WorldWideScience

Sample records for weather forecasting

  1. Weather Forecasting Systems and Methods

    Science.gov (United States)

    Mecikalski, John (Inventor); MacKenzie, Wayne M., Jr. (Inventor); Walker, John Robert (Inventor)

    2014-01-01

    A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.

  2. Severe Weather Forecast Decision Aid

    Science.gov (United States)

    Bauman, William H., III; Wheeler, Mark M.; Short, David A.

    2005-01-01

    This report presents a 15-year climatological study of severe weather events and related severe weather atmospheric parameters. Data sources included local forecast rules, archived sounding data, Cloud-to-Ground Lightning Surveillance System (CGLSS) data, surface and upper air maps, and two severe weather event databases covering east-central Florida. The local forecast rules were used to set threat assessment thresholds for stability parameters that were derived from the sounding data. The severe weather events databases were used to identify days with reported severe weather and the CGLSS data was used to differentiate between lightning and non-lightning days. These data sets provided the foundation for analyzing the stability parameters and synoptic patterns that were used to develop an objective tool to aid in forecasting severe weather events. The period of record for the analysis was May - September, 1989 - 2003. The results indicate that there are certain synoptic patterns more prevalent on days with severe weather and some of the stability parameters are better predictors of severe weather days based on locally tuned threat values. The results also revealed the stability parameters that did not display any skill related to severe weather days. An interactive web-based Severe Weather Decision Aid was developed to assist the duty forecaster by providing a level of objective guidance based on the analysis of the stability parameters, CGLSS data, and synoptic-scale dynamics. The tool will be tested and evaluated during the 2005 warm season.

  3. Using Forecasting to Teach Weather Science

    Science.gov (United States)

    Tsubota, Y.; Takahashi, T.

    2009-09-01

    Weather affects our lives and hence, is a popular topic in daily conversations and in the media. Therefore, it is not only important to teach weather, but is also a good idea to use 'weather' as a topic in science teaching. Science education has two main objectives: to acquire scientific concepts and methods. Weather forecasting is an adequate theme to teach scientific methods because it is dependent on observation. However, it is not easy to forecast weather using only temporal observation. We need to know the tendency of 'weather change' via consecutive and/or continuous weather observation. Students will acquire scientific-observation skills through weather observation. Data-processing skills would be enhanced through a weather-forecasting contest. A contest should be announced within 5 days of school events, such as a school excursion and field day. Students submit their own weather forecast by gathering weather information through the internet, news paper and so on. A weather-forecasting contest compels the student to observe the weather more often. We currently have some different weather forecasts. For example, American weather-related companies such as ACCU weather and Weather Channel provide weather forecast for the many locations all over the world. Comparing these weather forecasting with actual weather, participants such as students could evaluate the differences between forecasted and actual temperatures. Participants will judge the best weather forecast based on the magnitude of the difference. Also, participants evaluate the 'hitting ratio' of each weather forecast. Students can learn elementary statistics by comparing various weather forecasts. We have developed our weather web-site that provides our own weather forecasting and observation. Students acquire science skills using our weather web-site. We will report our lessen plans and explain our weather web-site.

  4. Convective Weather Avoidance with Uncertain Weather Forecasts

    Science.gov (United States)

    Karahan, Sinan; Windhorst, Robert D.

    2009-01-01

    Convective weather events have a disruptive impact on air traffic both in terminal area and in en-route airspaces. In order to make sure that the national air transportation system is safe and efficient, it is essential to respond to convective weather events effectively. Traffic flow control initiatives in response to convective weather include ground delay, airborne delay, miles-in-trail restrictions as well as tactical and strategic rerouting. The rerouting initiatives can potentially increase traffic density and complexity in regions neighboring the convective weather activity. There is a need to perform rerouting in an intelligent and efficient way such that the disruptive effects of rerouting are minimized. An important area of research is to study the interaction of in-flight rerouting with traffic congestion or complexity and developing methods that quantitatively measure this interaction. Furthermore, it is necessary to find rerouting solutions that account for uncertainties in weather forecasts. These are important steps toward managing complexity during rerouting operations, and the paper is motivated by these research questions. An automated system is developed for rerouting air traffic in order to avoid convective weather regions during the 20- minute - 2-hour time horizon. Such a system is envisioned to work in concert with separation assurance (0 - 20-minute time horizon), and longer term air traffic management (2-hours and beyond) to provide a more comprehensive solution to complexity and safety management. In this study, weather is dynamic and uncertain; it is represented as regions of airspace that pilots are likely to avoid. Algorithms are implemented in an air traffic simulation environment to support the research study. The algorithms used are deterministic but periodically revise reroutes to account for weather forecast updates. In contrast to previous studies, in this study convective weather is represented as regions of airspace that pilots

  5. Medium-range fire weather forecasts

    Science.gov (United States)

    J.O. Roads; K. Ueyoshi; S.C. Chen; J. Alpert; F. Fujioka

    1991-01-01

    The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has...

  6. Adaptive Weather Forecasting using Local Meteorological Information

    NARCIS (Netherlands)

    Doeswijk, T.G.; Keesman, K.J.

    2005-01-01

    In general, meteorological parameters such as temperature, rain and global radiation are important for agricultural systems. Anticipating on future conditions is most often needed in these systems. Weather forecasts then become of substantial importance. As weather forecasts are subject to

  7. Improving Local Weather Forecasts for Agricultural Applications

    NARCIS (Netherlands)

    Doeswijk, T.G.; Keesman, K.J.

    2005-01-01

    For controlling agricultural systems, weather forecasts can be of substantial importance. Studies have shown that forecast errors can be reduced in terms of bias and standard deviation using forecasts and meteorological measurements from one specific meteorological station. For agricultural systems

  8. Improving Local Weather Forecasts for Agricultural Applications

    NARCIS (Netherlands)

    Doeswijk, T.G.; Keesman, K.J.

    2005-01-01

    For controlling agricultural systems, weather forecasts can be of substantial importance. Studies have shown that forecast errors can be reduced in terms of bias and standard deviation using forecasts and meteorological measurements from one specific meteorological station. For agricultural systems

  9. Space Weather Forecasting: An Enigma

    Science.gov (United States)

    Sojka, J. J.

    2012-12-01

    -pipe" disciplines. The perceived progress in space weather understanding differs significantly depending upon which community (scientific, technology, forecaster, society) is addressing the question. Even more divergent are these thoughts when the question is how valuable is the scientific capability of forecasting space weather. This talk will discuss present day as well as future potential for forecasting space weather for a few selected examples. The author will attempt to straddle the divergent community opinions.

  10. Verification of Space Weather Forecasts using Terrestrial Weather Approaches

    Science.gov (United States)

    Henley, E.; Murray, S.; Pope, E.; Stephenson, D.; Sharpe, M.; Bingham, S.; Jackson, D.

    2015-12-01

    The Met Office Space Weather Operations Centre (MOSWOC) provides a range of 24/7 operational space weather forecasts, alerts, and warnings, which provide valuable information on space weather that can degrade electricity grids, radio communications, and satellite electronics. Forecasts issued include arrival times of coronal mass ejections (CMEs), and probabilistic forecasts for flares, geomagnetic storm indices, and energetic particle fluxes and fluences. These forecasts are produced twice daily using a combination of output from models such as Enlil, near-real-time observations, and forecaster experience. Verification of forecasts is crucial for users, researchers, and forecasters to understand the strengths and limitations of forecasters, and to assess forecaster added value. To this end, the Met Office (in collaboration with Exeter University) has been adapting verification techniques from terrestrial weather, and has been working closely with the International Space Environment Service (ISES) to standardise verification procedures. We will present the results of part of this work, analysing forecast and observed CME arrival times, assessing skill using 2x2 contingency tables. These MOSWOC forecasts can be objectively compared to those produced by the NASA Community Coordinated Modelling Center - a useful benchmark. This approach cannot be taken for the other forecasts, as they are probabilistic and categorical (e.g., geomagnetic storm forecasts give probabilities of exceeding levels from minor to extreme). We will present appropriate verification techniques being developed to address these forecasts, such as rank probability skill score, and comparing forecasts against climatology and persistence benchmarks. As part of this, we will outline the use of discrete time Markov chains to assess and improve the performance of our geomagnetic storm forecasts. We will also discuss work to adapt a terrestrial verification visualisation system to space weather, to help

  11. Decision Making Models Using Weather Forecast Information

    OpenAIRE

    Hiramatsu, Akio; Huynh, Van-Nam; Nakamori, Yoshiteru

    2007-01-01

    The quality of weather forecast has gradually improved, but weather information such as precipitation forecast is still uncertainty. Meteorologists have studied the use and economic value of weather information, and users have to translate weather information into their most desirable action. To maximize the economic value of users, the decision maker should select the optimum course of action for his company or project, based on an appropriate decision strategy under uncertain situations. In...

  12. Forecasting Space Weather from Magnetograms

    Science.gov (United States)

    Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F.; Khazanov, Igor

    2012-01-01

    Large flares and fast CMEs are the drivers of the most severe space weather including Solar Energetic Particle Events (SEP Events). Large flares and their co-produced CMEs are powered by the explosive release of free magnetic energy stored in non-potential magnetic fields of sunspot active regions. The free energy is stored in and released from the low-beta regime of the active region s magnetic field above the photosphere, in the chromosphere and low corona. From our work over the past decade and from similar work of several other groups, it is now well established that (1) a proxy of the free magnetic energy stored above the photosphere can be measured from photospheric magnetograms, maps of the measured field in the photosphere, and (2) an active region s rate of production of major CME/flare eruptions in the coming day or so is strongly correlated with its present measured value of the free-energy proxy. These results have led us to use the large database of SOHO/MDI full-disk magnetograms spanning Solar Cycle 23 to obtain empirical forecasting curves that from an active region s present measured value of the free-energy proxy give the active region s expected rates of production of major flares, CMEs, fast CMEs, and SEP Events in the coming day or so (Falconer et al 2011, Space Weather, 9, S04003). For each type of event, the expected rate is readily converted to the chance that the active region will produce such an event in any given forward time window of a day or so. If the chance is small enough (e.g. <5%), the forecast is All Clear for that type of event. We will present these forecasting curves and demonstrate the accuracy of their forecasts. In addition, we will show that the forecasts for major flares and fast CMEs can be made significantly more accurate by taking into account not only the value of the free energy proxy but also the active region s recent productivity of major flares; specifically, whether the active region has produced a major flare

  13. Adaptive Weather Forecasting using Local Meteorological Information

    NARCIS (Netherlands)

    Doeswijk, T.G.; Keesman, K.J.

    2005-01-01

    In general, meteorological parameters such as temperature, rain and global radiation are important for agricultural systems. Anticipating on future conditions is most often needed in these systems. Weather forecasts then become of substantial importance. As weather forecasts are subject to uncertain

  14. On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models

    OpenAIRE

    Fay, D; Ringwood, John; Condon, M.

    2004-01-01

    Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...

  15. Operational, regional-scale, chemical weather forecasting models in Europe

    NARCIS (Netherlands)

    Kukkonen, J.; Balk, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.H.; Poupkou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, K.

    2011-01-01

    Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed

  16. Prediction Techniques in Operational Space Weather Forecasting

    Science.gov (United States)

    Zhukov, Andrei

    2016-07-01

    The importance of forecasting space weather conditions is steadily increasing as our society is becoming more and more dependent on advanced technologies that may be affected by disturbed space weather. Operational space weather forecasting is still a difficult task that requires the real-time availability of input data and specific prediction techniques that are reviewed in this presentation, with an emphasis on solar and interplanetary weather. Key observations that are essential for operational space weather forecasting are listed. Predictions made on the base of empirical and statistical methods, as well as physical models, are described. Their validation, accuracy, and limitations are discussed in the context of operational forecasting. Several important problems in the scientific basis of predicting space weather are described, and possible ways to overcome them are discussed, including novel space-borne observations that could be available in future.

  17. An operation problem in weather forecasting

    Directory of Open Access Journals (Sweden)

    S. L. Mlurkrar

    1953-07-01

    Full Text Available The weather forecaster has to get ready his forecasts at definite hours of the day on the basis of isopleths. Provided the observations from most of the main stations had been received, the deduction that could be had by decreasing the plotting tome and studying the chart at greater leisure was often better than if the plotting time had not been decreased. A consideration of the weather chart plotted for a given forecast would also help the subsequent, forecasts, due account having been taken of the diurnal changes

  18. Probability Forecast of Regional Landslide Based on Numerical Weather Forecast

    Institute of Scientific and Technical Information of China (English)

    GAO Kechang; WEI Fangqiang; CUI Peng; HU Kaiheng; XU Jing; ZHANG Guoping; BI Baogui

    2006-01-01

    The regional forecast of landslide is one of the key points of hazard mitigation. It is also a hot and difficult point in research field. To solve this problem has become urgent task along with Chinese economy fast development. This paper analyzes the principle of regional landslide forecast and the factors for forecasting. The method of a combination of Information Value Model and Extension Model has been put forward to be as the forecast model. Using new result of Numerical Weather Forecast Research and that combination model, we discuss the implementation feasibility of regional landslide forecast. Finally, with the help of Geographic Information System, an operation system for southwest of China landslide forecast has been developed. It can carry out regional landslide forecast daily and has been pilot run in NMC. Since this is the first time linking theoretical research with meteorological service, further works are needed to enhance it.

  19. School Science Inspired by Improving Weather Forecasts

    Science.gov (United States)

    Reid, Heather; Renfrew, Ian A.; Vaughan, Geraint

    2014-01-01

    High winds and heavy rain are regular features of the British weather, and forecasting these events accurately is a major priority for the Met Office and other forecast providers. This is the challenge facing DIAMET, a project involving university groups from Manchester, Leeds, Reading, and East Anglia, together with the Met Office. DIAMET is part…

  20. How MAG4 Improves Space Weather Forecasting

    Science.gov (United States)

    Falconer, David; Khazanov, Igor; Barghouty, Nasser

    2013-01-01

    Dangerous space weather is driven by solar flares and Coronal Mass Ejection (CMEs). Forecasting flares and CMEs is the first step to forecasting either dangerous space weather or All Clear. MAG4 (Magnetogram Forecast), developed originally for NASA/SRAG (Space Radiation Analysis Group), is an automated program that analyzes magnetograms from the HMI (Helioseismic and Magnetic Imager) instrument on NASA SDO (Solar Dynamics Observatory), and automatically converts the rate (or probability) of major flares (M- and X-class), Coronal Mass Ejections (CMEs), and Solar Energetic Particle Events.

  1. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-02-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  2. Interactive Forecasting with the National Weather Service River Forecast System

    Science.gov (United States)

    Smith, George F.; Page, Donna

    1993-01-01

    The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

  3. 14 CFR 135.213 - Weather reports and forecasts.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 3 2010-01-01 2010-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that...

  4. Space weather research and forecast in USA

    CERN Document Server

    Pevtsov, Alexei A

    2016-01-01

    In the United States, scientific research in space weather is funded by several Government Agencies including the National Science Foundation (NSF) and the National Aeronautics and Space Agency (NASA). For commercial purposes, space weather forecast is made by the Space Weather Prediction Center (SWPC) of the National Oceanic and Atmospheric Administration (NOAA). Observations come from the network of groundbased observatories funded via various sources, as well as from the instruments on spacecraft. Numerical models used in forecast are developed in the framework of individual research projects. Later, the most promising models are selected for additional testing at SWPC. In order to increase the application of models in research and education, NASA in collaboration with other agencies created Community Coordinated Modeling Center (CCMC). In mid-1990, US scientific community presented compelling evidence for developing the National Program on Space Weather, and in 1995, such program has been formally created...

  5. Visually Comparing Weather Features in Forecasts.

    Science.gov (United States)

    Quinan, P Samuel; Meyer, Miriah

    2016-01-01

    Meteorologists process and analyze weather forecasts using visualization in order to examine the behaviors of and relationships among weather features. In this design study conducted with meteorologists in decision support roles, we identified and attempted to address two significant common challenges in weather visualization: the employment of inconsistent and often ineffective visual encoding practices across a wide range of visualizations, and a lack of support for directly visualizing how different weather features relate across an ensemble of possible forecast outcomes. In this work, we present a characterization of the problems and data associated with meteorological forecasting, we propose a set of informed default encoding choices that integrate existing meteorological conventions with effective visualization practice, and we extend a set of techniques as an initial step toward directly visualizing the interactions of multiple features over an ensemble forecast. We discuss the integration of these contributions into a functional prototype tool, and also reflect on the many practical challenges that arise when working with weather data.

  6. 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.

  7. Accurate Weather Forecasting for Radio Astronomy

    Science.gov (United States)

    Maddalena, Ronald J.

    2010-01-01

    The NRAO Green Bank Telescope routinely observes at wavelengths from 3 mm to 1 m. As with all mm-wave telescopes, observing conditions depend upon the variable atmospheric water content. The site provides over 100 days/yr when opacities are low enough for good observing at 3 mm, but winds on the open-air structure reduce the time suitable for 3-mm observing where pointing is critical. Thus, to maximum productivity the observing wavelength needs to match weather conditions. For 6 years the telescope has used a dynamic scheduling system (recently upgraded; www.gb.nrao.edu/DSS) that requires accurate multi-day forecasts for winds and opacities. Since opacity forecasts are not provided by the National Weather Services (NWS), I have developed an automated system that takes available forecasts, derives forecasted opacities, and deploys the results on the web in user-friendly graphical overviews (www.gb.nrao.edu/ rmaddale/Weather). The system relies on the "North American Mesoscale" models, which are updated by the NWS every 6 hrs, have a 12 km horizontal resolution, 1 hr temporal resolution, run to 84 hrs, and have 60 vertical layers that extend to 20 km. Each forecast consists of a time series of ground conditions, cloud coverage, etc, and, most importantly, temperature, pressure, humidity as a function of height. I use the Liebe's MWP model (Radio Science, 20, 1069, 1985) to determine the absorption in each layer for each hour for 30 observing wavelengths. Radiative transfer provides, for each hour and wavelength, the total opacity and the radio brightness of the atmosphere, which contributes substantially at some wavelengths to Tsys and the observational noise. Comparisons of measured and forecasted Tsys at 22.2 and 44 GHz imply that the forecasted opacities are good to about 0.01 Nepers, which is sufficient for forecasting and accurate calibration. Reliability is high out to 2 days and degrades slowly for longer-range forecasts.

  8. Forecasting the space weather impact

    DEFF Research Database (Denmark)

    Crosby, N. B.; Veronig, A.; Robbrecht, E.

    2012-01-01

    The FP7 COronal Mass Ejections and Solar Energetic Particles (COMESEP) project is developing tools for forecasting geomagnetic storms and solar energetic particle (SEP) radiation storms. By analysis of historical data, complemented by the extensive data coverage of solar cycle 23, the key ingredi...

  9. WOD - Weather On Demand forecasting system

    Science.gov (United States)

    Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina

    2017-04-01

    The backbone of the Belgingur forecasting system (called WOD - Weather On Demand) is the WRF-Chem atmospheric model, with a number of in-house customisations. Initial and boundary data are taken from the Global Forecasting System, operated by the National Oceanic and Atmospheric Administration (NOAA). Operational forecasts use cycling of a number of parameters, mainly deep soil and surface fields. This is done to minimise spin-up effects and to ensure proper book-keeping of hydrological fields such as snow accumulation and runoff, as well as the constituents of various chemical parameters. The WOD system can be used to create conventional short- to medium-range weather forecasts for any location on the globe. The WOD system can also be used for air quality purposes (e.g. dispersion forecasts from volcanic eruptions) and as a tool to provide input to other modelling systems, such as hydrological models. A wide variety of post-processing options are also available, making WOD an ideal tool for creating highly customised output that can be tailored to the specific needs of individual end-users. The most recent addition to the WOD system is an integrated verification system where forecasts can be compared to surface observations from chosen locations. Forecast visualisation, such as weather charts, meteograms, weather icons and tables, is done via number of web components that can be configured to serve the varying needs of different end-users. The WOD system itself can be installed in an automatic way on hardware running a range of Linux based OS. System upgrades can also be done in semi-automatic fashion, i.e. upgrades and/or bug-fixes can be pushed to the end-user hardware without system downtime. Importantly, the WOD system requires only rudimentary knowledge of the WRF modelling, and the Linux operating systems on behalf of the end-user, making it an ideal NWP tool in locations with limited IT infrastructure.

  10. Tomorrow's Forecast: Oceans and Weather.

    Science.gov (United States)

    Smigielski, Alan

    1995-01-01

    This issue of "Art to Zoo" focuses on weather and climate and is tied to the traveling exhibition Ocean Planet from the Smithsonian's National Museum of Natural History. The lessons encourage students to think about the profound influence the oceans have on planetary climate and life on earth. Sections of the lesson plan include: (1) "Ocean…

  11. The Quest for the Perfect Weather Forecaster

    Science.gov (United States)

    Kahl, Jonathan; Horwitz, Kevin; Berg, Craig; Gruhl, Mary

    2004-01-01

    It is said that meteorology is the only profession where a person can be wrong half the time and still keep his or her job. The truth is not quite so bleak, but one can still ask, "Just how accurate are weather forecasters, anyway?" This article presents two projects for middle level students to investigate this issue in a hands-on,…

  12. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the islands of Samoa at...

  13. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the island of Guam at...

  14. Weather Research and Forecasting (WRF) Regional Atmospheric Model: CNMI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Commonwealth of the Northern...

  15. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 3.5-day hourly forecast for the region surrounding the Hawaiian island of Oahu at...

  16. Generating Weather Forecast Texts with Case Based Reasoning

    OpenAIRE

    Adeyanju, Ibrahim

    2015-01-01

    Several techniques have been used to generate weather forecast texts. In this paper, case based reasoning (CBR) is proposed for weather forecast text generation because similar weather conditions occur over time and should have similar forecast texts. CBR-METEO, a system for generating weather forecast texts was developed using a generic framework (jCOLIBRI) which provides modules for the standard components of the CBR architecture. The advantage in a CBR approach is that systems can be built...

  17. 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

  18. Weather forecasting with open source software

    Science.gov (United States)

    Rautenhaus, Marc; Dörnbrack, Andreas

    2013-04-01

    To forecast the weather situation during aircraft-based atmospheric field campaigns, we employ a tool chain of existing and self-developed open source software tools and open standards. Of particular value are the Python programming language with its extension libraries NumPy, SciPy, PyQt4, Matplotlib and the basemap toolkit, the NetCDF standard with the Climate and Forecast (CF) Metadata conventions, and the Open Geospatial Consortium Web Map Service standard. These open source libraries and open standards helped to implement the "Mission Support System", a Web Map Service based tool to support weather forecasting and flight planning during field campaigns. The tool has been implemented in Python and has also been released as open source (Rautenhaus et al., Geosci. Model Dev., 5, 55-71, 2012). In this presentation we discuss the usage of free and open source software for weather forecasting in the context of research flight planning, and highlight how the field campaign work benefits from using open source tools and open standards.

  19. Convective Weather Forecast Accuracy Analysis at Center and Sector Levels

    Science.gov (United States)

    Wang, Yao; Sridhar, Banavar

    2010-01-01

    This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in

  20. Can We Trust Long-Range Weather Forecasts ?

    Science.gov (United States)

    Mailier, Pascal

    2010-05-01

    Long-range weather forecasts are widely used in key sectors of the economy, but too often their properties and limitations are not understood well enough. This poster reviews the characteristics, methods and reliability of long-range weather forecasts, making recommendations regarding their use and quality assessment. Despite their limited skill, long-range weather forecasts can still be a valuable tool for managing weather risk provided the necessary caution is exercised.

  1. Flare forecasting at the Met Office Space Weather Operations Centre

    Science.gov (United States)

    Murray, S. A.; Bingham, S.; Sharpe, M.; Jackson, D. R.

    2017-04-01

    The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end-users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms: forecasts for each active region on the solar disk over the next 24 h and full-disk forecasts for the next 4 days. Here the forecasting process is described in detail, as well as first verification of archived forecasts using methods commonly used in operational weather prediction. Real-time verification available for operational flare forecasting use is also described. The influence of human forecasters is highlighted, with human-edited forecasts outperforming original model results and forecasting skill decreasing over longer forecast lead times.

  2. Resolution of Probabilistic Weather Forecasts with Application in Disease Management.

    Science.gov (United States)

    Hughes, G; McRoberts, N; Burnett, F J

    2017-02-01

    Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

  3. Communicating Environmental Uncertainty: The Nature of Weather Forecasts.

    Science.gov (United States)

    Travis, Richard W.; Riebsame, William E.

    1979-01-01

    Traces the path of weather forecasts from the time they are made by the National Oceanic and Atmospheric Administration until the time they are received by the public through the mass media. The purpose of the article is to provide geography teachers with basic information on weather forecasts, interpretation of forecast terms, and indications…

  4. Economic Value of Weather and Climate Forecasts

    Science.gov (United States)

    Yohe, Gary W.

    Researchers who ponder the value of information work in the world of integrated assessment. They study the creation of scientific knowledge from models and data and view the processing and analysis of knowledge through the creation and distribution of information. They understand that the value of this information is derived fundamentally from how it is used. Therefore, such researchers must investigate how and where individuals (and the institutions and systems that individuals create) uncover information that they find credible and how and why those same individuals, institutions, and systems discard information that they find incredible. The essays presented in Economic Value of Weather and Climate Forecasts are a perfect example of how practicing researchers confront this integration process in an area in which just about everyone has an opinion—the weather.

  5. Visualizing uncertainty : Towards a better understanding of weather forecasts

    NARCIS (Netherlands)

    Toet, A.; Tak, S.; Erp, J.B.F. van

    2016-01-01

    Uncertainty visualizations are increasingly used in communications to the general public. A well-known example is the weather forecast. Rather than providing an exact temperature value, weather forecasts often show the range in which the temperature will lie. But uncertainty visualizations are also

  6. Now, Here's the Weather Forecast...

    Science.gov (United States)

    Richardson, Mathew

    2013-01-01

    The Met Office has a long history of weather forecasting, creating tailored weather forecasts for customers across the world. Based in Exeter, the Met Office is also home to the Met Office Hadley Centre, a world-leading centre for the study of climate change and its potential impacts. Climate information from the Met Office Hadley Centre is used…

  7. A review of operational, regional-scale, chemical weather forecasting models in Europe

    NARCIS (Netherlands)

    Kukkonen, J.; Olsson, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.-H.; Poupkou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.E.J.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, K.

    2012-01-01

    Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed

  8. Incorporating weather uncertainty in demand forecasts for electricity market planning

    Science.gov (United States)

    Ziser, C. J.; Dong, Z. Y.; Wong, K. P.

    2012-07-01

    A major component of electricity network planning is to ensure supply capability into the future, through generation and transmission development. Accurate forecasts of maximum demand are a crucial component of this process, with future weather conditions having a large impact on forecast accuracy. This article presents an improved methodology for the consideration of weather uncertainty in electricity demand forecasts. Case studies based on the Australian national electricity market are used to validate the proposed methodology.

  9. 24-Hour Forecast of Air Temperatures from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of air temperature. In...

  10. 72-Hour Forecast of Precipitation Amounts from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of precipitation amounts. In...

  11. 48-Hour Forecast of Precipitation Amounts from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of precipitation amounts. In...

  12. 24-Hour Forecast of Precipitation Amounts from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of precipitation amounts. In...

  13. 72-Hour Forecast of Air Temperatures from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of air temperature. In...

  14. 48-Hour Forecast of Air Temperatures from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of air temperature. In...

  15. Study on Hail Weather Analysis and Forecast in Guilin

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    [Objective] The hail weather and forecast storm in Guilin were studied. [Method] In allusion to the occurrence of hailstorm in Guilin and considering the observation data of a new generation of Doppler weather radar in Guilin, the weather situation and radar echo characteristics in Guilin was summarized so as to explore the short time forecast method. [Result] There were one or two icy storms every year in Guilin. The occurrence of icy storm was distinctly influenced by terrain. A new generation of Doppler ...

  16. Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

    Science.gov (United States)

    Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

    2015-12-01

    Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

  17. System Science approach to Space Weather forecast

    Science.gov (United States)

    Balikhin, Michael A.

    There are many dynamical systems in nature that are so complex that mathematical models of their behaviour can not be deduced from first principles with the present level of our knowledge. Obvious examples are organic cell, human brain, etc often attract system scientists. A example that is closer to space physics is the terrestrial magnetosphere. The system approach has been developed to understand such complex objects from the observation of their dynamics. The systems approach employs advanced data analysis methodologies to identify patterns in the overall system behaviour and provides information regarding the linear and nonlinear processes involved in the dynamics of the system. This, in combination with the knowledge deduced from the first principles, creates the opportunity to find mathematical relationships that govern the evolution of a particular physical system. Advances and problems of systems science applications to provide a reliable forecasts of space weather phenomena such as geomagnetic storms, substorms and radiation belts particle fluxes are reviewed and compared with the physics based models.

  18. How accurate are the weather forecasts for Bierun (southern Poland)?

    Science.gov (United States)

    Gawor, J.

    2012-04-01

    Weather forecast accuracy has increased in recent times mainly thanks to significant development of numerical weather prediction models. Despite the improvements, the forecasts should be verified to control their quality. The evaluation of forecast accuracy can also be an interesting learning activity for students. It joins natural curiosity about everyday weather and scientific process skills: problem solving, database technologies, graph construction and graphical analysis. The examination of the weather forecasts has been taken by a group of 14-year-old students from Bierun (southern Poland). They participate in the GLOBE program to develop inquiry-based investigations of the local environment. For the atmospheric research the automatic weather station is used. The observed data were compared with corresponding forecasts produced by two numerical weather prediction models, i.e. COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) developed by Naval Research Laboratory Monterey, USA; it runs operationally at the Interdisciplinary Centre for Mathematical and Computational Modelling in Warsaw, Poland and COSMO (The Consortium for Small-scale Modelling) used by the Polish Institute of Meteorology and Water Management. The analysed data included air temperature, precipitation, wind speed, wind chill and sea level pressure. The prediction periods from 0 to 24 hours (Day 1) and from 24 to 48 hours (Day 2) were considered. The verification statistics that are commonly used in meteorology have been applied: mean error, also known as bias, for continuous data and a 2x2 contingency table to get the hit rate and false alarm ratio for a few precipitation thresholds. The results of the aforementioned activity became an interesting basis for discussion. The most important topics are: 1) to what extent can we rely on the weather forecasts? 2) How accurate are the forecasts for two considered time ranges? 3) Which precipitation threshold is the most predictable? 4) Why

  19. Is It Going to Rain Today? Understanding the Weather Forecast.

    Science.gov (United States)

    Allsopp, Jim; And Others

    1996-01-01

    Presents a resource for science teachers to develop a better understanding of weather forecasts, including outlooks, watches, warnings, advisories, severe local storms, winter storms, floods, hurricanes, nonprecipitation hazards, precipitation probabilities, sky condition, and UV index. (MKR)

  20. Seasonal Forecasting of Fire Weather Based on a New Global Fire Weather Database

    Science.gov (United States)

    Dowdy, Andrew J.; Field, Robert D.; Spessa, Allan C.

    2016-01-01

    Seasonal forecasting of fire weather is examined based on a recently produced global database of the Fire Weather Index (FWI) system beginning in 1980. Seasonal average values of the FWI are examined in relation to measures of the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The results are used to examine seasonal forecasts of fire weather conditions throughout the world.

  1. Computational Analysis of Optical Neural Network Models to Weather Forecasting

    OpenAIRE

    A. C. Subhajini; V. Joseph Raj

    2010-01-01

    Neural networks have been in use in numerous meteorological applications including weather forecasting. They are found to be more powerful than any traditional expert system in the classification of meteorological patterns, in performing pattern classification tasks as they learn from examples without explicitly stating the rules and being non linear they solve complex problems more than linear techniques. A weather forecasting problem - rain fall estimation has been experimented using differ...

  2. 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.

  3. Next-Generation Severe Weather Forecasting and Communication

    Science.gov (United States)

    Rothfusz, Lans P.; Karstens, Christopher; Hilderband, Douglas

    2014-09-01

    Despite advances in the hazardous weather predictive skills of forecasters from the National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS) [Simmons and Sutter, 2011], the underlying methodologies used to generate severe weather watches (i.e., announcements that the potential for severe weather exists) and warnings (i.e., announcements that severe weather conditions are occurring or imminent) have changed little since they were first issued in 1965. The resulting text-based, deterministic (i.e., a single, most accurate value) messages lack the detail and flexibility to match the technology, science, diversity, lifestyles, and vulnerability of society today.

  4. Post Processing Numerical Weather Prediction Model Rainfall Forecasts for Use in Ensemble Streamflow Forecasting in Australia

    Science.gov (United States)

    Shrestha, D. L.; Robertson, D.; Bennett, J.; Ward, P.; Wang, Q. J.

    2012-12-01

    Through the water information research and development alliance (WIRADA) project, CSIRO is conducting research to improve flood and short-term streamflow forecasting services delivered by the Australian Bureau of Meteorology. WIRADA aims to build and test systems to generate ensemble flood and short-term streamflow forecasts with lead times of up to 10 days by integrating rainfall forecasts from Numerical Weather Prediction (NWP) models and hydrological modelling. Here we present an overview of the latest progress towards developing this system. Rainfall during the forecast period is a major source of uncertainty in streamflow forecasting. Ensemble rainfall forecasts are used in streamflow forecasting to characterise the rainfall uncertainty. In Australia, NWP models provide forecasts of rainfall and other weather conditions for lead times of up to 10 days. However, rainfall forecasts from Australian NWP models are deterministic and often contain systematic errors. We use a simplified Bayesian joint probability (BJP) method to post-process rainfall forecasts from the latest generation of Australian NWP models. The BJP method generates reliable and skilful ensemble rainfall forecasts. The post-processed rainfall ensembles are then used to force a semi-distributed conceptual rainfall runoff model to produce ensemble streamflow forecasts. The performance of the ensemble streamflow forecasts is evaluated on a number of Australian catchments and the benefits of using post processed rainfall forecasts are demonstrated.

  5. Progress in Climate Prediction and Weather Forecast Operations in China

    Institute of Scientific and Technical Information of China (English)

    XIAO Ziniu; LIU Bo; LIU Hua; ZHANG De

    2012-01-01

    The current status of weather forecasting and climate prediction,and the main progress China has made in recent years,are summarized in this paper. The characteristics and requirements of modern weather forecast operations are described briefly,and the significance of Numerical Weather Prediction (NWP) for future development is emphasized.The objectives and critical tasks for seamless short-term climate prediction that covers the extended-range (15 30 days),monthly,seasonal,annual,interannual and interdecadal timescales,are proposed.

  6. Aerosol Radiative Forcing and Weather Forecasts in the ECMWF Model

    Science.gov (United States)

    Bozzo, A.; Benedetti, A.; Rodwell, M. J.; Bechtold, P.; Remy, S.

    2015-12-01

    Aerosols play an important role in the energy balance of the Earth system via direct scattering and absorpiton of short-wave and long-wave radiation and indirect interaction with clouds. Diabatic heating or cooling by aerosols can also modify the vertical stability of the atmosphere and influence weather pattern with potential impact on the skill of global weather prediction models. The Copernicus Atmosphere Monitoring Service (CAMS) provides operational daily analysis and forecast of aerosol optical depth (AOD) for five aerosol species using a prognostic model which is part of the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF-IFS). The aerosol component was developed during the research project Monitoring Atmospheric Composition and Climate (MACC). Aerosols can have a large impact on the weather forecasts in case of large aerosol concentrations as found during dust storms or strong pollution events. However, due to its computational burden, prognostic aerosols are not yet feasible in the ECMWF operational weather forecasts, and monthly-mean climatological fields are used instead. We revised the aerosol climatology used in the operational ECMWF IFS with one derived from the MACC reanalysis. We analyse the impact of changes in the aerosol radiative effect on the mean model climate and in medium-range weather forecasts, also in comparison with prognostic aerosol fields. The new climatology differs from the previous one by Tegen et al 1997, both in the spatial distribution of the total AOD and the optical properties of each aerosol species. The radiative impact of these changes affects the model mean bias at various spatial and temporal scales. On one hand we report small impacts on measures of large-scale forecast skill but on the other hand details of the regional distribution of aerosol concentration have a large local impact. This is the case for the northern Indian Ocean where the radiative impact of the mineral

  7. Forecasts of time averages with a numerical weather prediction model

    Science.gov (United States)

    Roads, J. O.

    1986-01-01

    Forecasts of time averages of 1-10 days in duration by an operational numerical weather prediction model are documented for the global 500 mb height field in spectral space. Error growth in very idealized models is described in order to anticipate various features of these forecasts and in order to anticipate what the results might be if forecasts longer than 10 days were carried out by present day numerical weather prediction models. The data set for this study is described, and the equilibrium spectra and error spectra are documented; then, the total error is documented. It is shown how forecasts can immediately be improved by removing the systematic error, by using statistical filters, and by ignoring forecasts beyond about a week. Temporal variations in the error field are also documented.

  8. The impact of Sun-weather research on forecasting

    Science.gov (United States)

    Larsen, M. F.

    1979-01-01

    The possible impact of Sun-weather research on forecasting is examined. The type of knowledge of the effect is evaluated to determine if it is in a form that can be used for forecasting purposes. It is concluded that the present understanding of the effect does not lend itself readily to applications for forecast purposes. The limits of present predictive skill are examined and it is found that skill is most lacking for prediction of the smallest scales of atmospheric motion. However, it is not expected that Sun-weather research will have any significant impact on forecasting the smaller scales since predictability at these scales is limited by the finite grid size resolution and the time scales of turbulent diffusion. The predictability limits for the largest scales are on the order of several weeks although presently only a one week forecast is achievable.

