transmission. The findings help build a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs.

  10. EnKF OSSE Experiments Assessing the Impact of HIRAD Wind Speed and HIWRAP Radial Velocity Data on Analysis of Hurricane Karl (2010)

    Science.gov (United States)

    Albers, Cerese; Sippel, Jason A.; Braun, Scott A.; Miller, Timothy

    2012-01-01

    Previous studies (e.g., Zhang et al. 2009, Weng et al. 2011) have shown that radial velocity data from airborne and ground-based radars can be assimilated into ensemble Kalman filter (EnKF) systems to produce accurate analyses of tropical cyclone vortices, which can reduce forecast intensity error. Recently, wind speed data from SFMR technology has also been assimilated into the same types of systems and has been shown to improve the forecast intensity of mature tropical cyclones. Two instruments that measure these properties were present during the NASA Genesis and Rapid Intensification Processes (GRIP) field experiment in 2010 which sampled Hurricane Karl, and will next be co-located on the same aircraft for the subsequent NASA HS3 experiment. The High Altitude Wind and Rain Profiling Radar (HIWRAP) is a conically scanning Doppler radar mounted upon NASAs Global Hawk unmanned aerial vehicle, and the usefulness of its radial velocity data for assimilation has not been previously examined. Since the radar scans from above with a fairly large fixed elevation angle, it observes a large component of the vertical wind, which could degrade EnKF analyses compared to analyses with data taken from lesser elevation angles. The NASA Hurricane Imaging Radiometer (HIRAD) is a passive microwave radiometer similar to SFMR, and measures emissivity and retrieves hurricane surface wind speeds and rain rates over a much wider swath. Thus, this study examines the impact of assimilating simulated HIWRAP radial velocity data into an EnKF system, simulated HIRAD wind speed, and HIWRAP+HIRAD with the Weather Research and Forecasting (WRF) model and compares the results to no data assimilation and also to the Truth from which the data was simulated for both instruments.

  11. Forecasting Dry Bulk Freight Index with Improved SVM

    Directory of Open Access Journals (Sweden)

    Qianqian Han

    2014-01-01

    Full Text Available An improved SVM model is presented to forecast dry bulk freight index (BDI in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

  12. Studying and Improving Human Response to Natural Hazards: Lessons from the Virtual Hurricane Lab

    Science.gov (United States)

    Meyer, R.; Broad, K.; Orlove, B. S.

    2010-12-01

    threat. From a basic research perspective the data provide valuable potential insights into the dynamics of information gathering prior to hurricane impacts, as well as laboratory in which we can study how both information gathering and responses varies in responses to controlled variations in such factors as the complexity of forecast information. From an applied perspective the simulations provide an opportunity for residents in hazard-prone areas to learn about different kinds of information and receive feedback on their potential biases prior to an actual encounter with a hazard. The presentation concludes with a summary of some of the basic research findings that have emerged from the hurricane lab to date, as well as a discussion of the prospects for extending the technology to a broad range of environmental hazards.

  13. Improved NN-GM(1,1 for Postgraduates’ Employment Confidence Index Forecasting

    Directory of Open Access Journals (Sweden)

    Lu Wang

    2014-01-01

    Full Text Available Postgraduates’ employment confidence index (ECI forecasting can help the university to predict the future trend of postgraduates’ employment. However, the common forecast method based on the grey model (GM has unsatisfactory performance to a certain extent. In order to forecast postgraduates’ ECI efficiently, this paper discusses a novel hybrid forecast model using limited raw samples. Different from previous work, the residual modified GM(1,1 model is combined with the improved neural network (NN in this work. In particullar, the hybrid model reduces the residue of the standard GM(1,1 model as well as accelerating the convergence rate of the standard NN. After numerical studies, the illustrative results are provided to demonstrate the forecast performance of the proposed model. In addition, some strategies for improving the postgraduates’ employment confidence have been discussed.

  14. Year-ahead prediction of US landfalling hurricane numbers: intense hurricanes

    OpenAIRE

    Khare, Shree; Jewson, Stephen

    2005-01-01

    We continue with our program to derive simple practical methods that can be used to predict the number of US landfalling hurricanes a year in advance. We repeat an earlier study, but for a slightly different definition landfalling hurricanes, and for intense hurricanes only. We find that the averaging lengths needed for optimal predictions of numbers of intense hurricanes are longer than those needed for optimal predictions of numbers of hurricanes of all strengths.

  15. The communicative process of weather forecasts issued in the probabilistic form

    Directory of Open Access Journals (Sweden)

    Alessio Raimondi

    2009-03-01

    Full Text Available One of the main purposes of weather forecasting is that of protecting weather-sensitive human activities. Forecasts issued in the probabilistic form have a higher informative content, as opposed to deterministic one, since they bear information that give also a measure of their own uncertainty. However, in order to make an appropriate and effective use of this kind of forecasts in an operational setting, communication becomes significatively relevant.The present paper, after having briefly examined the weather forecasts concerning Hurricane Charley (August 2004, tackles the issue of the communicative process in detail.The bottom line of this study is that for the weather forecast to achieve its best predictive potential, an in-depth analysis of communication issues is necessary.

  16. Environmental Modeling, Technology, and Communication for Land Falling Tropical Cyclone/Hurricane Prediction

    Directory of Open Access Journals (Sweden)

    Paul Tchounwou

    2010-04-01

    Full Text Available Katrina (a tropical cyclone/hurricane began to strengthen reaching a Category 5 storm on 28th August, 2005 and its winds reached peak intensity of 175 mph and pressure levels as low as 902 mb. Katrina eventually weakened to a category 3 storm and made a landfall in Plaquemines Parish, Louisiana, Gulf of Mexico, south of Buras on 29th August 2005. We investigate the time series intensity change of the hurricane Katrina using environmental modeling and technology tools to develop an early and advanced warning and prediction system. Environmental Mesoscale Model (Weather Research Forecast, WRF simulations are used for prediction of intensity change and track of the hurricane Katrina. The model is run on a doubly nested domain centered over the central Gulf of Mexico, with grid spacing of 90 km and 30 km for 6 h periods, from August 28th to August 30th. The model results are in good agreement with the observations suggesting that the model is capable of simulating the surface features, intensity change and track and precipitation associated with hurricane Katrina. We computed the maximum vertical velocities (Wmax using Convective Available Kinetic Energy (CAPE obtained at the equilibrium level (EL, from atmospheric soundings over the Gulf Coast stations during the hurricane land falling for the period August 21–30, 2005. The large vertical atmospheric motions associated with the land falling hurricane Katrina produced severe weather including thunderstorms and tornadoes 2–3 days before landfall. The environmental modeling simulations in combination with sounding data show that the tools may be used as an advanced prediction and communication system (APCS for land falling tropical cyclones/hurricanes.

  17. An intercomparison of approaches for improving operational seasonal streamflow forecasts

    Science.gov (United States)

    Mendoza, Pablo A.; Wood, Andrew W.; Clark, Elizabeth; Rothwell, Eric; Clark, Martyn P.; Nijssen, Bart; Brekke, Levi D.; Arnold, Jeffrey R.

    2017-07-01

    For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches - statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) - and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction - HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically

  18. Requirements and benefits of flow forecasting for improving hydropower generation

    NARCIS (Netherlands)

    Dong, Xiaohua; Vrijling, J.K.; Dohmen-Janssen, Catarine M.; Ruigh, E.; Booij, Martijn J.; Stalenberg, B.; Hulscher, Suzanne J.M.H.; van Gelder, P.H.A.J.M.; Verlaan, M.; Zijderveld, A.; Waarts, P.

    2005-01-01

    This paper presents a methodology to identify the required lead time and accuracy of flow forecasting for improving hydropower generation of a reservoir, by simulating the benefits (in terms of electricity generated) obtained from the forecasting with varying lead times and accuracies. The

  19. Power Scaling of the Size Distribution of Economic Loss and Fatalities due to Hurricanes, Earthquakes, Tornadoes, and Floods in the USA

    Science.gov (United States)

    Tebbens, S. F.; Barton, C. C.; Scott, B. E.

    2016-12-01

    Traditionally, the size of natural disaster events such as hurricanes, earthquakes, tornadoes, and floods is measured in terms of wind speed (m/sec), energy released (ergs), or discharge (m3/sec) rather than by economic loss or fatalities. Economic loss and fatalities from natural disasters result from the intersection of the human infrastructure and population with the size of the natural event. This study investigates the size versus cumulative number distribution of individual natural disaster events for several disaster types in the United States. Economic losses are adjusted for inflation to 2014 USD. The cumulative number divided by the time over which the data ranges for each disaster type is the basis for making probabilistic forecasts in terms of the number of events greater than a given size per year and, its inverse, return time. Such forecasts are of interest to insurers/re-insurers, meteorologists, seismologists, government planners, and response agencies. Plots of size versus cumulative number distributions per year for economic loss and fatalities are well fit by power scaling functions of the form p(x) = Cx-β; where, p(x) is the cumulative number of events with size equal to and greater than size x, C is a constant, the activity level, x is the event size, and β is the scaling exponent. Economic loss and fatalities due to hurricanes, earthquakes, tornadoes, and floods are well fit by power functions over one to five orders of magnitude in size. Economic losses for hurricanes and tornadoes have greater scaling exponents, β = 1.1 and 0.9 respectively, whereas earthquakes and floods have smaller scaling exponents, β = 0.4 and 0.6 respectively. Fatalities for tornadoes and floods have greater scaling exponents, β = 1.5 and 1.7 respectively, whereas hurricanes and earthquakes have smaller scaling exponents, β = 0.4 and 0.7 respectively. The scaling exponents can be used to make probabilistic forecasts for time windows ranging from 1 to 1000 years

  20. Examining Dense Data Usage near the Regions with Severe Storms in All-Sky Microwave Radiance Data Assimilation and Impacts on GEOS Hurricane Analyses

    Science.gov (United States)

    Kim, Min-Jeong; Jin, Jianjun; McCarty, Will; El Akkraoui, Amal; Todling, Ricardo; Gelaro, Ron

    2018-01-01

    Many numerical weather prediction (NWP) centers assimilate radiances affected by clouds and precipitation from microwave sensors, with the expectation that these data can provide critical constraints on meteorological parameters in dynamically sensitive regions to make significant impacts on forecast accuracy for precipitation. The Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center assimilates all-sky microwave radiance data from various microwave sensors such as all-sky GPM Microwave Imager (GMI) radiance in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS), which includes the GEOS atmospheric model, the Gridpoint Statistical Interpolation (GSI) atmospheric analysis system, and the Goddard Aerosol Assimilation System (GAAS). So far, most of NWP centers apply same large data thinning distances, that are used in clear-sky radiance data to avoid correlated observation errors, to all-sky microwave radiance data. For example, NASA GMAO is applying 145 km thinning distances for most of satellite radiance data including microwave radiance data in which all-sky approach is implemented. Even with these coarse observation data usage in all-sky assimilation approach, noticeable positive impacts from all-sky microwave data on hurricane track forecasts were identified in GEOS-5 system. The motivation of this study is based on the dynamic thinning distance method developed in our all-sky framework to use of denser data in cloudy and precipitating regions due to relatively small spatial correlations of observation errors. To investigate the benefits of all-sky microwave radiance on hurricane forecasts, several hurricane cases selected between 2016-2017 are examined. The dynamic thinning distance method is utilized in our all-sky approach to understand the sources and mechanisms to explain the benefits of all-sky microwave radiance data from various microwave radiance sensors like Advanced Microwave Sounder Unit

  1. Impact of GPM Rainrate Data Assimilation on Simulation of Hurricane Harvey (2017)

    Science.gov (United States)

    Li, Xuanli; Srikishen, Jayanthi; Zavodsky, Bradley; Mecikalski, John

    2018-01-01

    Built upon Tropical Rainfall Measuring Mission (TRMM) legacy for next-generation global observation of rain and snow. The GPM was launched in February 2014 with Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) onboard. The GPM has a broad global coverage approximately 70deg S -70deg N with a swath of 245/125-km for the Ka (35.5 GHz)/Ku (13.6 GHz) band radar, and 850-km for the 13-channel GMI. GPM also features better retrievals for heavy, moderate, and light rain and snowfall To develop methodology to assimilate GPM surface precipitation data with Grid-point Statistical Interpolation (GSI) data assimilation system and WRF ARW model To investigate the potential and the value of utilizing GPM observation into NWP for operational environment The GPM rain rate data has been successfully assimilated using the GSI rain data assimilation package. Impacts of rain rate data have been found in temperature and moisture fields of initial conditions. 2.Assimilation of either GPM IMERG or GPROF rain product produces significant improvement in precipitation amount and structure for Hurricane Harvey (2017) forecast. Since IMERG data is available half-hourly, further forecast improvement is expected with continuous assimilation of IMERG data

  2. Hurricane Impacts to Tropical and Temperate Forest Landscapes

    OpenAIRE

    Boose, Emery Robert; Foster, David Russell; Fluet, Marcheterre

    1994-01-01

    Hurricanes represent an important natural disturbance process to tropical and temperate forests in many coastal areas of the world. The complex patterns of damage created in forests by hurricane winds result from the interaction of meteorological, physiographic, and biotic factors on a range of spatial scales. To improve our understanding of these factors and of the role of catastrophic hurricane wind as a disturbance process, we take an integrative approach. A simple meteorological model (HU...

  3. Hurricane Havoc - Mapping the Mayhem with NOAA's National Water Model

    Science.gov (United States)

    Aggett, G. R.; Stone, M.

    2017-12-01

    With Hurricane Irene as an example, this work demonstrates the versatility of NOAA's new National Water Model (NWM) as a tool for analyzing hydrologic hazards before, during, and after events. Hurricane Irene made landfall on the coast of North Carolina on August 27, 2011, and made its way up the East Coast over the next 3 days. This storm caused widespread flooding across the Northeast, where rain totals over 20" and wind speeds of 100mph were recorded, causing loss of life and significant damage to infrastructure. Large portions of New York and Vermont were some of the hardest hit areas. This poster will present a suite of post-processed products, derived from NWM output, that are currently being developed at NOAA's National Water Center in Tuscaloosa, AL. The National Water Model is allowing NOAA to expand its water prediction services to the approximately 2.7 million stream reaches across the U.S. The series of forecasted and real-time analysis products presented in this poster will demonstrate the strides NOAA is taking to increase preparedness and aid response to severe hydrologic events, like Hurricane Irene.

  4. A Comparison of HWRF, ARW and NMM Models in Hurricane Katrina (2005 Simulation

    Directory of Open Access Journals (Sweden)

    Anjaneyulu Yerramilli

    2011-06-01

    Full Text Available The life cycle of Hurricane Katrina (2005 was simulated using three different modeling systems of Weather Research and Forecasting (WRF mesoscale model. These are, HWRF (Hurricane WRF designed specifically for hurricane studies and WRF model with two different dynamic cores as the Advanced Research WRF (ARW model and the Non-hydrostatic Mesoscale Model (NMM. The WRF model was developed and sourced from National Center for Atmospheric Research (NCAR, incorporating the advances in atmospheric simulation system suitable for a broad range of applications. The HWRF modeling system was developed at the National Centers for Environmental Prediction (NCEP based on the NMM dynamic core and the physical parameterization schemes specially designed for tropics. A case study of Hurricane Katrina was chosen as it is one of the intense hurricanes that caused severe destruction along the Gulf Coast from central Florida to Texas. ARW, NMM and HWRF models were designed to have two-way interactive nested domains with 27 and 9 km resolutions. The three different models used in this study were integrated for three days starting from 0000 UTC of 27 August 2005 to capture the landfall of hurricane Katrina on 29 August. The initial and time varying lateral boundary conditions were taken from NCEP global FNL (final analysis data available at 1 degree resolution for ARW and NMM models and from NCEP GFS data at 0.5 degree resolution for HWRF model. The results show that the models simulated the intensification of Hurricane Katrina and the landfall on 29 August 2005 agreeing with the observations. Results from these experiments highlight the superior performance of HWRF model over ARW and NMM models in predicting the track and intensification of Hurricane Katrina.

  5. Sales Growth Rate Forecasting Using Improved PSO and SVM

    Directory of Open Access Journals (Sweden)

    Xibin Wang

    2014-01-01

    Full Text Available Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO for sales growth rate forecasting. We use support vector machine (SVM as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

  6. Simulating the effects of social networks on a population's hurricane evacuation participation

    Science.gov (United States)

    Widener, Michael J.; Horner, Mark W.; Metcalf, Sara S.

    2013-04-01

    Scientists have noted that recent shifts in the earth's climate have resulted in more extreme weather events, like stronger hurricanes. Such powerful storms disrupt societal function and result in a tremendous number of casualties, as demonstrated by recent hurricane experience in the US Planning for and facilitating evacuations of populations forecast to be impacted by hurricanes is perhaps the most effective strategy for reducing risk. A potentially important yet relatively unexplored facet of people's evacuation decision-making involves the interpersonal communication processes that affect whether at-risk residents decide to evacuate. While previous research has suggested that word-of-mouth effects are limited, data supporting these assertions were collected prior to the widespread adoption of digital social media technologies. This paper argues that the influence of social network effects on evacuation decisions should be revisited given the potential of new social media for impacting and augmenting information dispersion through real-time interpersonal communication. Using geographic data within an agent-based model of hurricane evacuation in Bay County, Florida, we examine how various types of social networks influence participation in evacuation. It is found that strategies for encouraging evacuation should consider the social networks influencing individuals during extreme events, as it can be used to increase the number of evacuating residents.

  7. Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements

    Energy Technology Data Exchange (ETDEWEB)

    Optis, Michael [National Renewable Energy Lab. (NREL), Golden, CO (United States); Scott, George N. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Draxl, Caroline [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2018-02-02

    The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present. Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.

  8. An intercomparison of approaches for improving operational seasonal streamflow forecasts

    Directory of Open Access Journals (Sweden)

    P. A. Mendoza

    2017-07-01

    Full Text Available For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches – statistical regression against IHCs and model-based ensemble streamflow prediction (ESP – and then systematically intercompare WSFs across a range of lead times. Additional methods include (i statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction – HESP provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1 objective approaches supporting

  9. Data Assimilation at FLUXNET to Improve Models towards Ecological Forecasting (Invited)

    Science.gov (United States)

    Luo, Y.

    2009-12-01

    Dramatically increased volumes of data from observational and experimental networks such as FLUXNET call for transformation of ecological research to increase its emphasis on quantitative forecasting. Ecological forecasting will also meet the societal need to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-based models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today’s ecological models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting is data assimilation, which uses data to inform initial conditions and to help constrain a model during simulation to yield results that approximate reality as closely as possible. In an era with dramatically increased availability of data from observational and experimental networks, data assimilation is a key technique that helps convert the raw data into ecologically meaningful products so as to accelerate our understanding of ecological processes, test ecological theory, forecast changes in ecological services, and better serve the society. This talk will use examples to illustrate how data from FLUXNET have been assimilated with process-based models to improve estimates of model parameters and state variables; to quantify uncertainties in ecological forecasting arising from observations, models and their interactions; and to evaluate information contributions of data and model toward short- and long-term forecasting of ecosystem responses to global change.

  10. Improving operational flood forecasting through data assimilation

    Science.gov (United States)

    Rakovec, Oldrich; Weerts, Albrecht; Uijlenhoet, Remko; Hazenberg, Pieter; Torfs, Paul

    2010-05-01

    Accurate flood forecasts have been a challenging topic in hydrology for decades. Uncertainty in hydrological forecasts is due to errors in initial state (e.g. forcing errors in historical mode), errors in model structure and parameters and last but not least the errors in model forcings (weather forecasts) during the forecast mode. More accurate flood forecasts can be obtained through data assimilation by merging observations with model simulations. This enables to identify the sources of uncertainties in the flood forecasting system. Our aim is to assess the different sources of error that affect the initial state and to investigate how they propagate through hydrological models with different levels of spatial variation, starting from lumped models. The knowledge thus obtained can then be used in a data assimilation scheme to improve the flood forecasts. This study presents the first results of this framework and focuses on quantifying precipitation errors and its effect on discharge simulations within the Ourthe catchment (1600 km2), which is situated in the Belgian Ardennes and is one of the larger subbasins of the Meuse River. Inside the catchment, hourly rain gauge information from 10 different locations is available over a period of 15 years. Based on these time series, the bootstrap method has been applied to generate precipitation ensembles. These were then used to simulate the catchment's discharges at the outlet. The corresponding streamflow ensembles were further assimilated with observed river discharges to update the model states of lumped hydrological models (R-PDM, HBV) through Residual Resampling. This particle filtering technique is a sequential data assimilation method and takes no prior assumption of the probability density function for the model states, which in contrast to the Ensemble Kalman filter does not have to be Gaussian. Our further research will be aimed at quantifying and reducing the sources of uncertainty that affect the initial

  11. Improving Garch Volatility Forecasts

    NARCIS (Netherlands)

    Klaassen, F.J.G.M.

    1998-01-01

    Many researchers use GARCH models to generate volatility forecasts. We show, however, that such forecasts are too variable. To correct for this, we extend the GARCH model by distinguishing two regimes with different volatility levels. GARCH effects are allowed within each regime, so that our model

  12. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid (Spanish Version)

    Energy Technology Data Exchange (ETDEWEB)

    Tian, Tian; Chernyakhovskiy, Ilya; Brancucci Martinez-Anido, Carlo

    2016-04-01

    This document is the Spanish version of 'Greening the Grid- Forecasting Wind and Solar Generation Improving System Operations'. It discusses improving system operations with forecasting with and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  13. Estimating the human influence on Hurricanes Harvey, Irma and Maria

    Science.gov (United States)

    Wehner, M. F.; Patricola, C. M.; Risser, M. D.

    2017-12-01

    Attribution of the human-induced climate change influence on the physical characteristics of individual extreme weather events has become an advanced science over the past decade. However, it is only recently that such quantification of anthropogenic influences on event magnitudes and probability of occurrence could be applied to very extreme storms such as hurricanes. We present results from two different classes of attribution studies for the impactful Atlantic hurricanes of 2017. The first is an analysis of the record rainfall amounts during Hurricane Harvey in the Houston, Texas area. We analyzed observed precipitation from the Global Historical Climatology Network with a covariate-based extreme value statistical analysis, accounting for both the external influence of global warming and the internal influence of ENSO. We found that human-induced climate change likely increased Hurricane Harvey's total rainfall by at least 19%, and likely increased the chances of the observed rainfall by a factor of at least 3.5. This suggests that changes exceeded Clausius-Clapeyron scaling, motivating attribution studies using dynamical climate models. The second analysis consists of two sets of hindcast simulations of Hurricanes Harvey, Irma, and Maria using the Weather Research and Forecasting model (WRF) at 4.5 km resolution. The first uses realistic boundary and initial conditions and present-day greenhouse gas forcings while the second uses perturbed conditions and pre-industrial greenhouse has forcings to simulate counterfactual storms without anthropogenic influences. These simulations quantify the fraction of Harvey's precipitation attributable to human activities and test the super Clausius-Clapeyron scaling suggested by the observational analysis. We will further quantify the human influence on intensity for Harvey, Irma, and Maria.

  14. Rapid-response flood mapping during Hurricanes Harvey, Irma and Maria by the Global Flood Partnership (GFP)

    Science.gov (United States)

    Cohen, S.; Alfieri, L.; Brakenridge, G. R.; Coughlan, E.; Galantowicz, J. F.; Hong, Y.; Kettner, A.; Nghiem, S. V.; Prados, A. I.; Rudari, R.; Salamon, P.; Trigg, M.; Weerts, A.

    2017-12-01

    The Global Flood Partnership (GFP; https://gfp.jrc.ec.europa.eu) is a multi-disciplinary group of scientists, operational agencies and flood risk managers focused on developing efficient and effective global flood management tools. Launched in 2014, its aim is to establish a partnership for global flood forecasting, monitoring and impact assessment to strengthen preparedness and response and to reduce global disaster losses. International organizations, the private sector, national authorities, universities and research agencies contribute to the GFP on a voluntary basis and benefit from a global network focused on flood risk reduction. At the onset of Hurricane Harvey, GFP was `activated' using email requests via its mailing service. Soon after, flood inundation maps, based on remote sensing analysis and modeling, were shared by different agencies, institutions, and individuals. These products were disseminated, to varying degrees of effectiveness, to federal, state and local agencies via emails and data-sharing services. This generated a broad data-sharing network which was utilized at the early stages of Hurricane Irma's impact, just two weeks after Harvey. In this presentation, we will describe the extent and chronology of the GFP response to both Hurricanes Harvey, Irma and Maria. We will assess the potential usefulness of this effort for event managers in various types of organizations and discuss future improvements to be implemented.

  15. An Extended Forecast of the Frequencies of North Atlantic Basin Tropical Cyclone Activity for 2009

    Science.gov (United States)

    Wilson, Robert M.

    2009-01-01

    An extended forecast of the frequencies for the 2009 North Atlantic basin hurricane season is presented. Continued increased activity during the 2009 season with numbers of tropical cyclones, hurricanes, and major hurricanes exceeding long-term averages are indicated. Poisson statistics for the combined high-activity intervals (1950-1965 and 1995-2008) give the central 50% intervals to be 9-14, 5-8, and 2-4, respectively, for the number of tropical cyclones, hurricanes, and major hurricanes, with a 23.4% chance of exceeding 14 tropical cyclones, a 28% chance of exceeding 8 hurricanes, and a 31.9% chance of exceeding 4 major hurricanes. Based strictly on the statistics of the current high-activity interval (1995-2008), the central 50% intervals for the numbers of tropical cyclones, hurricanes, and major hurricanes are 12-18, 6-10, and 3-5, respectively, with only a 5% chance of exceeding 23, 13, or 7 storms, respectively. Also examined are the first differences in 10-yr moving averages and the effects of global warming and decadal-length oscillations on the frequencies of occurrence for North Atlantic basin tropical cyclones. In particular, temperature now appears to be the principal driver of increased activity and storm strength during the current high-activity interval, with near-record values possible during the 2009 season.

  16. Daily variation in natural disaster casualties: information flows, safety, and opportunity costs in tornado versus hurricane strikes.

    Science.gov (United States)

    Zahran, Sammy; Tavani, Daniele; Weiler, Stephan

    2013-07-01

    Casualties from natural disasters may depend on the day of the week they strike. With data from the Spatial Hazard Events and Losses Database for the United States (SHELDUS), daily variation in hurricane and tornado casualties from 5,043 tornado and 2,455 hurricane time/place events is analyzed. Hurricane forecasts provide at-risk populations with considerable lead time. Such lead time allows strategic behavior in choosing protective measures under hurricane threat; opportunity costs in terms of lost income are higher during weekdays than during weekends. On the other hand, the lead time provided by tornadoes is near zero; hence tornados generate no opportunity costs. Tornado casualties are related to risk information flows, which are higher during workdays than during leisure periods, and are related to sheltering-in-place opportunities, which are better in permanent buildings like businesses and schools. Consistent with theoretical expectations, random effects negative binomial regression results indicate that tornado events occurring on the workdays of Monday through Thursday are significantly less lethal than tornados that occur on weekends. In direct contrast, and also consistent with theory, the expected count of hurricane casualties increases significantly with weekday occurrences. The policy implications of observed daily variation in tornado and hurricane events are considered. © 2012 Society for Risk Analysis.

  17. Synergizing two NWP models to improve hub-height wind speed forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Liu, H. [Ortech International, Mississauga, ON (Canada); Taylor, P. [York Univ., Toronto, ON (Canada)

    2010-07-01

    This PowerPoint presentation discussed some of the methods used to optimize hub-height wind speed forecasts. Statistical and physical forecast paradigms were considered. Forecast errors are often dictated by phase error, while refined NWP modelling is limited by data availability. A nested meso-scale NWP model was combined with a physical model to predict wind and power forecasts. Maps of data sources were included as well as equations used to derive predictions. Data from meteorological masts located near the Great Lakes were used to demonstrate the model. The results were compared with other modelling prediction methods. Forecasts obtained using the modelling approach can help operators in scheduling and trading procedures. Further research is being conducted to determine if the model can be used to improve ramp forecasts. tabs., figs.

  18. Hurricane Evacuation Routes

    Data.gov (United States)

    Department of Homeland Security — Hurricane Evacuation Routes in the United States A hurricane evacuation route is a designated route used to direct traffic inland in case of a hurricane threat. This...

  19. Hurricane Resource Reel

    Data.gov (United States)

    National Aeronautics and Space Administration — This Reel Includes the Following Sections TRT 50:10 Hurricane Overviews 1:02; Hurricane Arthur 15:07; Cyclone Pam 19:48; Typhoon Hagupit 21:27; Hurricane Bertha...

  20. Coastal emergency managers' preferences for storm surge forecast communication.

    Science.gov (United States)

    Morrow, Betty Hearn; Lazo, Jeffrey K

    2014-01-01

    Storm surge, the most deadly hazard associated with tropical and extratropical cyclones, is the basis for most evacuation decisions by authorities. One factor believed to be associated with evacuation noncompliance is a lack of understanding of storm surge. To address this problem, federal agencies responsible for cyclone forecasts are seeking more effective ways of communicating storm surge threat. To inform this process, they are engaging various partners in the forecast and warning process.This project focuses on emergency managers. Fifty-three emergency managers (EMs) from the Gulf and lower Atlantic coasts were surveyed to elicit their experience with, sources of, and preferences for storm surge information. The emergency managers-who are well seasoned in hurricane response and generally rate the surge risk in their coastal areas above average or extremely high-listed storm surge as their major concern with respect to hurricanes. They reported a general lack of public awareness about surge. Overall they support new ways to convey the potential danger to the public, including the issuance of separate storm surge watches and warnings, and the expression of surge heights using feet above ground level. These EMs would like more maps, graphics, and visual materials for use in communicating with the public. An important concern is the timing of surge forecasts-whether they receive them early enough to be useful in their evacuation decisions.

  1. Ocean surface waves in Hurricane Ike (2008) and Superstorm Sandy (2012): Coupled model predictions and observations

    Science.gov (United States)

    Chen, Shuyi S.; Curcic, Milan

    2016-07-01

    Forecasting hurricane impacts of extreme winds and flooding requires accurate prediction of hurricane structure and storm-induced ocean surface waves days in advance. The waves are complex, especially near landfall when the hurricane winds and water depth varies significantly and the surface waves refract, shoal and dissipate. In this study, we examine the spatial structure, magnitude, and directional spectrum of hurricane-induced ocean waves using a high resolution, fully coupled atmosphere-wave-ocean model and observations. The coupled model predictions of ocean surface waves in Hurricane Ike (2008) over the Gulf of Mexico and Superstorm Sandy (2012) in the northeastern Atlantic and coastal region are evaluated with the NDBC buoy and satellite altimeter observations. Although there are characteristics that are general to ocean waves in both hurricanes as documented in previous studies, wave fields in Ike and Sandy possess unique properties due mostly to the distinct wind fields and coastal bathymetry in the two storms. Several processes are found to significantly modulate hurricane surface waves near landfall. First, the phase speed and group velocities decrease as the waves become shorter and steeper in shallow water, effectively increasing surface roughness and wind stress. Second, the bottom-induced refraction acts to turn the waves toward the coast, increasing the misalignment between the wind and waves. Third, as the hurricane translates over land, the left side of the storm center is characterized by offshore winds over very short fetch, which opposes incoming swell. Landfalling hurricanes produce broader wave spectra overall than that of the open ocean. The front-left quadrant is most complex, where the combination of windsea, swell propagating against the wind, increasing wind-wave stress, and interaction with the coastal topography requires a fully coupled model to meet these challenges in hurricane wave and surge prediction.

  2. Ratio-based lengths of intervals to improve fuzzy time series forecasting.

    Science.gov (United States)

    Huarng, Kunhuang; Yu, Tiffany Hui-Kuang

    2006-04-01

    The objective of this study is to explore ways of determining the useful lengths of intervals in fuzzy time series. It is suggested that ratios, instead of equal lengths of intervals, can more properly represent the intervals among observations. Ratio-based lengths of intervals are, therefore, proposed to improve fuzzy time series forecasting. Algebraic growth data, such as enrollments and the stock index, and exponential growth data, such as inventory demand, are chosen as the forecasting targets, before forecasting based on the various lengths of intervals is performed. Furthermore, sensitivity analyses are also carried out for various percentiles. The ratio-based lengths of intervals are found to outperform the effective lengths of intervals, as well as the arbitrary ones in regard to the different statistical measures. The empirical analysis suggests that the ratio-based lengths of intervals can also be used to improve fuzzy time series forecasting.

  3. Investigating the sensitivity of hurricane intensity and trajectory to sea surface temperatures using the regional model WRF

    Directory of Open Access Journals (Sweden)

    Cevahir Kilic

    2013-12-01

    Full Text Available The influence of sea surface temperature (SST anomalies on the hurricane characteristics are investigated in a set of sensitivity experiments employing the Weather Research and Forecasting (WRF model. The idealised experiments are performed for the case of Hurricane Katrina in 2005. The first set of sensitivity experiments with basin-wide changes of the SST magnitude shows that the intensity goes along with changes in the SST, i.e., an increase in SST leads to an intensification of Katrina. Additionally, the trajectory is shifted to the west (east, with increasing (decreasing SSTs. The main reason is a strengthening of the background flow. The second set of experiments investigates the influence of Loop Current eddies idealised by localised SST anomalies. The intensity of Hurricane Katrina is enhanced with increasing SSTs close to the core of a tropical cyclone. Negative nearby SST anomalies reduce the intensity. The trajectory only changes if positive SST anomalies are located west or north of the hurricane centre. In this case the hurricane is attracted by the SST anomaly which causes an additional moisture source and increased vertical winds.

  4. Improving 7-Day Forecast Skill by Assimilation of Retrieved AIRS Temperature Profiles

    Science.gov (United States)

    Susskind, Joel; Rosenberg, Bob

    2016-01-01

    We conducted a new set of Data Assimilation Experiments covering the period January 1 to February 29, 2016 using the GEOS-5 DAS. Our experiments assimilate all data used operationally by GMAO (Control) with some modifications. Significant improvement in Global and Southern Hemisphere Extra-tropical 7-day forecast skill was obtained when: We assimilated AIRS Quality Controlled temperature profiles in place of observed AIRS radiances, and also did not assimilate CrISATMS radiances, nor did we assimilate radiosonde temperature profiles or aircraft temperatures. This new methodology did not improve or degrade 7-day Northern Hemispheric Extra-tropical forecast skill. We are conducting experiments aimed at further improving of Northern Hemisphere Extra-tropical forecast skill.

  5. Short-term electricity price forecast based on the improved hybrid model

    International Nuclear Information System (INIS)

    Dong Yao; Wang Jianzhou; Jiang He; Wu Jie

    2011-01-01

    Highlights: → The proposed models can detach high volatility and daily seasonality of electricity price. → The improved hybrid forecast models can make full use of the advantages of individual models. → The proposed models create commendable improvements that are relatively satisfactorily for current research. → The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

  6. Short-term electricity price forecast based on the improved hybrid model

    Energy Technology Data Exchange (ETDEWEB)

    Dong Yao, E-mail: dongyao20051987@yahoo.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Wang Jianzhou, E-mail: wjz@lzu.edu.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Jiang He; Wu Jie [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China)

    2011-08-15

    Highlights: {yields} The proposed models can detach high volatility and daily seasonality of electricity price. {yields} The improved hybrid forecast models can make full use of the advantages of individual models. {yields} The proposed models create commendable improvements that are relatively satisfactorily for current research. {yields} The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

  7. The communicative process of weather forecasts issued in the probabilistic form (Italian original version

    Directory of Open Access Journals (Sweden)

    Alessio Raimondi

    2009-03-01

    Full Text Available One of the main purposes of weather forecasting is that of protecting weather-sensitive human activities. Forecasts issued in the probabilistic form have a higher informative content, as opposed to deterministic one, since they bear information that give also a measure of their own uncertainty. However, in order to make an appropriate and effective use of this kind of forecasts in an operational setting, communication becomes significatively relevant.The present paper, after having briefly examined the weather forecasts concerning Hurricane Charley (August 2004, tackles the issue of the communicative process in detail.The bottom line of this study is that for the weather forecast to achieve its best predictive potential, an in-depth analysis of communication issues is necessary.

  8. Hurricane Imaging Radiometer

    Science.gov (United States)

    Cecil, Daniel J.; Biswas, Sayak K.; James, Mark W.; Roberts, J. Brent; Jones, W. Linwood; Johnson, James; Farrar, Spencer; Sahawneh, Saleem; Ruf, Christopher S.; Morris, Mary; hide

    2014-01-01

    The Hurricane Imaging Radiometer (HIRAD) is a synthetic thinned array passive microwave radiometer designed to allow retrieval of surface wind speed in hurricanes, up through category five intensity. The retrieval technology follows the Stepped Frequency Microwave Radiometer (SFMR), which measures surface wind speed in hurricanes along a narrow strip beneath the aircraft. HIRAD maps wind speeds in a swath below the aircraft, about 50-60 km wide when flown in the lower stratosphere. HIRAD has flown in the NASA Genesis and Rapid Intensification Processes (GRIP) experiment in 2010 on a WB-57 aircraft, and on a Global Hawk unmanned aircraft system (UAS) in 2012 and 2013 as part of NASA's Hurricane and Severe Storms Sentinel (HS3) program. The GRIP program included flights over Hurricanes Earl and Karl (2010). The 2012 HS3 deployment did not include any hurricane flights for the UAS carrying HIRAD. The 2013 HS3 flights included one flight over the predecessor to TS Gabrielle, and one flight over Hurricane Ingrid. This presentation will describe the HIRAD instrument, its results from the 2010 and 2013 flights, and potential future developments.

  9. Enhanced outage prediction modeling for strong extratropical storms and hurricanes in the Northeastern United States

    Science.gov (United States)

    Cerrai, D.; Anagnostou, E. N.; Wanik, D. W.; Bhuiyan, M. A. E.; Zhang, X.; Yang, J.; Astitha, M.; Frediani, M. E.; Schwartz, C. S.; Pardakhti, M.

    2016-12-01

    The overwhelming majority of human activities need reliable electric power. Severe weather events can cause power outages, resulting in substantial economic losses and a temporary worsening of living conditions. Accurate prediction of these events and the communication of forecasted impacts to the affected utilities is necessary for efficient emergency preparedness and mitigation. The University of Connecticut Outage Prediction Model (OPM) uses regression tree models, high-resolution weather reanalysis and real-time weather forecasts (WRF and NCAR ensemble), airport station data, vegetation and electric grid characteristics and historical outage data to forecast the number and spatial distribution of outages in the power distribution grid located within dense vegetation. Recent OPM improvements consist of improved storm classification and addition of new predictive weather-related variables and are demonstrated using a leave-one-storm-out cross-validation based on 130 severe extratropical storms and two hurricanes (Sandy and Irene) in the Northeast US. We show that it is possible to predict the number of trouble spots causing outages in the electric grid with a median absolute percentage error as low as 27% for some storm types, and at most around 40%, in a scale that varies between four orders of magnitude, from few outages to tens of thousands. This outage information can be communicated to the electric utility to manage allocation of crews and equipment and minimize the recovery time for an upcoming storm hazard.

  10. Hurricane Ike: Observations and Analysis of Coastal Change

    Science.gov (United States)

    Doran, Kara S.; Plant, Nathaniel G.; Stockdon, Hilary F.; Sallenger, Asbury H.; Serafin, Katherine A.

    2009-01-01

    Understanding storm-induced coastal change and forecasting these changes require knowledge of the physical processes associated with the storm and the geomorphology of the impacted coastline. The primary physical processes of interest are the wind field, storm surge, and wave climate. Not only does wind cause direct damage to structures along the coast, but it is ultimately responsible for much of the energy that is transferred to the ocean and expressed as storm surge, mean currents, and large waves. Waves and currents are the processes most responsible for moving sediments in the coastal zone during extreme storm events. Storm surge, the rise in water level due to the wind, barometric pressure, and other factors, allows both waves and currents to attack parts of the coast not normally exposed to those processes. Coastal geomorphology, including shapes of the shoreline, beaches, and dunes, is equally important to the coastal change observed during extreme storm events. Relevant geomorphic variables include sand dune elevation, beach width, shoreline position, sediment grain size, and foreshore beach slope. These variables, in addition to hydrodynamic processes, can be used to predict coastal vulnerability to storms The U.S. Geological Survey's (USGS) National Assessment of Coastal Change Hazards Project (http://coastal.er.usgs.gov/hurricanes), strives to provide hazard information to those interested in the Nation's coastlines, including residents of coastal areas, government agencies responsible for coastal management, and coastal researchers. As part of the National Assessment, observations were collected to measure coastal changes associated with Hurricane Ike, which made landfall near Galveston, Texas, on September 13, 2008. Methods of observation included aerial photography and airborne topographic surveys. This report documents these data-collection efforts and presents qualitative and quantitative descriptions of hurricane-induced changes to the shoreline

  11. Hurricane Katrina Wind Investigation Report

    Energy Technology Data Exchange (ETDEWEB)

    Desjarlais, A. O.

    2007-08-15

    This investigation of roof damage caused by Hurricane Katrina is a joint effort of the Roofing Industry Committee on Weather Issues, Inc. (RICOWI) and the Oak Ridge National Laboratory/U.S. Department of Energy (ORNL/DOE). The Wind Investigation Program (WIP) was initiated in 1996. Hurricane damage that met the criteria of a major windstorm event did not materialize until Hurricanes Charley and Ivan occurred in August 2004. Hurricane Katrina presented a third opportunity for a wind damage investigation in August 29, 2005. The major objectives of the WIP are as follows: (1) to investigate the field performance of roofing assemblies after major wind events; (2) to factually describe roofing assembly performance and modes of failure; and (3) to formally report results of the investigations and damage modes for substantial wind speeds The goal of the WIP is to perform unbiased, detailed investigations by credible personnel from the roofing industry, the insurance industry, and academia. Data from these investigations will, it is hoped, lead to overall improvement in roofing products, systems, roofing application, and durability and a reduction in losses, which may lead to lower overall costs to the public. This report documents the results of an extensive and well-planned investigative effort. The following program changes were implemented as a result of the lessons learned during the Hurricane Charley and Ivan investigations: (1) A logistics team was deployed to damage areas immediately following landfall; (2) Aerial surveillance--imperative to target wind damage areas--was conducted; (3) Investigation teams were in place within 8 days; (4) Teams collected more detailed data; and (5) Teams took improved photographs and completed more detailed photo logs. Participating associations reviewed the results and lessons learned from the previous investigations and many have taken the following actions: (1) Moved forward with recommendations for new installation procedures

  12. 77 FR 64564 - Implementation of Regulatory Guide 1.221 on Design-Basis Hurricane and Hurricane Missiles

    Science.gov (United States)

    2012-10-22

    ...-Basis Hurricane and Hurricane Missiles AGENCY: Nuclear Regulatory Commission. ACTION: Proposed interim...-ISG-024, ``Implementation of Regulatory Guide 1.221 on Design-Basis Hurricane and Hurricane Missiles....221, ``Design-Basis Hurricane and Hurricane Missiles for Nuclear Power Plants.'' DATES: Submit...

  13. Retention of Displaced Students after Hurricanes Katrina and Rita

    Science.gov (United States)

    Coco, Joshua Christian

    2017-01-01

    The purpose of the study was to investigate the strategies that university leaders implemented to improve retention of displaced students in the aftermaths of Hurricanes Katrina and Rita. The universities that participated in this study admitted displaced students after Hurricanes Katrina and Rita. This study utilized a qualitative…

  14. Short-term Inundation Forecasting for Tsunamis Version 4.0 Brings Forecasting Speed, Accuracy, and Capability Improvements to NOAA's Tsunami Warning Centers

    Science.gov (United States)

    Sterling, K.; Denbo, D. W.; Eble, M. C.

    2016-12-01

    Short-term Inundation Forecasting for Tsunamis (SIFT) software was developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) for use in tsunami forecasting and has been used by both U.S. Tsunami Warning Centers (TWCs) since 2012, when SIFTv3.1 was operationally accepted. Since then, advancements in research and modeling have resulted in several new features being incorporated into SIFT forecasting. Following the priorities and needs of the TWCs, upgrades to SIFT forecasting were implemented into SIFTv4.0, scheduled to become operational in October 2016. Because every minute counts in the early warning process, two major time saving features were implemented in SIFT 4.0. To increase processing speeds and generate high-resolution flooding forecasts more quickly, the tsunami propagation and inundation codes were modified to run on Graphics Processing Units (GPUs). To reduce time demand on duty scientists during an event, an automated DART inversion (or fitting) process was implemented. To increase forecasting accuracy, the forecasted amplitudes and inundations were adjusted to include dynamic tidal oscillations, thereby reducing the over-estimates of flooding common in SIFTv3.1 due to the static tide stage conservatively set at Mean High Water. Further improvements to forecasts were gained through the assimilation of additional real-time observations. Cabled array measurements from Bottom Pressure Recorders (BPRs) in the Oceans Canada NEPTUNE network are now available to SIFT for use in the inversion process. To better meet the needs of harbor masters and emergency managers, SIFTv4.0 adds a tsunami currents graphical product to the suite of disseminated forecast results. When delivered, these new features in SIFTv4.0 will improve the operational tsunami forecasting speed, accuracy, and capabilities at NOAA's Tsunami Warning Centers.

  15. Forecasting space weather: Can new econometric methods improve accuracy?

    Science.gov (United States)

    Reikard, Gordon

    2011-06-01

    Space weather forecasts are currently used in areas ranging from navigation and communication to electric power system operations. The relevant forecast horizons can range from as little as 24 h to several days. This paper analyzes the predictability of two major space weather measures using new time series methods, many of them derived from econometrics. The data sets are the A p geomagnetic index and the solar radio flux at 10.7 cm. The methods tested include nonlinear regressions, neural networks, frequency domain algorithms, GARCH models (which utilize the residual variance), state transition models, and models that combine elements of several techniques. While combined models are complex, they can be programmed using modern statistical software. The data frequency is daily, and forecasting experiments are run over horizons ranging from 1 to 7 days. Two major conclusions stand out. First, the frequency domain method forecasts the A p index more accurately than any time domain model, including both regressions and neural networks. This finding is very robust, and holds for all forecast horizons. Combining the frequency domain method with other techniques yields a further small improvement in accuracy. Second, the neural network forecasts the solar flux more accurately than any other method, although at short horizons (2 days or less) the regression and net yield similar results. The neural net does best when it includes measures of the long-term component in the data.

  16. Can we use Earth Observations to improve monthly water level forecasts?

    Science.gov (United States)

    Slater, L. J.; Villarini, G.

    2017-12-01

    Dynamical-statistical hydrologic forecasting approaches benefit from different strengths in comparison with traditional hydrologic forecasting systems: they are computationally efficient, can integrate and `learn' from a broad selection of input data (e.g., General Circulation Model (GCM) forecasts, Earth Observation time series, teleconnection patterns), and can take advantage of recent progress in machine learning (e.g. multi-model blending, post-processing and ensembling techniques). Recent efforts to develop a dynamical-statistical ensemble approach for forecasting seasonal streamflow using both GCM forecasts and changing land cover have shown promising results over the U.S. Midwest. Here, we use climate forecasts from several GCMs of the North American Multi Model Ensemble (NMME) alongside 15-minute stage time series from the National River Flow Archive (NRFA) and land cover classes extracted from the European Space Agency's Climate Change Initiative 300 m annual Global Land Cover time series. With these data, we conduct systematic long-range probabilistic forecasting of monthly water levels in UK catchments over timescales ranging from one to twelve months ahead. We evaluate the improvement in model fit and model forecasting skill that comes from using land cover classes as predictors in the models. This work opens up new possibilities for combining Earth Observation time series with GCM forecasts to predict a variety of hazards from space using data science techniques.