  9. Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

    Directory of Open Access Journals (Sweden)

    D. E. Robertson

    2013-09-01

    Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short

  10. Trends in the predictive performance of raw ensemble weather forecasts

    Science.gov (United States)

    Hemri, Stephan; Scheuerer, Michael; Pappenberger, Florian; Bogner, Konrad; Haiden, Thomas

    2015-04-01

    Over the last two decades the paradigm in weather forecasting has shifted from being deterministic to probabilistic. Accordingly, numerical weather prediction (NWP) models have been run increasingly as ensemble forecasting systems. The goal of such ensemble forecasts is to approximate the forecast probability distribution by a finite sample of scenarios. Global ensemble forecast systems, like the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, are prone to probabilistic biases, and are therefore not reliable. They particularly tend to be underdispersive for surface weather parameters. Hence, statistical post-processing is required in order to obtain reliable and sharp forecasts. In this study we apply statistical post-processing to ensemble forecasts of near-surface temperature, 24-hour precipitation totals, and near-surface wind speed from the global ECMWF model. Our main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the post-processed forecasts. The ECMWF ensemble is under continuous development, and hence its forecast skill improves over time. Parts of these improvements may be due to a reduction of probabilistic bias. Thus, we first hypothesize that the gain by post-processing decreases over time. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations we generate post-processed forecasts by ensemble model output statistics (EMOS) for each station and variable. Parameter estimates are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over rolling training periods that consist of the n days preceding the initialization dates. Given the higher average skill in terms of CRPS of the post-processed forecasts for all three variables, we analyze the evolution of the difference in skill between raw ensemble and EMOS forecasts. The fact that the gap in skill remains almost constant over time, especially for near

  11. Probability Forecasting: A Guide for Forecasters and Staff Weather Officers

    Science.gov (United States)

    1991-12-01

    100% 40 1 1 6 17% 0 20 0 0 1 0 0 0 0 0 9 0% 0 0 0 0 0 0 0 0 0 0 Totals Climo 1 2 3 4 5 6 7 8 9 10 11 12 031 32% Forecast Frequency S- Legend: 1...Corresponding to Expected Brier Scores for Forecasts with Two Categories Shown in Table 4-9. (Multiply by 100 to obtain percentages.) CORRELATION CLIMO % 0

  12. Time Relevance of Convective Weather Forecast for Air Traffic Automation

    Science.gov (United States)

    Chan, William N.

    2006-01-01

    The Federal Aviation Administration (FAA) is handling nearly 120,000 flights a day through its Air Traffic Management (ATM) system and air traffic congestion is expected to increse substantially over the next 20 years. Weather-induced impacts to throughput and efficiency are the leading cause of flight delays accounting for 70% of all delays with convective weather accounting for 60% of all weather related delays. To support the Next Generation Air Traffic System goal of operating at 3X current capacity in the NAS, ATC decision support tools are being developed to create advisories to assist controllers in all weather constraints. Initial development of these decision support tools did not integrate information regarding weather constraints such as thunderstorms and relied on an additional system to provide that information. Future Decision Support Tools should move towards an integrated system where weather constraints are factored into the advisory of a Decision Support Tool (DST). Several groups such at NASA-Ames, Lincoln Laboratories, and MITRE are integrating convective weather data with DSTs. A survey of current convective weather forecast and observation data show they span a wide range of temporal and spatial resolutions. Short range convective observations can be obtained every 5 mins with longer range forecasts out to several days updated every 6 hrs. Today, the short range forecasts of less than 2 hours have a temporal resolution of 5 mins. Beyond 2 hours, forecasts have much lower temporal. resolution of typically 1 hour. Spatial resolutions vary from 1km for short range to 40km for longer range forecasts. Improving the accuracy of long range convective forecasts is a major challenge. A report published by the National Research Council states improvements for convective forecasts for the 2 to 6 hour time frame will only be achieved for a limited set of convective phenomena in the next 5 to 10 years. Improved longer range forecasts will be probabilistic

  13. Weather Forecasting for Ka-band Operations: Initial Study Results

    Science.gov (United States)

    Morabito, D.; Wu, L.; Slobin, S.

    2016-08-01

    As lower frequency bands (e.g., 2.3 GHz and 8.4 GHz) have become oversubscribed during the past several decades, NASA has become interested in using higher frequency bands (e.g., 26 GHz and 32 GHz) for telemetry, thus making use of the available wider bandwidth. However, these bands are more susceptible to atmospheric degradation. Currently, flight projects tend to be conservative in preparing their communications links by using worst-case or conservative assumptions. Such assumptions result in nonoptimum data return. We explore the use of weather forecasting for Goldstone and Madrid for different weather condition scenarios to determine more optimal values of atmospheric attenuation and atmospheric noise temperature for use in telecommunication link design. We find that the use of weather forecasting can provide up to 2 dB or more of increased data return when more favorable conditions are forecast. Future plans involve further developing the technique for operational scenarios with interested flight projects.

  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. Post processing rainfall forecasts from numerical weather prediction models for short term streamflow forecasting

    Directory of Open Access Journals (Sweden)

    D. E. Robertson

    2013-05-01

    Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post processing raw NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast periods. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed multivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast periods and for cumulative totals throughout the forecast periods. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post processing method for a wider range of climatic conditions and also investigate the benefits of using post processed rainfall forecast for flood and short term streamflow forecasting.

  16. 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.

  17. WRF-Fire: coupled weather-wildland fire modeling with the weather research and forecasting model

    Science.gov (United States)

    Janice L. Coen; Marques Cameron; John Michalakes; Edward G. Patton; Philip J. Riggan; Kara M. Yedinak

    2012-01-01

    A wildland fire behavior module (WRF-Fire) was integrated into the Weather Research and Forecasting (WRF) public domain numerical weather prediction model. The fire module is a surface fire behavior model that is two-way coupled with the atmospheric model. Near-surface winds from the atmospheric model are interpolated to a finer fire grid and used, with fuel properties...

  18. Anticipatory Water Management: Using ensemble weather forecasts for critical events

    OpenAIRE

    Van Andel, S.J.

    2009-01-01

    Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring evacuation of local inhabitants. Therefore, the use of weather forecast information with hydrological models can be invaluable for the operational water manager to expand the forecast horizon and to have ti...

  19. Hourly weather forecasts for gas turbine power generation

    OpenAIRE

    G. Giunta; R. Vernazza; Salerno, R.; Ceppi, A.; Ercolani, G.; Mancini, M

    2017-01-01

    An hourly short-term weather forecast can optimize processes in Combined Cycle Gas Turbine (CCGT) plants by helping to reduce imbalance charges on the national power grid. Consequently, a reliable meteorological prediction for a given power plant is crucial for obtaining competitive prices for the electric market, better planning and stock management, sales and supplies of energy sources. The paper discusses the short-term hourly temperature forecasts, at lead time day+1 and day+2, over a per...

  20. Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

    Science.gov (United States)

    Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan

    2016-04-01

    Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more

  1. 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...

  2. Extended time weather forecasts contributes to agricultural productivity estimates

    Science.gov (United States)

    de Oliveira Cardoso, Andrea; Pinto, Hilton Silveira; de Ávila, Ana Maria Heuminski; da Silva Dias, Pedro Leite; Marin, Fabio Ricardo; Pilau, Felipe

    2010-11-01

    Weather conditions in critical periods of the vegetative crop development influence crop productivity, thus being a basic parameter for crop forecast. Reliable extended period weather forecasts may contribute to improve the estimation of agricultural productivity. The production of soybean plays an important role in the Brazilian economy, because this country is ranked among the largest producers of soybeans in the world. This culture can be significantly affected by water conditions, depending on the intensity of water deficit. This work explores the role of extended period weather forecasts for estimating soybean productivity in the southern part of Brazil, Passo Fundo, and Londrina (State of Rio Grande do Sul and Paraná, respectively) in the 2005/2006 harvest. The goal was to investigate the possible contribution of precipitation forecasts as a substitute for the use of climatological data on crop forecasts. The results suggest that the use of meteorological forecasts generate more reliable productivity estimates during the growth period than those generated only through climatological information.

  3. Weather-based forecasts of California crop yields

    Energy Technology Data Exchange (ETDEWEB)

    Lobell, D B; Cahill, K N; Field, C B

    2005-09-26

    Crop yield forecasts provide useful information to a range of users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, while for many crops official forecasts do not exist. As broad-scale crop yields are largely dependent on weather, measurements from existing meteorological stations have the potential to provide a reliable, timely, and cost-effective means to anticipate crop yields. We developed weather-based models of state-wide yields for 12 major California crops (wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios), and tested their accuracy using cross-validation over the 1980-2003 period. Many crops were forecast with high accuracy, as judged by the percent of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, allowing for lead times that were longer than existing procedures in many cases.

  4. GPS Estimates of Integrated Precipitable Water Aid Weather Forecasters

    Science.gov (United States)

    Moore, Angelyn W.; Gutman, Seth I.; Holub, Kirk; Bock, Yehuda; Danielson, David; Laber, Jayme; Small, Ivory

    2013-01-01

    Global Positioning System (GPS) meteorology provides enhanced density, low-latency (30-min resolution), integrated precipitable water (IPW) estimates to NOAA NWS (National Oceanic and Atmospheric Adminis tration Nat ional Weather Service) Weather Forecast Offices (WFOs) to provide improved model and satellite data verification capability and more accurate forecasts of extreme weather such as flooding. An early activity of this project was to increase the number of stations contributing to the NOAA Earth System Research Laboratory (ESRL) GPS meteorology observing network in Southern California by about 27 stations. Following this, the Los Angeles/Oxnard and San Diego WFOs began using the enhanced GPS-based IPW measurements provided by ESRL in the 2012 and 2013 monsoon seasons. Forecasters found GPS IPW to be an effective tool in evaluating model performance, and in monitoring monsoon development between weather model runs for improved flood forecasting. GPS stations are multi-purpose, and routine processing for position solutions also yields estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV (precipitable water vapor) using in situ pressure and temperature measurements, the basis for GPS meteorology. NOAA ESRL has implemented this concept with a nationwide distribution of more than 300 "GPSMet" stations providing IPW estimates at sub-hourly resolution currently used in operational weather models in the U.S.

  5. Activity to Forecast Weather Using a Computer and GMS Pictures

    OpenAIRE

    榊原, 保志; 池本, 博司

    2003-01-01

    This paper presents an activity to forecast weather using a computer and Geostationary Meteorology Satellite (GMS) images. Students obtained visual images and infrared images scanned by GMS from web pages and investigated the distribution of cumulonimbus and nimbostratus, which often bring precipitation, using functions of digital imaging solutions such as color balance, posterization and layer. Two trial lesson at a high school suggested that this activity helped students think weather forec...

  6. Downscaling Global Weather Forecast Outputs Using ANN for Flood Prediction

    OpenAIRE

    Nam Do Hoai; Keiko Udo; Akira Mano

    2011-01-01

    Downscaling global weather prediction model outputs to individual locations or local scales is a common practice for operational weather forecast in order to correct the model outputs at subgrid scales. This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron (MLP) neural network. The MLP architecture was optimized by considering physical bases that determine the circulation of atmospheric va...

  7. How some nonmeteorological professionals view meteorology and weather forecasting.

    Science.gov (United States)

    Scoggins, J. R.; Vaughan, W. W.

    1971-01-01

    The results of a questionnaire designed to gather information on how nonmeteorological scientists and engineers view meteorology and weather forecasting are summarized in this paper. The respondents were from two organizations, Texas A & M University and NASA's Marshall Space Flight Center, the first representing the academic community and the latter the engineering community. While there were some differences between the groups, in most cases answers expressed in the opinionnaire by the two groups were essentially identical. The results revealed the following: Approximately three-fourths of the respondents use meteorological data and/or weather forecasts in their profession; the meaning of probability forecasts is very unclear with only 13% indicating the correct answer; television is the main source of weather information; approximately half of the respondents had never heard of the Global Atmospheric Research Program; and the opinion was almost unanimous that satellites had contributed significantly to weather observations and/or forecasts. Also, the results indicated a number of other ?problem' areas where some improvements are desired.

  8. Review on space weather in Latin America. 3. Development of space weather forecasting centers

    Science.gov (United States)

    Denardini, Clezio Marcos; Dasso, Sergio; Gonzalez-Esparza, J. Americo

    2016-11-01

    The present work is the third of a three-part review of space weather in Latin America, specifically observing its evolution in three countries (Argentina, Brazil and Mexico). This work presents the decision process for the spinning off of space weather prediction centers from space science groups with our interpretation of the reasons/opportunities that lead to this. Lastly, the constraints for the progress in space weather monitoring, research, and forecast are listed with recommendations to overcome them, which we believe will lead to the access of key variables for the monitoring and forecasting space weather, which will allow these centers to better monitor space weather and issue warnings, ​watches and alerts.

  9. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Maui-Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Hawaiian islands of Oahu,...

  10. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Main Hawaiian Islands

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Main Hawaiian Islands (MHI)...

  11. Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast.

    Science.gov (United States)

    Moran, Kelly R; Fairchild, Geoffrey; Generous, Nicholas; Hickmann, Kyle; Osthus, Dave; Priedhorsky, Reid; Hyman, James; Del Valle, Sara Y

    2016-12-01

    Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. We conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting. Published by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

  12. Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System

    Directory of Open Access Journals (Sweden)

    Tien Du Duc

    2016-01-01

    Full Text Available The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl. For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.

  13. A Modeler's Perspective on Space Weather Forecasting (Invited)

    Science.gov (United States)

    Wiltberger, M. J.

    2010-12-01

    Space physics is moving into a new era where numerical models originally developed for answering science questions are used as the basis for making operational space weather forecasts. Answering this challenge requires developments on multiple fronts requiring collaborations across space physics disciplines and between the research and operations communities. Since space weather in geospace is driven by the solar wind conditions a natural solution to improving the forecast lead time is to couple geospace models to heliospheric models. The quality of these forecast is dependent upon the ability of the heliospheric models to accurately model IMF Bz. Another challenge presented by moving into the forecasting arena is preparing the models for real-time operation which includes both computational performance and data redundancy issues. Moving into operations also presents modelers with an opportunity to assess their models performance over calculation intervals unprecedented duration. A key collaboration in the transition of models to operation is the discussion between forecasters and developers on what forecast parameters can accurately be predicted by the current generation of numerical models. This collaboration naturally includes a discussion of the definition of the best metrics to be used in quantitatively assessing performance.

  14. Improvements in medium range weather forecasting system of India

    Indian Academy of Sciences (India)

    V S Prasad; Saji Mohandas; Surya Kanti Dutta; M Das Gupta; G R Iyengar; E N Rajagopal; Swati Basu

    2014-03-01

    Medium range weather forecasts are being generated in real time using Global Data Assimilation Forecasting System (GDAFS) at NCMRWF since 1994. The system has been continuously upgraded in terms of data usage, assimilation and forecasting system. Recently this system was upgraded to a horizontal resolution of T574 (about 22 km) with 64 levels in vertical. The assimilation scheme of this upgraded system is based on the latest Grid Statistical Interpolation (GSI) scheme and it has the provision to use most of available meteorological and oceanographic satellite datasets besides conventional meteorological observations. The new system has an improved procedure for relocating tropical cyclone to its observed position with the correct intensity. All these modifications have resulted in improvement of skill of medium range forecasts by about 1 day.

  15. Weather forecast-based optimization of integrated energy systems.

    Energy Technology Data Exchange (ETDEWEB)

    Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

    2009-03-01

    In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.

  16. Flood forecasting using medium-range probabilistic weather prediction

    Directory of Open Access Journals (Sweden)

    B. T. Gouweleeuw

    2005-01-01

    Full Text Available Following the developments in short- and medium-range weather forecasting over the last decade, operational flood forecasting also appears to show a shift from a so-called single solution or 'best guess' deterministic approach towards a probabilistic approach based on ensemble techniques. While this probabilistic approach is now more or less common practice and well established in the meteorological community, operational flood forecasters have only started to look for ways to interpret and mitigate for end-users the prediction products obtained by combining so-called Ensemble Prediction Systems (EPS of Numerical Weather Prediction (NWP models with rainfall-runoff models. This paper presents initial results obtained by combining deterministic and EPS hindcasts of the global NWP model of the European Centre for Medium-Range Weather Forecasts (ECMWF with the large-scale hydrological model LISFLOOD for two historic flood events: the river Meuse flood in January 1995 and the river Odra flood in July 1997. In addition, a possible way to interpret the obtained ensemble based stream flow prediction is proposed.

  17. Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

    Directory of Open Access Journals (Sweden)

    D. L. Shrestha

    2013-05-01

    Full Text Available The quality of precipitation forecasts from four Numerical Weather Prediction (NWP models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT and regional (ACCESS-R NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly, the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

  18. 72-Hour Forecast of 12 Hour Probability of Precipitation from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of precipitation probabilities. In...

  19. 48-Hour Forecast of 12 Hour Probability of Precipitation from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of precipitation probabilities. In...

  20. 24-Hour Forecast of 12 Hour Probability of Precipitation from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of precipitation probabilities. In...

  1. Expert Systems and Weather Forecasting in the 4th and 5th Grade.

    Science.gov (United States)

    Kirkwood, James J.; Gimblett, Randy H.

    1992-01-01

    Fourth and fifth graders built weather measuring instruments, entered data into a computer program that forecasted weather, and compared the resultant forecast with actual weather. As a result of their activities, students took a greater interest in weather phenomena, understood the computer program, and learned to think more logically. (LB)

  2. 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

  3. Weather modeling and forecasting of PV systems operation

    CERN Document Server

    Paulescu, Marius; Gravila, Paul; Badescu, Viorel

    2012-01-01

    In the past decade, there has been a substantial increase of grid-feeding photovoltaic applications, thus raising the importance of solar electricity in the energy mix. This trend is expected to continue and may even increase. Apart from the high initial investment cost, the fluctuating nature of the solar resource raises particular insertion problems in electrical networks. Proper grid managing demands short- and long-time forecasting of solar power plant output. Weather modeling and forecasting of PV systems operation is focused on this issue. Models for predicting the state of the sky, nowc

  4. [50 years of the methodology of weather forecasting for medicine].

    Science.gov (United States)

    Grigor'ev, K I; Povazhnaia, E L

    2014-01-01

    The materials reported in the present article illustrate the possibility of weather forecasting for the medical purposes in the historical aspect. The main characteristics of the relevant organizational and methodological approaches to meteoprophylaxis based of the standard medical forecasts are presented. The emphasis is laid on the priority of the domestic medical school in the development of the principles of diagnostics and treatment of meteosensitivity and meteotropic complications in the patients presenting with various diseases with special reference to their age-related characteristics.

  5. Fifty Years of Space Weather Forecasting from Boulder

    Science.gov (United States)

    Berger, T. E.

    2015-12-01

    The first official space weather forecast was issued by the Space Disturbances Laboratory in Boulder, Colorado, in 1965, ushering in an era of operational prediction that continues to this day. Today, the National Oceanic and Atmospheric Administration (NOAA) charters the Space Weather Prediction Center (SWPC) as one of the nine National Centers for Environmental Prediction (NCEP) to provide the nation's official watches, warnings, and alerts of space weather phenomena. SWPC is now integral to national and international efforts to predict space weather events, from the common and mild, to the rare and extreme, that can impact critical technological infrastructure. In 2012, the Strategic National Risk Assessment included extreme space weather events as low-to-medium probability phenomena that could, unlike any other meteorogical phenomena, have an impact on the government's ability to function. Recognizing this, the White House chartered the Office of Science and Technology Policy (OSTP) to produce the first comprehensive national strategy for the prediction, mitigation, and response to an extreme space weather event. The implementation of the National Strategy is ongoing with NOAA, its partners, and stakeholders concentrating on the goal of improving our ability to observe, model, and predict the onset and severity of space weather events. In addition, work continues with the research community to improve our understanding of the physical mechanisms - on the Sun, in the heliosphere, and in the Earth's magnetic field and upper atmosphere - of space weather as well as the effects on critical infrastructure such as electrical power transmission systems. In fifty years, people will hopefully look back at the history of operational space weather prediction and credit our efforts today with solidifying the necessary developments in observational systems, full-physics models of the entire Sun-Earth system, and tools for predicting the impacts to infrastructure to protect

  6. Anvil Forecast Tool in the Advanced Weather Interactive Processing System

    Science.gov (United States)

    Barrett, Joe H., III; Hood, Doris

    2009-01-01

    Meteorologists from the 45th Weather Squadron (45 WS) and National Weather Service Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Shuttle Flight Rules. As a result, the Applied Meteorology Unit (AMU) was tasked to create a graphical overlay tool for the Meteorological Interactive Data Display System (MIDDS) that indicates the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input. The tool creates a graphic depicting the potential location of thunderstorm anvils one, two, and three hours into the future. The locations are based on the average of the upper level observed or forecasted winds. The graphic includes 10 and 20 n mi standoff circles centered at the location of interest, as well as one-, two-, and three-hour arcs in the upwind direction. The arcs extend outward across a 30 sector width based on a previous AMU study that determined thunderstorm anvils move in a direction plus or minus 15 of the upper-level wind direction. The AMU was then tasked to transition the tool to the Advanced Weather Interactive Processing System (AWIPS). SMG later requested the tool be updated to provide more flexibility and quicker access to model data. This presentation describes the work performed by the AMU to transition the tool into AWIPS, as well as the subsequent improvements made to the tool.

  7. Fine Forecasts: Encouraging the Media to Include Ultraviolet Radiation Information in Summertime Weather Forecasts

    Science.gov (United States)

    Richards, R.; Reeder, A. I.; Bulliard, J.-L.

    2004-01-01

    Melanoma and skin cancer are largely attributable to over-exposure to solar ultraviolet radiation (UVR). Reports of UVR levels within media weather forecasts appear to be well received by the public and have good potential to communicate the need for appropriate sun protection to a broad audience. This study describes provision of UVR messages by…

  8. Impact of High Resolution SST Data on Regional Weather Forecasts

    Science.gov (United States)

    Jedlovec, Gary J.; Case, Jonathon; LaFontaine, Frank; Vazquez, Jorge; Mattocks, Craig

    2010-01-01

    Past studies have shown that the use of coarse resolution SST products such as from the real-time global (RTG) SST analysis[1] or other coarse resolution once-a-day products do not properly portray the diurnal variability of fluxes of heat and moisture from the ocean that drive the formation of low level clouds and precipitation over the ocean. For example, the use of high resolution MODIS SST composite [2] to initialize the Advanced Research Weather Research and Forecast (WRF) (ARW) [3] has been shown to improve the prediction of sensible weather parameters in coastal regions [4][5}. In an extend study, [6] compared the MODIS SST composite product to the RTG SST analysis and evaluated forecast differences for a 6 month period from March through August 2007 over the Florida coastal regions. In a comparison to buoy data, they found that that the MODIS SST composites reduced the bias and standard deviation over that of the RTG data. These improvements led to significant changes in the initial and forecasted heat fluxes and the resulting surface temperature fields, wind patterns, and cloud distributions. They also showed that the MODIS composite SST product, produced for the Terra and Aqua satellite overpass times, captured a component of the diurnal cycle in SSTs not represented in the RTG or other one-a-day SST analyses. Failure to properly incorporate these effects in the WRF initialization cycle led to temperature biases in the resulting short term forecasts. The forecast impact was limited in some situations however, due to composite product inaccuracies brought about by data latency during periods of long-term cloud cover. This paper focuses on the forecast impact of an enhanced MODIS/AMSR-E composite SST product designed to reduce inaccuracies due data latency in the MODIS only composite product.

  9. Space weather forecasting with a Multimodel Ensemble Prediction System (MEPS)

    Science.gov (United States)

    Schunk, R. W.; Scherliess, L.; Eccles, V.; Gardner, L. C.; Sojka, J. J.; Zhu, L.; Pi, X.; Mannucci, A. J.; Butala, M.; Wilson, B. D.; Komjathy, A.; Wang, C.; Rosen, G.

    2016-07-01

    The goal of the Multimodel Ensemble Prediction System (MEPS) program is to improve space weather specification and forecasting with ensemble modeling. Space weather can have detrimental effects on a variety of civilian and military systems and operations, and many of the applications pertain to the ionosphere and upper atmosphere. Space weather can affect over-the-horizon radars, HF communications, surveying and navigation systems, surveillance, spacecraft charging, power grids, pipelines, and the Federal Aviation Administration (FAA's) Wide Area Augmentation System (WAAS). Because of its importance, numerous space weather forecasting approaches are being pursued, including those involving empirical, physics-based, and data assimilation models. Clearly, if there are sufficient data, the data assimilation modeling approach is expected to be the most reliable, but different data assimilation models can produce different results. Therefore, like the meteorology community, we created a Multimodel Ensemble Prediction System (MEPS) for the Ionosphere-Thermosphere-Electrodynamics (ITE) system that is based on different data assimilation models. The MEPS ensemble is composed of seven physics-based data assimilation models for the ionosphere, ionosphere-plasmasphere, thermosphere, high-latitude ionosphere-electrodynamics, and middle to low latitude ionosphere-electrodynamics. Hence, multiple data assimilation models can be used to describe each region. A selected storm event that was reconstructed with four different data assimilation models covering the middle and low latitude ionosphere is presented and discussed. In addition, the effect of different data types on the reconstructions is shown.

  10. Operational forecasting based on a modified Weather Research and Forecasting model

    Energy Technology Data Exchange (ETDEWEB)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

    Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

  11. Improving Weather Forecasts Through Reduced Precision Data Assimilation

    Science.gov (United States)

    Hatfield, Samuel; Düben, Peter; Palmer, Tim

    2017-04-01

    We present a new approach for improving the efficiency of data assimilation, by trading numerical precision for computational speed. Future supercomputers will allow a greater choice of precision, so that models can use a level of precision that is commensurate with the model uncertainty. Previous studies have already indicated that the quality of climate and weather forecasts is not significantly degraded when using a precision less than double precision [1,2], but so far these studies have not considered data assimilation. Data assimilation is inherently uncertain due to the use of relatively long assimilation windows, noisy observations and imperfect models. Thus, the larger rounding errors incurred from reducing precision may be within the tolerance of the system. Lower precision arithmetic is cheaper, and so by reducing precision in ensemble data assimilation, we can redistribute computational resources towards, for example, a larger ensemble size. Because larger ensembles provide a better estimate of the underlying distribution and are less reliant on covariance inflation and localisation, lowering precision could actually allow us to improve the accuracy of weather forecasts. We will present results on how lowering numerical precision affects the performance of an ensemble data assimilation system, consisting of the Lorenz '96 toy atmospheric model and the ensemble square root filter. We run the system at half precision (using an emulation tool), and compare the results with simulations at single and double precision. We estimate that half precision assimilation with a larger ensemble can reduce assimilation error by 30%, with respect to double precision assimilation with a smaller ensemble, for no extra computational cost. This results in around half a day extra of skillful weather forecasts, if the error-doubling characteristics of the Lorenz '96 model are mapped to those of the real atmosphere. Additionally, we investigate the sensitivity of these results

  12. Improving High-resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with Upgraded Kain-Fritsch Cumulus Scheme

    Science.gov (United States)

    High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of inco...

  13. Calls Forecast for the Moscow Ambulance Service. The Impact of Weather Forecast

    Science.gov (United States)

    Gordin, Vladimir; Bykov, Philipp

    2015-04-01

    We use the known statistics of the calls for the current and previous days to predict them for tomorrow and for the following days. We assume that this algorithm will work operatively, will cyclically update the available information and will move the horizon of the forecast. Sure, the accuracy of such forecasts depends on their lead time, and from a choice of some group of diagnoses. For comparison we used the error of the inertial forecast (tomorrow there will be the same number of calls as today). Our technology has demonstrated accuracy that is approximately two times better compared to the inertial forecast. We obtained the following result: the number of calls depends on the actual weather in the city as well as on its rate of change. We were interested in the accuracy of the forecast for 12-hour sum of the calls in real situations. We evaluate the impact of the meteorological errors [1] on the forecast errors of the number of Ambulance calls. The weather and the Ambulance calls number both have seasonal tendencies. Therefore, if we have medical information from one city only, we should separate the impacts of such predictors as "annual variations in the number of calls" and "weather". We need to consider the seasonal tendencies (associated, e. g. with the seasonal migration of the population) and the impact of the air temperature simultaneously, rather than sequentially. We forecasted separately the number of calls with diagnoses of cardiovascular group, where it was demonstrated the advantage of the forecasting method, when we use the maximum daily air temperature as a predictor. We have a chance to evaluate statistically the influence of meteorological factors on the dynamics of medical problems. In some cases it may be useful for understanding of the physiology of disease and possible treatment options. We can assimilate some personal archives of medical parameters for the individuals with concrete diseases and the relative meteorological archive. As a

  14. Engaging Earth- and Environmental-Science Undergraduates through Weather Discussions and an eLearning Weather Forecasting Contest

    Science.gov (United States)

    Schultz, David M.; Anderson, Stuart; Seo-Zindy, Ryo

    2013-01-01

    For students who major in meteorology, engaging in weather forecasting can motivate learning, develop critical-thinking skills, improve their written communication, and yield better forecasts. Whether such advances apply to students who are not meteorology majors has been less demonstrated. To test this idea, a weather discussion and an eLearning…

  15. Engaging Earth- and Environmental-Science Undergraduates through Weather Discussions and an eLearning Weather Forecasting Contest

    Science.gov (United States)

    Schultz, David M.; Anderson, Stuart; Seo-Zindy, Ryo

    2013-01-01

    For students who major in meteorology, engaging in weather forecasting can motivate learning, develop critical-thinking skills, improve their written communication, and yield better forecasts. Whether such advances apply to students who are not meteorology majors has been less demonstrated. To test this idea, a weather discussion and an eLearning…

  16. Looking toward to the next-generation space weather forecast system. Comments former a former space weather forecaster

    Energy Technology Data Exchange (ETDEWEB)

    Tomita, Fumihiko [Communications Research Laboratory, Koganei, Tokyo (Japan)

    1999-03-01

    In the 21st century, man's space-based activities will increase significantly and many kinds of space utilization technologies will assume a vital role in the infrastructure, creating new businesses, securing the global environment, contributing much to human welfare in the world. Communications Research Laboratory (CRL) has been contributing to the safety of human activity in space and to the further understanding of the solar terrestrial environment through the study of space weather, including the upper atmosphere, magnetosphere, interplanetary space, and the sun. The next-generation Space Weather Integrated Monitoring System (SWIMS) for future space activities based on the present international space weather forecasting system is introduced in this paper. (author)

  17. WEATHER FORECAST DATA SEMANTIC ANALYSIS IN F-LOGIC

    Directory of Open Access Journals (Sweden)

    Ana Meštrović

    2007-06-01

    Full Text Available This paper addresses the semantic analysis problem in a spoken dialog system developed for the domain of weather forecasts. The main goal of semantic analysis is to extract the meaning from the spoken utterances and to transform it into a domain database format. In this work a semantic database for the domain of weather forecasts is represented using the F-logic formalism. Semantic knowledge is captured through semantic categories a semantic dictionary using phrases and output templates. Procedures for semantic analysis of Croatian weather data combine parsing techniques for Croatian language and slot filling approach. Semantic analysis is conducted in three phases. In the first phase the main semantic category for the input utterance is determined. The lattices are used for hierarchical semantic relation representation and main category derivation. In the second phase semantic units are analyzed and knowledge slots in the database are filled. Since some slot values of input data are missing in the third phase, incomplete data is updated with missing values. All rules for semantic analysis are defined in the F-logic and implemented using the FLORA-2 system. The results of semantic analysis evaluation in terms of frame and slot error rates are presented.

  18. Flood Forecast and Early Warning with High-Resolution Ensemble Rainfall from Numerical Weather Prediction Model

    OpenAIRE

    Yu, Wansik; NAKAKITA, Eiichi; Jung, Kwansue

    2016-01-01

    This paper investigates the applicability of ensemble forecasts of numerical weather prediction (NWP) model for flood forecasting. In this study, 10 km resolution ensemble rainfalls forecast and their downscaled forecasts of 2 km resolution were used in the hydrologic model as input data for flood forecasting and application of flood early warning. Ensemble data consists of 51 members and 48 hr forecast time. Ensemble outputs are verified spatially whether they can produce suitable rainfall p...

  19. Hourly weather forecasts for gas turbine power generation

    Directory of Open Access Journals (Sweden)

    G. Giunta

    2017-06-01

    Full Text Available An hourly short-term weather forecast can optimize processes in Combined Cycle Gas Turbine (CCGT plants by helping to reduce imbalance charges on the national power grid. Consequently, a reliable meteorological prediction for a given power plant is crucial for obtaining competitive prices for the electric market, better planning and stock management, sales and supplies of energy sources. The paper discusses the short-term hourly temperature forecasts, at lead time day+1 and day+2, over a period of thirteen months in 2012 and 2013 for six Italian CCGT power plants of 390 MW each (260 MW from the gas turbine and 130 MW from the steam turbine. These CCGT plants are placed in three different Italian climate areas: the Po Valley, the Adriatic coast, and the North Tyrrhenian coast. The meteorological model applied in this study is the eni-Kassandra Meteo Forecast (e‑kmf™, a multi-model approach system to provide probabilistic forecasts with a Kalman filter used to improve accuracy of local temperature predictions. Performance skill scores, computed by the output data of the meteorological model, are compared with local observations, and used to evaluate forecast reliability. In the study, the approach has shown good overall scores encompassing more than 50,000 hourly temperature values. Some differences from one site to another, due to local meteorological phenomena, can affect the short-term forecast performance, with consequent impacts on gas-to-power production and related negative imbalances. For operational application of the methodology in CCGT power plant, the benefits and limits have been successfully identified.

  20. Mountain range specific analog weather forecast model for northwest Himalaya in India

    Indian Academy of Sciences (India)

    D Singh; A Ganju

    2008-10-01

    Mountain range specific analog weather forecast model is developed utilizing surface weather observations of reference stations in each mountain range in northwest Himalaya (NW-Himalaya).The model searches past similar cases from historical dataset of reference observatory in each mountain range based on current situation.The searched past similar cases of each mountain range are used to draw weather forecast for that mountain range in operational weather forecasting mode, three days in advance.The developed analog weather forecast model is tested with the independent dataset of more than 717 days (542 days for Pir Panjal range in HP)of the past 4 winters (2003 –2004 to 2006 –2007).Independent test results are reasonably good and suggest that there is some possibility of forecasting weather in operational weather forecasting mode employing analog method over different mountain ranges in NW-Himalaya.Significant difference in overall accuracy of the model is found for prediction of snow day and no-snow day over different mountain ranges, when weather is predicted under snow day and no-snow day weather forecast categories respectively.In the same mountain range,signi ficant difference is also found in overall accuracy of the model for prediction of snow day and no-snow day for different areas.This can be attributed to their geographical position and topographical differences.The analog weather forecast model performs better than persistence and climatological forecast for day-1 predictions for all the mountain ranges except Karakoram range in NW-Himalaya.The developed analog weather forecast model may help as a guidance tool for forecasting weather in operational weather forecasting mode in different mountain ranges in NW-Himalaya.

  1. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv

  2. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv

  3. Forecasting Safe or Dangerous Space Weather from HMI Magnetograms

    Science.gov (United States)

    Falconer, David; Barghouty, Abdulnasser F.; Khazanov, Igor; Moore, Ron

    2011-01-01

    We have developed a space-weather forecasting tool using an active-region free-energy proxy that was measured from MDI line-of-sight magnetograms. To develop this forecasting tool (Falconer et al 2011, Space Weather Journal, in press), we used a database of 40,000 MDI magnetograms of 1300 active regions observed by MDI during the previous solar cycle (cycle 23). From each magnetogram we measured our free-energy proxy and for each active region we determined its history of major flare, CME and Solar Particle Event (SPE) production. This database determines from the value of an active region s free-energy proxy the active region s expected rate of production of 1) major flares, 2) CMEs, 3) fast CMEs, and 4) SPEs during the next few days. This tool was delivered to NASA/SRAG in 2010. With MDI observations ending, we have to be able to use HMI magnetograms instead of MDI magnetograms. One of the difficulties is that the measured value of the free-energy proxy is sensitive to the spatial resolution of the measured magnetogram: the 0.5 /pixel resolution of HMI gives a different value for the free-energy proxy than the 2 /pixels resolution of MDI. To use our MDI-database forecasting curves until a comparably large HMI database is accumulated, we smooth HMI line-of-sight magnetograms to MDI resolution, so that we can use HMI to find the value of the free-energy proxy that MDI would have measured, and then use the forecasting curves given by the MDI database. The new version for use with HMI magnetograms was delivered to NASA/SRAG (March 2011). It can also use GONG magnetograms, as a backup.

  4. Origins of forecast skill of weather and climate events on verifiable time scales

    CSIR Research Space (South Africa)

    Landman, WA

    2012-07-01

    Full Text Available Verification of weather and seasonal forecasts, as well as the statistical analysis of the spatial and temporal description of forecast and observed fields, are necessary to improve on our understanding of the capabilities of models to describe...

  5. A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

    Directory of Open Access Journals (Sweden)

    Yiqi Chu

    2016-10-01

    Full Text Available Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28–52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.

  6. The Scientific Foundations of Forecasting Magnetospheric Space Weather

    Science.gov (United States)

    Eastwood, J. P.; Nakamura, R.; Turc, L.; Mejnertsen, L.; Hesse, M.

    2017-08-01

    The magnetosphere is the lens through which solar space weather phenomena are focused and directed towards the Earth. In particular, the non-linear interaction of the solar wind with the Earth's magnetic field leads to the formation of highly inhomogenous electrical currents in the ionosphere which can ultimately result in damage to and problems with the operation of power distribution networks. Since electric power is the fundamental cornerstone of modern life, the interruption of power is the primary pathway by which space weather has impact on human activity and technology. Consequently, in the context of space weather, it is the ability to predict geomagnetic activity that is of key importance. This is usually stated in terms of geomagnetic storms, but we argue that in fact it is the substorm phenomenon which contains the crucial physics, and therefore prediction of substorm occurrence, severity and duration, either within the context of a longer-lasting geomagnetic storm, but potentially also as an isolated event, is of critical importance. Here we review the physics of the magnetosphere in the frame of space weather forecasting, focusing on recent results, current understanding, and an assessment of probable future developments.

  7. Ionospheric forecasts for the European region for space weather applications

    Directory of Open Access Journals (Sweden)

    Tsagouri Ioanna

    2015-01-01

    Full Text Available This paper discusses recent advances in the implementation and validation of the Solar Wind driven autoregression model for Ionospheric short-term Forecast (SWIF that is running in the European Digital upper Atmosphere Server (DIAS to release ionospheric forecasting products for the European region. The upgraded implementation plan expands SWIF’s capabilities in the high latitude ionosphere while the extensive validation tests in the two solar cycles 23 and 24 allow the comprehensive analysis of the model’s performance in all terms. Focusing on disturbed conditions, the results demonstrate that SWIF’s alert detection algorithm forecasts the occurrence of ionospheric storm time disturbances with probability of detection up to 98% under intense geomagnetic storm conditions and up to 63% when storms of moderate intensity are also considered. The forecasts show relative improvement over climatology of about 30% in middle-to-low and high latitudes and 40% in middle-to-high latitudes. This indicates that SWIF is able to capture on average more than one third (35% of the storm-associated ionospheric disturbances. Regarding the accuracy, the averaged mean relative error during storm conditions usually ranges around 20% in middle-to-low and high latitudes and 24% in the middle-to-high latitudes. Our analysis shows clearly that SWIF alert criteria were designed to effectively anticipate the ionospheric storm time effects that occurred under specific interplanetary conditions, e.g., cloud Interplanetary Coronal Mass Ejections (ICMEs and/or associated sheaths. The results provide valuable input in advancing our ability in predicting the space weather effects in the ionosphere for future developments, and further work is proposed to enhance the model forecasting efficiency to support operational applications.

  8. Engaging Earth- and Environmental-Science Undergraduates Through Weather Discussions and an eLearning Weather Forecasting Contest

    Science.gov (United States)

    Schultz, David M.; Anderson, Stuart; Seo-Zindy, Ryo

    2013-06-01

    For students who major in meteorology, engaging in weather forecasting can motivate learning, develop critical-thinking skills, improve their written communication, and yield better forecasts. Whether such advances apply to students who are not meteorology majors has been less demonstrated. To test this idea, a weather discussion and an eLearning weather forecasting contest were devised for a meteorology course taken by third-year undergraduate earth- and environmental-science students. The discussion consisted of using the recent, present, and future weather to amplify the topics of the week's lectures. Then, students forecasted the next day's high temperature and the probability of precipitation for Woodford, the closest official observing site to Manchester, UK. The contest ran for 10 weeks, and the students received credit for participation. The top students at the end of the contest received bonus points on their final grade. A Web-based forecast contest application was developed to register the students, receive their forecasts, and calculate weekly standings. Students who were successful in the forecast contest were not necessarily those who achieved the highest scores on the tests, demonstrating that the contest was possibly testing different skills than traditional learning. Student evaluations indicate that the weather discussion and contest were reasonably successful in engaging students to learn about the weather outside of the classroom, synthesize their knowledge from the lectures, and improve their practical understanding of the weather. Therefore, students taking a meteorology class, but not majoring in meteorology, can derive academic benefits from weather discussions and forecast contests. Nevertheless, student evaluations also indicate that better integration of the lectures, weather discussions, and the forecasting contests is necessary.

  9. Downscaling Global Weather Forecast Outputs Using ANN for Flood Prediction

    Directory of Open Access Journals (Sweden)

    Nam Do Hoai

    2011-01-01

    Full Text Available Downscaling global weather prediction model outputs to individual locations or local scales is a common practice for operational weather forecast in order to correct the model outputs at subgrid scales. This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron (MLP neural network. The MLP architecture was optimized by considering physical bases that determine the circulation of atmospheric variables. Downscaled precipitation was then used as inputs to the super tank model (runoff model for flood prediction. The case study was conducted for the Thu Bon River Basin, located in Central Vietnam. Study results showed that the precipitation predicted by MLP outperformed that directly obtained from model outputs or downscaled using multiple linear regression. Consequently, flood forecast based on the downscaled precipitation was very encouraging. It has demonstrated as a robust technology, simple to implement, reliable, and universal application for flood prediction through the combination of downscaling model and super tank model.

  10. Improving Weather Research and Forecasting Model Initial Conditions via Surface Pressure Analysis

    Science.gov (United States)

    2015-09-01

    Obsgrid) that creates input data for the Advanced Research version of the Weather Research and Forecasting model (WRF-ARW) is modified to perform a...Configuration  The Advanced Research version of the Weather Research and Forecasting model (WRF-ARW) V3.6.1 (Skamarock et al. 2008) is applied with 56 vertical...those with more benign weather. On 7 February a trough moved onshore and led to widespread precipitation in the region . More quiescent weather was in

  11. Forecasting Space Weather Events for a Neighboring World

    Science.gov (United States)

    Zheng, Yihua; Mason, Tom; Wood, Erin L.

    2015-01-01

    Shortly after NASA's Mars Atmosphere and Volatile EvolutioN mission (MAVEN) spacecraft entered Mars' orbit on 21 September 2014, scientists glimpsed the Martian atmosphere's response to a front of solar energetic particles (SEPs) and an associated coronal mass ejection (CME). In response to some solar flares and CMEs, streams of SEPs burst from the solar atmosphere and are further accelerated in the interplanetary medium between the Sun and the planets. These particles deposit their energy and momentum into anything in their path, including the Martian atmosphere and MAVEN particle detectors. MAVEN scientists had been alerted to the likely CME-Mars encounter by a space weather prediction system that had its origins in space weather forecasting for Earth but now forecasts space weather for Earth's neighboring planets. The two Solar Terrestrial Relations Observatory spacecraft and Solar Heliospheric Observatory observed a CME on 26 September, with a trajectory that suggested a Mars intercept. A computer model developed for solar wind prediction, the Wang-Sheeley-Arge-Enlil cone model [e.g., Zheng et al., 2013; Parsons et al., 2011], running in real time at the Community Coordinated Modeling Center (CCMC) located at NASA Goddard since 2006, showed the CME propagating in the direction of Mars (Figure 1). According to MAVEN particle detectors, the disturbance and accompanying SEP enhancement at the leading edge of the CME reached Mars at approximately 17 hours Universal Time on 29 September 2014. Such SEPs may have a profound effect on atmospheric escape - they are believed to be a possible means for driving atmospheric loss. SEPs can cause loss of Mars' upper atmosphere through several loss mechanisms including sputtering of the atmosphere. Sputtering occurs when atoms are ejected from the atmosphere due to impacts with energetic particles.

  12. Forecasting Space Weather Events for a Neighboring World

    Science.gov (United States)

    Zheng, Yihua; Mason, Tom; Wood, Erin L.

    2015-01-01

    Shortly after NASA's Mars Atmosphere and Volatile EvolutioN mission (MAVEN) spacecraft entered Mars' orbit on 21 September 2014, scientists glimpsed the Martian atmosphere's response to a front of solar energetic particles (SEPs) and an associated coronal mass ejection (CME). In response to some solar flares and CMEs, streams of SEPs burst from the solar atmosphere and are further accelerated in the interplanetary medium between the Sun and the planets. These particles deposit their energy and momentum into anything in their path, including the Martian atmosphere and MAVEN particle detectors. MAVEN scientists had been alerted to the likely CME-Mars encounter by a space weather prediction system that had its origins in space weather forecasting for Earth but now forecasts space weather for Earth's neighboring planets. The two Solar Terrestrial Relations Observatory spacecraft and Solar Heliospheric Observatory observed a CME on 26 September, with a trajectory that suggested a Mars intercept. A computer model developed for solar wind prediction, the Wang-Sheeley-Arge-Enlil cone model [e.g., Zheng et al., 2013; Parsons et al., 2011], running in real time at the Community Coordinated Modeling Center (CCMC) located at NASA Goddard since 2006, showed the CME propagating in the direction of Mars (Figure 1). According to MAVEN particle detectors, the disturbance and accompanying SEP enhancement at the leading edge of the CME reached Mars at approximately 17 hours Universal Time on 29 September 2014. Such SEPs may have a profound effect on atmospheric escape - they are believed to be a possible means for driving atmospheric loss. SEPs can cause loss of Mars' upper atmosphere through several loss mechanisms including sputtering of the atmosphere. Sputtering occurs when atoms are ejected from the atmosphere due to impacts with energetic particles.

  13. Weather Stations as Educational and Hazard-Forecasting Tools

    Science.gov (United States)

    Bowman, L. J.; Gierke, J. S.; Gochis, E. E.; Dominguez, R.; Mayer, A. S.

    2014-12-01

    Small, relatively inexpensive (educational opportunities at all grade levels, while also facilitating compilation of climate data for longer term research. Weather stations and networks of stations have been installed both locally and abroad in mostly rural and resource-limited settings. The data are being used either in the classroom to engage students in place-based, scientific investigations and/or research to improve hydrometeorological hazard forecasting, including water scarcity. The San Vicente (El Salvador) Network of six stations monitors rainfall to aid warning and evacuations for landslide and flooding hazards. Other parameters are used in modeling the watershed hydrology. A station installed in Hermosillo, Mexico is used in both Geography and Ecology Classes. Trends in temperature and rainfall are graphed and compared to historic data gathered over the last 30 years by CONAGUA. These observations are linked to local water-related problems, including well salinization, diminished agriculture, depleted aquifers, and social conflict regarding access to water. Two weather stations were installed at the Hannahville Indian Community School (Nah Tah Wahsh) in Michigan for educational purposes of data collection, analysis, and presentation. Through inquiry-based explorations of local hydrological processes, students are introduced to how meteorological data are used in understanding watershed hydrology and the sustainable management of groundwater resources. Several Michigan Technological University Peace Corps Masters International students have deployed weather stations in and around the communities where they serve, and the data are used in research to help in understanding water resource availability and irrigation needs.

  14. Types of Forecast and Weather-Related Information Used among Tourism Businesses in Coastal North Carolina

    Science.gov (United States)

    Ayscue, Emily P.

    This study profiles the coastal tourism sector, a large and diverse consumer of climate and weather information. It is crucial to provide reliable, accurate and relevant resources for the climate and weather-sensitive portions of this stakeholder group in order to guide them in capitalizing on current climate and weather conditions and to prepare them for potential changes. An online survey of tourism business owners, managers and support specialists was conducted within the eight North Carolina oceanfront counties asking respondents about forecasts they use and for what purposes as well as why certain forecasts are not used. Respondents were also asked about their perceived dependency of their business on climate and weather as well as how valuable different forecasts are to their decision-making. Business types represented include: Agriculture, Outdoor Recreation, Accommodations, Food Services, Parks and Heritage, and Other. Weekly forecasts were the most popular forecasts with Monthly and Seasonal being the least used. MANOVA and ANOVA analyses revealed outdoor-oriented businesses (Agriculture and Outdoor Recreation) as perceiving themselves significantly more dependent on climate and weather than indoor-oriented ones (Food Services and Accommodations). Outdoor businesses also valued short-range forecasts significantly more than indoor businesses. This suggests a positive relationship between perceived climate and weather dependency and forecast value. The low perceived dependency and value of short-range forecasts of indoor businesses presents an opportunity to create climate and weather information resources directed at how they can capitalize on positive climate and weather forecasts and how to counter negative effects with forecasted adverse conditions. The low use of long-range forecasts among all business types can be related to the low value placed on these forecasts. However, these forecasts are still important in that they are used to make more

  15. Reducing Probabilistic Weather Forecasts to the Worst-Case Scenario: Anchoring Effects

    Science.gov (United States)

    Joslyn, Susan; Savelli, Sonia; Nadav-Greenberg, Limor

    2011-01-01

    Many weather forecast providers believe that forecast uncertainty in the form of the worst-case scenario would be useful for general public end users. We tested this suggestion in 4 studies using realistic weather-related decision tasks involving high winds and low temperatures. College undergraduates, given the statistical equivalent of the…

  16. Space Weather Forecasting and Research at the Community Coordinated Modeling Center

    Science.gov (United States)

    Aronne, M.

    2015-12-01

    The Space Weather Research Center (SWRC), within the Community Coordinated Modeling Center (CCMC), provides experimental research forecasts and analysis for NASA's robotic mission operators. Space weather conditions are monitored to provide advance warning and forecasts based on observations and modeling using the integrated Space Weather Analysis Network (iSWA). Space weather forecasters come from a variety of backgrounds, ranging from modelers to astrophysicists to undergraduate students. This presentation will discuss space weather operations and research from an undergraduate perspective. The Space Weather Research, Education, and Development Initiative (SW REDI) is the starting point for many undergraduate opportunities in space weather forecasting and research. Space weather analyst interns play an active role year-round as entry-level space weather analysts. Students develop the technical and professional skills to forecast space weather through a summer internship that includes a two week long space weather boot camp, mentorship, poster session, and research opportunities. My unique development of research projects includes studying high speed stream events as well as a study of 20 historic, high-impact solar energetic particle events. This unique opportunity to combine daily real-time analysis with related research prepares students for future careers in Heliophysics.

  17. Weather factors in the short-term forecasting of daily ambulance calls.

    Science.gov (United States)

    Wong, Ho-Ting; Lai, Poh-Chin

    2014-07-01

    The daily ambulance demand for Hong Kong is rising, and it has been shown that weather factors (temperature and humidity) play a role in the demand for ambulance services. This study aimed at developing short-term forecasting models of daily ambulance calls using the 7-day weather forecast data as predictors. We employed the autoregressive integrated moving average (ARIMA) method to analyze over 1.3 million cases of emergency attendance in May 2006 through April 2009 and the 7-day weather forecast data for the same period. Our results showed that the ARIMA model could offer reasonably accurate forecasts of daily ambulance calls at 1-7 days ahead of time and with improved accuracy by including weather factors. Specifically, the inclusion of average temperature alone in our ARIMA model improved the predictability of the 1-day forecast when compared to that of a simple ARIMA model (8.8% decrease in the root mean square error, RMSE=53 vs 58). The improvement in the 7-day forecast with average temperature as a predictor was more pronounced, with a 10% drop in prediction error (RMSE=62 vs 69). These findings suggested that weather forecast data can improve the 1- to 7-day forecasts of daily ambulance demand. As weather forecast data are readily accessible from Hong Kong Observatory's official website, there is virtually no cost to including them in the ARIMA models, which yield better prediction for forward planning and deployment of ambulance manpower.

  18. Weather factors in the short-term forecasting of daily ambulance calls

    Science.gov (United States)

    Wong, Ho-Ting; Lai, Poh-Chin

    2014-07-01

    The daily ambulance demand for Hong Kong is rising, and it has been shown that weather factors (temperature and humidity) play a role in the demand for ambulance services. This study aimed at developing short-term forecasting models of daily ambulance calls using the 7-day weather forecast data as predictors. We employed the autoregressive integrated moving average (ARIMA) method to analyze over 1.3 million cases of emergency attendance in May 2006 through April 2009 and the 7-day weather forecast data for the same period. Our results showed that the ARIMA model could offer reasonably accurate forecasts of daily ambulance calls at 1-7 days ahead of time and with improved accuracy by including weather factors. Specifically, the inclusion of average temperature alone in our ARIMA model improved the predictability of the 1-day forecast when compared to that of a simple ARIMA model (8.8 % decrease in the root mean square error, RMSE = 53 vs 58). The improvement in the 7-day forecast with average temperature as a predictor was more pronounced, with a 10 % drop in prediction error (RMSE = 62 vs 69). These findings suggested that weather forecast data can improve the 1- to 7-day forecasts of daily ambulance demand. As weather forecast data are readily accessible from Hong Kong Observatory's official website, there is virtually no cost to including them in the ARIMA models, which yield better prediction for forward planning and deployment of ambulance manpower.

  19. Influence of Met-Ocean Condition Forecasting Uncertainties on Weather Window Predictions for Offshore Operations

    DEFF Research Database (Denmark)

    Gintautas, Tomas; Sørensen, John Dalsgaard

    2017-01-01

    The article briefly presents a novel methodology of weather window estimation for offshore operations and mainly focuses on effects of met-ocean condition forecasting uncertainties on weather window predictions when using the proposed methodology. It is demonstrated that the proposed methodology...... to include stochastic variables, representing met-ocean forecasting uncertainties and the results of such modification are given in terms of predicted weather windows for a selected test case....

  20. Opportunities and challenges of indigenous biotic weather forecasting among the Borena herders of southern Ethiopia.

    Science.gov (United States)

    Ayal, Desalegn Yayeh; Desta, Solomon; Gebru, Getachew; Kinyangi, James; Recha, John; Radeny, Maren

    2015-01-01

    The practical utilization of available modern as well as traditional weather forecasting systems builds herders' resiliency capacity to climatic shocks. The precision and reliability of the forecasting system determines its creditability and acceptance by the users to be proactive in the decisions they make based on the forecasted information. It has been postulated that traditional weather forecasting systems are becoming less reliable due to repeated faulty forecasts. The study assesses the current status of the Borana traditional weather forecasting system and how traditional experts make weather forecasts based on biotic indicators such as intestinal readings, changes in plant and animal body languages. Questionnaire survey, field observations, focus group discussions and interviews with relevant key informants were employed to obtain data. Collected field data was compared with National Metrological Service Agency instrumental data for consistency. Results reveal that herders made short term weather forecasts using intestinal readings, and observed changes in plant and animal body languages. The study shows the extent how public confidence in the accuracy of indigenous weather forecasting skills has been gradually eroded overtime due to faulty forecasts. The precision and credibility of the traditional weather forecast steadily declined and led to repeated faulty predictions. Poor documentation, oral based knowledge transfer system, influence of religion and modern education, aging and extinction of traditional experts were identified as the major causes undermining the vitality of traditional climate forecast. Traditional weather foresting knowledge and skill could have some utility and also serve as a starting point to scientifically study the relationship between various signs and implied climatic events. This article recommends before traditional Borana weather forecasting system completely disappears, a remedial action should be carried out to rescue this

  1. 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.

  2. 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.

  3. Tools and Products of Real-Time Modeling: Opportunities for Space Weather Forecasting

    Science.gov (United States)

    Hesse, Michael

    2009-01-01

    The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involves model evaluations, model transitions to operations, and the development of draft Space Weather forecasting tools. This presentation will focus on the last element. Specifically, we will discuss present capabilities, and the potential to derive further tools. These capabilities will be interpreted in the context of a broad-based, bootstrapping activity for modern Space Weather forecasting.

  4. Use of weather forecasts to control night cooling

    Energy Technology Data Exchange (ETDEWEB)

    Wittchen, K.B. [Danish Building Research Inst., Horsholm (Denmark); Logberg, E.; Pedersen, S.; Djurtoft, R. [Birch and Krogboe Consultants and Planners, Virum (Denmark); Thiesen, J. [Vejr2 Private Weather Service of Denmark, Roskilde (Denmark)

    2005-07-01

    Night cooling can reduce the energy consumption for conditioning spaces in a building by allowing cool night air to flow as free cooling through a mechanical ventilation system or as ventilation driven by natural buoyancy. This study examined the feasibility of saving energy by having a better control strategy for night cooling in an office building. The work was based on the premise that having accurate knowledge of the weather would allow for greater control of night cooling. The enhanced control should result in both energy savings and thermal comfort. Traditional night cooling strategies create challenges in cold or moderate climates during the first hours of work because if the buildings are cooled to eliminate the cooling load of the following day, the indoor operative temperature will likely be too low to be acceptable. The solution is to provide a boost of hot air in the morning to compensate for the cool construction. The energy savings are attributed to the minimization of the boost of heated air in the morning to overcome thermal discomfort due to cold construction, and to the storage of the appropriate amount of cooling in the construction when the external loads of the following day are known. It was concluded that the use of local weather forecasts is a promising approach to control passive night cooling. Night ventilation driven by mechanical ventilation system will increase the energy use of the fans and this increase may be in the same order of magnitude as the energy savings obtained by reducing the cooling load due to efficient night cooling. It was noted that further development of the method is needed before implementation in real buildings. 9 refs., 2 tabs., 9 figs.

  5. How to judge the quality and value of weather forecast products

    Science.gov (United States)

    Thornes, John E.; Stephenson, David B.

    2001-09-01

    In order to decide whether or not a weather service supplier is giving good value for money we need to monitor the quality of the forecasts and the use that is made of the forecasts to estimate their value. A number of verification statistics are examined to measure the quality of forecasts - including Miss Rate, False Alarm Rate, the Peirce Skill Score and the Odds Ratio Skill Score - and a means of testing the significance of these values is presented. In order to assess the economic value of the forecasts a value index is suggested that takes into account the cost-loss ratio and forecast errors. It is suggested that a combination of these quality and value statistics could be used by weather forecast customers to choose the best forecast provider and to set limits for performance related contracts.

  6. Sequential correction of ensemble regional weather predictions for forecasting reference evapotranspiration

    Science.gov (United States)

    Pelosi, Anna; Medina Gonzalez, Hanoi; Villani, Paolo; D'Urso, Guido; Battista Chirico, Giovanni

    2016-04-01

    This study explores the performance of an adaptive procedure for correcting the ensemble numerical weather outputs applied to the probabilistic forecast of reference evapotranspiration (ETo). This procedure is proposed as an effective forecast correction method when the available dataset is not large enough for the calibration of statistical batch procedures. The numerical weather prediction outputs are those provided by COSMO-LEPS, an ensemble-based Limited Area Model, with 16 members and 7.5 km spatial resolution, with forecast lead-time up to 5 days. ETo forecasts are computed according to the FAO Penman-Monteith (FAO-PM) equation, which requires data of five weather variables: air temperature, relative humidity, solar radiation and wind speed. The performance of the proposed procedure is evaluated at eighteen monitoring stations, located in Campania region (Southern Italy), with two alternative strategies: i) correction applied to the raw ensemble forecasts of the five weather variables prior applying the FAO-PM equation; ii) correction applied to the ensemble output of the ETo forecasts obtained with FAO-PM equation after using the raw ensemble weather forecasts as input. In both cases the suggested post-processing procedure was able to significantly increase the accuracy and reduce the uncertainty of the ETo forecasts.

  7. A review of operational, regional-scale, chemical weather forecasting models in Europe

    Directory of Open Access Journals (Sweden)

    J. Kukkonen

    2012-01-01

    Full Text Available Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.

  8. Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

    Science.gov (United States)

    Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

    2012-01-01

    Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

  9. State-of-the-art Space Weather Forecast with AFFECTS and HELCATS

    Science.gov (United States)

    Bothmer, Volker; Affects Team; Helcats Team

    2016-04-01

    The space weather projects fostered through the European Union FP7 and Horizon 2020 programs have opened up new horizons in the field of space weather research and have facilitated state-of-the-art-forecasts. Here we present an overview on the services and space weather forecasts the EU FP7 project AFFECTS (Advanced Forecast For Ensuring Communications Through Space) is providing and how the precision of the forecast is qualitatively greatly enhanced by new results derived from the EU FP7 project HELCATS (Heliospheric Cataloguing, Analysis, and Techniques Services). The forecast techniques base on near-real time multipoint analysis of coronal mass ejections observed by SOHO and STEREO and simulations of their Sun to Earth evolution.

  10. Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa

    Science.gov (United States)

    Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh

    2014-01-01

    Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

  11. The Joint Calibration Model in probabilistic weather forecasting: some preliminary issues

    Directory of Open Access Journals (Sweden)

    Patrizia Agati

    2013-05-01

    Full Text Available Ensemble Prediction Systems play today a fundamental role in weather forecasting. They can represent and measure uncertainty, thereby allowing distributional forecasting as well as deterministic-style forecasts. In this context, we show how the Joint Calibration Model (Agati et al., 2007 – based on a modelization of the Probability Integral Transform distribution – can provide a solution to the problem of information combining in probabilistic forecasting of continuous variables. A case study is presented, where the potentialities of the method are explored and the accuracy of deterministic-style forecasts from JCM is compared with that from Bayesian Model Averaging (Raftery et al., 2005.

  12. An integrated user-oriented weather forecast system for air traffic using real-time observations and model data

    OpenAIRE

    Forster, Caroline; Tafferner, Arnold

    2009-01-01

    This paper presents the Weather Forecast User-oriented System Including Object Nowcasting (WxFUSION), an integrated weather forecast system for air traffic. The system is currently under development within a new project named “Weather and Flying” under the leadership of the Institute of Atmospheric Physics (IPA) at the German Aerospace Center (DLR). WxFUSION aims at combining data from various sources, as there are weather observations, remote sensing, nowcasting and numerical model forecast ...

  13. Evaluating winds and vertical wind shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes

    DEFF Research Database (Denmark)

    Draxl, Caroline; Hahmann, Andrea N.; Pena Diaz, Alfredo

    2014-01-01

    The existence of vertical wind shear in the atmosphere close to the ground requires that wind resource assessment and prediction with numerical weather prediction (NWP) models use wind forecasts at levels within the full rotor span of modern large wind turbines. The performance of NWP models...... regarding wind energy at these levels partly depends on the formulation and implementation of planetary boundary layer (PBL) parameterizations in these models. This study evaluates wind speeds and vertical wind shears simulated by theWeather Research and Forecasting model using seven sets of simulations...

  14. Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

    Directory of Open Access Journals (Sweden)

    J. Schmidt

    2008-04-01

    Full Text Available A project established at the National Institute of Water and Atmospheric Research (NIWA in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO Numerical Weather Prediction model (NWP are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%.

  15. Development and Implementation of Dynamic Scripts to Support Local Model Verification at National Weather Service Weather Forecast Offices

    Science.gov (United States)

    Zavodsky, Bradley; Case, Jonathan L.; Gotway, John H.; White, Kristopher; Medlin, Jeffrey; Wood, Lance; Radell, Dave

    2014-01-01

    Local modeling with a customized configuration is conducted at National Weather Service (NWS) Weather Forecast Offices (WFOs) to produce high-resolution numerical forecasts that can better simulate local weather phenomena and complement larger scale global and regional models. The advent of the Environmental Modeling System (EMS), which provides a pre-compiled version of the Weather Research and Forecasting (WRF) model and wrapper Perl scripts, has enabled forecasters to easily configure and execute the WRF model on local workstations. NWS WFOs often use EMS output to help in forecasting highly localized, mesoscale features such as convective initiation, the timing and inland extent of lake effect snow bands, lake and sea breezes, and topographically-modified winds. However, quantitatively evaluating model performance to determine errors and biases still proves to be one of the challenges in running a local model. Developed at the National Center for Atmospheric Research (NCAR), the Model Evaluation Tools (MET) verification software makes performing these types of quantitative analyses easier, but operational forecasters do not generally have time to familiarize themselves with navigating the sometimes complex configurations associated with the MET tools. To assist forecasters in running a subset of MET programs and capabilities, the Short-term Prediction Research and Transition (SPoRT) Center has developed and transitioned a set of dynamic, easily configurable Perl scripts to collaborating NWS WFOs. The objective of these scripts is to provide SPoRT collaborating partners in the NWS with the ability to evaluate the skill of their local EMS model runs in near real time with little prior knowledge of the MET package. The ultimate goal is to make these verification scripts available to the broader NWS community in a future version of the EMS software. This paper provides an overview of the SPoRT MET scripts, instructions for how the scripts are run, and example use

  16. Training the next generation of scientists in Weather Forecasting: new approaches with real models

    Science.gov (United States)

    Carver, Glenn; Váňa, Filip; Siemen, Stephan; Kertesz, Sandor; Keeley, Sarah

    2014-05-01

    The European Centre for Medium Range Weather Forecasts operationally produce medium range forecasts using what is internationally acknowledged as the world leading global weather forecast model. Future development of this scientifically advanced model relies on a continued availability of experts in the field of meteorological science and with high-level software skills. ECMWF therefore has a vested interest in young scientists and University graduates developing the necessary skills in numerical weather prediction including both scientific and technical aspects. The OpenIFS project at ECMWF maintains a portable version of the ECMWF forecast model (known as IFS) for use in education and research at Universities, National Meteorological Services and other research and education organisations. OpenIFS models can be run on desktop or high performance computers to produce weather forecasts in a similar way to the operational forecasts at ECMWF. ECMWF also provide the Metview desktop application, a modern, graphical, and easy to use tool for analysing and visualising forecasts that is routinely used by scientists and forecasters at ECMWF and other institutions. The combination of Metview with the OpenIFS models has the potential to deliver classroom-friendly tools allowing students to apply their theoretical knowledge to real-world examples using a world-leading weather forecasting model. In this paper we will describe how the OpenIFS model has been used for teaching. We describe the use of Linux based 'virtual machines' pre-packaged on USB sticks that support a technically easy and safe way of providing 'classroom-on-a-stick' learning environments for advanced training in numerical weather prediction. We welcome discussions with interested parties.

  17. Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

    Science.gov (United States)

    O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

    2015-04-01

    Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

  18. Using Flow Charts to Visualize the Decision-Making Process in Space Weather Forecasting

    Science.gov (United States)

    Aung, M. T. Y.; Myat, T.; Zheng, Y.; Mays, M. L.; Ngwira, C.; Damas, M. C.

    2016-12-01

    Our society today relies heavily on technological systems such as satellites, navigation systems, power grids and aviation. These systems are very sensitive to space weather disturbances. When Earth-directed space weather driven by the Sun arrives at the Earth, it causes changes to the Earth's radiation environment and the magnetosphere. Strong disturbances in the magnetosphere of the Earth are responsible for geomagnetic storms that can last from hours to days depending on strength of storms. Geomagnetic storms can severely impact critical infrastructure on Earth, such as the electric power grid, and Solar Energetic Particles that can endanger life in outer space. How can we lessen these adverse effects? They can be lessened through the early warning signals sent by space weather forecasters before CME or high-speed stream arrives. A space weather forecaster's duty is to send predicted notifications to high-tech industries and NASA missions so that they could take extra measures for protection. NASA space weather forecasters make prediction decisions by following certain steps and processes from the time an event occurs at the sun all the way to the impact locations. However, there has never been a tool that helps these forecasters visualize the decision process until now. A flow chart is created to help forecasters visualize the decision process. This flow chart provides basic knowledge of space weather and can be used to train future space weather forecasters. It also helps to cut down the training period and increase consistency in forecasting. The flow chart is also a great reference for people who are already familiar with space weather.

  19. Optimal climate control of a storage facility using local weather forecasts

    NARCIS (Netherlands)

    Keesman, K.J.; Peters, D.; Lukasse, L.J.S.

    2003-01-01

    in this paper, the problem of optimal climate control of a potato storage facility exploiting favourable weather conditions is considered. A receding horizon optimal controller, allowing incorporation of real-time weather forecasts and input/state constraints and based on a reduced-order model, is c

  20. Forecasting and prospecting criteria of Au-bearing weathering mantles in southern Siberia, Russia

    Science.gov (United States)

    Kalinin, Yu. A.; Roslyakov, N. A.

    2012-04-01

    The geological and genetic model of gold concentration at different stages of weathering has been developed on the basis of long-standing geological and geochemical research of Au-bearing weathering mantles in the main ore fields of Siberia. This model makes it possible to substantiate forecasting and prospecting criteria of gold deposits hosted in linear karst zones of weathering, to delineate prospective areas in southern Siberia, and to estimate their resource potential.

  1. Opportunities and Challenges for Space Weather Forecasting (Invited)

    Science.gov (United States)

    Onsager, T. G.

    2010-12-01

    Although space weather disturbances have been relatively minor throughout the recent minimum of solar cycle, the demand for space weather information has increased dramatically. This has occurred among the commercial and government sectors within the U.S. as well as throughout an expanding number of countries internationally. This presentation will discuss the opportunities and the challenges associated with this growth in space weather interest. One key opportunity is with the increasing access to data and shared products from partner organizations throughout the world. For example, the World Meteorological Organization is now participating in the coordination of space weather data and products and raising the awareness of the importance of space weather globally. Our major challenge is to provide the timely and accurate space weather information needed to support the increasing demand. Priorities for new capabilities needed today as well as activities focused on coordinating the growing international interest in space weather will be presented.

  2. The Mauna Kea Weather Center: Custom Atmospheric Forecasting Support for Mauna Kea

    Science.gov (United States)

    Businger, Steven

    2011-03-01

    The success of operations at Mauna Kea Observatories is strongly influenced by weather conditions. The Mauna Kea Weather Center, an interdisciplinary research program, was established in 1999 to develop and provide custom weather support for Mauna Kea Observatories. The operational forecasting goals of the program are to facilitate the best possible use of favorable atmospheric conditions for scientific benefit and to ensure operational safety. During persistent clear periods, astronomical observing quality varies substantially due to changes in the vertical profiles of temperature, wind, moisture, and turbulence. Cloud and storm systems occasionally cause adverse or even hazardous conditions. A dedicated, daily, real-time mesoscale numerical modeling effort provides crucial forecast guidance in both cases. Several key atmospheric variables are forecast with sufficient skill to be of operational and scientific benefit to the telescopes on Mauna Kea. Summit temperature forecasts allow mirrors to be set to the ambient temperature to reduce image distortion. Precipitable water forecasts allow infrared observations to be prioritized according to atmospheric opacity. Forecasts of adverse and hazardous conditions protect the safety of personnel and allow for scheduling of maintenance when observing is impaired by cloud. The research component of the project continues to improve the accuracy and content of the forecasts. In particular, case studies have resulted in operational forecasts of astronomical observing quality, or seeing.

  3. Activities of the Japanese space weather forecast center at Communications Research Laboratory.

    Science.gov (United States)

    Watari, Shinichi; Tomita, Fumihiko

    2002-12-01

    The International Space Environment Service (ISES) is an international organization for space weather forecasts and belongs to the International Union of Radio Science (URSI). There are eleven ISES forecast centers in the world, and Communications Research Laboratory (CRL) runs the Japanese one. We make forecasts on the space environment and deliver them over the phones and through the Internet. Our forecasts could be useful for human activities in space. Currently solar activity is near maximum phase of the solar cycle 23. We report the several large disturbances of space environment occurred in 2001, during which low-latitude auroras were observed several times in Japan.

  4. Space Weather Education: Learning to Forecast at the Community Coordinated Modeling Center

    Science.gov (United States)

    Wold, A.

    2015-12-01

    Enhancing space weather education is important to space science endeavors. While participating in the Space Weather Research, Education and Development Initiative (SW REDI) Bootcamp and working as a Space Weather Analyst Intern, several innovative technologies and tools were integral to my learning and understanding of space weather analysis and forecasting. Two of the tools utilized in learning about space weather were the Integrated Space Weather Analysis System (iSWA) and the Space Weather Database Of Notifications, Knowledge, Information (DONKI). iSWA, a web-based dissemination system, hosts many state-of-the-art space weather models as well as real time space weather data from spacecraft such as Solar Dynamics Observatory and the Advanced Composition Explorer. As a customizable tool that operates in real-time while providing access also to historical data, iSWA proved essential in my understanding the drivers and impacts of space weather. DONKI was instrumental in accessing historical space weather events to understand the connections between solar phenomena and their effects on Earth environments. DONKI operates as a database of space weather events including linkages between causes and effects of space weather events. iSWA and DONKI are tools available also to the public. They not only enrich the space weather learning process but also allow researchers and model developers access to essential heliophysics and magnetospheric data.

  5. An overview on the Space Weather in Latin America: from Space Research to Space Weather and its Forecast

    Science.gov (United States)

    De Nardin, C. M.; Gonzalez-Esparza, A.; Dasso, S.

    2015-12-01

    We present an overview on the Space Weather in Latin America, highlighting the main findings from our review the recent advances in the space science investigations in Latin America focusing in the solar-terrestrial interactions, modernly named space weather, which leaded to the creation of forecast centers. Despite recognizing advances in the space research over the whole Latin America, this review is restricted to the evolution observed in three countries (Argentina, Brazil and Mexico) only, due to the fact that these countries have recently developed operational center for monitoring the space weather. The work starts with briefly mentioning the first groups that started the space science in Latin America. The current status and research interest of such groups are then described together with the most referenced works and the challenges for the next decade to solve space weather puzzles. A small inventory of the networks and collaborations being built is also described. Finally, the decision process for spinning off the space weather prediction centers from the space science groups is reported with an interpretation of the reason/opportunities that lead to it. Lastly, the constraints for the progress in the space weather monitoring, research, and forecast are listed with recommendations to overcome them.

  6. InFlight Weather Forecasts at Your Fingertips

    Science.gov (United States)

    2003-01-01

    A new information system is delivering real-time weather reports to pilots where they need it the most - inside their aircraft cockpits. Codeveloped by NASA and ViGYAN, Inc., the WSI InFlight(trademark) Cockpit Weather System provides a continuous, satellite-based broadcast of weather information to a portable or panel-mounted display inside the cockpit. With complete coverage and content for the continental United States at any altitude, the system is specifically designed for inflight use.

  7. FARMERS’ WILLINGNESS TO PAY FOR WEATHER FORECAST INFORMATION IN SAVELUGU-NANTON MUNICIPALITY OF THE NORTHERN REGION

    OpenAIRE

    Franklin, Nantui; Prince, Nketiah; Daniel, Darko

    2014-01-01

    In the quest for farmers to get maximum output and improve their livelihood within the major constraint of depending on rain-fed agriculture, the issue of having access to weather forecast information is very important. A contingent valuation method was used to elicit the amount farmers are willing to pay for accessing unpriced weather forecast information in the Savelugu-Nanton Municipality of the Northern Region. Farmers were also asked to rank weather forecast variables according to their ...

  8. National Weather Service (NWS) Implementation of the Hydrologic Ensemble Forecast Service

    Science.gov (United States)

    Hartman, R. K.; Fresch, M. A.; Wells, E.