  17. Analysis of Hurricane Irene’s Wind Field Using the Advanced Research Weather Research and Forecast (WRF-ARW Model

    Directory of Open Access Journals (Sweden)

    Alfred M. Klausmann

    2014-01-01

    Full Text Available Hurricane Irene caused widespread and significant impacts along the U.S. east coast during 27–29 August 2011. During this period, the storm moved across eastern North Carolina and then tracked northward crossing into Long Island and western New England. Impacts included severe flooding from the mid-Atlantic states into eastern New York and western New England, widespread wind damage and power outages across a large portion of southern and central New England, and a major storm surge along portions of the Long Island coast. The objective of this study was to conduct retrospective simulations using the Advanced Research Weather Research and Forecast (WRF-ARW model in an effort to reconstruct the storm’s surface wind field during the period of 27–29 August 2011. The goal was to evaluate how to use the WRF modeling system as a tool for reconstructing the surface wind field from historical storm events to support storm surge studies. The results suggest that, with even modest data assimilation applied to these simulations, the model was able to resolve the detailed structure of the storm, the storm track, and the spatial surface wind field pattern very well. The WRF model shows real potential for being used as a tool to analyze historical storm events to support storm surge studies.

  18. Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting

    KAUST Repository

    Zhu, Xinxin; Bowman, Kenneth P.; Genton, Marc G.

    2014-01-01

    pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal

  19. Improving GEFS Weather Forecasts for Indian Monsoon with Statistical Downscaling

    Science.gov (United States)

    Agrawal, Ankita; Salvi, Kaustubh; Ghosh, Subimal

    2014-05-01

    Weather forecast has always been a challenging research problem, yet of a paramount importance as it serves the role of 'key input' in formulating modus operandi for immediate future. Short range rainfall forecasts influence a wide range of entities, right from agricultural industry to a common man. Accurate forecasts actually help in minimizing the possible damage by implementing pre-decided plan of action and hence it is necessary to gauge the quality of forecasts which might vary with the complexity of weather state and regional parameters. Indian Summer Monsoon Rainfall (ISMR) is one such perfect arena to check the quality of weather forecast not only because of the level of intricacy in spatial and temporal patterns associated with it, but also the amount of damage it can cause (because of poor forecasts) to the Indian economy by affecting agriculture Industry. The present study is undertaken with the rationales of assessing, the ability of Global Ensemble Forecast System (GEFS) in predicting ISMR over central India and the skill of statistical downscaling technique in adding value to the predictions by taking them closer to evidentiary target dataset. GEFS is a global numerical weather prediction system providing the forecast results of different climate variables at a fine resolution (0.5 degree and 1 degree). GEFS shows good skills in predicting different climatic variables but fails miserably over rainfall predictions for Indian summer monsoon rainfall, which is evident from a very low to negative correlation values between predicted and observed rainfall. Towards the fulfilment of second rationale, the statistical relationship is established between the reasonably well predicted climate variables (GEFS) and observed rainfall. The GEFS predictors are treated with multicollinearity and dimensionality reduction techniques, such as principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Statistical relationship is

  20. HURRICANE AND SEVERE STORM SENTINEL (HS3) HURRICANE IMAGING RADIOMETER (HIRAD) V1

    Data.gov (United States)

    National Aeronautics and Space Administration — The Hurricane and Severe Storm Sentinel (HS3) Hurricane Imaging Radiometer (HIRAD) was collected by the Hurricane Imaging Radiometer (HIRAD), which was a multi-band...

  1. Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

    International Nuclear Information System (INIS)

    Chitsaz, Hamed; Amjady, Nima; Zareipour, Hamidreza

    2015-01-01

    Highlights: • Presenting a Morlet wavelet neural network for wind power forecasting. • Proposing improved Clonal selection algorithm for training the model. • Applying Maximum Correntropy Criterion to evaluate the training performance. • Extensive testing of the proposed wind power forecast method on real-world data. - Abstract: With the integration of wind farms into electric power grids, an accurate wind power prediction is becoming increasingly important for the operation of these power plants. In this paper, a new forecasting engine for wind power prediction is proposed. The proposed engine has the structure of Wavelet Neural Network (WNN) with the activation functions of the hidden neurons constructed based on multi-dimensional Morlet wavelets. This forecast engine is trained by a new improved Clonal selection algorithm, which optimizes the free parameters of the WNN for wind power prediction. Furthermore, Maximum Correntropy Criterion (MCC) has been utilized instead of Mean Squared Error as the error measure in training phase of the forecasting model. The proposed wind power forecaster is tested with real-world hourly data of system level wind power generation in Alberta, Canada. In order to demonstrate the efficiency of the proposed method, it is compared with several other wind power forecast techniques. The obtained results confirm the validity of the developed approach

  2. Simulation of the Impact of New Aircraft- and Satellite-Based Ocean Surface Wind Measurements on H*Wind Analyses and Numerical Forecasts

    Science.gov (United States)

    Miller, Timothy; Atlas, Robert; Black, Peter; Chen, Shuyi; Hood, Robbie; Johnson, James; Jones, Linwood; Ruf, Chris; Uhlhorn, Eric; Krishnamurti, T. N.; hide

    2009-01-01

    The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor for hurricane observations that is currently under development by NASA Marshall Space Flight Center, NOAA Hurricane Research Division, the University of Central Florida and the University of Michigan. HIRAD is being designed to enhance the realtime airborne ocean surface winds observation capabilities of NOAA and USAF Weather Squadron hurricane hunter aircraft using the operational airborne Stepped Frequency Microwave Radiometer (SFMR). Unlike SFMR, which measures wind speed and rain rate along the ground track directly beneath the aircraft, HIRAD will provide images of the surface wind and rain field over a wide swath ( 3 x the aircraft altitude). The present paper describes a set of Observing System Simulation Experiments (OSSEs) in which measurements from the new instrument as well as those from existing instruments (air, surface, and space-based) are simulated from the output of a detailed numerical model, and those results are used to construct H*Wind analyses. The H*Wind analysis, a product of the Hurricane Research Division of NOAA s Atlantic Oceanographic and Meteorological Laboratory, brings together wind measurements from a variety of observation platforms into an objective analysis of the distribution of wind speeds in a tropical cyclone. This product is designed to improve understanding of the extent and strength of the wind field, and to improve the assessment of hurricane intensity. See http://www.aoml.noaa.gov/hrd/data_sub/wind.html. Evaluations will be presented on the impact of the HIRAD instrument on H*Wind analyses, both in terms of adding it to the full suite of current measurements, as well as using it to replace instrument(s) that may not be functioning at the future time the HIRAD instrument is implemented. Also shown will be preliminary results of numerical weather prediction OSSEs in which the impact of the addition of HIRAD observations to the initial state

  3. Recent Atlantic Hurricanes, Pacific Super Typhoons, and Tropical Storm Awareness in Underdeveloped Island and Coastal Regions

    Science.gov (United States)

    Plondke, D. L.

    2017-12-01

    Hurricane Harvey was the first major hurricane to make landfall in the continental U.S. in 12 years. The next tropical storm in the 2017 Atlantic Hurricane Season was Hurricane Irma, a category 5 storm and the strongest storm to strike the U.S. mainland since Hurricane Wilma in 2005. These two storms were the third and fourth in a sequence of 10 consecutive storms to reach hurricane status in this season that ranks at least seventh among the most active seasons as measured by the Accumulate Cyclone Energy (ACE) index. Assessment of damage from Harvey may prove it to be the costliest storm in U.S. history, approaching $190 billion. Irma was the first category 5 hurricane to hit the Leeward Islands, devastating island environments including Puerto Rico, the Virgin Islands, Barbuda, Saint Barthelemy, and Anguilla with sustained winds reaching at times 185 mph. Together with the two super typhoons of the 2017 Pacific season, Noru and Lan, the two Atlantic hurricanes rank among the strongest, longest-lasting tropical cyclones on record. How many more billions of dollars will be expended in recovery and reconstruction efforts following future mega-disasters comparable to those of Hurricanes Harvey and Irma? Particularly on Caribbean and tropical Pacific islands with specialized and underdeveloped economies, aging and substandard infrastructure often cannot even partially mitigate against the impacts of major hurricanes. The most frequently used measurements of storm impact are insufficient to assess the economic impact. Analysis of the storm tracks and periods of greatest storm intensity of Hurricanes Harvey and Irma, and Super Typhoons Lan and Noru, in spatial relationship with island and coastal administrative regions, shows that rainfall totals, flooded area estimates, and property/infrastructure damage dollar estimates are all quantitative indicators of storm impact, but do not measure the costs that result from lack of storm preparedness and education of residents

  4. Short-Term City Electric Load Forecasting with Considering Temperature Effects: An Improved ARIMAX Model

    Directory of Open Access Journals (Sweden)

    Herui Cui

    2015-01-01

    Full Text Available Short-term electric load is significantly affected by weather, especially the temperature effects in summer. External factors can result in mutation structures in load data. Under the influence of the external temperature factors, city electric load cannot be easily forecasted as usual. This research analyzes the relationship between electricity load and daily temperature in city. An improved ARIMAX model is proposed in this paper to deal with the mutation data structures. It is found that information amount of the improved ARIMAX model is smaller than that of the classic method and its relative error is less than AR, ARMA and Sigmoid-Function ANN models. The forecasting results are more accurately fitted. This improved model is highly valuable when dealing with mutation data structure in the field of load forecasting. And it is also an effective technique in forecasting electric load with temperature effects.

  5. Hurricane-related emergency department visits in an inland area: an analysis of the public health impact of Hurricane Hugo in North Carolina.

    Science.gov (United States)

    Brewer, R D; Morris, P D; Cole, T B

    1994-04-01

    To evaluate the public health impact of a hurricane on an inland area. Descriptive study. Seven hospital emergency departments. Patients who were treated from September 22 to October 6, 1989, for an injury or illness related to Hurricane Hugo. None. Over the two-week study period, 2,090 patients were treated for injuries or illnesses related to the hurricane. Of these, 1,833 (88%) were treated for injuries. Insect stings and wounds accounted for almost half of the total cases. A substantial proportion (26%) of the patients suffering from stings had a generalized reaction (eg, hives, wheezing, or both). Nearly one-third of the wounds were caused by chain saws. Hurricanes can lead to substantial morbidity in an inland area. Disaster plans should address risks associated with stinging insects and hazardous equipment and should address ways to improve case reporting.

  6. Operational aspects of asynchronous filtering for improved flood forecasting

    Science.gov (United States)

    Rakovec, Oldrich; Weerts, Albrecht; Sumihar, Julius; Uijlenhoet, Remko

    2014-05-01

    Hydrological forecasts can be made more reliable and less uncertain by recursively improving initial conditions. A common way of improving the initial conditions is to make use of data assimilation (DA), a feedback mechanism or update methodology which merges model estimates with available real world observations. The traditional implementation of the Ensemble Kalman Filter (EnKF; e.g. Evensen, 2009) is synchronous, commonly named a three dimensional (3-D) assimilation, which means that all assimilated observations correspond to the time of update. Asynchronous DA, also called four dimensional (4-D) assimilation, refers to an updating methodology, in which observations being assimilated into the model originate from times different to the time of update (Evensen, 2009; Sakov 2010). This study investigates how the capabilities of the DA procedure can be improved by applying alternative Kalman-type methods, e.g., the Asynchronous Ensemble Kalman Filter (AEnKF). The AEnKF assimilates observations with smaller computational costs than the original EnKF, which is beneficial for operational purposes. The results of discharge assimilation into a grid-based hydrological model for the Upper Ourthe catchment in Belgian Ardennes show that including past predictions and observations in the AEnKF improves the model forecasts as compared to the traditional EnKF. Additionally we show that elimination of the strongly non-linear relation between the soil moisture storage and assimilated discharge observations from the model update becomes beneficial for an improved operational forecasting, which is evaluated using several validation measures. In the current study we employed the HBV-96 model built within a recently developed open source modelling environment OpenStreams (2013). The advantage of using OpenStreams (2013) is that it enables direct communication with OpenDA (2013), an open source data assimilation toolbox. OpenDA provides a number of algorithms for model calibration

  7. BAYESIAN FORECASTS COMBINATION TO IMPROVE THE ROMANIAN INFLATION PREDICTIONS BASED ON ECONOMETRIC MODELS

    Directory of Open Access Journals (Sweden)

    Mihaela Simionescu

    2014-12-01

    Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.

  8. Data Assimilation using observed streamflow and remotely-sensed soil moisture for improving sub-seasonal-to-seasonal forecasting

    Science.gov (United States)

    Arumugam, S.; Mazrooei, A.; Lakshmi, V.; Wood, A.

    2017-12-01

    Subseasonal-to-seasonal (S2S) forecasts of soil moisture and streamflow provides critical information for water and agricultural systems to support short-term planning and mangement. This study evaluates the role of observed streamflow and remotely-sensed soil moisture from SMAP (Soil Moisture Active Passive) mission in improving S2S streamflow and soil moisture forecasting using data assimilation (DA). We first show the ability to forecast soil moisture at monthly-to-seaasonal time scale by forcing climate forecasts with NASA's Land Information System and then compares the developed soil moisture forecast with the SMAP data over the Southeast US. Our analyses show significant skill in forecasting real-time soil moisture over 1-3 months using climate information. We also show that the developed soil moisture forecasts capture the observed severe drought conditions (2007-2008) over the Southeast US. Following that, we consider both SMAP data and observed streamflow for improving S2S streamflow and soil moisture forecasts for a pilot study area, Tar River basin, in NC. Towards this, we consider variational assimilation (VAR) of gauge-measured daily streamflow data in improving initial hydrologic conditions of Variable Infiltration Capacity (VIC) model. The utility of data assimilation is then assessed in improving S2S forecasts of streamflow and soil moisture through a retrospective analyses. Furthermore, the optimal frequency of data assimilation and optimal analysis window (number of past observations to use) are also assessed in order to achieve the maximum improvement in S2S forecasts of streamflow and soil moisture. Potential utility of updating initial conditions using DA and providing skillful forcings are also discussed.

  9. Hurricane Gustav: Observations and Analysis of Coastal Change

    Science.gov (United States)

    Doran, Kara S.; Stockdon, Hilary F.; Plant, Nathaniel G.; Sallenger, Asbury H.; Guy, Kristy K.; Serafin, Katherine A.

    2009-01-01

    Understanding storm-induced coastal change and forecasting these changes require knowledge of the physical processes associated with a storm and the geomorphology of the impacted coastline. The primary physical processes of interest are the wind field, storm surge, currents, and wave field. Not only does wind cause direct damage to structures along the coast, but it is ultimately responsible for much of the energy that is transferred to the ocean and expressed as storm surge, mean currents, and surface waves. Waves and currents are the processes most responsible for moving sediments in the coastal zone during extreme storm events. Storm surge, which is the rise in water level due to the wind, barometric pressure, and other factors, allows both waves and currents to attack parts of the coast not normally exposed to these processes. Coastal geomorphology, including shapes of the shoreline, beaches, and dunes, is also a significant aspect of the coastal change observed during extreme storms. Relevant geomorphic variables include sand dune elevation, beach width, shoreline position, sediment grain size, and foreshore beach slope. These variables, in addition to hydrodynamic processes, can be used to predict coastal vulnerability to storms. The U.S. Geological Survey (USGS) National Assessment of Coastal Change Hazards project (http://coastal.er.usgs.gov/hurricanes) strives to provide hazard information to those concerned about the Nation's coastlines, including residents of coastal areas, government agencies responsible for coastal management, and coastal researchers. As part of the National Assessment, observations were collected to measure morphological changes associated with Hurricane Gustav, which made landfall near Cocodrie, Louisiana, on September 1, 2008. Methods of observation included oblique aerial photography, airborne topographic surveys, and ground-based topographic surveys. This report documents these data-collection efforts and presents qualitative and

  10. Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

    Science.gov (United States)

    Susskind, Joel; Reale, Oreste

    2010-01-01

    AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best

  11. Hurricane Gustav Poster

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Gustav poster. Multi-spectral image from NOAA-17 shows Hurricane Gustav having made landfall along the Louisiana coastline. Poster size is 36"x27"

  12. A multi-scale ensemble-based framework for forecasting compound coastal-riverine flooding: The Hackensack-Passaic watershed and Newark Bay

    Science.gov (United States)

    Saleh, F.; Ramaswamy, V.; Wang, Y.; Georgas, N.; Blumberg, A.; Pullen, J.

    2017-12-01

    Estuarine regions can experience compound impacts from coastal storm surge and riverine flooding. The challenges in forecasting flooding in such areas are multi-faceted due to uncertainties associated with meteorological drivers and interactions between hydrological and coastal processes. The objective of this work is to evaluate how uncertainties from meteorological predictions propagate through an ensemble-based flood prediction framework and translate into uncertainties in simulated inundation extents. A multi-scale framework, consisting of hydrologic, coastal and hydrodynamic models, was used to simulate two extreme flood events at the confluence of the Passaic and Hackensack rivers and Newark Bay. The events were Hurricane Irene (2011), a combination of inland flooding and coastal storm surge, and Hurricane Sandy (2012) where coastal storm surge was the dominant component. The hydrodynamic component of the framework was first forced with measured streamflow and ocean water level data to establish baseline inundation extents with the best available forcing data. The coastal and hydrologic models were then forced with meteorological predictions from 21 ensemble members of the Global Ensemble Forecast System (GEFS) to retrospectively represent potential future conditions up to 96 hours prior to the events. Inundation extents produced by the hydrodynamic model, forced with the 95th percentile of the ensemble-based coastal and hydrologic boundary conditions, were in good agreement with baseline conditions for both events. The USGS reanalysis of Hurricane Sandy inundation extents was encapsulated between the 50th and 95th percentile of the forecasted inundation extents, and that of Hurricane Irene was similar but with caveats associated with data availability and reliability. This work highlights the importance of accounting for meteorological uncertainty to represent a range of possible future inundation extents at high resolution (∼m).

  13. Hurricane Ike Poster

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Ike poster. Multi-spectral image from NOAA-15 shows Hurricane Ike in the Gulf of Mexico heading toward Galveston Island, Texas. Poster size is 36"x27".

  14. Development and testing of improved statistical wind power forecasting methods.

    Energy Technology Data Exchange (ETDEWEB)

    Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)

    2011-12-06

    Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios

  15. Hurricane Isaac: observations and analysis of coastal change

    Science.gov (United States)

    Guy, Kristy K.; Stockdon, Hilary F.; Plant, Nathaniel G.; Doran, Kara S.; Morgan, Karen L.M.

    2013-01-01

    Understanding storm-induced coastal change and forecasting these changes require knowledge of the physical processes associated with a storm and the geomorphology of the impacted coastline. The primary physical process of interest is sediment transport that is driven by waves, currents, and storm surge associated with storms. Storm surge, which is the rise in water level due to the wind, barometric pressure, and other factors, allows both waves and currents to impact parts of the coast not normally exposed to these processes. Coastal geomorphology reflects the coastal changes associated with extreme-storm processes. Relevant geomorphic variables that are observable before and after storms include sand dune elevation, beach width, shoreline position, sediment grain size, and foreshore beach slope. These variables, in addition to hydrodynamic processes, can be used to quantify coastal change and are used to predict coastal vulnerability to storms (Stockdon and others, 2007). The U.S. Geological Survey (USGS) National Assessment of Coastal Change Hazards (NACCH) project (http://coastal.er.usgs.gov/national-assessment/) provides hazard information to those concerned about the Nation’s coastlines, including residents of coastal areas, government agencies responsible for coastal management, and coastal researchers. Extreme-storm research is a component of the NACCH project (http://coastal.er.usgs.gov/hurricanes/) that includes development of predictive understanding, vulnerability assessments using models, and updated observations in response to specific storm events. In particular, observations were made to determine morphological changes associated with Hurricane Isaac, which made landfall in the United States first at Southwest Pass, at the mouth of the Mississippi River, at 0000 August 29, 2012 UTC (Coordinated Universal Time) and again, 8 hours later, west of Port Fourchon, Louisiana (Berg, 2013). Methods of observation included oblique aerial photography

  16. Land area change analysis following hurricane impacts in Delacroix, Louisiana, 2004--2009

    Science.gov (United States)

    Palaseanu-Lovejoy, Monica; Kranenburg, Christine J.; Brock, John C.

    2012-01-01

    The purpose of this project is to provide improved estimates of Louisiana wetland land loss due to hurricane impacts between 2004 and 2009 based upon a change detection mapping analysis that incorporates pre- and post-landfall (Hurricanes Katrina, Rita, Gustav, and Ike) fractional water classification of a combination of high resolution (QuickBird, IKONOS and Geoeye-1) and medium resolution (Landsat) satellite imagery. This second dataset focuses on Hurricanes Katrina and Gustav, which made landfall on August 29, 2005, and September 1, 2008, respectively. The study area is an approximately 1208-square-kilometer region surrounding Delacroix, Louisiana, in the eastern Delta Plain. Overall, 77 percent of the area remained unchanged between 2004 and 2009, and over 11 percent of the area was changed permanently by Hurricane Katrina (including both land gain and loss). Less than 3 percent was affected, either temporarily or permanently, by Hurricane Gustav. A related dataset (SIM 3141) focused on Hurricane Rita, which made landfall on the Louisiana/Texas border on September 24, 2005, as a Category 3 hurricane.

  17. Filters or Holt Winters Technique to Improve the SPF Forecasts for USA Inflation Rate?

    Directory of Open Access Journals (Sweden)

    Mihaela Bratu (Simionescu

    2013-02-01

    Full Text Available In this study, transformations of SPF inflation forecasts were made in order to get moreaccurate predictions. The filters application and Holt Winters technique were chosen as possiblestrategies of improving the predictions accuracy. The quarterly inflation rate forecasts (1975 Q1-2012Q3 of USAmade by SPF were transformed using an exponential smoothing technique-HoltWinters-and these new predictions are better than the initial ones for all forecasting horizons of 4quarters. Some filters were applied to SPF forecasts (Hodrick-Prescott,Band-Pass and Christiano-Fitzegerald filters, but Holt Winters method was superior.Full sample asymmetric (Christiano-Fitzegerald and Band-Pass filtersmoothed values are more accurate than the SPF expectations onlyfor some forecast horizons.

  18. Mapping and Visualization of Storm-Surge Dynamics for Hurricane Katrina and Hurricane Rita

    Science.gov (United States)

    Gesch, Dean B.

    2009-01-01

    The damages caused by the storm surges from Hurricane Katrina and Hurricane Rita were significant and occurred over broad areas. Storm-surge maps are among the most useful geospatial datasets for hurricane recovery, impact assessments, and mitigation planning for future storms. Surveyed high-water marks were used to generate a maximum storm-surge surface for Hurricane Katrina extending from eastern Louisiana to Mobile Bay, Alabama. The interpolated surface was intersected with high-resolution lidar elevation data covering the study area to produce a highly detailed digital storm-surge inundation map. The storm-surge dataset and related data are available for display and query in a Web-based viewer application. A unique water-level dataset from a network of portable pressure sensors deployed in the days just prior to Hurricane Rita's landfall captured the hurricane's storm surge. The recorded sensor data provided water-level measurements with a very high temporal resolution at surveyed point locations. The resulting dataset was used to generate a time series of storm-surge surfaces that documents the surge dynamics in a new, spatially explicit way. The temporal information contained in the multiple storm-surge surfaces can be visualized in a number of ways to portray how the surge interacted with and was affected by land surface features. Spatially explicit storm-surge products can be useful for a variety of hurricane impact assessments, especially studies of wetland and land changes where knowledge of the extent and magnitude of storm-surge flooding is critical.

  19. Swamp tours in Louisiana post Hurricane Katrina and Hurricane Rita

    Science.gov (United States)

    Dawn J. Schaffer; Craig A. Miller

    2007-01-01

    Hurricanes Katrina and Rita made landfall in southern Louisiana during August and September 2005. Prior to these storms, swamp tours were a growing sector of nature-based tourism that entertained visitors while teaching about local flora, fauna, and culture. This study determined post-hurricane operating status of tours, damage sustained, and repairs made. Differences...

  20. Sizing Up a Superstorm: Exploring the Role of Recalled Experience and Attribution of Responsibility in Judgments of Future Hurricane Risk.

    Science.gov (United States)

    Rickard, Laura N; Yang, Z Janet; Schuldt, Jonathon P; Eosco, Gina M; Scherer, Clifford W; Daziano, Ricardo A

    2017-12-01

    Research suggests that hurricane-related risk perception is a critical predictor of behavioral response, such as evacuation. Less is known, however, about the precursors of these subjective risk judgments, especially when time has elapsed from a focal event. Drawing broadly from the risk communication, social psychology, and natural hazards literature, and specifically from concepts adapted from the risk information seeking and processing model and the protective action decision model, we examine how individuals' distant recollections, including attribution of responsibility for the effects of a storm, attitude toward relevant information, and past hurricane experience, relate to risk judgment for a future, similar event. The present study reports on a survey involving U.S. residents in Connecticut, New Jersey, and New York (n = 619) impacted by Hurricane Sandy. While some results confirm past findings, such as that hurricane experience increases risk judgment, others suggest additional complexity, such as how various types of experience (e.g., having evacuated vs. having experienced losses) may heighten or attenuate individual-level judgments of responsibility. We suggest avenues for future research, as well as implications for federal agencies involved in severe weather/natural hazard forecasting and communication with public audiences. © 2017 Society for Risk Analysis.

  1. Four methodologies to improve healthcare demand forecasting.

    Science.gov (United States)

    Côté, M J; Tucker, S L

    2001-05-01

    Forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. This task, which often is assumed by financial managers, first requires the compilation and examination of historical information. Although many quantitative forecasting methods exist, four common methods of forecasting are percent adjustment, 12-month moving average, trendline, and seasonalized forecast. These four methods are all based upon the organization's recent historical demand. Healthcare financial managers who want to project demand for healthcare services in their facility should understand the advantages and disadvantages of each method and then select the method that will best meet the organization's needs.

  2. Hurricane Katrina: A Teachable Moment

    Science.gov (United States)

    Bertrand, Peggy

    2009-01-01

    This article presents suggestions for integrating the phenomenon of hurricanes into the teaching of high school fluid mechanics. Students come to understand core science concepts in the context of their impact upon both the environment and human populations. Suggestions for using information about hurricanes, particularly Hurricane Katrina, in a…

  3. Forecast combinations

    OpenAIRE

    Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

    2010-01-01

    We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...

  4. Improving Arctic Sea Ice Edge Forecasts by Assimilating High Horizontal Resolution Sea Ice Concentration Data into the US Navy’s Ice Forecast Systems

    Science.gov (United States)

    2016-06-13

    1735-2015 © Author(s) 2015. CC Attribution 3.0 License. Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice...concentration data into the US Navy’s ice forecast systems P. G. Posey1, E. J. Metzger1, A. J. Wallcraft1, D. A. Hebert1, R. A. Allard1, O. M. Smedstad2...error within the US Navy’s operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration

  5. Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model

    Directory of Open Access Journals (Sweden)

    Cristina Forbes

    2014-05-01

    Full Text Available Numerical simulations of the storm tide that flooded the US Atlantic coastline during Hurricane Sandy (2012 are carried out using the National Weather Service (NWS Sea Lakes and Overland Surges from Hurricanes (SLOSH storm surge prediction model to quantify its ability to replicate the height, timing, evolution and extent of the water that was driven ashore by this large, destructive storm. Recent upgrades to the numerical model, including the incorporation of astronomical tides, are described and simulations with and without these upgrades are contrasted to assess their contributions to the increase in forecast accuracy. It is shown, through comprehensive verifications of SLOSH simulation results against peak water surface elevations measured at the National Oceanic and Atmospheric Administration (NOAA tide gauge stations, by storm surge sensors deployed and hundreds of high water marks collected by the U.S. Geological Survey (USGS, that the SLOSH-simulated water levels at 71% (89% of the data measurement locations have less than 20% (30% relative error. The RMS error between observed and modeled peak water levels is 0.47 m. In addition, the model’s extreme computational efficiency enables it to run large, automated ensembles of predictions in real-time to account for the high variability that can occur in tropical cyclone forecasts, thus furnishing a range of values for the predicted storm surge and inundation threat.

  6. Can energy forecasts be improved?

    International Nuclear Information System (INIS)

    Rech, O.; Alban, P.

    2000-01-01

    Within the present day context of energy, characterized by the gap between short term trends and long term risks, forecasting takes on particular interest. We based our study on the evaluation of the results of some of these long term (2020) and very long term (2050) forecasts. This article looks at the overall demand for energy, whereas the evolution of each primary energy will be handled in a future article. We are restricting our analysis to a global level despite the inherent limitations of such a choice. Our approach mainly concentrates on the dynamics of the phenomena. Thus, we have noticed a simultaneous slowing down since the 1960's of the demography, economy and energy. The revenue and energy consumption per capita do not elude this tendency. At the same time, energy production leads a steep downward tendency. All in all, the forecasts have a tendency to conflict more or less with these changes. In the majority of the scenarios the anticipated rhythms of economic change and energy consumption would indicate a sudden and abrupt inverse of current dynamics. We have noticed that the single use of the average annual rate of change is insufficient to clearly present the long term tendencies that follow curved and not linear paths. Diagnostic errors made in past analyses are likely to affect the models for forecasting, for which the inferred dynamics have not been fully apprehended

  7. Influences of the Saharan Air Layer on the Formation and Intensification of Hurricane Isabel (2003): Analysis of AIRS data and Numerical Simulation

    Science.gov (United States)

    Wu, L.; Braun, S. A.

    2006-12-01

    Over the past two decades, little advance has been made in prediction of tropical cyclone intensity while substantial improvements have been made in forecasting hurricane tracks. One reason is that we don't well understand the physical processes that govern tropical cyclone intensity. Recent studies have suggested that the Saharan Air Layer (SAL) may be yet another piece of the puzzle in advancing our understanding of tropical cyclone intensity change in the Atlantic basin. The SAL is an elevated mixed layer, forming as air moves across the vast Sahara Desert, in particular during boreal summer months. The SAL contains warm, dry air as well as a substantial amount of mineral dust, which can affect radiative heating and modify cloud processes. Using the retrieved temperature and humidity profiles from the AIRS suite on the NASA Aqua satellite, the SAL and its influences on the formation and intensification of Hurricane Isabel (2003) are analyzed and simulated with MM5. When the warmth and dryness of the SAL (the thermodynamic effect) is considered by relaxing the model thermodynamic state to the AIRS profiles, MM5 can well simulate the large-scale flow patterns and the activity of Hurricane Isabel in terms of the timing and location of formation and the subsequent track. Compared with the experiment without nudging the AIRS data, it is suggested that the simulated SAL effect may delay the formation and intensification of Hurricane Isabel. This case study generally confirms the argument by Dunion and Velden (2004) that the SAL can suppress Atlantic tropical cyclone activity by increasing the vertical wind shear, reducing the mean relative humidity, and stabilizing the environment at lower levels.

  8. A new deterministic Ensemble Kalman Filter with one-step-ahead smoothing for storm surge forecasting

    KAUST Repository

    Raboudi, Naila

    2016-11-01

    The Ensemble Kalman Filter (EnKF) is a popular data assimilation method for state-parameter estimation. Following a sequential assimilation strategy, it breaks the problem into alternating cycles of forecast and analysis steps. In the forecast step, the dynamical model is used to integrate a stochastic sample approximating the state analysis distribution (called analysis ensemble) to obtain a forecast ensemble. In the analysis step, the forecast ensemble is updated with the incoming observation using a Kalman-like correction, which is then used for the next forecast step. In realistic large-scale applications, EnKFs are implemented with limited ensembles, and often poorly known model errors statistics, leading to a crude approximation of the forecast covariance. This strongly limits the filter performance. Recently, a new EnKF was proposed in [1] following a one-step-ahead smoothing strategy (EnKF-OSA), which involves an OSA smoothing of the state between two successive analysis. At each time step, EnKF-OSA exploits the observation twice. The incoming observation is first used to smooth the ensemble at the previous time step. The resulting smoothed ensemble is then integrated forward to compute a "pseudo forecast" ensemble, which is again updated with the same observation. The idea of constraining the state with future observations is to add more information in the estimation process in order to mitigate for the sub-optimal character of EnKF-like methods. The second EnKF-OSA "forecast" is computed from the smoothed ensemble and should therefore provide an improved background. In this work, we propose a deterministic variant of the EnKF-OSA, based on the Singular Evolutive Interpolated Ensemble Kalman (SEIK) filter. The motivation behind this is to avoid the observations perturbations of the EnKF in order to improve the scheme\\'s behavior when assimilating big data sets with small ensembles. The new SEIK-OSA scheme is implemented and its efficiency is demonstrated

  9. A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Chengshi Tian

    2018-03-01

    Full Text Available Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.

  10. Assimilation of ZDR Columns for Improving the Spin-Up and Forecasts of Convective Storms

    Science.gov (United States)

    Carlin, J.; Gao, J.; Snyder, J.; Ryzhkov, A.

    2017-12-01

    A primary motivation for assimilating radar reflectivity data is the reduction of spin-up time for modeled convection. To accomplish this, cloud analysis techniques seek to induce and sustain convective updrafts in storm-scale models by inserting temperature and moisture increments and hydrometeor mixing ratios into the model analysis from simple relations with reflectivity. Polarimetric radar data provide additional insight into the microphysical and dynamic structure of convection. In particular, the radar meteorology community has known for decades that convective updrafts cause, and are typically co-located with, differential reflectivity (ZDR) columns - vertical protrusions of enhanced ZDR above the environmental 0˚C level. Despite these benefits, limited work has been done thus far to assimilate dual-polarization radar data into numerical weather prediction models. In this study, we explore the utility of assimilating ZDR columns to improve storm-scale model analyses and forecasts of convection. We modify the existing Advanced Regional Prediction System's (ARPS) cloud analysis routine to adjust model temperature and moisture state variables using detected ZDR columns as proxies for convective updrafts, and compare the resultant cycled analyses and forecasts with those from the original reflectivity-based cloud analysis formulation. Results indicate qualitative and quantitative improvements from assimilating ZDR columns, including more coherent analyzed updrafts, forecast updraft helicity swaths that better match radar-derived rotation tracks, more realistic forecast reflectivity fields, and larger equitable threat scores. These findings support the use of dual-polarization radar signatures to improve storm-scale model analyses and forecasts.

  11. Improved Weather Forecasting for the Dynamic Scheduling System of the Green Bank Telescope

    Science.gov (United States)

    Henry, Kari; Maddalena, Ronald

    2018-01-01

    The Robert C Byrd Green Bank Telescope (GBT) uses a software system that dynamically schedules observations based on models of vertical weather forecasts produced by the National Weather Service (NWS). The NWS provides hourly forecasted values for ~60 layers that extend into the stratosphere over the observatory. We use models, recommended by the Radiocommunication Sector of the International Telecommunications Union, to derive the absorption coefficient in each layer for each hour in the NWS forecasts and for all frequencies over which the GBT has receivers, 0.1 to 115 GHz. We apply radiative transfer models to derive the opacity and the atmospheric contributions to the system temperature, thereby deriving forecasts applicable to scheduling radio observations for up to 10 days into the future. Additionally, the algorithms embedded in the data processing pipeline use historical values of the forecasted opacity to calibrate observations. Until recently, we have concentrated on predictions for high frequency (> 15 GHz) observing, as these need to be scheduled carefully around bad weather. We have been using simple models for the contribution of rain and clouds since we only schedule low-frequency observations under these conditions. In this project, we wanted to improve the scheduling of the GBT and data calibration at low frequencies by deriving better algorithms for clouds and rain. To address the limitation at low frequency, the observatory acquired a Radiometrics Corporation MP-1500A radiometer, which operates in 27 channels between 22 and 30 GHz. By comparing 16 months of measurements from the radiometer against forecasted system temperatures, we have confirmed that forecasted system temperatures are indistinguishable from those measured under good weather conditions. Small miss-calibrations of the radiometer data dominate the comparison. By using recalibrated radiometer measurements, we looked at bad weather days to derive better models for forecasting the

  12. 2005 Atlantic Hurricanes Poster

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The 2005 Atlantic Hurricanes poster features high quality satellite images of 15 hurricanes which formed in the Atlantic Basin (includes Gulf of Mexico and Caribbean...

  13. How Disasters Affect Local Labor Markets: The Effects of Hurricanes in Florida

    Science.gov (United States)

    Belasen, Ariel R.; Polachek, Solomon W.

    2009-01-01

    This study improves upon the Difference in Difference approach by examining exogenous shocks using a Generalized Difference in Difference (GDD) technique that identifies economic effects of hurricanes. Based on the Quarterly Census of Employment and Wages data, worker earnings in Florida counties hit by a hurricane increase up to 4 percent,…

  14. An Improved Artificial Colony Algorithm Model for Forecasting Chinese Electricity Consumption and Analyzing Effect Mechanism

    Directory of Open Access Journals (Sweden)

    Jingmin Wang

    2016-01-01

    Full Text Available Electricity consumption forecast is perceived to be a growing hot topic in such a situation that China’s economy has entered a period of new normal and the demand of electric power has slowed down. Therefore, exploring Chinese electricity consumption influence mechanism and forecasting electricity consumption are crucial to formulate electrical energy plan scientifically and guarantee the sustainable economic and social development. Research has identified medium and long term electricity consumption forecast as a difficult study influenced by various factors. This paper proposed an improved Artificial Bee Colony (ABC algorithm which combined with multivariate linear regression (MLR for exploring the influencing mechanism of various factors on Chinese electricity consumption and forecasting electricity consumption in the future. The results indicated that the improved ABC algorithm in view of the various factors is superior to traditional models just considering unilateralism in accuracy and persuasion. The overall findings cast light on this model which provides a new scientific and effective way to forecast the medium and long term electricity consumption.

  15. Land Use Adaptation to Climate Change: Economic Damages from Land-Falling Hurricanes in the Atlantic and Gulf States of the USA, 1900–2005

    Directory of Open Access Journals (Sweden)

    Asim Zia

    2012-05-01

    Full Text Available Global climate change, especially the phenomena of global warming, is expected to increase the intensity of land-falling hurricanes. Societal adaptation is needed to reduce vulnerability from increasingly intense hurricanes. This study quantifies the adaptation effects of potentially policy driven caps on housing densities and agricultural cover in coastal (and adjacent inland areas vulnerable to hurricane damages in the Atlantic and Gulf Coastal regions of the U.S. Time series regressions, especially Prais-Winston and Autoregressive Moving Average (ARMA models, are estimated to forecast the economic impacts of hurricanes of varying intensity, given that various patterns of land use emerge in the Atlantic and Gulf coastal states of the U.S. The Prais-Winston and ARMA models use observed time series data from 1900 to 2005 for inflation adjusted hurricane damages and socio-economic and land-use data in the coastal or inland regions where hurricanes caused those damages. The results from this study provide evidence that increases in housing density and agricultural cover cause significant rise in the de-trended inflation-adjusted damages. Further, higher intensity and frequency of land-falling hurricanes also significantly increase the economic damages. The evidence from this study implies that a medium to long term land use adaptation in the form of capping housing density and agricultural cover in the coastal (and adjacent inland states can significantly reduce economic damages from intense hurricanes. Future studies must compare the benefits of such land use adaptation policies against the costs of development controls implied in housing density caps and agricultural land cover reductions.

  16. 2004 Landfalling Hurricanes Poster

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The 2004 U.S. Landfalling Hurricanes poster is a special edition poster which contains two sets of images of Hurricanes Charley, Frances, Ivan, and Jeanne, created...

  17. Hurricane Katrina Poster (August 28, 2005)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Katrina poster. Multi-spectral image from NOAA-18 shows a very large Hurricane Katrina as a category 5 hurricane in the Gulf of Mexico on August 28, 2005....

  18. Hurricane Rita Poster (September 22, 2005)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Rita poster. Multi-spectral image from NOAA-16 shows Hurricane Rita as a category-4 hurricane in the Gulf of Mexico on September 22, 2005. Poster size is...

  19. Forecasting and analyzing high O3 time series in educational area through an improved chaotic approach

    Science.gov (United States)

    Hamid, Nor Zila Abd; Adenan, Nur Hamiza; Noorani, Mohd Salmi Md

    2017-08-01

    Forecasting and analyzing the ozone (O3) concentration time series is important because the pollutant is harmful to health. This study is a pilot study for forecasting and analyzing the O3 time series in one of Malaysian educational area namely Shah Alam using chaotic approach. Through this approach, the observed hourly scalar time series is reconstructed into a multi-dimensional phase space, which is then used to forecast the future time series through the local linear approximation method. The main purpose is to forecast the high O3 concentrations. The original method performed poorly but the improved method addressed the weakness thereby enabling the high concentrations to be successfully forecast. The correlation coefficient between the observed and forecasted time series through the improved method is 0.9159 and both the mean absolute error and root mean squared error are low. Thus, the improved method is advantageous. The time series analysis by means of the phase space plot and Cao method identified the presence of low-dimensional chaotic dynamics in the observed O3 time series. Results showed that at least seven factors affect the studied O3 time series, which is consistent with the listed factors from the diurnal variations investigation and the sensitivity analysis from past studies. In conclusion, chaotic approach has been successfully forecast and analyzes the O3 time series in educational area of Shah Alam. These findings are expected to help stakeholders such as Ministry of Education and Department of Environment in having a better air pollution management.

  20. Climatic Changes and Consequences on the French West Indies (C3AF), Hurricane and Tsunami Hazards Assessment

    Science.gov (United States)

    Arnaud, G.; Krien, Y.; Zahibo, N.; Dudon, B.

    2017-12-01

    Coastal hazards are among the most worrying threats of our time. In a context of climate change coupled to a large population increase, tropical areas could be the most exposed zones of the globe. In such circumstances, understanding the underlying processes can help to better predict storm surges and the associated global risks.Here we present the partial preliminary results integrated in a multidisciplinary project focused on climatic change effects over the coastal threat in the French West Indies and funded by the European Regional Development Fund. The study aims to provide a coastal hazard assessment based on hurricane surge and tsunami modeling including several aspects of climate changes that can affect hazards such as sea level rise, crustal subsidence/uplift, coastline changes etc. Several tsunamis scenarios have been simulated including tele-tsunamis to ensure a large range of tsunami hazards. Surge level of hurricane have been calculated using a large number of synthetic hurricanes to cover the actual and forecasted climate over the tropical area of Atlantic ocean. This hazard assessment will be later coupled with stakes assessed over the territory to provide risk maps.

  1. Hurricane risk assessment to rollback or ride out a cost versus loss decision making approach

    Science.gov (United States)

    Wohlman, Richard A.

    1992-01-01

    The potential exists that a hurricane striking the Kennedy Space Center while a Space Shuttle is on the pad. Winds in excess of 74.5 knots could cause the failure of the holddown bolts bringing about the catastrophic loss of the entire vehicle. Current plans call for the rollback of the shuttle when winds of that magnitude are forecast to strike the center. As this is costly, a new objective method for making rollback/rideout decisions based upon Bayesian Analysis and economic cost versus loss is presented.

  2. Recovery from PTSD following Hurricane Katrina.

    Science.gov (United States)

    McLaughlin, Katie A; Berglund, Patricia; Gruber, Michael J; Kessler, Ronald C; Sampson, Nancy A; Zaslavsky, Alan M

    2011-06-01

    We examined patterns and correlates of speed of recovery of estimated posttraumatic stress disorder (PTSD) among people who developed PTSD in the wake of Hurricane Katrina. A probability sample of prehurricane residents of areas affected by Hurricane Katrina was administered a telephone survey 7-19 months following the hurricane and again 24-27 months posthurricane. The baseline survey assessed PTSD using a validated screening scale and assessed a number of hypothesized predictors of PTSD recovery that included sociodemographics, prehurricane history of psychopathology, hurricane-related stressors, social support, and social competence. Exposure to posthurricane stressors and course of estimated PTSD were assessed in a follow-up interview. An estimated 17.1% of respondents had a history of estimated hurricane-related PTSD at baseline and 29.2% by the follow-up survey. Of the respondents who developed estimated hurricane-related PTSD, 39.0% recovered by the time of the follow-up survey with a mean duration of 16.5 months. Predictors of slow recovery included exposure to a life-threatening situation, hurricane-related housing adversity, and high income. Other sociodemographics, history of psychopathology, social support, social competence, and posthurricane stressors were unrelated to recovery from estimated PTSD. The majority of adults who developed estimated PTSD after Hurricane Katrina did not recover within 18-27 months. Delayed onset was common. Findings document the importance of initial trauma exposure severity in predicting course of illness and suggest that pre- and posttrauma factors typically associated with course of estimated PTSD did not influence recovery following Hurricane Katrina. © 2011 Wiley-Liss, Inc.

  3. Visualizing uncertainties in a storm surge ensemble data assimilation and forecasting system

    KAUST Repository

    Hollt, Thomas

    2015-01-15

    We present a novel integrated visualization system that enables the interactive visual analysis of ensemble simulations and estimates of the sea surface height and other model variables that are used for storm surge prediction. Coastal inundation, caused by hurricanes and tropical storms, poses large risks for today\\'s societies. High-fidelity numerical models of water levels driven by hurricane-force winds are required to predict these events, posing a challenging computational problem, and even though computational models continue to improve, uncertainties in storm surge forecasts are inevitable. Today, this uncertainty is often exposed to the user by running the simulation many times with different parameters or inputs following a Monte-Carlo framework in which uncertainties are represented as stochastic quantities. This results in multidimensional, multivariate and multivalued data, so-called ensemble data. While the resulting datasets are very comprehensive, they are also huge in size and thus hard to visualize and interpret. In this paper, we tackle this problem by means of an interactive and integrated visual analysis system. By harnessing the power of modern graphics processing units for visualization as well as computation, our system allows the user to browse through the simulation ensembles in real time, view specific parameter settings or simulation models and move between different spatial and temporal regions without delay. In addition, our system provides advanced visualizations to highlight the uncertainty or show the complete distribution of the simulations at user-defined positions over the complete time series of the prediction. We highlight the benefits of our system by presenting its application in a real-world scenario using a simulation of Hurricane Ike.

  4. 48 CFR 1852.236-73 - Hurricane plan.

    Science.gov (United States)

    2010-10-01

    ... 48 Federal Acquisition Regulations System 6 2010-10-01 2010-10-01 true Hurricane plan. 1852.236-73... Hurricane plan. As prescribed in 1836.570(c), insert the following clause: Hurricane Plan (DEC 1988) In the event of a hurricane warning, the Contractor shall— (a) Inspect the area and place all materials...

  5. Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting

    KAUST Repository

    Zhu, Xinxin

    2014-09-01

    Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth’s rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space-time methods in this setting.

  6. Short-term wind power combined forecasting based on error forecast correction

    International Nuclear Information System (INIS)

    Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

    2016-01-01

    Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed

  7. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    for the third and fourth day precipitation forecasts. A marked improvement was shown for the consensus 24 hour precipitation forecast, and small... Zuckerberg (1980) found a small long term skill increase in forecasts of heavy snow events for nine eastern cities. Other National Weather Service...and maximum temperature) are each awarded marks 2, 1, or 0 according to whether the forecast is correct, 8 - *- -**■*- ———"—- - -■ t0m 1 MM—IB I

  8. Combining Passive Microwave Sounders with CYGNSS information for improved retrievals: Observations during Hurricane Harvey

    Science.gov (United States)

    Schreier, M. M.

    2017-12-01

    The launch of CYGNSS (Cyclone Global Navigation Satellite System) has added an interesting component to satellite observations: it can provide wind speeds in the tropical area with a high repetition rate. Passive microwave sounders that are overpassing the same region can benefit from this information, when it comes to the retrieval of temperature or water profiles: the uncertainty about wind speeds has a strong impact on emissivity and reflectivity calculations with respect to surface temperature. This has strong influences on the uncertainty of retrieval of temperature and water content, especially under extreme weather conditions. Adding CYGNSS information to the retrieval can help to reduce errors and provide a significantly better sounder retrieval. Based on observations during Hurricane Harvey, we want to show the impact of CYGNSS data on the retrieval of passive microwave sensors. We will show examples on the impact on the retrieval from polar orbiting instruments, like the Advanced Technology Microwave Sounder (ATMS) and AMSU-A/B on NOAA-18 and 19. In addition we will also show the impact on retrievals from HAMSR (High Altitude MMIC Sounding Radiometer), which was flying on the Global Hawk during the EPOCH campaign. We will compare the results with other observations and estimate the impact of additional CYGNSS information on the microwave retrieval, especially on the impact in error and uncertainty reduction. We think, that a synergetic use of these different data sources could significantly help to produce better assimilation products for forecast assimilation.

  9. A Look Inside Hurricane Alma

    Science.gov (United States)

    2002-01-01

    Hurricane season in the eastern Pacific started off with a whimper late last month as Alma, a Category 2 hurricane, slowly made its way up the coast of Baja California, packing sustained winds of 110 miles per hour and gusts of 135 miles per hour. The above image of the hurricane was acquired on May 29, 2002, and displays the rainfall rates occurring within the storm. Click the image above to see an animated data visualization (3.8 MB) of the interior of Hurricane Alma. The images of the clouds seen at the beginning of the movie were retrieved from the National Oceanic and Atmospheric Association's (NOAA's) Geostationary Orbiting Environmental Satellite (GOES) network. As the movie continues, the clouds are peeled away to reveal an image of rainfall levels in the hurricane. The rainfall data were obtained by the Precipitation Radar aboard NASA's Tropical Rainfall Measuring Mission (TRMM) satellite. The Precipitation Radar bounces radio waves off of clouds to retrieve a reading of the number of large, rain-sized droplets within the clouds. Using these data, scientists can tell how much precipitation is occurring within and beneath a hurricane. In the movie, yellow denotes areas where 0.5 inches of rain is falling per hour, green denotes 1 inch per hour, and red denotes over 2 inches per hour. (Please note that high resolution still images of Hurricane Alma are available in the NASA Visible Earth in TIFF format.) Image and animation courtesy Lori Perkins, NASA Goddard Space Flight Center Scientific Visualization Studio

  10. State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

    Science.gov (United States)

    Gibbs, Matthew S.; McInerney, David; Humphrey, Greer; Thyer, Mark A.; Maier, Holger R.; Dandy, Graeme C.; Kavetski, Dmitri

    2018-02-01

    Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall-runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.

  11. A Lagrangian trajectory view on transport and mixing processes between the eye, eyewall, and environment using a high resolution simulation of Hurricane Bonnie (1998)

    Science.gov (United States)

    Cram, Thomas A.; Persing, John; Montgomery, Michael T.; Braun, Scott A.

    2006-01-01

    The transport and mixing characteristics of a large sample of air parcels within a mature and vertically sheared hurricane vortex is examined. Data from a high-resolution (2 km grid spacing) numerical simulation of "real-case" Hurricane Bonnie (1998) is used to calculate Lagrangian trajectories of air parcels in various subdomains of the hurricane (namely, the eye, eyewall, and near-environment) to study the degree of interaction (transport and mixing) between these subdomains. It is found that 1) there is transport and mixing from the low-level eye to the eyewall that carries high- Be air which can enhance the efficiency of the hurricane heat engine; 2) a portion of the low-level inflow of the hurricane bypasses the eyewall to enter the eye, that both replaces the mass of the low-level eye and lingers for a sufficient time (order 1 hour) to acquire enhanced entropy characteristics through interaction with the ocean beneath the eye; 3) air in the mid- to upper-level eye is exchanged with the eyewall such that more than half the air of the eye is exchanged in five hours in this case of a sheared hurricane; and 4) that one-fifth of the mass in the eyewall at a height of 5 km has an origin in the mid- to upper-level environment where thet(sub e) is much less than in the eyewall, which ventilates the ensemble average eyewall theta(sub e) by about 1 K. Implications of these findings to the problem of hurricane intensity forecasting are discussed.

  12. Recent improvements in Hurricane Imaging Radiometer’s brightness temperature image reconstruction

    Directory of Open Access Journals (Sweden)

    Sayak K. Biswas

    Full Text Available NASA MSFCs airborne Hurricane Imaging Radiometer (HIRAD uses interferometric aperture synthesis to produce high resolution wide swath images of scene brightness temperature (Tb distribution at four discrete C-band microwave frequencies (4.0, 5.0, 6.0 and 6.6 GHz. Images of ocean surface wind speed under heavy precipitation such as in tropical cyclones, is inferred from these measurements. The baseline HIRAD Tb reconstruction algorithm had produced prominent along-track streaks in the Tb images. Particularly the 4.0 GHz channel had been so dominated by the streaks as to be unusable.The loss of a frequency channel had compromised the final wind speed retrievals. During 2016, the HIRAD team made substantial progress in developing a quality controlled signal processing technique for the HIRAD data collected in 2015’s Tropical Cyclone Intensity (TCI experiment and reduced the effect of streaks in all channels including 4.0 GHz. 2000 MSC: 41A05, 41A10, 65D05, 65D17, Keywords: Microwave radiometry, Aperture synthesis, Image reconstruction, Hurricane winds

  13. Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

    Science.gov (United States)

    Yang, J.; Astitha, M.; Schwartz, C. S.

    2017-12-01

    Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

  14. Uncertainty Forecasts Improve Weather-Related Decisions and Attenuate the Effects of Forecast Error

    Science.gov (United States)

    Joslyn, Susan L.; LeClerc, Jared E.

    2012-01-01

    Although uncertainty is inherent in weather forecasts, explicit numeric uncertainty estimates are rarely included in public forecasts for fear that they will be misunderstood. Of particular concern are situations in which precautionary action is required at low probabilities, often the case with severe events. At present, a categorical weather…

  15. Validation of a probabilistic model for hurricane insurance loss projections in Florida

    International Nuclear Information System (INIS)

    Pinelli, J.-P.; Gurley, K.R.; Subramanian, C.S.; Hamid, S.S.; Pita, G.L.

    2008-01-01

    The Florida Public Hurricane Loss Model is one of the first public models accessible for scrutiny to the scientific community, incorporating state of the art techniques in hurricane and vulnerability modeling. The model was developed for Florida, and is applicable to other hurricane-prone regions where construction practice is similar. The 2004 hurricane season produced substantial losses in Florida, and provided the means to validate and calibrate this model against actual claim data. This paper presents the predicted losses for several insurance portfolios corresponding to hurricanes Andrew, Charley, and Frances. The predictions are validated against the actual claim data. Physical damage predictions for external building components are also compared to observed damage. The analyses show that the predictive capabilities of the model were substantially improved after the calibration against the 2004 data. The methodology also shows that the predictive capabilities of the model could be enhanced if insurance companies report more detailed information about the structures they insure and the types of damage they suffer. This model can be a powerful tool for the study of risk reduction strategies

  16. Exploring the interactions between forecast accuracy, risk perception and perceived forecast reliability in reservoir operator's decision to use forecast

    Science.gov (United States)

    Shafiee-Jood, M.; Cai, X.

    2017-12-01

    Advances in streamflow forecasts at different time scales offer a promise for proactive flood management and improved risk management. Despite the huge potential, previous studies have found that water resources managers are often not willing to incorporate streamflow forecasts information in decisions making, particularly in risky situations. While low accuracy of forecasts information is often cited as the main reason, some studies have found that implementation of streamflow forecasts sometimes is impeded by institutional obstacles and behavioral factors (e.g., risk perception). In fact, a seminal study by O'Connor et al. (2005) found that risk perception is the strongest determinant of forecast use while managers' perception about forecast reliability is not significant. In this study, we aim to address this issue again. However, instead of using survey data and regression analysis, we develop a theoretical framework to assess the user-perceived value of streamflow forecasts. The framework includes a novel behavioral component which incorporates both risk perception and perceived forecast reliability. The framework is then used in a hypothetical problem where reservoir operator should react to probabilistic flood forecasts with different reliabilities. The framework will allow us to explore the interactions among risk perception and perceived forecast reliability, and among the behavioral components and information accuracy. The findings will provide insights to improve the usability of flood forecasts information through better communication and education.

  17. 7 CFR 701.50 - 2005 hurricanes.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 7 2010-01-01 2010-01-01 false 2005 hurricanes. 701.50 Section 701.50 Agriculture... ADMINISTERED UNDER THIS PART § 701.50 2005 hurricanes. In addition benefits elsewhere allowed by this part, claims related to calendar year 2005 hurricane losses may be allowed to the extent provided for in §§ 701...

  18. An Examination of Hurricane Emergency Preparedness Planning at Institutions of Higher Learning of the Gulf South Region Post Hurricane Katrina

    Science.gov (United States)

    Ventura, Caterina Gulli

    2010-01-01

    The purpose of the study was to examine hurricane emergency preparedness planning at institutions of higher learning of the Gulf South region following Hurricane Katrina. The problem addressed the impact of Hurricane Katrina on decision-making and policy planning processes. The focus was on individuals that administer the hurricane emergency…

  19. Coastal and Riverine Flood Forecast Model powered by ADCIRC

    Science.gov (United States)

    Khalid, A.; Ferreira, C.

    2017-12-01

    Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which

  20. Brief communication "Hurricane Irene: a wake-up call for New York City?"

    Directory of Open Access Journals (Sweden)

    J. C. J. H. Aerts

    2012-06-01

    Full Text Available The weakening of Irene from a Category 3 hurricane to a tropical storm resulted in less damage in New York City (NYC than initially was anticipated. It is widely recognized that the storm surge and associated flooding could have been much more severe. In a recent study, we showed that a direct hit to the city from a hurricane may expose an enormous number of people to flooding. A major hurricane has the potential to cause large-scale damage in NYC. The city's resilience to flooding can be increased by improving and integrating flood insurance, flood zoning, and building code policies.

  1. Forecasting Water Levels Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Shreenivas N. Londhe

    2011-06-01

    Full Text Available For all Ocean related activities it is necessary to predict the actual water levels as accurate as possible. The present work aims at predicting the water levels with a lead time of few hours to a day using the technique of artificial neural networks. Instead of using the previous and current values of observed water level time series directly as input and output the water level anomaly (difference between the observed water level and harmonically predicted tidal level is calculated for each hour and the ANN model is developed using this time series. The network predicted anomaly is then added to harmonic tidal level to predict the water levels. The exercise is carried out at six locations, two in The Gulf of Mexico, two in The Gulf of Maine and two in The Gulf of Alaska along the USA coastline. The ANN models performed reasonably well for all forecasting intervals at all the locations. The ANN models were also run in real time mode for a period of eight months. Considering the hurricane season in Gulf of Mexico the models were also tested particularly during hurricanes.

  2. Hurricane Katrina impacts on Mississippi forests

    Science.gov (United States)

    Sonja N. Oswalt; Christopher Oswalt; Jeffery Turner

    2008-01-01

    Hurricane Katrina triggered public interest and concern for forests in Mississippi that required rapid responses from the scientific community. A uniform systematic sample of 3,590 ground plots were established and measured in 687 days immediately after the impact of Hurricane Katrina on the Gulf Coast. The hurricane damaged an estimated 521 million trees with more...

  3. Constraining Big Hurricanes: Remotely sensing Galveston Islands' changing coastal landscape from days to millennia

    Science.gov (United States)

    Dougherty, A. J.; Choi, J. H.; Heo, S.; Dosseto, A.

    2017-12-01

    Climate change models forecast increased storm intensity, which will drive coastal erosion as sea-level rise accelerates with global warming. Over the last five years the largest hurricanes ever recorded in the Pacific (Patricia) and the Atlantic (Irma) occurred as well as the devastation of Harvey. The preceding decade was marked with Super Storm Sandy, Katrina and Ike. A century prior, the deadliest natural disaster in North America occurred as a category 4 hurricane known as `The 1900 Storm' hit Galveston Island. This research aims to contextualize the impact of storms long before infrastructure and historical/scientific accounts documented erosion. Unlike the majority of barrier islands in the US, Galveston built seaward over the Holocene. As the beach prograded it preserved a history of storms and shoreline change over millennia to the present-day. These systems (called prograded barriers) were first studied over 50 years ago using topographic profiles, sediment cores and radiocarbon dating. This research revisits some of these benchmark study sites to augment existing data utilizing state-of-the-art Light Detection and Ranging (LiDAR), Ground Penetrating Radar (GPR), and Optically Stimulated Luminescence (OSL) techniques. In 2016 GPR and OSL data were collected from Galveston Island, with the aim to combine GPR, OSL and LiDAR (GOaL) to extract a high-resolution geologic record spanning 6,000 years. The resulting millennia-scale coastal evolution can be used to contextualize the impact of historic hurricanes over the past century (`The 1900 Storm'), decade (Ike in 2008) and year (now with Harvey). Preliminary results reveal a recent change in shoreline behaviour, and data from Harvey are currently being accessed within the perspective of these initial findings. This dataset will be discussed with respect to the other two benchmark prograded barriers studied in North America: Nayarit Barrier (Mexico) that Hurricane Patricia passed directly over in 2013 and

  4. Inflation Forecast Contracts

    OpenAIRE

    Gersbach, Hans; Hahn, Volker

    2012-01-01

    We introduce a new type of incentive contract for central bankers: inflation forecast contracts, which make central bankers’ remunerations contingent on the precision of their inflation forecasts. We show that such contracts enable central bankers to influence inflation expectations more effectively, thus facilitating more successful stabilization of current inflation. Inflation forecast contracts improve the accuracy of inflation forecasts, but have adverse consequences for output. On balanc...

  5. Genesis of tornadoes associated with hurricanes

    Science.gov (United States)

    Gentry, R. C.

    1983-01-01

    The climatological history of hurricane-tornadoes is brought up to date through 1982. Most of the tornadoes either form near the center of the hurricane, from the outer edge of the eyewall outward, or in an area between north and east-southeast of the hurricane center. The blackbody temperatures of the cloud tops which were analyzed for several hurricane-tornadoes that formed in the years 1974, 1975, and 1979, did not furnish strong precursor signals of tornado formation, but followed one of two patterns: either the temperatures were very low, or the tornado formed in areas of strong temperature gradients. Tornadoes with tropical cyclones most frequently occur at 1200-1800 LST, and although most are relatively weak, they can reach the F3 intensity level. Most form in association with the outer rainbands of the hurricane.

  6. The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

    Science.gov (United States)

    Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

    2013-10-01

    Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

  7. Safety and design impact of hurricane Andrew

    International Nuclear Information System (INIS)

    Guey, Ching N.

    2004-01-01

    Turkey Point completed the IPE in June of 1991. Hurricane Andrew landed at Turkey Point on August 24, 1992. Although the safety related systems, components and structures were not damaged by the Hurricane Andrew, certain nonsafety related components and the neighboring fossil plant sustained noticeable damage. Among the major components that were nonsafety related but would affect the PRA of the plant included the service water pumps and the high tower. This paper discusses the safety and design impact of Hurricane Andrew on Turkey Point Nuclear Power Plant. The risk of hurricanes on the interim and evolving plant configurations are briefly described. The risk of the plant from internal events as a result of damage incurred during Hurricane Andrew are discussed. The design change as the result of Hurricane Andrew and its impact on the PRA are presented. (author)

  8. Continental United States Hurricane Strikes

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Continental U.S. Hurricane Strikes Poster is our most popular poster which is updated annually. The poster includes all hurricanes that affected the U.S. since...

  9. 75 FR 54918 - Draft Regulatory Guide, DG-1247, “Design-Basis Hurricane and Hurricane Missiles for Nuclear Power...

    Science.gov (United States)

    2010-09-09

    .... This series was developed to describe and make available to the public such information as methods that... maximum hurricane windspeeds for hurricanes that originate in the Atlantic and make landfall along the... connected and provides an aerodynamic sail area on which the wind can act. An automobile hurricane missile...

  10. Improved forecasting with leading indicators: the principal covariate index

    NARCIS (Netherlands)

    C. Heij (Christiaan)

    2007-01-01

    textabstractWe propose a new method of leading index construction that combines the need for data compression with the objective of forecasting. This so-called principal covariate index is constructed to forecast growth rates of the Composite Coincident Index. The forecast performance is compared

  11. JLAB Hurricane recovery

    International Nuclear Information System (INIS)

    A. Hutton; D. Arenius; J. Benesch; S. Chattopadhyay; E. F. Daly; O. Garza; R. Kazimi; R. Lauzi; L. Merminga; W. Merz; R. Nelson; W. Oren; M. Poelker; P. Powers; J. Preble; V. Ganni; C. R. Reece; R. Rimmer; M. Spata; S. Suhring

    2004-01-01

    Hurricane Isabel, originally a Category 5 storm, arrived at Jefferson Lab on September 18, 2003 with winds of only 75 mph, creating little direct damage to the infrastructure. However, electric power was lost for four days allowing the superconducting cryomodules to warm up and causing a total loss of the liquid helium. The subsequent recovery of the cryomodules and the impact of the considerable amount of opportunistic preventive maintenance provides important lessons for all accelerator complexes, not only those with superconducting elements. The details of how the recovery process was structured and the resulting improvement in accelerator availability will be discussed in detail

  12. Identification of Caribbean basin hurricanes from Spanish documentary sources

    Energy Technology Data Exchange (ETDEWEB)

    Garcia-Herrera, R. [Depto. Fisica de la Tierra II, Facultad de Ciencias Fisicas, Universidad Complutense de Madrid, Ciudad Universitaria, 28040 Madrid (Spain); Gimeno, L. [Universidad de Vigo, Ourense (Spain); Ribera, P.; Gonzalez, E.; Fernandez, G. [Universidad Pablo de Olavide, Sevilla (Spain); Hernandez, E. [Universidad Complutense de Madrid, Madrid (Spain)

    2007-07-15

    This paper analyses five hurricanes that occurred in the period 1600 to 1800. These examples were identified during a systematic search in the General Archive of the Indies (AGI) in Seville. The research combined the expertise of climatologists and historians in order to optimise the search and analysis strategies. Results demonstrate the potential of this archive for the assessment of hurricanes in this period and show some of the difficulties involved in the collection of evidence of hurricane activity. The documents provide detailed descriptions of a hurricane's impacts and allow us to identify previously unreported hurricanes, obtain more precise dates for hurricanes previously identified, better define the area affected by a given hurricane and, finally, better assess a hurricane's intensity.

  13. Predicting hurricane wind damage by claim payout based on Hurricane Ike in Texas

    Directory of Open Access Journals (Sweden)

    Ji-Myong Kim

    2016-09-01

    Full Text Available The increasing occurrence of natural disasters and their related damage have led to a growing demand for models that predict financial loss. Although considerable research on the financial losses related to natural disasters has found significant predictors, there has been a lack of comprehensive study that addresses the relationship among vulnerabilities, natural disasters, and the economic losses of individual buildings. This study identifies the vulnerability indicators for hurricanes to establish a metric to predict the related financial loss. We classify hurricane-prone areas by highlighting the spatial distribution of losses and vulnerabilities. This study used a Geographical Information System (GIS to combine and produce spatial data and a multiple regression method to establish a wind damage prediction model. As the dependent variable, we used the value of the Texas Windstorm Insurance Association (TWIA claim payout divided by the appraised values of the buildings to predict real economic loss. As independent variables, we selected a hurricane indicator and built environment vulnerability indicators. The model we developed can be used by government agencies and insurance companies to predict hurricane wind damage.

  14. Hurricane Season: Are You Ready?

    Centers for Disease Control (CDC) Podcasts

    Hurricanes are one of Mother Nature’s most powerful forces. Host Bret Atkins talks with CDC’s National Center for Environmental Health Director Dr. Chris Portier about the main threats of a hurricane and how you can prepare.

  15. The puzzle of Fran: home healthcare in a hurricane.

    Science.gov (United States)

    King, D

    1998-10-01

    A natural disaster in the form of Hurricane Fran resulted not only in stories of ingenuity and compassion, but in a major performance improvement (PI) process for the entire agency. Through this PI process we learned about ourselves as a home health agency and discovered ways to improve our performance. More importantly we discovered ways to improve patient tracking and care during a disaster.

  16. Constraining the ensemble Kalman filter for improved streamflow forecasting

    Science.gov (United States)

    Maxwell, Deborah H.; Jackson, Bethanna M.; McGregor, James

    2018-05-01

    Data assimilation techniques such as the Ensemble Kalman Filter (EnKF) are often applied to hydrological models with minimal state volume/capacity constraints enforced during ensemble generation. Flux constraints are rarely, if ever, applied. Consequently, model states can be adjusted beyond physically reasonable limits, compromising the integrity of model output. In this paper, we investigate the effect of constraining the EnKF on forecast performance. A "free run" in which no assimilation is applied is compared to a completely unconstrained EnKF implementation, a 'typical' hydrological implementation (in which mass constraints are enforced to ensure non-negativity and capacity thresholds of model states are not exceeded), and then to a more tightly constrained implementation where flux as well as mass constraints are imposed to force the rate of water movement to/from ensemble states to be within physically consistent boundaries. A three year period (2008-2010) was selected from the available data record (1976-2010). This was specifically chosen as it had no significant data gaps and represented well the range of flows observed in the longer dataset. Over this period, the standard implementation of the EnKF (no constraints) contained eight hydrological events where (multiple) physically inconsistent state adjustments were made. All were selected for analysis. Mass constraints alone did little to improve forecast performance; in fact, several were significantly degraded compared to the free run. In contrast, the combined use of mass and flux constraints significantly improved forecast performance in six events relative to all other implementations, while the remaining two events showed no significant difference in performance. Placing flux as well as mass constraints on the data assimilation framework encourages physically consistent state estimation and results in more accurate and reliable forward predictions of streamflow for robust decision-making. We also

  17. Improving Forecast Skill by Assimilation of Quality Controlled AIRS Version 5 Temperature Soundings

    Science.gov (United States)

    Susskind, Joel; Reale, Oreste

    2009-01-01

    The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations are now used primarily in the generation of cloud cleared radiances R(sub i). This approach allows for the generation of accurate values of R(sub i) and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel error estimates for R(sub i). These error estimates are used for Quality Control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of Quality Control using the NASA GEOS-5 data assimilation system. Assimilation of Quality Controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done operationally by ECMWF and NCEP. Forecast resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

  18. A diary of hurricane Hugo.

    Science.gov (United States)

    Counts, C S

    1989-12-01

    Charleston, South Carolina was the recent victim of Hurricane Hugo. This article recalls the events that occurred before, during, and after the hurricane struck. The focus is on four outpatient dialysis units in that area. It is a story from which others may learn more about emergency preparedness.

  19. Telehealth at the US Department of Veterans Affairs after Hurricane Sandy.

    Science.gov (United States)

    Der-Martirosian, Claudia; Griffin, Anne R; Chu, Karen; Dobalian, Aram

    2018-01-01

    Background Like other integrated health systems, the US Department of Veterans Affairs has widely implemented telehealth during the past decade to improve access to care for its patient population. During major crises, the US Department of Veterans Affairs has the potential to transition healthcare delivery from traditional care to telecare. This paper identifies the types of Veterans Affairs telehealth services used during Hurricane Sandy (2012), and examines the patient characteristics of those users. Methods This study conducted both quantitative and qualitative analyses. Veterans Affairs administrative and clinical data files were used to illustrate the use of telehealth services 12 months pre- and 12 months post- Hurricane Sandy. In-person interviews with 31 key informants at the Manhattan Veterans Affairs Medical Center three-months post- Hurricane Sandy were used to identify major themes related to telecare. Results During the seven-month period of hospital closure at the Manhattan Veterans Affairs Medical Center after Hurricane Sandy, in-person patient visits decreased dramatically while telehealth visits increased substantially, suggesting that telecare was used in lieu of in-person care for some vulnerable patients. The most commonly used types of Veterans Affairs telehealth services included primary care, triage, mental health, home health, and ancillary services. Using qualitative analyses, three themes emerged from the interviews regarding the use of Veterans Affairs telecare post- Hurricane Sandy: patient safety, provision of telecare, and patient outreach. Conclusion Telehealth offers the potential to improve post-disaster access to and coordination of care. More information is needed to better understand how telehealth can change the processes and outcomes during disasters. Future studies should also evaluate key elements, such as adequate resources, regulatory and technology issues, workflow integration, provider resistance, diagnostic fidelity and

  20. Hurricane Frances Poster (September 5, 2004)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Frances poster. Multi-spectral image from NOAA-17 shows Hurricane Frances over central Florida on September 5, 2004. Poster dimension is approximately...

  1. An Assessment of the Subseasonal Forecast Performance in the Extended Global Ensemble Forecast System (GEFS)

    Science.gov (United States)

    Sinsky, E.; Zhu, Y.; Li, W.; Guan, H.; Melhauser, C.

    2017-12-01

    Optimal forecast quality is crucial for the preservation of life and property. Improving monthly forecast performance over both the tropics and extra-tropics requires attention to various physical aspects such as the representation of the underlying SST, model physics and the representation of the model physics uncertainty for an ensemble forecast system. This work focuses on the impact of stochastic physics, SST and the convection scheme on forecast performance for the sub-seasonal scale over the tropics and extra-tropics with emphasis on the Madden-Julian Oscillation (MJO). A 2-year period is evaluated using the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). Three experiments with different configurations than the operational GEFS were performed to illustrate the impact of the stochastic physics, SST and convection scheme. These experiments are compared against a control experiment (CTL) which consists of the operational GEFS but its integration is extended from 16 to 35 days. The three configurations are: 1) SPs, which uses a Stochastically Perturbed Physics Tendencies (SPPT), Stochastic Perturbed Humidity (SHUM) and Stochastic Kinetic Energy Backscatter (SKEB); 2) SPs+SST_bc, which uses a combination of SPs and a bias-corrected forecast SST from the NCEP Climate Forecast System Version 2 (CFSv2); and 3) SPs+SST_bc+SA_CV, which combines SPs, a bias-corrected forecast SST and a scale aware convection scheme. When comparing to the CTL experiment, SPs shows substantial improvement. The MJO skill has improved by about 4 lead days during the 2-year period. Improvement is also seen over the extra-tropics due to the updated stochastic physics, where there is a 3.1% and a 4.2% improvement during weeks 3 and 4 over the northern hemisphere and southern hemisphere, respectively. Improvement is also seen when the bias-corrected CFSv2 SST is combined with SPs. Additionally, forecast performance enhances when the scale aware

  2. The impact of weather and ocean forecasting on hydrocarbon production and pollution management in the Gulf of Mexico

    International Nuclear Information System (INIS)

    Kaiser, Mark J.; Pulsipher, Allan G.

    2007-01-01

    Over the past 2 years, the vulnerability of offshore production in the Gulf of Mexico (GOM) has been brought to light by extensive damage to oil and gas facilities and pipelines resulting from Hurricanes Ivan, Katrina, and Rita. The occurrences of extreme weather regularly force operators to shut-down production, cease drilling and construction activities, and evacuate personnel. Loop currents and eddies can also impact offshore operations and delay installation and drilling activities and reduce the effectiveness of oil spill response strategies. The purpose of this paper is to describe how weather and ocean forecasting impact production activities and pollution management in the GOM. Physical outcome and decision models in support of production and development activities and oil spill response management are presented, and the expected economic benefits that may result from the implementation of an integrated ocean observation network in the region are summarized. Improved ocean observation systems are expected to reduce the uncertainty of forecasting and to enhance the value of ocean/weather information throughout the Gulf region. The source of benefits and the size of activity from which improved ocean observation benefits may be derived are estimated for energy development and production activities and oil spill response management

  3. Hurricane Isabel Poster (September 18, 2003)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Isabel poster. Multi-spectral image from NOAA-17 shows Hurricane Isabel making landfall on the North Carolina Outer Banks on September 18, 2003. Poster...

  4. Hurricane Sandy Poster (October 29, 2012)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Sandy poster. Multi-spectral image from Suomi-NPP shows Hurricane Sandy approaching the New Jersey Coast on October 29, 2012. Poster size is approximately...

  5. Hurricane Charley Poster (August 13, 2004)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Charley poster. Multi-spectral image from NOAA-17 shows a small but powerful hurricane heading toward southern Florida on August 13, 2004. Poster dimension...

  6. Hurricane Jeanne Poster (September 25, 2004)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Jeanne poster. Multi-spectral image from NOAA-16 shows Hurricane Jeanne near Grand Bahama Island on September 25, 2004. Poster size is 34"x30".

  7. Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods

    Science.gov (United States)

    Quilty, J.; Adamowski, J. F.

    2015-12-01

    Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.

  8. Hurricane impacts on a pair of coastal forested watersheds: implications of selective hurricane damage to forest structure and streamflow dynamics

    Science.gov (United States)

    Jayakaran, A. D.; Williams, T. M.; Ssegane, H.; Amatya, D. M.; Song, B.; Trettin, C. C.

    2014-03-01

    Hurricanes are infrequent but influential disruptors of ecosystem processes in the southeastern Atlantic and Gulf coasts. Every southeastern forested wetland has the potential to be struck by a tropical cyclone. We examined the impact of Hurricane Hugo on two paired coastal South Carolina watersheds in terms of streamflow and vegetation dynamics, both before and after the hurricane's passage in 1989. The study objectives were to quantify the magnitude and timing of changes including a reversal in relative streamflow difference between two paired watersheds, and to examine the selective impacts of a hurricane on the vegetative composition of the forest. We related these impacts to their potential contribution to change watershed hydrology through altered evapotranspiration processes. Using over 30 years of monthly rainfall and streamflow data we showed that there was a significant transformation in the hydrologic character of the two watersheds - a transformation that occurred soon after the hurricane's passage. We linked the change in the rainfall-runoff relationship to a catastrophic change in forest vegetation due to selective hurricane damage. While both watersheds were located in the path of the hurricane, extant forest structure varied between the two watersheds as a function of experimental forest management techniques on the treatment watershed. We showed that the primary damage was to older pines, and to some extent larger hardwood trees. We believe that lowered vegetative water use impacted both watersheds with increased outflows on both watersheds due to loss of trees following hurricane impact. However, one watershed was able to recover to pre hurricane levels of evapotranspiration at a quicker rate due to the greater abundance of pine seedlings and saplings in that watershed.

  9. Assessment of Risk of Cholera in Haiti following Hurricane Matthew.

    Science.gov (United States)

    Khan, Rakib; Anwar, Rifat; Akanda, Shafqat; McDonald, Michael D; Huq, Anwar; Jutla, Antarpreet; Colwell, Rita

    2017-09-01

    Damage to the inferior and fragile water and sanitation infrastructure of Haiti after Hurricane Matthew has created an urgent public health emergency in terms of likelihood of cholera occurring in the human population. Using satellite-derived data on precipitation, gridded air temperature, and hurricane path and with information on water and sanitation (WASH) infrastructure, we tracked changing environmental conditions conducive for growth of pathogenic vibrios. Based on these data, we predicted and validated the likelihood of cholera cases occurring past hurricane. The risk of cholera in the southwestern part of Haiti remained relatively high since November 2016 to the present. Findings of this study provide a contemporary process for monitoring ground conditions that can guide public health intervention to control cholera in human population by providing access to vaccines, safe WASH facilities. Assuming current social and behavioral patterns remain constant, it is recommended that WASH infrastructure should be improved and considered a priority especially before 2017 rainy season.

  10. Using snow data assimilation to improve ensemble streamflow forecasting for the Upper Colorado River Basin

    Science.gov (United States)

    Micheletty, P. D.; Perrot, D.; Day, G. N.; Lhotak, J.; Quebbeman, J.; Park, G. H.; Carney, S.

    2017-12-01

    Water supply forecasting in the western United States is inextricably linked to snowmelt processes, as approximately 70-85% of total annual runoff comes from water stored in seasonal mountain snowpacks. Snowmelt-generated streamflow is vital to a variety of downstream uses; the Upper Colorado River Basin (UCRB) alone provides water supply for 25 million people, irrigation water for 3.5 million acres, and drives hydropower generation at Lake Powell. April-July water supply forecasts produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC) are critical to basin water management. The primary objective of this project as part of the NASA Water Resources Applied Science Program, is to improve water supply forecasting for the UCRB by assimilating satellite and ground snowpack observations into a distributed hydrologic model at various times during the snow accumulation and melt seasons. To do this, we have built a framework that uses an Ensemble Kalman Filter (EnKF) to update modeled snow water equivalent (SWE) states in the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) with spatially interpolated SNOTEL snow water equivalent (SWE) observations and products from the MODIS Snow Covered-Area and Grain size retrieval algorithm (when available). We have generated April-July water supply reforecasts for a 20-year period (1991-2010) for several headwater catchments in the UCRB using HL-RDHM and snow data assimilation in the Ensemble Streamflow Prediction (ESP) framework. The existing CBRFC ESP reforecasts will provide a baseline for comparison to determine whether the data assimilation process adds skill to the water supply forecasts. Preliminary results from one headwater basin show improved skill in water supply forecasting when HL-RDHM is run with the data assimilation step compared to HL-RDHM run without the data assimilation step, particularly in years when MODSCAG data were available (2000-2010). The final

  11. Shelf sediment transport during hurricanes Katrina and Rita

    Science.gov (United States)

    Xu, Kehui; Mickey, Rangley C.; Chen, Qin; Harris, Courtney K.; Hetland, Robert D.; Hu, Kelin; Wang, Jiaze

    2016-05-01

    Hurricanes can greatly modify the sedimentary record, but our coastal scientific community has rather limited capability to predict hurricane-induced sediment deposition. A three-dimensional sediment transport model was developed in the Regional Ocean Modeling System (ROMS) to study seabed erosion and deposition on the Louisiana shelf in response to Hurricanes Katrina and Rita in the year 2005. Sensitivity tests were performed on both erosional and depositional processes for a wide range of erosional rates and settling velocities, and uncertainty analysis was done on critical shear stresses using the polynomial chaos approximation method. A total of 22 model runs were performed in sensitivity and uncertainty tests. Estimated maximum erosional depths were sensitive to the inputs, but horizontal erosional patterns seemed to be controlled mainly by hurricane tracks, wave-current combined shear stresses, seabed grain sizes, and shelf bathymetry. During the passage of two hurricanes, local resuspension and deposition dominated the sediment transport mechanisms. Hurricane Katrina followed a shelf-perpendicular track before making landfall and its energy dissipated rapidly within about 48 h along the eastern Louisiana coast. In contrast, Hurricane Rita followed a more shelf-oblique track and disturbed the seabed extensively during its 84-h passage from the Alabama-Mississippi border to the Louisiana-Texas border. Conditions to either side of Hurricane Rita's storm track differed substantially, with the region to the east having stronger winds, taller waves and thus deeper erosions. This study indicated that major hurricanes can disturb the shelf at centimeter to meter levels. Each of these two hurricanes suspended seabed sediment mass that far exceeded the annual sediment inputs from the Mississippi and Atchafalaya Rivers, but the net transport from shelves to estuaries is yet to be determined. Future studies should focus on the modeling of sediment exchange between

  12. Hurricane Hugo Poster (September 21, 1989)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Hugo poster. Multi-spectral image from NOAA-11 captures Hurricane Hugo slamming into South Carolina coast on September 21, 1989. Poster size is 36"x36".

  13. Hurricane Ivan Poster (September 15, 2004)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Ivan poster. Multi-spectral image from NOAA-16 shows Hurricane Ivan in the Gulf of Mexico on September 15, 2004. Poster size is 34"x30".

  14. Meeting the Science Needs of the Nation in the Wake of Hurricane Sandy-- A U.S. Geological Survey Science Plan for Support of Restoration and Recovery

    Science.gov (United States)

    Buxton, Herbert T.; Andersen, Matthew E.; Focazio, Michael J.; Haines, John W.; Hainly, Robert A.; Hippe, Daniel J.; Sugarbaker, Larry J.

    2013-01-01

    n late October 2012, Hurricane Sandy came ashore during a spring high tide on the New Jersey coastline, delivering hurricane-force winds, storm tides exceeding 19 feet, driving rain, and plummeting temperatures. Hurricane Sandy resulted in 72 direct fatalities in the mid-Atlantic and northeastern United States, and widespread and substantial physical, environmental, ecological, social, and economic impacts estimated at near $50 billion. Before the landfall of Hurricane Sandy, the USGS provided forecasts of potential coastal change; collected oblique aerial photography of pre-storm coastal morphology; deployed storm-surge sensors, rapid-deployment streamgages, wave sensors, and barometric pressure sensors; conducted Light Detection And Ranging (lidar) aerial topographic surveys of coastal areas; and issued a landslide alert for landslide prone areas. During the storm, Tidal Telemetry Networks provided real-time water-level information along the coast. Long-term network and rapid-deployment real-time streamgages and water-quality monitors reported on river levels and changes in water quality. Immediately after the storm, the USGS serviced real-time instrumentation, retrieved data from over 140 storm-surge sensors, and collected other essential environmental data, including more than 830 high-water marks mapping the extent and elevation of the storm surge. Post-storm lidar surveys documented storm impacts to coastal barriers informing response and recovery and providing a new baseline to assess vulnerability of the reconfigured coast. The USGS Hazard Data Distribution System served storm related information from many agencies on the Internet on a daily basis. This science plan was developed immediately following Hurricane Sandy to coordinate continuing USGS activities with other agencies and to guide continued data collection and analysis to ensure support for recovery and restoration efforts. The data, information, and tools that are produced by implementing this

  15. Satellite Remote Sensing of Ocean Winds, Surface Waves and Surface Currents during the Hurricanes

    Science.gov (United States)

    Zhang, G.; Perrie, W. A.; Liu, G.; Zhang, L.

    2017-12-01

    , waves and currents in hurricanes can be useful for intensity prediction, which has had relatively few improvements in the past 25 years. In 2018 RADARSAT Constellation Mission will be launched, increasing SAR coverage by 10×, allowing increased observations during the next hurricane season.

  16. Reducing uncertainty in load forecasts and using real options for improving capacity dispatch management through the utilization of weather and hydrologic forecasts

    International Nuclear Information System (INIS)

    Davis, T.

    2004-01-01

    The effect of weather on electricity markets was discussed with particular focus on reducing weather uncertainty by improving short term weather forecasts. The implications of weather for hydroelectric power dispatch and use were also discussed. Although some errors in weather forecasting can result in economic benefits, most errors are associated with more costs than benefits. This presentation described how a real options analysis can make weather a favorable option. Four case studies were presented for exploratory data analysis of regional weather phenomena. These included: (1) the 2001 California electricity crisis, (2) the delta breeze effects on the California ISO, (3) the summer 2002 weather forecast error for ISO New England, and (4) the hydro plant asset valuation using weather uncertainty. It was concluded that there is a need for more economic methodological studies on the effect of weather on energy markets and costs. It was suggested that the real options theory should be applied to weather planning and utility applications. tabs., figs

  17. Forecasting China’s Annual Biofuel Production Using an Improved Grey Model

    Directory of Open Access Journals (Sweden)

    Nana Geng

    2015-10-01

    Full Text Available Biofuel production in China suffers from many uncertainties due to concerns about the government’s support policy and supply of biofuel raw material. Predicting biofuel production is critical to the development of this energy industry. Depending on the biofuel’s characteristics, we improve the prediction precision of the conventional prediction method by creating a dynamic fuzzy grey–Markov prediction model. Our model divides random time series decomposition into a change trend sequence and a fluctuation sequence. It comprises two improvements. We overcome the problem of considering the status of future time from a static angle in the traditional grey model by using the grey equal dimension new information and equal dimension increasing models to create a dynamic grey prediction model. To resolve the influence of random fluctuation data and weak anti-interference ability in the Markov chain model, we improve the traditional grey–Markov model with classification of states using the fuzzy set theory. Finally, we use real data to test the dynamic fuzzy prediction model. The results prove that the model can effectively improve the accuracy of forecast data and can be applied to predict biofuel production. However, there are still some defects in our model. The modeling approach used here predicts biofuel production levels based upon past production levels dictated by economics, governmental policies, and technological developments but none of which can be forecast accurately based upon past events.

  18. Hurricane Wilma Poster (October 24, 2005)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hurricane Wilma poster. Multi-spectral image from NOAA-18 shows Hurricane Wilma exiting Florida off the east Florida coast on October 24, 2005. Poster size is 34"x30".

  19. Spatial grids for hurricane climate research

    Energy Technology Data Exchange (ETDEWEB)

    Elsner, James B.; Hodges, Robert E.; Jagger, Thomas H. [Florida State University, Tallahassee, FL (United States)

    2012-07-15

    The authors demonstrate a spatial framework for studying hurricane climatology. The framework consists of a spatial tessellation of the hurricane basin using equal-area hexagons. The hexagons are efficient at covering hurricane tracks and provide a scaffolding to combine attribute data from tropical cyclones with spatial climate data. The framework's utility is demonstrated using examples from recent hurricane seasons. Seasons that have similar tracks are quantitatively assessed and grouped. Regional cyclone frequency and intensity variations are mapped. A geographically-weighted regression of cyclone intensity on sea-surface temperature emphasizes the importance of a warm ocean in the intensification of cyclones over regions where the heat content is greatest. The largest differences between model predictions and observations occur near the coast. The authors suggest the framework is ideally suited for comparing tropical cyclones generated from different numerical simulations. (orig.)

  20. Directional analysis of the storm surge from Hurricane Sandy 2012, with applications to Charleston, New Orleans, and the Philippines.

    Science.gov (United States)

    Drews, Carl; Galarneau, Thomas J

    2015-01-01

    Hurricane Sandy in late October 2012 drove before it a storm surge that rose to 4.28 meters above mean lower low water at The Battery in lower Manhattan, and flooded the Hugh L. Carey automobile tunnel between Brooklyn and The Battery. This study examines the surge event in New York Harbor using the Weather Research and Forecasting (WRF) atmospheric model and the Coupled-Ocean-Atmosphere-Wave- Sediment Transport/Regional Ocean Modeling System (COAWST/ROMS). We present a new technique using directional analysis to calculate and display maps of a coastline's potential for storm surge; these maps are constructed from wind fields blowing from eight fixed compass directions. This analysis approximates the surge observed during Hurricane Sandy. The directional analysis is then applied to surge events at Charleston, South Carolina, New Orleans, Louisiana, and Tacloban City, the Philippines. Emergency managers could use these directional maps to prepare their cities for an approaching storm, on planning horizons from days to years.

  1. The wind forecasting improvement project. Description and results from the Southern study region

    Energy Technology Data Exchange (ETDEWEB)

    Freedman, Jeffrey [AWS Truepower LLC, Albany, NY (United States); Benjamin, Stan; Wilczak, James [National Oceanic and Atmospheric Administration, Washington, DC and Boulder, CO (United States)] [and others

    2012-07-01

    The Wind Forecasting Improvement Project (WFIP) is a multi-year U.S. Department of Energy (DOE)/National Oceanographic and Atmospheric Administration (NOAA) sponsored study whose main purpose is to demonstrate the scientific and economic benefits of additional atmospheric observations and model enhancements on wind energy production forecasts. WFIP covers two geographical regions of the U.S.: (1) the upper Great Plains, or Northern Study Area, and (2) most of Texas-the Southern Study Area. The Southern campaign is being led by AWS Truepower LLC, and includes a team of private, government, and academic partners with collective experience and expertise in all facets required to ensure a successful completion of the project. In addition presenting a summary of the state-of-the-art forecasting techniques used and phenomena-based analysis mentioned above, a brief synopsis of how ''lessons learned'' from the WFIP Southern Study Area can be articulated and applied to other wind resource regions will be described. (orig.)

  2. Automation of energy demand forecasting

    Science.gov (United States)

    Siddique, Sanzad

    Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

  3. Using integrated modeling for generating watershed-scale dynamic flood maps for Hurricane Harvey

    Science.gov (United States)

    Saksena, S.; Dey, S.; Merwade, V.; Singhofen, P. J.

    2017-12-01

    Hurricane Harvey, which was categorized as a 1000-year return period event, produced unprecedented rainfall and flooding in Houston. Although the expected rainfall was forecasted much before the event, there was no way to identify which regions were at higher risk of flooding, the magnitude of flooding, and when the impacts of rainfall would be highest. The inability to predict the location, duration, and depth of flooding created uncertainty over evacuation planning and preparation. This catastrophic event highlighted that the conventional approach to managing flood risk using 100-year static flood inundation maps is inadequate because of its inability to predict flood duration and extents for 500-year or 1000-year return period events in real-time. The purpose of this study is to create models that can dynamically predict the impacts of rainfall and subsequent flooding, so that necessary evacuation and rescue efforts can be planned in advance. This study uses a 2D integrated surface water-groundwater model called ICPR (Interconnected Channel and Pond Routing) to simulate both the hydrology and hydrodynamics for Hurricane Harvey. The methodology involves using the NHD stream network to create a 2D model that incorporates rainfall, land use, vadose zone properties and topography to estimate streamflow and generate dynamic flood depths and extents. The results show that dynamic flood mapping captures the flood hydrodynamics more accurately and is able to predict the magnitude, extent and time of occurrence for extreme events such as Hurricane Harvey. Therefore, integrated modeling has the potential to identify regions that are more susceptible to flooding, which is especially useful for large-scale planning and allocation of resources for protection against future flood risk.

  4. Blink Number Forecasting Based on Improved Bayesian Fusion Algorithm for Fatigue Driving Detection

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-01-01

    Full Text Available An improved Bayesian fusion algorithm (BFA is proposed for forecasting the blink number in a continuous video. It assumes that, at one prediction interval, the blink number is correlated with the blink numbers of only a few previous intervals. With this assumption, the weights of the component predictors in the improved BFA are calculated according to their prediction performance only from a few intervals rather than from all intervals. Therefore, compared with the conventional BFA, the improved BFA is more sensitive to the disturbed condition of the component predictors for adjusting their weights more rapidly. To determine the most relevant intervals, the grey relation entropy-based analysis (GREBA method is proposed, which can be used analyze the relevancy between the historical data flows of blink number and the data flow at the current interval. Three single predictors, that is, the autoregressive integrated moving average (ARIMA, radial basis function neural network (RBFNN, and Kalman filter (KF, are designed and incorporated linearly into the BFA. Experimental results demonstrate that the improved BFA obviously outperforms the conventional BFA in both accuracy and stability; also fatigue driving can be accurately warned against in advance based on the blink number forecasted by the improved BFA.