    2015-12-01

    Operational hydrologic forecasters as well as the communities that they serve have long recognized the value of including uncertainty in hydrologic projections. While single value (deterministic) forecasts are easy to understand and link to specific mitigation actions, the potential for using modern risk management strategies is very limited. This is particularly evident at lead times beyond a few days when forecast skill may be low but the value (and costs) of mitigation actions may be quite high. Based on nearly ten years of research and development, the NWS's National Water Center (NWC, formerly the Office of Hydrologic Development) implemented and evaluated the Hydrologic Ensemble Forecast Service (HEFS, see Demargne et al. 2014 Brown et al., 2013, Brown et al., 2014a/b/c). The HEFS provides hydrologic forecasts that reflect the total uncertainty, including that contributed by the meteorological forcing and the hydrologic modeling. The HEFS leverages the skill in weather and climate forecasts to produce ensemble forecasts of precipitation, temperature and streamflow at forecast lead times ranging from one hour to one year. The resulting ensembles represent a rich dataset from which a wide variety of risk-based decision support information can be derived. The NWS River Forecast Centers (RFCs) are starting to incorporate the Hydrologic Ensemble Forecast Service (HEFS,) into their routine operations. In 2012, five (of thirteen) RFCs began running and testing HEFS in an experimental mode. In 2015, HEFS was deployed (including training and software support) to the eight remaining RFCs. Currently, all RFCs are running the HEFS every day in real-time for an increasing number of forecast locations. Eventually, forecasts from the HEFS will be integrated into the warning/hazard services at the NWS Weather Forecast Offices (WFOs). This contribution describes the HEFS framework, the development and deployment strategy, and the operational plans for HEFS going forward.

  9. Impact of prescribed diabatic heating on short range weather forecasts

    Science.gov (United States)

    Marx, L.; Shukla, J.

    1984-01-01

    Using the 9 layer general circulation model developed at the Goddard Laboratory for Atmospheric Sciences (GLAS), several 4 to 5 day integrations were made to assess the impact that latent heating processes (supersaturation and moist convective) have on the model forecasts. In an earlier study by Shukla (1981) it was hypothesized that because of strong interaction between dynamics and moist convection, small initial errors grow very fast and make short range forecasting difficult. The purpose of this study was to examine if prescribed heating rates can improve the forecasts for a few days.

  10. Densified GPS Estimates of Integrated Precipitable Water Vapor Improve Weather Forecasting during the North American Monsoon

    Science.gov (United States)

    Moore, A. W.; Small, I.; Gutman, S. I.; Bock, Y.; Dumas, J.; Haase, J. S.; Laber, J. L.

    2013-12-01

    Continuous GPS (CGPS) stations for observing crustal motion in the western U.S. now number more than 1200, with over 500 of them operating in real time. Tropospheric wet delay from real-time processing of the GPS data, along with co-located or nearby surface and temperature measurements, are being operationally converted to Integrated Precipitable Water Vapor (IPW) for evaluation as a forecasting tool (Gutman, 2011). The available density of real-time GPS in southern California now allows us to explore usage of densified GPS IPW in operational weather forecasting during weather conditions involving moisture extremes. Under a NASA Advanced Information Systems Technology (AIST) project, 27 southern California stations have been added to the NOAA GPS-Met observing network providing 30-minute estimates of IPW for ingestion into operational NOAA weather models, as well as for direct use by National Weather Service forecasters in monitoring developing weather conditions. The densified network proved advantageous in the 2013 North American Monsoon season, allowing forecasters to visualize rapid moisture increases at intervals between model runs and radiosonde observations and assisting in flood watch/warning decisions. We discuss the observed relationship between IPW and onset of precipitation in monsoon events in southern California and possibilities for additional decision support tools for forecasters.

  11. Forecasting irrigation demand by assimilating satellite images and numerical weather predictions

    Science.gov (United States)

    Pelosi, Anna; Medina, Hanoi; Villani, Paolo; Falanga Bolognesi, Salvatore; D'Urso, Guido; Battista Chirico, Giovanni

    2016-04-01

    Forecasting irrigation water demand, with small predictive uncertainty in the short-medium term, is fundamental for an efficient planning of water resource allocation among multiple users and for decreasing water and energy consumptions. In this study we present an innovative system for forecasting irrigation water demand, applicable at different spatial scales: from the farm level to the irrigation district level. The forecast system is centred on a crop growth model assimilating data from satellite images and numerical weather forecasts, according to a stochastic ensemble-based approach. Different sources of uncertainty affecting model predictions are represented by an ensemble of model trajectories, each generated by a possible realization of the model components (model parameters, input weather data and model state variables). The crop growth model is based on a set of simplified analytical relations, with the aim to assess biomass, leaf area index (LAI) growth and evapotranspiration rate with a daily time step. Within the crop growth model, LAI dynamics is let be governed by temperature and leaf dry matter supply, according to the development stage of the crop. The model assimilates LAI data retrieved from VIS-NIR high-resolution multispectral satellite images. Numerical weather model outputs are those from the European limited area ensemble prediction system (COSMO-LEPS), which provides forecasts up to five days with a spatial resolution of seven kilometres. Weather forecasts are sequentially bias corrected based on data from ground weather stations. The forecasting system is evaluated in experimental areas of southern Italy during three irrigation seasons. The performance analysis shows very accurate irrigation water demand forecasts, which make the proposed system a valuable support for water planning and saving at farm level as well as for water management at larger spatial scales.

  12. SEASAT economic assessment. Volume 9: Ports and harbors case study and generalization. [economic benefits of SEASAT satellites to harbors and shipping industries through improved weather forecasting

    Science.gov (United States)

    1975-01-01

    This case study and generalization quantify benefits made possible through improved weather forecasting resulting from the integration of SEASAT data into local weather forecasts. The major source of avoidable economic losses to shipping from inadequate weather forecasting data is shown to be dependent on local precipitation forecasting. The ports of Philadelphia and Boston were selected for study.

  13. Assimilation of microwave, infrared, and radio occultation satellite observations with a weather research and forecasting model for heavy rainfall forecasting

    Science.gov (United States)

    Boonyuen, Pakornpop; Wu, Falin; Phunthirawuth, Parwapath; Zhao, Yan

    2016-10-01

    In this research, satellite observation data were assimilated into Weather Research and Forecasting Model (WRF) by using Three-dimensional Variational Data Assimilation System (3DVAR) to analyze its impacts on heavy rainfall forecasts. The weather case for this research was during 13-18 September 2015. Tropical cyclone VAMCO, forming in South China Sea near with Vietnam, moved on west direction to the Northeast of Thailand. After passed through Vietnam, the tropical cyclone was become to depression and there was heavy rainfall throughout the area of Thailand. Observation data, used in this research, included microwave radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A), infrared radiance observations from Infrared Atmospheric Sounding Interferometer (IASI), and GPS radio occultation (RO) from the COSMIC and CHAMP missions. The experiments were designed in five cases, namely, 1) without data assimilation (CTRL); 2) with only RO data (RO); 3) with only AMSU-A data (AMSUA); 4) with only IASI data (IASI); and 5) with all of RO, AMSU-A and IASI data assimilation (ALL). Then all experiment results would be compared with both NCEP FNL (Final) Operational Global Analysis and the observation data from Thai Meteorological Department weather stations. The experiments result demonstrated that with microwave (AMSU-A), infrared (IASI) and GPS radio occultation (RO) data assimilation can produce the positive impact on analyses and forecast. All of satellite data assimilations have corresponding positive effects in term of temperature and humidity forecasting, and the GPS-RO assimilation produces the best of temperature and humidity forecast biases. The satellite data assimilation has a good impact on temperature and humidity in lower troposphere and vertical distribution that very helpful for heavy rainfall forecast improvement.

  14. Shallow Analysis on the First Spring Soaking Rain Weather and Its Precipitation Enhancement Potential Forecast in Jinzhou Area in 2010

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    [Objective] The research aimed to study the first spring soaking rain weather and its precipitation enhancement potential forecast in Jinzhou area in 2010. [Method] From the weather situation characteristics, main influence system, forecast service, artificial precipitation enhancement operation and so on, the first spring soaking rain weather process in Jinzhou area in 2010 was summarized comprehensively. [Result] The weather situation characteristics of soaking rain were that the high-altitude cold air wa...

  15. Cloud based N-dimensional weather forecast visualization tool with image analysis capabilities

    Science.gov (United States)

    Laka-Iñurrategi, M.; Alberdi, I.; Alonso, K.; Quartulli, M.

    2013-10-01

    Until recently the majority of data analysis and visualization tools were desktop applications that demanded high requirement hardware to carry out those processes. However, nowadays there is a trend to evolve this kind of applications to service based solutions that can be accessed remotely. Considering the implications that the weather has in the health and the safety of the human beings, authorities require a further knowledge of the weather forecasts and their impacts but they have difficulties to properly understand the raw forecasts since they usually are not experts in the field of meteorology. For this purpose, we have designed and implemented a framework that permits a remote access to weather forecasts. With this tool, the practitioners can access, visualise and interact with the data from a web browser. Furthermore, it contains an image and numeric analysis module that permits the generation of new information what is helpful in decision making processes.

  16. Realtime Space Weather Forecasts Via Android Phone App

    Science.gov (United States)

    Crowley, G.; Haacke, B.; Reynolds, A.

    2010-12-01

    For the past several years, ASTRA has run a first-principles global 3-D fully coupled thermosphere-ionosphere model in real-time for space weather applications. The model is the Thermosphere-Ionosphere Mesosphere Electrodynamics General Circulation Model (TIMEGCM). ASTRA also runs the Assimilative Mapping of Ionospheric Electrodynamics (AMIE) in real-time. Using AMIE to drive the high latitude inputs to the TIMEGCM produces high fidelity simulations of the global thermosphere and ionosphere. These simulations can be viewed on the Android Phone App developed by ASTRA. The SpaceWeather app for the Android operating system is free and can be downloaded from the Google Marketplace. We present the current status of realtime thermosphere-ionosphere space-weather forcasting and discuss the way forward. We explore some of the issues in maintaining real-time simulations with assimilative data feeds in a quasi-operational setting. We also discuss some of the challenges of presenting large amounts of data on a smartphone. The ASTRA SpaceWeather app includes the broadest and most unique range of space weather data yet to be found on a single smartphone app. This is a one-stop-shop for space weather and the only app where you can get access to ASTRA’s real-time predictions of the global thermosphere and ionosphere, high latitude convection and geomagnetic activity. Because of the phone's GPS capability, users can obtain location specific vertical profiles of electron density, temperature, and time-histories of various parameters from the models. The SpaceWeather app has over 9000 downloads, 30 reviews, and a following of active users. It is clear that real-time space weather on smartphones is here to stay, and must be included in planning for any transition to operational space-weather use.

  17. Convective Weather Forecast Quality Metrics for Air Traffic Management Decision-Making

    Science.gov (United States)

    Chatterji, Gano B.; Gyarfas, Brett; Chan, William N.; Meyn, Larry A.

    2006-01-01

    Since numerical weather prediction models are unable to accurately forecast the severity and the location of the storm cells several hours into the future when compared with observation data, there has been a growing interest in probabilistic description of convective weather. The classical approach for generating uncertainty bounds consists of integrating the state equations and covariance propagation equations forward in time. This step is readily recognized as the process update step of the Kalman Filter algorithm. The second well known method, known as the Monte Carlo method, consists of generating output samples by driving the forecast algorithm with input samples selected from distributions. The statistical properties of the distributions of the output samples are then used for defining the uncertainty bounds of the output variables. This method is computationally expensive for a complex model compared to the covariance propagation method. The main advantage of the Monte Carlo method is that a complex non-linear model can be easily handled. Recently, a few different methods for probabilistic forecasting have appeared in the literature. A method for computing probability of convection in a region using forecast data is described in Ref. 5. Probability at a grid location is computed as the fraction of grid points, within a box of specified dimensions around the grid location, with forecast convection precipitation exceeding a specified threshold. The main limitation of this method is that the results are dependent on the chosen dimensions of the box. The examples presented Ref. 5 show that this process is equivalent to low-pass filtering of the forecast data with a finite support spatial filter. References 6 and 7 describe the technique for computing percentage coverage within a 92 x 92 square-kilometer box and assigning the value to the center 4 x 4 square-kilometer box. This technique is same as that described in Ref. 5. Characterizing the forecast, following

  18. COST ES0602: towards a European network on chemical weather forecasting and information systems

    Directory of Open Access Journals (Sweden)

    J. Kukkonen

    2009-04-01

    Full Text Available The COST ES0602 action provides a forum for benchmarking approaches and practices in data exchange and multi-model capabilities for chemical weather forecasting and near real-time information services in Europe. The action includes approximately 30 participants from 19 countries, and its duration is from 2007 to 2011 (http://www.chemicalweather.eu/. Major efforts have been dedicated in other actions and projects to the development of infrastructures for data flow. We have therefore aimed for collaboration with ongoing actions towards developing near real-time exchange of input data for air quality forecasting. We have collected information on the operational air quality forecasting models on a regional and continental scale in a structured form, and inter-compared and evaluated the physical and chemical structure of these models. We have also constructed a European chemical weather forecasting portal that includes links to most of the available chemical weather forecasting systems in Europe. The collaboration also includes the examination of the case studies that have been organized within COST-728, in order to inter-compare and evaluate the models against experimental data. We have also constructed an operational model forecasting ensemble. Data from a representative set of regional background stations have been selected, and the operational forecasts for this set of sites will be inter-compared and evaluated. The Action has investigated, analysed and reviewed existing chemical weather information systems and services, and will provide recommendations on best practices concerning the presentation and dissemination of chemical weather information towards the public and decision makers.

  19. Forecasting Space Weather in the Upper Atmosphere: From Science to Prediction

    Science.gov (United States)

    Mannucci, A. J.; Meng, X.; Verkhoglyadova, O. P.; Tsurutani, B.; Wang, C.; Rosen, G.; Sharma, S.

    2016-12-01

    An objective of the original National Space Weather Program Strategic Plan (1999) was to foster science that has applications for the benefit of society. It was envisaged that scientific knowledge would lead to predictability of impactful space weather phenomena, similarly to what has occurred with numerical prediction of tropospheric weather. The link between scientific knowledge and predictability of natural phenomena is not a straightforward one, however, as the tropospheric community learned when trying to forecast weather systems several days ahead - the limitations of which led to the discovery of "chaos". In the thermosphere-ionosphere domain of space weather, the global behavior of the system is strongly dependent on driving from above and below, creating challenges to accurate prediction. We will describe our effort, part of the NASA/NSF Partnership For Collaborative Space Weather Modeling, to develop methods of improving predictive skill as scientific knowledge increases. Our approach is meant to succeed despite varying degrees of scientific understanding, and despite environmental factors that are often poorly constrained. Rather than focusing exclusively on increasing the complexity of, or coupling between models, we are developing forecasting tools that help understand what limits predictability in different situations. We will describe our algorithms for "forecast variables" (FVs) that are quantities derived from model outputs and observations. FVs are designed to provide insight into what limits predictive skill under geomagnetic storm conditions. We will present assessments of simulated "forecasts" for several upper atmosphere storms initiated by high-speed solar wind streams and coronal mass ejections, using the first principles-based Global Ionosphere Thermosphere Model (GITM). We will describe our approaches to data-driven and statistical forecasting, which serve as benchmarks that physics-based forecasts should improve upon.

  20. Weather forecasts, users' economic expenses and decision strategies

    Science.gov (United States)

    Carter, G. M.

    1972-01-01

    Differing decision models and operational characteristics affecting the economic expenses (i.e., the costs of protection and losses suffered if no protective measures have been taken) associated with the use of predictive weather information have been examined.

  1. Computer Models Used by AFGWC and NMC for Weather Analysis and Forecasting

    Science.gov (United States)

    1992-08-01

    significant amount of reference material available for the computer models used by Air Force weather forecasters, there is no single reference to all... material in this north-south plane is much more difficult to chapter includes the First-Guess forecast describe without the mathematics, but one model...VA 22304-6146 ............ 2 WSO, H & HSB Maimn Station Wee, MCA Tot.in CA 92710-000 ....... AULAE . Mazwell APB, AL 36112-6164

  2. Anvil Forecast Tool in the Advanced Weather Interactive Processing System, Phase II

    Science.gov (United States)

    Barrett, Joe H., III

    2008-01-01

    Meteorologists from the 45th Weather Squadron (45 WS) and Spaceflight Meteorology Group have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Light Rules. As a result, the Applied Meteorology Unit (AMU) created a graphical overlay tool for the Meteorological Interactive Data Display Systems (MIDDS) to indicate the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input.

  3. Weather Regimes: The Challenge in Extended-Range Forecasting

    Science.gov (United States)

    Reinhold, Brian

    1987-01-01

    A hypothesis to explain the low-frequency (10- to 90-day) variance of the mid-latitude atmosphere is presented. In this hypothesis it is proposed that the planetary-scale waves forced by topography or other zonal inhomogeneities and the day-to-day weather disturbances (synoptic scales) influence each other to generate weather regimes. These quasi-stable flow configurations are responsible for short-range climate anomalies such as droughts, heat waves, deep freezes, and excessive precipitation, as the weather-producing disturbances are organized into storm tracks. Onset and disruption of the weather regimes may be induced by the anomalous development of perhaps a single cyclonic disturbance, which can throw the quasi equilibrium out of balance. Thus, rapid changes in flow regimes can occur almost at random. The development and transition of weather regimes may then be purely internal (that is, depending only upon the properties of the fluid motions themselves) to the atmospheric dynamics. This internal quality suggests that the chaotic, abrupt short-range climatic behavior of the mid-latitudes is a natural behavior of the system that requires no assistance from the outside. The weather regime concept presents a different view of the extended-range atmospheric behavior than the stimulus-response model, such as the atmospheric response to the El Nino.

  4. The Art and Science of Long-Range Space Weather Forecasting

    Science.gov (United States)

    Hathaway, David H.; Wilson, Robert M.

    2006-01-01

    Long-range space weather forecasts are akin to seasonal forecasts of terrestrial weather. We don t expect to forecast individual events but we do hope to forecast the underlying level of activity important for satellite operations and mission pl&g. Forecasting space weather conditions years or decades into the future has traditionally been based on empirical models of the solar cycle. Models for the shape of the cycle as a function of its amplitude become reliable once the amplitude is well determined - usually two to three years after minimum. Forecasting the amplitude of a cycle well before that time has been more of an art than a science - usually based on cycle statistics and trends. Recent developments in dynamo theory -the theory explaining the generation of the Sun s magnetic field and the solar activity cycle - have now produced models with predictive capabilities. Testing these models with historical sunspot cycle data indicates that these predictions may be highly reliable one, or even two, cycles into the future.

  5. The Application of Synoptic Weather Forecasting Rules to Selected Weather Situations in the United States.

    Science.gov (United States)

    Kohler, Fred E.

    The document describes the use of weather maps and data in teaching introductory college courses in synoptic meteorology. Students examine weather changes at three-hour intervals from data obtained from the "Monthly Summary of Local Climatological Data." Weather variables in the local summary include sky cover, air temperature, dew point, relative…

  6. Current problems in communication from the weather forecast in the prevention of hydraulic and hydrogeological risk

    Science.gov (United States)

    Fazzini, Massimiliano; Vaccaro, Carmela

    2014-05-01

    The Italian territory is one of the most fragile hydraulic and hydro geologic of the world, due to its complexity physiographic, lithological and above meteo-climatic too. Moreover, In recent years, the unhappy urbanization, the abandonment of mountain areas and countryside have fostered hydro geological instability, ever more devastating, in relation to the extremes of meteorological events. After the dramatic floods and landscapes of the last 24 months - in which more than 50 people died - it is actually open a public debate on the issues related to prevention, forecasting and management of hydro-meteorological risk. Aim of the correct weather forecasting at different spatial and temporal scales is to avoid or minimize the potential occurrence of damage or human losses resulting from the increasingly of frequent extreme weather events. In Italy, there are two major complex problems that do not allow for effective dissemination of the correct weather forecasting. First, the absence of a national meteorological service - which can ensure the quality of information. In this regard, it is at an advanced stage the establishment of a unified national weather service - formed by technicians to national and regional civil protection and the Meteorological Service of the Air Force, which will ensure the quality of the prediction, especially through exclusive processing of national and local weather forecasting and hydro geological weather alert. At present, however, this lack favors the increasing diffusion of meteorological sites more or less professional - often totally not "ethical" - which, at different spatial scales, tend to amplify the signals from the weather prediction models, describing them the users of the web such as exceptional or rare phenomena and often causing unjustified alarmism. This behavior is almost always aimed at the desire of give a forecast before other sites and therefore looking for new commercial sponsors, with easy profits. On the other hand

  7. Description of Mixed-Phase Clouds in Weather Forecast and Climate Models

    Science.gov (United States)

    2014-09-30

    1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Description of Mixed-Phase Clouds in Weather Forecast...TERM GOALS To develop improved parameterizations of so-called mixed-phase stratocumulus in numerical models of weather and climate, and of their...impact on the surface energy budget over the Arctic Ocean, their impact on the vertical structure of the lower troposphere and relationships to larger

  8. Norway and Cuba Continue Collaborating to Build Capacity to Improve Weather Forecasting

    Science.gov (United States)

    Antuña, Juan Carlos; Kalnay, Eugenia; Mesquita, Michel D. S.

    2014-06-01

    The Future of Climate Extremes in the Caribbean Extreme Cuban Climate (XCUBE) project, which is funded by the Norwegian Directorate for Civil Protection as part of an assignment for the Norwegian Ministry of Foreign Affairs to support scientific cooperation between Norway and Cuba, carried out a training workshop on seasonal forecasting, reanalysis data, and weather research and forecasting (WRF). The workshop was a follow-up to the XCUBE workshop conducted in Havana in 2013 and provided Cuban scientists with access to expertise on seasonal forecasting, the WRF model developed by the National Center for Atmospheric Research (NCAR) and the community, data assimilation, and reanalysis.

  9. Comparison of radar and numerical weather model rainfall forecasts in the perspective of urban flood prediction

    DEFF Research Database (Denmark)

    Lovring, M. M.; Löwe, Roland; Courdent, Vianney Augustin Thomas

    (NWP) with assimilation of radar and cloud data (RA3), and Ensemble NWP with 25 members (S05) is conducted by comparing against rain gauge measurements and flood extent. Despite lower spatial and temporal resolution, the ensemble product seems promising for forecasting extreme events. A combination......An early flood warning system has been developed for urban catchments and is currently running in online operation in Copenhagen. The system is highly dependent on the quality of rainfall forecast inputs. An investigation of precipitation inputs from Radar Nowcast (RN), Numerical Weather Prediction...... of the three forecast products is expected to yield the optimal input for flood warning....

  10. Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS)

    OpenAIRE

    Pappenberger, F.; K. J. Beven; N. M. Hunter; Bates, P. D.; B. T. Gouweleeuw; Thielen, J.; A. P. J. De De Roo

    2005-01-01

    International audience; The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall-runoff forecast and flood inundation forecast) which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessa...

  11. Numerical Weather Forecasting at the Savannah River Site

    Energy Technology Data Exchange (ETDEWEB)

    Buckley, R.L.

    1999-01-26

    Facilities such as the Savannah River Site (SRS), which contain the potential for hazardous atmospheric releases, rely on the predictive capabilities of dispersion models to assess possible emergency response actions. The operational design in relation to domain size and forecast time is presented, along with verification of model results over extended time periods with archived surface observations.

  12. A verification of monthly weather forecasts in the seventies

    NARCIS (Netherlands)

    Oerlemans, J.; Nap, J.L.; Dool, H.M. van den

    1981-01-01

    Monthly forecasts of temperature, rainfall and sunshine have been verified during the period 1970–79. The predictions were based on seven different schemes. Of the seven methods, five refer to De Bilt (The Netherlands), one to southeast England and one to the Federal Republic of Germany. The results

  13. Rain Forecasting for Ho Chi Minh City Using Doppler Weather Radar Dwsr-2500C

    Directory of Open Access Journals (Sweden)

    Dung Dang Quoc

    2016-03-01

    Full Text Available Rainfall amounts vary randomly over time and space. Rainfall monitoring and forecasting is a difficult task, especially for a short-term period from 30 minutes to 3 hours. Recently Doppler weather radars have been used as one of the new solutions in the short-term forecasting of extreme rain or storm. This research presents some results of forecasting the wind direction, velocity, and rainfall of a typical rainy day, 14 September 2010, based on CAPPI images of a DWSR-2500C radar in the Nha Be district, Ho Chi Minh City (HCMC. The results showed that the Doppler radar, in a scanning radius of 30 km, is very effective in forecasting extreme rainfall for each region and district when reflected radar signals from clouds moving towards the city are detected. This research provides useful information in the forecast of extreme rainfall for flood prevention works in the HCM City.

  14. Initial weather regimes as predictors of numerical 30-day mean forecast accuracy

    Science.gov (United States)

    Colucci, Stephen J.; Baumhefner, David P.

    1992-01-01

    Thirty 30-day mean 500-mb-height anomaly forecasts generated by the NCAR Community Climate Model (CCM) for the year 1978 are examined in order to determine if the forecast accuracy can be estimated with the initial conditions. The initial weather regimes were defined in such a way that the regimes could discriminate between the best and the worst 30-day mean forecasts run from the initial fields in this data set. On the basis of the CCM experiments, it is suggested that the accuracy of numerical 30-day mean forecasts may depend upon the accuracy with which the cyclones and their interactions with the planetary scale are predicted early in the forecast cycle, and that this accuracy may depend upon the initial conditions.

  15. Using ensemble weather forecast in a risk based real time optimization of urban drainage systems

    DEFF Research Database (Denmark)

    Courdent, Vianney Augustin Thomas; Vezzaro, Luca; Mikkelsen, Peter Steen

    2015-01-01

    on DORA's approach, this study investigated the implementation of long forecast horizon using an ensemble forecast from a Numerical Weather Prediction (NWP) model. The uncertainty of the prediction is characterized by an ensemble of 25 forecast scenarios. According to the status of the UDS......) strategy was developed to operate Urban Drainage Systems (UDS) in order to minimize the expected overflow risk by considering the water volume presently stored in the drainage network, the expected runoff volume based on a 2-hours radar forecast model and an estimated uncertainty of the runoff forecast....... However, such temporal horizon (1-2 hours) is relatively short when used for the operation of large storage facilities, which may require a few days to be emptied. This limits the performance of the optimization and control in reducing combined sewer overflow and in preparing for possible flooding. Based...

  16. Microcontroller-based network for meteorological sensing and weather forecast calculations

    Directory of Open Access Journals (Sweden)

    A. Vas

    2012-06-01

    Full Text Available Weather forecasting needs a lot of computing power. It is generally accomplished by using supercomputers which are expensive to rent and to maintain. In addition, weather services also have to maintain radars, balloons and pay for worldwide weather data measured by stations and satellites. Weather forecasting computations usually consist of solving differential equations based on the measured parameters. To do that, the computer uses the data of close and distant neighbor points. Accordingly, if small-sized weather stations, which are capable of making measurements, calculations and communication, are connected through the Internet, then they can be used to run weather forecasting calculations like a supercomputer does. It doesn’t need any central server to achieve this, because this network operates as a distributed system. We chose Microchip’s PIC18 microcontroller (μC platform in the implementation of the hardware, and the embedded software uses the TCP/IP Stack v5.41 provided by Microchip.

  17. Modeling and Forecasting Average Temperature for Weather Derivative Pricing

    Directory of Open Access Journals (Sweden)

    Zhiliang Wang

    2015-01-01

    Full Text Available The main purpose of this paper is to present a feasible model for the daily average temperature on the area of Zhengzhou and apply it to weather derivatives pricing. We start by exploring the background of weather derivatives market and then use the 62 years of daily historical data to apply the mean-reverting Ornstein-Uhlenbeck process to describe the evolution of the temperature. Finally, Monte Carlo simulations are used to price heating degree day (HDD call option for this city, and the slow convergence of the price of the HDD call can be found through taking 100,000 simulations. The methods of the research will provide a frame work for modeling temperature and pricing weather derivatives in other similar places in China.

  18. Report on the First International Symposium on Operational Weather Forecasting in Antarctica.

    Science.gov (United States)

    Turner, John; Pendlebury, Stephen; Cowled, Lance; Jacka, Kieran; Jones, Marjorie; Targett, Philip

    2000-01-01

    The First International Symposium on Operational Weather Forecasting in Antarctica was held in Hobart, Australia, from 31 August to 3 September 1998. There were 40 attendees at the meeting from Australia, Belgium, Brazil, China, France, Italy, Russia, and the United Kingdom. In recent years there has been considerable growth in the requirement for weather forecasts for the Antarctic because of the increases in complex scientific research activities and the rapid growth of tourism to the continent. At many of the research stations there are now sophisticated forecasting operations that make use of the data available from drifting buoys and automatic weather stations, the output from numerical weather prediction systems, and high resolution satellite imagery. The models have considerable success at predicting the synoptic-scale depressions that occur over the ocean and in the coastal region. However, the many mesoscale systems that occur, which are very important for forecasting local conditions, are not well represented in the model fields and their movement is mainly predicted via the satellite data. In the future it is anticipated that high resolution, limited-area models will be run for selected parts of the continent. The symposium showed that great advances had been made during recent years in forecasting for the Antarctic as a result of our better understanding of atmospheric processes at high latitudes, along with the availability of high resolution satellite imagery and the output of numerical models. Outstanding problems include the difficulty of getting all of the observations to the main analysis centers outside the Antarctic in a timely fashion, the lack of upper air data from the Antarctic Peninsula and the interior of the continent, and the poor representation of the Antarctic orography and high latitude processes in numerical models. An outcome of the symposium will be a weather forecasting handbook dealing with the entire continent.

  19. Real-time dynamic control of the Three Gorges Reservoir by coupling numerical weather rainfall prediction and flood forecasting

    DEFF Research Database (Denmark)

    Wang, Y.; Chen, H.; Rosbjerg, Dan

    2013-01-01

    In reservoir operation improvement of the accuracy of forecast flood inflow and extension of forecast lead-time can effectively be achieved by using rainfall forecasts from numerical weather predictions with a hydrological catchment model. In this study, the Regional Spectrum Model (RSM), which...

  20. From the weather forecast to the prognostic moisture content of dry agricultural crops.

    NARCIS (Netherlands)

    Atzema, A.J.

    1994-01-01

    Part 1The aim of the study of grass is to forecast the drying of cut grass up to five days ahead, hourly. The first investigated problem is the response of the drying of cut grass to the weather elements. Next a simple model and an advanced model for the drying of cut grass are presented. Errors in

  1. From the weather forecast to the prognostic moisture content of dry agricultural crops

    NARCIS (Netherlands)

    Atzema, A.J.

    1994-01-01

    Part 1

    The aim of the study of grass is to forecast the drying of cut grass up to five days ahead, hourly. The first investigated problem is the response of the drying of cut grass to the weather elements. Next a simple model and an advanced model for the drying of cut

  2. Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

    Science.gov (United States)

    Watson, Leela R.; Bauman, William H., III

    2008-01-01

    NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.

  3. Short period forecasting of catchment-scale precipitation. Part I: the role of Numerical Weather Prediction

    Directory of Open Access Journals (Sweden)

    M. A. Pedder

    2000-01-01

    Full Text Available A deterministic forecast of surface precipitation involves solving a time-dependent moisture balance equation satisfying conservation of total water substance. A realistic solution needs to take into account feedback between atmospheric dynamics and the diabatic sources of heat energy associated with phase changes, as well as complex microphysical processes controlling the conversion between cloud water (or ice and precipitation. Such processes are taken into account either explicitly or via physical parameterisation schemes in many operational numerical weather prediction models; these can therefore generate precipitation forecasts which are fully consistent with the predicted evolution of the atmospheric state as measured by observations of temperature, wind, pressure and humidity. This paper reviews briefly the atmospheric moisture balance equation and how it may be solved in practice. Solutions are obtained using the Meteorological Office Mesoscale version of its operational Unified Numerical Weather Prediction (NWP model; they verify predicted precipitation rates against catchment-scale values based on observations collected during an Intensive Observation Period (IOP of HYREX. Results highlight some limitations of an operational NWP forecast in providing adequate time and space resolution, and its sensitivity to initial conditions. The large-scale model forecast can, nevertheless, provide important information about the moist dynamical environment which could be incorporated usefully into a higher resolution, ‘storm-resolving’ prediction scheme. Keywords: Precipitation forecasting; moisture budget; numerical weather prediction

  4. Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route.

    Science.gov (United States)

    Inoue, Jun; Yamazaki, Akira; Ono, Jun; Dethloff, Klaus; Maturilli, Marion; Neuber, Roland; Edwards, Patti; Yamaguchi, Hajime

    2015-01-01

    During ice-free periods, the Northern Sea Route (NSR) could be an attractive shipping route. The decline in Arctic sea-ice extent, however, could be associated with an increase in the frequency of the causes of severe weather phenomena, and high wind-driven waves and the advection of sea ice could make ship navigation along the NSR difficult. Accurate forecasts of weather and sea ice are desirable for safe navigation, but large uncertainties exist in current forecasts, partly owing to the sparse observational network over the Arctic Ocean. Here, we show that the incorporation of additional Arctic observations improves the initial analysis and enhances the skill of weather and sea-ice forecasts, the application of which has socioeconomic benefits. Comparison of 63-member ensemble atmospheric forecasts, using different initial data sets, revealed that additional Arctic radiosonde observations were useful for predicting a persistent strong wind event. The sea-ice forecast, initialised by the wind fields that included the effects of the observations, skilfully predicted rapid wind-driven sea-ice advection along the NSR.

  5. Assessment of marine weather forecasts over the Indian sector of Southern Ocean

    Science.gov (United States)

    Gera, Anitha; Mahapatra, D. K.; Sharma, Kuldeep; Prakash, Satya; Mitra, A. K.; Iyengar, G. R.; Rajagopal, E. N.; Anilkumar, N.

    2017-09-01

    The Southern Ocean (SO) is one of the important regions where significant processes and feedbacks of the Earth's climate take place. Expeditions to the SO provide useful data for improving global weather/climate simulations and understanding many processes. Some of the uncertainties in these weather/climate models arise during the first few days of simulation/forecast and do not grow much further. NCMRWF issued real-time five day weather forecasts of mean sea level pressure, surface winds, winds at 500 hPa & 850 hPa and rainfall, daily to NCAOR to provide guidance for their expedition to Indian sector of SO during the austral summer of 2014-2015. Evaluation of the skill of these forecasts indicates possible error growth in the atmospheric model at shorter time scales. The error growth is assessed using the model analysis/reanalysis, satellite data and observations made during the expedition. The observed variability of sub-seasonal rainfall associated with mid-latitude systems is seen to exhibit eastward propagations and are well reproduced in the model forecasts. All cyclonic disturbances including the sub-polar lows and tropical cyclones that occurred during this period were well captured in the model forecasts. Overall, this model performs reasonably well over the Indian sector of the SO in medium range time scale.

  6. The benefit of high-resolution operational weather forecasts for flash flood warning

    Directory of Open Access Journals (Sweden)

    J. Younis

    2008-07-01

    Full Text Available In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of loss of human life and infrastructures. Over the last two decades, flash floods have caused damage costing a billion Euros in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available short-range numerical weather forecasts together with a rainfall-runoff model can be used for early indication of the occurrence of flash floods.

    One of the challenges in flash flood forecasting is that the watersheds are typically small, and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground measurements. The lack of observations in most flash flood prone basins, therefore, necessitates the development of a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area, with lead times of the order of weather forecasts.

    This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. This paper describes the main aspects of using numerical weather forecasting for flash flood forecasting, together with a threshold – exceedance. As a case study the severe flash flood event which took place on 8–9 September 2002 has been chosen.

    Short-range weather forecasts, from the Lokalmodell of the German national weather service, are used as input for the LISFLOOD model, a hybrid between a conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to

  7. Verification of ensemble forecasts of Mediterranean high-impact weather events against satellite observations

    Directory of Open Access Journals (Sweden)

    J.-P. Chaboureau

    2012-08-01

    Full Text Available Ensemble forecasts at kilometre scale of two severe storms over the Mediterranean region are verified against satellite observations. In complement to assessing the forecasts against ground-based measurements, brightness temperature (BT images are computed from forecast fields and directly compared to BTs observed from satellite. The so-called model-to-satellite approach is very effective in identifying systematic errors in the prediction of cloud cover for BTs in the infrared window and in verifying the forecasted convective activity with BTs in the microwave range. This approach is combined with the calculation of meteorological scores for an objective evaluation of ensemble forecasts. The application of the approach is shown in the context of two Mediterranean case studies, a tropical-like storm and a heavy precipitating event. Assessment of cloud cover and convective activity using satellite observations in the infrared (10.8 μm and microwave regions (183–191 GHz provides results consistent with other traditional methods using rainfall measurements. In addition, for the tropical-like storm, differences among forecasts occur much earlier in terms of cloud cover and deep convective activity than they do in terms of deepening and track. Further, the underdispersion of the ensemble forecasts of the two high-impact weather events is easily identified with satellite diagnostics. This suggests that such an approach could be a useful method for verifying ensemble forecasts, particularly in data-sparse regions.