  5. Physical aspects of Hurricane Hugo in Puerto Rico

    Science.gov (United States)

    Scatena, F.N.; Larsen, Matthew C.

    1991-01-01

    On 18 September 1989 the western part ofHurricane Hugo crossed eastern Puerto Rico and the Luquillo Experimental Forest (LEF). Storm-facing slopes on the northeastern part of the island that were within 15 km of the eye and received greater than 200 mm of rain were most affected by the storm. In the LEF and nearby area, recurrence intervals associated with Hurricane Hugo were 50 yr for wind velocity, 10 to 31 yr for stream discharge, and 5 yr for rainfall intensity. To compare the magnitudes of the six hurricanes to pass over PuertoRico since 1899, 3 indices were developed using the standardized values of the product of: the maximum sustained wind speed at San Juan squared and storm duration; the square of the product of the maximum sustained wind velocity at San Juan and the ratio of the distance between the hurricane eye and San Juan to the distance between the eye and percentage of average annual rainfall delivered by the storm. Based on these indices, HurricaneHugo was of moderate intensity. However, because of the path of Hurricane Hugo, only one of these six storms (the 1932 storm) caused more damage to the LEF than Hurricane Hugo. Hurricanes of Hugo's magnitude are estimated to pass over the LEF once every 50-60 yr, on average. 

  6. Subscriber Number Forecasting Tool Based on Subscriber Attribute Distribution for Evaluating Improvement Strategies

    OpenAIRE

    Hiramatsu, Ayako; Shono, Yuji; Oiso, Hiroaki; Komoda, Norihisa

    2005-01-01

    In this paper, a subscriber number forecasting tool that evaluates quiz game mobile content improvement strategies is developed. Unsubscription rates depend on such subscriber attributes such as consecutive months, stages, rankings, and so on. In addition, content providers can anticipate change in unsubscription rates for each content improvement strategy. However, subscriber attributes change dynamically. Therefore, a method that deals with dynamic subscriber attribute changes is proposed. ...

  7. On the Influence of Global Warming on Atlantic Hurricane Frequency

    Science.gov (United States)

    Hosseini, S. R.; Scaioni, M.; Marani, M.

    2018-04-01

    In this paper, the possible connection between the frequency of Atlantic hurricanes to the climate change, mainly the variation in the Atlantic Ocean surface temperature has been investigated. The correlation between the observed hurricane frequency for different categories of hurricane's intensity and Sea Surface Temperature (SST) has been examined over the Atlantic Tropical Cyclogenesis Regions (ACR). The results suggest that in general, the frequency of hurricanes have a high correlation with SST. In particular, the frequency of extreme hurricanes with Category 5 intensity has the highest correlation coefficient (R = 0.82). In overall, the analyses in this work demonstrates the influence of the climate change condition on the Atlantic hurricanes and suggest a strong correlation between the frequency of extreme hurricanes and SST in the ACR.

  8. Evaluation of weather forecast systems for storm surge modeling in the Chesapeake Bay

    Science.gov (United States)

    Garzon, Juan L.; Ferreira, Celso M.; Padilla-Hernandez, Roberto

    2018-01-01

    Accurate forecast of sea-level heights in coastal areas depends, among other factors, upon a reliable coupling of a meteorological forecast system to a hydrodynamic and wave system. This study evaluates the predictive skills of the coupled circulation and wind-wave model system (ADCIRC+SWAN) for simulating storm tides in the Chesapeake Bay, forced by six different products: (1) Global Forecast System (GFS), (2) Climate Forecast System (CFS) version 2, (3) North American Mesoscale Forecast System (NAM), (4) Rapid Refresh (RAP), (5) European Center for Medium-Range Weather Forecasts (ECMWF), and (6) the Atlantic hurricane database (HURDAT2). This evaluation is based on the hindcasting of four events: Irene (2011), Sandy (2012), Joaquin (2015), and Jonas (2016). By comparing the simulated water levels to observations at 13 monitoring stations, we have found that the ADCIR+SWAN System forced by the following: (1) the HURDAT2-based system exhibited the weakest statistical skills owing to a noteworthy overprediction of the simulated wind speed; (2) the ECMWF, RAP, and NAM products captured the moment of the peak and moderately its magnitude during all storms, with a correlation coefficient ranging between 0.98 and 0.77; (3) the CFS system exhibited the worst averaged root-mean-square difference (excepting HURDAT2); (4) the GFS system (the lowest horizontal resolution product tested) resulted in a clear underprediction of the maximum water elevation. Overall, the simulations forced by NAM and ECMWF systems induced the most accurate results best accuracy to support water level forecasting in the Chesapeake Bay during both tropical and extra-tropical storms.

  9. Gulf of Mexico hurricane wave simulations using SWAN : Bulk formula-based drag coefficient sensitivity for Hurricane Ike

    NARCIS (Netherlands)

    Huang, Y.; Weisberg, R.H.; Zheng, L.; Zijlema, M.

    2013-01-01

    The effects of wind input parameterizations on wave estimations under hurricane conditions are examined using the unstructured grid, third-generation wave model, Simulating WAves Nearshore (SWAN). Experiments using Hurricane Ike wind forcing, which impacted the Gulf of Mexico in 2008, illustrate

  10. Improving wave forecasting by integrating ensemble modelling and machine learning

    Science.gov (United States)

    O'Donncha, F.; Zhang, Y.; James, S. C.

    2017-12-01

    Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

  11. NOAA predicts active 2013 Atlantic hurricane season

    Science.gov (United States)

    (discussion) El Niño/Southern Oscillation (ENSO) Diagnostic Discussion National Hurricane Preparedness Week in both English and Spanish, featuring NOAA hurricane experts and the FEMA administrator at

  12. Hurricane Resilient Wind Plant Concept Study Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Dibra, Besart [Keystone Engineering Inc., Vonore, TN (United States); Finucane, Zachary [Keystone Engineering Inc., Vonore, TN (United States); Foley, Benjamin [Keystone Engineering Inc., Vonore, TN (United States); Hall, Rudy [Keystone Engineering Inc., Vonore, TN (United States); Damiani, Rick [National Renewable Energy Lab. (NREL), Golden, CO (United States); Maples, Benjamin [National Renewable Energy Lab. (NREL), Golden, CO (United States); Parker, Zachary [National Renewable Energy Lab. (NREL), Golden, CO (United States); Robertson, Amy [National Renewable Energy Lab. (NREL), Golden, CO (United States); Scott, George [National Renewable Energy Lab. (NREL), Golden, CO (United States); Stehly, Tyler [National Renewable Energy Lab. (NREL), Golden, CO (United States); Wendt, Fabian [National Renewable Energy Lab. (NREL), Golden, CO (United States); Andersen, Mads Boel Overgaard [Siemens Wind Power A/S, Brande (Denmark); Standish, Kevin [Siemens Wind Power A/S, Brande (Denmark); Lee, Ken [Wetzel Engineering Inc., Round Rock, TX (United States); Raina, Amool [Wetzel Engineering Inc., Round Rock, TX (United States); Wetzel, Kyle [Wetzel Engineering Inc., Round Rock, TX (United States); Musial, Walter [National Renewable Energy Lab. (NREL), Golden, CO (United States); Schreck, Scott [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2016-10-01

    Hurricanes occur over much of the U.S. Atlantic and Gulf coasts, from Long Island to the U.S.-Mexico border, encompassing much of the nation's primary offshore wind resource. Category 5 hurricanes have made landfall as far north as North Carolina, with Category 3 hurricanes reaching New York with some frequency. Along the US West coast, typhoons strike with similar frequency and severity. At present, offshore wind turbine design practices do not fully consider the severe operating conditions imposed by hurricanes. Although universally applied to most turbine designs, International Electrotechnical Commission (IEC) standards do not sufficiently address the duration, directionality, magnitude, or character of hurricanes. To assess advanced design features that could mitigate hurricane loading in various ways, this Hurricane-Resilient Wind Plant Concept Study considered a concept design study of a 500-megawatt (MW) wind power plant consisting of 10-MW wind turbines deployed in 25-meter (m) water depths in the Western Gulf of Mexico. This location was selected because hurricane frequency and severity provided a unique set of design challenges that would enable assessment of hurricane risk and projection of cost of energy (COE) changes, all in response to specific U.S. Department of Energy (DOE) objectives. Notably, the concept study pursued a holistic approach that incorporated multiple advanced system elements at the wind turbine and wind power plant levels to meet objectives for system performance and reduced COE. Principal turbine system elements included a 10-MW rotor with structurally efficient, low-solidity blades; a lightweight, permanent-magnet, direct-drive generator, and an innovative fixed substructure. At the wind power plant level, turbines were arrayed in a large-scale wind power plant in a manner aimed at balancing energy production against capital, installation, and operation and maintenance (O&M) costs to achieve significant overall reductions in

  13. Implementation of bayesian model averaging on the weather data forecasting applications utilizing open weather map

    Science.gov (United States)

    Rahmat, R. F.; Nasution, F. R.; Seniman; Syahputra, M. F.; Sitompul, O. S.

    2018-02-01

    Weather is condition of air in a certain region at a relatively short period of time, measured with various parameters such as; temperature, air preasure, wind velocity, humidity and another phenomenons in the atmosphere. In fact, extreme weather due to global warming would lead to drought, flood, hurricane and other forms of weather occasion, which directly affects social andeconomic activities. Hence, a forecasting technique is to predict weather with distinctive output, particullary mapping process based on GIS with information about current weather status in certain cordinates of each region with capability to forecast for seven days afterward. Data used in this research are retrieved in real time from the server openweathermap and BMKG. In order to obtain a low error rate and high accuracy of forecasting, the authors use Bayesian Model Averaging (BMA) method. The result shows that the BMA method has good accuracy. Forecasting error value is calculated by mean square error shows (MSE). The error value emerges at minumum temperature rated at 0.28 and maximum temperature rated at 0.15. Meanwhile, the error value of minimum humidity rates at 0.38 and the error value of maximum humidity rates at 0.04. Afterall, the forecasting error rate of wind speed is at 0.076. The lower the forecasting error rate, the more optimized the accuracy is.

  14. Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility Resource Planning

    Energy Technology Data Exchange (ETDEWEB)

    Gagnon, Pieter [National Renewable Energy Lab. (NREL), Golden, CO (United States); Barbose, Galen L. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Stoll, Brady [National Renewable Energy Lab. (NREL), Golden, CO (United States); Ehlen, Ali [National Renewable Energy Lab. (NREL), Golden, CO (United States); Zuboy, Jarret [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mai, Trieu [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mills, Andrew D. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2018-05-15

    Misforecasting the adoption of customer-owned distributed photovoltaics (DPV) can have operational and financial implications for utilities; forecasting capabilities can be improved, but generally at a cost. This paper informs this decision-space by using a suite of models to explore the capacity expansion and operation of the Western Interconnection over a 15-year period across a wide range of DPV growth rates and misforecast severities. The system costs under a misforecast are compared against the costs under a perfect forecast, to quantify the costs of misforecasting. Using a simplified probabilistic method applied to these modeling results, an analyst can make a first-order estimate of the financial benefit of improving a utility’s forecasting capabilities, and thus be better informed about whether to make such an investment. For example, under our base assumptions, a utility with 10 TWh per year of retail electric sales who initially estimates that DPV growth could range from 2% to 7.5% of total generation over the next 15 years could expect total present-value savings of approximately $4 million if they could reduce the severity of misforecasting to within ±25%. Utility resource planners can compare those savings against the costs needed to achieve that level of precision, to guide their decision on whether to make an investment in tools or resources.

  15. A Novel Hydro-information System for Improving National Weather Service River Forecast System

    Science.gov (United States)

    Nan, Z.; Wang, S.; Liang, X.; Adams, T. E.; Teng, W. L.; Liang, Y.

    2009-12-01

    A novel hydro-information system has been developed to improve the forecast accuracy of the NOAA National Weather Service River Forecast System (NWSRFS). An MKF-based (Multiscale Kalman Filter) spatial data assimilation framework, together with the NOAH land surface model, is employed in our system to assimilate satellite surface soil moisture data to yield improved evapotranspiration. The latter are then integrated into the distributed version of the NWSRFS to improve its forecasting skills, especially for droughts, but also for disaster management in general. Our system supports an automated flow into the NWSRFS of daily satellite surface soil moisture data, derived from the TRMM Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and the forcing information of the North American Land Data Assimilation System (NLDAS). All data are custom processed, archived, and supported by the NASA Goddard Earth Sciences Data Information and Services Center (GES DISC). An optional data fusing component is available in our system, which fuses NEXRAD Stage III precipitation data with the NLDAS precipitation data, using the MKF-based framework, to provide improved precipitation inputs. Our system employs a plug-in, structured framework and has a user-friendly, graphical interface, which can display, in real-time, the spatial distributions of assimilated state variables and other model-simulated information, as well as their behaviors in time series. The interface can also display watershed maps, as a result of the integration of the QGIS library into our system. Extendibility and flexibility of our system are achieved through the plug-in design and by an extensive use of XML-based configuration files. Furthermore, our system can be extended to support multiple land surface models and multiple data assimilation schemes, which would further increase its capabilities. Testing of the integration of the current system into the NWSRFS is

  16. Controlling a hurricane by altering its internal climate

    Science.gov (United States)

    Mardhekar, D.

    2010-09-01

    Atmospheric hazards, like the fury of a hurricane, can be controlled by altering its internal climate. The hurricane controlling technique suggested is eco-friendly, compatible with hurricane size, has a sound scientific base and is practically possible. The key factor is a large scale dilution of the hurricane fuel, vapour, in the eye wall and spiral rain bands where condensation causing vapor volume reduction (a new concept which can be explained by Avogadro's law) and latent heat release drive the storm. This can be achieved by installing multiple storage tanks containing dry liquefied air on the onshore and offshore coastal regions and islands, preferably underground, in the usual path of a hurricane. Each storage tank is designed to hold and release dry liquefied air of around 100,000 tons. Satellite tracking of hurricanes can locate the eye wall and the spiral rain bands. The installed storage tanks coming under these areas will rapidly inject dry air in huge quantities thereby diluting the vapour content of the vapour-rich air in the eye wall and in the spiral rain bands. This will result in reduced natural input of vapour-rich air, reduced release of latent heat, reduced formation of the low pressure zone due to condensation and volume reduction of the vapor, expansion of the artificially introduced dry air as it goes up occupying a larger space with the diluted fuel, absorption of energy from the system by low temperature of the artificially introduced air. It will effect considerable condensation of the vapor near the sea surface thus further starving the hurricane of its fuel in its engine. Seeding materials, or microscopic dust as suggested by Dr. Daniel Rosenfeld in large quantities may also be introduced via the flow of the injected dry air in order to enhance the hurricane controlling ability. All the above factors are in favour of retarding the hurricane's wind speed and power. The sudden weakening of hurricane Lili was found to be partially caused

  17. Using Large-Eddy Simulations to Define Spectral and Coherence Characteristics of the Hurricane Boundary Layer for Wind-Energy Applications

    Science.gov (United States)

    Worsnop, Rochelle P.; Bryan, George H.; Lundquist, Julie K.; Zhang, Jun A.

    2017-10-01

    Offshore wind-energy development is planned for regions where hurricanes commonly occur, such as the USA Atlantic Coast. Even the most robust wind-turbine design (IEC Class I) may be unable to withstand a Category-2 hurricane (hub-height wind speeds >50 m s^{-1}). Characteristics of the hurricane boundary layer that affect the structural integrity of turbines, especially in major hurricanes, are poorly understood, primarily due to a lack of adequate observations that span typical turbine heights (wind profiles of an idealized Category-5 hurricane at high spatial (10 m) and temporal (0.1 s) resolution. By comparison with unique flight-level observations from a field project, we find that a relatively simple configuration of the Cloud Model I model accurately represents the properties of Hurricane Isabel (2003) in terms of mean wind speeds, wind-speed variances, and power spectra. Comparisons of power spectra and coherence curves derived from our hurricane simulations to those used in current turbine design standards suggest that adjustments to these standards may be needed to capture characteristics of turbulence seen within the simulated hurricane boundary layer. To enable improved design standards for wind turbines to withstand hurricanes, we suggest modifications to account for shifts in peak power to higher frequencies and greater spectral coherence at large separations.

  18. Improving MJO Prediction and Simulation Using AGCM Coupled Ocean Model with Refined Vertical Resolution

    Science.gov (United States)

    Tu, Chia-Ying; Tseng, Wan-Ling; Kuo, Pei-Hsuan; Lan, Yung-Yao; Tsuang, Ben-Jei; Hsu, Huang-Hsiung

    2017-04-01

    Precipitation in Taiwan area is significantly influenced by MJO (Madden-Julian Oscillation) in the boreal winter. This study is therefore conducted by toggling the MJO prediction and simulation with a unique model structure. The one-dimensional TKE (Turbulence Kinetic Energy) type ocean model SIT (Snow, Ice, Thermocline) with refined vertical resolution near surface is able to resolve cool skin, as well as diurnal warm layer. SIT can simulate accurate SST and hence give precise air-sea interaction. By coupling SIT with ECHAM5 (MPI-Meteorology), CAM5 (NCAR) and HiRAM (GFDL), the MJO simulations in 20-yrs climate integrations conducted by three SIT-coupled AGCMs are significant improved comparing to those driven by prescribed SST. The horizontal resolutions in ECHAM5, CAM5 and HiRAM are 2-deg., 1-deg and 0.5-deg., respectively. This suggests that the improvement of MJO simulation by coupling SIT is AGCM-resolution independent. This study further utilizes HiRAM coupled SIT to evaluate its MJO forecast skill. HiRAM has been recognized as one of the best model for seasonal forecasts of hurricane/typhoon activity (Zhao et al., 2009; Chen & Lin, 2011; 2013), but was not as successful in MJO forecast. The preliminary result of the HiRAM-SIT experiment during DYNAMO period shows improved success in MJO forecast. These improvements of MJO prediction and simulation in both hindcast experiments and climate integrations are mainly from better-simulated SST diurnal cycle and diurnal amplitude, which is contributed by the refined vertical resolution near ocean surface in SIT. Keywords: MJO Predictability, DYNAMO

  19. Hurricane Recovery and Ecological Resilience: Measuring the Impacts of Wetland Alteration Post Hurricane Ike on the Upper TX Coast

    Science.gov (United States)

    Reja, Md Y.; Brody, Samuel D.; Highfield, Wesley E.; Newman, Galen D.

    2017-12-01

    Recovery after hurricane events encourages new development activities and allows reconstruction through the conversion of naturally occurring wetlands to other land uses. This research investigates the degree to which hurricane recovery activities in coastal communities are undermining the ability of these places to attenuate the impacts of future storm events. Specifically, it explores how and to what extent wetlands are being affected by the CWA Section 404 permitting program in the context of post-Hurricane Ike 2008 recovery. Wetland alteration patterns are examined by selecting a control group (Aransas and Brazoria counties with no hurricane impact) vs. study group (Chambers and Galveston counties with hurricane impact) research design with a pretest-posttest measurement analyzing the variables such as permit types, pre-post Ike permits, land cover classes, and within-outside the 100-year floodplain. Results show that permitting activities in study group have increased within the 100-year floodplain and palustrine wetlands continue to be lost compare to the control group. Simultaneously, post-Ike individual and nationwide permits increased in the Hurricane Ike impacted area. A binomial logistic regression model indicated that permits within the study group, undeveloped land cover class, and individual and nationwide permit type have a substantial effect on post-Ike permits, suggesting that post-Ike permits have significant impact on wetland losses. These findings indicate that recovery after the hurricane is compromising ecological resiliency in coastal communities. The study outcome may be applied to policy decisions in managing wetlands during a long-term recovery process to maintain natural function for future flood mitigation.

  20. Results of verification and investigation of wind velocity field forecast. Verification of wind velocity field forecast model

    International Nuclear Information System (INIS)

    Ogawa, Takeshi; Kayano, Mitsunaga; Kikuchi, Hideo; Abe, Takeo; Saga, Kyoji

    1995-01-01

    In Environmental Radioactivity Research Institute, the verification and investigation of the wind velocity field forecast model 'EXPRESS-1' have been carried out since 1991. In fiscal year 1994, as the general analysis, the validity of weather observation data, the local features of wind field, and the validity of the positions of monitoring stations were investigated. The EXPRESS which adopted 500 m mesh so far was improved to 250 m mesh, and the heightening of forecast accuracy was examined, and the comparison with another wind velocity field forecast model 'SPEEDI' was carried out. As the results, there are the places where the correlation with other points of measurement is high and low, and it was found that for the forecast of wind velocity field, by excluding the data of the points with low correlation or installing simplified observation stations to take their data in, the forecast accuracy is improved. The outline of the investigation, the general analysis of weather observation data and the improvements of wind velocity field forecast model and forecast accuracy are reported. (K.I.)

  1. An improved market penetration model for wind energy technology forecasting

    International Nuclear Information System (INIS)

    Lund, P.D.

    1995-01-01

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  2. An improved market penetration model for wind energy technology forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Lund, P D [Helsinki Univ. of Technology, Espoo (Finland). Advanced Energy Systems

    1996-12-31

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  3. An improved market penetration model for wind energy technology forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Lund, P.D. [Helsinki Univ. of Technology, Espoo (Finland). Advanced Energy Systems

    1995-12-31

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  4. Tsunamis and Hurricanes A Mathematical Approach

    CERN Document Server

    Cap, Ferdinand

    2006-01-01

    Tsunamis and hurricanes have had a devastating impact on the population living near the coast during the year 2005. The calculation of the power and intensity of tsunamis and hurricanes are of great importance not only for engineers and meteorologists but also for governments and insurance companies. This book presents new research on the mathematical description of tsunamis and hurricanes. A combination of old and new approaches allows to derive a nonlinear partial differential equation of fifth order describing the steepening up and the propagation of tsunamis. The description includes dissipative terms and does not contain singularities or two valued functions. The equivalence principle of solutions of nonlinear large gas dynamics waves and of solutions of water wave equations will be used. An extension of the continuity equation by a source term due to evaporation rates of salt seawater will help to understand hurricanes. Detailed formula, tables and results of the calculations are given.

  5. Forecasting experiments of a dynamical-statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

    Science.gov (United States)

    Hong, Mei; Chen, Xi; Zhang, Ren; Wang, Dong; Shen, Shuanghe; Singh, Vijay P.

    2018-04-01

    With the objective of tackling the problem of inaccurate long-term El Niño-Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical-statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical-statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

  6. Landscape and regional impacts of hurricanes in Puerto Rico

    OpenAIRE

    Boose, Emery Robert; Serrano, Mayra I.; Foster, David Russell

    2004-01-01

    Puerto Rico is subject to frequent and severe impacts from hurricanes, whose long-term ecological role must be assessed on a scale of centuries. In this study we applied a method for reconstructing hurricane disturbance regimes developed in an earlier study of hurricanes in New England. Patterns of actual wind damage from historical records were analyzed for 85 hurricanes since European settlement in 1508. A simple meteorological model (HURRECON) was used to reconstruct the impacts of 43 hurr...

  7. Improving Regional Forecast by Assimilating Atmospheric InfraRed Sounder (AIRS) Profiles into WRF Model

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.

    2009-01-01

    In data sparse regions, remotely-sensed observations can be used to improve analyses and produce improved forecasts. One such source comes from the Atmospheric InfraRed Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The purpose of this paper is to describe a procedure to optimally assimilate high resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background type, and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics. The AIRS thermodynamic profiles are derived from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators were used to select the highest quality temperature and moisture data for each profile location and pressure level. The analyses were then used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impacts of AIRS profiles on forecast were assessed against verifying NAM analyses and stage IV precipitation data.

  8. Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion

    Directory of Open Access Journals (Sweden)

    Jiandong Duan

    2018-02-01

    Full Text Available In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM model with a maximum correntropy criterion (MCC to forecast the electricity consumption (EC. Firstly, the electricity characteristics of various industries are analyzed to determine the factors that mainly affect the changes in electricity, such as the gross domestic product (GDP, temperature, and so on. Secondly, according to the statistics of the status quo of the small sample data, the LSSVM model is employed as the prediction model. In order to optimize the parameters of the LSSVM model, we further use the local similarity function MCC as the evaluation criterion. Thirdly, we employ the K-fold cross-validation and grid searching methods to improve the learning ability. In the experiments, we have used the EC data of Shaanxi Province in China to evaluate the proposed prediction scheme, and the results show that the proposed prediction scheme outperforms the method based on the traditional LSSVM model.

  9. 2005 Significant U.S. Hurricane Strikes Poster

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The 2005 Significant U.S. Hurricane Strikes poster is one of two special edition posters for the Atlantic Hurricanes. This beautiful poster contains two sets of...

  10. Improving volcanic ash forecasts with ensemble-based data assimilation

    NARCIS (Netherlands)

    Fu, Guangliang

    2017-01-01

    The 2010 Eyjafjallajökull volcano eruption had serious consequences to civil aviation. This has initiated a lot of research on volcanic ash forecasting in recent years. For forecasting the volcanic ash transport after eruption onset, a volcanic ash transport and diffusion model (VATDM) needs to be

  11. Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

    Science.gov (United States)

    Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

    2015-01-01

    The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

  12. Continental United States Hurricane Strikes 1950-2012

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Continental U.S. Hurricane Strikes Poster is our most popular poster which is updated annually. The poster includes all hurricanes that affected the U.S. since...

  13. Vintage errors: do real-time economic data improve election forecasts?

    Directory of Open Access Journals (Sweden)

    Mark Andreas Kayser

    2015-07-01

    Full Text Available Economic performance is a key component of most election forecasts. When fitting models, however, most forecasters unwittingly assume that the actual state of the economy, a state best estimated by the multiple periodic revisions to official macroeconomic statistics, drives voter behavior. The difference in macroeconomic estimates between revised and original data vintages can be substantial, commonly over 100% (two-fold for economic growth estimates, making the choice of which data release to use important for the predictive validity of a model. We systematically compare the predictions of four forecasting models for numerous US presidential elections using real-time and vintage data. We find that newer data are not better data for election forecasting: forecasting error increases with data revisions. This result suggests that voter perceptions of economic growth are influenced more by media reports about the economy, which are based on initial economic estimates, than by the actual state of the economy.

  14. Satellite sar detection of hurricane helene (2006)

    DEFF Research Database (Denmark)

    Ju, Lian; Cheng, Yongcun; Xu, Qing

    2013-01-01

    In this paper, the wind structure of hurricane Helene (2006) over the Atlantic Ocean is investigated from a C-band RADARSAT-1 synthetic aperture radar (SAR) image acquired on 20 September 2006. First, the characteristics, e.g., the center, scale and area of the hurricane eye (HE) are determined. ...... observations from the stepped frequency microwave radiometer (SFMR) on NOAA P3 aircraft. All the results show the capability of hurricane monitoring by satellite SAR. Copyright © 2013 by the International Society of Offshore and Polar Engineers (ISOPE)....

  15. A decision model for intergenerational life-cycle risk assessment of civil infrastructure exposed to hurricanes under climate change

    International Nuclear Information System (INIS)

    Lee, Ji Yun; Ellingwood, Bruce R.

    2017-01-01

    Public awareness of civil infrastructure performance has increased considerably in recent years as a result of repeated natural disasters. Risks from natural hazards may increase dramatically in the future, given current patterns of urbanization and population growth in hazard-prone areas. Risk assessments for infrastructure with expected service periods of a century or more are highly uncertain, and there is compelling evidence that climatology will evolve over such intervals. Thus, current natural hazard and risk assessment models, which are based on a presumption of stationarity in hazard occurrence and intensity, may not be adequate to assess the potential risks from hazards occurring in the distant future. This paper addresses two significant intergenerational elements – the potential impact of non-stationarity in hazard due to climate change and intergenerational discounting practices – that are essential to provide an improved decision support framework that accommodates the needs and values of future generations. The framework so developed is tested through two benchmark problems involving buildings exposed to hurricanes. - Highlights: • Difficulties of conventional life-cycle engineering decision-making over multiple generations are clearly elaborated. • Two intergenerational elements are proposed to reflect equitable allocations of risk between generations. • A data-based approach to forecast future hurricanes is provided to bridge the gap between models at large and local scales. • The feasibility and practicability of a refined framework are examined through two lifecycle cost assessment examples. • The two intergenerational elements suggested in this study have a wide range of applicability.

  16. Improving Seasonal Crop Monitoring and Forecasting for Soybean and Corn in Iowa

    Science.gov (United States)

    Togliatti, K.; Archontoulis, S.; Dietzel, R.; VanLoocke, A.

    2016-12-01

    Accurately forecasting crop yield in advance of harvest could greatly benefit farmers, however few evaluations have been conducted to determine the effectiveness of forecasting methods. We tested one such method that used a combination of short-term weather forecasting from the Weather Research and Forecasting Model (WRF) to predict in season weather variables, such as, maximum and minimum temperature, precipitation and radiation at 4 different forecast lengths (2 weeks, 1 week, 3 days, and 0 days). This forecasted weather data along with the current and historic (previous 35 years) data from the Iowa Environmental Mesonet was combined to drive Agricultural Production Systems sIMulator (APSIM) simulations to forecast soybean and corn yields in 2015 and 2016. The goal of this study is to find the forecast length that reduces the variability of simulated yield predictions while also increasing the accuracy of those predictions. APSIM simulations of crop variables were evaluated against bi-weekly field measurements of phenology, biomass, and leaf area index from early and late planted soybean plots located at the Agricultural Engineering and Agronomy Research Farm in central Iowa as well as the Northwest Research Farm in northwestern Iowa. WRF model predictions were evaluated against observed weather data collected at the experimental fields. Maximum temperature was the most accurately predicted variable, followed by minimum temperature and radiation, and precipitation was least accurate according to RMSE values and the number of days that were forecasted within a 20% error of the observed weather. Our analysis indicated that for the majority of months in the growing season the 3 day forecast performed the best. The 1 week forecast came in second and the 2 week forecast was the least accurate for the majority of months. Preliminary results for yield indicate that the 2 week forecast is the least variable of the forecast lengths, however it also is the least accurate

  17. Initial evaluations of a Gulf of Mexico/Caribbean ocean forecast system in the context of the Deepwater Horizon disaster

    Science.gov (United States)

    Zaron, Edward D.; Fitzpatrick, Patrick J.; Cross, Scott L.; Harding, John M.; Bub, Frank L.; Wiggert, Jerry D.; Ko, Dong S.; Lau, Yee; Woodard, Katharine; Mooers, Christopher N. K.

    2015-12-01

    In response to the Deepwater Horizon (DwH) oil spill event in 2010, the Naval Oceanographic Office deployed a nowcast-forecast system covering the Gulf of Mexico and adjacent Caribbean Sea that was designated Americas Seas, or AMSEAS, which is documented in this manuscript. The DwH disaster provided a challenge to the application of available ocean-forecast capabilities, and also generated a historically large observational dataset. AMSEAS was evaluated by four complementary efforts, each with somewhat different aims and approaches: a university research consortium within an Integrated Ocean Observing System (IOOS) testbed; a petroleum industry consortium, the Gulf of Mexico 3-D Operational Ocean Forecast System Pilot Prediction Project (GOMEX-PPP); a British Petroleum (BP) funded project at the Northern Gulf Institute in response to the oil spill; and the Navy itself. Validation metrics are presented in these different projects for water temperature and salinity profiles, sea surface wind, sea surface temperature, sea surface height, and volume transport, for different forecast time scales. The validation found certain geographic and time biases/errors, and small but systematic improvements relative to earlier regional and global modeling efforts. On the basis of these positive AMSEAS validation studies, an oil spill transport simulation was conducted using archived AMSEAS nowcasts to examine transport into the estuaries east of the Mississippi River. This effort captured the influences of Hurricane Alex and a non-tropical cyclone off the Louisiana coast, both of which pushed oil into the western Mississippi Sound, illustrating the importance of the atmospheric influence on oil spills such as DwH.

  18. Strategic Forecasting

    DEFF Research Database (Denmark)

    Duus, Henrik Johannsen

    2016-01-01

    Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...... as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts...

  19. Operational Impact of Data Collected from the Global Hawk Unmanned Aircraft During SHOUT

    Science.gov (United States)

    Wick, G. A.; Dunion, J. P.; Sippel, J.; Cucurull, L.; Aksoy, A.; Kren, A.; Christophersen, H.; Black, P.

    2017-12-01

    The primary scientific goal of the Sensing Hazards with Operational Unmanned Technology (SHOUT) Project was to determine the potential utility of observations from high-altitude, long-endurance unmanned aircraft systems such as the Global Hawk (GH) aircraft to improve operational forecasts of high-impact weather events or mitigate potential degradation of forecasts in the event of a future gap in satellite coverage. Hurricanes and tropical cyclones are among the most potentially destructive high-impact weather events and pose a major forecasting challenge to NOAA. Major winter storms over the Pacific Ocean, including atmospheric river events, which make landfall and bring strong winds and extreme precipitation to the West Coast and Alaska are also important to forecast accurately because of their societal impact in those parts of the country. In response, the SHOUT project supported three field campaigns with the GH aircraft and dedicated data impact studies exploring the potential for the real-time data from the aircraft to improve the forecasting of both tropical cyclones and landfalling Pacific storms. Dropsonde observations from the GH aircraft were assimilated into the operational Hurricane Weather Research and Forecasting (HWRF) and Global Forecast System (GFS) models. The results from several diverse but complementary studies consistently demonstrated significant positive forecast benefits spanning the regional and global models. Forecast skill improvements within HWRF reached up to about 9% for track and 14% for intensity. Within GFS, track skill improvements for multi-storm averages exceeded 10% and improvements for individual storms reached over 20% depending on forecast lead time. Forecasted precipitation was also improved. Impacts for Pacific winter storms were smaller but still positive. The results are highly encouraging and support the potential for operational utilization of data from a platform like the GH. This presentation summarizes the

  20. Ensemble forecasting of species distributions.

    Science.gov (United States)

    Araújo, Miguel B; New, Mark

    2007-01-01

    Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.

  1. Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.

    Science.gov (United States)

    Wang, Wen-chuan; Chau, Kwok-wing; Qiu, Lin; Chen, Yang-bo

    2015-05-01

    Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for the effective reservoir management. In this research, an artificial neural network (ANN) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting medium and long-term runoff time series. First, the original runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and a residual series using EEMD technique for attaining deeper insight into the data characteristics. Then all IMF components and residue are predicted, respectively, through appropriate ANN models. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Two annual reservoir runoff time series from Biuliuhe and Mopanshan in China, are investigated using the developed model based on four performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. Enhancing Famine Early Warning Systems with Improved Forecasts, Satellite Observations and Hydrologic Simulations

    Science.gov (United States)

    Funk, C. C.; Verdin, J.; Thiaw, W. M.; Hoell, A.; Korecha, D.; McNally, A.; Shukla, S.; Arsenault, K. R.; Magadzire, T.; Novella, N.; Peters-Lidard, C. D.; Robjohn, M.; Pomposi, C.; Galu, G.; Rowland, J.; Budde, M. E.; Landsfeld, M. F.; Harrison, L.; Davenport, F.; Husak, G. J.; Endalkachew, E.

    2017-12-01

    Drought early warning science, in support of famine prevention, is a rapidly advancing field that is helping to save lives and livelihoods. In 2015-2017, a series of extreme droughts afflicted Ethiopia, Southern Africa, Eastern Africa in OND and Eastern Africa in MAM, pushing more than 50 million people into severe food insecurity. Improved drought forecasts and monitoring tools, however, helped motivate and target large and effective humanitarian responses. Here we describe new science being developed by a long-established early warning system - the USAID Famine Early Warning Systems Network (FEWS NET). FEWS NET is a leading provider of early warning and analysis on food insecurity. FEWS NET research is advancing rapidly on several fronts, providing better climate forecasts and more effective drought monitoring tools that are being used to support enhanced famine early warning. We explore the philosophy and science underlying these successes, suggesting that a modal view of climate change can support enhanced seasonal prediction. Under this modal perspective, warming of the tropical oceans may interact with natural modes of variability, like the El Niño-Southern Oscillation, to enhance Indo-Pacific sea surface temperature gradients during both El Niño and La Niña-like climate states. Using empirical data and climate change simulations, we suggest that a sequence of droughts may commence in northern Ethiopia and Southern Africa with the advent of a moderate-to-strong El Niño, and then continue with La Niña/West Pacific related droughts in equatorial eastern East Africa. Scientifically, we show that a new hybrid statistical-dynamic precipitation forecast system, the FEWS NET Integrated Forecast System (FIFS), based on reformulations of the Global Ensemble Forecast System weather forecasts and National Multi-Model Ensemble (NMME) seasonal climate predictions, can effectively anticipate recent East and Southern African drought events. Using cross-validation, we

  3. Hurricane Sandy: Shared Trauma and Therapist Self-Disclosure.

    Science.gov (United States)

    Rao, Nyapati; Mehra, Ashwin

    2015-01-01

    Hurricane Sandy was one of the most devastating storms to hit the United States in history. The impact of the hurricane included power outages, flooding in the New York City subway system and East River tunnels, disrupted communications, acute shortages of gasoline and food, and a death toll of 113 people. In addition, thousands of residences and businesses in New Jersey and New York were destroyed. This article chronicles the first author's personal and professional experiences as a survivor of the hurricane, more specifically in the dual roles of provider and trauma victim, involving informed self-disclosure with a patient who was also a victim of the hurricane. The general analytic framework of therapy is evaluated in the context of the shared trauma faced by patient and provider alike in the face of the hurricane, leading to important implications for future work on resilience and recovery for both the therapist and patient.

  4. Wind power forecast

    Energy Technology Data Exchange (ETDEWEB)

    Pestana, Rui [Rede Electrica Nacional (REN), S.A., Lisboa (Portugal). Dept. Systems and Development System Operator; Trancoso, Ana Rosa; Delgado Domingos, Jose [Univ. Tecnica de Lisboa (Portugal). Seccao de Ambiente e Energia

    2012-07-01

    Accurate wind power forecast are needed to reduce integration costs in the electric grid caused by wind inherent variability. Currently, Portugal has a significant wind power penetration level and consequently the need to have reliable wind power forecasts at different temporal scales, including localized events such as ramps. This paper provides an overview of the methodologies used by REN to forecast wind power at national level, based on statistical and probabilistic combinations of NWP and measured data with the aim of improving accuracy of pure NWP. Results show that significant improvement can be achieved with statistical combination with persistence in the short-term and with probabilistic combination in the medium-term. NWP are also able to detect ramp events with 3 day notice to the operational planning. (orig.)

  5. Hurricane impacts on a pair of coastal forested watersheds: implications of selective hurricane damage to forest structure and streamflow dynamics

    OpenAIRE

    A. D. Jayakaran; T. M. Williams; H. Ssegane; D. M. Amatya; B. Song; C. C. Trettin

    2014-01-01

    Hurricanes are infrequent but influential disruptors of ecosystem processes in the southeastern Atlantic and Gulf coasts. Every southeastern forested wetland has the potential to be struck by a tropical cyclone. We examined the impact of Hurricane Hugo on two paired coastal watersheds in South Carolina in terms of stream flow and vegetation dynamics, both before and after the hurricane's passage in 1989. The study objectives were to quantify the magnitude and timing of changes including a rev...

  6. ON THE INFLUENCE OF GLOBAL WARMING ON ATLANTIC HURRICANE FREQUENCY

    Directory of Open Access Journals (Sweden)

    S. R. Hosseini

    2018-04-01

    Full Text Available In this paper, the possible connection between the frequency of Atlantic hurricanes to the climate change, mainly the variation in the Atlantic Ocean surface temperature has been investigated. The correlation between the observed hurricane frequency for different categories of hurricane’s intensity and Sea Surface Temperature (SST has been examined over the Atlantic Tropical Cyclogenesis Regions (ACR. The results suggest that in general, the frequency of hurricanes have a high correlation with SST. In particular, the frequency of extreme hurricanes with Category 5 intensity has the highest correlation coefficient (R = 0.82. In overall, the analyses in this work demonstrates the influence of the climate change condition on the Atlantic hurricanes and suggest a strong correlation between the frequency of extreme hurricanes and SST in the ACR.

  7. Longitudinal Impact of Hurricane Sandy Exposure on Mental Health Symptoms.

    Science.gov (United States)

    Schwartz, Rebecca M; Gillezeau, Christina N; Liu, Bian; Lieberman-Cribbin, Wil; Taioli, Emanuela

    2017-08-24

    Hurricane Sandy hit the eastern coast of the United States in October 2012, causing billions of dollars in damage and acute physical and mental health problems. The long-term mental health consequences of the storm and their predictors have not been studied. New York City and Long Island residents completed questionnaires regarding their initial Hurricane Sandy exposure and mental health symptoms at baseline and 1 year later (N = 130). There were statistically significant decreases in anxiety scores (mean difference = -0.33, p Hurricane Sandy has an impact on PTSD symptoms that persists over time. Given the likelihood of more frequent and intense hurricanes due to climate change, future hurricane recovery efforts must consider the long-term effects of hurricane exposure on mental health, especially on PTSD, when providing appropriate assistance and treatment.

  8. Simulation of the Impact of New Aircraft-and Satellite-based Ocean Surface Wind Measurements on Wind Analyses and Numerical Forecasts

    Science.gov (United States)

    Miller, TImothy; Atlas, Robert; Black, Peter; Chen, Shuyi; Jones, Linwood; Ruf, Chris; Uhlhorn, Eric; Gamache, John; Amarin, Ruba; El-Nimri, Salem; hide

    2010-01-01

    The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor for hurricane observations that is currently under development by NASA Marshall Space Flight Center, NOAA Hurricane Research Division, the University of Central Florida and the University of Michigan. HIRAD is being designed to enhance the realtime airborne ocean surface winds observation capabilities of NOAA and USAF Weather Squadron hurricane hunter aircraft currently using the operational airborne Stepped Frequency Microwave Radiometer (SFMR). Unlike SFMR, which measures wind speed and rain rate along the ground track directly beneath the aircraft, HIRAD will provide images of the surface wind and rain field over a wide swath (approx. 3 x the aircraft altitude). The present paper describes a set of Observing System Simulation Experiments (OSSEs) in which measurements from the new instrument as well as those from existing instruments (air, surface, and space-based) are simulated from the output of a detailed numerical model, and those results are used to construct H*Wind analyses, a product of the Hurricane Research Division of NOAA s Atlantic Oceanographic and Meteorological Laboratory. Evaluations will be presented on the impact of the HIRAD instrument on H*Wind analyses, both in terms of adding it to the full suite of current measurements, as well as using it to replace instrument(s) that may not be functioning at the future time the HIRAD instrument is implemented. Also shown will be preliminary results of numerical weather prediction OSSEs in which the impact of the addition of HIRAD observations to the initial state on numerical forecasts of the hurricane intensity and structure is assessed.

  9. Efficient training schemes that improve the forecast quality of a supermodel

    Science.gov (United States)

    Schevenhoven, Francine; Selten, Frank; Duane, Gregory; Keenlyside, Noel

    2017-04-01

    Weather and climate models have improved steadily over time as witnessed by objective skill scores, although they remain imperfect. Given these imperfect models, predictions might be improved by combining them dynamically into a so-called "supermodel". In contrast to the standard multi-model ensemble approach, the models exchange information during the simulation, which leads to new solutions. In this study we explore different techniques to create such a supermodel. The techniques are applied to global climate models. The results indicate that the techniques are computationally efficient and lead to supermodels with superior forecast quality and climatology compared to the individual models or the standard multi-model ensemble approach.

  10. Counteracting structural errors in ensemble forecast of influenza outbreaks.

    Science.gov (United States)

    Pei, Sen; Shaman, Jeffrey

    2017-10-13

    For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. As a consequence, quantification of the nonlinear error structure in current forecast models is needed so that this growth can be corrected and forecast skill improved. Here, we inspect the error growth of a compartmental influenza model and find that a robust error structure arises naturally from the nonlinear model dynamics. By counteracting these structural errors, diagnosed using error breeding, we develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models.Inaccuracy of influenza forecasts based on dynamical models is partly due to nonlinear error growth. Here the authors address the error structure of a compartmental influenza model, and develop a new improved forecast approach combining dynamical error correction and statistical filtering techniques.

  11. Ecological forecasts: An emerging imperative

    Science.gov (United States)

    James S. Clark; Steven R. Carpenter; Mary Barber; Scott Collins; Andy Dobson; Jonathan A. Foley; David M. Lodge; Mercedes Pascual; Roger Pielke; William Pizer; Cathy Pringle; Walter V. Reid; Kenneth A. Rose; Osvaldo Sala; William H. Schlesinger; Diana H. Wall; David Wear

    2001-01-01

    Planning and decision-making can be improved by access to reliable forecasts of ecosystem state, ecosystem services, and natural capital. Availability of new data sets, together with progress in computation and statistics, will increase our ability to forecast ecosystem change. An agenda that would lead toward a capacity to produce, evaluate, and communicate forecasts...

  12. Numerical modeling of the effects of Hurricane Sandy and potential future hurricanes on spatial patterns of salt marsh morphology in Jamaica Bay, New York City

    Science.gov (United States)

    Wang, Hongqing; Chen, Qin; Hu, Kelin; Snedden, Gregg A.; Hartig, Ellen K.; Couvillion, Brady R.; Johnson, Cody L.; Orton, Philip M.

    2017-03-29

    The salt marshes of Jamaica Bay, managed by the New York City Department of Parks & Recreation and the Gateway National Recreation Area of the National Park Service, serve as a recreational outlet for New York City residents, mitigate flooding, and provide habitat for critical wildlife species. Hurricanes and extra-tropical storms have been recognized as one of the critical drivers of coastal wetland morphology due to their effects on hydrodynamics and sediment transport, deposition, and erosion processes. However, the magnitude and mechanisms of hurricane effects on sediment dynamics and associated coastal wetland morphology in the northeastern United States are poorly understood. In this study, the depth-averaged version of the Delft3D modeling suite, integrated with field measurements, was utilized to examine the effects of Hurricane Sandy and future potential hurricanes on salt marsh morphology in Jamaica Bay, New York City. Hurricane Sandy-induced wind, waves, storm surge, water circulation, sediment transport, deposition, and erosion were simulated by using the modeling system in which vegetation effects on flow resistance, surge reduction, wave attenuation, and sedimentation were also incorporated. Observed marsh elevation change and accretion from a rod surface elevation table and feldspar marker horizons and cesium-137- and lead-210-derived long-term accretion rates were used to calibrate and validate the wind-waves-surge-sediment transport-morphology coupled model.The model results (storm surge, waves, and marsh deposition and erosion) agreed well with field measurements. The validated modeling system was then used to detect salt marsh morphological change due to Hurricane Sandy across the entire Jamaica Bay over the short-term (for example, 4 days and 1 year) and long-term (for example, 5 and 10 years). Because Hurricanes Sandy (2012) and Irene (2011) were two large and destructive tropical cyclones which hit the northeast coast, the validated coupled

  13. Space-time wind speed forecasting for improved power system dispatch

    KAUST Repository

    Zhu, Xinxin

    2014-02-27

    To support large-scale integration of wind power into electric energy systems, state-of-the-art wind speed forecasting methods should be able to provide accurate and adequate information to enable efficient, reliable, and cost-effective scheduling of wind power. Here, we incorporate space-time wind forecasts into electric power system scheduling. First, we propose a modified regime-switching, space-time wind speed forecasting model that allows the forecast regimes to vary with the dominant wind direction and with the seasons, hence avoiding a subjective choice of regimes. Then, results from the wind forecasts are incorporated into a power system economic dispatch model, the cost of which is used as a loss measure of the quality of the forecast models. This, in turn, leads to cost-effective scheduling of system-wide wind generation. Potential economic benefits arise from the system-wide generation of cost savings and from the ancillary service cost savings. We illustrate the economic benefits using a test system in the northwest region of the United States. Compared with persistence and autoregressive models, our model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars annually in regions with high wind penetration, such as Texas and the Pacific northwest. © 2014 Sociedad de Estadística e Investigación Operativa.

  14. Improving the accuracy of flood forecasting with transpositions of ensemble NWP rainfall fields considering orographic effects

    Science.gov (United States)

    Yu, Wansik; Nakakita, Eiichi; Kim, Sunmin; Yamaguchi, Kosei

    2016-08-01

    The use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue flood warnings. However, space scale of the hydrological domain is still much finer than meteorological model, and NWP models have challenges with displacement. The main objective of this study to enhance the transposition method proposed in Yu et al. (2014) and to suggest the post-processing ensemble flood forecasting method for the real-time updating and the accuracy improvement of flood forecasts that considers the separation of the orographic rainfall and the correction of misplaced rain distributions using additional ensemble information through the transposition of rain distributions. In the first step of the proposed method, ensemble forecast rainfalls from a numerical weather prediction (NWP) model are separated into orographic and non-orographic rainfall fields using atmospheric variables and the extraction of topographic effect. Then the non-orographic rainfall fields are examined by the transposition scheme to produce additional ensemble information and new ensemble NWP rainfall fields are calculated by recombining the transposition results of non-orographic rain fields with separated orographic rainfall fields for a generation of place-corrected ensemble information. Then, the additional ensemble information is applied into a hydrologic model for post-flood forecasting with a 6-h interval. The newly proposed method has a clear advantage to improve the accuracy of mean value of ensemble flood forecasting. Our study is carried out and verified using the largest flood event by typhoon 'Talas' of 2011 over the two catchments, which are Futatsuno (356.1 km2) and Nanairo (182.1 km2) dam catchments of Shingu river basin (2360 km2), which is located in the Kii peninsula, Japan.

  15. Geologic record of Hurricane impacts on the New Jersey coast

    Science.gov (United States)

    Nikitina, Daria; Horton, Benjamin; Khan, Nicole; Clear, Jennifer; Shaw, Timothy; Enache, Mihaela; Frizzera, Dorina; Procopio, Nick; Potapova, Marina

    2016-04-01

    Hurricanes along the US Atlantic coast have caused significant damage and loss of human life over the last century. Recent studies suggest that intense-hurricane activity is closely related to changes of sea surface temperatures and therefore the risk of hurricane strikes may increase in the future. A clear understanding of the role of recent warming on tropical cyclone activity is limited by the shortness of the instrumental record. However, the sediment preserved beneath coastal wetlands is an archive of when hurricanes impacted the coast. We present two complimenting approaches that help to extend pre-historic record and assess frequency and intensity of hurricane landfalls along the New Jersey cost; dating overwash deposits and hurricane-induced salt-marsh erosion documented at multiple sites. The stratigraphic investigation of estuarine salt marshes in the southern New Jersey documented seven distinctive erosion events that correlate among different sites. Radiocarbon dates suggest the prehistoric events occurred in AD 558-673, AD 429-966, AD 558-673, Ad 1278-1438, AD 1526-1558 or AD 1630-1643 (Nikitina et al., 2014). Younger sequences correspond with historical land-falling hurricanes in AD 1903 and AD 1821 or AD 1788. Four events correlate well with barrier overwash deposits documented along the New Jersey coast (Donnelley et al., 2001 and 2004). The stratigraphic sequence of salt High resolution sedimentary-based reconstructions of past intense-hurricane landfalls indicate that significant variability in the frequency of intense hurricanes occurred over the last 2000 years.

  16. Analysis of PG&E`s residential end-use metered data to improve electricity demand forecasts -- final report

    Energy Technology Data Exchange (ETDEWEB)

    Eto, J.H.; Moezzi, M.M.

    1993-12-01

    This report summarizes findings from a unique project to improve the end-use electricity load shape and peak demand forecasts made by the Pacific Gas and Electric Company (PG&E) and the California Energy Commission (CEC). First, the direct incorporation of end-use metered data into electricity demand forecasting models is a new approach that has only been made possible by recent end-use metering projects. Second, and perhaps more importantly, the joint-sponsorship of this analysis has led to the development of consistent sets of forecasting model inputs. That is, the ability to use a common data base and similar data treatment conventions for some of the forecasting inputs frees forecasters to concentrate on those differences (between their competing forecasts) that stem from real differences of opinion, rather than differences that can be readily resolved with better data. The focus of the analysis is residential space cooling, which represents a large and growing demand in the PG&E service territory. Using five years of end-use metered, central air conditioner data collected by PG&E from over 300 residences, we developed consistent sets of new inputs for both PG&E`s and CEC`s end-use load shape forecasting models. We compared the performance of the new inputs both to the inputs previously used by PG&E and CEC, and to a second set of new inputs developed to take advantage of a recently added modeling option to the forecasting model. The testing criteria included ability to forecast total daily energy use, daily peak demand, and demand at 4 P.M. (the most frequent hour of PG&E`s system peak demand). We also tested the new inputs with the weather data used by PG&E and CEC in preparing their forecasts.

  17. Load forecasting method considering temperature effect for distribution network

    Directory of Open Access Journals (Sweden)

    Meng Xiao Fang

    2016-01-01

    Full Text Available To improve the accuracy of load forecasting, the temperature factor was introduced into the load forecasting in this paper. This paper analyzed the characteristics of power load variation, and researched the rule of the load with the temperature change. Based on the linear regression analysis, the mathematical model of load forecasting was presented with considering the temperature effect, and the steps of load forecasting were given. Used MATLAB, the temperature regression coefficient was calculated. Using the load forecasting model, the full-day load forecasting and time-sharing load forecasting were carried out. By comparing and analyzing the forecast error, the results showed that the error of time-sharing load forecasting method was small in this paper. The forecasting method is an effective method to improve the accuracy of load forecasting.

  18. Hurricane Season: Are You Ready?

    Centers for Disease Control (CDC) Podcasts

    2012-09-24

    Hurricanes are one of Mother Nature’s most powerful forces. Host Bret Atkins talks with CDC’s National Center for Environmental Health Director Dr. Chris Portier about the main threats of a hurricane and how you can prepare.  Created: 9/24/2012 by Office of Public Health Preparedness and Response (OPHPR), National Center for Environmental Health (NCEH), and the Agency for Toxic Substances and Disease Registry (ATSDR).   Date Released: 9/24/2012.

  19. Improved Local Weather Forecasts Using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Jørgensen, Bo Nørregaard

    2015-01-01

    Solar irradiance and temperature forecasts are used in many different control systems. Such as intelligent climate control systems in commercial greenhouses, where the solar irradiance affects the use of supplemental lighting. This paper proposes a novel method to predict the forthcoming weather...... using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show...

  20. Ensemble-based simultaneous emission estimates and improved forecast of radioactive pollution from nuclear power plant accidents: application to ETEX tracer experiment

    International Nuclear Information System (INIS)

    Zhang, X.L.; Li, Q.B.; Su, G.F.; Yuan, M.Q.

    2015-01-01

    The accidental release of radioactive materials from nuclear power plant leads to radioactive pollution. We apply an augmented ensemble Kalman filter (EnKF) with a chemical transport model to jointly estimate the emissions of Perfluoromethylcyclohexane (PMCH), a tracer substitute for radionuclides, from a point source during the European Tracer Experiment, and to improve the forecast of its dispersion downwind. We perturb wind fields to account for meteorological uncertainties. We expand the state vector of PMCH concentrations through continuously adding an a priori emission rate for each succeeding assimilation cycle. We adopt a time-correlated red noise to simulate the temporal emission fluctuation. The improved EnKF system rapidly updates (and reduces) the excessively large initial first-guess emissions, thereby significantly improves subsequent forecasts (r = 0.83, p < 0.001). It retrieves 94% of the total PMCH released and substantially reduces transport error (>80% average reduction of the normalized mean square error). - Highlights: • EnKF is augmented for estimating emission and improving dispersion forecast. • The improved system retrieves 94% of the actual total tracer release in ETEX. • The system substantially improves the 3-h forecast of the tracer dispersion. • The method is robust and insensitive to the first-guess emissions. • The meteorological uncertainties exert strong influence on the performance

  1. On the relationship between hurricane cost and the integrated wind profile

    Science.gov (United States)

    Wang, S.; Toumi, R.

    2016-11-01

    It is challenging to identify metrics that best capture hurricane destructive potential and costs. Although it has been found that the sea surface temperature and vertical wind shear can both make considerable changes to the hurricane destructive potential metrics, it is still unknown which plays a more important role. Here we present a new method to reconstruct the historical wind structure of hurricanes that allows us, for the first time, to calculate the correlation of damage with integrated power dissipation and integrated kinetic energy of all hurricanes at landfall since 1988. We find that those metrics, which include the horizontal wind structure, rather than just maximum intensity, are much better correlated with the hurricane cost. The vertical wind shear over the main development region of hurricanes plays a more dominant role than the sea surface temperature in controlling these metrics and therefore also ultimately the cost of hurricanes.

  2. Worldwide historical hurricane tracks from 1848 through the previous hurricane season

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This Historical Hurricane Tracks web site provides visualizations of storm tracks derived from the 6-hourly (0000, 0600, 1200, 1800 UTC) center locations and...

  3. High Resolution Modeling of Hurricanes in a Climate Context

    Science.gov (United States)

    Knutson, T. R.

    2007-12-01

    Modeling of tropical cyclone activity in a climate context initially focused on simulation of relatively weak tropical storm-like disturbances as resolved by coarse grid (200 km) global models. As computing power has increased, multi-year simulations with global models of grid spacing 20-30 km have become feasible. Increased resolution also allowed for simulation storms of increasing intensity, and some global models generate storms of hurricane strength, depending on their resolution and other factors, although detailed hurricane structure is not simulated realistically. Results from some recent high resolution global model studies are reviewed. An alternative for hurricane simulation is regional downscaling. An early approach was to embed an operational (GFDL) hurricane prediction model within a global model solution, either for 5-day case studies of particular model storm cases, or for "idealized experiments" where an initial vortex is inserted into an idealized environments derived from global model statistics. Using this approach, hurricanes up to category five intensity can be simulated, owing to the model's relatively high resolution (9 km grid) and refined physics. Variants on this approach have been used to provide modeling support for theoretical predictions that greenhouse warming will increase the maximum intensities of hurricanes. These modeling studies also simulate increased hurricane rainfall rates in a warmer climate. The studies do not address hurricane frequency issues, and vertical shear is neglected in the idealized studies. A recent development is the use of regional model dynamical downscaling for extended (e.g., season-length) integrations of hurricane activity. In a study for the Atlantic basin, a non-hydrostatic model with grid spacing of 18km is run without convective parameterization, but with internal spectral nudging toward observed large-scale (basin wavenumbers 0-2) atmospheric conditions from reanalyses. Using this approach, our

  4. Regional-seasonal weather forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.

    1980-08-01

    In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)

  5. The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing

    Directory of Open Access Journals (Sweden)

    Tangao Hu

    2018-05-01

    Full Text Available As the worst natural disaster on record in Dominica and Puerto Rico, Hurricane Maria in September 2017 had a large impact on the vegetation of these islands. In this paper, multitemporal Landsat 8 OLI and Sentinel-2 data are used to investigate vegetation damage on Dominica and Puerto Rico by Hurricane Maria, and related influencing factors are analyzed. Moreover, the changes in the normalized difference vegetation index (NDVI in the year 2017 are compared to reference years (2015 and 2016. The results show that (1 there is a sudden drop in NDVI values after Hurricane Maria’s landfall (decreased about 0.2 which returns to near normal vegetation after 1.5 months; (2 different land cover types have different sensitivities to Hurricane Maria, whereby forest is the most sensitive type, then followed by wetland, built-up, and natural grassland; and (3 for Puerto Rico, the vegetation damage is highly correlated with distance from the storm center and elevation. For Dominica, where the whole island is within Hurricane Maria’s radius of maximum wind, the vegetation damage has no obvious relationship to elevation or distance. The study provides insight into the sensitivity and recovery of vegetation after a major land-falling hurricane, and may lead to improved vegetation protection strategies.

  6. Performance of social network sensors during Hurricane Sandy.

    Directory of Open Access Journals (Sweden)

    Yury Kryvasheyeu

    Full Text Available Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the "friendship paradox", is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users' network centrality effectively translate into moderate awareness advantage (up to 26 hours; and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple "sentiment sensing" technique that can detect and locate disasters.

  7. Performance of Social Network Sensors during Hurricane Sandy

    Science.gov (United States)

    Kryvasheyeu, Yury; Chen, Haohui; Moro, Esteban; Van Hentenryck, Pascal; Cebrian, Manuel

    2015-01-01

    Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the “friendship paradox”, is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users’ network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple “sentiment sensing” technique that can detect and locate disasters. PMID:25692690

  8. Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

    Directory of Open Access Journals (Sweden)

    Shasha Jiang

    2016-05-01

    Full Text Available Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectral–textural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes.

  9. Bayesian flood forecasting methods: A review

    Science.gov (United States)

    Han, Shasha; Coulibaly, Paulin

    2017-08-01

    developed and widely applied, but there is still room for improvements. Future research in the context of Bayesian flood forecasting should be on assimilation of various sources of newly available information and improvement of predictive performance assessment methods.

  10. Physical trajectory profile data from glider SG610 deployed by Atlantic Oceanographic and Meteorological Laboratory in the Caribbean Sea from 2015-02-06 to 2015-04-27 (NCEI Accession 0137961)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Seaglider data gathered as part of the Sustained Ocean Observations for Improving Atlantic Tropical Cyclone Intensity and Hurricane Seasonal Forecast project funded...

  11. Physical trajectory profile data from glider SG609 deployed by Atlantic Oceanographic and Meteorological Laboratory in the North Atlantic Ocean from 2014-07-19 to 2014-11-18 (NCEI Accession 0131705)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Seaglider data gathered as part of the Sustained Ocean Observations for Improving Atlantic Tropical Cyclone Intensity and Hurricane Seasonal Forecast project funded...

  12. Physical trajectory profile data from glider SG547 deployed by US DOC; NOAA; OAR; Atlantic Oceanographic and Meteorological Laboratory in the North Atlantic Ocean from 2016-08-04 to 2016-11-02 (NCEI Accession 0157712)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Seaglider data gathered as part of the Sustained Ocean Observations for Improving Atlantic Tropical Cyclone Intensity and Hurricane Seasonal Forecast project funded...

  13. Physical trajectory profile data from glider SG610 deployed by US DOC; NOAA; OAR; Atlantic Oceanographic and Meteorological Laboratory in the North Atlantic Ocean from 2016-08-04 to 2016-11-01 (NCEI Accession 0157656)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Seaglider data gathered as part of the Sustained Ocean Observations for Improving Atlantic Tropical Cyclone Intensity and Hurricane Seasonal Forecast project funded...

  14. Physical trajectory profile data from glider SG609 deployed by Atlantic Oceanographic and Meteorological Laboratory in the Caribbean Sea from 2016-03-10 to 2016-06-02 (NCEI Accession 0154495)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Seaglider data gathered as part of the Sustained Ocean Observations for Improving Atlantic Tropical Cyclone Intensity and Hurricane Seasonal Forecast project funded...

  15. Physical trajectory profile data from glider SG610 deployed by Atlantic Oceanographic and Meteorological Laboratory in the North Atlantic Ocean from 2015-08-11 to 2015-11-18 (NCEI Accession 0145656)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Seaglider data gathered as part of the Sustained Ocean Observations for Improving Atlantic Tropical Cyclone Intensity and Hurricane Seasonal Forecast project funded...

  16. Physical trajectory profile data from glider SG630 deployed by US DOC; NOAA; OAR; Atlantic Oceanographic and Meteorological Laboratory in the Caribbean Sea from 2016-07-21 to 2016-11-10 (NCEI Accession 0157713)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Seaglider data gathered as part of the Sustained Ocean Observations for Improving Atlantic Tropical Cyclone Intensity and Hurricane Seasonal Forecast project funded...

  17. Physical trajectory profile data from glider SG609 deployed by US DOC; NOAA; OAR; Atlantic Oceanographic and Meteorological Laboratory in the Caribbean Sea from 2016-07-21 to 2016-11-10 (NCEI Accession 0157655)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Seaglider data gathered as part of the Sustained Ocean Observations for Improving Atlantic Tropical Cyclone Intensity and Hurricane Seasonal Forecast project funded...

  18. Physical trajectory profile data from glider SG609 deployed by Atlantic Oceanographic and Meteorological Laboratory in the Caribbean Sea from 2015-07-15 to 2015-11-16 (NCEI Accession 0145655)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Seaglider data gathered as part of the Sustained Ocean Observations for Improving Atlantic Tropical Cyclone Intensity and Hurricane Seasonal Forecast project funded...

  19. Space-time wind speed forecasting for improved power system dispatch

    KAUST Repository

    Zhu, Xinxin; Genton, Marc G.; Gu, Yingzhong; Xie, Le

    2014-01-01

    direction and with the seasons, hence avoiding a subjective choice of regimes. Then, results from the wind forecasts are incorporated into a power system economic dispatch model, the cost of which is used as a loss measure of the quality of the forecast

  20. Hurricane Sandy beach response and recovery at Fire Island, New York: Shoreline and beach profile data, October 2012 to October 2014

    Science.gov (United States)

    Hehre Henderson, Rachel E.; Hapke, Cheryl J.; Brenner, Owen T.; Reynolds, Billy J.

    2015-04-30

    In response to the forecasted impact of Hurricane Sandy, which made landfall on October 29, 2012, the U.S. Geological Survey (USGS) began a substantial data-collection effort to assess the morphological impacts to the beach and dune system at Fire Island, New York. Global positioning system (GPS) field surveys of the beach and dunes were conducted just prior to and after landfall and these data were used to quantify change in several focus areas. In order to quantify morphologic change along the entire length of the island, pre-storm (May 2012) and post-storm (November 2012) lidar and aerial photography were used to assess changes to the shoreline and beach.As part of the USGS Hurricane Sandy Supplemental Fire Island Study, the beach is monitored periodically to enable better understanding of post-Sandy recovery. The alongshore state of the beach is recorded using a differential global positioning system (DGPS) to collect data around the mean high water (MHW; 0.46 meter North American Vertical Datum of 1988) to derive a shoreline, and the cross-shore response and recovery are measured along a series of 10 profiles.Overall, Hurricane Sandy substantially altered the morphology of Fire Island. However, the coastal system rapidly began to recover after the 2012­–13 winter storm season and continues to recover in the form of volume gains and shoreline adjustment.

  1. Hurricane Harvey Report : A fact-finding effort in the direct aftermath of Hurricane Harvey in the Greater Houston Region

    NARCIS (Netherlands)

    Sebastian, A.G.; Lendering, K.T.; Kothuis, B.L.M.; Brand, A.D.; Jonkman, S.N.; van Gelder, P.H.A.J.M.; Kolen, B.; Comes, M.; Lhermitte, S.L.M.; Meesters, K.J.M.G.; van de Walle, B.A.; Ebrahimi Fard, A.; Cunningham, S.; Khakzad Rostami, N.; Nespeca, V.

    2017-01-01

    On August 25, 2017, Hurricane Harvey made landfall near Rockport, Texas as a Category 4 hurricane with maximum sustained winds of approximately 200 km/hour. Harvey caused severe damages in coastal Texas due to extreme winds and storm surge, but will go down in history for record-setting rainfall

  2. Improving Ecological Forecasting: Data Assimilation Enhances the Ecological Forecast Horizon of a Complex Food Web

    Science.gov (United States)

    Massoud, E. C.; Huisman, J.; Benincà, E.; Bouten, W.; Vrugt, J. A.

    2017-12-01

    Species abundances in ecological communities can display chaotic non-equilibrium dynamics. A characteristic feature of chaotic systems is that long-term prediction of the system's trajectory is fundamentally impossible. How then should we make predictions for complex multi-species communities? We explore data assimilation (DA) with the Ensemble Kalman Filter (EnKF) to fuse a two-predator-two-prey model with abundance data from a long term experiment of a plankton community which displays chaotic dynamics. The results show that DA improves substantially the predictability and ecological forecast horizon of complex community dynamics. In addition, we show that DA helps provide guidance on measurement design, for instance on defining the frequency of observations. The study presented here is highly innovative, because DA methods at the current stage are almost unknown in ecology.

  3. Tracks of Major Hurricanes of the Western Hemisphere

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This 36"x24" National Hurricane Center poster depicts the complete tracks of all major hurricanes in the north Atlantic and eastern north Pacific basins since as...

  4. Hurricanes accelerated the Florida-Bahamas lionfish invasion.

    Science.gov (United States)

    Johnston, Matthew W; Purkis, Sam J

    2015-06-01

    In this study, we demonstrate how perturbations to the Florida Current caused by hurricanes are relevant to the spread of invasive lionfish from Florida to the Bahamas. Without such perturbations, this current represents a potential barrier to the transport of planktonic lionfish eggs and larvae across the Straits of Florida. We further show that once lionfish became established in the Bahamas, hurricanes significantly hastened their spread through the island chain. We gain these insights through: (1) an analysis of the direction and velocity of simulated ocean currents during the passage of hurricanes through the Florida Straits and (2) the development of a biophysical model that incorporates the tolerances of lionfish to ocean climate, their reproductive strategy, and duration that the larvae remain viable in the water column. On the basis of this work, we identify 23 occasions between the years 1992 and 2006 in which lionfish were provided the opportunity to breach the Florida Current. We also find that hurricanes during this period increased the rate of spread of lionfish through the Bahamas by more than 45% and magnified its population by at least 15%. Beyond invasive lionfish, we suggest that extreme weather events such as hurricanes likely help to homogenize the gene pool for all Caribbean marine species susceptible to transport. © 2015 John Wiley & Sons Ltd.

  5. An assimilation test of Doppler radar reflectivity and radial velocity from different height layers in improving the WRF rainfall forecasts

    Science.gov (United States)

    Tian, Jiyang; Liu, Jia; Yan, Denghua; Li, Chuanzhe; Chu, Zhigang; Yu, Fuliang

    2017-12-01

    Hydrological forecasts require high-resolution and accurate rainfall information, which is one of the most difficult variables to be captured by the mesoscale Numerical Weather Prediction (NWP) systems. Radar data assimilation is an effective method for improving rainfall forecasts by correcting the initial and lateral boundary conditions of the NWP system. The aim of this study is to explore an efficient way of utilizing the Doppler radar observations for data assimilation, which is implemented by exploring the effect of assimilating radar data from different height layers on the improvement of the NWP rainfall accuracy. The Weather Research and Forecasting (WRF) model is used for numerical rainfall forecast in the Zijingguan catchment located in the ;Jing-Jin-Ji; (Beijing-Tianjin-Hebei) Region of Northern China, and the three-dimensional variational data assimilation (3-DVar) technique is adopted to assimilate the radar data. Radar reflectivity and radial velocity are assimilated separately and jointly. Each type of radar data is divided into seven data sets according to the height layers: (1) 2000 m, and (7) all layers. The results show that radar reflectivity assimilation leads to better results than radial velocity assimilation. The accuracy of the forecasted rainfall deteriorates with the rise of the height of the assimilated radar reflectivity. The same results can be found when assimilating radar reflectivity and radial velocity at the same time. The conclusions of this study provide a reference for efficient assimilation of the radar data in improving the NWP rainfall products.

  6. Meteor Shower Forecast Improvements from a Survey of All-Sky Network Observations

    Science.gov (United States)

    Moorhead, Althea V.; Sugar, Glenn; Brown, Peter G.; Cooke, William J.

    2015-01-01

    Meteoroid impacts are capable of damaging spacecraft and potentially ending missions. In order to help spacecraft programs mitigate these risks, NASA's Meteoroid Environment Office (MEO) monitors and predicts meteoroid activity. Temporal variations in near-Earth space are described by the MEO's annual meteor shower forecast, which is based on both past shower activity and model predictions. The MEO and the University of Western Ontario operate sister networks of all-sky meteor cameras. These networks have been in operation for more than 7 years and have computed more than 20,000 meteor orbits. Using these data, we conduct a survey of meteor shower activity in the "fireball" size regime using DBSCAN. For each shower detected in our survey, we compute the date of peak activity and characterize the growth and decay of the shower's activity before and after the peak. These parameters are then incorporated into the annual forecast for an improved treatment of annual activity.

  7. Hurricane impacts on US forest carbon sequestration

    Science.gov (United States)

    Steven G. McNulty

    2002-01-01

    Recent focus has been given to US forests as a sink for increases in atmospheric carbon dioxide. Current estimates of US Forest carbon sequestration average approximately 20 Tg (i.e. 1012 g) year. However, predictions of forest carbon sequestration often do not include the influence of hurricanes on forest carbon storage. Intense hurricanes...

  8. Spatial structure of directional wave spectra in hurricanes

    Science.gov (United States)

    Esquivel-Trava, Bernardo; Ocampo-Torres, Francisco J.; Osuna, Pedro

    2015-01-01

    The spatial structure of the wave field during hurricane conditions is studied using the National Data Buoy Center directional wave buoy data set from the Caribbean Sea and the Gulf of Mexico. The buoy information, comprising the directional wave spectra during the passage of several hurricanes, was referenced to the center of the hurricane using the path of the hurricane, the propagation velocity, and the radius of the maximum winds. The directional wave spectra were partitioned into their main components to quantify the energy corresponding to the observed wave systems and to distinguish between wind-sea and swell. The findings are consistent with those found using remote sensing data (e.g., Scanning Radar Altimeter data). Based on the previous work, the highest waves are found in the right forward quadrant of the hurricane, where the spectral shape tends to become uni-modal, in the vicinity of the region of maximum winds. More complex spectral shapes are observed in distant regions at the front of and in the rear quadrants of the hurricane, where there is a tendency of the spectra to become bi- and tri-modal. The dominant waves generally propagate at significant angles to the wind direction, except in the regions next to the maximum winds of the right quadrants. Evidence of waves generated by concentric eyewalls associated with secondary maximum winds was also found. The frequency spectra display some of the characteristics of the JONSWAP spectrum adjusted by Young (J Geophys Res 111:8020, 2006); however, at the spectral peak, the similarity with the Pierson-Moskowitz spectrum is clear. These results establish the basis for the use in assessing the ability of numerical models to simulate the wave field in hurricanes.

  9. Hurricane Imaging Radiometer (HIRAD) Wind Speed Retrievals and Assessment Using Dropsondes

    Science.gov (United States)

    Cecil, Daniel J.; Biswas, Sayak K.

    2018-01-01

    The Hurricane Imaging Radiometer (HIRAD) is an experimental C-band passive microwave radiometer designed to map the horizontal structure of surface wind speed fields in hurricanes. New data processing and customized retrieval approaches were developed after the 2015 Tropical Cyclone Intensity (TCI) experiment, which featured flights over Hurricanes Patricia, Joaquin, Marty, and the remnants of Tropical Storm Erika. These new approaches produced maps of surface wind speed that looked more realistic than those from previous campaigns. Dropsondes from the High Definition Sounding System (HDSS) that was flown with HIRAD on a WB-57 high altitude aircraft in TCI were used to assess the quality of the HIRAD wind speed retrievals. The root mean square difference between HIRAD-retrieved surface wind speeds and dropsonde-estimated surface wind speeds was 6.0 meters per second. The largest differences between HIRAD and dropsonde winds were from data points where storm motion during dropsonde descent compromised the validity of the comparisons. Accounting for this and for uncertainty in the dropsonde measurements themselves, we estimate the root mean square error for the HIRAD retrievals as around 4.7 meters per second. Prior to the 2015 TCI experiment, HIRAD had previously flown on the WB-57 for missions across Hurricanes Gonzalo (2014), Earl (2010), and Karl (2010). Configuration of the instrument was not identical to the 2015 flights, but the methods devised after the 2015 flights may be applied to that previous data in an attempt to improve retrievals from those cases.

  10. Family Structures, Relationships, and Housing Recovery Decisions after Hurricane Sandy

    Directory of Open Access Journals (Sweden)

    Ali Nejat

    2016-04-01

    Full Text Available Understanding of the recovery phase of a disaster cycle is still in its infancy. Recent major disasters such as Hurricane Sandy have revealed the inability of existing policies and planning to promptly restore infrastructure, residential properties, and commercial activities in affected communities. In this setting, a thorough grasp of housing recovery decisions can lead to effective post-disaster planning by policyholders and public officials. The objective of this research is to integrate vignette and survey design to study how family bonds affected rebuilding/relocating decisions after Hurricane Sandy. Multinomial logistic regression was used to investigate respondents’ family structures before Sandy and explore whether their relationships with family members changed after Sandy. The study also explores the effect of the aforementioned relationship and its changes on households’ plans to either rebuild/repair their homes or relocate. These results were compared to another multinomial logistic regression which was applied to examine the impact of familial bonds on respondents’ suggestions to a vignette family concerning rebuilding and relocating after a hurricane similar to Sandy. Results indicate that respondents who lived with family members before Sandy were less likely to plan for relocating than those who lived alone. A more detailed examination shows that this effect was driven by those who improved their relationships with family members; those who did not improve their family relationships were not significantly different from those who lived alone, when it came to rebuilding/relocation planning. Those who improved their relationships with family members were also less likely to suggest that the vignette family relocate. This study supports the general hypothesis that family bonds reduce the desire to relocate, and provides empirical evidence that family mechanisms are important for the rebuilding/relocating decision

  11. Hurricane Sandy science plan: coastal topographic and bathymetric data to support hurricane impact assessment and response

    Science.gov (United States)

    Stronko, Jakob M.

    2013-01-01

    Hurricane Sandy devastated some of the most heavily populated eastern coastal areas of the Nation. With a storm surge peaking at more than 19 feet, the powerful landscape-altering destruction of Hurricane Sandy is a stark reminder of why the Nation must become more resilient to coastal hazards. In response to this natural disaster, the U.S. Geological Survey (USGS) received a total of $41.2 million in supplemental appropriations from the Department of the Interior (DOI) to support response, recovery, and rebuilding efforts. These funds support a science plan that will provide critical scientific information necessary to inform management decisions for recovery of coastal communities, and aid in preparation for future natural hazards. This science plan is designed to coordinate continuing USGS activities with stakeholders and other agencies to improve data collection and analysis that will guide recovery and restoration efforts. The science plan is split into five distinct themes: • Coastal topography and bathymetry • Impacts to coastal beaches and barriers • Impacts of storm surge, including disturbed estuarine and bay hydrology • Impacts on environmental quality and persisting contaminant exposures • Impacts to coastal ecosystems, habitats, and fish and wildlife This fact sheet focuses on coastal topography and bathymetry. This fact sheet focuses on coastal topography and bathymetry.

  12. Hurricane preparedness among elderly residents in South Florida.

    Science.gov (United States)

    Kleier, Jo Ann; Krause, Deirdre; Ogilby, Terry

    2018-01-01

    The purpose of this study was to describe factors associated with hurricane preparation and to test a theoretical model of hurricane preparation decision process among a group of elderly residents living in a high-risk geographical area. This is a descriptive, correlational study. A convenience sample consisted of 188 English-speaking individuals who were aged 55 years or older. In addition to demographic information, two survey instruments were used. Theoretical constructs were operationalized through Moon's Hurricane Preparation Questionnaire. Hurricane preparedness was measured by self-reported responses to FEMA's inventory checklist, which addresses the recommended basic steps of preparation. The theoretical model of hurricane preparation decision process was supported. Main barriers to preparation are the need for cooperation from others and cost of preparation. Participants reported having taken many preparatory steps to shelter-in-place, but too few are prepared if their home were storm-damaged or they should have to evacuate. Findings are consistent with previous studies of samples drawn from similar populations. This report provides guidance as to how public health nurses can become involved with the population and develop interventions based on the constructs of the theoretical model. © 2017 Wiley Periodicals, Inc.

  13. A comparison of the nursing home evacuation experience between hurricanes katrina (2005) and gustav (2008).

    Science.gov (United States)

    Blanchard, Gary; Dosa, David

    2009-11-01

    One of the tragic legacies of Hurricane Katrina was the loss of life among Louisiana (LA) nursing home (NH) residents. Katrina revealed a staggering lack of emergency preparation and understanding of how to safely evacuate frail populations. Three years later, LA braced for Hurricane Gustav, a storm heralded to rival Katrina's power. Although its magnitude of destruction ultimately paled to Katrina, the warnings and predicted path preceding Gustav yielded a process of NH evacuations similar to Katrina. The goal of this article was to ascertain whether NH administrative directors (ADs) felt more prepared to evacuate before Gustav. In 2006, Dosa et al(5) (J Am Med Dir Assoc, 3/07), interviewed 20 NH ADs by qualitative telephone survey to evaluate their lessons learned from Katrina. Administrators at these 20 participating nursing homes were contacted and asked to participate in a follow-up survey to compare hurricane preparedness between 2005 and 2008. Specifically, ADs were asked if they evacuated before Gustav, their destination, and about logistical issues with evacuation (eg, transportation, injuries). ADs were asked to rate their confidence with state assistance, hurricane transportation, and evacuation preparedness on a 10-point scale (10=most confident) and compare their preparedness to Katrina. Sixteen of the 20 NHs that participated in 2006 agreed to be surveyed-11 of whom held the same position before Katrina. Unlike Katrina, when only 45% evacuated before the storm, all 16 NHs evacuated before Gustav (56% to another NH and 46% to a church, gym, college, or other facility). Overall, ADs rated their confidence in preparedness for Gustav as a mean of 8.3 (range 5 to 10) compared with a mean of 5.4 (range 3 to 8) for Katrina, a 54% improvement. Of the 11 ADs employed pre-Katrina, 73% reported improved collaboration with the state and 55% noted improved transportation. Nevertheless, 7 ADs noted significant logistical problems during evacuation (mostly

  14. Weather uncertainty versus climate change uncertainty in a short television weather broadcast

    Science.gov (United States)

    Witte, J.; Ward, B.; Maibach, E.

    2011-12-01

    For TV meteorologists talking about uncertainty in a two-minute forecast can be a real challenge. It can quickly open the way to viewer confusion. TV meteorologists understand the uncertainties of short term weather models and have different methods to convey the degrees of confidence to the viewing public. Visual examples are seen in the 7-day forecasts and the hurricane track forecasts. But does the public really understand a 60 percent chance of rain or the hurricane cone? Communication of climate model uncertainty is even more daunting. The viewing public can quickly switch to denial of solid science. A short review of the latest national survey of TV meteorologists by George Mason University and lessons learned from a series of climate change workshops with TV broadcasters provide valuable insights into effectively using visualizations and invoking multimedia-learning theories in weather forecasts to improve public understanding of climate change.

  15. "Just-in-Time" Personal Preparedness: Downloads and Usage Patterns of the American Red Cross Hurricane Application During Hurricane Sandy.

    Science.gov (United States)

    Kirsch, Thomas D; Circh, Ryan; Bissell, Richard A; Goldfeder, Matthew

    2016-10-01

    Personal preparedness is a core activity but has been found to be frequently inadequate. Smart phone applications have many uses for the public, including preparedness. In 2012 the American Red Cross began releasing "disaster" apps for family preparedness and recovery. The Hurricane App was widely used during Hurricane Sandy in 2012. Patterns of download of the application were analyzed by using a download tracking tool by the American Red Cross and Google Analytics. Specific variables included date, time, and location of individual downloads; number of page visits and views; and average time spent on pages. As Hurricane Sandy approached in late October, daily downloads peaked at 152,258 on the day of landfall and by mid-November reached 697,585. Total page views began increasing on October 25 with over 4,000,000 page views during landfall compared to 3.7 million the first 3 weeks of October with a 43,980% increase in views of the "Right Before" page and a 76,275% increase in views of the "During" page. The Hurricane App offered a new type of "just-in-time" training that reached tens of thousands of families in areas affected by Hurricane Sandy. The app allowed these families to access real-time information before and after the storm to help them prepare and recover. (Disaster Med Public Health Preparedness. 2016;page 1 of 6).

  16. An Axisymmetric View of Concentric Eyewall Evolution in Hurricane Rita (2005)

    Science.gov (United States)

    2012-08-01

    of Hurricane Hugo (1989). Mon. Wea. Rev., 136, 1237–1259. Martinez, Y., G. Brunet, and M. K. Yau, 2010: On the dynamics of two-dimensional hurricane ...An Axisymmetric View of Concentric Eyewall Evolution in Hurricane Rita (2005) MICHAEL M. BELL Naval Postgraduate School, Monterey, California, and... Hurricane Research Division, Miami, Florida WEN-CHAU LEE National Center for Atmospheric Research,* Boulder, Colorado (Manuscript received 23 June 2011, in

  17. Evaluation of TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis)'s predictive skill for hurricane-triggered landslides: A case study in Macon County, North Carolina

    Science.gov (United States)

    Liao, Z.; Hong, Y.; Kirschbaum, D.; Adler, R.F.; Gourley, J.J.; Wooten, R.

    2011-01-01

    The key to advancing the predictability of rainfall-triggered landslides is to use physically based slope-stability models that simulate the transient dynamical response of the subsurface moisture to spatiotemporal variability of rainfall in complex terrains. TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis) is a USGS landslide prediction model, coded in Fortran, that accounts for the influences of hydrology, topography, and soil physics on slope stability. In this study, we quantitatively evaluate the spatiotemporal predictability of a Matlab version of TRIGRS (MaTRIGRS) in the Blue Ridge Mountains of Macon County, North Carolina where Hurricanes Ivan triggered widespread landslides in the 2004 hurricane season. High resolution digital elevation model (DEM) data (6-m LiDAR), USGS STATSGO soil database, and NOAA/NWS combined radar and gauge precipitation are used as inputs to the model. A local landslide inventory database from North Carolina Geological Survey is used to evaluate the MaTRIGRS' predictive skill for the landslide locations and timing, identifying predictions within a 120-m radius of observed landslides over the 30-h period of Hurricane Ivan's passage in September 2004. Results show that within a radius of 24 m from the landslide location about 67% of the landslide, observations could be successfully predicted but with a high false alarm ratio (90%). If the radius of observation is extended to 120 m, 98% of the landslides are detected with an 18% false alarm ratio. This study shows that MaTRIGRS demonstrates acceptable spatiotemporal predictive skill for landslide occurrences within a 120-m radius in space and a hurricane-event-duration (h) in time, offering the potential to serve as a landslide warning system in areas where accurate rainfall forecasts and detailed field data are available. The validation can be further improved with additional landslide information including the exact time of failure for each

  18. A New Strategy for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Yi Yang

    2013-01-01

    Full Text Available Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.

  19. Air Pollution Forecasts: An Overview.

    Science.gov (United States)

    Bai, Lu; Wang, Jianzhou; Ma, Xuejiao; Lu, Haiyan

    2018-04-17

    Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

  20. Air Pollution Forecasts: An Overview

    Directory of Open Access Journals (Sweden)

    Lu Bai

    2018-04-01

    Full Text Available Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

  1. Air Pollution Forecasts: An Overview

    Science.gov (United States)

    Bai, Lu; Wang, Jianzhou; Lu, Haiyan

    2018-01-01

    Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies. PMID:29673227

  2. Improved ensemble-mean forecast skills of ENSO events by a zero-mean stochastic model-error model of an intermediate coupled model

    Science.gov (United States)

    Zheng, F.; Zhu, J.

    2015-12-01

    To perform an ensemble-based ENSO probabilistic forecast, the crucial issue is to design a reliable ensemble prediction strategy that should include the major uncertainties of a forecast system. In this study, we developed a new general ensemble perturbation technique to improve the ensemble-mean predictive skill of forecasting ENSO using an intermediate coupled model (ICM). The model uncertainties are first estimated and analyzed from EnKF analysis results through assimilating observed SST. Then, based on the pre-analyzed properties of the model errors, a zero-mean stochastic model-error model is developed to mainly represent the model uncertainties induced by some important physical processes missed in the coupled model (i.e., stochastic atmospheric forcing/MJO, extra-tropical cooling and warming, Indian Ocean Dipole mode, etc.). Each member of an ensemble forecast is perturbed by the stochastic model-error model at each step during the 12-month forecast process, and the stochastical perturbations are added into the modeled physical fields to mimic the presence of these high-frequency stochastic noises and model biases and their effect on the predictability of the coupled system. The impacts of stochastic model-error perturbations on ENSO deterministic predictions are examined by performing two sets of 21-yr retrospective forecast experiments. The two forecast schemes are differentiated by whether they considered the model stochastic perturbations, with both initialized by the ensemble-mean analysis states from EnKF. The comparison results suggest that the stochastic model-error perturbations have significant and positive impacts on improving the ensemble-mean prediction skills during the entire 12-month forecast process. Because the nonlinear feature of the coupled model can induce the nonlinear growth of the added stochastic model errors with model integration, especially through the nonlinear heating mechanism with the vertical advection term of the model, the

  3. Sub-Seasonal Climate Forecast Rodeo

    Science.gov (United States)

    Webb, R. S.; Nowak, K.; Cifelli, R.; Brekke, L. D.

    2017-12-01

    The Bureau of Reclamation, as the largest water wholesaler and the second largest producer of hydropower in the United States, benefits from skillful forecasts of future water availability. Researchers, water managers from local, regional, and federal agencies, and groups such as the Western States Water Council agree that improved precipitation and temperature forecast information at the sub-seasonal to seasonal (S2S) timescale is an area with significant potential benefit to water management. In response, and recognizing NOAA's leadership in forecasting, Reclamation has partnered with NOAA to develop and implement a real-time S2S forecasting competition. For a year, solvers are submitting forecasts of temperature and precipitation for weeks 3&4 and 5&6 every two weeks on a 1x1 degree grid for the 17 western state domain where Reclamation operates. The competition began on April 18, 2017 and the final real-time forecast is due April 3, 2018. Forecasts are evaluated once observational data become available using spatial anomaly correlation. Scores are posted on a competition leaderboard hosted by the National Integrated Drought Information System (NIDIS). The leaderboard can be accessed at: https://www.drought.gov/drought/sub-seasonal-climate-forecast-rodeo. To be eligible for cash prizes - which total $800,000 - solvers must outperform two benchmark forecasts during the real-time competition as well as in a required 11-year hind-cast. To receive a prize, competitors must grant a non-exclusive license to practice their forecast technique and make it available as open source software. At approximately one quarter complete, there are teams outperforming the benchmarks in three of the four competition categories. With prestige and monetary incentives on the line, it is hoped that the competition will spur innovation of improved S2S forecasts through novel approaches, enhancements to established models, or otherwise. Additionally, the competition aims to raise

  4. Economic impact analysis of load forecasting

    International Nuclear Information System (INIS)

    Ranaweera, D.K.; Karady, G.G.; Farmer, R.G.

    1997-01-01

    Short term load forecasting is an essential function in electric power system operations and planning. Forecasts are needed for a variety of utility activities such as generation scheduling, scheduling of fuel purchases, maintenance scheduling and security analysis. Depending on power system characteristics, significant forecasting errors can lead to either excessively conservative scheduling or very marginal scheduling. Either can induce heavy economic penalties. This paper examines the economic impact of inaccurate load forecasts. Monte Carlo simulations were used to study the effect of different load forecasting accuracy. Investigations into the effect of improving the daily peak load forecasts, effect of different seasons of the year and effect of utilization factors are presented

  5. Increased Sensitization to Mold Allergens Measured by Intradermal Skin Testing following Hurricanes.

    Science.gov (United States)

    Saporta, Diego; Hurst, David

    2017-01-01

    Objective . To report on changes in sensitivity to mold allergens determined by changes in intradermal skin testing reactivity, after exposure to two severe hurricanes. Methods . A random, retrospective allergy charts review divided into 2 groups of 100 patients each: Group A, patients tested between 2003 and 2010 prior to hurricanes, and Group B, patients tested in 2014 and 2015 following hurricanes. Reactivity to eighteen molds was determined by intradermal skin testing. Test results, age, and respiratory symptoms were recorded. Chi-square test determined reactivity/sensitivity differences between groups. Results . Posthurricane patients had 34.6 times more positive results ( p hurricanes ( p hurricanes ( p hurricanes. This supports climatologists' hypothesis that environmental changes resulting from hurricanes can be a health risk as reflected in increased allergic sensitivities and symptoms and has significant implications for physicians treating patients from affected areas.

  6. Evaluation of the NCEP CFSv2 45-day Forecasts for Predictability of Intraseasonal Tropical Storm Activities

    Science.gov (United States)

    Schemm, J. E.; Long, L.; Baxter, S.

    2013-12-01

    Evaluation of the NCEP CFSv2 45-day Forecasts for Predictability of Intraseasonal Tropical Storm Activities Jae-Kyung E. Schemm, Lindsey Long and Stephen Baxter Climate Prediction Center, NCEP/NWS/NOAA Predictability of intraseasonal tropical storm (TS) activities is assessed using the 1999-2010 CFSv2 hindcast suite. Weekly TS activities in the CFSv2 45-day forecasts were determined using the TS detection and tracking method devised by Carmago and Zebiak (2002). The forecast periods are divided into weekly intervals for Week 1 through Week 6, and also the 30-day mean. The TS activities in those intervals are compared to the observed activities based on the NHC HURDAT and JTWC Best Track datasets. The CFSv2 45-day hindcast suite is made of forecast runs initialized at 00, 06, 12 and 18Z every day during the 1999 - 2010 period. For predictability evaluation, forecast TS activities are analyzed based on 20-member ensemble forecasts comprised of 45-day runs made during the most recent 5 days prior to the verification period. The forecast TS activities are evaluated in terms of the number of storms, genesis locations and storm tracks during the weekly periods. The CFSv2 forecasts are shown to have a fair level of skill in predicting the number of storms over the Atlantic Basin with the temporal correlation scores ranging from 0.73 for Week 1 forecasts to 0.63 for Week 6, and the average RMS errors ranging from 0.86 to 1.07 during the 1999-2010 hurricane season. Also, the forecast track density distribution and false alarm statistics are compiled using the hindcast analyses. In real-time applications of the intraseasonal TS activity forecasts, the climatological TS forecast statistics will be used to make the model bias corrections in terms of the storm counts, track distribution and removal of false alarms. An operational implementation of the weekly TS activity prediction is planned for early 2014 to provide an objective input for the CPC's Global Tropical Hazards

  7. Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

    Directory of Open Access Journals (Sweden)

    Hugo Carrão

    2018-06-01

    America are obtainable from improvements in near-real-time precipitation observations for the region. In the longer term, improvements in precipitation forecast skill from dynamical models, like the fifth generation of the ECMWF seasonal forecasting system, will be essential in this effort.