  8. The Impact of Atmospheric InfraRed Sounder (AIRS) Profiles on Short-term Weather Forecasts

    Science.gov (United States)

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

    2007-01-01

    The Atmospheric Infrared Sounder (AIRS), together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced spacebased atmospheric sounding systems. The combined AlRS/AMSU system provides radiance measurements used to retrieve temperature profiles with an accuracy of 1 K over 1 km layers under both clear and partly cloudy conditions, while the accuracy of the derived humidity profiles is 15% in 2 km layers. Critical to the successful use of AIRS profiles for weather and climate studies is the use of profile quality indicators and error estimates provided with each profile Aside form monitoring changes in Earth's climate, one of the objectives of AIRS is to provide sounding information of sufficient accuracy such that the assimilation of the new observations, especially in data sparse region, will lead to an improvement in weather forecasts. The purpose of this paper is to describe a procedure to optimally assimilate highresolution AIRS profile data in a regional analysis/forecast model. The paper will focus on the impact of AIRS profiles on a rapidly developing east coast storm and will also discuss preliminary results for a 30-day forecast period, simulating a quasi-operation environment. Temperature and moisture profiles were obtained from the prototype version 5.0 EOS science team retrieval algorithm which includes explicit error information for each profile. The error profile information was used to select the highest quality temperature and moisture data for every profile location and pressure level for assimilation into the ARPS Data Analysis System (ADAS). The AIRS-enhanced analyses were used as initial fields for the Weather Research and Forecast (WRF) system used by the SPORT project for regional weather forecast studies. The ADASWRF system will be run on CONUS domain with an emphasis on the east coast. The preliminary assessment of the impact of the AIRS profiles will focus on quality control issues associated with AIRS

  9. The potential economic benefits of improvements in weather forecasting

    Science.gov (United States)

    Thompson, J. C.

    1972-01-01

    The study was initiated as a consequence of the increased use of weather satellites, electronic computers and other technological developments which have become a virtual necessity for solving the complex problems of the earth's atmosphere. Neither the economic emphasis, nor the monetary results of the study, are intended to imply their sole use as criteria for making decisions concerning the intrinsic value of technological improvements in meteorology.

  10. Anvil Forecast Tool in the Advanced Weather Interactive Processing System (AWIPS)

    Science.gov (United States)

    Barrett, Joe H., III; Hood, Doris

    2009-01-01

    Launch Weather Officers (LWOs) from the 45th Weather Squadron (45 WS) and forecasters from the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violating the Lightning Launch Commit Criteria (LLCC) (Krider et al. 2006; Space Shuttle Flight Rules (FR), NASA/JSC 2004)). As a result, the Applied Meteorology Unit (AMU) developed a tool that creates an anvil threat corridor graphic that can be overlaid on satellite imagery using the Meteorological Interactive Data Display System (MIDDS, Short and Wheeler, 2002). The tool helps forecasters estimate the locations of thunderstorm anvils at one, two, and three hours into the future. It has been used extensively in launch and landing operations by both the 45 WS and SMG. The Advanced Weather Interactive Processing System (AWIPS) is now used along with MIDDS for weather analysis and display at SMG. In Phase I of this task, SMG tasked the AMU to transition the tool from MIDDS to AWIPS (Barrett et aI., 2007). For Phase II, SMG requested the AMU make the Anvil Forecast Tool in AWIPS more configurable by creating the capability to read model gridded data from user-defined model files instead of hard-coded files. An NWS local AWIPS application called AGRID was used to accomplish this. In addition, SMG needed to be able to define the pressure levels for the model data, instead of hard-coding the bottom level as 300 mb and the top level as 150 mb. This paper describes the initial development of the Anvil Forecast Tool for MIDDS, followed by the migration of the tool to AWIPS in Phase I. It then gives a detailed presentation of the Phase II improvements to the AWIPS tool.

  11. Implementation of the Immersed Boundary Method in the Weather Research and Forecasting model

    Energy Technology Data Exchange (ETDEWEB)

    Lundquist, Katherine Ann [Univ. of California, Berkeley, CA (United States)

    2006-01-01

    Accurate simulations of atmospheric boundary layer flow are vital for predicting dispersion of contaminant releases, particularly in densely populated urban regions where first responders must react within minutes and the consequences of forecast errors are potentially disastrous. Current mesoscale models do not account for urban effects, and conversely urban scale models do not account for mesoscale weather features or atmospheric physics. The ultimate goal of this research is to develop and implement an immersed boundary method (IBM) along with a surface roughness parameterization into the mesoscale Weather Research and Forecasting (WRF) model. IBM will be used in WRF to represent the complex boundary conditions imposed by urban landscapes, while still including forcing from regional weather patterns and atmospheric physics. This document details preliminary results of this research, including the details of three distinct implementations of the immersed boundary method. Results for the three methods are presented for the case of a rotation influenced neutral atmospheric boundary layer over flat terrain.

  12. Integrating weather and climate predictions for seamless hydrologic ensemble forecasting: A case study in the Yalong River basin

    Science.gov (United States)

    Ye, Aizhong; Deng, Xiaoxue; Ma, Feng; Duan, Qingyun; Zhou, Zheng; Du, Chao

    2017-04-01

    Despite the tremendous improvement made in numerical weather and climate models over the recent years, the forecasts generated by those models still cannot be used directly for hydrological forecasting. A post-processor like the Ensemble Pre-Processor (EPP) developed by U.S. National Weather Service must be used to remove various biases and to extract useful predictive information from those forecasts. In this paper, we investigate how different designs of canonical events in the EPP can help post-process precipitation forecasts from the Global Ensemble Forecast System (GEFS) and Climate Forecast System Version 2 (CFSv2). The use of canonical events allow those products to be linked seamlessly and then the post-processed ensemble precipitation forecasts can be generated using the Schaake Shuffle procedure. We used the post-processed ensemble precipitation forecasts to drive a distributed hydrological model to obtain ensemble streamflow forecasts and evaluated those forecasts against the observed streamflow. We found that the careful design of canonical events can help extract more useful information, especially when up-to-date observed precipitation is used to setup the canonical events. We also found that streamflow forecasts using post-processed precipitation forecasts have longer lead times and higher accuracy than streamflow forecasts made by traditional Extend Streamflow Prediction (ESP) and the forecasts based on original GEFS and CFSv2 precipitation forecasts.

  13. Technology Reconciliation in the Remote Sensing ERA of United States Civilian Weather Forecasting: 1957 -1987.

    Science.gov (United States)

    Courain, Margaret Eileen

    This dissertation seeks to advance an understanding of the management of a major technological change in meteorology. The study examines the connection between changes in production and real-time use of data products derived from remote -sensing data collection and the evolution of U.S. civilian weather forecasting 1957-1987. The role of data collection in weather forecasting throughout history is examined, giving most attention to the 1957-1987 period. Critical to the real-time use of remote-sensing data was technology reconciliation. As defined by the author, it is the function or process by which data products and information derived from a new technology are made consistent or congruent with the existing data representations of a science in order to be used effectively. No model had been developed for a technology reconciliation process, or definition of the major role technology reconciliators played in the 30-year evolution of the science of weather forecasting. In order to assess the new remote-sensing data resource and its use in U.S. civilian weather forecasting, a Data Accountability and Review Technique (DART) was developed by the author in 1989. This technique was used to identify 16 of the technology reconciliators who developed and reconciled 25 new remote-sensing data products with the weather charts, maps and computer models of the National Weather Service. In five separate program teams, they were responsible for 15 improvements in the products--forecasts--and 18 improvement in the process of weather forecasting. A model of the technology reconciliation is proposed which can be applied to understanding the contemporary history of other sciences. The model, as well as the methods developed by the author to recognize the process of technology reconciliation has a much more general applicability beyond the sciences. Any field implementing new technology that promises to improve its whole way of working will be faced with the task of technology

  14. Assessing reference evapotranspiration at regional scale based on remote sensing, weather forecast and GIS tools

    Science.gov (United States)

    Ramírez-Cuesta, J. M.; Cruz-Blanco, M.; Santos, C.; Lorite, I. J.

    2017-03-01

    Reference evapotranspiration (ETo) is a key component in efficient water management, especially in arid and semi-arid environments. However, accurate ETo assessment at the regional scale is complicated by the limited number of weather stations and the strict requirements in terms of their location and surrounding physical conditions for the collection of valid weather data. In an attempt to overcome this limitation, new approaches based on the use of remote sensing techniques and weather forecast tools have been proposed. Use of the Land Surface Analysis Satellite Application Facility (LSA SAF) tool and Geographic Information Systems (GIS) have allowed the design and development of innovative approaches for ETo assessment, which are especially useful for areas lacking available weather data from weather stations. Thus, by identifying the best-performing interpolation approaches (such as the Thin Plate Splines, TPS) and by developing new approaches (such as the use of data from the most similar weather station, TS, or spatially distributed correction factors, CITS), errors as low as 1.1% were achieved for ETo assessment. Spatial and temporal analyses reveal that the generated errors were smaller during spring and summer as well as in homogenous topographic areas. The proposed approaches not only enabled accurate calculations of seasonal and daily ETo values, but also contributed to the development of a useful methodology for evaluating the optimum number of weather stations to be integrated into a weather station network and the appropriateness of their locations. In addition to ETo, other variables included in weather forecast datasets (such as temperature or rainfall) could be evaluated using the same innovative methodology proposed in this study.

  15. From Early Exploration to Space Weather Forecasts: Canada's Geomagnetic Odyssey

    Science.gov (United States)

    Lam, Hing-Lan

    2011-05-01

    Canada is a region ideally suited for the study of space weather: The north magnetic pole is encompassed within its territory, and the auroral oval traverses its vast landmass from east to west. Magnetic field lines link the country directly to the outer magnetosphere. In light of this geographic suitability, it has been a Canadian tradition to install ground monitors to remotely sense the space above Canadian territory. The beginning of this tradition dates back to 1840, when Edward Sabine, a key figure in the “magnetic crusade” to establish magnetic observatories throughout the British Empire in the nineteenth century, founded the first Canadian magnetic observatory on what is now the campus of the University of Toronto, 27 years before the birth of Canada. This observatory, which later became the Toronto Magnetic and Meteorological Observatory, marked the beginning of the Canadian heritage of installing magnetic stations and other ground instruments in the years to come. This extensive network of ground-based measurement devices, coupled with space-based measurements in more modern times, has enabled Canadian researchers to contribute significantly to studies related to space weather.

  16. Improving Multimodel Weather Forecast of Monsoon Rain Over China Using FSU Superensemble

    Institute of Scientific and Technical Information of China (English)

    T.N.KRISHNAMURTI; A.D.SAGADEVAN; A.CHAKRABORTY; A.K.MISHRA; A.SIMON

    2009-01-01

    In this paper we present the current capabilities for numerical weather prediction of precipitation over China using a suite of ten multimodels and our superensemble based forecasts.Our suite of models includes the operational suite selected by NCARs TIGGE archives for the THORPEX Program.These are:ECMWF,UKMO,JMA,NCEP,CMA,CMC,BOM,MF,KMA and the CPTEC models.The superensemble strategy includes a training and a forecasts phase,for these the periods chosen for this study include the months February through September for the years 2007 and 2008.This paper addresses precipitation forecasts for the mcdium range i.e.Days 1 to 3 and extending out to Day 10 of forecasts using this suite of global models.For training and forecasts validations we have made use of an advanced TRMM satellite based rainfall product.We make use of standard metrics for forecast validations that include the RMS errors,spatial correlations and the equitable threat scores.The results of skill forecasts of precipitation clearly demonstrate that it is possible to obtain higher skills for precipitation forecasts for Days 1 through 3 of forecasts from the use of the multimodel superensemble as compared to the best model of this suite.Between Days 4 to 10 it is possible to have very high skills from the multimodel superensemble for the RMS error of precipitation.Those skills are shown for a global belt and especially over China.Phenomenologically this product was also found very useful for precipitation forecasts for the Onset of the South China Sea monsoon,the life cycle of the mci-yu rains and post typhoon landfall heavy rains and flood events.The higher skills of the multimodel superensemble make it a very useful product for such real time events.

  17. A New Tool for Forecasting Solar Drivers of Severe Space Weather

    Science.gov (United States)

    Adams, J. H.; Falconer, D.; Barghouty, A. F.; Khazanov, I.; Moore, R.

    2010-01-01

    This poster describes a tool that is designed to forecast solar drivers for severe space weather. Since most severe space weather is driven by Solar flares and Coronal Mass Ejections (CMEs) - the strongest of these originate in active regions and are driven by the release of coronal free magnetic energy and There is a positive correlation between an active region's free magnetic energy and the likelihood of flare and CME production therefore we can use this positive correlation as the basis of our empirical space weather forecasting tool. The new tool takes a full disk Michelson Doppler Imager (MDI) magnetogram, identifies strong magnetic field areas, identifies these with NOAA active regions, and measures a free-magnetic-energy proxy. It uses an empirically derived forecasting function to convert the free-magnetic-energy proxy to an expected event rate. It adds up the expected event rates from all active regions on the disk to forecast the expected rate and probability of each class of events -- X-class flares, X&M class flares, CMEs, fast CMEs, and solar particle events (SPEs).

  18. Economic consequences of improved temperature forecasts: An experiment with the Florida citrus growers (control group results). Executive summary. [weather forecasting

    Science.gov (United States)

    1977-01-01

    A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions so as to significantly reduce the cost for frost and freeze protection and crop losses. The design and implementation of the first phase of an economic experiment which will monitor citrus growers decisions, actions, costs and losses, and meteorological forecasts and actual weather events was carried out. The economic experiment was designed to measure the change in annual protection costs and crop losses which are the direct result of improved temperature forecasts. To estimate the benefits that may result from improved temperature forecasting capability, control and test groups were established with effective separation being accomplished temporally. The control group, utilizing current forecasting capability, was observed during the 1976-77 frost season and the results are reported. A brief overview is given of the economic experiment, the results obtained to date, and the work which still remains to be done.

  19. Predicting and Mitigating Socioeconomic Impacts of Extreme Space Weather: Benefits of Improved Forecasts (Invited)

    Science.gov (United States)

    Kanekal, S. G.; Baker, D. N.

    2013-12-01

    Vulnerability of society to severe space weather is an issue of increasing worldwide concern. A notable example is that electric power networks connecting widely separated geographic areas may incur debilitating damage induced by geomagnetic storms. The conclusion of a recent National Research Council report was that harsh space weather events can cause tens of millions to many billions of dollars of damage to space and ground-based assets during major solar storms. The most extreme events could cause months-long power outages and could cost in excess of one trillion dollars. In this presentation, we discuss broad socioeconomic impacts of space weather and also discuss the immense potential benefits of improved space weather forecasts. Such forecasts would be based on continuous observations of disturbances on the Sun and would take advantage of our increased understanding of the Earth's space environmental conditions and the causative solar drivers. We consider scenarios of how such observation-based forecasts could be used most effectively by policy makers and technology management officials.

  20. A space weather forecasting system with multiple satellites based on a self-recognizing network.

    Science.gov (United States)

    Tokumitsu, Masahiro; Ishida, Yoshiteru

    2014-05-05

    This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

  1. A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network

    Directory of Open Access Journals (Sweden)

    Masahiro Tokumitsu

    2014-05-01

    Full Text Available This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV. The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

  2. Space Weather Products and Tools Used in Auroral Monitoring and Forecasting at CCMC/SWRC

    Science.gov (United States)

    Zheng, Yihua; Rastaetter, Lutz

    2015-01-01

    Key points discussed in this chapter are (1) the importance of aurora research to scientific advances and space weather applications, (2) space weather products at CCMC that are relevant to aurora monitoring and forecasting, and (3) the need for more effort from the whole community to achieve a better and long-lead-time forecast of auroral activity. Aurora, as manifestations of solar wind-magnetosphere-ionosphere coupling that occurs in a region of space that is relatively easy to access for sounding rockets, satellites, and other types of observational platforms, serves as a natural laboratory for studying the underlying physics of the complex system. From a space weather application perspective, auroras can cause surface charging of technological assets passing through the region, result in scintillation effects affecting communication and navigation, and cause radar cluttering that hinders military and civilian applications. Indirectly, an aurora and its currents can induce geomagnetically induced currents (GIC) on the ground, which poses major concerns for the wellbeing and operation of power grids, particularly during periods of intense geomagnetic activity. In addition, accurate auroral forecasting is desired for auroral tourism. In this chapter, we first review some of the existing auroral models and discuss past validation efforts. Such efforts are crucial in transitioning a model(s) from research to operations and for further model improvement and development that also benefits scientific endeavors. Then we will focus on products and tools that are used for auroral monitoring and forecasting at the Space Weather Research Center (SWRC). As part of the CCMC (Community Coordinated Modeling Center), SWRC has been providing space weather services since 2010.

  3. Maintaining a Local Data Integration System in Support of Weather Forecast Operations

    Science.gov (United States)

    Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian

    2010-01-01

    Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LDIS) as part of their forecast and warning operations. Each has benefited from 3-dimensional analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive and complete understanding of evolving fine-scale weather features. Recent efforts have been undertaken to update the LDIS through the formal tasking process of NASA's Applied Meteorology Unit. The goals include upgrading LDIS with the latest version of ADAS, incorporating new sources of observational data, and making adjustments to shell scripts written to govern the system. A series of scripts run a complete modeling system consisting of the preprocessing step, the main model integration, and the post-processing step. The preprocessing step prepares the terrain, surface characteristics data sets, and the objective analysis for model initialization. Data ingested through ADAS include (but are not limited to) Level II Weather Surveillance Radar- 1988 Doppler (WSR-88D) data from six Florida radars, Geostationary Operational Environmental Satellites (GOES) visible and infrared satellite imagery, surface and upper air observations throughout Florida from NOAA's Earth System Research Laboratory/Global Systems Division

  4. On the dynamic estimation of relative weights for observation and forecast in numerical weather prediction

    Science.gov (United States)

    Wahba, Grace; Deepak, A. (Editor)

    1988-01-01

    The problem of merging direct and remotely sensed (indirect) data with forecast data to get an estimate of the present state of the atmosphere for the purpose of numerical weather prediction is examined. To carry out this merging optimally, it is necessary to provide an estimate of the relative weights to be given to the observations and forecast. It is possible to do this dynamically from the information to be merged, if the correlation structure of the errors from the various sources is sufficiently different. Some new statistical approaches to doing this are described, and conditions quantified in which such estimates are likely to be good.

  5. Statistical Correction of Air Temperature Forecasts for City and Road Weather Applications

    Science.gov (United States)

    Mahura, Alexander; Petersen, Claus; Sass, Bent; Gilet, Nicolas

    2014-05-01

    The method for statistical correction of air /road surface temperatures forecasts was developed based on analysis of long-term time-series of meteorological observations and forecasts (from HIgh Resolution Limited Area Model & Road Conditions Model; 3 km horizontal resolution). It has been tested for May-Aug 2012 & Oct 2012 - Mar 2013, respectively. The developed method is based mostly on forecasted meteorological parameters with a minimal inclusion of observations (covering only a pre-history period). Although the st iteration correction is based taking into account relevant temperature observations, but the further adjustment of air and road temperature forecasts is based purely on forecasted meteorological parameters. The method is model independent, e.g. it can be applied for temperature correction with other types of models having different horizontal resolutions. It is relatively fast due to application of the singular value decomposition method for matrix solution to find coefficients. Moreover, there is always a possibility for additional improvement due to extra tuning of the temperature forecasts for some locations (stations), and in particular, where for example, the MAEs are generally higher compared with others (see Gilet et al., 2014). For the city weather applications, new operationalized procedure for statistical correction of the air temperature forecasts has been elaborated and implemented for the HIRLAM-SKA model runs at 00, 06, 12, and 18 UTCs covering forecast lengths up to 48 hours. The procedure includes segments for extraction of observations and forecast data, assigning these to forecast lengths, statistical correction of temperature, one-&multi-days statistical evaluation of model performance, decision-making on using corrections by stations, interpolation, visualisation and storage/backup. Pre-operational air temperature correction runs were performed for the mainland Denmark since mid-April 2013 and shown good results. Tests also showed

  6. Forcing the snow-cover model SNOWPACK with forecasted weather data

    Directory of Open Access Journals (Sweden)

    S. Bellaire

    2011-12-01

    Full Text Available Avalanche danger is often estimated based on snow cover stratigraphy and snow stability data. In Canada, single forecasting regions are very large (>50 000 km2 and snow cover data are often not available. To provide additional information on the snow cover and its seasonal evolution the Swiss snow cover model SNOWPACK was therefore coupled with a regional weather forecasting model GEM15. The output of GEM15 was compared to meteorological as well as snow cover data from Mt. Fidelity, British Columbia, Canada, for five winters between 2005 and 2010. Precipitation amounts are most difficult to predict for weather forecasting models. Therefore, we first assess the capability of the model chain to forecast new snow amounts and consequently snow depth. Forecasted precipitation amounts were generally over-estimated. The forecasted data were therefore filtered and used as input for the snow cover model. Comparison between the model output and manual observations showed that after pre-processing the input data the snow depth and new snow events were well modelled. In a case study two key factors of snow cover instability, i.e. surface hoar formation and crust formation were investigated at a single point. Over half of the relevant critical layers were reproduced. Overall, the model chain shows promising potential as a future forecasting tool for avalanche warning services in Canadian data sparse areas and could thus well be applied to similarly large regions elsewhere. However, a more detailed analysis of the simulated snow cover structure is still required.

  7. Overview of the Diagnostic Cloud Forecast Model at the Air Force Weather Agency

    Science.gov (United States)

    Hildebrand, E. P.

    2014-12-01

    The Air Force Weather Agency (AFWA) is responsible for running and maintaining the Diagnostic Cloud Forecast (DCF) model to support DoD missions and those of their external partners. The DCF model generates three-dimensional cloud forecasts for global and regional domains at various resolutions. Regional domains are chosen based on Air Force mission needs. DCF is purely a statistical model that can be appended to any numerical weather prediction (NWP) model. Operationally, AFWA runs the DCF model deterministically using GFS data from NCEP and WRF data that are created in-house. In addition, AFWA also runs an ensemble version of the DCF model using the Mesoscale Ensemble Prediction System (MEPS). The deterministic DCF uses predictor variables from the WRF or GFS models, depending on whether the domain is regional or global, and statistically relates them to observed cloud cover from the World-Wide Merged Cloud Analysis (WWMCA). The forecast process of the model uses an ordinal logistic regression to predict membership in one of 101 groups (every 1% from 0-100%). The predicted group membership then is translated into a cloud amount. This is performed on 21 pressure levels ranging from 1000 hPa to 100 hPa. Cloud amount forecasts on these 21 levels are used along with the NWP geopotential height forecasts to estimate the base and top heights of cloud layers in the vertical. DCF also includes routines to estimate the amount and type of cloud within each layer. Forecasts of total cloud amount are verified using the WWMCA, as well as independent sources of cloud data. This presentation will include an overview of the DCF model and its use at AFWA. Results will be presented to show that DCF adds value over the raw cloud forecasts from NWP models. Ideas for future work also will be addressed.

  8. Real Time Data in Synoptic Meteolorolgy and Weather Forecasting Education

    Science.gov (United States)

    Campetella, C. M.; Gassmann, M. I.

    2006-05-01

    from GOES-12 from the Unidata Internet Data Distribution (IDD) system. The data now being routinely received have impacted not only the meteorological education in the DAOS, but also have been instructive in techniques for Internet-based data sharing for our students. The DAOS has made a substantial effort to provide undergraduate students with experience in manipulating, displaying, and analyzing weather data in real-time through interactive displays of data using visualization tools provided by Unidata. Two of the specific courses whose curriculum have been improved are synoptic meteorology and a laboratory on weather prediction. Some laboratory materials have been developed to reflect current data as applied to the lecture material. This talk will briefly describe the data compiled and the fields used to analyze an intense cyclogenesis event that occurred over the La Plata River in August, 2005. This event was used as a case study for discussions in the Synoptic Weather Laboratory degree course of Atmospheric Sciences Licentiate.

  9. Models and applications for space weather forecasting and analysis at the Community Coordinated Modeling Center.

    Science.gov (United States)

    Kuznetsova, Maria

    The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) was established at the dawn of the new millennium as a long-term flexible solution to the problem of transition of progress in space environment modeling to operational space weather forecasting. CCMC hosts an expanding collection of state-of-the-art space weather models developed by the international space science community. Over the years the CCMC acquired the unique experience in preparing complex models and model chains for operational environment and developing and maintaining custom displays and powerful web-based systems and tools ready to be used by researchers, space weather service providers and decision makers. In support of space weather needs of NASA users CCMC is developing highly-tailored applications and services that target specific orbits or locations in space and partnering with NASA mission specialists on linking CCMC space environment modeling with impacts on biological and technological systems in space. Confidence assessment of model predictions is an essential element of space environment modeling. CCMC facilitates interaction between model owners and users in defining physical parameters and metrics formats relevant to specific applications and leads community efforts to quantify models ability to simulate and predict space environment events. Interactive on-line model validation systems developed at CCMC make validation a seamless part of model development circle. The talk will showcase innovative solutions for space weather research, validation, anomaly analysis and forecasting and review on-going community-wide model validation initiatives enabled by CCMC applications.

  10. Flow intake control using dry-weather forecast

    Directory of Open Access Journals (Sweden)

    O. Icke

    2017-08-01

    Full Text Available Level-based control of the influent flow causes peak discharges at a waste water treatment plant (WWTP after rainfall events. Furthermore, the capacity of the post-treatment is in general smaller than the maximum hydraulic capacity of the WWTP. This results in a significant bypass of the post-treatment during peak discharge. The optimisation of influent flow reduces peak discharge, and increases the treatment efficiency of the whole water cycle, which benefits the surface water quality. In this paper, it is shown that half of the bypasses of the post-treatment can be prevented by predictive control. A predictive controller for influent flow is implemented using the Aquasuite® Advanced Monitoring and Control platform. Based on real-time measured water levels in the sewerage and both rainfall and dry-weather flow (DWF predictions, a discharge limitation is determined by a volume optimisation technique. For the analysed period (February–September 2016 results at WWTP Bennekom show that about 50 % of bypass volume can be prevented. Analysis of single rainfall events shows that the used approach is still conservative and that the bypass can be even further decreased by allowing discharge limitation during precipitation.

  11. Flow intake control using dry-weather forecast

    Science.gov (United States)

    Icke, Otto; van Schagen, Kim; Huising, Christian; Wuister, Jasper; van Dijk, Edward; Budding, Arjan

    2017-08-01

    Level-based control of the influent flow causes peak discharges at a waste water treatment plant (WWTP) after rainfall events. Furthermore, the capacity of the post-treatment is in general smaller than the maximum hydraulic capacity of the WWTP. This results in a significant bypass of the post-treatment during peak discharge. The optimisation of influent flow reduces peak discharge, and increases the treatment efficiency of the whole water cycle, which benefits the surface water quality. In this paper, it is shown that half of the bypasses of the post-treatment can be prevented by predictive control. A predictive controller for influent flow is implemented using the Aquasuite® Advanced Monitoring and Control platform. Based on real-time measured water levels in the sewerage and both rainfall and dry-weather flow (DWF) predictions, a discharge limitation is determined by a volume optimisation technique. For the analysed period (February-September 2016) results at WWTP Bennekom show that about 50 % of bypass volume can be prevented. Analysis of single rainfall events shows that the used approach is still conservative and that the bypass can be even further decreased by allowing discharge limitation during precipitation.

  12. Impact of radiosonde data over the Arctic ice on forecasting winter extreme weather over mid latitude

    Science.gov (United States)

    Sato, Kazutoshi; Inoue, Jun; Yamazaki, Akira; Kim, Joo-hong; Maturilli, Marion; Dethloff, Klaus; Hudson, Stephen

    2016-04-01

    In February 2015, the Arctic air outbreak caused extreme cold events and heavy snowfall over the mid latitude, in particular over the North America. During the winter, special radiosonde observations were made on the Norwegian RV Lance around the north of Svalbard under the N-ICE2015 project. We investigated the impact of the radiosonde data on forecasting of a cold extreme event over the eastern North America using the AFES-LETKF experimental ensemble reanalysis version2 (ALERA2) data set. ALERA2 was used as the reference reanalysis (CTL) while the observing-system experiment (OSE) assimilated the same observational data set, except for the radiosonde data obtained by the RV Lance. Using these two reanalysis data as initial values, ensemble forecasting experiments were conducted. Comparing these ensemble forecasts, there were large differences in the position and depth of a predicted tropopause polar vortex. The CTL forecast well predicted the southward intrusion of the polar vortex which pushed a cold air over the eastern North America from the Canadian Archipelago. In the OSE forecast, in contrast, the trough associated with southward intrusion of the polar vortex was weak, which prevented a cold outbreak from Arctic. This result suggested that the radiosonde observations over the central Arctic would improve the skill of weather forecasts during winter.

  13. Integration of Space Weather Forecasts into Space Protection

    Science.gov (United States)

    Reeves, G.

    2012-09-01

    How would the US respond to a clandestine attack that disabled one of our satellites? How would we know that it was an attack, not a natural failure? The goal of space weather programs as applied to space protection are simple: Provide a rapid and reliable assessment of the probability that satellite or system failure was caused by the space environment. Achieving that goal is not as simple. However, great strides are being made on a number of fronts. We will report on recent successes in providing rapid, automated anomaly/attack assessment for the penetrating radiation environment in the Earth's radiation belts. We have previously reported on the Dynamic Radiation Environment Assimilation Model (DREAM) that was developed at Los Alamos National Laboratory to assess hazards posed by the natural and by nuclear radiation belts. This year we will report on recent developments that are moving this program from the research, test, and evaluation phases to real-time implementation and application. We will discuss the challenges of leveraging space environment data sets for applications that are beyond the scope of mission requirements, the challenges of moving data from where they exist to where they are needed, the challenges of turning data into actionable information, and how those challenges were overcome. We will discuss the state-of-the-art as it exists in 2012 including the new capabilities that have been enabled and the limitations that still exist. We will also discuss how currently untapped data resources could advance the state-of-the-art and the future steps for implementing automatic real-time anomaly forensics.

  14. Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecasts

    Science.gov (United States)

    Jones, Thomas A.; Koch, Steven; Li, Zhenglong

    2017-04-01

    Assimilation of hyperspectral sounder data into numerical weather prediction (NWP) models has proven vital to generating accurate model analyses of tropospheric temperature and humidity where few conventional observations exist. Applications to storm-scale models are limited since the low temporal resolution provided by polar orbiting sensors cannot adequately sample rapidly changing environments associated with high impact weather events. To address this limitation, hyperspectral sounders have been proposed for geostationary orbiting satellites, but these have yet to be built and launched in part due to much higher engineering costs and a lack of a definite requirement for the data. This study uses an Observation System Simulation Experiment (OSSE) approach to simulate temperature and humidity profiles from a hypothetical geostationary-based sounder from a nature run of a high impact weather event on 20 May 2013. The simulated observations are then assimilated using an ensemble adjustment Kalman filter approach, testing both hourly and 15 minute cycling to determine their relative effectiveness at improving the near storm environment. Results indicate that assimilating both temperature and humidity profiles reduced mid-tropospheric both mean and standard deviation of analysis and forecast errors compared to assimilating conventional observations alone. The 15 minute cycling generally produced the lowest errors while also generating the best 2-4 hour updraft helicity forecasts of ongoing convection. This study indicates the potential for significant improvement in short-term forecasting of severe storms from the assimilation of hyperspectral geostationary satellite data. However, more studies are required using improved OSSE designs encompassing multiple storm environments and additional observation types such as radar reflectivity to fully define the effectiveness of assimilating geostationary hyperspectral observations for high impact weather forecasting

  15. Intel Xeon Phi accelerated Weather Research and Forecasting (WRF Goddard microphysics scheme

    Directory of Open Access Journals (Sweden)

    J. Mielikainen

    2014-12-01

    Full Text Available The Weather Research and Forecasting (WRF model is a numerical weather prediction system designed to serve both atmospheric research and operational forecasting needs. The WRF development is a done in collaboration around the globe. Furthermore, the WRF is used by academic atmospheric scientists, weather forecasters at the operational centers and so on. The WRF contains several physics components. The most time consuming one is the microphysics. One microphysics scheme is the Goddard cloud microphysics scheme. It is a sophisticated cloud microphysics scheme in the Weather Research and Forecasting (WRF model. The Goddard microphysics scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. Compared to the earlier microphysics schemes, the Goddard scheme incorporates a large number of improvements. Thus, we have optimized the Goddard scheme code. In this paper, we present our results of optimizing the Goddard microphysics scheme on Intel Many Integrated Core Architecture (MIC hardware. The Intel Xeon Phi coprocessor is the first product based on Intel MIC architecture, and it consists of up to 61 cores connected by a high performance on-die bidirectional interconnect. The Intel MIC is capable of executing a full operating system and entire programs rather than just kernels as the GPU does. The MIC coprocessor supports all important Intel development tools. Thus, the development environment is one familiar to a vast number of CPU developers. Although, getting a maximum performance out of MICs will require using some novel optimization techniques. Those optimization techniques are discussed in this paper. The results show that the optimizations improved performance of Goddard microphysics scheme on Xeon Phi 7120P by a factor of 4.7×. In addition, the optimizations reduced the Goddard microphysics scheme's share of the total WRF processing time from 20.0 to 7.5%. Furthermore, the same

  16. Application of numerical weather prediction in wind power forecasting: Assessment of the diurnal cycle

    Directory of Open Access Journals (Sweden)

    Tobias Heppelmann

    2017-06-01

    Full Text Available For a secure integration of weather dependent renewable energies in Germany's mixed power supply, precise forecasts of expected wind power are indispensable. These in turn are heavily dependent on numerical weather prediction (NWP. With this relevant area of application, NWP models need to be evaluated concerning new variables such as wind speed at hub heights of wind power plants. This article presents verification results of the deterministic NWP forecasts of the global ICON model, its ICON-EU nest, the COSMO-EU, and the COSMO-DE as well as of the ensemble prediction system COSMO-DE-EPS of the German National Weather Service (DWD, against wind mast observations. The focus is on the diurnal cycle in the Planetary Boundary Layer as wind power forecasts for Germany exhibit pronounced systematic amplitude and phase errors in the morning and evening hours. NWP forecasts with lead times up to 48 hours are examined. All considered NWP models reveal shortcomings concerning the representation of the diurnal cycle. Especially in summertime at onshore locations, when Low-Level Jets form, nocturnal wind speeds at hub height are underestimated. In the COSMO model, stable conditions are not sufficiently reflected in the first part of the night and the vertical mixing after sunrise establishes too late. The verification results of the COSMO-DE-EPS confirm the deficiencies of the deterministic forecasts. The deficiencies are present in all ensemble members and thus indicate potential for improvement not only in the model physics parameterization but also concerning the physical ensemble perturbations.

  17. Economic consequences of improved temperature forecasts: An experiment with the Florida citrus growers (control group results). [weather forecasting

    Science.gov (United States)

    1977-01-01

    A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions. An economic experiment was carried out which will monitor citrus growers' decisions, actions, costs and losses, and meteorological forecasts and actual weather events and will establish the economic benefits of improved temperature forecasts. A summary is given of the economic experiment, the results obtained to date, and the work which still remains to be done. Specifically, the experiment design is described in detail as are the developed data collection methodology and procedures, sampling plan, data reduction techniques, cost and loss models, establishment of frost severity measures, data obtained from citrus growers, National Weather Service, and Federal Crop Insurance Corp., resulting protection costs and crop losses for the control group sample, extrapolation of results of control group to the Florida citrus industry and the method for normalization of these results to a normal or average frost season so that results may be compared with anticipated similar results from test group measurements.

  18. Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites.

    Science.gov (United States)

    Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

    2016-04-15

    This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons.

  19. Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

    Science.gov (United States)

    Dreher, Joseph; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian; Van Speybroeck, Kurt

    2009-01-01

    The National Weather Service Forecast Office in Melbourne, FL (NWS MLB) is responsible for providing meteorological support to state and county emergency management agencies across East Central Florida in the event of incidents involving the significant release of harmful chemicals, radiation, and smoke from fires and/or toxic plumes into the atmosphere. NWS MLB uses the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to provide trajectory, concentration, and deposition guidance during such events. Accurate and timely guidance is critical for decision makers charged with protecting the health and well-being of populations at risk. Information that can describe the geographic extent of areas possibly affected by a hazardous release, as well as to indicate locations of primary concern, offer better opportunity for prompt and decisive action. In addition, forecasters at the NWS Spaceflight Meteorology Group (SMG) have expressed interest in using the HYSPLIT model to assist with Weather Flight Rules during Space Shuttle landing operations. In particular, SMG would provide low and mid-level HYSPLIT trajectory forecasts for cumulus clouds associated with smoke plumes, and high-level trajectory forecasts for thunderstorm anvils. Another potential benefit for both NWS MLB and SMG is using the HYSPLIT model concentration and deposition guidance in fog situations.

  20. Forecasting Optimal Solar Energy Supply in Jiangsu Province (China: A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

    Directory of Open Access Journals (Sweden)

    Xiuli Zhao

    2014-01-01

    Full Text Available The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

  1. Forecasting Optimal Solar Energy Supply in Jiangsu Province (China): A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

    Science.gov (United States)

    Zhao, Xiuli; Yiranbon, Ethel

    2014-01-01

    The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor. PMID:24511292

  2. Forecasting optimal solar energy supply in Jiangsu Province (China): a systematic approach using hybrid of weather and energy forecast models.

    Science.gov (United States)

    Zhao, Xiuli; Asante Antwi, Henry; Yiranbon, Ethel

    2014-01-01

    The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, "least-cost," and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

  3. Sensitivity analysis of numerical weather prediction radiative schemes to forecast direct solar radiation over Australia

    Science.gov (United States)

    Mukkavilli, S. K.; Kay, M. J.; Taylor, R.; Prasad, A. A.; Troccoli, A.

    2014-12-01

    The Australian Solar Energy Forecasting System (ASEFS) project requires forecasting timeframes which range from nowcasting to long-term forecasts (minutes to two years). As concentrating solar power (CSP) plant operators are one of the key stakeholders in the national energy market, research and development enhancements for direct normal irradiance (DNI) forecasts is a major subtask. This project involves comparing different radiative scheme codes to improve day ahead DNI forecasts on the national supercomputing infrastructure running mesoscale simulations on NOAA's Weather Research & Forecast (WRF) model. ASEFS also requires aerosol data fusion for improving accurate representation of spatio-temporally variable atmospheric aerosols to reduce DNI bias error in clear sky conditions over southern Queensland & New South Wales where solar power is vulnerable to uncertainities from frequent aerosol radiative events such as bush fires and desert dust. Initial results from thirteen years of Bureau of Meteorology's (BOM) deseasonalised DNI and MODIS NASA-Terra aerosol optical depth (AOD) anomalies demonstrated strong negative correlations in north and southeast Australia along with strong variability in AOD (~0.03-0.05). Radiative transfer schemes, DNI and AOD anomaly correlations will be discussed for the population and transmission grid centric regions where current and planned CSP plants dispatch electricity to capture peak prices in the market. Aerosol and solar irradiance datasets include satellite and ground based assimilations from the national BOM, regional aerosol researchers and agencies. The presentation will provide an overview of this ASEFS project task on WRF and results to date. The overall goal of this ASEFS subtask is to develop a hybrid numerical weather prediction (NWP) and statistical/machine learning multi-model ensemble strategy that meets future operational requirements of CSP plant operators.

  4. Impact of various observing systems on weather analysis and forecast over the Indian region

    Science.gov (United States)

    Singh, Randhir; Ojha, Satya P.; Kishtawal, C. M.; Pal, P. K.

    2014-09-01

    To investigate the potential impact of various types of data on weather forecast over the Indian region, a set of data-denial experiments spanning the entire month of July 2012 is executed using the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system. The experiments are designed to allow the assessment of mass versus wind observations and terrestrial versus space-based instruments, to evaluate the relative importance of the classes of conventional instrument such as radiosonde, and finally to investigate the role of individual spaceborne instruments. The moist total energy norm is used for validation and forecast skill assessment. The results show that the contribution of wind observations toward error reduction is larger than mass observations in the short range (48 h) forecast. Terrestrial-based observations generally contribute more than space-based observations except for the moisture fields, where the role of the space-based instruments becomes more prevalent. Only about 50% of individual instruments are found to be beneficial in this experiment configuration, with the most important role played by radiosondes. Thereafter, Meteosat Atmospheric Motion Vectors (AMVs) (only for short range forecast) and Special Sensor Microwave Imager (SSM/I) are second and third, followed by surface observations, Sounder for Probing Vertical Profiles of Humidity (SAPHIR) radiances and pilot observations. Results of the additional experiments of comparative performance of SSM/I total precipitable water (TPW), Microwave Humidity Sounder (MHS), and SAPHIR radiances indicate that SSM/I is the most important instrument followed by SAPHIR and MHS for improving the quality of the forecast over the Indian region. Further, the impact of single SAPHIR instrument (onboard Megha-Tropiques) is significantly larger compared to three MHS instruments (onboard NOAA-18/19 and MetOp-A).

  5. Case studies of NOAA 6/TIROS N data impact on numerical weather forecasts

    Science.gov (United States)

    Druyan, L. M.; Alperson, Z.; Ben-Amram, T.

    1984-01-01

    The impact of satellite temperatures from systems which predate the launching of the third generation of vertical sounding instruments aboard TIROS N (13 Oct 1978) and NOAA 6 (27 June 1979) is reported. The first evaluation of soundings from TIROS N found that oceanic, cloudy retrievals over NH mid latitudes show a cold bias in winter. It is confirmed for both satellite systems using a larger data base. It is shown that RMS differences between retrievals and colocated radiosonde observations within the swath 30-60N during the 1979-80 winter were generally 2-3K in clear air and higher for cloudy columns. A positive impact of TIROS N temperatures on the analysis of synoptic weather systems is shown. Analyses prepared from only satellite temperatures seemed to give a better definition to weather systems' thermal structure than that provided by corresponding NMC analyses without satellite data. The results of a set of 14 numerical forecast experiments performed with the PE model of the Israel Meteorological Service (IMS) are summarized; these were designed to test the impact of TIROS N and NOAA 6 temperatures within the IMS analysis and forecast cycle. The satellite data coverage over the NH, the mean area/period S1 and RMS verification scores and the spatial distribution of SAT versus NO SAT forecast differences are discussed and it is concluded that positive forecast impact occurs over ocean areas where the extra data improve the specification which is otherwise available from conventional observations. The forecast impact for three cases from the same set of experiments was examined and it is found that satellite temperatures, observed over the Atlantic Ocean contribute to better forecasts over Iceland and central Europe although a worse result was verified over Spain. It is also shown that the better scores of a forecast based also on satellite data and verified over North America actually represent a mixed impact on the forecast synoptic patterns. A superior 48 hr

  6. Characteristics of occasional poor medium-range weather forecasts for Europe

    Science.gov (United States)

    Rodwell, Mark; Magnusson, Linus; Bauer, Peter; Bechtold, Peter; Bonavita, Massimo; Cardinali, Carla; Diamantakis, Michail; Earnshaw, Paul; Garcia-Mendez, Antonio; Isaksen, Lars; Kallen, Erland; Klocke, Daniel; Lopez, Philippe; McNally, Tony; Persson, Anders; Prates, Fernando; Wedi, Nils

    2013-04-01

    Weather prediction at a range of a few days has become more skilful over recent decades, but forecast centres still suffer from occasional very poor forecasts - often referred to as a 'drop-outs' or 'busts'. This study focuses on European Centre for Medium-range Weather Forecasts (ECMWF) day-6 forecasts for Europe. The focus on particularly poor forecasts means that one must be careful what can be concluded for a single case-study, and what requires the use of a large composite of cases. Although busts are defined by area-mean scores, bust composites reveal a coherent 'Rex-type' blocking situation; with a High over North Europe and a Low over the Mediterranean. Initial conditions for these busts also reveal a coherent flow, but this is located over North America and involves a trough over the Rockies, with high convective instability to its east. This flow-type occurs particularly in spring, and is often associated with a Rossby wave that has crossed the Pacific. A composite of this initial flow-type does indeed display enhanced day-6 random forecast errors - indicating reduced inherent predictability. In the probabilistic forecasting system, ensemble spread is also enhanced but not sufficiently to match the increased error. Composite analysis of the Potential Vorticity budget shows that mesoscale convective systems, associated with the convective instability, act to slow the motion of the trough. Hence convection errors play an active role in the busts. The sub-grid-scale nature of convection highlights the importance of the representation of model uncertainty in probabilistic forecasts. The cloud and extreme conditions associated with mesoscale convective systems also reduce the availability and utility of observations provided to the data assimilation. A question of relevance to the wider community is do we have observations with sufficient accuracy to better constrain the important error-structures in the initial-conditions? Meanwhile, improvements to ensemble

  7. Real-time dynamic control of the Three Gorges Reservoir by coupling numerical weather rainfall prediction and flood forecasting

    DEFF Research Database (Denmark)

    Wang, Y.; Chen, H.; Rosbjerg, Dan

    2013-01-01

    season 2012 as example, real-time dynamic control of the FLWL was implemented by using the forecasted reservoir flood inflow as input. The forecasted inflow with 5 days lead-time rainfall forecast was evaluated by several performance indices, including the mean relative error of the volumetric reservoir......In reservoir operation improvement of the accuracy of forecast flood inflow and extension of forecast lead-time can effectively be achieved by using rainfall forecasts from numerical weather predictions with a hydrological catchment model. In this study, the Regional Spectrum Model (RSM), which...... is developed by the Japan Meteorological Agency, was used to forecast rainfall with 5 days lead-time in the upper region of the Three Gorges Reservoir (TGR). A conceptual hydrological model, the Xinanjiang Model, has been set up to forecast the inflow flood of TGR by the Ministry of Water Resources Information...

  8. Implementation of an atmospheric sulfur scheme in the HIRLAM regional weather forecast model

    Energy Technology Data Exchange (ETDEWEB)

    Ekman, Annica [Stockholm Univ. (Sweden). Dept. of Meteorology

    2000-02-01

    Sulfur chemistry has been implemented into the regional weather forecast model HIRLAM in order to simulate sulfur fields during specific weather situations. The model calculates concentrations of sulfur dioxide in air (SO{sub 2}(a)), sulfate in air (SO{sub 4}(a)), sulfate in cloud water (SO{sub 4}(aq)) and hydrogen peroxide (H{sub 2}O{sub 2}). Modeled concentrations of SO{sub 2}(a), SO{sub 4}(a) and SO{sub 4}(aq) in rain water are compared with observations for two weather situations, one winter case with an extensive stratiform cloud cover and one summer case with mostly convective clouds. A comparison of the weather forecast parameters precipitation, relative humidity, geopotential and temperature with observations is also performed. The results show that the model generally overpredicts the SO{sub 2}(a) concentration and underpredicts the SO{sub 4}(a) concentration. The agreement between modeled and observed SO{sub 4}(aq) in rain water is poor. Calculated turnover times are approximately 1 day for SO{sub 2}(a) and 2-2.5 days for SO{sub 4}(a). For SO{sub 2}(a) this is in accordance with earlier simulated global turnover times, but for SO{sub 4}(a) it is substantially lower. Several sensitivity simulations show that the fractional mean bias and root mean square error decreases, mainly for SO{sub 4}(a) and SO{sub 4}(aq), if an additional oxidant for converting SO{sub 2}(a) to SO{sub 4}(a) is included in the model. All weather forecast parameters, except precipitation, agree better with observations than the sulfur variables do. Wet scavenging is responsible for about half of the deposited sulfur and in addition, a major part of the sulfate production occurs through in-cloud oxidation. Hence, the distribution of clouds and precipitation must be better simulated by the weather forecast model in order to improve the agreement between observed and simulated sulfur concentrations.

  9. Implementation of an atmospheric sulfur scheme in the HIRLAM regional weather forecast model

    Energy Technology Data Exchange (ETDEWEB)

    Ekman, Annica [Stockholm Univ. (Sweden). Dept. of Meteorology

    2000-02-01

    Sulfur chemistry has been implemented into the regional weather forecast model HIRLAM in order to simulate sulfur fields during specific weather situations. The model calculates concentrations of sulfur dioxide in air (SO{sub 2}(a)), sulfate in air (SO{sub 4}(a)), sulfate in cloud water (SO{sub 4}(aq)) and hydrogen peroxide (H{sub 2}O{sub 2}). Modeled concentrations of SO{sub 2}(a), SO{sub 4}(a) and SO{sub 4}(aq) in rain water are compared with observations for two weather situations, one winter case with an extensive stratiform cloud cover and one summer case with mostly convective clouds. A comparison of the weather forecast parameters precipitation, relative humidity, geopotential and temperature with observations is also performed. The results show that the model generally overpredicts the SO{sub 2}(a) concentration and underpredicts the SO{sub 4}(a) concentration. The agreement between modeled and observed SO{sub 4}(aq) in rain water is poor. Calculated turnover times are approximately 1 day for SO{sub 2}(a) and 2-2.5 days for SO{sub 4}(a). For SO{sub 2}(a) this is in accordance with earlier simulated global turnover times, but for SO{sub 4}(a) it is substantially lower. Several sensitivity simulations show that the fractional mean bias and root mean square error decreases, mainly for SO{sub 4}(a) and SO{sub 4}(aq), if an additional oxidant for converting SO{sub 2}(a) to SO{sub 4}(a) is included in the model. All weather forecast parameters, except precipitation, agree better with observations than the sulfur variables do. Wet scavenging is responsible for about half of the deposited sulfur and in addition, a major part of the sulfate production occurs through in-cloud oxidation. Hence, the distribution of clouds and precipitation must be better simulated by the weather forecast model in order to improve the agreement between observed and simulated sulfur concentrations.

  10. Determining optimal clothing ensembles based on weather forecasts, with particular reference to outdoor winter military activities.

    Science.gov (United States)

    Morabito, Marco; Pavlinic, Daniela Z; Crisci, Alfonso; Capecchi, Valerio; Orlandini, Simone; Mekjavic, Igor B

    2011-07-01

    Military and civil defense personnel are often involved in complex activities in a variety of outdoor environments. The choice of appropriate clothing ensembles represents an important strategy to establish the success of a military mission. The main aim of this study was to compare the known clothing insulation of the garment ensembles worn by soldiers during two winter outdoor field trials (hike and guard duty) with the estimated optimal clothing thermal insulations recommended to maintain thermoneutrality, assessed by using two different biometeorological procedures. The overall aim was to assess the applicability of such biometeorological procedures to weather forecast systems, thereby developing a comprehensive biometeorological tool for military operational forecast purposes. Military trials were carried out during winter 2006 in Pokljuka (Slovenia) by Slovene Armed Forces personnel. Gastrointestinal temperature, heart rate and environmental parameters were measured with portable data acquisition systems. The thermal characteristics of the clothing ensembles worn by the soldiers, namely thermal resistance, were determined with a sweating thermal manikin. Results showed that the clothing ensemble worn by the military was appropriate during guard duty but generally inappropriate during the hike. A general under-estimation of the biometeorological forecast model in predicting the optimal clothing insulation value was observed and an additional post-processing calibration might further improve forecast accuracy. This study represents the first step in the development of a comprehensive personalized biometeorological forecast system aimed at improving recommendations regarding the optimal thermal insulation of military garment ensembles for winter activities.

  11. Application of polar orbiter products in weather forecasting using open source tools and open standards

    Science.gov (United States)

    Plieger, Maarten; de Vreede, Ernst

    2015-04-01

    EUMETSAT disseminates data for a number of polar satellites. At KNMI these data are not fully used for operational weather forecasting mainly because of the irregular coverage and lack of tools for handling these different types of data and products. For weather forecasting there is a lot of interest in the application of products from these polar orbiters. One of the key aspects is the high-resolution of these products, which can complement the information provided by numerical weather forecasts. Another advantage over geostationary satellites is the high coverage at higher latitudes and lack of parallax. Products like the VIIRS day-night band offer many possibilities for this application. This presentation will describe a project that aims to make available a number of products from polar satellites to the forecasting operation. The goal of the project is to enable easy and timely access to polar orbiter products and enable combined presentations of satellite imagery with model data. The system will be able to generate RGB composites (“false colour images”) for operational use. The system will be built using open source components and open standards. Pytroll components are used for data handling, reprojection and derived product generation. For interactive presentation of imagery the browser based ADAGUC WMS viewer component is used. Image generation is done by ADAGUC server components, which provide OGC WMS services. Polar satellite products are stored as true color RGBA data in the NetCDF file format, the satellite swaths are stored as regular grids with their own custom geographical projection. The ADAGUC WMS system is able to reproject, render and combine these data in a webbrowser interactively. Results and lessons learned will be presented at the conference.

  12. Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Bradley T.; Jedlovee, Gary J.

    2010-01-01

    In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

  13. Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

    Science.gov (United States)

    Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

    2015-01-01

    The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building

  14. Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

    Science.gov (United States)

    Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Yan, Banghua; Zavodsky, Bradley; Zhao, Limin; Dong, Jun; Wang, Nai-Yu

    2015-01-01

    (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.

  15. Verification of Monthly Mean Forecasts for Fire Weather Elements in the Contiguous United States.

    Science.gov (United States)

    Klein, Willlam H.; Charney, Joseph J.; McCutchan, Morris H.; Benoit, John W.

    1996-12-01

    The authors first review a system for specifying monthly mean anomalies of midday temperature (T), dew-point (D), and wind speed (W) at a large network of surface stations across the United States. Multiple regression equations containing approximately three terms were derived for each element and month from concurrent fields of 700-mb height anomaly (H) observed over North America and vicinity, plus the previous month's observed local anomaly of T, D, or W, during the 20-year period 1964-1983. Results of testing this forecast system on prognostic values of H prepared twice a month at the National Weather Service from 1973 to 1990 and on observed values of H during the independent period from 1984 to 1990 are presented.The authors pooled the data for all months and 122 stations to compute Heidke skill scores (HSS) for two, three, four, and five classes for each weather element. All scores showed skill that dropped steadily as the number of classes increased. In all cases skill was highest for T and lowest for W, the same result as that obtained in the original derivation. In order to examine the distribution of skill, we computed the HSS separately for each class. The skill scores for the first and last classes were greater than those for the middle class for all elements and months. It is encouraging that forecasts above the 90th percentile, the most critical category for fire potential, consistently showed greater skill than forecasts for any other class. Geographical and seasonal variations of skill based on prognostic height was also examined. The results showed that skill is strongly dependent on location, month, and weather element. Mean annual scores were positive for each element in all parts of the country, while monthly scores were highest in January and lowest in October.Although the forecasts based on prognostic heights were more skillful than climatology for all weather elements, they improved over month-to-month persistence only for T and D. If the

  16. Evaluating the use of high-resolution numerical weather forecast for debris flow prediction.

    Science.gov (United States)

    Nikolopoulos, Efthymios I.; Bartsotas, Nikolaos S.; Borga, Marco; Kallos, George

    2015-04-01

    The sudden occurrence combined with the high destructive power of debris flows pose a significant threat to human life and infrastructures. Therefore, developing early warning procedures for the mitigation of debris flows risk is of great economical and societal importance. Given that rainfall is the predominant factor controlling debris flow triggering, it is indisputable that development of effective debris flows warning procedures requires accurate knowledge of the properties (e.g. duration, intensity) of the triggering rainfall. Moreover, efficient and timely response of emergency operations depends highly on the lead-time provided by the warning systems. Currently, the majority of early warning systems for debris flows are based on nowcasting procedures. While the latter may be successful in predicting the hazard, they provide warnings with a relatively short lead-time (~6h). Increasing the lead-time is necessary in order to improve the pre-incident operations and communication of the emergency, thus coupling warning systems with weather forecasting is essential for advancing early warning procedures. In this work we evaluate the potential of using high-resolution (1km) rainfall fields forecasted with a state-of-the-art numerical weather prediction model (RAMS/ICLAMS), in order to predict the occurrence of debris flows. Analysis is focused over the Upper Adige region, Northeast Italy, an area where debris flows are frequent. Seven storm events that generated a large number (>80) of debris flows during the period 2007-2012 are analyzed. Radar-based rainfall estimates, available from the operational C-band radar located at Mt Macaion, are used as the reference to evaluate the forecasted rainfall fields. Evaluation is mainly focused on assessing the error in forecasted rainfall properties (magnitude, duration) and the correlation in space and time with the reference field. Results show that the forecasted rainfall fields captured very well the magnitude and

  17. Applying Forecast Models from the Center for Integrated Space Weather Modeling

    Science.gov (United States)

    Gehmeyr, M.; Baker, D. N.; Millward, G.; Odstrcil, D.

    2007-12-01

    The Center for Integrated Space Weather Modeling (CISM) has developed three forecast models (FMs) for the Sun-Earth chain. They have been matured by various degrees toward the operational stage. The Sun-Earth FM suite comprises empirical and physical models: the Planetary Equivalent Amplitude (AP-FM), the Solar Wind (SW- FM), and the Geospace (GS-FM) models. We give a brief overview of these forecast models and touch briefly on the associated validation studies. We demonstrate the utility of the models: AP-FM supporting the operations of the AIM (Aeronomy of Ice in the Mesosphere) mission soon after launch; SW-FM providing assistance with the interpretation of the STEREO beacon data; and GS-FM combining model and observed data to characterize the aurora borealis. We will then discuss space weather tools in a more general sense, point out where the current capabilities and shortcomings are, and conclude with a look forward to what areas need improvement to facilitate better real-time forecasts.

  18. The Sydney 2000 World Weather Research Programme Forecast Demonstration Project: Overview and Current Status.

    Science.gov (United States)

    Keenan, T.; Joe, P.; Wilson, J.; Collier, C.; Golding, B.; Burgess, D.; May, P.; Pierce, C.; Bally, J.; Crook, A.; Seed, A.; Sills, D.; Berry, L.; Potts, R.; Bell, I.; Fox, N.; Ebert, E.; Eilts, M.;  O'Loughlin, K.;  Webb, R.;  Carbone, R.;  Browning, K.;  Roberts, R.;  Mueller, C.

    2003-08-01

    The first World Weather Research Programme (WWRP) Forecast Demonstration Project (FDP), with a focus on nowcasting, was conducted in Sydney, Australia, from 4 September to 21 November 2000 during a period associated with the Sydney 2000 Olympic Games. Through international collaboration, nine nowcasting systems from the United States, United Kingdom, Canada, and Australia were deployed at the Sydney Office of the Bureau of Meteorology (BOM) to demonstrate the capability of modern forecast systems and to quantify the associated benefits in the delivery of a real-time nowcast service. On-going verification and impact studies supported by international committees assisted by the WWRP formed an integral part of this project. A description is given of the project, including component systems, the weather, and initial outcomes. Initial results show that the nowcasting systems tested were transferable and able to provide valuable information enhancing BOM nowcasts. The project provided for unprecedented interchange of concepts and ideas between forecasters, researchers, and end users in an operational framework where they all faced common issues relevant to real time nowcast decision making. A training workshop sponsored by the World Meteorological Organization (WMO) was also held in conjunction with the project so that other member nations could benefit from the FDP.

  19. 1. The impact of weather forecast improvements on large scale hydrology: analysing a decade of forecasts of the European Flood Alert System

    Science.gov (United States)

    Pappenberger, Florian; Thielen, Jutta; Del Medico, Mauro

    2010-05-01

    The European Flood Alert System (EFAS) provides early flood alerts on a pre-operational basis to National hydrological services. EFAS river discharge forecasts are based on probabilistic techniques, using ensemble system and deterministic numerical weather prediction data. The performance of EFAS is regularly analysed with regard to individual flood events and case studies. Although this analysis provides important insight into the strengths and weaknesses of the forecast system, it lacks statistical and independent measures of its long-term performance. In this paper an assessment of EFAS results based on ECMWF weather forecasts over a period of 10 years is presented. EFAS river discharge forecasts have been rerun every week for a period of 10 years using the weather forecast available at the time. These are evaluated for a total of 500 river gauging stations distributed across Europe.. The selected stations are sufficiently separated in space to avoid autocorrelation of station time series. Also, analysis is performed with a gap of 3 days between each forecast which reduces the temporal correlation of the time series of the same station. The data are analysed with regard to skill, bias and quality of river discharge forecast. The 10 year simulations clearly show that the skill of the river discharge forecasts have undergone an evolution linked to the quality of the operational meteorological forecast. Overall, over the period of 10 years, the skill of the EFAS forecasts has steadily increased. Important hydrological extreme events cannot be clearly identified with the skill score analysis, highlighting the necessity for event based analysis in addition to statistical long-term assessments for a better understanding of the EFAS system and large scale river discharge predictions in general. he predictability is shown to depend on catchment size and geographical location.

  20. Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

    Science.gov (United States)

    Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara

    2016-06-01

    Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

  1. Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Nebula Cloud Computing Environment

    Science.gov (United States)

    Molthan, Andrew L.; Case, Jonathan L.; Venner, Jason; Moreno-Madrinan, Max. J.; Delgado, Francisco

    2012-01-01

    Over the past two years, scientists in the Earth Science Office at NASA fs Marshall Space Flight Center (MSFC) have explored opportunities to apply cloud computing concepts to support near real ]time weather forecast modeling via the Weather Research and Forecasting (WRF) model. Collaborators at NASA fs Short ]term Prediction Research and Transition (SPoRT) Center and the SERVIR project at Marshall Space Flight Center have established a framework that provides high resolution, daily weather forecasts over Mesoamerica through use of the NASA Nebula Cloud Computing Platform at Ames Research Center. Supported by experts at Ames, staff at SPoRT and SERVIR have established daily forecasts complete with web graphics and a user interface that allows SERVIR partners access to high resolution depictions of weather in the next 48 hours, useful for monitoring and mitigating meteorological hazards such as thunderstorms, heavy precipitation, and tropical weather that can lead to other disasters such as flooding and landslides. This presentation will describe the framework for establishing and providing WRF forecasts, example applications of output provided via the SERVIR web portal, and early results of forecast model verification against available surface ] and satellite ]based observations.

  2. Tailored high-resolution numerical weather forecasts for energy efficient predictive building control

    Science.gov (United States)

    Stauch, V. J.; Gwerder, M.; Gyalistras, D.; Oldewurtel, F.; Schubiger, F.; Steiner, P.

    2010-09-01

    The high proportion of the total primary energy consumption by buildings has increased the public interest in the optimisation of buildings' operation and is also driving the development of novel control approaches for the indoor climate. In this context, the use of weather forecasts presents an interesting and - thanks to advances in information and predictive control technologies and the continuous improvement of numerical weather prediction (NWP) models - an increasingly attractive option for improved building control. Within the research project OptiControl (www.opticontrol.ethz.ch) predictive control strategies for a wide range of buildings, heating, ventilation and air conditioning (HVAC) systems, and representative locations in Europe are being investigated with the aid of newly developed modelling and simulation tools. Grid point predictions for radiation, temperature and humidity of the high-resolution limited area NWP model COSMO-7 (see www.cosmo-model.org) and local measurements are used as disturbances and inputs into the building system. The control task considered consists in minimizing energy consumption whilst maintaining occupant comfort. In this presentation, we use the simulation-based OptiControl methodology to investigate the impact of COSMO-7 forecasts on the performance of predictive building control and the resulting energy savings. For this, we have selected building cases that were shown to benefit from a prediction horizon of up to 3 days and therefore, are particularly suitable for the use of numerical weather forecasts. We show that the controller performance is sensitive to the quality of the weather predictions, most importantly of the incident radiation on differently oriented façades. However, radiation is characterised by a high temporal and spatial variability in part caused by small scale and fast changing cloud formation and dissolution processes being only partially represented in the COSMO-7 grid point predictions. On the

  3. Modeled Forecasts of Dengue Fever in San Juan, PR Using NASA Satellite Enhanced Weather Forecasts

    Science.gov (United States)

    Morin, Cory; Quattrochi, Dale; Zavodsky, Bradley; Case, Jonathan

    2015-01-01

    Dengue virus is transmitted between humans and mosquitoes of the genus Aedes and causes approximately 96 million cases of disease (dengue fever) each year (Bhatet al. 2013). Symptoms of dengue fever include fever, headache, nausea, vomiting, and eye, muscle and joint pain (CDC). More sever manifestations such as abdominal pain, bleeding from nose and gums, vomiting of blood, and clammy skin occur in rare cases of dengue hemorrhagic fever (CDC). Dengue fever occurs throughout tropical and sub-tropical regions worldwide, however, the geographical range and size of epidemics is increasing. Weather and climate are drivers of dengue virus transmission dynamics (Morin et al. 2013) by affecting mosquito proliferation and the virus extrinsic incubation period (i.e. required time for the virus to replicate and disseminate within the mosquito before it can retransmit the virus).

  4. The Impact of Weather Forecasts of Various Lead Times on Snowmaking Decisions Made for the 2010 Vancouver Olympic Winter Games

    Science.gov (United States)

    Doyle, Chris

    2014-01-01

    The Vancouver 2010 Winter Olympics were held from 12 to 28 February 2010, and the Paralympic events followed 2 weeks later. During the Games, the weather posed a grave threat to the viability of one venue and created significant complications for the event schedule at others. Forecasts of weather with lead times ranging from minutes to days helped organizers minimize disruptions to sporting events and helped ensure all medal events were successfully completed. Of comparable importance, however, were the scenarios and forecasts of probable weather for the winter in advance of the Games. Forecasts of mild conditions at the time of the Games helped the Games' organizers mitigate what would have been very serious potential consequences for at least one venue. Snowmaking was one strategy employed well in advance of the Games to prepare for the expected conditions. This short study will focus on how operational decisions were made by the Games' organizers on the basis of both climatological and snowmaking forecasts during the pre-Games winter. An attempt will be made to quantify, economically, the value of some of the snowmaking forecasts made for the Games' operators. The results obtained indicate that although the economic value of the snowmaking forecast was difficult to determine, the Games' organizers valued the forecast information greatly. This suggests that further development of probabilistic forecasts for applications like pre-Games snowmaking would be worthwhile.

  5. Forecasting severe ice storms using numerical weather prediction: the March 2010 Newfoundland event

    Directory of Open Access Journals (Sweden)

    J. Hosek

    2011-02-01

    Full Text Available The northeast coast of North America is frequently hit by severe ice storms. These freezing rain events can produce large ice accretions that damage structures, frequently power transmission and distribution infrastructure. For this reason, it is highly desirable to model and forecast such icing events, so that the consequent damages can be prevented or mitigated. The case study presented in this paper focuses on the March 2010 ice storm event that took place in eastern Newfoundland. We apply a combination of a numerical weather prediction model and an ice accretion algorithm to simulate a forecast of this event.

    The main goals of this study are to compare the simulated meteorological variables to observations, and to assess the ability of the model to accurately predict the ice accretion load for different forecast horizons. The duration and timing of the freezing rain event that occurred between the night of 4 March and the morning of 6 March was simulated well in all model runs. The total precipitation amounts in the model, however, differed by up to a factor of two from the observations. The accuracy of the model air temperature strongly depended on the forecast horizon, but it was acceptable for all simulation runs. The simulated accretion loads were also compared to the design values for power delivery structures in the region. The results indicated that the simulated values exceeded design criteria in the areas of reported damage and power outages.

  6. Forecasting severe ice storms using numerical weather prediction: the March 2010 Newfoundland event

    Science.gov (United States)

    Hosek, J.; Musilek, P.; Lozowski, E.; Pytlak, P.

    2011-02-01

    The northeast coast of North America is frequently hit by severe ice storms. These freezing rain events can produce large ice accretions that damage structures, frequently power transmission and distribution infrastructure. For this reason, it is highly desirable to model and forecast such icing events, so that the consequent damages can be prevented or mitigated. The case study presented in this paper focuses on the March 2010 ice storm event that took place in eastern Newfoundland. We apply a combination of a numerical weather prediction model and an ice accretion algorithm to simulate a forecast of this event. The main goals of this study are to compare the simulated meteorological variables to observations, and to assess the ability of the model to accurately predict the ice accretion load for different forecast horizons. The duration and timing of the freezing rain event that occurred between the night of 4 March and the morning of 6 March was simulated well in all model runs. The total precipitation amounts in the model, however, differed by up to a factor of two from the observations. The accuracy of the model air temperature strongly depended on the forecast horizon, but it was acceptable for all simulation runs. The simulated accretion loads were also compared to the design values for power delivery structures in the region. The results indicated that the simulated values exceeded design criteria in the areas of reported damage and power outages.

  7. Performance of Chaos Theory in Weather Forecasts (Case Study: Tehran-Temperate Climate

    Directory of Open Access Journals (Sweden)

    SohrabHajjam

    2016-06-01

    Full Text Available Accurate weather forecast is of great importance for providing the suitable substrates for water resources management and crisis management. Therefore, the use of methods with high accuracy and updating the forecast models seem to be necessary in this regard. In evaluation of hydrological and climate data, the investigation of precipitation parameter is in non-linear time series method. The present research aimed to compare the performance of intelligent systems based on nonlinear methods, chaos theory, and neural network system in estimating monthly precipitation in temperate climate of Tehran. The results of neural network system, local model methods, and the nearest neighbor showed that chaos-based methods not only are sensitive to the range of data but also influenced by the length of data and attitude towards data review process based on conditions. Evaluation of results indicated that chaos-based have an acceptable and high precision and accuracy and chaos theory produces better results than neural network system in temperate climates. Considering the nature of data, the studied climate, and the procedures required in forecast of meteorological parameters, chaos theory can bring very good results. Due to the sensitivity of meteorological forecasts, the use of this theory can be helpful and beneficial.

  8. The Effect of NEXRAD Image Looping and National Convective Weather Forecast Product on Pilot Decision Making in the Use of a Cockpit Weather Information Display

    Science.gov (United States)

    Burgess, Malcolm A.; Thomas, Rickey P.

    2004-01-01

    This experiment investigated improvements to cockpit weather displays to better support the hazardous weather avoidance decision-making of general aviation pilots. Forty-eight general aviation pilots were divided into three equal groups and presented with a simulated flight scenario involving embedded convective activity. The control group had access to conventional sources of pre-flight and in-flight weather products. The two treatment groups were provided with a weather display that presented NEXRAD mosaic images, graphic depiction of METARs, and text METARs. One treatment group used a NEXRAD image looping feature and the second group used the National Convective Weather Forecast (NCWF) product overlaid on the NEXRAD display. Both of the treatment displays provided a significant increase in situation awareness but, they provided incomplete information required to deal with hazardous convective weather conditions, and would require substantial pilot training to permit their safe and effective use.

  9. PRECURSOR WEATHER CONDITIONS FOR HAIL-EVENT FORECASTING IN THE MOLDAVIA

    Directory of Open Access Journals (Sweden)

    VASILICĂ ISTRATE

    2016-03-01

    Full Text Available he present work analysed a statistically representative number of severe convective events which caused hail and material damage during 1990-2015. For these episodes heights of 0oC, -6oC, -10oC and -20oC isotherms were analyzed. In the layers between these four isotherms, the formation and growth of hailstones occur. The height of these isotherms in the period preceding hail is essential in weather forecast of this phenomenon. Lifted index values recorded before hail events are also correlated with height isotherms. The altitudinal differences between the height of -6oC and -20oC isotherm and freezing level which represents intervals of initiation and formation of hail are identified. Not at last the values of the meteorological indices used to forecast the potential formation and hail falling in extracarpathian Moldavia are presented in details.

  10. Numerical simulation of birch pollen dispersion with an operational weather forecast system.

    Science.gov (United States)

    Vogel, Heike; Pauling, Andreas; Vogel, Bernhard

    2008-11-01

    We included a parameterisation of the emissions of pollen grains into the comprehensive model system COSMO-ART. In addition, a detailed density distribution of birch trees within Switzerland was derived. Based on these new developments, we carried out numerical simulations of the dispersion of pollen grains for an episode that occurred in April 2006 over Switzerland and the adjacent regions. Since COSMO-ART is based on the operational forecast model of the German Weather Service, we are presenting a feasibility study of daily pollen forecast based on methods which have been developed during the last two decades for the treatment of anthropogenic aerosol. A comparison of the model results and very detailed pollen counts documents the current possibilities and the shortcomings of the method and gives hints for necessary improvements.

  11. Combining stochastic weather generation and ensemble streamflow predictions for short to medium term flow forecasting over Quebec, Canada

    Science.gov (United States)

    Brissette, F.; Chen, J.; Li, Z.; Turcotte, R.

    2012-04-01

    Probabilistic streamflow forecasting has been an important research avenue over the past decade and such approaches are now more commonly being incorporated into operational forecasting systems within government agencies and industries dealing with water management. This work details a prototype for a streamflow forecast operational system in southern Quebec, Canada. The system uses ensemble meteorological forecasts for short term (less than 10 days) forecasting, switching to a stochastic weather generator for the period exceeding 10 days all the way to a three-month lead time. Precipitation and temperature series are then fed to one (or many) hydrological models to produce streamflow forecasts. The ensemble weather forecasts are corrected for biases and under dispersion using logistic regression. Results show that the ensemble streamflow forecasts resulting from the ensemble meteorological forecast have more skill than the deterministic forecasts. Preliminary results indicate that ensemble meteorological forecasts displayed skill for a period up to 5 days for precipitation and up to about 10 days for temperature. Past ten days, probabilistic streamflow forecasts are based on multiple synthetic times series obtained from a stochastic weather generator. The use of stochastic time series result in better forecasts then resampling the historical record and allows for better evaluation of extreme events. The weather generator can easily be linked to large scale seasonal global predictors, is such links exist. Over the tested basins (continental climate), the system forecast has skills up to a lead time of 4 weeks in the best case. For a lead-time between one and three months, using the forecast prototype yielded no better results than using the historical streamflow record. This work also investigated the uncertainty linked to the choice of one hydrology model and the ability of a multi-model approach to improve streamflow forecasting. Preliminary results showed that

  12. The Doppler Wind and Temperature Sensor(DWTS): Advancing weather forecasts with a simple instrument.

    Science.gov (United States)

    Gordley, Larry; Marshall, Benjamin; Fritts, David; Evans, Wayne

    DWTS is a simple infrared earth limb imager, employing a novel gas correlation approach, that will provide global winds and temperature day and night from cloud top to over 200 km. It is now established that medium to long term weather forecasting skill can be dramatically advanced given global stratospheric wind fields. This presentation describes the latest design of the DWTS sensor and plans to establish a DWTS constellation for monitoring global high altitude winds and temperature at an unprecedented temporal and spatial resolution.

  13. SPARX: a modelling system for Solar Energetic Particle Radiation Space Weather forecasting

    CERN Document Server

    Marsh, M S; Dierckxsens, M; Laitinen, T; Crosby, N B

    2014-01-01

    The capability to predict the parameters of an SEP event such as its onset, peak flux, and duration is critical to assessing any potential space weather impact. We present a new operational modelling system simulating the propagation of Solar Energetic Particles (SEPs) from locations near the Sun to any given location in the heliosphere. The model is based on the test particle approach and is spatially 3D, thus allowing for the possibility of transport in the direction perpendicular to the magnetic field. The model naturally includes the effects of perpendicular propagation due to drifts and drift-induced deceleration. The modelling framework and the way in which parameters of relevance for Space Weather are obtained within a forecasting context are described. The first results from the modelling system are presented. These results demonstrate that corotation and drift of SEP streams play an essential role in shaping SEP flux profiles.