  8. Improvement of inventory control and forecast according to activity-based classifications: T company as an example

    Science.gov (United States)

    Huang, Jui-Chan; Wu, Tzu-Jung; Chiu, Yen-Chun; Lu, Chunwei

    2017-06-01

    Inventory management is a major issue for all the industries. The supply of products to customers requires the readiness of the inventory. This allows rapid delivery and reduces waiting time for customers so that companies can profit from it. Any stock out or insufficiency will lead to loss of customers because their needs cannot be met. This will hurt firm profitability and market competitiveness. Inventory control is critical to retain liquidity and avoid overstocking. This is also the key to firm's survival and sustainability. To ensure an appropriate level of inventory, it is necessary to manage the inventory levels with sales forecast on an on-going basis. This paper seeks to assist Company T to improve its inventory control. Firstly, the products offered by Company T are classified into groups. The R programming language is used to stimulate and forecast future sales of different products. Different techniques are applied to manage the inventory levels according to the results of categorizations and forecasts that are consolidation of all the product items and grouping them into activity-based classifications, simulation and forecasting of future sales according to the categorization results, and formulation of different control techniques based on the simulations and forecasts. The results and the inventory management can be used to enhance the inventory control as well.

  9. An Approach to Remove the Systematic Bias from the Storm Surge forecasts in the Venice Lagoon

    Science.gov (United States)

    Canestrelli, A.

    2017-12-01

    In this work a novel approach is proposed for removing the systematic bias from the storm surge forecast computed by a two-dimensional shallow-water model. The model covers both the Adriatic and Mediterranean seas and provides the forecast at the entrance of the Venice Lagoon. The wind drag coefficient at the water-air interface is treated as a calibration parameter, with a different value for each range of wind velocities and wind directions. This sums up to a total of 16-64 parameters to be calibrated, depending on the chosen resolution. The best set of parameters is determined by means of an optimization procedure, which minimizes the RMS error between measured and modeled water level in Venice for the period 2011-2015. It is shown that a bias is present, for which the peaks of wind velocities provided by the weather forecast are largely underestimated, and that the calibration procedure removes this bias. When the calibrated model is used to reproduce events not included in the calibration dataset, the forecast error is strongly reduced, thus confirming the quality of our procedure. The proposed approach it is not site-specific and could be applied to different situations, such as storm surges caused by intense hurricanes.

  10. Improving Arctic Sea Ice Observations and Data Access to Support Advances in Sea Ice Forecasting

    Science.gov (United States)

    Farrell, S. L.

    2017-12-01

    The economic and strategic importance of the Arctic region is becoming apparent. One of the most striking and widely publicized changes underway is the declining sea ice cover. Since sea ice is a key component of the climate system, its ongoing loss has serious, and wide-ranging, socio-economic implications. Increasing year-to-year variability in the geographic location, concentration, and thickness of the Arctic ice cover will pose both challenges and opportunities. The sea ice research community must be engaged in sustained Arctic Observing Network (AON) initiatives so as to deliver fit-for-purpose remote sensing data products to a variety of stakeholders including Arctic communities, the weather forecasting and climate modeling communities, industry, local, regional and national governments, and policy makers. An example of engagement is the work currently underway to improve research collaborations between scientists engaged in obtaining and assessing sea ice observational data and those conducting numerical modeling studies and forecasting ice conditions. As part of the US AON, in collaboration with the Interagency Arctic Research Policy Committee (IARPC), we are developing a strategic framework within which observers and modelers can work towards the common goal of improved sea ice forecasting. Here, we focus on sea ice thickness, a key varaible of the Arctic ice cover. We describe multi-sensor, and blended, sea ice thickness data products under development that can be leveraged to improve model initialization and validation, as well as support data assimilation exercises. We will also present the new PolarWatch initiative (polarwatch.noaa.gov) and discuss efforts to advance access to remote sensing satellite observations and improve communication with Arctic stakeholders, so as to deliver data products that best address societal needs.

  11. A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network

    International Nuclear Information System (INIS)

    Yu, Feng; Xu, Xiaozhong

    2014-01-01

    Highlights: • A detailed data processing will make more accurate results prediction. • Taking a full account of more load factors to improve the prediction precision. • Improved BP network obtains higher learning convergence. • Genetic algorithm optimized by chaotic cat map enhances the global search ability. • The combined GA–BP model improved by modified additional momentum factor is superior to others. - Abstract: This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to accelerate the convergence speed of network, including improved additional momentum factor, improved self-adaptive learning rate and improved momentum and self-adaptive learning rate. Then, it is available to use the global search capability of optimized genetic algorithm to determine the initial weights and thresholds of BP neural network to avoid being trapped in local minima. The ability of GA is enhanced by cat chaotic mapping. In light of the characteristic of natural gas load for Shanghai, a series of data preprocessing methods are adopted and more comprehensive load factors are taken into account to improve the prediction accuracy. Such improvements facilitate forecasting efficiency and exert maximum performance of the model. As a result, the integration model improved by modified additional momentum factor gets more ideal solutions for short-term gas load forecasting, through analyses and comparisons of the above several different combinational algorithms

  12. Evaluation of long-term community recovery from Hurricane Andrew: sources of assistance received by population sub-groups.

    Science.gov (United States)

    McDonnell, S; Troiano, R P; Barker, N; Noji, E; Hlady, W G; Hopkins, R

    1995-12-01

    Two three-stage cluster surveys were conducted in South Dade County, Florida, 14 months apart, to assess recovery following Hurricane Andrew. Response rates were 75 per cent and 84 per cent. Sources of assistance used in recovery from Hurricane Andrew differed according to race, per capita income, ethnicity, and education. Reports of improved living situation post-hurricane were not associated with receiving relief assistance, but reports of a worse situation were associated with loss of income, being exploited, or job loss. The number of households reporting problems with crime and community violence doubled between the two surveys. Disaster relief efforts had less impact on subjective long-term recovery than did job or income loss or housing repair difficulties. Existing sources of assistance were used more often than specific post-hurricane relief resources. The demographic make-up of a community may determine which are the most effective means to inform them after a disaster and what sources of assistance may be useful.

  13. Nurses respond to Hurricane Hugo victims' disaster stress.

    Science.gov (United States)

    Weinrich, S; Hardin, S B; Johnson, M

    1990-06-01

    Hugo, a class IV hurricane, hit South Carolina September 22, 1989, and left behind a wake of terror and destruction. Sixty-one nursing students and five faculty were involved in disaster relief with families devastated by the hurricane. A review of the literature led these authors to propose a formulation of the concept of disaster stress, a synthesis of theories that explains response to disaster as a crisis response, a stress response, or as posttraumatic stress. With the concept of disaster stress serving as a theoretical foundation, the nurses observed, assessed, and intervened with one population of hurricane Hugo victims, noting their immediate psychosocial reactions and coping mechanisms. Victims' reactions to disaster stress included confusion, irritability, lethargy, withdrawal, and crying. The most frequently observed coping strategy of these hurricane Hugo victims was talking about their experiences; other coping tactics involved humor, religion, and altruism.

  14. Hurricane Sandy, Disaster Preparedness, and the Recovery Model.

    Science.gov (United States)

    Pizzi, Michael A

    2015-01-01

    Hurricane Sandy was the second largest and costliest hurricane in U.S. history to affect multiple states and communities. This article describes the lived experiences of 24 occupational therapy students who lived through Hurricane Sandy using the Recovery Model to frame the research. Occupational therapy student narratives were collected and analyzed using qualitative methods and framed by the Recovery Model. Directed content and thematic analysis was performed using the 10 components of the Recovery Model. The 10 components of the Recovery Model were experienced by or had an impact on the occupational therapy students as they coped and recovered in the aftermath of the natural disaster. This study provides insight into the lived experiences and recovery perspectives of occupational therapy students who experienced Hurricane Sandy. Further research is indicated in applying the Recovery Model to people who survive disasters. Copyright © 2015 by the American Occupational Therapy Association, Inc.

  15. Magnetogram Forecast: An All-Clear Space Weather Forecasting System

    Science.gov (United States)

    Barghouty, Nasser; Falconer, David

    2015-01-01

    Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

  16. Genesis of Hurricane Sandy (2012) Simulated with a Global Mesoscale Model

    Science.gov (United States)

    Shen, Bo-Wen; DeMaria, Mark; Li, J.-L. F.; Cheung, S.

    2013-01-01

    In this study, we investigate the formation predictability of Hurricane Sandy (2012) with a global mesoscale model. We first present five track and intensity forecasts of Sandy initialized at 00Z 22-26 October 2012, realistically producing its movement with a northwestward turn prior to its landfall. We then show that three experiments initialized at 00Z 16-18 October captured the genesis of Sandy with a lead time of up to 6 days and simulated reasonable evolution of Sandy's track and intensity in the next 2 day period of 18Z 21-23 October. Results suggest that the extended lead time of formation prediction is achieved by realistic simulations of multiscale processes, including (1) the interaction between an easterly wave and a low-level westerly wind belt (WWB) and (2) the appearance of the upper-level trough at 200 hPa to Sandy's northwest. The low-level WWB and upper-level trough are likely associated with a Madden-Julian Oscillation.

  17. Hurricane Katrina Sediment Sampling

    Data.gov (United States)

    U.S. Environmental Protection Agency — Hurricane Katrina made landfall in August 2005, causing widespread devastation along the Gulf Coast of the United States. EPA emergency response personnel worked...

  18. Hurricane Katrina Water Sampling

    Data.gov (United States)

    U.S. Environmental Protection Agency — Hurricane Katrina made landfall in August 2005, causing widespread devastation along the Gulf Coast of the United States. EPA emergency response personnel worked...

  19. Hurricane Katrina Soil Sampling

    Data.gov (United States)

    U.S. Environmental Protection Agency — Hurricane Katrina made landfall in August 2005, causing widespread devastation along the Gulf Coast of the United States. EPA emergency response personnel worked...

  20. Disaster preparedness of dialysis patients for Hurricanes Gustav and Ike 2008.

    Science.gov (United States)

    Kleinpeter, Myra A

    2009-01-01

    Hurricanes Katrina and Rita resulted in massive devastation of the Gulf Coast at Mississippi, Louisiana, and Texas during 2005. Because of those disasters, dialysis providers, nephrologists, and dialysis patients used disaster planning activities to work to mitigate the morbidity and mortality associated with the 2005 hurricane season for future events affecting dialysis patients. As Hurricane Gustav approached, anniversary events for Hurricane Katrina were postponed because of evacuation orders for nearly the entire Louisiana Gulf Coast. As part of the hurricane preparation, dialysis units reviewed the disaster plans of patients, and patients made preparation for evacuation. Upon evacuation, many patients returned to the dialysis units that had provided services during their exile from Hurricane Katrina; other patients went to other locations as part of their evacuation plan. Patients uniformly reported positive experiences with dialysis providers in their temporary evacuation communities, provided that those communities did not experience the effects of Hurricane Gustav. With the exception of evacuees to Baton Rouge, patients continued to receive their treatments uninterrupted. Because of extensive damage in the Baton Rouge area, resulting in widespread power losses and delayed restoration of power to hospitals and other health care facilities, some patients missed one treatment. However, as a result of compliance with disaster fluid and dietary recommendations, no adverse outcomes occurred. In most instances, patients were able to return to their home dialysis unit or a nearby unit to continue dialysis treatments within 4 - 5 days of Hurricane Gustav. Hurricane Ike struck the Texas Gulf Coast near Galveston, resulting in devastation of that area similar to the devastation seen in New Orleans after Katrina. The storm surge along the Louisiana Gulf Coast resulted in flooding that temporarily closed coastal dialysis units. Patients were prepared and experienced

  1. Coal production forecast and low carbon policies in China

    International Nuclear Information System (INIS)

    Wang Jianzhou; Dong Yao; Wu Jie; Mu Ren; Jiang He

    2011-01-01

    With rapid economic growth and industrial expansion, China consumes more coal than any other nation. Therefore, it is particularly crucial to forecast China's coal production to help managers make strategic decisions concerning China's policies intended to reduce carbon emissions and concerning the country's future needs for domestic and imported coal. Such decisions, which must consider results from forecasts, will have important national and international effects. This article proposes three improved forecasting models based on grey systems theory: the Discrete Grey Model (DGM), the Rolling DGM (RDGM), and the p value RDGM. We use the statistical data of coal production in China from 1949 to 2005 to validate the effectiveness of these improved models to forecast the data from 2006 to 2010. The performance of the models demonstrates that the p value RDGM has the best forecasting behaviour over this historical time period. Furthermore, this paper forecasts coal production from 2011 to 2015 and suggests some policies for reducing carbon and other emissions that accompany the rise in forecasted coal production. - Highlights: → Improved forecasting models make full use of the advantages of individual model. → Proposed models create commendable improvements for current research. → Proposed models do not make complicated decisions about the explicit form. → We forecast coal production of China from 2011 to 2015. → We suggest some policies for reducing carbon emissions.

  2. Coal production forecast and low carbon policies in China

    Energy Technology Data Exchange (ETDEWEB)

    Wang Jianzhou [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Dong Yao, E-mail: dongyao20051987@yahoo.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Wu Jie; Mu Ren; Jiang He [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China)

    2011-10-15

    With rapid economic growth and industrial expansion, China consumes more coal than any other nation. Therefore, it is particularly crucial to forecast China's coal production to help managers make strategic decisions concerning China's policies intended to reduce carbon emissions and concerning the country's future needs for domestic and imported coal. Such decisions, which must consider results from forecasts, will have important national and international effects. This article proposes three improved forecasting models based on grey systems theory: the Discrete Grey Model (DGM), the Rolling DGM (RDGM), and the p value RDGM. We use the statistical data of coal production in China from 1949 to 2005 to validate the effectiveness of these improved models to forecast the data from 2006 to 2010. The performance of the models demonstrates that the p value RDGM has the best forecasting behaviour over this historical time period. Furthermore, this paper forecasts coal production from 2011 to 2015 and suggests some policies for reducing carbon and other emissions that accompany the rise in forecasted coal production. - Highlights: > Improved forecasting models make full use of the advantages of individual model. > Proposed models create commendable improvements for current research. > Proposed models do not make complicated decisions about the explicit form. > We forecast coal production of China from 2011 to 2015. > We suggest some policies for reducing carbon emissions.

  3. On the Impact Angle of Hurricane Sandy's New Jersey Landfall

    Science.gov (United States)

    Hall, Timothy M.; Sobel, Adam H.

    2013-01-01

    Hurricane Sandy's track crossed the New Jersey coastline at an angle closer to perpendicular than any previous hurricane in the historic record, one of the factors contributing to recordsetting peak-water levels in parts of New Jersey and New York. To estimate the occurrence rate of Sandy-like tracks, we use a stochastic model built on historical hurricane data from the entire North Atlantic to generate a large sample of synthetic hurricanes. From this synthetic set we calculate that under long-term average climate conditions, a hurricane of Sandy's intensity or greater (category 1+) makes NJ landfall at an angle at least as close to perpendicular as Sandy's at an average annual rate of 0.0014 yr-1 (95% confidence range 0.0007 to 0.0023); i.e., a return period of 714 years (95% confidence range 435 to 1429).

  4. The value of feedback in forecasting competitions

    OpenAIRE

    George Athanasopoulos; Rob J Hyndman

    2011-01-01

    In this paper we challenge the traditional design used for forecasting competitions. We implement an online competition with a public leaderboard that provides instant feedback to competitors who are allowed to revise and resubmit forecasts. The results show that feedback significantly improves forecasting accuracy.

  5. Deaths associated with Hurricane Sandy - October-November 2012.

    Science.gov (United States)

    2013-05-24

    On October 29, 2012, Hurricane Sandy hit the northeastern U.S. coastline. Sandy's tropical storm winds stretched over 900 miles (1,440 km), causing storm surges and destruction over a larger area than that affected by hurricanes with more intensity but narrower paths. Based on storm surge predictions, mandatory evacuations were ordered on October 28, including for New York City's Evacuation Zone A, the coastal zone at risk for flooding from any hurricane. By October 31, the region had 6-12 inches (15-30 cm) of precipitation, 7-8 million customers without power, approximately 20,000 persons in shelters, and news reports of numerous fatalities (Robert Neurath, CDC, personal communication, 2013). To characterize deaths related to Sandy, CDC analyzed data on 117 hurricane-related deaths captured by American Red Cross (Red Cross) mortality tracking during October 28-November 30, 2012. This report describes the results of that analysis, which found drowning was the most common cause of death related to Sandy, and 45% of drowning deaths occurred in flooded homes in Evacuation Zone A. Drowning is a leading cause of hurricane death but is preventable with advance warning systems and evacuation plans. Emergency plans should ensure that persons receive and comprehend evacuation messages and have the necessary resources to comply with them.

  6. Climate forecasts for corn producer decision making

    Science.gov (United States)

    Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertai...

  7. Cooperative Hurricane Network Obs

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Observations from the Cooperative Hurricane Reporting Network (CHURN), a special network of stations that provided observations when tropical cyclones approached the...

  8. Forecasting biodiversity in breeding birds using best practices

    Science.gov (United States)

    Taylor, Shawn D.; White, Ethan P.

    2018-01-01

    Biodiversity forecasts are important for conservation, management, and evaluating how well current models characterize natural systems. While the number of forecasts for biodiversity is increasing, there is little information available on how well these forecasts work. Most biodiversity forecasts are not evaluated to determine how well they predict future diversity, fail to account for uncertainty, and do not use time-series data that captures the actual dynamics being studied. We addressed these limitations by using best practices to explore our ability to forecast the species richness of breeding birds in North America. We used hindcasting to evaluate six different modeling approaches for predicting richness. Hindcasts for each method were evaluated annually for a decade at 1,237 sites distributed throughout the continental United States. All models explained more than 50% of the variance in richness, but none of them consistently outperformed a baseline model that predicted constant richness at each site. The best practices implemented in this study directly influenced the forecasts and evaluations. Stacked species distribution models and “naive” forecasts produced poor estimates of uncertainty and accounting for this resulted in these models dropping in the relative performance compared to other models. Accounting for observer effects improved model performance overall, but also changed the rank ordering of models because it did not improve the accuracy of the “naive” model. Considering the forecast horizon revealed that the prediction accuracy decreased across all models as the time horizon of the forecast increased. To facilitate the rapid improvement of biodiversity forecasts, we emphasize the value of specific best practices in making forecasts and evaluating forecasting methods. PMID:29441230

  9. Forecasting biodiversity in breeding birds using best practices

    Directory of Open Access Journals (Sweden)

    David J. Harris

    2018-02-01

    Full Text Available Biodiversity forecasts are important for conservation, management, and evaluating how well current models characterize natural systems. While the number of forecasts for biodiversity is increasing, there is little information available on how well these forecasts work. Most biodiversity forecasts are not evaluated to determine how well they predict future diversity, fail to account for uncertainty, and do not use time-series data that captures the actual dynamics being studied. We addressed these limitations by using best practices to explore our ability to forecast the species richness of breeding birds in North America. We used hindcasting to evaluate six different modeling approaches for predicting richness. Hindcasts for each method were evaluated annually for a decade at 1,237 sites distributed throughout the continental United States. All models explained more than 50% of the variance in richness, but none of them consistently outperformed a baseline model that predicted constant richness at each site. The best practices implemented in this study directly influenced the forecasts and evaluations. Stacked species distribution models and “naive” forecasts produced poor estimates of uncertainty and accounting for this resulted in these models dropping in the relative performance compared to other models. Accounting for observer effects improved model performance overall, but also changed the rank ordering of models because it did not improve the accuracy of the “naive” model. Considering the forecast horizon revealed that the prediction accuracy decreased across all models as the time horizon of the forecast increased. To facilitate the rapid improvement of biodiversity forecasts, we emphasize the value of specific best practices in making forecasts and evaluating forecasting methods.

  10. Not so close but still extremely loud: recollection of the World Trade Center terror attack and previous hurricanes moderates the association between exposure to hurricane Sandy and posttraumatic stress symptoms.

    Science.gov (United States)

    Palgi, Yuval; Shrira, Amit; Hamama-Raz, Yaira; Palgi, Sharon; Goodwin, Robin; Ben-Ezra, Menachem

    2014-05-01

    The present study examined whether recollections of the World Trade Center (WTC) terror attack and previous hurricanes moderated the relationship between exposure to Hurricane Sandy and related posttraumatic stress disorder (PTSD) symptoms. An online sample of 1000 participants from affected areas completed self-report questionnaires a month after Hurricane Sandy hit the East Coast of the United States. Participants reported their exposure to Hurricane Sandy, their PTSD symptoms, and recollections of the WTC terror attack and previous hurricanes elicited due to Hurricane Sandy. Exposure to Hurricane Sandy was related to PTSD symptoms among those with high level of recollections of the WTC terror attack and past hurricanes, but not among those with low level of recollections. The aftermath of exposure to Hurricane Sandy is related not only to exposure, but also to its interaction with recollections of past traumas. These findings have theoretical and practical implications for practitioners and health policy makers in evaluating and interpreting the impact of past memories on future natural disasters. This may help in intervention plans of social and psychological services. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. Error sensitivity analysis in 10-30-day extended range forecasting by using a nonlinear cross-prediction error model

    Science.gov (United States)

    Xia, Zhiye; Xu, Lisheng; Chen, Hongbin; Wang, Yongqian; Liu, Jinbao; Feng, Wenlan

    2017-06-01

    Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous meteorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear crossprediction error (NCPE) model, and their stability in the prediction validity period in 10-30-day extended range forecasting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small (10-6-10-2), minor variation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random error has minimal effect on the prediction. When the ratio is in the range of 10-1-2 (i.e., random error dominates), attention should be paid to the random error instead of only the initial error. When the ratio is around 10-2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecasting, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depicted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect ( m > 4) is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperature or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.

  12. Hurricane Sandy science plan: coastal impact assessments

    Science.gov (United States)

    Stronko, Jakob M.

    2013-01-01

    Hurricane Sandy devastated some of the most heavily populated eastern coastal areas of the Nation. With a storm surge peaking at more than 19 feet, the powerful landscape-altering destruction of Hurricane Sandy is a stark reminder of why the Nation must become more resilient to coastal hazards. In response to this natural disaster, the U.S. Geological Survey (USGS) received a total of $41.2 million in supplemental appropriations from the Department of the Interior (DOI) to support response, recovery, and rebuilding efforts. These funds support a science plan that will provide critical scientific information necessary to inform management decisions for recovery of coastal communities, and aid in preparation for future natural hazards. This science plan is designed to coordinate continuing USGS activities with stakeholders and other agencies to improve data collection and analysis that will guide recovery and restoration efforts. The science plan is split into five distinct themes: coastal topography and bathymetry, impacts to coastal beaches and barriers, impacts of storm surge, including disturbed estuarine and bay hydrology, impacts on environmental quality and persisting contaminant exposures, impacts to coastal ecosystems, habitats, and fish and wildlife. This fact sheet focuses assessing impacts to coastal beaches and barriers.

  13. Forecasting metal prices: Do forecasters herd?

    DEFF Research Database (Denmark)

    Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

    2013-01-01

    We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

  14. Probing magma reservoirs to improve volcano forecasts

    Science.gov (United States)

    Lowenstern, Jacob B.; Sisson, Thomas W.; Hurwitz, Shaul

    2017-01-01

    When it comes to forecasting eruptions, volcano observatories rely mostly on real-time signals from earthquakes, ground deformation, and gas discharge, combined with probabilistic assessments based on past behavior [Sparks and Cashman, 2017]. There is comparatively less reliance on geophysical and petrological understanding of subsurface magma reservoirs.

  15. A convection-allowing ensemble forecast based on the breeding growth mode and associated optimization of precipitation forecast

    Science.gov (United States)

    Li, Xiang; He, Hongrang; Chen, Chaohui; Miao, Ziqing; Bai, Shigang

    2017-10-01

    A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP) methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipitation tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the precipitation center produced by the ensemble forecast was also better. The Fractions Skill Score (FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of precipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could improve precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved.

  16. Improved El Nino forecasting by cooperativity detection.

    Science.gov (United States)

    Ludescher, Josef; Gozolchiani, Avi; Bogachev, Mikhail I; Bunde, Armin; Havlin, Shlomo; Schellnhuber, Hans Joachim

    2013-07-16

    Although anomalous episodic warming of the eastern equatorial Pacific, dubbed El Niño by Peruvian fishermen, has major (and occasionally devastating) impacts around the globe, robust forecasting is still limited to about 6 mo ahead. A significant extension of the prewarning time would be instrumental for avoiding some of the worst damages such as harvest failures in developing countries. Here we introduce a unique avenue toward El Niño prediction based on network methods, inspecting emerging teleconnections. Our approach starts from the evidence that a large-scale cooperative mode--linking the El Niño basin (equatorial Pacific corridor) and the rest of the ocean--builds up in the calendar year before the warming event. On this basis, we can develop an efficient 12-mo forecasting scheme, i.e., achieve some doubling of the early-warning period. Our method is based on high-quality observational data available since 1950 and yields hit rates above 0.5, whereas false-alarm rates are below 0.1.

  17. Real-time emergency forecasting technique for situation management systems

    Science.gov (United States)

    Kopytov, V. V.; Kharechkin, P. V.; Naumenko, V. V.; Tretyak, R. S.; Tebueva, F. B.

    2018-05-01

    The article describes the real-time emergency forecasting technique that allows increasing accuracy and reliability of forecasting results of any emergency computational model applied for decision making in situation management systems. Computational models are improved by the Improved Brown’s method applying fractal dimension to forecast short time series data being received from sensors and control systems. Reliability of emergency forecasting results is ensured by the invalid sensed data filtering according to the methods of correlation analysis.

  18. Hurricane impacts on a pair of coastal forested watersheds: implications of selective hurricane damage to forest structure and streamflow dynamics

    Science.gov (United States)

    A.D. Jayakaran; T.M. Williams; H. Ssegane; D.M. Amatya; B. Song; C.C. Trettin

    2014-01-01

    Hurricanes are infrequent but influential disruptors of ecosystem processes in the southeastern Atlantic and Gulf coasts. Every southeastern forested wetland has the potential to be struck by a tropical cyclone. We examined the impact of Hurricane Hugo on two paired coastal South Carolina watersheds in terms of streamflow and vegetation dynamics, both before and after...

  19. SKU demand forecasting in the presence of promotions

    NARCIS (Netherlands)

    Gür Ali, Ö.; Sayin, S.; Woensel, van T.; Fransoo, J.C.

    2009-01-01

    Promotions and shorter life cycles make grocery sales forecasting more difficult, requiring more complicated models. We identify methods of increasing complexity and data preparation cost yielding increasing improvements in forecasting accuracy, by varying the forecasting technique, the input

  20. EarthLabs - Investigating Hurricanes: Earth's Meteorological Monsters

    Science.gov (United States)

    McDaris, J. R.; Dahlman, L.; Barstow, D.

    2007-12-01

    Earth science is one of the most important tools that the global community needs to address the pressing environmental, social, and economic issues of our time. While, at times considered a second-rate science at the high school level, it is currently undergoing a major revolution in the depth of content and pedagogical vitality. As part of this revolution, labs in Earth science courses need to shift their focus from cookbook-like activities with known outcomes to open-ended investigations that challenge students to think, explore and apply their learning. We need to establish a new model for Earth science as a rigorous lab science in policy, perception, and reality. As a concerted response to this need, five states, a coalition of scientists and educators, and an experienced curriculum team are creating a national model for a lab-based high school Earth science course named EarthLabs. This lab course will comply with the National Science Education Standards as well as the states' curriculum frameworks. The content will focus on Earth system science and environmental literacy. The lab experiences will feature a combination of field work, classroom experiments, and computer access to data and visualizations, and demonstrate the rigor and depth of a true lab course. The effort is being funded by NOAA's Environmental Literacy program. One of the prototype units of the course is Investigating Hurricanes. Hurricanes are phenomena which have tremendous impact on humanity and the resources we use. They are also the result of complex interacting Earth systems, making them perfect objects for rigorous investigation of many concepts commonly covered in Earth science courses, such as meteorology, climate, and global wind circulation. Students are able to use the same data sets, analysis tools, and research techniques that scientists employ in their research, yielding truly authentic learning opportunities. This month-long integrated unit uses hurricanes as the story line by

  1. Numerical study of sediment dynamics during hurricane Gustav

    Science.gov (United States)

    Zang, Zhengchen; Xue, Z. George; Bao, Shaowu; Chen, Qin; Walker, Nan D.; Haag, Alaric S.; Ge, Qian; Yao, Zhigang

    2018-06-01

    In this study, the coupled ocean-atmosphere-wave-and-sediment transport (COAWST) modeling system was employed to explore sediment dynamics in the northern Gulf of Mexico during hurricane Gustav in 2008. The performance of the model was evaluated quantitatively and qualitatively against in-situ and remote sensing measurements, respectively. After Gustav's landfall in coastal Louisiana, the maximum significant wave heights reached more than 8 m offshore and they decreased quickly as it moved toward the inner shelf, where the vertical stratification was largely destroyed. Alongshore currents were dominant westward on the eastern sector of the hurricane track, and offshoreward currents prevailed on the western sector. High suspended sediment concentrations (>1000 mg/l) were confined to the inner shelf at surface layers and the simulated high concentrations at the bottom layer extended to the 200 m isobaths. The stratification was restored one week after landfall, although not fully. The asymmetric hurricane winds induced stronger hydrodynamics in the eastern sector, which led to severe erosion. The calculated suspended sediment flux (SSF) was convergent to the hurricane center and the maximum SSF was simulated near the south and southeast of the Mississippi river delta. The averaged post-hurricane deposition over the Louisiana shelf was 4.0 cm, which was 3.2-26 times higher than the annual accumulation rate under normal weather conditions.

  2. Improving the Forecasting Accuracy of Crude Oil Prices

    Directory of Open Access Journals (Sweden)

    Xuluo Yin

    2018-02-01

    Full Text Available Currently, oil is the key element of energy sustainability, and its prices and economy have a strong mutual influence. Modeling a good method to accurately predict oil prices over long future horizons is challenging and of great interest to investors and policymakers. This paper forecasts oil prices using many predictor variables with a new time-varying weight combination approach. In doing so, we first use five single-variable time-varying parameter models to predict crude oil prices separately. Second, every special model is assigned a time-varying weight by the new combination approach. Finally, the forecasting results of oil prices are calculated. The results show that the paper’s method is robust and performs well compared to random walk.

  3. Analog forecasting with dynamics-adapted kernels

    Science.gov (United States)

    Zhao, Zhizhen; Giannakis, Dimitrios

    2016-09-01

    Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning and state-space reconstruction for dynamical systems. A key ingredient of our approach is to replace single-analog forecasting with weighted ensembles of analogs constructed using local similarity kernels. The kernels used here employ a number of dynamics-dependent features designed to improve forecast skill, including Takens’ delay-coordinate maps (to recover information in the initial data lost through partial observations) and a directional dependence on the dynamical vector field generating the data. Mathematically, our approach is closely related to kernel methods for out-of-sample extension of functions, and we discuss alternative strategies based on the Nyström method and the multiscale Laplacian pyramids technique. We illustrate these techniques in applications to forecasting in a low-order deterministic model for atmospheric dynamics with chaotic metastability, and interannual-scale forecasting in the North Pacific sector of a comprehensive climate model. We find that forecasts based on kernel-weighted ensembles have significantly higher skill than the conventional approach following a single analog.

  4. Options to Improve the Quality of Wind Generation Output Forecasting with the Use of Available Information as Explanatory Variables

    Directory of Open Access Journals (Sweden)

    Rafał Magulski

    2015-06-01

    Full Text Available Development of wind generation, besides its positive aspects related to the use of renewable energy, is a challenge from the point of view of power systems’ operational security and economy. The uncertain and variable nature of wind generation sources entails the need for the for the TSO to provide adequate reserves of power, necessary to maintain the grid’s stable operation, and the actors involved in the trading of energy from these sources incur additional of balancing unplanned output deviations. The paper presents the results of analyses concerning the options to forecast a selected wind farm’s output exercised by means of different methods of prediction, using a different range of measurement and forecasting data available on the farm and its surroundings. The analyses focused on the evaluation of forecast errors, and selection of input data for forecasting models and assessment of their impact on prediction quality improvement.

  5. Oceanic control of Northeast Pacific hurricane activity at interannual timescales

    International Nuclear Information System (INIS)

    Balaguru, Karthik; Ruby Leung, L; Yoon, Jin-ho

    2013-01-01

    Sea surface temperature (SST) is not the only oceanic parameter that can play a key role in the interannual variability of Northeast Pacific hurricane activity. Using several observational data sets and the statistical technique of multiple linear regression analysis, we show that, along with SST, the thermocline depth (TD) plays an important role in hurricane activity at interannual timescales in this basin. Based on the parameter that dominates, the ocean basin can be divided into two sub-regions. In the Southern sub-region, which includes the hurricane main development area, interannual variability of the upper-ocean heat content (OHC) is primarily controlled by TD variations. Consequently, the interannual variability in the hurricane power dissipation index (PDI), which is a measure of the intensity of hurricane activity, is driven by that of the TD. On the other hand, in the Northern sub-region, SST exerts the major control over the OHC variability and, in turn, the PDI. Our study suggests that both SST and TD have a significant influence on the Northeast Pacific hurricane activity at interannual timescales and that their respective roles are more clearly delineated when sub-regions along an approximate north–south demarcation are considered rather than the basin as a whole. (letter)

  6. An assessment of the ECMWF tropical cyclone ensemble forecasting system and its use for insurance loss predictions

    Science.gov (United States)

    Aemisegger, F.; Martius, O.; Wüest, M.

    2010-09-01

    Tropical cyclones (TC) are amongst the most impressive and destructive weather systems of Earth's atmosphere. The costs related to such intense natural disasters have been rising in recent years and may potentially continue to increase in the near future due to changes in magnitude, timing, duration or location of tropical storms. This is a challenging situation for numerical weather prediction, which should provide a decision basis for short term protective measures through high quality medium range forecasts on the one hand. On the other hand, the insurance system bears great responsibility in elaborating proactive plans in order to face these extreme events that individuals cannot manage independently. Real-time prediction and early warning systems are needed in the insurance sector in order to face an imminent hazard and minimise losses. Early loss estimates are important in order to allocate capital and to communicate to investors. The ECMWF TC identification algorithm delivers information on the track and intensity of storms based on the ensemble forecasting system. This provides a physically based framework to assess the uncertainty in the forecast of a specific event. The performance of the ECMWF TC ensemble forecasts is evaluated in terms of cyclone intensity and location in this study and the value of such a physically-based quantification of uncertainty in the meteorological forecast for the estimation of insurance losses is assessed. An evaluation of track and intensity forecasts of hurricanes in the North Atlantic during the years 2005 to 2009 is carried out. Various effects are studied like the differences in forecasts over land or sea, as well as links between storm intensity and forecast error statistics. The value of the ECMWF TC forecasting system for the global re-insurer Swiss Re was assessed by performing insurance loss predictions using their in-house loss model for several case studies of particularly devastating events. The generally known

  7. Cash Flow Forecasting : Proposal for New Long-Term Cash Flow Forecast in the Case Company

    OpenAIRE

    Pitkänen, Annika

    2016-01-01

    The purpose of this study was to develop a cash flow forecast model for the case company. The case company in this thesis was a Finnish building construction company. The group controlling set a target to improve the corporate treasury’s current long-term cash flow forecast because it was inaccurate and it often had outstanding deficiencies between actual and forecasted figures. A project team was set up to investigate on this issue and this research and development project is documented in t...

  8. Assimilating InSAR Maps of Water Vapor to Improve Heavy Rainfall Forecasts: A Case Study With Two Successive Storms

    Science.gov (United States)

    Mateus, Pedro; Miranda, Pedro M. A.; Nico, Giovanni; Catalão, João.; Pinto, Paulo; Tomé, Ricardo

    2018-04-01

    Very high resolution precipitable water vapor maps obtained by the Sentinel-1 A synthetic aperture radar (SAR), using the SAR interferometry (InSAR) technique, are here shown to have a positive impact on the performance of severe weather forecasts. A case study of deep convection which affected the city of Adra, Spain, on 6-7 September 2015, is successfully forecasted by the Weather Research and Forecasting model initialized with InSAR data assimilated by the three-dimensional variational technique, with improved space and time distributions of precipitation, as observed by the local weather radar and rain gauge. This case study is exceptional because it consisted of two severe events 12 hr apart, with a timing that allows for the assimilation of both the ascending and descending satellite images, each for the initialization of each event. The same methodology applied to the network of Global Navigation Satellite System observations in Iberia, at the same times, failed to reproduce observed precipitation, although it also improved, in a more modest way, the forecast skill. The impact of precipitable water vapor data is shown to result from a direct increment of convective available potential energy, associated with important adjustments in the low-level wind field, favoring its release in deep convection. It is suggested that InSAR images, complemented by dense Global Navigation Satellite System data, may provide a new source of water vapor data for weather forecasting, since their sampling frequency could reach the subdaily scale by merging different SAR platforms, or when future geosynchronous radar missions become operational.

  9. Combining forecast weights: Why and how?

    Science.gov (United States)

    Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim

    2012-09-01

    This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.

  10. Maternal exposure to hurricane destruction and fetal mortality.

    Science.gov (United States)

    Zahran, Sammy; Breunig, Ian M; Link, Bruce G; Snodgrass, Jeffrey G; Weiler, Stephan; Mielke, Howard W

    2014-08-01

    The majority of research documenting the public health impacts of natural disasters focuses on the well-being of adults and their living children. Negative effects may also occur in the unborn, exposed to disaster stressors when critical organ systems are developing and when the consequences of exposure are large. We exploit spatial and temporal variation in hurricane behaviour as a quasi-experimental design to assess whether fetal death is dose-responsive in the extent of hurricane damage. Data on births and fetal deaths are merged with Parish-level housing wreckage data. Fetal outcomes are regressed on housing wreckage adjusting for the maternal, fetal, placental and other risk factors. The average causal effect of maternal exposure to hurricane destruction is captured by difference-in-differences analyses. The adjusted odds of fetal death are 1.40 (1.07-1.83) and 2.37 (1.684-3.327) times higher in parishes suffering 10-50% and >50% wreckage to housing stock, respectively. For every 1% increase in the destruction of housing stock, we observe a 1.7% (1.1-2.4%) increase in fetal death. Of the 410 officially recorded fetal deaths in these parishes, between 117 and 205 may be attributable to hurricane destruction and postdisaster disorder. The estimated fetal death toll is 17.4-30.6% of the human death toll. The destruction caused by Hurricanes Katrina and Rita imposed significant measurable losses in terms of fetal death. Postdisaster migratory dynamics suggest that the reported effects of maternal exposure to hurricane destruction on fetal death may be conservative. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  11. Sleep disturbance and its relationship to psychiatric morbidity after Hurricane Andrew.

    Science.gov (United States)

    Mellman, T A; David, D; Kulick-Bell, R; Hebding, J; Nolan, B

    1995-11-01

    Sleep disturbance is an important dimension of posttraumatic stress disorder (PTSD), but most of the limited available data were obtained years after the original traumatic event. This study provides information on sleep disturbance and its relationship to posttraumatic morbidity from evaluations done within a year after the trauma. Sleep and psychiatric symptoms of 54 victims (12 men and 42 women) of Hurricane Andrew who had no psychiatric illness in the 6 months before the hurricane were evaluated. A subset of hurricane victims with active psychiatric morbidity (N = 10) and nine comparison subjects who were unaffected by the hurricane were examined in a sleep laboratory. A broad range of sleep-related complaints were rated as being greater after the hurricane, and psychiatric morbidity (which was most commonly PTSD, followed by depression) had a significant effect on most of the subjective sleep measures. In addition, subjects with active morbidity endorsed greater frequencies of "bad dreams" and general sleep disturbances before the hurricane. Polysomnographic results for the hurricane victims revealed a greater number of arousals and entries into stage 1 sleep. REM density correlated positively with both the PTSD symptom of reexperiencing trauma and global distress. Subjects affected by Hurricane Andrew reported sleep disturbances, particularly those subjects with psychiatric morbidity. Tendencies to experience bad dreams and interrupted sleep before a trauma appear to mark vulnerability to posttraumatic morbidity. Results of sleep laboratory evaluations suggested brief shifts toward higher arousal levels during sleep for PTSD subjects and a relationship of REM phasic activity and symptom severity.

  12. Influences of Appalachian orography on heavy rainfall and rainfall variability associated with the passage of hurricane Isabel by ensemble simulations

    Science.gov (United States)

    Oldaker, Guy; Liu, Liping; Lin, Yuh-Lang

    2017-12-01

    This study focuses on the heavy rainfall event associated with hurricane Isabel's (2003) passage over the Appalachian mountains of the eastern United States. Specifically, an ensemble consisting of two groups of simulations using the Weather Research and Forecasting model (WRF), with and without topography, is performed to investigate the orographic influences on heavy rainfall and rainfall variability. In general, the simulated ensemble mean with full terrain is able to reproduce the key observed 24-h rainfall amount and distribution, while the flat-terrain mean lacks in this respect. In fact, 30-h rainfall amounts are reduced by 75% with the removal of topography. Rainfall variability is also significantly increased with the presence of orography. Further analysis shows that the complex interaction between the hurricane and terrain along with contributions from varied microphysics, cumulus parametrization, and planetary boundary layer schemes have a pronounced effect on rainfall and rainfall variability. This study follows closely with a previous study, but for a different TC case of Isabel (2003). It is an important sensitivity test for a different TC in a very different environment. This study reveals that the rainfall variability behaves similarly, even with different settings of the environment.

  13. Hurricane-induced failure of low salinity wetlands

    Science.gov (United States)

    Howes, Nick C.; FitzGerald, Duncan M.; Hughes, Zoe J.; Georgiou, Ioannis Y.; Kulp, Mark A.; Miner, Michael D.; Smith, Jane M.; Barras, John A.

    2010-01-01

    During the 2005 hurricane season, the storm surge and wave field associated with Hurricanes Katrina and Rita eroded 527 km2 of wetlands within the Louisiana coastal plain. Low salinity wetlands were preferentially eroded, while higher salinity wetlands remained robust and largely unchanged. Here we highlight geotechnical differences between the soil profiles of high and low salinity regimes, which are controlled by vegetation and result in differential erosion. In low salinity wetlands, a weak zone (shear strength 500–1450 Pa) was observed ∼30 cm below the marsh surface, coinciding with the base of rooting. High salinity wetlands had no such zone (shear strengths > 4500 Pa) and contained deeper rooting. Storm waves during Hurricane Katrina produced shear stresses between 425–3600 Pa, sufficient to cause widespread erosion of the low salinity wetlands. Vegetation in low salinity marshes is subject to shallower rooting and is susceptible to erosion during large magnitude storms; these conditions may be exacerbated by low inorganic sediment content and high nutrient inputs. The dramatic difference in resiliency of fresh versus more saline marshes suggests that the introduction of freshwater to marshes as part of restoration efforts may therefore weaken existing wetlands rendering them vulnerable to hurricanes. PMID:20660777

  14. Low ionospheric reactions on tropical depressions prior hurricanes

    Science.gov (United States)

    Nina, Aleksandra; Radovanović, Milan; Milovanović, Boško; Kovačević, Andjelka; Bajčetić, Jovan; Popović, Luka Č.

    2017-10-01

    We study the reactions of the low ionosphere during tropical depressions (TDs) which have been detected before the hurricane appearances in the Atlantic Ocean. We explore 41 TD events using very low frequency (VLF) radio signals emitted by NAA transmitter located in the USA and recorded by VLF receiver located in Belgrade (Serbia). We found VLF signal deviations (caused ionospheric turbulence) in the case of 36 out of 41 TD events (88%). Additionally, we explore 27 TDs which have not been developed in hurricanes and found similar low ionospheric reactions. However, in the sample of 41 TDs which are followed by hurricanes the typical low ionosphere perturbations seem to be more frequent than other TDs.

  15. Weather Forecasts are for Wimps. Why Water Resource Managers Do Not Use Climate Forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Rayner, S. [James Martin Institute of Science and Civilization, Said Business School, University of Oxford, OX1 1HP (United Kingdom); Lach, D. [Oregon State University, Corvallis, OR, 97331-4501 (United States); Ingram, H. [School of Social Ecology, University of California Irvine, Irvine, CA, 92697-7075 (United States)

    2005-04-15

    Short-term climate forecasting offers the promise of improved hydrologic management strategies. However, water resource managers in the United States have proven reluctant to incorporate them in decision making. While managers usually cite poor reliability of the forecasts as the reason for this, they are seldom able to demonstrate knowledge of the actual performance of forecasts or to consistently articulate the level of reliability that they would require. Analysis of three case studies in California, the Pacific Northwest, and metro Washington DC identifies institutional reasons that appear to lie behind managers reluctance to use the forecasts. These include traditional reliance on large built infrastructure, organizational conservatism and complexity, mismatch of temporal and spatial scales of forecasts to management needs, political disincentives to innovation, and regulatory constraints. The paper concludes that wider acceptance of the forecasts will depend on their being incorporated in existing organizational routines and industrial codes and practices, as well as changes in management incentives to innovation. Finer spatial resolution of forecasts and the regional integration of multi-agency functions would also enhance their usability. The title of this article is taken from an advertising slogan for the Oldsmobile Bravura SUV.

  16. Impacts of a large array of offshore wind farms on precipitation during hurricane Harvey

    Science.gov (United States)

    Pan, Y.; Archer, C. L.

    2017-12-01

    Hurricane Harvey brought to the Texas coast possibly the heaviest rain ever recorded in U.S. history, which then caused flooding at unprecedented levels. Previous studies have shown that large arrays of offshore wind farms can extract kinetic energy from a hurricane and thus reduce the wind and storm surge. This study will quantitatively test weather the offshore turbines may also affect precipitation patterns. The Weather Research Forecast model is employed to model Harvey and the offshore wind farms are parameterized as elevated drag and turbulence kinetic energy sources. The turbines (7.8 MW Enercon-126 with rotor diameter D=127 m) are placed along the coast of Texas and Louisiana within 100 km from the shore, where the water depth is below 200 meters. Three spacing between turbines are considered (with the number of turbines in parenthesis): 7D×7D (149,936), 9D×9D (84,339), and 11D×11D (56,226). A fourth case (9D×9D) with a smaller area and thus less turbines (33,363) is added to the simulations to emphasize the impacts of offshore turbines installed specifically to protect the city of Houston, which was flooded heavily during hurricane Harvey. The model is integrated for 24 hours from 00UTC Aug 26th, 2017 to 00UTC Aug 27th, 2017. Model results indicate that the offshore wind farms have a strong impact on the distribution of 24-hour accumulated precipitation, with an obvious decrease onshore, downstream of the wind farms, and an increase in the offshore areas, upstream of or within the wind farms. A sector covering the metro-Houston area is chosen to study the sensitivity of the four different wind farm layouts. The spatial-average 24-hour accumulated precipitation is decreased by 37%, 28%, 20% and 25% respectively for the four cases. Compared with the control case with no wind turbines, increased horizontal wind divergence and lower vertical velocity are found where the precipitation is reduced onshore, whereas increased horizontal wind convergence and

  17. Forecasting winds over nuclear power plants statistics

    International Nuclear Information System (INIS)

    Marais, Ch.