  14. Medical weather forecast as the risk management facilities of meteopathia with population

    Science.gov (United States)

    Efimenko, Natalya; Chalaya, Elena; Povolotskaia, Nina; Senik, Irina; Topuriya, David

    2013-04-01

    Frequent cases of extreme deviations of weather conditions and anthropogenic press on the Earth atmosphere are external stressors and provoke the development of meteopathic reactions (DMR) with people suffering from dysadaptation (DA). [EGU2011-6740-3; EGU2012-6103]. The influence of weather factors on the person is multivariate which complicates the search of physiological indicators of this exposure. The results of long-term researches of meteodependence and risks development of weather-conditional pathologic reactions with people suffering from DA (1640 observed people) in various systems and human body subsystems (thermal control, cardiovascular, respiratory, vegetative and central nervous systems) were taken as a principle of calculation methodology of estimation of weather pathogenicity (EWP). This estimation is used in the system of medical weather forecast (MWF) in the resorts of Caucasian Mineral Waters and is marked as an organized structure in prevention of DMR risks. Nowadays MWF efficiency is from 78% to 95% as it depends not only on the performance of models of dynamic, synoptic, heliogeophysical forecasts, but also on the underestimation of environmental factors which often act as dominating stressors. The program of atmospheric global system monitoring and real-time forecasts doesn`t include atmospheric electricity factors, ionization factors, range and chemistry factors of aerosol particles and organic volatile plant matters in atmospheric boundary layer. New fractality researches of control mechanisms processes providing adaptation to external and internal environmental conditions with patients suffering from DA allowed us to understand the meaning of the phenomenon of structural similarity and similarity of physiological response processes to the influence of weather types with similar dominating environmental factors. Particularly, atmospheric conditions should be regarded as stressor natural factors that create deionization conditions of the

  15. Community Coordinated Modeling Center: Addressing Needs of Operational Space Weather Forecasting

    Science.gov (United States)

    Kuznetsova, M.; Maddox, M.; Pulkkinen, A.; Hesse, M.; Rastaetter, L.; Macneice, P.; Taktakishvili, A.; Berrios, D.; Chulaki, A.; Zheng, Y.; Mullinix, R.

    2012-01-01

    Models are key elements of space weather forecasting. The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) hosts a broad range of state-of-the-art space weather models and enables access to complex models through an unmatched automated web-based runs-on-request system. Model output comparisons with observational data carried out by a large number of CCMC users open an unprecedented mechanism for extensive model testing and broad community feedback on model performance. The CCMC also evaluates model's prediction ability as an unbiased broker and supports operational model selections. The CCMC is organizing and leading a series of community-wide projects aiming to evaluate the current state of space weather modeling, to address challenges of model-data comparisons, and to define metrics for various user s needs and requirements. Many of CCMC models are continuously running in real-time. Over the years the CCMC acquired the unique experience in developing and maintaining real-time systems. CCMC staff expertise and trusted relations with model owners enable to keep up to date with rapid advances in model development. The information gleaned from the real-time calculations is tailored to specific mission needs. Model forecasts combined with data streams from NASA and other missions are integrated into an innovative configurable data analysis and dissemination system (http://iswa.gsfc.nasa.gov) that is accessible world-wide. The talk will review the latest progress and discuss opportunities for addressing operational space weather needs in innovative and collaborative ways.

  16. CFORS - Regional Chemical and Weather Forecast System in Support of Field Experiments

    Science.gov (United States)

    Yienger, J. J.; Uno, I.; Guttikunda, S. K.; Carmichael, G. R.; Tang, Y.; Thongboonchoo, N.; Woo, J.; Dorwart, J.; Streets, D.

    2001-12-01

    In this paper we will present the development, evaluation, and use of improved modeling techniques and methodologies for the integration of meteorological forecasts with air pollution forecasts in support of field operations during the TRACE-P and Ace-Asia experiments in East Asia. During the campaign period we provided a variety of forecast products using our regional modeling system built upon the dynamic meteorological model RAMS and the 3-D regional chemical transport models STEM-III. These models were run in both on-line and off-line modes, and the results integrated into an interactive web-based data mining and analysis framework. This resulting Chemical Weather Forecasting System CFORS, was run operationally for the period February through May 2001, and provided 72-hr forecasts of a variety of aerosol, chemical and air mass and emission marker quantities. These included aerosol mass distribution and optical depth by major component (e.g., dust, sea salt, black carbon, organic carbon, and sulfate), photochemical quantities including ozone and OH/HO2, and air mass & emissions markers including lightning, volcanic, mega-cities, and biomass burning. These model products were presented along with meteorological forecasts and satellite products, and used to help determine the flight plans, the positioning of the ship, and to alert surface stations of upcoming events (such as dust storms). The use of CFORS forecasts (along with other model results) models were shown to provide important new information and level of detail into mission planning. For example many of the mission objectives required designing flight paths that sampled across gradients of optical depth, or flew above, below and through vertical layers of aerosol, intercepted biomass emission plumes, or sampled dust storms. CFORS, forecasts of dust outbreaks and plume locations, etc., proved to be very useful in designing missions that meet these objective. In this paper we will present an overview of

  17. Constraining storm-scale forecasts of deep convective initiation with surface weather observations

    Science.gov (United States)

    Madaus, Luke

    Successfully forecasting when and where individual convective storms will form remains an elusive goal for short-term numerical weather prediction. In this dissertation, the convective initiation (CI) challenge is considered as a problem of insufficiently resolved initial conditions and dense surface weather observations are explored as a possible solution. To better quantify convective-scale surface variability in numerical simulations of discrete convective initiation, idealized ensemble simulations of a variety of environments where CI occurs in response to boundary-layer processes are examined. Coherent features 1-2 hours prior to CI are found in all surface fields examined. While some features were broadly expected, such as positive temperature anomalies and convergent winds, negative temperature anomalies due to cloud shadowing are the largest surface anomaly seen prior to CI. Based on these simulations, several hypotheses about the required characteristics of a surface observing network to constrain CI forecasts are developed. Principally, these suggest that observation spacings of less than 4---5 km would be required, based on correlation length scales. Furthermore, it is anticipated that 2-m temperature and 10-m wind observations would likely be more relevant for effectively constraining variability than surface pressure or 2-m moisture observations based on the magnitudes of observed anomalies relative to observation error. These hypotheses are tested with a series of observing system simulation experiments (OSSEs) using a single CI-capable environment. The OSSE results largely confirm the hypotheses, and with 4-km and particularly 1-km surface observation spacing, skillful forecasts of CI are possible, but only within two hours of CI time. Several facets of convective-scale assimilation, including the need for properly-calibrated localization and problems from non-Gaussian ensemble estimates of the cloud field are discussed. Finally, the characteristics

  18. Reflections on Improving the Accuracy of Weather Forecasts%关于提高天气预报准确率的思考

    Institute of Scientific and Technical Information of China (English)

    李学欣

    2014-01-01

    The weather forecast meteorological services in the most basic work. Analyzes the importance of weather forecast accuracy and factors affecting the accuracy of weather forecasts, made several on improving the accuracy of weather forecasts measures for reference.%天气预报是气象服务业中最基础的工作。分析了天气预报准确率的重要性和影响天气预报准确率的因素,提出了几点关于提高天气预报准确率的措施,以供参考。

  19. The Impact of the Assimilation of AIRS Radiance Measurements on Short-term Weather Forecasts

    Science.gov (United States)

    McCarty, Will; Jedlovec, Gary; Miller, Timothy L.

    2009-01-01

    Advanced spaceborne instruments have the ability to improve the horizontal and vertical characterization of temperature and water vapor in the atmosphere through the explicit use of hyperspectral thermal infrared radiance measurements. The incorporation of these measurements into a data assimilation system provides a means to continuously characterize a three-dimensional, instantaneous atmospheric state necessary for the time integration of numerical weather forecasts. Measurements from the National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) are incorporated into the gridpoint statistical interpolation (GSI) three-dimensional variational (3D-Var) assimilation system to provide improved initial conditions for use in a mesoscale modeling framework mimicking that of the operational North American Mesoscale (NAM) model. The methodologies for the incorporation of the measurements into the system are presented. Though the measurements have been shown to have a positive impact in global modeling systems, the measurements are further constrained in this system as the model top is physically lower than the global systems and there is no ozone characterization in the background state. For a study period, the measurements are shown to have positive impact on both the analysis state as well as subsequently spawned short-term (0-48 hr) forecasts, particularly in forecasted geopotential height and precipitation fields. At 48 hr, height anomaly correlations showed an improvement in forecast skill of 2.3 hours relative to a system without the AIRS measurements. Similarly, the equitable threat and bias scores of precipitation forecasts of 25 mm (6 hr)-1 were shown to be improved by 8% and 7%, respectively.

  20. Interactions of physical, chemical, and biological weather calling for an integrated approach to assessment, forecasting, and communication of air quality.

    Science.gov (United States)

    Klein, Thomas; Kukkonen, Jaakko; Dahl, Aslög; Bossioli, Elissavet; Baklanov, Alexander; Vik, Aasmund Fahre; Agnew, Paul; Karatzas, Kostas D; Sofiev, Mikhail

    2012-12-01

    This article reviews interactions and health impacts of physical, chemical, and biological weather. Interactions and synergistic effects between the three types of weather call for integrated assessment, forecasting, and communication of air quality. Today's air quality legislation falls short of addressing air quality degradation by biological weather, despite increasing evidence for the feasibility of both mitigation and adaptation policy options. In comparison with the existing capabilities for physical and chemical weather, the monitoring of biological weather is lacking stable operational agreements and resources. Furthermore, integrated effects of physical, chemical, and biological weather suggest a critical review of air quality management practices. Additional research is required to improve the coupled modeling of physical, chemical, and biological weather as well as the assessment and communication of integrated air quality. Findings from several recent COST Actions underline the importance of an increased dialog between scientists from the fields of meteorology, air quality, aerobiology, health, and policy makers.

  1. Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

    Science.gov (United States)

    Dreher, Joseph G.

    2009-01-01

    For expedience in delivering dispersion guidance in the diversity of operational situations, National Weather Service Melbourne (MLB) and Spaceflight Meteorology Group (SMG) are becoming increasingly reliant on the PC-based version of the HYSPLIT model run through a graphical user interface (GUI). While the GUI offers unique advantages when compared to traditional methods, it is difficult for forecasters to run and manage in an operational environment. To alleviate the difficulty in providing scheduled real-time trajectory and concentration guidance, the Applied Meteorology Unit (AMU) configured a Linux version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) (HYSPLIT) model that ingests the National Centers for Environmental Prediction (NCEP) guidance, such as the North American Mesoscale (NAM) and the Rapid Update Cycle (RUC) models. The AMU configured the HYSPLIT system to automatically download the NCEP model products, convert the meteorological grids into HYSPLIT binary format, run the model from several pre-selected latitude/longitude sites, and post-process the data to create output graphics. In addition, the AMU configured several software programs to convert local Weather Research and Forecast (WRF) model output into HYSPLIT format.

  2. Potential of 4d-VAR for exigent forecasting of severe weather

    CERN Document Server

    Hoffman, Ross N; Nehrkorn, Thomas

    2011-01-01

    Severe storms, tropical cyclones, and associated tornadoes, floods, lightning, and microbursts threaten life and property. Reliable, precise, and accurate alerts of these phenomena can trigger defensive actions and preparations. However, these crucial weather phenomena are difficult to forecast. The objective of this paper is to demonstrate the potential of 4d-VAR (four dimensional variational data assimilation) for exigent forecasting (XF) of severe storm precursors and to thereby characterize the probability of a worst-case scenario. 4d-VAR is designed to adjust the initial conditions (IC) of a numerical weather prediction model consistent with the uncertainty of the prior estimate of the IC while at the same time minimizing the misfit to available observations. For XF the same approach is taken but instead of fitting observations, a measure of damage or loss or an equivalent proxy is maximized or minimized. To accomplish this will require development of a specialized cost function for 4d-VAR. When 4d-VAR s...

  3. A combined road weather forecast system to prevent road ice formation in the Adige Valley (Italy)

    Science.gov (United States)

    Di Napoli, Claudia; Piazza, Andrea; Antonacci, Gianluca; Todeschini, Ilaria; Apolloni, Roberto; Pretto, Ilaria

    2016-04-01

    Road ice is a dangerous meteorological hazard to a nation's transportation system and economy. By reducing the pavement friction with vehicle tyres, ice formation on pavements increases accident risk and delays travelling times thus posing a serious threat to road users' safety and the running of economic activities. Keeping roads clear and open is therefore essential, especially in mountainous areas where ice is likely to form during the winter period. Winter road maintenance helps to restore road efficiency and security, and its benefits are up to 8 times the costs sustained for anti-icing strategies [1]. However, the optimization of maintenance costs and the reduction of the environmental damage from over-salting demand further improvements. These can be achieved by reliable road weather forecasts, and in particular by the prediction of road surface temperatures (RSTs). RST is one of the most important parameters in determining road surface conditions. It is well known from literature that ice forms on pavements in high-humidity conditions when RSTs are below 0°C. We have therefore implemented an automatic forecast system to predict critical RSTs on a test route along the Adige Valley complex terrain, in the Italian Alps. The system considers two physical models, each computing heat and energy fluxes between the road and the atmosphere. One is Reuter's radiative cooling model, which predicts RSTs at sunrise as a function of surface temperatures at sunset and the time passed since then [2]. One is METRo (Model of the Environment and Temperature of Roads), a road weather forecast software which also considers heat conduction through road material [3]. We have applied the forecast system to a network of road weather stations (road weather information system, RWIS) installed on the test route [4]. Road and atmospheric observations from RWIS have been used as initial conditions for both METRo and Reuter's model. In METRo observations have also been coupled to

  4. Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

    Science.gov (United States)

    Watson, Leela R.; Bauman, William H., III; Hoeth, Brian

    2009-01-01

    This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

  5. Evaluating the skill of seasonal weather forecasts in predicting aflatoxin contamination of groundnut in Senegal

    Science.gov (United States)

    Brak, B.; Challinor, A.

    2011-12-01

    skill in seasonal weather forecasting in West Africa (Senegal) sufficient to predict the occurrence of high (median) aflatoxin concentrations in groundnut at harvest and after some period of storage? For multiple locations in Senegal, aflatoxin contamination (AC) indices estimated using observed weather data from 1999-2010 were compared with AC indices based on gridded seasonal weather forecasts for the same location and year. Pearson correlation coefficients for ACobs and ACpred indices were calculated using all locations combined and, if sufficient weather years without missing values were available, for individual locations to test for regional differences in skill.

  6. Weather requirements and rain forecast to time fungicide application for Fusarium head blight control in wheat

    Directory of Open Access Journals (Sweden)

    Ricardo Brustolin

    2013-12-01

    Full Text Available The aim of this study was to determine the minimum conditions of wetness duration and mean temperature required for Fusarium head blight infection in wheat. The weather model developed by Zoldan (2008 was tested in field experiments for two wheat cultivars grown in 2005 (five sowing dates and 2006 (six sowing dates in 10 m² plots with three replicates. The disease was assessed according to head incidence (HI, spikelet incidence (SI, and the interaction between these two methods was called head blight severity (HBS. Starting at the beginning of anthesis, air temperature and head wetness duration were daily recorded with an automatic weather station. With the combination of these two factors, a weather favorability table was built for the disease occurrence. Starting on the day of flowering beginning (1 - 5% fully exserted anthers, the sum of daily values for infection favorability (SDVIF was calculated by means of a computer program, according to Zoldan (2008 table. The initial symptoms of the disease were observed at 3.7% spikelet incidence, corresponding to 2.6 SVDFI. The infection occurs in wheat due to rainfall which results in spike wetting of > 61.4 h duration. Rainfall events forecast can help time fungicide application to control FHB. The name of this alert system is proposed as UPF-scab alert.

  7. Transition of Suomi National Polar-Orbiting Partnership (S-NPP) Data Products for Operational Weather Forecasting Applications

    Science.gov (United States)

    Smith, Matthew R.; Molthan, Andrew L.; Fuell, Kevin K.; Jedlovec, Gary J.

    2012-01-01

    SPoRT is a team of NASA/NOAA scientists focused on demonstrating the utility of NASA and future NOAA data and derived products on improving short-term weather forecasts. Work collaboratively with a suite of unique products and selected WFOs in an end-to-end transition activity. Stable funding from NASA and NOAA. Recognized by the science community as the "go to" place for transitioning experimental and research data to the operational weather community. Endorsed by NWS ESSD/SSD chiefs. Proven paradigm for transitioning satellite observations and modeling capabilities to operations (R2O). SPoRT s transition of NASA satellite instruments provides unique or higher resolution data products to complement the baseline suite of geostationary data available to forecasters. SPoRT s partnership with NWS WFOs provides them with unique imagery to support disaster response and local forecast challenges. SPoRT has years of proven experience in developing and transitioning research products to the operational weather community. SPoRT has begun work with CONUS and OCONUS WFOs to determine the best products for maximum benefit to forecasters. VIIRS has already proven to be another extremely powerful tool, enhancing forecasters ability to handle difficult forecasting situations.

  8. Operational use of VIIRS Multispectral Imagery and NUCAPS Soundings in Short-term Weather Forecasting

    Science.gov (United States)

    Molthan, A.; Fuell, K. K.; Berndt, E.; Schultz, L. A.

    2016-12-01

    The NASA/SPoRT Program supports the NOAA/JPSS program through the transition of S-NPP VIIRS and CrIS/ATMS products to prepare users for the upcoming JPSS-1/-2 missions. Several multispectral (i.e. RGB) imagery products can be created from VIIRS based on internationally-accepted recipes developed by EUMETSAT. Initial transition of a Nighttime Microphysics RGB to operations revealed improved distinction between low clouds and fog compared with legacy satellite imagery, and hence, improvement in short-term aviation and public forecasts. An increased number of S-NPP passes at high latitude combined with other instruments led to a series of "microphysical" RGBs to be introduced to NWS forecasters in Alaska at both local weather offices as well as regional aviation centers. Forecasters in Alaska also applied VIIRS microphysical RGBs to identify small scale features such as valley/coastal fog, volcanic ash, and convective precipitation. Further use of a "Dust" RGB in the U.S. southwest led to changes in NWS forecast products due to improvements in detection and monitoring of dust aloft. As multispectral imagery has gained operational acceptance, additional work has begun to develop quantitative products to assist users with their interpretation of RGB imagery. For example, National Center forecasters often use an "Air Mass" RGB to differentiate between possible stratospheric /tropospheric interactions, moist tropical air masses, and cool, continental/maritime air masses. Research was done to demonstrate how the NUCAPS CrIS/ATMS infrared retrieved temperature, moisture, and ozone profiles can aid Air Mass RGB imagery interpretation as well as how these quantitative values are important for anticipating tropical to extratropical transition events. In addition, an enhanced stratospheric depth product was developed to identify the dynamic tropopause from the NUCAPS retrieved ozone profiles to aid identification of stratospheric air influence. Forecasters from National Centers

  9. Evaluation of fire weather forecasts using PM2.5 sensitivity analysis

    Science.gov (United States)

    Balachandran, Sivaraman; Baumann, Karsten; Pachon, Jorge E.; Mulholland, James A.; Russell, Armistead G.

    2017-01-01

    Fire weather forecasts are used by land and wildlife managers to determine when meteorological and fuel conditions are suitable to conduct prescribed burning. In this work, we investigate the sensitivity of ambient PM2.5 to various fire and meteorological variables in a spatial setting that is typical for the southeastern US, where prescribed fires are the single largest source of fine particulate matter. We use the method of principle components regression to estimate sensitivity of PM2.5, measured at a monitoring site in Jacksonville, NC (JVL), to fire data and observed and forecast meteorological variables. Fire data were gathered from prescribed fire activity used for ecological management at Marine Corps Base Camp Lejeune, extending 10-50 km south from the PM2.5 monitor. Principal components analysis (PCA) was run on 10 data sets that included acres of prescribed burning activity (PB) along with meteorological forecast data alone or in combination with observations. For each data set, observed PM2.5 (unitless) was regressed against PCA scores from the first seven principal components (explaining at least 80% of total variance). PM2.5 showed significant sensitivity to PB: 3.6 ± 2.2 μg m-3 per 1000 acres burned at the investigated distance scale of ∼10-50 km. Applying this sensitivity to the available activity data revealed a prescribed burning source contribution to measured PM2.5 of up to 25% on a given day. PM2.5 showed a positive sensitivity to relative humidity and temperature, and was also sensitive to wind direction, indicating the capture of more regional aerosol processing and transport effects. As expected, PM2.5 had a negative sensitivity to dispersive variables but only showed a statistically significant negative sensitivity to ventilation rate, highlighting the importance of this parameter to fire managers. A positive sensitivity to forecast precipitation was found, consistent with the practice of conducting prescribed burning on days when rain

  10. Forecasting New Hampshire Power Outages through the Analysis of Weather Station Observations

    Science.gov (United States)

    Fessenden, Ross T.

    Eversource Energy, formerly Public Service of New Hampshire (PSNH), has worked closely with Plymouth State University (PSU) in the past, and present, to better predict weather-related power outage events and maximize the efficiency with which Eversource responds to them. This research paired weather data from thirteen stations throughout New Hampshire, Vermont, and Massachusetts with Eversource Trouble Report and Unsatisfactory Performance of Equipment Report (TRUPER) data in an effort to quantify weather situations that lead to power outages. The ultimate goal involved developing a predictive model that uses weather data to forecast the magnitude of power outages. The study focused on the Eversource Western/Central service territory and utilized data from 2006-2010. The first four years, 2006-2009, were analyzed using Classification and Regression Tree (CART) statistical analysis. The results of this CART analysis trained a predictive model, while the fifth year, 2010, served as the testing set for the predictive model. To conduct the statistical analysis, a database was created pairing TRUPER reports with the closest available hourly weather observations. The database included nine weather variables matched with three variables from the TRUPER data: 1) customers, 2) customer minutes, and 3) outage duration. While the entire Eversource service territory saw 91,286 TRUPERs from 2006-2010, the Western/Central service territory, the focus of this study, accounted for 29,430. Before conducting the CART analysis, correlations between single weather variables and TRUPER data were calculated and, in general, proved xi weak. In addition to analyzing the complete four-year training data set, many portions/variations of the data set were analyzed. The analyses included a yearly analysis, time lag analysis, cold/warm-season analysis, and a single-station analysis. Although individual years and smaller data sets showed moderately higher correlations between weather and outage

  11. Coupling of weather forecasts and smart grid-control of wastewater inlet to Kolding WWTP (Denmark)

    DEFF Research Database (Denmark)

    Evald Bjerg, Julie; Grum, Morten; Courdent, Vianney Augustin Thomas

    2015-01-01

    , releasing the water when the price decreases. A lumped conceptual model was constructed based an existing highly detailed hydrodynamic model of the catchment. The conceptual model was used to assess the performance of the four control strategies, which were evaluated based on savings in operation cost...... four strategies for controlling the wastewater flow to Kolding Central wastewater treatment plant (WWTP) based on the Smart Grid concept are investigated. The control strategies use the storage volume in the pipe system upstream the WWTP to detain water during hours with high electricity prices...... and emitted CO2 equivalents. Weather forecasts were used to empty out the system prior to a rain event, ensuring that the control strategies did not lead to increases in combined sewer overflow. The largest savings obtained were 833 EUR/month and 3909 kg CO2 equivalents/month, which were achieved by only...

  12. Diabatic heating rate estimates from European Centre for Medium-Range Weather Forecasts analyses

    Science.gov (United States)

    Christy, John R.

    1991-01-01

    Vertically integrated diabatic heating rate estimates (H) calculated from 32 months of European Center for Medium-Range Weather Forecasts daily analyses (May 1985-December 1987) are determined as residuals of the thermodynamic equation in pressure coordinates. Values for global, hemispheric, zonal, and grid point H are given as they vary over the time period examined. The distribution of H is compared with previous results and with outgoing longwave radiation (OLR) measurements. The most significant negative correlations between H and OLR occur for (1) tropical and Northern-Hemisphere mid-latitude oceanic areas and (2) zonal and hemispheric mean values for periods less than 90 days. Largest positive correlations are seen in periods greater than 90 days for the Northern Hemispheric mean and continental areas of North Africa, North America, northern Asia, and Antarctica. The physical basis for these relationships is discussed. An interyear comparison between 1986 and 1987 reveals the ENSO signal.

  13. Performance tuning Weather Research and Forecasting (WRF) Goddard longwave radiative transfer scheme on Intel Xeon Phi

    Science.gov (United States)

    Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.

    2015-10-01

    Next-generation mesoscale numerical weather prediction system, the Weather Research and Forecasting (WRF) model, is a designed for dual use for forecasting and research. WRF offers multiple physics options that can be combined in any way. One of the physics options is radiance computation. The major source for energy for the earth's climate is solar radiation. Thus, it is imperative to accurately model horizontal and vertical distribution of the heating. Goddard solar radiative transfer model includes the absorption duo to water vapor,ozone, ozygen, carbon dioxide, clouds and aerosols. The model computes the interactions among the absorption and scattering by clouds, aerosols, molecules and surface. Finally, fluxes are integrated over the entire longwave spectrum.In this paper, we present our results of optimizing the Goddard longwave radiative transfer scheme on Intel Many Integrated Core Architecture (MIC) hardware. The Intel Xeon Phi coprocessor is the first product based on Intel MIC architecture, and it consists of up to 61 cores connected by a high performance on-die bidirectional interconnect. The coprocessor supports all important Intel development tools. Thus, the development environment is familiar one to a vast number of CPU developers. Although, getting a maximum performance out of MICs will require using some novel optimization techniques. Those optimization techniques are discusses in this paper. The optimizations improved the performance of the original Goddard longwave radiative transfer scheme on Xeon Phi 7120P by a factor of 2.2x. Furthermore, the same optimizations improved the performance of the Goddard longwave radiative transfer scheme on a dual socket configuration of eight core Intel Xeon E5-2670 CPUs by a factor of 2.1x compared to the original Goddard longwave radiative transfer scheme code.

  14. Evaluating weather research and forecasting (WRF) model predictions of turbulent flow parameters in a dry convective boundary layer

    NARCIS (Netherlands)

    Gibbs, J.A.; Fedorovich, E.; Eijk, A.M.J. van

    2011-01-01

    Weather Research and Forecasting (WRF) model predictions using different boundary layer schemes and horizontal grid spacings were compared with observational and numerical large-eddy simulation data for conditions corresponding to a dry atmospheric convective boundary layer (CBL) over the southern

  15. Evaluation of Radiation Belt Space Weather Forecasts for Internal Charging Analyses

    Science.gov (United States)

    Minow, Joseph I.; Coffey, Victoria N.; Jun, Insoo; Garrett, Henry B.

    2007-01-01

    A variety of static electron radiation belt models, space weather prediction tools, and energetic electron datasets are used by spacecraft designers and operations support personnel as internal charging code inputs to evaluate electrostatic discharge risks in space systems due to exposure to relativistic electron environments. Evaluating the environment inputs is often accomplished by comparing whether the data set or forecast tool reliability predicts measured electron flux (or fluence over a given period) for some chosen period. While this technique is useful as a model metric, it does not provide the information necessary to evaluate whether short term deviances of the predicted flux is important in the charging evaluations. In this paper, we use a 1-D internal charging model to compute electric fields generated in insulating materials as a function of time when exposed to relativistic electrons in the Earth's magnetosphere. The resulting fields are assumed to represent the "true" electric fields and are compared with electric field values computed from relativistic electron environments derived from a variety of space environment and forecast tools. Deviances in predicted fields compared to the "true" fields which depend on insulator charging time constants will be evaluated as a potential metric for determining the importance of predicted and measured relativistic electron flux deviations over a range of time scales.

  16. Assessing the Impact of Observations on Numerical Weather Forecasts Using the Adjoint Method

    Science.gov (United States)

    Gelaro, Ronald

    2012-01-01

    The adjoint of a data assimilation system provides a flexible and efficient tool for estimating observation impacts on short-range weather forecasts. The impacts of any or all observations can be estimated simultaneously based on a single execution of the adjoint system. The results can be easily aggregated according to data type, location, channel, etc., making this technique especially attractive for examining the impacts of new hyper-spectral satellite instruments and for conducting regular, even near-real time, monitoring of the entire observing system. This talk provides a general overview of the adjoint method, including the theoretical basis and practical implementation of the technique. Results are presented from the adjoint-based observation impact monitoring tool in NASA's GEOS-5 global atmospheric data assimilation and forecast system. When performed in conjunction with standard observing system experiments (OSEs), the adjoint results reveal both redundancies and dependencies between observing system impacts as observations are added or removed from the assimilation system. Understanding these dependencies may be important for optimizing the use of the current observational network and defining requirements for future observing systems

  17. Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

    Science.gov (United States)

    Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

    2014-06-01

    Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme.

  18. Sensitivity of Short-Term Weather Forecasts to Assimilated AIRS Data: Implications for NPOESS Applications

    Science.gov (United States)

    Zavodsky, Bradley; McCarty, Will; Chou, Shih-Hung; Jedlovec, Gary

    2009-01-01

    The Atmospheric Infrared Sounder (AIRS) is acting as a heritage and risk reduction instrument for the Cross-track lnfrared Sounder (CrIS) to be flown aboard the NPP and NPOESS satellites. The hyperspectral nature of AIRS and CrIS provides high-quality soundings that, along with their asynoptic observation time over North America, make them attractive sources to fill the spatial and temporal data voids in upper air temperature and moisture measurements for use in data assimilation and numerical weather prediction. Observations from AlRS can be assimilated either as direct radiances or retrieved thermodynamic profiles, and the Short-Term Prediction Research and Transition (SPORT) Center at NASA's Marshall Space Flight Center has used both data types to improve short-term (0-48h), regional forecasts. The purpose of this paper is to share SPORT'S experiences using AlRS radiances and retrieved profiles in regional data assimilation activities by showing that proper handling of issues-including cloud contamination and land emissivity characterization-are necessary to produce optimal analyses and forecasts.

  19. An industry perspective on the use of seasonal forecasts and weather information for evaluating sensitivities in traded commodity supply chains

    Science.gov (United States)

    Domeisen, Daniela; Slavov, Georgi

    2015-04-01

    Weather information on seasonal timescales is crucial to various end users, from the level of subsistence farming to the government level. Also the financial industry is ever more aware of and interested in the benefits that early and correctly interpreted forecast information provides. Straight forward and often cited applications include the estimation of rainfall and temperature anomalies for drought - prone agricultural areas producing traded commodities, as well as some of the rather direct impacts of weather on energy production. Governments, weather services, as well as both academia and private companies are working on tailoring climate and weather information to a growing number of customers. However, also other large markets, such as coal, iron ore, and gas, are crucially dependent on seasonal weather information and forecasts, while the needs are again very dependent on the direction of the predicted signal. So far, relatively few providers in climate services address these industries. All of these commodities show a strong seasonal and weather dependence, and an unusual winter or summer can crucially impact their demand and supply. To name a few impacts, gas is crucially driven by heating demand, iron ore excavation is dependent on the available water resources, and coal mining is dependent on winter temperatures and rainfall. This contribution will illustrate and provide an inside view of the type of climate and weather information needed for the various large commodity industries.

  20. 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

  1. Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 2

    Science.gov (United States)

    Keitz, J. F.

    1982-01-01

    The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 2 of the four major tasks included in the study. Task 2 compares various catagories of flight plans and flight tracking data produced by a simulation system developed for the Federal Aviation Administrations by SRI International. (Flight tracking data simulate actual flight tracks of all aircraft operating at a given time and provide for rerouting of flights as necessary to resolve traffic conflicts.) The comparisons of flight plans on the forecast to flight plans on the verifying analysis confirm Task 1 findings that wind speeds are generally underestimated. Comparisons involving flight tracking data indicate that actual fuel burn is always higher than planned, in either direction, and even when the same weather data set is used. Since the flight tracking model output results in more diversions than is known to be the case, it was concluded that there is an error in the flight tracking algorithm.

  2. Latitude belt convection permitting simulation using the Weather Research and Forecasting (WRF) model

    Science.gov (United States)

    Warrach-Sagi, Kirsten; Schwitalla, Thomas; Wulfmeyer, Volker

    2015-04-01

    Extreme events like the heat wave in summer 2003 in Central Europe and in August 2010 in Russia (which was associated with floodings of the Odra an in Pakistan) and severe floodings in Germany were caused by persistent so-called omega and blocking Vb weather situations in Europe. They are caused when quasi-stationary, quasi-resonant enhanced and quasi-resonant Rossby waves develop in mid-latitudes. To simulate quasi-stationary Rossby waves in numerical weather prediction and climate models at least a resolution of 20 km is required, however, to simulate the associated extremes the simulations need to be convection permitting. Further the high resolution allows the small scale structures to feed back to the large scale systems. Most of the current limited area, high-resolution models apply a domain which is centered over the region of interest. Such limited area model applications may suffer from a deterioration of synoptic features like low pressure systems due to effects in the boundary relaxation zone when downscaling reanalysis or global model simulation data. For Europe this is mainly caused by the longitudinal boundaries. A way to overcome these types of difficulties is to run a latitude belt simulation model. We applied the Weather Research and Forecasting (WRF) model with 3 km horizontal resolution for July and August 2013 forcing the model 6-hourly with ECMWF analyses data at 20°N and 65°N and with daily sea surface temperature data from the OSTIA project of the UK Met Office at 6 km resolution. The model domain encompasses 12000*1500*57 grid cells. First results of this so far unique simulation will be presented.

  3. Classification of rainfall events for weather forecasting purposes in andean region of Colombia

    Science.gov (United States)

    Suárez Hincapié, Joan Nathalie; Romo Melo, Liliana; Vélez Upegui, Jorge Julian; Chang, Philippe

    2016-04-01

    contribution which is done with this research is the obtainment elements to optimize and to improve the spatial resolution of the results obtained with mesoscale models such as the Weather Research & Forecasting Model- WRF, used in Colombia for the purposes of weather forecasting and that in addition produces other tools used in current issues such as risk management.

  4. Cascading model uncertainty from medium range weather forecasts (10 days through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS

    Directory of Open Access Journals (Sweden)

    F. Pappenberger

    2005-01-01

    Full Text Available The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall-runoff forecast and flood inundation forecast which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessary requirement to provide robust predictions. In this paper, 10-day ahead rainfall forecasts, consisting of one deterministic, one control and 50 ensemble forecasts, are fed into a rainfall-runoff model (LisFlood for which parameter uncertainty is represented by six different parameter sets identified through a Generalised Likelihood Uncertainty Estimation (GLUE analysis and functional hydrograph classification. The runoff of these 52 * 6 realisations form the input to a flood inundation model (LisFlood-FP which acknowledges uncertainty by utilising ten different sets of roughness coefficients identified using the same GLUE methodology. Likelihood measures for each parameter set computed on historical data are used to give uncertain predictions of flow hydrographs as well as spatial inundation extent. This analysis demonstrates that a full uncertainty analysis of such an integrated system is limited mainly by computer power as well as by how well the rainfall predictions represent potential future conditions. However, these restrictions may be overcome or lessened in the future and this paper establishes a computationally feasible methodological approach to the uncertainty cascade problem.

  5. Hourly forecasts of renewable energy sources by an operating MOS-system of the German Weather Service

    Science.gov (United States)

    Vogt, Gernot; Sebastian, Trepte

    2016-04-01

    Model Output Statistics (MOS) is a powerful tool for optimizing the direct output of numerical weather forecast models. By developing multiple linear regressions with predictors, derived from observations and numerical weather prediction (NWP) at DWD (German Meteorological Service), a reduction of 50% of the error variance in the forecast has been achieved. Moreover, statistical post-processing yields numerous advantages in forecasting, e. g. down-scaling to point forecasts at observation stations with specific topographic and climatologic characteristics, correction of biases and systematic errors produced by numerical models, the derivation of further predictands of interest (e. g. exceedance probabilities) and the combination of several models. In the German project EWeLiNE (Simultaneous improvement of weather and power forecasts for the grid integration of renewable energies), which is carried out in collaboration by DWD and IWES (Fraunhofer Institute for Wind Energy and Energy System Technology), one of the main goals is an adjustment of the DWD-system MOSMIX (combining numerical forecasts of the global models IFS and ICON) to the demands of transmission system operators (TSO). This includes the implementation of new predictands like wind elements in altitudes > 10m or solar radiance. To meet the demands of the TSOs the temporal resolution of MOSMIX, currently delivering forecasts in 3-hour time-steps, needs to be enhanced to 1-hour time-steps. This can be achieved by adjusting the statistical equations to take account of hourly SYNOP observations. Thus, diverse input parameters and internal processing schemes have to be re-specified for example in terms of precipitation. We show a comparative verification of 1-hour MOS and 3-hour MOS for different forecast elements. Raw data comprising of acquired point measurements of wind observations have been converted and implemented into the MOS-system. Sensitivity studies have then been conducted investigating the fit

  6. Surface Temperature Variation Prediction Model Using Real-Time Weather Forecasts

    Science.gov (United States)

    Karimi, M.; Vant-Hull, B.; Nazari, R.; Khanbilvardi, R.

    2015-12-01

    Combination of climate change and urbanization are heating up cities and putting the lives of millions of people in danger. More than half of the world's total population resides in cities and urban centers. Cities are experiencing urban Heat Island (UHI) effect. Hotter days are associated with serious health impacts, heart attaches and respiratory and cardiovascular diseases. Densely populated cities like Manhattan, New York can be affected by UHI impact much more than less populated cities. Even though many studies have been focused on the impact of UHI and temperature changes between urban and rural air temperature, not many look at the temperature variations within a city. These studies mostly use remote sensing data or typical measurements collected by local meteorological station networks. Local meteorological measurements only have local coverage and cannot be used to study the impact of UHI in a city and remote sensing data such as MODIS, LANDSAT and ASTER have with very low resolution which cannot be used for the purpose of this study. Therefore, predicting surface temperature in urban cities using weather data can be useful.Three months of Field campaign in Manhattan were used to measure spatial and temporal temperature variations within an urban setting by placing 10 fixed sensors deployed to measure temperature, relative humidity and sunlight. Fixed instrument shelters containing relative humidity, temperature and illumination sensors were mounted on lampposts in ten different locations in Manhattan (Vant-Hull et al, 2014). The shelters were fixed 3-4 meters above the ground for the period of three months from June 23 to September 20th of 2013 making measurements with the interval of 3 minutes. These high resolution temperature measurements and three months of weather data were used to predict temperature variability from weather forecasts. This study shows that the amplitude of spatial and temporal variation in temperature for each day can be predicted

  7. Use of weather research and forecasting model outputs to obtain near-surface refractive index structure constant over the ocean.