    1997-01-01

    In the event of an accident at nuclear power plant, it is essential to forecast the wind velocity at the level where the efflux occurs (about 100 m). At present meteorologists refine the wind forecast from the coarse grid of numerical weather prediction (NWP) models. The purpose of this study is to improve the forecasts by developing a statistical adaptation method which corrects the NWP forecasts by using statistical comparisons between wind forecasts and observations. The Multiple Linear Regression method is used here to forecast the 100 m wind at 12 and 24 hours range for three Electricite de France (EDF) sites. It turns out that this approach gives better forecasts than the NWP model alone and is worthy of operational use. (author)

  18. Teacher Guidelines for Helping Students after a Hurricane

    Science.gov (United States)

    National Child Traumatic Stress Network, 2013

    2013-01-01

    Being in a hurricane can be very frightening, and the days, weeks, and months following the storm can be very stressful. Most families recover over time, especially with the support of relatives, friends, and their community. But different families may have different experiences during and after a hurricane, and how long it takes them to recover…

  19. Morphological responses of the Wax Lake Delta, Louisiana, to Hurricanes Rita

    Directory of Open Access Journals (Sweden)

    Fei Xing

    2017-12-01

    Full Text Available This study examines the morphodynamic response of a deltaic system to extreme weather events. The Wax Lake Delta (WLD in Louisiana, USA, is used to illustrate the impact of extreme events (hurricanes on a river-dominated deltaic system. Simulations using the open source Delft3D model reveal that Hurricane Rita, which made landfall 120 km to the west of WLD as a Category 3 storm in 2005, caused erosion on the right side and deposition on the left side of the hurricane eye track on the continental shelf line (water depth 10 m to 50 m. Erosion over a wide area occurred both on the continental shelf line and in coastal areas when the hurricane moved onshore, while deposition occurred along the Gulf coastline (water depth < 5 m when storm surge water moved back offshore. The numerical model estimated that Hurricane Rita’s storm surge reached 2.5 m, with maximum currents of 2.0 m s–1, and wave heights of 1.4 m on the WLD. The northwestern-directed flow and waves induced shear stresses, caused erosion on the eastern banks of the deltaic islands and deposition in channels located west of these islands. In total, Hurricane Rita eroded more than 500,000 m3 of sediments on the WLD area. Including waves in the analysis resulted in doubling the amount of erosion in the study area, comparing to the wave-excluding scenario. The exclusion of fluvial input caused minor changes in deltaic morphology during the event. Vegetation cover was represented as rigid rods in the model which add extra source terms for drag and turbulence to influence the momentum and turbulence equations. Vegetation slowed down the floodwater propagation and decreased flow velocity on the islands, leading to a 47% reduction in the total amount of erosion. Morphodynamic impact of the hurricane track relative to the delta was explored. Simulations indicate that the original track of Hurricane Rita (landfall 120 km west of the WLD produced twice as much erosion and deposition at the delta

  20. Radial profiles of velocity and pressure for condensation-induced hurricanes

    International Nuclear Information System (INIS)

    Makarieva, A.M.; Gorshkov, V.G.

    2011-01-01

    The Bernoulli integral in the form of an algebraic equation is obtained for the hurricane air flow as the sum of the kinetic energy of wind and the condensational potential energy. With an account for the eye rotation energy and the decrease of angular momentum towards the hurricane center it is shown that the theoretical profiles of pressure and velocity agree well with observations for intense hurricanes. The previous order of magnitude estimates obtained in pole approximation are confirmed.

  1. Radial profiles of velocity and pressure for condensation-induced hurricanes

    Science.gov (United States)

    Makarieva, A. M.; Gorshkov, V. G.

    2011-02-01

    The Bernoulli integral in the form of an algebraic equation is obtained for the hurricane air flow as the sum of the kinetic energy of wind and the condensational potential energy. With an account for the eye rotation energy and the decrease of angular momentum towards the hurricane center it is shown that the theoretical profiles of pressure and velocity agree well with observations for intense hurricanes. The previous order of magnitude estimates obtained in pole approximation are confirmed.

  2. Radial profiles of velocity and pressure for condensation-induced hurricanes

    Energy Technology Data Exchange (ETDEWEB)

    Makarieva, A.M., E-mail: ammakarieva@gmail.co [Theoretical Physics Division, Petersburg Nuclear Physics Institute, Gatchina, St. Petersburg (Russian Federation); Gorshkov, V.G. [Theoretical Physics Division, Petersburg Nuclear Physics Institute, Gatchina, St. Petersburg (Russian Federation)

    2011-02-14

    The Bernoulli integral in the form of an algebraic equation is obtained for the hurricane air flow as the sum of the kinetic energy of wind and the condensational potential energy. With an account for the eye rotation energy and the decrease of angular momentum towards the hurricane center it is shown that the theoretical profiles of pressure and velocity agree well with observations for intense hurricanes. The previous order of magnitude estimates obtained in pole approximation are confirmed.

  3. Uncertainty Analysis of Multi-Model Flood Forecasts

    Directory of Open Access Journals (Sweden)

    Erich J. Plate

    2015-12-01

    Full Text Available This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf, calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables.

  4. External factors impacting hospital evacuations caused by Hurricane Rita: the role of situational awareness.

    Science.gov (United States)

    Downey, Erin L; Andress, Knox; Schultz, Carl H

    2013-06-01

    identify and coordinate with community resources. Hospital evacuation requires coordinated processes and resources, including situational awareness that reflects the condition of the community as a result of the incident. Successful hospital evacuation decision making is influenced by community-wide situational awareness and transportation deficits. Planning with the community to create realistic EOPs that accurately reflect available resources and protocols is critical to informing hospital decision making during a crisis. Knowledge of these factors could improve decision making and evacuation practices, potentially reducing evacuation times in future hurricanes.

  5. Human-model hybrid Korean air quality forecasting system.

    Science.gov (United States)

    Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

    2016-09-01

    The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the

  6. Changes in trace metals in Thalassia testudinum after hurricane impacts.

    Science.gov (United States)

    Whelan, T; Van Tussenbroek, B I; Santos, M G Barba

    2011-12-01

    Major hurricanes Emily and Wilma hit the Mexican Caribbean in 2005. Changes in trace metals in the seagrass Thalassia testudinum prior to (May 2004, 2005) and following passage of these hurricanes (May, June 2006) were determined at four locations along a ≈ 130 km long stretch of coast. Before the hurricanes, essential metals were likely limiting and concentrations of potentially toxic Pb were high in a contaminated lagoon (27.5 μg g(-1)) and near submarine springs (6.10 μg g(-1)); the likely sources were inland sewage disposal or excessive boat traffic. After the hurricanes, Pb decreased to 2.0 μg g(-1) in the contaminated lagoon probably through flushing. At the northern sites, essential Fe increased >2-fold (from 26.8 to 68.3 μg g(-1) on average), possibly from remobilization of anoxic sediments or upwelling of deep seawater during Wilma. Thus, hurricanes can be beneficial to seagrass beds in flushing toxic metals and replenishing essential elements. Copyright © 2011 Elsevier Ltd. All rights reserved.

  7. Dependence of US hurricane economic loss on maximum wind speed and storm size

    International Nuclear Information System (INIS)

    Zhai, Alice R; Jiang, Jonathan H

    2014-01-01

    Many empirical hurricane economic loss models consider only wind speed and neglect storm size. These models may be inadequate in accurately predicting the losses of super-sized storms, such as Hurricane Sandy in 2012. In this study, we examined the dependences of normalized US hurricane loss on both wind speed and storm size for 73 tropical cyclones that made landfall in the US from 1988 through 2012. A multi-variate least squares regression is used to construct a hurricane loss model using both wind speed and size as predictors. Using maximum wind speed and size together captures more variance of losses than using wind speed or size alone. It is found that normalized hurricane loss (L) approximately follows a power law relation with maximum wind speed (V max ) and size (R), L = 10 c V max a R b , with c determining an overall scaling factor and the exponents a and b generally ranging between 4–12 and 2–4 respectively. Both a and b tend to increase with stronger wind speed. Hurricane Sandy’s size was about three times of the average size of all hurricanes analyzed. Based on the bi-variate regression model that explains the most variance for hurricanes, Hurricane Sandy’s loss would be approximately 20 times smaller if its size were of the average size with maximum wind speed unchanged. It is important to revise conventional empirical hurricane loss models that are only dependent on maximum wind speed to include both maximum wind speed and size as predictors. (letters)

  8. Developing Local Scale, High Resolution, Data to Interface with Numerical Hurricane Models

    Science.gov (United States)

    Witkop, R.; Becker, A.

    2017-12-01

    In 2017, the University of Rhode Island's (URI's) Graduate School of Oceanography (GSO) developed hurricane models that specify wind speed, inundation, and erosion around Rhode Island with enough precision to incorporate impacts on individual facilities. At the same time, URI's Marine Affairs Visualization Lab (MAVL) developed a way to realistically visualize these impacts in 3-D. Since climate change visualizations and water resource simulations have been shown to promote resiliency action (Sheppard, 2015) and increase credibility (White et al., 2010) when local knowledge is incorporated, URI's hurricane models and visualizations may also more effectively enable hurricane resilience actions if they include Facility Manager (FM) and Emergency Manager (EM) perceived hurricane impacts. This study determines how FM's and EM's perceive their assets as being vulnerable to quantifiable hurricane-related forces at the individual facility scale while exploring methods to elicit this information from FMs and EMs in a format usable for incorporation into URI GSO's hurricane models.

  9. Empirical testing of forecast update procedure forseasonal products

    DEFF Research Database (Denmark)

    Wong, Chee Yew; Johansen, John

    2008-01-01

    Updating of forecasts is essential for successful collaborative forecasting, especially for seasonal products. This paper discusses the results of a theoretical simulation and an empirical test of a proposed time-series forecast updating procedure. It involves a two-stage longitudinal case study...... of a toy supply chain. The theoretical simulation involves historical weekly consumer demand data for 122 toy products. The empirical test is then carried out in real-time with 291 toy products. The results show that the proposed forecast updating procedure: 1) reduced forecast errors of the annual...... provided less forecast accuracy improvement and it needed a longer time to achieve relatively acceptable forecast uncertainty....

  10. Crop Insurance Inaccurate FCIC Price Forecasts Increase Program Costs

    National Research Council Canada - National Science Library

    1991-01-01

    ...) how FCIC can improve its forecast accuracy. We found that FCIC's corn, wheat, and soybeans price forecasts exhibit large bias errors that exceed those of other available alternative forecasts and that FCIC would have spent...

  11. Hurricane Impact on Seepage Water in Larga Cave, Puerto Rico

    Science.gov (United States)

    Vieten, Rolf; Warken, Sophie; Winter, Amos; Schröder-Ritzrau, Andrea; Scholz, Denis; Spötl, Christoph

    2018-03-01

    Hurricane-induced rainfall over Puerto Rico has characteristic δ18O values which are more negative than local rainfall events. Thus, hurricanes may be recorded in speleothems from Larga cave, Puerto Rico, as characteristic oxygen isotope excursions. Samples of 84 local rainfall events between 2012 and 2013 ranged from -6.2 to +0.3‰, whereas nine rainfall samples belonging to a rainband of hurricane Isaac (23-24 August 2012) ranged from -11.8 to -7.1‰. Cave monitoring covered the hurricane season of 2014 and investigated the impact of hurricane rainfall on drip water chemistry. δ18O values were measured in cumulative monthly rainwater samples above the cave. Inside the cave, δ18O values of instantaneous drip water samples were analyzed and drip rates were recorded at six drip sites. Most effective recharge appears to occur during the wet months (April-May and August-November). δ18O values of instantaneous drip water samples ranged from -3.5 to -2.4‰. In April 2014 and April 2015 some drip sites showed more negative δ18O values than the effective rainfall (-2.9‰), implying an influence of hurricane rainfall reaching the cave via stratified seepage flow months to years after the event. Speleothems from these drip sites in Larga cave have a high potential for paleotempestology studies.

  12. Estimating the spatial distribution of power outages during hurricanes in the Gulf coast region

    International Nuclear Information System (INIS)

    Han, S.-R.; Guikema, Seth D.; Quiring, Steven M.; Lee, Kyung-Ho; Rosowsky, David; Davidson, Rachel A.

    2009-01-01

    Hurricanes have caused severe damage to the electric power system throughout the Gulf coast region of the US, and electric power is critical to post-hurricane disaster response as well as to long-term recovery for impacted areas. Managing power outage risk and preparing for post-storm recovery efforts requires accurate methods for estimating the number and location of power outages. This paper builds on past work on statistical power outage estimation models to develop, test, and demonstrate a statistical power outage risk estimation model for the Gulf Coast region of the US. Previous work used binary hurricane-indicator variables representing particular hurricanes in order to achieve a good fit to the past data. To use these models for predicting power outages during future hurricanes, one must implicitly assume that an approaching hurricane is similar to the average of the past hurricanes. The model developed in this paper replaces these indicator variables with physically measurable variables, enabling future predictions to be based on only well-understood characteristics of hurricanes. The models were developed using data about power outages during nine hurricanes in three states served by a large, investor-owned utility company in the Gulf Coast region

  13. Significant Wave Height under Hurricane Irma derived from SAR Sentinel-1 Data

    Science.gov (United States)

    Lehner, S.; Pleskachevsky, A.; Soloviev, A.; Fujimura, A.

    2017-12-01

    The 2017 Atlantic hurricane season was with three major hurricanes a particular active one. The Category 4 hurricane Irma made landfall on the Florida Keys on September 10th 2017 and was imaged several times by ESAs Sentinel-1 satellites in C-band and the TerraSAR-X satellite in X-band. The high resolution TerraSAR-X imagery showed the footprint of individual tornadoes on the sea surface together with their turbulent wake imaged as a dark line due to increased turbulence. The water-cloud structures of the tornadoes are analyzed and their sea surface structure is compared to optical and IR cloud imagery. An estimate of the wind field using standard XMOD algorithms is provided, although saturating under the strong rain and high wind speed conditions. Imaging the hurricanes by space radar gives the opportunity to observe the sea surface and thus measure the wind field and the sea state under hurricane conditions through the clouds even in this severe weather, although rain features, which are usually not observed in SAR become visible due to damping effects. The Copernicus Sentinel-1 A and B satellites, which are operating in C-band provided several images of the sea surface under hurricane Irma, Jose and Maria. The data were acquired daily and converted into measurements of sea surface wind field u10 and significant wave height Hs over a swath width of 280km about 1000 km along the orbit. The wind field of the hurricanes as derived by CMOD is provided by NOAA operationally on their web server. In the hurricane cases though the wind speed saturates at 20 m/sec and is thus too low in the area of hurricane wind speed. The technique to derive significant wave height is new though and does not show any calibration issues. This technique provides for the first time measurements of the areal coverage and distribution of the ocean wave height as caused by a hurricane on SAR wide swath images. Wave heights up to 10 m were measured under the forward quadrant of the hurricane

  14. Science and the storms: The USGS response to the hurricanes of 2005

    Science.gov (United States)

    Farris, G. S.; Smith, G.J.; Crane, M.P.; Demas, C.R.; Robbins, L.L.; Lavoie, D.L.

    2007-01-01

    This report is designed to give a view of the immediate response of the U.S. Geological Survey (USGS) to four major hurricanes of 2005: Dennis, Katrina, Rita, and Wilma. Some of this response took place days after the hurricanes; other responses included fieldwork and analysis through the spring. While hurricane science continues within the USGS, this overview of work following these hurricanes reveals how a Department of the Interior bureau quickly brought together a diverse array of its scientists and technologies to assess and analyze many hurricane effects. Topics vary from flooding and water quality to landscape and ecosystem impacts, from geotechnical reconnaissance to analyzing the collapse of bridges and estimating the volume of debris. Thus, the purpose of this report is to inform the American people of the USGS science that is available and ongoing in regard to hurricanes. It is the hope that such science will help inform the decisions of those citizens and officials tasked with coastal restoration and planning for future hurricanes. Chapter 1 is an essay establishing the need for science in building a resilient coast. The second chapter includes some hurricane facts that provide hurricane terminology, history, and maps of the four hurricanes’ paths. Chapters that follow give the scientific response of USGS to the storms. Both English and metric measurements are used in the articles in anticipation of both general and scientific audiences in the United States and elsewhere. Chapter 8 is a compilation of relevant ongoing and future hurricane work. The epilogue marks the 2-year anniversary of Hurricane Katrina. An index of authors follows the report to aid in finding articles that are cross-referenced within the report. In addition to performing the science needed to understand the effects of hurricanes, USGS employees helped in the rescue of citizens by boat and through technology by “geoaddressing” 911 calls after Katrina and Rita so that other

  15. Post-hurricane forest damage assessment using satellite remote sensing

    Science.gov (United States)

    W. Wang; J.J. Qu; X. Hao; Y. Liu; J.A. Stanturf

    2010-01-01

    This study developed a rapid assessment algorithm for post-hurricane forest damage estimation using moderate resolution imaging spectroradiometer (MODIS) measurements. The performance of five commonly used vegetation indices as post-hurricane forest damage indicators was investigated through statistical analysis. The Normalized Difference Infrared Index (NDII) was...

  16. A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

    Directory of Open Access Journals (Sweden)

    Hongzhuan Zhao

    2016-04-01

    Full Text Available With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS. Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

  17. Device for forecasting reactor power-up routes

    International Nuclear Information System (INIS)

    Fukuzaki, Takaharu.

    1980-01-01

    Purpose: To improve the reliability and forecasting accuracy for a device forecasting the change of the state on line in BWR type reactors. Constitution: The present state in a nuclear reactor is estimated in a present state judging section based on measuring signals for thermal power, core flow rate, control rod density and the like from the nuclear reactor, and the estimated results are accumulated in an operation result collecting section. While on the other hand, a forecasting section forecasts the future state in the reactor based on the signals from the forecasting condition setting section. The actual result values from the collecting section and the forecasting results are compared to each other. If they are not equal, new setting signals are outputted from the setting section to perform the forecasting again. These procedures are repeated till the difference between the forecast results and the actual result values is minimized, by which accurate forecasting for the state of the reactor is made possible. (Furukawa, Y.)

  18. A Climatological Study of Hurricane Force Extratropical Cyclones

    Science.gov (United States)

    2012-03-01

    extratropical cyclone by months in the Pacific basin. Most of the storms occur from October through March...hurricane force extratropical cyclone. Starting from left to right; the first column is the storm name, second column is the year, month, day, hour (UTC...2000 through 2007 illustrates that the number of hurricane-force extratropical cyclones is quite significant: approximately 500 storms , nearly evenly

  19. Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

    Directory of Open Access Journals (Sweden)

    S. W. D. Turner

    2017-09-01

    Full Text Available Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

  20. Evacuation Shelters - MDC_HurricaneShelter

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — A label feature class of Miami-Dade County Hurricane Evacuation Shelters (HEC) including Special Need Evacuation Centers (SNEC) and Medical Management Facilities...

  1. Psychological distress of adolescents exposed to Hurricane Hugo.

    Science.gov (United States)

    Hardin, S B; Weinrich, M; Weinrich, S; Hardin, T L; Garrison, C

    1994-07-01

    To ascertain the effects of a natural disaster on adolescents, 1482 South Carolina high school students who were exposed to Hurricane Hugo were surveyed 1 year after the disaster. Subjects completed a self-administered questionnaire measuring Hugo exposure, nonviolent and violent life events, social support, self-efficacy, and psychological distress. Results showed that the students reported minimal exposure to the hurricane and psychological distress variables approximated national norms. As exposure increased, adolescents reported increased symptoms of psychological distress; i.e., anger, depression, anxiety, and global mental distress. Females and white students experienced higher levels of distress. In most cases, other stressful life events were at least as strong a predictor of psychological distress as was exposure to the hurricane. Self-efficacy and social support were protective.

  2. Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

    Energy Technology Data Exchange (ETDEWEB)

    Hoff, Thomas Hoff [Clean Power Research, L.L.C., Napa, CA (United States); Kankiewicz, Adam [Clean Power Research, L.L.C., Napa, CA (United States)

    2016-02-26

    Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP) forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest

  3. How uncertain are day-ahead wind forecasts?

    Energy Technology Data Exchange (ETDEWEB)

    Grimit, E. [3TIER Environmental Forecast Group, Seattle, WA (United States)

    2006-07-01

    Recent advances in the combination of weather forecast ensembles with Bayesian statistical techniques have helped to address uncertainties in wind forecasting. Weather forecast ensembles are a collection of numerical weather predictions. The combination of several equally-skilled forecasts typically results in a consensus forecast with greater accuracy. The distribution of forecasts also provides an estimate of forecast inaccuracy. However, weather forecast ensembles tend to be under-dispersive, and not all forecast uncertainties can be taken into account. In order to address these issues, a multi-variate linear regression approach was used to correct the forecast bias for each ensemble member separately. Bayesian model averaging was used to provide a predictive probability density function to allow for multi-modal probability distributions. A test location in eastern Canada was used to demonstrate the approach. Results of the test showed that the method improved wind forecasts and generated reliable prediction intervals. Prediction intervals were much shorter than comparable intervals based on a single forecast or on historical observations alone. It was concluded that the approach will provide economic benefits to both wind energy developers and investors. refs., tabs., figs.

  4. An Observational Study of Tropical Cyclone Spin-Up in Supertyphoon Jangmi and Hurricane Georges

    Science.gov (United States)

    2011-12-01

    Marks et al. (2008) flight level and radar observations from Hurricane Hugo shown in Figure 9 (their Figure 3) and Hurricane Isabel (Montgomery et al...Figure 3c and Figure 6c) and Persing and Montgomery (2003, their Figures 8, 9, and 12). For the case of Hurricane Hugo , a cross-section of the... Hurricane Hugo (1989). Mon. Wea. Rev., 136, 1237–1259. McTaggart-Cowan, R., L. F. Bosart, J. R. Gyakum, and E. H. Atallah, 2007: Hurricane Katrina

  5. Performance assessment of topologically diverse power systems subjected to hurricane events

    International Nuclear Information System (INIS)

    Winkler, James; Duenas-Osorio, Leonardo; Stein, Robert; Subramanian, Devika

    2010-01-01

    Large tropical cyclones cause severe damage to major cities along the United States Gulf Coast annually. A diverse collection of engineering and statistical models are currently used to estimate the geographical distribution of power outage probabilities stemming from these hurricanes to aid in storm preparedness and recovery efforts. Graph theoretic studies of power networks have separately attempted to link abstract network topology to transmission and distribution system reliability. However, few works have employed both techniques to unravel the intimate connection between network damage arising from storms, topology, and system reliability. This investigation presents a new methodology combining hurricane damage predictions and topological assessment to characterize the impact of hurricanes upon power system reliability. Component fragility models are applied to predict failure probability for individual transmission and distribution power network elements simultaneously. The damage model is calibrated using power network component failure data for Harris County, TX, USA caused by Hurricane Ike in September of 2008, resulting in a mean outage prediction error of 15.59% and low standard deviation. Simulated hurricane events are then applied to measure the hurricane reliability of three topologically distinct transmission networks. The rate of system performance decline is shown to depend on their topological structure. Reliability is found to correlate directly with topological features, such as network meshedness, centrality, and clustering, and the compact irregular ring mesh topology is identified as particularly favorable, which can influence regional lifeline policy for retrofit and hardening activities to withstand hurricane events.

  6. Forecasting Hong Kong economy using factor augmented vector autoregression

    OpenAIRE

    Pang, Iris Ai Jao

    2010-01-01

    This work applies the FAVAR model to forecast GDP growth rate, unemployment rate and inflation rate of the Hong Kong economy. There is no factor model forecasting literature on the Hong Kong economy. The objective is to find out whether factor forecasting of using a large dataset can improve forecast performance of the Hong Kong economy. To avoid misspecification of the number of factors in the FAVAR, combination forecasts are constructed. It is found that forecasts from FAVAR model overall o...

  7. Adaptive time-variant models for fuzzy-time-series forecasting.

    Science.gov (United States)

    Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

    2010-12-01

    A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

  8. Resilience of Professional Counselors Following Hurricanes Katrina and Rita

    Science.gov (United States)

    Lambert, Simone F.; Lawson, Gerard

    2013-01-01

    Professional counselors who provided services to those affected by Hurricanes Katrina and Rita completed the K6+ (screen for severe mental illness), the Posttraumatic Growth Inventory, and the Professional Quality of Life Scale. Results indicated that participants who survived the hurricanes had higher levels of posttraumatic growth than…

  9. Litterfall Production Prior to and during Hurricanes Irma and Maria in Four Puerto Rican Forests

    Directory of Open Access Journals (Sweden)

    Xianbin Liu

    2018-06-01

    Full Text Available Hurricanes Irma and Maria struck Puerto Rico on the 6th and 20th of September 2017, respectively. These two powerful Cat 5 hurricanes severely defoliated forest canopy and deposited massive amounts of litterfall in the forests across the island. We established a 1-ha research plot in each of four forests (Guánica State Forest, Río Abajo State Forest, Guayama Research Area and Luquillo Experiment Forest before September 2016, and had collected one full year data of litterfall production prior to the arrival of Hurricanes Irma and Maria. Hurricane-induced litterfall was collected within one week after Hurricane Irma, and within two weeks after Hurricane Maria. Each litterfall sample was sorted into leaves, wood (branches and barks, reproductive organs (flowers, fruits and seeds and miscellaneous materials (mostly dead animal bodies or feces after oven-drying to constant weight. Annual litterfall production prior to the arrival of Hurricanes Irma and Maria varied from 4.68 to 25.41 Mg/ha/year among the four forests, and annual litterfall consisted of 50–81% leaffall, 16–44% woodfall and 3–6% fallen reproductive organs. Hurricane Irma severely defoliated the Luquillo Experimental Forest, but had little effect on the other three forests, whereas Hurricane Maria defoliated all four forests. Total hurricane-induced litterfall from Hurricanes Irma and Maria amounted to 95–171% of the annual litterfall production, with leaffall and woodfall from hurricanes amounting to 63–88% and 122–763% of their corresponding annual leaffall and woodfall, respectively. Hurricane-induced litterfall consisted of 30–45% leaves and 55–70% wood. Our data showed that Hurricanes Irma and Maria deposited a pulse of litter deposition equivalent to or more than the total annual litterfall input with at least a doubled fraction of woody materials. This pulse of hurricane-induced debris and elevated proportion of woody component may trigger changes in

  10. Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

    Science.gov (United States)

    Manzanas, R.; Lucero, A.; Weisheimer, A.; Gutiérrez, J. M.

    2018-02-01

    Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.

  11. Race differences in depression vulnerability following Hurricane Katrina.

    Science.gov (United States)

    Ali, Jeanelle S; Farrell, Amy S; Alexander, Adam C; Forde, David R; Stockton, Michelle; Ward, Kenneth D

    2017-05-01

    This study investigated whether racial disparities in depression were present after Hurricane Katrina. Data were gathered from 932 New Orleans residents who were present when Hurricane Katrina struck, and who returned to New Orleans the following year. Multiple logistic regression models evaluated racial differences in screening positive for depression (a score ≥16 on the Center for Epidemiologic Studies Depression Scale), and explored whether differential vulnerability (prehurricane physical and mental health functioning and education level), differential exposure to hurricane-related stressors, and loss of social support moderated and/or reduced the association of race with depression. A univariate logistic regression analysis showed the odds for screening positive for depression were 86% higher for African Americans than for Caucasians (odds ratio [OR] = 1.86 [1.28-2.71], p = .0012). However, after controlling simultaneously for sociodemographic characteristics, preexisting vulnerabilities, social support, and trauma-specific factors, race was no longer a significant correlate for screening positive for depression (OR = 1.54 [0.95-2.48], p = .0771). The racial disparity in postdisaster depression seems to be confounded by sociodemographic characteristics, preexisting vulnerabilities, social support, and trauma-specific factors. Nonetheless, even after adjusting for these factors, there was a nonsignificant trend effect for race, which could suggest race played an important role in depression outcomes following Hurricane Katrina. Future studies should examine these associations prospectively, using stronger assessments for depression, and incorporate measures for discrimination and segregation, to further understand possible racial disparities in depression after Hurricane Katrina. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  12. The effects of hurricanes on birds, with special reference to Caribbean islands

    Science.gov (United States)

    Wiley, J.W.; Wunderle, J.M.

    1993-01-01

    Cyclonic storms, variously called typhoons, cyclones, or hurricanes (henceforth, hurricanes), are common in many parts of the world, where their frequent occurrence can have both direct and indirect effects on bird populations. Direct effects of hurricanes include mortality from exposure to hurricane winds, rains, and storm surges, and geographic displacement of individuals by storm winds. Indirect effects become apparent in the storm's aftermath and include loss of food supplies or foraging substrates; loss of nests and nest or roost sites; increased vulnerability to predation; microclimate changes; and increased conflict with humans. The short-term response of bird populations to hurricane damage, before changes in plant succession, includes shifts in diet, foraging sites or habitats, and reproductive changes. Bird populations may show long-term responses to changes in plant succession as second-growth vegetation increases in storm-damaged old-growth forests. The greatest stress of a hurricane to most upland terrestrial bird populations occurs after its passage rather than during its impact. The most important effect of a hurricane is the destruction of vegetation, which secondarily affects wildlife in the storm's aftermath. The most vulnerable terrestrial wildlife populations have a diet of nectar, fruit, or seeds; nest, roost, or forage on large old trees; require a closed forest canopy; have special microclimate requirements and/or live in a habitat in which vegetation has a slow recovery rate. Small populations with these traits are at greatest risk to hurricane-induced extinction, particularly if they exist in small isolated habitat fragments. Recovery of avian populations from hurricane effects is partially dependent on the extent and degree of vegetation damage as well as its rate of recovery. Also, the reproductive rate of the remnant local population and recruitment from undisturbed habitat patches influence the rate at which wildlife populations recover

  13. Avifauna response to hurricanes: regional changes in community similarity

    Science.gov (United States)

    Chadwick D. Rittenhouse; Anna M. Pidgeon; Thomas P. Albright; Patrick D. Culbert; Murray K. Clayton; Curtis H. Flather; Chengquan Huang; Jeffrey G. Masek; Volker C. Radeloff

    2010-01-01

    Global climate models predict increases in the frequency and intensity of extreme climatic events such as hurricanes, which may abruptly alter ecological processes in forests and thus affect avian diversity. Developing appropriate conservation measures necessitates identifying patterns of avifauna response to hurricanes. We sought to answer two questions: (1) does...

  14. On the molecular dynamics in the hurricane interactions with its environment

    Science.gov (United States)

    Meyer, Gabriel; Vitiello, Giuseppe

    2018-06-01

    By resorting to the Burgers model for hurricanes, we study the molecular motion involved in the hurricane dynamics. We show that the Lagrangian canonical formalism requires the inclusion of the environment degrees of freedom. This also allows the description of the motion of charged particles. In view of the role played by moist convection, cumulus and cloud water droplets in the hurricane dynamics, we discuss on the basis of symmetry considerations the role played by the molecular electrical dipoles and the formation of topologically non-trivial structures. The mechanism of energy storage and dissipation, the non-stationary time dependent Ginzburg-Landau equation and the vortex equation are studied. Finally, we discuss the fractal self-similarity properties of hurricanes.

  15. Study on network traffic forecast model of SVR optimized by GAFSA

    International Nuclear Information System (INIS)

    Liu, Yuan; Wang, RuiXue

    2016-01-01

    There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.

  16. Longitudinal Impact of Hurricane Sandy Exposure on Mental Health Symptoms

    Directory of Open Access Journals (Sweden)

    Rebecca M. Schwartz

    2017-08-01

    Full Text Available Hurricane Sandy hit the eastern coast of the United States in October 2012, causing billions of dollars in damage and acute physical and mental health problems. The long-term mental health consequences of the storm and their predictors have not been studied. New York City and Long Island residents completed questionnaires regarding their initial Hurricane Sandy exposure and mental health symptoms at baseline and 1 year later (N = 130. There were statistically significant decreases in anxiety scores (mean difference = −0.33, p < 0.01 and post-traumatic stress disorder (PTSD scores (mean difference = −1.98, p = 0.001 between baseline and follow-up. Experiencing a combination of personal and property damage was positively associated with long-term PTSD symptoms (ORadj 1.2, 95% CI [1.1–1.4] but not with anxiety or depression. Having anxiety, depression, or PTSD at baseline was a significant predictor of persistent anxiety (ORadj 2.8 95% CI [1.1–6.8], depression (ORadj 7.4 95% CI [2.3–24.1 and PTSD (ORadj 4.1 95% CI [1.1–14.6] at follow-up. Exposure to Hurricane Sandy has an impact on PTSD symptoms that persists over time. Given the likelihood of more frequent and intense hurricanes due to climate change, future hurricane recovery efforts must consider the long-term effects of hurricane exposure on mental health, especially on PTSD, when providing appropriate assistance and treatment.

  17. High Resolution Hurricane Storm Surge and Inundation Modeling (Invited)

    Science.gov (United States)

    Luettich, R.; Westerink, J. J.

    2010-12-01

    Coastal counties are home to nearly 60% of the U.S. population and industry that accounts for over 16 million jobs and 10% of the U.S. annual gross domestic product. However, these areas are susceptible to some of the most destructive forces in nature, including tsunamis, floods, and severe storm-related hazards. Since 1900, tropical cyclones making landfall on the US Gulf of Mexico Coast have caused more than 9,000 deaths; nearly 2,000 deaths have occurred during the past half century. Tropical cyclone-related adjusted, annualized losses in the US have risen from 1.3 billion from 1949-1989, to 10.1 billion from 1990-1995, and $35.8 billion per year for the period 2001-2005. The risk associated with living and doing business in the coastal areas that are most susceptible to tropical cyclones is exacerbated by rising sea level and changes in the characteristics of severe storms associated with global climate change. In the five years since hurricane Katrina devastated the northern Gulf of Mexico Coast, considerable progress has been made in the development and utilization of high resolution coupled storm surge and wave models. Recent progress will be presented with the ADCIRC + SWAN storm surge and wave models. These tightly coupled models use a common unstructured grid in the horizontal that is capable of covering large areas while also providing high resolution (i.e., base resolution down to 20m plus smaller subgrid scale features such as sea walls and levees) in areas that are subject to surge and inundation. Hydrodynamic friction and overland winds are adjusted to account for local land cover. The models scale extremely well on modern high performance computers allowing rapid turnaround on large numbers of compute cores. The models have been adopted for FEMA National Flood Insurance Program studies, hurricane protection system design and risk analysis, and quasi-operational forecast systems for several regions of the country. They are also being evaluated as

  18. Impact of AIRS Thermodynamic Profile on Regional Weather Forecast

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Brad; Jedlovee, Gary

    2010-01-01

    Prudent assimilation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. AIRS-enhanced analysis has warmer and moister PBL. Forecasts with AIRS profiles are generally closer to NAM analyses than CNTL. Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecasts. Including AIRS profiles in assimilation process enhances the moist instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.

  19. Better Forecasting for Better Planning: A Systems Approach.

    Science.gov (United States)

    Austin, W. Burnet

    Predictions and forecasts are the most critical features of rational planning as well as the most vulnerable to inaccuracy. Because plans are only as good as their forecasts, current planning procedures could be improved by greater forecasting accuracy. Economic factors explain and predict more than any other set of factors, making economic…

  20. Using constructed analogs to improve the skill of National Multi-Model Ensemble March–April–May precipitation forecasts in equatorial East Africa

    International Nuclear Information System (INIS)

    Shukla, Shraddhanand; Funk, Christopher; Hoell, Andrew

    2014-01-01

    In this study we implement and evaluate a simple ‘hybrid’ forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble’s (NMME) March–April–May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2°S to 8°N and 36°E to 46°E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The ‘hybrid approach’ described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30°S to 30°N and 30°E to 270°E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45. (letter)

  1. Forecasting interest rates with shifting endpoints

    DEFF Research Database (Denmark)

    Van Dijk, Dick; Koopman, Siem Jan; Wel, Michel van der

    2014-01-01

    We consider forecasting the term structure of interest rates with the assumption that factors driving the yield curve are stationary around a slowly time-varying mean or ‘shifting endpoint’. The shifting endpoints are captured using either (i) time series methods (exponential smoothing) or (ii......) long-range survey forecasts of either interest rates or inflation and output growth, or (iii) exponentially smoothed realizations of these macro variables. Allowing for shifting endpoints in yield curve factors provides substantial and significant gains in out-of-sample predictive accuracy, relative...... to stationary and random walk benchmarks. Forecast improvements are largest for long-maturity interest rates and for long-horizon forecasts....

  2. Verification of ECMWF and ECMWF/MACC's global and direct irradiance forecasts with respect to solar electricity production forecasts

    Directory of Open Access Journals (Sweden)

    M. Schroedter-Homscheidt

    2017-02-01

    Full Text Available The successful electricity grid integration of solar energy into day-ahead markets requires at least hourly resolved 48 h forecasts. Technologies as photovoltaics and non-concentrating solar thermal technologies make use of global horizontal irradiance (GHI forecasts, while all concentrating technologies both from the photovoltaic and the thermal sector require direct normal irradiances (DNI. The European Centre for Medium-Range Weather Forecasts (ECMWF has recently changed towards providing direct as well as global irradiances. Additionally, the MACC (Monitoring Atmospheric Composition & Climate near-real time services provide daily analysis and forecasts of aerosol properties in preparation of the upcoming European Copernicus programme. The operational ECMWF/IFS (Integrated Forecast System forecast system will in the medium term profit from the Copernicus service aerosol forecasts. Therefore, within the MACC‑II project specific experiment runs were performed allowing for the assessment of the performance gain of these potential future capabilities. Also the potential impact of providing forecasts with hourly output resolution compared to three-hourly resolved forecasts is investigated. The inclusion of the new aerosol climatology in October 2003 improved both the GHI and DNI forecasts remarkably, while the change towards a new radiation scheme in 2007 only had minor and partly even unfavourable impacts on the performance indicators. For GHI, larger RMSE (root mean square error values are found for broken/overcast conditions than for scattered cloud fields. For DNI, the findings are opposite with larger RMSE values for scattered clouds compared to overcast/broken cloud situations. The introduction of direct irradiances as an output parameter in the operational IFS version has not resulted in a general performance improvement with respect to biases and RMSE compared to the widely used Skartveit et al. (1998 global to direct irradiance

  3. Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA

    Energy Technology Data Exchange (ETDEWEB)

    Hou, Zhangshuan; Etingov, Pavel V.; Makarov, Yuri V.; Samaan, Nader A.

    2014-10-27

    In this paper, we introduce a new approach without implying normal distributions and stationarity of power generation forecast errors. In addition, it is desired to more accurately quantify the forecast uncertainty by reducing prediction intervals of forecasts. We use automatically coupled wavelet transform and autoregressive integrated moving-average (ARIMA) forecasting to reflect multi-scale variability of forecast errors. The proposed analysis reveals slow-changing “quasi-deterministic” components of forecast errors. This helps improve forecasts produced by other means, e.g., using weather-based models, and reduce forecast errors prediction intervals.

  4. Hurricane Harvey Report: A fact-finding effort in the direct aftermath of Hurricane Harvey in the Greater Houston Region

    OpenAIRE

    Sebastian, A.G.; Lendering, K.T.; Kothuis, B.L.M.; Brand, A.D.; Jonkman, S.N.; van Gelder, P.H.A.J.M.; Kolen, B.; Comes, M.; Lhermitte, S.L.M.; Meesters, K.J.M.G.; van de Walle, B.A.; Ebrahimi Fard, A.; Cunningham, S.; Khakzad Rostami, N.; Nespeca, V.

    2017-01-01

    On August 25, 2017, Hurricane Harvey made landfall near Rockport, Texas as a Category 4 hurricane with maximum sustained winds of approximately 200 km/hour. Harvey caused severe damages in coastal Texas due to extreme winds and storm surge, but will go down in history for record-setting rainfall totals and flood-related damages. Across large portions of southeast Texas, rainfall totals during the six-day period between August 25 and 31, 2017 were amongst the highest ever recorded, causing flo...

  5. Application of seasonal forecasting for the drought forecasting in Catalonia (Spain)

    Science.gov (United States)

    Llasat, Maria-Carmen; Zaragoza, Albert; Aznar, Blanca; Cabot, Jordi

    2010-05-01

    Low flows and droughts are a hydro-climatic feature in Spain (Alvarez et al, 2008). The construction of dams as water reservoirs has been a usual tool to manage the water resources for agriculture and livestock, industries and human needs (MIMAM, 2000, 2007). The last drought that has affected Spain has last four years in Catalonia, from 2004 to the spring of 2008, and it has been particularly hard as a consequence of the precipitation deficit in the upper part of the rivers that nourish the main dams. This problem increases when the water scarcity affects very populated areas, like big cities. The Barcelona city, with more than 3.000.000 people concentrated in the downtown and surrounding areas is a clear example. One of the objectives of the SOSTAQUA project is to improve the water resources management in real time, in order to improve the water supply in the cities in the framework of sustainable development. The work presented here deals with the application of seasonal forecasting to improve the water management in Catalonia, particularly in drought conditions. A seasonal prediction index has been created as a linear combination of climatic data and the ECM4 prediction that has been validated too. This information has implemented into a hydrological model and it has been applied to the last drought considering the real water demands of population, as well as to the water storage evolution in the last months. It has been found a considerable advance in the forecasting of water volume into reservoirs. The advantage of this methodology is that it only requires seasonal forecasting free through internet. Due to the fact that the principal rivers that supply water to Barcelona, birth on the Pyrenees and Pre-Pyrenees region, the analysis and precipitation forecasting is focused on this region (Zaragoza, 2008).

  6. Hurricane Satellite (HURSAT) Microwave (MW)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Hurricane Satellite (HURSAT) from Microwave (MW) observations of tropical cyclones worldwide data consist of raw satellite observations. The data derive from the...

  7. Contrasting Hydrodynamic and Environmental Effects of Hurricanes Harvey and Ike in a Highly Industrialized Estuary

    Science.gov (United States)

    Kiaghadi, A.; Rifai, H. S.

    2017-12-01

    It is commonly believed that storm surge is the most destructive aspect of hurricanes. However, massive rainfall with a return period of 100 years or more induced by hurricanes can cause more catastrophic damage than losses caused by storm surge as demonstrated recently by hurricanes Harvey, Irma and Maria. In this study the hydrodynamics and environmental effects of hurricanes Ike and Harvey were compared and contrasted by linking hydrodynamic flow models with water quality models to simulate spills from storage tanks located in the Houston Ship Channel (HSC). Hurricane Ike with a maximum surge of 5.3 meters in Galveston Bay and Harvey with a maximum rainfall of 1.25 meters both struck the HSC region in Texas in 2008 and 2017, respectively. Both events resulted in numerous spills from municipal and industrial facilities, hazardous waste sites, superfund sites, and landfills. The Environmental Fluid Dynamic Code (EFDC) was coupled with the SWAN+ADCIRC hurricane simulation model to simulate Hurricane Ike and EFDC was coupled with USGS flow boundary conditions to model Hurricane Harvey. A conservative dye release was used to simulate a chemical release during each event. The results showed Hurricane Harvey caused higher water surface elevations within the HSC accompanied by longer and wider-spread land inundation. In contrast, higher water surface elevations were observed within the shallow side bays during Hurricane Ike that caused sediment resuspension and repartitioning of pollutants. Rapid spill mass transportation was observed for both hurricanes; 50% of total spill mass reached Galveston Bay in 20 and 22 hours after a spill event for Hurricane Harvey and Ike, respectively, and more than 90% of the spill mass reached the bay in 36 and 48 hours, respectively. Unlike Hurricane Harvey, the conservative tracer was spread almost 2.5 km upstream of the releasing point for Hurricane Ike due to surge. However, during Harvey, 35% more land was affected by the spilled

  8. Predicting the Texas Windstorm Insurance Association claim payout of commercial buildings from Hurricane Ike

    Science.gov (United States)

    Kim, J. M.; Woods, P. K.; Park, Y. J.; Son, K.

    2013-08-01

    Following growing public awareness of the danger from hurricanes and tremendous demands for analysis of loss, many researchers have conducted studies to develop hurricane damage analysis methods. Although researchers have identified the significant indicators, there currently is no comprehensive research for identifying the relationship among the vulnerabilities, natural disasters, and economic losses associated with individual buildings. To address this lack of research, this study will identify vulnerabilities and hurricane indicators, develop metrics to measure the influence of economic losses from hurricanes, and visualize the spatial distribution of vulnerability to evaluate overall hurricane damage. This paper has utilized the Geographic Information System to facilitate collecting and managing data, and has combined vulnerability factors to assess the financial losses suffered by Texas coastal counties. A multiple linear regression method has been applied to develop hurricane economic damage predicting models. To reflect the pecuniary loss, insured loss payment was used as the dependent variable to predict the actual financial damage. Geographical vulnerability indicators, built environment vulnerability indicators, and hurricane indicators were all used as independent variables. Accordingly, the models and findings may possibly provide vital references for government agencies, emergency planners, and insurance companies hoping to predict hurricane damage.

  9. Empirical seasonal forecasts of the NAO

    Science.gov (United States)

    Sanchezgomez, E.; Ortizbevia, M.

    2003-04-01

    We present here seasonal forecasts of the North Atlantic Oscillation (NAO) issued from ocean predictors with an empirical procedure. The Singular Values Decomposition (SVD) of the cross-correlation matrix between predictor and predictand fields at the lag used for the forecast lead is at the core of the empirical model. The main predictor field are sea surface temperature anomalies, although sea ice cover anomalies are also used. Forecasts are issued in probabilistic form. The model is an improvement over a previous version (1), where Sea Level Pressure Anomalies were first forecast, and the NAO Index built from this forecast field. Both correlation skill between forecast and observed field, and number of forecasts that hit the correct NAO sign, are used to assess the forecast performance , usually above those values found in the case of forecasts issued assuming persistence. For certain seasons and/or leads, values of the skill are above the .7 usefulness treshold. References (1) SanchezGomez, E. and Ortiz Bevia M., 2002, Estimacion de la evolucion pluviometrica de la Espana Seca atendiendo a diversos pronosticos empiricos de la NAO, in 'El Agua y el Clima', Publicaciones de la AEC, Serie A, N 3, pp 63-73, Palma de Mallorca, Spain

  10. Monthly forecasting of agricultural pests in Switzerland

    Science.gov (United States)

    Hirschi, M.; Dubrovsky, M.; Spirig, C.; Samietz, J.; Calanca, P.; Weigel, A. P.; Fischer, A. M.; Rotach, M. W.

    2012-04-01

    Given the repercussions of pests and diseases on agricultural production, detailed forecasting tools have been developed to simulate the degree of infestation depending on actual weather conditions. The life cycle of pests is most successfully predicted if the micro-climate of the immediate environment (habitat) of the causative organisms can be simulated. Sub-seasonal pest forecasts therefore require weather information for the relevant habitats and the appropriate time scale. The pest forecasting system SOPRA (www.sopra.info) currently in operation in Switzerland relies on such detailed weather information, using hourly weather observations up to the day the forecast is issued, but only a climatology for the forecasting period. Here, we aim at improving the skill of SOPRA forecasts by transforming the weekly information provided by ECMWF monthly forecasts (MOFCs) into hourly weather series as required for the prediction of upcoming life phases of the codling moth, the major insect pest in apple orchards worldwide. Due to the probabilistic nature of operational monthly forecasts and the limited spatial and temporal resolution, their information needs to be post-processed for use in a pest model. In this study, we developed a statistical downscaling approach for MOFCs that includes the following steps: (i) application of a stochastic weather generator to generate a large pool of daily weather series consistent with the climate at a specific location, (ii) a subsequent re-sampling of weather series from this pool to optimally represent the evolution of the weekly MOFC anomalies, and (iii) a final extension to hourly weather series suitable for the pest forecasting model. Results show a clear improvement in the forecast skill of occurrences of upcoming codling moth life phases when incorporating MOFCs as compared to the operational pest forecasting system. This is true both in terms of root mean squared errors and of the continuous rank probability scores of the

  11. Skilful seasonal forecasts of streamflow over Europe?

    Science.gov (United States)

    Arnal, Louise; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; Prudhomme, Christel; Neumann, Jessica; Krzeminski, Blazej; Pappenberger, Florian

    2018-04-01

    This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate

  12. Sensitivity of monthly streamflow forecasts to the quality of rainfall forcing: When do dynamical climate forecasts outperform the Ensemble Streamflow Prediction (ESP) method?

    Science.gov (United States)

    Tanguy, M.; Prudhomme, C.; Harrigan, S.; Smith, K. A.; Parry, S.

    2017-12-01

    Forecasting hydrological extremes is challenging, especially at lead times over 1 month for catchments with limited hydrological memory and variable climates. One simple way to derive monthly or seasonal hydrological forecasts is to use historical climate data to drive hydrological models using the Ensemble Streamflow Prediction (ESP) method. This gives a range of possible future streamflow given known initial hydrologic conditions alone. The degree of skill of ESP depends highly on the forecast initialisation month and catchment type. Using dynamic rainfall forecasts as driving data instead of historical data could potentially improve streamflow predictions. A lot of effort is being invested within the meteorological community to improve these forecasts. However, while recent progress shows promise (e.g. NAO in winter), the skill of these forecasts at monthly to seasonal timescales is generally still limited, and the extent to which they might lead to improved hydrological forecasts is an area of active research. Additionally, these meteorological forecasts are currently being produced at 1 month or seasonal time-steps in the UK, whereas hydrological models require forcings at daily or sub-daily time-steps. Keeping in mind these limitations of available rainfall forecasts, the objectives of this study are to find out (i) how accurate monthly dynamical rainfall forecasts need to be to outperform ESP, and (ii) how the method used to disaggregate monthly rainfall forecasts into daily rainfall time series affects results. For the first objective, synthetic rainfall time series were created by increasingly degrading observed data (proxy for a `perfect forecast') from 0 % to +/-50 % error. For the second objective, three different methods were used to disaggregate monthly rainfall data into daily time series. These were used to force a simple lumped hydrological model (GR4J) to generate streamflow predictions at a one-month lead time for over 300 catchments

  13. Lessons Learnt From Hurricane Katrina.

    Science.gov (United States)

    Akundi, Murty

    2008-03-01

    Hurricane Katrina devastated New Orleans and its suburbs on Monday August 29^th, 2005. The previous Friday morning, August 26, the National Hurricane Center indicated that Katrina was a Category One Hurricane, which was expected to hit Florida. By Friday afternoon, it had changed its course, and neither the city nor Xavier University was prepared for this unexpected turn in the hurricane's path. The university had 6 to 7 ft of water in every building and Xavier was closed for four months. Students and university personnel that were unable to evacuate were trapped on campus and transportation out of the city became a logistical nightmare. Email and all electronic systems were unavailable for at least a month, and all cell phones with a 504 area code stopped working. For the Department, the most immediate problem was locating faculty and students. Xavier created a list of faculty and their new email addresses and began coordinating with faculty. Xavier created a web page with advice for students, and the chair of the department created a separate blog with contact information for students. The early lack of a clear method of communication made worse the confusion and dismay among the faculty on such issues as when the university would reopen, whether the faculty would be retained, whether they should seek temporary (or permanent) employment elsewhere, etc. With the vision and determination of President Dr. Francis, Xavier was able to reopen the university in January and ran a full academic year from January through August. Since Katrina, the university has asked every department and unit to prepare emergency preparedness plans. Each department has been asked to collect e-mail addresses (non-Xavier), cell phone numbers and out of town contact information. The University also established an emergency website to communicate. All faculty have been asked to prepare to teach classes electronically via Black board or the web. Questions remain about the longer term issues of

  14. Hurricane disturbance benefits nesting American Oystercatchers (Haematopus palliatus)

    Science.gov (United States)

    Simons, Theodore R.; Schulte, Shiloh A.

    2016-01-01

    Coastal ecosystems are under increasing pressure from human activity, introduced species, sea level rise, and storm activity. Hurricanes are a powerful destructive force, but can also renew coastal habitats. In 2003, Hurricane Isabel altered the barrier islands of North Carolina, flattening dunes and creating sand flats. American Oystercatchers (Haematopus palliatus) are large shorebirds that inhabit the coastal zone throughout the year. Alternative survival models were evaluated for 699 American Oystercatcher nests on North Core Banks and South Core Banks, North Carolina, USA, from 1999–2007. Nest survival on North Core Banks increased from 0.170 (SE = 0.002) to 0.772 (SE = 0.090) after the hurricane, with a carry-over effect lasting 2 years. A simple year effects model described nest survival on South Core Banks. Habitat had no effect on survival except when the overall rate of nest survival was at intermediate levels (0.300–0.600), when nests on open flats survived at a higher rate (0.600; SE = 0.112) than nests in dune habitat (0.243; SE = 0.094). Predator activity declined on North Core Banks after the hurricane and corresponded with an increase in nest survival. Periodic years with elevated nest survival may offset low annual productivity and contribute to the stability of American Oystercatcher populations.

  15. Mass Media Use by College Students during Hurricane Threat

    Science.gov (United States)

    Piotrowski, Chris

    2015-01-01

    There is a dearth of studies on how college students prepare for the threat of natural disasters. This study surveyed college students' preferences in mass media use prior to an approaching hurricane. The convenience sample (n = 76) were from a university located in the hurricane-prone area of the central Gulf of Mexico coast. Interestingly,…

  16. Influenza forecasting with Google Flu Trends.

    Science.gov (United States)

    Dugas, Andrea Freyer; Jalalpour, Mehdi; Gel, Yulia; Levin, Scott; Torcaso, Fred; Igusa, Takeru; Rothman, Richard E

    2013-01-01

    We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by

  17. Generic Hurricane Extreme Seas State

    DEFF Research Database (Denmark)

    Wehmeyer, Christof; Skourup, Jesper; Frigaard, Peter

    2012-01-01

    Extreme sea states, which the IEC 61400-3 (2008) standard requires for the ultimate limit state (ULS) analysis of offshore wind turbines are derived to establish the design basis for the conceptual layout of deep water floating offshore wind turbine foundations in hurricane affected areas....... Especially in the initial phase of floating foundation concept development, site specific metocean data are usually not available. As the areas of interest are furthermore not covered by any design standard, in terms of design sea states, generic and in engineering terms applicable environmental background...... data is required for a type specific conceptual design. ULS conditions for different return periods are developed, which can subsequently be applied in siteindependent analysis and conceptual design. Recordings provided by National Oceanic and Atmospheric Administration (NOAA), of hurricanes along...

  18. Call Forecasting for Inbound Call Center

    Directory of Open Access Journals (Sweden)

    Peter Vinje

    2009-01-01

    Full Text Available In a scenario of inbound call center customer service, the ability to forecast calls is a key element and advantage. By forecasting the correct number of calls a company can predict staffing needs, meet service level requirements, improve customer satisfaction, and benefit from many other optimizations. This project will show how elementary statistics can be used to predict calls for a specific company, forecast the rate at which calls are increasing/decreasing, and determine if the calls may stop at some point.

  19. Simulating Storm Surge Impacts with a Coupled Atmosphere-Inundation Model with Varying Meteorological Forcing

    Directory of Open Access Journals (Sweden)

    Alexandra N. Ramos Valle

    2018-04-01

    Full Text Available Storm surge events have the potential to cause devastating damage to coastal communities. The magnitude of their impacts highlights the need for increased accuracy and real-time forecasting and predictability of storm surge. In this study, we assess two meteorological forcing configurations to hindcast the storm surge of Hurricane Sandy, and ultimately support the improvement of storm surge forecasts. The Weather Research and Forecasting (WRF model is coupled to the ADvanced CIRCulation Model (ADCIRC to determine water elevations. We perform four coupled simulations and compare storm surge estimates resulting from the use of a parametric vortex model and a full-physics atmospheric model. One simulation is forced with track-based meteorological data calculated from WRF, while three simulations are forced with the full wind and pressure field outputs from WRF simulations of varying resolutions. Experiments were compared to an ADCIRC simulation forced by National Hurricane Center best track data, as well as to station observations. Our results indicated that given accurate meteorological best track data, a parametric vortex model can accurately forecast maximum water elevations, improving upon the use of a full-physics coupled atmospheric-surge model. In the absence of a best track, atmospheric forcing in the form of full wind and pressure field from a high-resolution atmospheric model simulation prove reliable for storm surge forecasting.

  20. Improving the health forecasting alert system for cold weather and heat-waves in England: a case-study approach using temperature-mortality relationships

    Science.gov (United States)

    Masato, Giacomo; Cavany, Sean; Charlton-Perez, Andrew; Dacre, Helen; Bone, Angie; Carmicheal, Katie; Murray, Virginia; Danker, Rutger; Neal, Rob; Sarran, Christophe

    2015-04-01

    The health forecasting alert system for cold weather and heatwaves currently in use in the Cold Weather and Heatwave plans for England is based on 5 alert levels, with levels 2 and 3 dependent on a forecast or actual single temperature action trigger. Epidemiological evidence indicates that for both heat and cold, the impact on human health is gradual, with worsening impact for more extreme temperatures. The 60% risk of heat and cold forecasts used by the alerts is a rather crude probabilistic measure, which could be substantially improved thanks to the state-of-the-art forecast techniques. In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office's (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. The prototype health forecasting alert system introduces an "impact vs likelihood matrix" for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. The prototype shows some clear improvements over the current alert system. It allows for a much greater

  1. Nonlinear analysis of the occurrence of hurricanes in the Gulf of Mexico and the Caribbean Sea

    Directory of Open Access Journals (Sweden)

    B. Rojo-Garibaldi

    2018-04-01

    Full Text Available Hurricanes are complex systems that carry large amounts of energy. Their impact often produces natural disasters involving the loss of human lives and materials, such as infrastructure, valued at billions of US dollars. However, not everything about hurricanes is negative, as hurricanes are the main source of rainwater for the regions where they develop. This study shows a nonlinear analysis of the time series of the occurrence of hurricanes in the Gulf of Mexico and the Caribbean Sea obtained from 1749 to 2012. The construction of the hurricane time series was carried out based on the hurricane database of the North Atlantic basin hurricane database (HURDAT and the published historical information. The hurricane time series provides a unique historical record on information about ocean–atmosphere interactions. The Lyapunov exponent indicated that the system presented chaotic dynamics, and the spectral analysis and nonlinear analyses of the time series of the hurricanes showed chaotic edge behavior. One possible explanation for this chaotic edge is the individual chaotic behavior of hurricanes, either by category or individually regardless of their category and their behavior on a regular basis.

  2. Nonlinear analysis of the occurrence of hurricanes in the Gulf of Mexico and the Caribbean Sea

    Science.gov (United States)

    Rojo-Garibaldi, Berenice; Salas-de-León, David Alberto; Adela Monreal-Gómez, María; Sánchez-Santillán, Norma Leticia; Salas-Monreal, David

    2018-04-01

    Hurricanes are complex systems that carry large amounts of energy. Their impact often produces natural disasters involving the loss of human lives and materials, such as infrastructure, valued at billions of US dollars. However, not everything about hurricanes is negative, as hurricanes are the main source of rainwater for the regions where they develop. This study shows a nonlinear analysis of the time series of the occurrence of hurricanes in the Gulf of Mexico and the Caribbean Sea obtained from 1749 to 2012. The construction of the hurricane time series was carried out based on the hurricane database of the North Atlantic basin hurricane database (HURDAT) and the published historical information. The hurricane time series provides a unique historical record on information about ocean-atmosphere interactions. The Lyapunov exponent indicated that the system presented chaotic dynamics, and the spectral analysis and nonlinear analyses of the time series of the hurricanes showed chaotic edge behavior. One possible explanation for this chaotic edge is the individual chaotic behavior of hurricanes, either by category or individually regardless of their category and their behavior on a regular basis.

  3. Two-sample Kalman filter and system error modelling for storm surge forecasting

    NARCIS (Netherlands)

    Sumihar, J.H.

    2009-01-01

    Two directions for improving the accuracy of sea level forecast are investigated in this study. The first direction seeks to improve the forecast accuracy of astronomical tide component. Here, a method is applied to analyze and forecast the remaining periodic components of harmonic analysis

  4. Using data envelopment analysis to evaluate the performance of post-hurricane electric power restoration activities

    International Nuclear Information System (INIS)

    Reilly, Allison C.; Davidson, Rachel A.; Nozick, Linda K.; Chen, Thomas; Guikema, Seth D.

    2016-01-01

    Post-hurricane restoration of electric power is attracting increasing scrutiny as customers’ tolerance for even short power interruptions decreases. At the peak, 8.5 million customers were without power after Hurricane Sandy and over 1 million customers were without power more than a week after the storm made landfall. Currently, restoration processes are typically evaluated on a case-by-case basis by a regional public service commission or similar body and lack systematic comparisons to other restoration experiences. This paper introduces a framework using data envelopment analysis to help evaluate post-hurricane restorations through comparison with the experiences of other companies in similar storms. The method accounts for the variable severity of the hurricanes themselves, so that companies are not penalized for outages that are long only because the hurricane that caused them was particularly severe. The analysis is illustrated through an application comparing 27 recent post-hurricane restoration experiences across 13 different electric power companies in the United States. The results of the study show some consistency in performance among individual utilities after the hurricanes they experience. The method could be applied to other types of infrastructure systems and other extreme events as well. - Highlights: • A Data Envelopment Analysis (DEA) framework is developed to compare post- hurricane power-outage restoration performance. • Hurricane severity is considered, so that utilities are not penalized for long outages caused by severe storms. • A case study using real data compares 27 recent post-hurricane restoration experiences. • The results of the study show utilities tend to perform consistently after the hurricanes they experience.

  5. Retrieving hurricane wind speeds using cross-polarization C-band measurements

    NARCIS (Netherlands)

    Van Zadelhoff, G.J.; Stoffelen, A.; Vachon, P.W.; Wolfe, J.; Horstmann, J.; Belmonte Rivas, M.

    2014-01-01

    Hurricane-force wind speeds can have a large societal impact and in this paper microwave C-band cross-polarized (VH) signals are investigated to assess if they can be used to derive extreme wind-speed conditions. European satellite scatterometers have excellent hurricane penetration capability at

  6. HURRICANE AND SEVERE STORM SENTINEL (HS3) FLIGHT REPORTS V1

    Data.gov (United States)

    National Aeronautics and Space Administration — The Hurricane and Severe Storm Sentinel (HS3) Flight Reports provide information about flights flown by the WB-57 and Global Hawk aircrafts during the Hurricane and...

  7. ECMWF seasonal forecast system 3 and its prediction of sea surface temperature

    Energy Technology Data Exchange (ETDEWEB)

    Stockdale, Timothy N.; Anderson, David L.T.; Balmaseda, Magdalena A.; Ferranti, Laura; Mogensen, Kristian; Palmer, Timothy N.; Molteni, Franco; Vitart, Frederic [ECMWF, Reading (United Kingdom); Doblas-Reyes, Francisco [ECMWF, Reading (United Kingdom); Institut Catala de Ciencies del Clima (IC3), Barcelona (Spain)

    2011-08-15

    The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1 year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3-6 months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean. (orig.)

  8. Analyzing after-action reports from Hurricanes Andrew and Katrina: repeated, modified, and newly created recommendations.

    Science.gov (United States)

    Knox, Claire Connolly

    2013-01-01

    Thirteen years after Hurricane Andrew struck Homestead, FL, Hurricane Katrina devastated the Gulf Coast of Mississippi, Alabama, and southeastern Louisiana. Along with all its destruction, the term "catastrophic" was redefined. This article extends the literature on these hurricanes by providing a macrolevel analysis of The Governor's Disaster Planning and Response Review Committee Final Report from Hurricane Andrew and three federal after-action reports from Hurricane Katrina, as well as a cursory review of relevant literature. Results provide evidence that previous lessons have not been learned or institutionalized with many recommendations being repeated or modified. This article concludes with a discussion of these lessons, as well as new issues arising during Hurricane Katrina.

  9. Coastal Sediment Distribution Patterns Following Category 5 Hurricanes (Irma and Maria): Pre and Post Hurricane High Resolution Multibeam Surveys of Eastern St. John, US Virgin Islands

    Science.gov (United States)

    Browning, T. N.; Sawyer, D. E.; Russell, P.

    2017-12-01

    In August of 2017 we collected high resolution multibeam data of the seafloor in a large embayment in eastern St. John, US Virgin Islands (USVI). One month later, the eyewall of Category 5 Hurricane Irma directly hit St. John as one of the largest hurricanes on record in the Atlantic Ocean. A week later, Category 5 Hurricane Maria passed over St. John. While the full extent of the impacts are still being assessed, the island experienced a severe loss of vegetation, infrastructure, buildings, roads, and boats. We mobilized less than two months afterward to conduct a repeat survey of the same area on St. John. We then compared these data to document and quantify the sediment influx and movement that occurred in coastal embayments as a result of Hurricanes Irma and Maria. The preliminary result of the intense rain, wind, and storm surge likely yields an event deposit that can be mapped and volumetrically quantified in the bays of eastern St. John. The results of this study allow for a detailed understanding of the post-hurricane pulse of sediment that enters the marine environment, the sediment flux seaward, and the morphological changes to the bay floor.

  10. Economic evaluation of short-term wind power forecast in ERCOT. Preliminary results

    Energy Technology Data Exchange (ETDEWEB)

    Orwig, Kirsten D.; Hodge, Bri-Mathias; Brinkman, Greg; Ela, Erik; Milligan, Michael [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Banunarayanan, Venkat; Nasir, Saleh [ICF International, Fairfax, VA (United States); Freedman, Jeff [AWS Truepower, Albany, NY (United States)

    2012-07-01

    A number of wind energy integration studies have investigated the monetary value of using day-ahead wind power forecasts for grid operation decisions. Historically, these studies have shown that large cost savings could be gained by grid operators implementing the forecasts in their system operations. To date, none of these studies have investigated the value of shorter term (0- to 6-h ahead) wind power forecasts. In 2010, the Department of Energy and the National Oceanic and Atmospheric Administration partnered to form the Wind Forecasting Improvement Project (WFIP) to fund improvements in short-term wind forecasts and determine the economic value of these improvements to grid operators. In this work, we discuss the preliminary results of the economic benefit analysis portion of the WFIP for the Electric Reliability Council of Texas. The improvements seen in the wind forecasts are examined and the economic results of a production cost model simulation are analyzed. (orig.)

  11. House Price Forecasts, Forecaster Herding, and the Recent Crisis

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Pierdzioch; Ruelke

    2013-01-01

    We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-)herding of forecasters. Forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) t......) the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time....

  12. Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

    Science.gov (United States)

    Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

    2009-04-01

    In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be

  13. Epidemic gasoline exposures following Hurricane Sandy.

    Science.gov (United States)

    Kim, Hong K; Takematsu, Mai; Biary, Rana; Williams, Nicholas; Hoffman, Robert S; Smith, Silas W

    2013-12-01

    Major adverse climatic events (MACEs) in heavily-populated areas can inflict severe damage to infrastructure, disrupting essential municipal and commercial services. Compromised health care delivery systems and limited utilities such as electricity, heating, potable water, sanitation, and housing, place populations in disaster areas at risk of toxic exposures. Hurricane Sandy made landfall on October 29, 2012 and caused severe infrastructure damage in heavily-populated areas. The prolonged electrical outage and damage to oil refineries caused a gasoline shortage and rationing unseen in the USA since the 1970s. This study explored gasoline exposures and clinical outcomes in the aftermath of Hurricane Sandy. Prospectively collected, regional poison control center (PCC) data regarding gasoline exposure cases from October 29, 2012 (hurricane landfall) through November 28, 2012 were reviewed and compared to the previous four years. The trends of gasoline exposures, exposure type, severity of clinical outcome, and hospital referral rates were assessed. Two-hundred and eighty-three gasoline exposures were identified, representing an 18 to 283-fold increase over the previous four years. The leading exposure route was siphoning (53.4%). Men comprised 83.0% of exposures; 91.9% were older than 20 years of age. Of 273 home-based calls, 88.7% were managed on site. Asymptomatic exposures occurred in 61.5% of the cases. However, minor and moderate toxic effects occurred in 12.4% and 3.5% of cases, respectively. Gastrointestinal (24.4%) and pulmonary (8.4%) symptoms predominated. No major outcomes or deaths were reported. Hurricane Sandy significantly increased gasoline exposures. While the majority of exposures were managed at home with minimum clinical toxicity, some patients experienced more severe symptoms. Disaster plans should incorporate public health messaging and regional PCCs for public health promotion and toxicological surveillance.

  14. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    International Nuclear Information System (INIS)

    Shafie-khah, M.; Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K.

    2011-01-01

    Research highlights: → A hybrid method is proposed to forecast the day-ahead prices in electricity market. → The method combines Wavelet-ARIMA and RBFN network models. → PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. → One of the merits of the proposed method is lower need to the input data. → The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  15. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    Energy Technology Data Exchange (ETDEWEB)

    Shafie-khah, M., E-mail: miadreza@gmail.co [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Moghaddam, M. Parsa, E-mail: parsa@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Sheikh-El-Eslami, M.K., E-mail: aleslam@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

    2011-05-15

    Research highlights: {yields} A hybrid method is proposed to forecast the day-ahead prices in electricity market. {yields} The method combines Wavelet-ARIMA and RBFN network models. {yields} PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. {yields} One of the merits of the proposed method is lower need to the input data. {yields} The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  16. Hurricanes and Climate: the U.S. CLIVAR Working Group on Hurricanes

    Science.gov (United States)

    Walsh, Kevin; Camargo, Suzana J.; Vecchi, Gabriel A.; Daloz, Anne Sophie; Elsner, James; Emanuel, Kerry; Horn, Michael; Lim, Young-Kwon; Roberts, Malcolm; Patricola, Christina; hide

    2015-01-01

    While a quantitative climate theory of tropical cyclone formation remains elusive, considerable progress has been made recently in our ability to simulate tropical cyclone climatologies and understand the relationship between climate and tropical cyclone formation. Climate models are now able to simulate a realistic rate of global tropical cyclone formation, although simulation of the Atlantic tropical cyclone climatology remains challenging unless horizontal resolutions finer than 50 km are employed. The idealized experiments of the Hurricane Working Group of U.S. CLIVAR, combined with results from other model simulations, have suggested relationships between tropical cyclone formation rates and climate variables such as mid-tropospheric vertical velocity. Systematic differences are shown between experiments in which only sea surface temperature is increases versus experiments where only atmospheric carbon dioxide is increased, with the carbon dioxide experiments more likely to demonstrate a decrease in numbers. Further experiments are proposed that may improve our understanding of the relationship between climate and tropical cyclone formation, including experiments with two-way interaction between the ocean and the atmosphere and variations in atmospheric aerosols.

  17. Long-term response of Caribbean palm forests to hurricanes

    Science.gov (United States)

    Ariel Lugo; J.L. Frangi

    2016-01-01

    We studied the response of Prestoea montana (Sierra Palm, hereafter Palm) brakes and a Palm floodplain forest to hurricanes in the Luquillo Experimental Forest in Puerto Rico. Over a span of 78 years, 3 hurricanes passed over the study sites for which we have 64 years of measurements for Palm brakes and 20 years for the Palm floodplain forest. For each stand, species...

  18. House Price Forecasts, Forecaster Herding, and the Recent Crisis

    Directory of Open Access Journals (Sweden)

    Christian Pierdzioch

    2012-11-01

    Full Text Available We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-herding of forecasters. Forecasts are consistent with herding (anti-herding of forecasters if forecasts are biased towards (away from the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time.

  19. Linking Science of Flood Forecasts to Humanitarian Actions for Improved Preparedness and Effective Response

    Science.gov (United States)

    Uprety, M.; Dugar, S.; Gautam, D.; Kanel, D.; Kshetri, M.; Kharbuja, R. G.; Acharya, S. H.

    2017-12-01

    Advances in flood forecasting have provided opportunities for humanitarian responders to employ a range of preparedness activities at different forecast time horizons. Yet, the science of prediction is less understood and realized across the humanitarian landscape, and often preparedness plans are based upon average level of flood risk. Working under the remit of Forecast Based Financing (FbF), we present a pilot from Nepal on how available flood and weather forecast products are informing specific pre-emptive actions in the local preparedness and response plans, thereby supporting government stakeholders and humanitarian agencies to take early actions before an impending flood event. In Nepal, forecasting capabilities are limited but in a state of positive flux. Whilst local flood forecasts based upon rainfall-runoff models are yet to be operationalized, streamflow predictions from Global Flood Awareness System (GLoFAS) can be utilized to plan and implement preparedness activities several days in advance. Likewise, 3-day rainfall forecasts from Nepal Department of Hydrology and Meteorology (DHM) can further inform specific set of early actions for potential flash floods due to heavy precipitation. Existing community based early warning systems in the major river basins of Nepal are utilizing real time monitoring of water levels and rainfall together with localised probabilistic flood forecasts which has increased warning lead time from 2-3 hours to 7-8 hours. Based on these available forecast products, thresholds and trigger levels have been determined for different flood scenarios. Matching these trigger levels and assigning responsibilities to relevant actors for early actions, a set of standard operating procedures (SOPs) are being developed, broadly covering general preparedness activities and science informed anticipatory actions for different forecast lead times followed by the immediate response activities. These SOPs are currently being rolled out and

  20. Automated time series forecasting for biosurveillance.

    Science.gov (United States)

    Burkom, Howard S; Murphy, Sean Patrick; Shmueli, Galit

    2007-09-30

    For robust detection performance, traditional control chart monitoring for biosurveillance is based on input data free of trends, day-of-week effects, and other systematic behaviour. Time series forecasting methods may be used to remove this behaviour by subtracting forecasts from observations to form residuals for algorithmic input. We describe three forecast methods and compare their predictive accuracy on each of 16 authentic syndromic data streams. The methods are (1) a non-adaptive regression model using a long historical baseline, (2) an adaptive regression model with a shorter, sliding baseline, and (3) the Holt-Winters method for generalized exponential smoothing. Criteria for comparing the forecasts were the root-mean-square error, the median absolute per cent error (MedAPE), and the median absolute deviation. The median-based criteria showed best overall performance for the Holt-Winters method. The MedAPE measures over the 16 test series averaged 16.5, 11.6, and 9.7 for the non-adaptive regression, adaptive regression, and Holt-Winters methods, respectively. The non-adaptive regression forecasts were degraded by changes in the data behaviour in the fixed baseline period used to compute model coefficients. The mean-based criterion was less conclusive because of the effects of poor forecasts on a small number of calendar holidays. The Holt-Winters method was also most effective at removing serial autocorrelation, with most 1-day-lag autocorrelation coefficients below 0.15. The forecast methods were compared without tuning them to the behaviour of individual series. We achieved improved predictions with such tuning of the Holt-Winters method, but practical use of such improvements for routine surveillance will require reliable data classification methods.

  1. The Variance-covariance Method using IOWGA Operator for Tourism Forecast Combination

    Directory of Open Access Journals (Sweden)

    Liangping Wu

    2014-08-01

    Full Text Available Three combination methods commonly used in tourism forecasting are the simple average method, the variance-covariance method and the discounted MSFE method. These methods assign the different weights that can not change at each time point to each individual forecasting model. In this study, we introduce the IOWGA operator combination method which can overcome the defect of previous three combination methods into tourism forecasting. Moreover, we further investigate the performance of the four combination methods through the theoretical evaluation and the forecasting evaluation. The results of the theoretical evaluation show that the IOWGA operator combination method obtains extremely well performance and outperforms the other forecast combination methods. Furthermore, the IOWGA operator combination method can be of well forecast performance and performs almost the same to the variance-covariance combination method for the forecasting evaluation. The IOWGA operator combination method mainly reflects the maximization of improving forecasting accuracy and the variance-covariance combination method mainly reflects the decrease of the forecast error. For future research, it may be worthwhile introducing and examining other new combination methods that may improve forecasting accuracy or employing other techniques to control the time for updating the weights in combined forecasts.

  2. Improving the principles of short-term electric load forecasting of the Irkutsk region

    Directory of Open Access Journals (Sweden)

    Kornilov Vladimir

    2017-01-01

    Full Text Available Forecasting of electric load (EL is an important task for both electric power entities and large consumers of electricity [1]. Large consumers are faced with the need to compose applications for the planned volume of EL, and the deviation of subsequent real consumption from previously announced leads to the appearance of penalties from the wholesale market. In turn, electricity producers are interested in forecasting the demand for electricity for prompt response to its fluctuations and for the purpose of optimal infrastructure development. The most difficult and urgent task is the hourly forecasting of EL, which is extremely important for the successful solution of problems of optimization of generating capacities, minimization of power losses, dispatching control, security assessment of power supply, etc. Ultimately, such forecasts allow optimizing the cash costs for electricity and fuel or water consumption during generation. This paper analyzes the experience of the branch of JSC "SO UPS" Irkutsk Regional Dispatch Office of the procedure for short-term forecasting of the EL of the Irkutsk region.

  3. Observations of the structure and evolution of surface and flight-level wind asymmetries in Hurricane Rita (2005)

    Science.gov (United States)

    Rogers, Robert; Uhlhorn, Eric

    2008-11-01

    Knowledge of the magnitude and distribution of surface winds, including the structure of azimuthal asymmetries in the wind field, are important factors for tropical cyclone forecasting. With its ability to remotely measure surface wind speeds, the stepped frequency microwave radiometer (SFMR) has assumed a prominent role for the operational tropical cyclone forecasting community. An example of this instrument's utility is presented here, where concurrent measurements of aircraft flight-level and SFMR surface winds are used to document the wind field evolution over three days in Hurricane Rita (2005). The amplitude and azimuthal location (phase) of the wavenumber-1 asymmetry in the storm-relative winds varied at both levels over time. The peak was found to the right of storm track at both levels on the first day. By the third day, the peak in flight-level storm-relative winds remained to the right of storm track, but it shifted to left of storm track at the surface, resulting in a 60-degree shift between the surface and flight-level and azimuthal variations in the ratio of surface to flight-level winds. The asymmetric differences between the surface and flight-level maximum wind radii also varied, indicating a vortex whose tilt was increasing.

  4. An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty

    Directory of Open Access Journals (Sweden)

    Nathaniel K. Newlands

    2014-06-01

    Full Text Available We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting it and comparing its forecasts against available historical data (1987-2011 for spring wheat (Triticum aestivum L.. The model was also validated for the 2012 growing season by comparing its forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1-4 % in mid-season and over-estimated by 1 % at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space.

  5. The new IEA Wind Task 36 on Wind Power Forecasting

    DEFF Research Database (Denmark)

    Giebel, Gregor; Cline, Joel; Frank, Helmut

    Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind E...... forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions....

  6. Ocean Observing Public-Private Collaboration to Improve Tropical Storm and Hurricane Predictions in the Gulf of Mexico

    Science.gov (United States)

    Perry, R.; Leung, P.; McCall, W.; Martin, K. M.; Howden, S. D.; Vandermeulen, R. A.; Kim, H. S. S.; Kirkpatrick, B. A.; Watson, S.; Smith, W.

    2016-02-01

    In 2008, Shell partnered with NOAA to explore opportunities for improving storm predictions in the Gulf of Mexico. Since, the collaboration has grown to include partners from Shell, NOAA National Data Buoy Center and National Center for Environmental Information, National Center for Environmental Prediction, University of Southern Mississippi, and the Gulf of Mexico Coastal Ocean Observing System. The partnership leverages complementary strengths of each collaborator to build a comprehensive and sustainable monitoring and data program to expand observing capacity and protect offshore assets and Gulf communities from storms and hurricanes. The program combines in situ and autonomous platforms with remote sensing and numerical modeling. Here we focus on profiling gliders and the benefits of a public-private partnership model for expanding regional ocean observing capacity. Shallow and deep gliders measure ocean temperature to derive ocean heat content (OHC), along with salinity, dissolved oxygen, fluorescence, and CDOM, in the central and eastern Gulf shelf and offshore. Since 2012, gliders have collected 4500+ vertical profiles and surveyed 5000+ nautical miles. Adaptive sampling and mission coordination with NCEP modelers provides specific datasets to assimilate into EMC's coupled HYCOM-HWRF model and 'connect-the-dots' between well-established Eulerian metocean measurements by obtaining (and validating) data between fixed stations (e.g. platform and buoy ADCPs) . Adaptive sampling combined with remote sensing provides satellite-derived OHC validation and the ability to sample productive coastal waters advected offshore by the Loop Current. Tracking coastal waters with remote sensing provides another verification of estimate Loop Current and eddy boundaries, as well as quantifying productivity and analyzing water quality on the Gulf coast, shelf break and offshore. Incorporating gliders demonstrates their value as tools to better protect offshore oil and gas assets

  7. The perfect storm of information: combining traditional and non-traditional data sources for public health situational awareness during hurricane response.

    Science.gov (United States)

    Bennett, Kelly J; Olsen, Jennifer M; Harris, Sara; Mekaru, Sumiko; Livinski, Alicia A; Brownstein, John S

    2013-12-16

    Hurricane Isaac made landfall in southeastern Louisiana in late August 2012, resulting in extensive storm surge and inland flooding. As the lead federal agency responsible for medical and public health response and recovery coordination, the Department of Health and Human Services (HHS) must have situational awareness to prepare for and address state and local requests for assistance following hurricanes. Both traditional and non-traditional data have been used to improve situational awareness in fields like disease surveillance and seismology. This study investigated whether non-traditional data (i.e., tweets and news reports) fill a void in traditional data reporting during hurricane response, as well as whether non-traditional data improve the timeliness for reporting identified HHS Essential Elements of Information (EEI). HHS EEIs provided the information collection guidance, and when the information indicated there was a potential public health threat, an event was identified and categorized within the larger scope of overall Hurricane Issac situational awareness. Tweets, news reports, press releases, and federal situation reports during Hurricane Isaac response were analyzed for information about EEIs. Data that pertained to the same EEI were linked together and given a unique event identification number to enable more detailed analysis of source content. Reports of sixteen unique events were examined for types of data sources reporting on the event and timeliness of the reports. Of these sixteen unique events identified, six were reported by only a single data source, four were reported by two data sources, four were reported by three data sources, and two were reported by four or more data sources. For five of the events where news tweets were one of multiple sources of information about an event, the tweet occurred prior to the news report, press release, local government\\emergency management tweet, and federal situation report. In all circumstances where

  8. Simulation of the Impact of New Aircraft- and Satellite-based Ocean Surface Wind Measurements on Estimates of Hurricane Intensity

    Science.gov (United States)

    Uhlhorn, Eric; Atlas, Robert; Black, Peter; Buckley, Courtney; Chen, Shuyi; El-Nimri, Salem; Hood, Robbie; Johnson, James; Jones, Linwood; Miller, Timothy; hide

    2009-01-01

    The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor currently under development to enhance real-time hurricane ocean surface wind observations. HIRAD builds on the capabilities of the Stepped Frequency Microwave Radiometer (SFMR), which now operates on NOAA P-3, G-4, and AFRC C-130 aircraft. Unlike the SFMR, which measures wind speed and rain rate along the ground track directly beneath the aircraft, HIRAD will provide images of the surface wind and rain field over a wide swath (approximately 3 times the aircraft altitude). To demonstrate potential improvement in the measurement of peak hurricane winds, we present a set of Observing System Simulation Experiments (OSSEs) in which measurements from the new instrument as well as those from existing platforms (air, surface, and space-based) are simulated from the output of a high-resolution (approximately 1.7 km) numerical model. Simulated retrieval errors due to both instrument noise as well as model function accuracy are considered over the expected range of incidence angles, wind speeds and rain rates. Based on numerous simulated flight patterns and data source combinations, statistics are developed to describe relationships between the observed and true (from the model s perspective) peak wind speed. These results have implications for improving the estimation of hurricane intensity (as defined by the peak sustained wind anywhere in the storm), which may often go un-observed due to sampling limitations.

  9. Spatial Ecology of Puerto Rican Boas (Epicrates inornatus) in a Hurricane Impacted Forest.

    Science.gov (United States)

    Joseph M. Wunderle Jr.; Javier E. Mercado Bernard Parresol Esteban Terranova 2

    2004-01-01

    Spatial ecology of Puerto Rican boas (Epicrates inornatus, Boidae) was studied with radiotelemetry in a subtropical wet forest recovering from a major hurricane (7–9 yr previous) when Hurricane Georges struck. Different boas were studied during three periods relative to Hurricane Georges: before only; before and after; and after only. Mean daily movement per month...

  10. Ensemble forecasting using sequential aggregation for photovoltaic power applications

    International Nuclear Information System (INIS)

    Thorey, Jean

    2017-01-01

    Our main objective is to improve the quality of photovoltaic power forecasts deriving from weather forecasts. Such forecasts are imperfect due to meteorological uncertainties and statistical modeling inaccuracies in the conversion of weather forecasts to power forecasts. First we gather several weather forecasts, secondly we generate multiple photovoltaic power forecasts, and finally we build linear combinations of the power forecasts. The minimization of the Continuous Ranked Probability Score (CRPS) allows to statistically calibrate the combination of these forecasts, and provides probabilistic forecasts under the form of a weighted empirical distribution function. We investigate the CRPS bias in this context and several properties of scoring rules which can be seen as a sum of quantile-weighted losses or a sum of threshold-weighted losses. The minimization procedure is achieved with online learning techniques. Such techniques come with theoretical guarantees of robustness on the predictive power of the combination of the forecasts. Essentially no assumptions are needed for the theoretical guarantees to hold. The proposed methods are applied to the forecast of solar radiation using satellite data, and the forecast of photovoltaic power based on high-resolution weather forecasts and standard ensembles of forecasts. (author) [fr

  11. Lessons from Hurricane Sandy: a community response in Brooklyn, New York.

    Science.gov (United States)

    Schmeltz, Michael T; González, Sonia K; Fuentes, Liza; Kwan, Amy; Ortega-Williams, Anna; Cowan, Lisa Pilar

    2013-10-01

    The frequency and intensity of extreme weather events have increased in recent decades; one example is Hurricane Sandy. If the frequency and severity continue or increase, adaptation and mitigation efforts are needed to protect vulnerable populations and improve daily life under changed weather conditions. This field report examines the devastation due to Hurricane Sandy experienced in Red Hook, Brooklyn, New York, a neighborhood consisting of geographically isolated low-lying commercial and residential units, with a concentration of low-income housing, and disproportionate rates of poverty and poor health outcomes largely experienced by Black and Latino residents. Multiple sources of data were reviewed, including street canvasses, governmental reports, community flyers, and meeting transcripts, as well as firsthand observations by a local nonprofit Red Hook Initiative (RHI) and community members, and social media accounts of the effects of Sandy and the response to daily needs. These data are considered within existing theory, evidence, and practice on protecting public health during extreme weather events. Firsthand observations show that a community-based organization in Red Hook, RHI, was at the center of the response to disaster relief, despite the lack of staff training in response to events such as Hurricane Sandy. Review of these data underscores that adaptation and response to climate change and likely resultant extreme weather is a dynamic process requiring an official coordinated governmental response along with on-the-ground volunteer community responders.

  12. A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Haupt, Sue Ellen [National Center for Atmospheric Research, Boulder, CO (United States)

    2016-04-19

    minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF

  13. An independent system operator's perspective on operational ramp forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Porter, G. [New Brunswick System Operator, Fredericton, NB (Canada)

    2010-07-01

    One of the principal roles of the power system operator is to select the most economical resources to reliably supply electric system power needs. Operational wind power production forecasts are required by system operators in order to understand the impact of ramp event forecasting on dispatch functions. A centralized dispatch approach can contribute to a more efficient use of resources that traditional economic dispatch methods. Wind ramping events can have a significant impact on system reliability. Power systems can have constrained or robust transmission systems, and may also be islanded or have large connections to neighbouring systems. Power resources can include both flexible and inflexible generation resources. Wind integration tools must be used by system operators to improve communications and connections with wind power plants. Improved wind forecasting techniques are also needed. Sensitivity to forecast errors is dependent on current system conditions. System operators require basic production forecasts, probabilistic forecasts, and event forecasts. Forecasting errors were presented as well as charts outlining the implications of various forecasts. tabs., figs.

  14. Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion

    OpenAIRE

    Jiandong Duan; Xinyu Qiu; Wentao Ma; Xuan Tian; Di Shang

    2018-01-01

    In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM) model with a...

  15. Hurricane Risk Variability along the Gulf of Mexico Coastline

    Science.gov (United States)

    Trepanier, Jill C.; Ellis, Kelsey N.; Tucker, Clay S.

    2015-01-01

    Hurricane risk characteristics are examined across the U. S. Gulf of Mexico coastline using a hexagonal tessellation. Using an extreme value model, parameters are collected representing the rate or λ (frequency), the scale or σ (range), and the shape or ξ (intensity) of the extreme wind distribution. These latent parameters and the 30-year return level are visualized across the grid. The greatest 30-year return levels are located toward the center of the Gulf of Mexico, and for inland locations, along the borders of Louisiana, Mississippi, and Alabama. Using a geographically weighted regression model, the relationship of these parameters to sea surface temperature (SST) is found to assess sensitivity to change. It is shown that as SSTs increase near the coast, the frequency of hurricanes in these grids decrease significantly. This reinforces the importance of SST in areas of likely tropical cyclogenesis in determining the number of hurricanes near the coast, along with SSTs along the lifespan of the storm, rather than simply local SST. The range of hurricane wind speeds experienced near Florida is shown to increase with increasing SSTs (insignificant), suggesting that increased temperatures may allow hurricanes to maintain their strength as they pass over the Florida peninsula. The modifiable areal unit problem is assessed using multiple grid sizes. Moran’s I and the local statistic G are calculated to examine spatial autocorrelation in the parameters. This research opens up future questions regarding rapid intensification and decay close to the coast and the relationship to changing SSTs. PMID:25767885

  16. Hurricane risk variability along the Gulf of Mexico coastline.

    Science.gov (United States)

    Trepanier, Jill C; Ellis, Kelsey N; Tucker, Clay S

    2015-01-01

    Hurricane risk characteristics are examined across the U. S. Gulf of Mexico coastline using a hexagonal tessellation. Using an extreme value model, parameters are collected representing the rate or λ (frequency), the scale or σ (range), and the shape or ξ (intensity) of the extreme wind distribution. These latent parameters and the 30-year return level are visualized across the grid. The greatest 30-year return levels are located toward the center of the Gulf of Mexico, and for inland locations, along the borders of Louisiana, Mississippi, and Alabama. Using a geographically weighted regression model, the relationship of these parameters to sea surface temperature (SST) is found to assess sensitivity to change. It is shown that as SSTs increase near the coast, the frequency of hurricanes in these grids decrease significantly. This reinforces the importance of SST in areas of likely tropical cyclogenesis in determining the number of hurricanes near the coast, along with SSTs along the lifespan of the storm, rather than simply local SST. The range of hurricane wind speeds experienced near Florida is shown to increase with increasing SSTs (insignificant), suggesting that increased temperatures may allow hurricanes to maintain their strength as they pass over the Florida peninsula. The modifiable areal unit problem is assessed using multiple grid sizes. Moran's I and the local statistic G are calculated to examine spatial autocorrelation in the parameters. This research opens up future questions regarding rapid intensification and decay close to the coast and the relationship to changing SSTs.

  17. Infrasonic ray tracing applied to mesoscale atmospheric structures: refraction by hurricanes.

    Science.gov (United States)

    Bedard, Alfred J; Jones, R Michael

    2013-11-01

    A ray-tracing program is used to estimate the refraction of infrasound by the temperature structure of the atmosphere and by hurricanes represented by a Rankine-combined vortex wind plus a temperature perturbation. Refraction by the hurricane winds is significant, giving rise to regions of focusing, defocusing, and virtual sources. The refraction of infrasound by the temperature anomaly associated with a hurricane is small, probably no larger than that from uncertainties in the wind field. The results are pertinent to interpreting ocean wave generated infrasound in the vicinities of tropical cyclones.

  18. Space weather at Low Latitudes: Considerations to improve its forecasting

    Science.gov (United States)

    Chau, J. L.; Goncharenko, L.; Valladares, C. E.; Milla, M. A.

    2013-05-01

    In this work we present a summary of space weather events that are unique to low-latitude regions. Special emphasis will be devoted to events that occur during so-called quiet (magnetically) conditions. One of these events is the occurrence of nighttime F-region irregularities, also known Equatorial Spread F (ESF). When such irregularities occur navigation and communications systems get disrupted or perturbed. After more than 70 years of studies, many features of ESF irregularities (climatology, physical mechanisms, longitudinal dependence, time dependence, etc.) are well known, but so far they cannot be forecast on time scales of minutes to hours. We present a summary of some of these features and some of the efforts being conducted to contribute to their forecasting. In addition to ESF, we have recently identified a clear connection between lower atmospheric forcing and the low latitude variability, particularly during the so-called sudden stratospheric warming (SSW) events. During SSW events and magnetically quiet conditions, we have observed changes in total electron content (TEC) that are comparable to changes that occur during strong magnetically disturbed conditions. We present results from recent events as well as outline potential efforts to forecast the ionospheric effects during these events.

  19. Five year prediction of Sea Surface Temperature in the Tropical Atlantic: a comparison of simple statistical methods

    OpenAIRE

    Laepple, Thomas; Jewson, Stephen; Meagher, Jonathan; O'Shay, Adam; Penzer, Jeremy

    2007-01-01

    We are developing schemes that predict future hurricane numbers by first predicting future sea surface temperatures (SSTs), and then apply the observed statistical relationship between SST and hurricane numbers. As part of this overall goal, in this study we compare the historical performance of three simple statistical methods for making five-year SST forecasts. We also present SST forecasts for 2006-2010 using these methods and compare them to forecasts made from two structural time series ...

  20. Forecasting risks of natural gas consumption in Slovenia

    Energy Technology Data Exchange (ETDEWEB)

    Potocnik, Primoz; Govekar, Edvard; Grabec, Igor [Laboratory of Synergetics, Ljubljana (Slovenia). Faculty of Mechanical Engineering; Thaler, Marko; Poredos, Alojz [Laboratory for Refrigeration, Ljubljana (Slovenia). Faculty of Mechanical Engineering

    2007-08-15

    Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic incentive model and a risk model. Basic guidelines are given for the construction of a forecasting model that combines past energy consumption data, weather data and weather forecast. The forecasting model is required to estimate expected forecasting errors that are the basis for forecasting risk estimation. The risk estimation strategy also requires an economic incentive model that describes the influence of forecasting accuracy on the energy distribution systems' cash flow. The economic model defines the critical forecasting error levels that most strongly influence cash flow. Based on the forecasting model and the economic model, the development of a risk model is proposed. The risk model is associated with critical forecasting error levels in the context of various influential parameters such as seasonal data, month, day of the week and temperature. The risk model is applicable to estimating the daily forecasting risk based on the influential parameters. The proposed approach is illustrated by a case study of a Slovenian natural gas distribution company. (author)