    Science.gov (United States)

    Qing, Chun; Wu, Xiaoqing; Li, Xuebin; Zhu, Wenyue; Qiao, Chunhong; Rao, Ruizhong; Mei, Haipin

    2016-06-13

    The methods to obtain atmospheric refractive index structure constant (Cn2) by instrument measurement are limited spatially and temporally and they are more difficult and expensive over the ocean. It is useful to forecast Cn2 effectively from Weather Research and Forecasting Model (WRF) outputs. This paper introduces a method that WRF Model is used to forecast the routine meteorological parameters firstly, and then Cn2 is calculated based on these parameters by the Bulk model from the Monin-Obukhov similarity theory (MOST) over the ocean near-surface. The corresponding Cn2 values measured by the micro-thermometer which is placed on the ship are compared with the ones forecasted by WRF model to determine how this method performs. The result shows that the forecasted Cn2 is consistent with the measured Cn2 in trend and the order of magnitude as a whole, as well as the correlation coefficient is up to 77.57%. This method can forecast some essential aspects of Cn2 and almost always captures the correct magnitude of Cn2, which experiences fluctuations of two orders of magnitude. Thus, it seems to be a feasible and meaningful method that using WRF model to forecast near-surface Cn2 value over the ocean.

  8. Impact of additional surface observation network on short range weather forecast during summer monsoon 2008 over Indian subcontinent

    Indian Academy of Sciences (India)

    Prashant Kumar; Randhir Singh; P C Joshi; P K Pal

    2011-02-01

    The three dimensional variational data assimilation scheme (3D-Var) is employed in the recently developed Weather Research and Forecasting (WRF) model. Assimilation experiments have been conducted to assess the impact of Indian Space Research Organisation’s (ISRO) Automatic Weather Stations (AWS) surface observations (temperature and moisture) on the short range forecast over the Indian region. In this study, two experiments, CNT (without AWS observations) and EXP (with AWS observations) were made for 24-h forecast starting daily at 0000 UTC during July 2008. The impact of assimilation of AWS surface observations were assessed in comparison to the CNT experiment. The spatial distribution of the improvement parameter for temperature, relative humidity and wind speed from one month assimilation experiments demonstrated that for 24-h forecast, AWS observations provide valuable information. Assimilation of AWS observed temperature and relative humidity improved the analysis as well as 24-h forecast. The rainfall prediction has been improved due to the assimilation of AWS data, with the largest improvement seen over the Western Ghat and eastern India.

  9. Improving solar wind persistence forecasts: Removing transient space weather events, and using observations away from the Sun-Earth line

    Science.gov (United States)

    Kohutova, Petra; Bocquet, François-Xavier; Henley, Edmund M.; Owens, Matthew J.

    2016-10-01

    This study demonstrates two significant ways of improving persistence forecasts of the solar wind, which exploit the relatively unchanging nature of the ambient solar wind to provide 27 day forecasts, when using data from the Lagrangian L1 point. Such forecasts are useful as a prediction tool for the ambient wind, and for benchmarking of solar wind models. We show that solar wind persistence forecasts can be improved by removing transient solar wind features such as coronal mass ejections (CMEs). Using CME indicators to automatically identify CME-contaminated periods in ACE data from 1998 to 2011, and replacing these with solar wind from a previous synodic rotation, persistence forecasts improve (relative to a baseline): skill scores for Bz, a crucial parameter for determining solar wind geoeffectiveness, improve by 7.7 percentage points when using a proton temperature-based indicator with good operational potential. We also show that persistence forecasts can be improved by using measurements away from L1, to reduce the requirement on coronal stability for an entire synodic period, at the cost of reduced lead time. Using STEREO-B data from 2007 to 2013 to create such a reduced lead time persistence forecast, we show that Bz skill scores improve by 17.1 percentage points relative to ACE. Finally, we report on implications for persistence forecasts from any future missions to the L5 Lagrangian point and on the successful operational implementation (in spring 2015) of the normal (ACE-based) and reduced lead time (STEREO-based) persistence forecasts in the Met Office's Space Weather Operations Centre, as well as plans for future improvements.

  10. The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts

    NARCIS (Netherlands)

    F. Ravazzolo (Francesco); C. Zhou (Chen); C. Huurman

    2007-01-01

    textabstractIn the literature the effects of weather on electricity sales are well-documented. However, studies that have investigated the impact of weather on electricity prices are still scarce (e.g. Knittel and Roberts, 2005), partly because the wholesale power markets have only recently been

  11. The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts

    NARCIS (Netherlands)

    F. Ravazzolo (Francesco); C. Zhou (Chen); C. Huurman

    2007-01-01

    textabstractIn the literature the effects of weather on electricity sales are well-documented. However, studies that have investigated the impact of weather on electricity prices are still scarce (e.g. Knittel and Roberts, 2005), partly because the wholesale power markets have only recently been der

  12. Forecast Issues in the Urban Zone: Report of the 10th Prospectus Development Team of the U.S. Weather Research Program.

    Science.gov (United States)

    Dabberdt, Walter F.; Hales, Jeremy; Zubrick, Steven; Crook, Andrew; Krajewski, Witold; Doran, J. Christopher; Mueller, Cynthia; King, Clark; Keener, Ronald N.; Bornstein, Robert; Rodenhuis, David; Kocin, Paul; Rossetti, Michael A.; Sharrocks, Fred; Stanley, Ellis M., Sr.

    2000-09-01

    The 10th Prospectus Development Team (PDT-10) of the U.S. Weather Research Program was charged with identifying research needs and opportunities related to the short-term prediction of weather and air quality in urban forecast zones. Weather has special and significant impacts on large numbers of the U.S. population who live in major urban areas. It is recognized that urban users have different weather information needs than do their rural counterparts. Further, large urban areas can impact local weather and hydrologic processes in various ways. The recommendations of the team emphasize that human life and well-being in urban areas can be protected and enjoyed to a significantly greater degree. In particular, PDT-10 supports the need for 1) improved access to real-time weather information, 2) improved tailoring of weather data to the specific needs of individual user groups, and 3) more user-specific forecasts of weather and air quality. Specific recommendations fall within nine thematic areas: 1) development of a user-oriented weather database; 2) focused research on the impacts of visibility and icing on transportation; 3) improved understanding and forecasting of winter storms; 4) improved understanding and forecasting of convective storms; 5) improved forecasting of intense/severe lightning; 6) further research into the impacts of large urban areas on the location and intensity of urban convection; 7) focused research on the application of mesoscale forecasting in support of emergency response and air quality; 8) quantification and reduction of uncertainty in hydrological, meteorological, and air quality modeling; and 9) the need for improved observing systems. An overarching recommendation of PDT-10 is that research into understanding and predicting weather impacts in urban areas should receive increased emphasis by the atmospheric science community at large, and that urban weather should be a focal point of the U.S. Weather Research Program.

  13. Effects of Real-Time NASA Vegetation Data on Model Forecasts of Severe Weather

    Science.gov (United States)

    Case, Jonathan L.; Bell, Jordan R.; LaFontaine, Frank J.; Peters-Lidard, Christa D.

    2012-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA-EOS Aqua and Terra satellites. NASA SPoRT started generating daily real-time GVF composites at 1-km resolution over the Continental United States beginning 1 June 2010. A companion poster presentation (Bell et al.) primarily focuses on impact results in an offline configuration of the Noah land surface model (LSM) for the 2010 warm season, comparing the SPoRT/MODIS GVF dataset to the current operational monthly climatology GVF available within the National Centers for Environmental Prediction (NCEP) and Weather Research and Forecasting (WRF) models. This paper/presentation primarily focuses on individual case studies of severe weather events to determine the impacts and possible improvements by using the real-time, high-resolution SPoRT-MODIS GVFs in place of the coarser-resolution NCEP climatological GVFs in model simulations. The NASA-Unified WRF (NU-WRF) modeling system is employed to conduct the sensitivity simulations of individual events. The NU-WRF is an integrated modeling system based on the Advanced Research WRF dynamical core that is designed to represents aerosol, cloud, precipitation, and land processes at satellite-resolved scales in a coupled simulation environment. For this experiment, the coupling between the NASA Land Information System (LIS) and the WRF model is utilized to measure the impacts of the daily SPoRT/MODIS versus the monthly NCEP climatology GVFs. First, a spin-up run of the LIS is integrated for two years using the Noah LSM to ensure that the land surface fields reach an equilibrium state on the 4-km grid mesh used. Next, the spin-up LIS is run in two separate modes beginning on 1 June 2010, one continuing with the climatology GVFs while the

  14. Forecasting of photovoltaic power at hourly intervals with artificial neural networks under fluctuating weather conditions

    Directory of Open Access Journals (Sweden)

    Stamatia Dimopoulou, Alice Oppermann, Ekkehard Boggasch, Andreas Rausch

    2017-01-01

    Full Text Available The requirement of the in advance knowledge of the future photovoltaic (PV production in the domestic field for a better allocation of the on-site PV generation to the local load demand and the available storage facilities is more and more emerging. In this study two different methods were applied so as to forecast the next hour PV power using artificial neural networks (ANN. In the first case the weather parameters of solar irradiance and ambient temperature were predicted, the output was fed to the developed model of the PV installation and the next hour PV power was computed. In the second case it was attempted to predict directly the PV power. The performance of the applied ANNs was compared with the respective outcomes from the persistence models. In each case the applied ANN outperforms the persistence model. In addition, during the evaluation phase the extracted annual energy results were compared with the respective registered data from the installed meters. Again in both cases the results approximated the reality, though in the first case the difficulty in identification and representation of malfunctions in operation of the PV plants due to snow accumulation on the panels caused minor deviations.

  15. Enhancing Hydrologic Modelling in the Coupled Weather Research and Forecasting-Urban Modelling System

    Science.gov (United States)

    Yang, Jiachuan; Wang, Zhi-Hua; Chen, Fei; Miao, Shiguang; Tewari, Mukul; Voogt, James A.; Myint, Soe

    2015-04-01

    Urbanization modifies surface energy and water budgets, and has significant impacts on local and regional hydroclimate. In recent decades, a number of urban canopy models have been developed and implemented into the Weather Research and Forecasting (WRF) model to capture urban land-surface processes. Most of these models are inadequate due to the lack of realistic representation of urban hydrological processes. Here, we implement physically-based parametrizations of urban hydrological processes into the single layer urban canopy model in the WRF model. The new single-layer urban canopy model features the integration of, (1) anthropogenic latent heat, (2) urban irrigation, (3) evaporation from paved surfaces, and (4) the urban oasis effect. The new WRF-urban modelling system is evaluated against field measurements for four different cities; results show that the model performance is substantially improved as compared to the current schemes, especially for latent heat flux. In particular, to evaluate the performance of green roofs as an urban heat island mitigation strategy, we integrate in the urban canopy model a multilayer green roof system, enabled by the physical urban hydrological schemes. Simulations show that green roofs are capable of reducing surface temperature and sensible heat flux as well as enhancing building energy efficiency.

  16. Future intensification of hydro-meteorological extremes: downscaling using the weather research and forecasting model

    Science.gov (United States)

    El-Samra, R.; Bou-Zeid, E.; Bangalath, H. K.; Stenchikov, G.; El-Fadel, M.

    2017-02-01

    A set of ten downscaling simulations at high spatial resolution (3 km horizontally) were performed using the Weather Research and Forecasting (WRF) model to generate future climate projections of annual and seasonal temperature and precipitation changes over the Eastern Mediterranean (with a focus on Lebanon). The model was driven with the High Resolution Atmospheric Model (HiRAM), running over the whole globe at a resolution of 25 km, under the conditions of two Representative Concentration Pathways (RCP) (4.5 and 8.5). Each downscaling simulation spanned one year. Two past years (2003 and 2008), also forced by HiRAM without data assimilation, were simulated to evaluate the model's ability to capture the cold and wet (2003) and hot and dry (2008) extremes. The downscaled data were in the range of recent observed climatic variability, and therefore corrected for the cold bias of HiRAM. Eight future years were then selected based on an anomaly score that relies on the mean annual temperature and accumulated precipitation to identify the worst year per decade from a water resources perspective. One hot and dry year per decade, from 2011 to 2050, and per scenario was simulated and compared to the historic 2008 reference. The results indicate that hot and dry future extreme years will be exacerbated and the study area might be exposed to a significant decrease in annual precipitation (rain and snow), reaching up to 30% relative to the current extreme conditions.

  17. Investigation of riming within mixed-phase stratiform clouds using Weather Research and Forecasting (WRF) model

    Science.gov (United States)

    Hou, Tuanjie; Lei, Hengchi; Yang, Jiefan; Hu, Zhaoxia; Feng, Qiujuan

    2016-09-01

    In this study, we investigated stratiform precipitation associated with an upper-level westerly trough and a cold front over northern China between 30 Apr. and 1 May 2009. We employed the Weather Research and Forecasting (WRF) model (version 3.4.1) to perform high-resolution numerical simulations of rainfall. We also conducted simulations with two microphysics schemes and sensitivity experiments without riming of snow and changing cloud droplet number concentrations (CDNCs) to determine the effect of snow riming on cloud structure and precipitation. Then we compared our results with CloudSat, Doppler radar and rain gauge observations. The comparison with the Doppler radar observations suggested that the WRF model was quite successful in capturing the timing and location of the stratiform precipitation region. Further comparisons with the CloudSat retrievals suggested that both microphysics schemes overestimated ice and liquid water contents. The sensitivity experiments without riming of snow suggested that the presence or absence of riming significantly influenced the precipitation distribution, but only slightly affected total accumulated precipitation. Without riming of snow, the changes of updrafts from the two microphysics schemes were different due to a different consideration of ice particle capacitance and latent heat effect of riming on deposition. While sensitivity experiments with three different CDNC values of 100, 250 and 1000 cm- 3 suggested variations in snow riming rates, changing CDNC had little impact on precipitation.

  18. High Impact Weather Forecasts and Warnings with the GOES-R Geostationary Lightning Mapper (GLM)

    Science.gov (United States)

    Goodman, Steven; Blakeslee, Richard; Koshak, William; Mach, Douglas

    2011-01-01

    The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. A major advancement over the current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM). The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, a GOES-R Risk Reduction Science Team and Algorithm Working Group Lightning Applications Team have begun to develop cal/val performance monitoring tools and new applications using the GLM alone, in conjunction with other instruments, and merged or blended integrated observing system products combining satellite, radar, in-situ and numerical models. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms.

  19. Future intensification of hydro-meteorological extremes: downscaling using the weather research and forecasting model

    KAUST Repository

    El-Samra, R.

    2017-02-15

    A set of ten downscaling simulations at high spatial resolution (3 km horizontally) were performed using the Weather Research and Forecasting (WRF) model to generate future climate projections of annual and seasonal temperature and precipitation changes over the Eastern Mediterranean (with a focus on Lebanon). The model was driven with the High Resolution Atmospheric Model (HiRAM), running over the whole globe at a resolution of 25 km, under the conditions of two Representative Concentration Pathways (RCP) (4.5 and 8.5). Each downscaling simulation spanned one year. Two past years (2003 and 2008), also forced by HiRAM without data assimilation, were simulated to evaluate the model’s ability to capture the cold and wet (2003) and hot and dry (2008) extremes. The downscaled data were in the range of recent observed climatic variability, and therefore corrected for the cold bias of HiRAM. Eight future years were then selected based on an anomaly score that relies on the mean annual temperature and accumulated precipitation to identify the worst year per decade from a water resources perspective. One hot and dry year per decade, from 2011 to 2050, and per scenario was simulated and compared to the historic 2008 reference. The results indicate that hot and dry future extreme years will be exacerbated and the study area might be exposed to a significant decrease in annual precipitation (rain and snow), reaching up to 30% relative to the current extreme conditions.

  20. The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

    Science.gov (United States)

    Prive, Nikki C.; Errico, Ronald M.

    2013-01-01

    A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

  1. Numerical simulation for regional ozone concentrations: A case study by weather research and forecasting/chemistry (WRF/Chem) model

    OpenAIRE

    Khandakar Md Habib Al Razi, Moritomi Hiroshi

    2013-01-01

    The objective of this research is to better understand and predict the atmospheric concentration distribution of ozone and its precursor (in particular, within the Planetary Boundary Layer (Within 110 km to 12 km) over Kasaki City and the Greater Tokyo Area using fully coupled online WRF/Chem (Weather Research and Forecasting/Chemistry) model. In this research, a serious and continuous high ozone episode in the Greater Tokyo Area (GTA) during the summer of 14–18 August 2010 was investigated u...

  2. Investigating Surface Bias Errors in the Weather Research and Forecasting (WRF) Model using a Geographic Information System (GIS)

    Science.gov (United States)

    2015-02-01

    horizontal grid spacing inner domain centered near San Diego, California. The San Diego area contains a mixture of urban , suburban, agricultural, and...Global Forecast System (GFS) model (Environmental Modeling Center 2003). The WRE–N is envisioned to be a rapid-update cycling application of WRF–ARW...surface– hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Monthly Weather Review. 2001a

  3. From Predicting Solar Activity to Forecasting Space Weather: Practical Examples of Research-to-Operations and Operations-to-Research

    CERN Document Server

    Steenburgh, R A; Millward, G H; 10.1007/s11207-013-0308-6

    2013-01-01

    The successful transition of research to operations (R2O) and operations to research (O2R) requires, above all, interaction between the two communities. We explore the role that close interaction and ongoing communication played in the successful fielding of three separate developments: an observation platform, a numerical model, and a visualization and specification tool. Additionally, we will examine how these three pieces came together to revolutionize interplanetary coronal mass ejection (ICME) arrival forecasts. A discussion of the importance of education and training in ensuring a positive outcome from R2O activity follows. We describe efforts by the meteorological community to make research results more accessible to forecasters and the applicability of these efforts to the transfer of space-weather research.We end with a forecaster "wish list" for R2O transitions. Ongoing, two-way communication between the research and operations communities is the thread connecting it all.

  4. Application of the NASA A-Train to Evaluate Clouds Simulated by the Weather Research and Forecast Model

    Science.gov (United States)

    Molthan, Andrew L.; Jedlovec, Gary J.; Lapenta, William M.

    2008-01-01

    The CloudSat Mission, part of the NASA A-Train, is providing the first global survey of cloud profiles and cloud physical properties, observing seasonal and geographical variations that are pertinent to evaluating the way clouds are parameterized in weather and climate forecast models. CloudSat measures the vertical structure of clouds and precipitation from space through the Cloud Profiling Radar (CPR), a 94 GHz nadir-looking radar measuring the power backscattered by clouds as a function of distance from the radar. One of the goals of the CloudSat mission is to evaluate the representation of clouds in forecast models, thereby contributing to improved predictions of weather, climate and the cloud-climate feedback problem. This paper highlights potential limitations in cloud microphysical schemes currently employed in the Weather Research and Forecast (WRF) modeling system. The horizontal and vertical structure of explicitly simulated cloud fields produced by the WRF model at 4-km resolution are being evaluated using CloudSat observations in concert with products derived from MODIS and AIRS. A radiative transfer model is used to produce simulated profiles of radar reflectivity given WRF input profiles of hydrometeor mixing ratios and ambient atmospheric conditions. The preliminary results presented in the paper will compare simulated and observed reflectivity fields corresponding to horizontal and vertical cloud structures associated with midlatitude cyclone events.

  5. Development of GNSS PWV information management system for very short-term weather forecast in the Korean Peninsula

    Science.gov (United States)

    Park, Han-Earl; Yoon, Ha Su; Yoo, Sung-Moon; Cho, Jungho

    2017-04-01

    Over the past decade, Global Navigation Satellite System (GNSS) was in the spotlight as a meteorological research tool. The Korea Astronomy and Space Science Institute (KASI) developed a GNSS precipitable water vapor (PWV) information management system to apply PWV to practical applications, such as very short-term weather forecast. The system consists of a DPR, DRS, and TEV, which are divided functionally. The DPR processes GNSS data using the Bernese GNSS software and then retrieves PWV from zenith total delay (ZTD) with the optimized mean temperature equation for the Korean Peninsula. The DRS collects data from eighty permanent GNSS stations in the southern part of the Korean Peninsula and provides the PWV retrieved from GNSS data to a user. The TEV is in charge of redundancy of the DPR. The whole process is performed in near real-time where the delay is ten minutes. The validity of the GNSS PWV was proved by means of a comparison with radiosonde data. In the experiment of numerical weather prediction model, the GNSS PWV was utilized as the initial value of the Weather Research & Forecasting (WRF) model for heavy rainfall event. As a result, we found that the forecasting capability of the WRF is improved by data assimilation of GNSS PWV.

  6. Development of an Operation Control System for Photovoltaics and Electric Storage Heaters for Houses Based on Information in Weather Forecasts

    Science.gov (United States)

    Obara, Shin'ya

    An all-electric home using an electric storage heater with safety and cleaning is expanded. However, the general electric storage heater leads to an unpleasant room temperature and energy loss by the overs and shorts of the amount of heat radiation when the climate condition changes greatly. Consequently, the operation of the electric storage heater introduced into an all-electric home, a storage type electric water heater, and photovoltaics was planned using weather forecast information distributed by a communication line. The comfortable evaluation (the difference between a room-temperature target and a room-temperature result) when the proposed system was employed based on the operation planning, purchase electric energy, and capacity of photovoltaics was investigated. As a result, comfortable heating operation was realized by using weather forecast data; furthermore, it is expected that the purchase cost of the commercial power in daytime can be reduced by introducing photovoltaics. Moreover, when the capacity of the photovoltaics was increased, the surplus power was stored in the electric storage heater, but an extremely unpleasant room temperature was not shown in the investigation ranges of this paper. By obtaining weather information from the forecast of the day from an external service using a communication line, the heating system of the all-electric home with low energy loss and comfort temperature is realizable.

  7. Operational on-line coupled chemical weather forecasts for Europe with WRF/Chem

    Science.gov (United States)

    Hirtl, Marcus; Mantovani, Simone; Krüger, Bernd C.; Flandorfer, Claudia; Langer, Matthias

    2014-05-01

    Air quality is a key element for the well-being and quality of life of European citizens. Air pollution measurements and modeling tools are essential for the assessment of air quality according to EU legislation. The responsibilities of ZAMG as the national weather service of Austria include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. ZAMG conducts daily Air-Quality forecasts using the on-line coupled model WRF/Chem. Meteorology is simulated simultaneously with the emissions, turbulent mixing, transport, transformation, and fate of trace gases and aerosols. The emphasis of the application is on predicting pollutants over Austria. Two domains are used for the simulations: the mother domain covers Europe with a resolution of 12 km, the inner domain includes the alpine region with a horizontal resolution of 4 km; 45 model levels are used in the vertical direction. The model runs 2 times per day for a period of 72 hours and is initialized with ECMWF forecasts. On-line coupled models allow considering two-way interactions between different atmospheric processes including chemistry (both gases and aerosols), clouds, radiation, boundary layer, emissions, meteorology and climate. In the operational set-up direct-, indirect and semi-direct effects between meteorology and air chemistry are enabled. The model is running on the HPCF (High Performance Computing Facility) of the ZAMG. In the current set-up 1248 CPUs are used. As the simulations need a big amount of computing resources, a method to safe I/O-time was implemented. Every MPI task writes all its output into the shared memory filesystem of the compute nodes. Once the WRF/Chem integration is finished, all split NetCDF-files are merged and saved on the global file system. The merge-routine is based on parallel-NetCDF. With this method the model runs about 30% faster on the SGI

  8. The Main Pillar: Assessment of Space Weather Observational Asset Performance Supporting Nowcasting, Forecasting and Research to Operations

    Science.gov (United States)

    Posner, Arik; Hesse, Michael; SaintCyr, Chris

    2014-01-01

    Space weather forecasting critically depends upon availability of timely and reliable observational data. It is therefore particularly important to understand how existing and newly planned observational assets perform during periods of severe space weather. Extreme space weather creates challenging conditions under which instrumentation and spacecraft may be impeded or in which parameters reach values that are outside the nominal observational range. This paper analyzes existing and upcoming observational capabilities for forecasting, and discusses how the findings may impact space weather research and its transition to operations. A single limitation to the assessment is lack of information provided to us on radiation monitor performance, which caused us not to fully assess (i.e., not assess short term) radiation storm forecasting. The assessment finds that at least two widely spaced coronagraphs including L4 would provide reliability for Earth-bound CMEs. Furthermore, all magnetic field measurements assessed fully meet requirements. However, with current or even with near term new assets in place, in the worst-case scenario there could be a near-complete lack of key near-real-time solar wind plasma data of severe disturbances heading toward and impacting Earth's magnetosphere. Models that attempt to simulate the effects of these disturbances in near real time or with archival data require solar wind plasma observations as input. Moreover, the study finds that near-future observational assets will be less capable of advancing the understanding of extreme geomagnetic disturbances at Earth, which might make the resulting space weather models unsuitable for transition to operations.

  9. The Use of Interactive Graphics Processing in Short-Range Terminal Weather Forecasting: An Initial Assessment.

    Science.gov (United States)

    1983-03-31

    ingest individual forecasts into a data file through the use of formatted interrogation/ response messages via the AlcIDAS ’ keyboard /alpha- numeric CRT...to 10 min of observation time. The AlcIDAS forecaster has many computer analyses to aid him that are not available to forecasters relying on

  10. Urban modelling for Budapest using the Weather Research and Forecasting model

    Science.gov (United States)

    Göndöcs, Júlia; Breuer, Hajnalka; Pongrácz, Rita; Bartholy, Judit

    2016-04-01

    The population of Earth is continuously growing, and due to urbanisation it is quite concentrated in metropolitan areas. Overall, cities cover almost 2% of the global surface causing several environmental and social issues. These artificial surface covers significantly modify the surface energy exchange processes through modification of naturally covered lands resulting in altered local wind and temperature patterns because of the presence of buildings. The architectures' three-dimensional extensions certainly affect the incoming radiation, the sky-view factors as well, as the 3D wind fields, resulting in specific local microclimate at each metropolitan area. The increased temperature in the central built-up areas and the cooler surrounding of the cities lead to the urban heat island phenomenon, which is widely studied both with observations and numerical models. The Weather Research and Forecasting (WRF) mesoscale model coupled to multilayer urban canopy parameterisation is used to investigate this phenomenon for Budapest and its surroundings. Before starting the simulations, the detailed surface has to be set up according to the actual conditions, for which CORINE and OpenStreetMap databases are used, both including buildings, different land use categories, and waterbodies. The new land use distribution serving as input for WRF runs distinguishes three urban categories: (i) low-intensity residential, (ii) high-intensity residential, and (iii) commercial/industrial. For the simulations the initial meteorological fields are derived from the publicly available GFS (Global Forecast System) outputs. Simulations are completed for one-week-long periods in summer and winter in 2015, for which we selected periods with the atmospheric conditions of weak wind and clear sky. In order to keep the stability of the simulations, the entire downscaling is carried out in several steps using gradually smaller domains embedded to each other. Thus, three embedded target areas have

  11. Global chemical weather forecasts for field campaign planning: predictions and observations of large-scale features during MINOS, CONTRACE, and INDOEX

    Directory of Open Access Journals (Sweden)

    M. G. Lawrence

    2002-10-01

    Full Text Available The first global tropospheric forecasts of O3 and its precursors have been used in the daily flight planning of field measurement campaigns. The 3-D chemistry-transport model MATCH-MPIC is driven by meteorological data from a weather center (NCEP to produce daily 3-day forecasts of the global distributions of O3 and related gases, as well as regional CO tracers. This paper describes the forecast system and its use in three field campaigns, MINOS, CONTRACE and INDOEX. An overview is given of the forecasts by MATCH-MPIC and by three other chemical weather forecast models (EURAD, ECHAM, and FLEXPART, focusing on O3 and CO. Total CO and regional CO tracers were found to be the most valuable gases for flight planning, due to their relatively well-defined anthropogenic source regions and lifetimes of one to a few months. CO was in good agreement with the observations on nearly all the flights (generally  r > 0.7, RMS  < 20%. In every case in which the chemical weather forecasts were primarily responsible for the flight plans, the targeted features were observed. Two forecasted phenomena are discussed in detail: outflow from Asia observed in the Mediterranean upper troposphere during MINOS, and outflow from North America observed in the middle troposphere over northern Europe during CONTRACE. It is shown that although such pollution plumes occur repeatedly during the months around the campaigns, their frequency is sufficiently low (~10--30% of the time that global chemical weather forecasts are important for enabling them to be observed during limited-duration field campaigns. The MATCH-MPIC chemical weather forecasts, including an interface for making customized figures from the output, are available for community use via  forecasts.html">http://www.mpch-mainz.mpg.de/~lawrence/forecasts.html .

  12. Nimble@ITCEcnoGrid: A Grid in Research Domain for Weather Forecasting

    Directory of Open Access Journals (Sweden)

    Vijay Dhir

    2012-01-01

    120 decimal points.Encouraged by the speed the same system has been utilized to computes the Momentum, Thermodynamics and Continuity equations for the Weather Forecasting using the Windows based Desktop computers..

  13. Operational Irrigation Scheduling for Citrus Trees with Soil Moisture Data Assimilation and Weather Forecast

    Science.gov (United States)

    Han, Xujun; Hendricks Franssen, Harrie-Jan; Martínez Alzamora, Fernando; Ángel Jiménez Bello, Miguel; Chanzy, André; Vereecken, Harry

    2015-04-01

    Agricultural areas in the Mediterranean are expected to face more drought stress in the future due to climate change and human activities. Irrigation scheduling is necessary to allocate the optimal water amount at the right time period to avoid unnecessary water losses. An operational data assimilation framework was set-up to combine model predictions and soil moisture measurements in an optimal way for characterizing the soil water status of the root zone. Irrigation amounts for the next days are optimized on the basis of the soil water status of the root zone and meteorological ensemble predictions. In these experiments, the uncertainties of atmospheric forcings and soil properties were considered. The uncertain model forcings were taken from an ensemble of weather forecasts by ECMWF, and delivered by MeteoFrance in this project. The improved soil moisture profile was used to calculate the irrigation requirement taking into account the root distribution of citrus trees in the subsurface. The approach was tested operationally for the experimental site near Picassent, Valencia, Spain. Three fields were irrigated according to our approach in the years 2013 and 2014. Three others were irrigated traditionally, based on FAO-criteria. Soil moisture was measured by FDR probes at 10 cm and 30 cm depth at various fields and these real time data were assimilated by the Local Ensemble Transform Kalman Filter (LETKF) into the Community Land Model (CLM) to improve the estimation of the soil moisture profile. The measured soil moisture was assimilated five times per day before the start of the next drip irrigation. The final results (total amount of irrigated water, stem water potential and citrus production) show that our strategy resulted in significantly less irrigated water compared to the FAO-irrigated fields, but without indications of increased water stress. Soil moisture contents did not decline over time in our approach, stem water potential measurements did not

  14. Forecasting challenges during the severe weather outbreak in Central Europe on 25 June 2008

    Science.gov (United States)

    Púčik, Tomáš; Francová, Martina; Rýva, David; Kolář, Miroslav; Ronge, Lukáš

    2011-06-01

    On 25 June 2008, severe thunderstorms caused widespread damage and two fatalities in the Czech Republic. Significant features of the storms included numerous downbursts on a squall line that exhibited a bow echo reflectivity pattern, with sustained wind gusts over 32 m/s at several reporting stations. Moreover, a tornado and several downbursts of F2 intensity occurred within the convective system, collocated with the development of mesovortices within the larger scale bow echo. The extent of the event was sufficient to call it a derecho, as the windstorm had affected Eastern Germany, Southern Poland, Slovakia, Austria and Northern Hungary as well. Ahead of the squall line, several well-organized isolated cells occurred, exhibiting supercellular characteristics, both from a radar and visual perspective. These storms produced large hail and also isolated severe wind gusts. This paper deals mostly with the forecasting challenges that were experienced by the meteorologist on duty during the evolution of this convective scenario. The main challenge of the day was to identify the region that would be most affected by severe convection, especially as the numerical weather prediction failed to anticipate the extent and the progress of the derecho-producing mesoscale convective systems (MCSs). Convective storms developed in an environment conducive to severe thunderstorms, with strong wind shear confined mostly to the lower half of the troposphere. These developments also were strongly influenced by mesoscale factors, especially a mesolow centered over Austria and its trough stretching to Eastern Bohemia. The paper demonstrates how careful mesoscale analysis could prove useful in dealing with such convective situations. Remote-sensing methods are also shown to be useful in such situations, especially when they can offer sufficient lead time to issue a warning, which is not always the case.

  15. Implementation of a new aerosol HAM model within the Weather Research and Forecasting (WRF modeling system

    Directory of Open Access Journals (Sweden)

    R. Mashayekhi

    2009-07-01

    Full Text Available A new coupled system of aerosol HAM model and the Weather, Research and Forecasting (WRF model is presented in this paper. Unlike the current aerosol schemes used in WRF model, the HAM is using a "pseudomodal" approach for the representation of the particle size distribution. The aerosol components considered are sulfate, black carbon, particulate organic matter, sea salt and mineral dust. The preliminary model results are presented for two different 6-day simulation periods from 22 to 28 February 2006 as a winter period and 6 to 12 May 2006 as a mild period. The mean shortwave radiation and thermal forcing were calculated from the model simulations with and without aerosols feedback for two simulation periods. A negative radiative forcing and cooling of the atmosphere were found mainly over the regions of high emission of mineral dust. The absorption of shortwave radiation by black carbon caused warming effects in some regions with positive radiative forcing. The simulated daily mean sulfate mass concentration showed a rather good agreement with the measurements in the European EMEP network. The diurnal variation of the simulated hourly PM10 mass concentration at Tehran was also qualitatively close to the observations in both simulation periods. The model captured diurnal cycle and the magnitude of the observed PM10 concentration during most of the simulation periods. The differences between the observed and simulated PM10 concentration resulted mostly from limitation of the model in simulating the clouds and precipitation, transport errors and uncertainties in the particulate emission rates. The inclusion of aerosols feedback in shortwave radiation scheme improved the simulated daily mean shortwave radiation fluxes in Tehran for both simulation periods.

  16. Climate Impacts Mid-1800's Deforestation in New England using the Weather, Research, and Forecasting Model

    Science.gov (United States)

    Burakowski, E. A.; Chen, M.; Birkel, S. D.; Wake, C. P.; Dibb, J. E.

    2012-12-01

    When colonists arrived in the New England region of the United States (US) in the 1600's, more than 90% of land area was forested. By the mid-1800's, half of the land area was deforested having been cleared extensively for timber, pasture, and to heat homes. Today, New Hampshire is one of the most forested states in the US, yet little is known about the local climate impacts resulting from reforestation. We hypothesize that the removal of forests in 1850 had a strong impact on wintertime climate through changes in surface albedo, roughness length, and other biogeophysical surface properties. This study investigates the climate impacts of historical deforestation on New England winter climate using the Weather, Research, and Forecasting model. The WRF simulations presented here utilize a triple-nested approach, with the innermost 4-km domain centered on the New England states for two land cover scenarios, (2) an historical 1850 deforested scenario derived from the History Database of the Global Environment (HYDE3) land cover dataset and (2) present-day reforested scenario derived from MODerate Resolution Imaging Spectroradiometer (MODIS) land cover data. ERA-Interim lateral boundary conditions are used to drive the model and results are compared for an above average snowfall winter (November 2008 through April 2009) and a below-average snowfall winter (November 2001 through April 2002). Simulations are ongoing but analysis of observational data suggests that nocturnal cooling is a dominant response to deforestation compared to forested areas. The results from the WRF modeling efforts in this study will help inform future land use decisions in the future.

  17. Assessing High-Resolution Weather Research and Forecasting (WRF) Forecasts Using an Object-Based Diagnostic Evaluation

    Science.gov (United States)

    2014-02-01

    Cold, normal, warm conditions Continuous Visual, continuous, probabilistic, spatial, ensemble Maximum temperature Object-, or event-oriented...errors are much smaller than the expected error in the forecast, allowing them to be ignored. Verification results tend to be more trustworthy when

  18. The power of weather

    OpenAIRE

    Christian Huurman; Francesco Ravazzolo; Chen Zhou

    2010-01-01

    This paper examines the predictive power of weather for electricity prices in day ahead markets in real time. We find that next-day weather forecasts improve the forecast accuracy of Scandinavian day-ahead electricity prices substantially in terms of point forecasts, suggesting that weather forecasts can price the weather premium. This improvement strengthens the confidence in the forecasting model, which results in high center-mass predictive densities. In density forecast, such a predictive...

  19. Rapid Retrieval and Assimilation of Ground Based GPS-Met Observations at the NOAA Forecast Systems Laboratory: Impact on Weather Forecasts

    Science.gov (United States)

    Gutman, S.

    2003-04-01

    This year, 2003, marks the tenth anniversary of ground-based Global Positioning System meteorology. GPS-Met as we now know it started in 1992 with the definition of the essential techniques to retrieve integrated (total column) precipitable water vapor (IPW) from zenith-scaled neutral atmospheric signal delays (Bevis et al., 1992). It culminated with the GPS/Storm experiment in 1993, which demonstrated the ability to make IPW measurements with about the predicted accuracy under warm-weather conditions (Rocken et al., 1995). Since then, most of the major advances in GPS-Met data processing have been in the form of improved mapping functions (Niell, 1996), the estimation of GPS signal delays in an absolute (Duan et al., 1996) versus a relative sense (Rocken et al., 1993), and improved GPS satellite orbit accuracy with reduced latency (Fang et al., 1998). Experiments with other GPS-Met data processing techniques, such as the estimation of line-of-sight GPS signal delays using a double-difference to zero-difference technique described by Alber et al. (2000) and Braun et al. (2001) are noted, but lingering questions about the validity of this approach (Gutman, 2002), and not the potential value of a slant-path measurements per se, (as enumerated by MacDonald and Xie, 2001 or Ha et al., 2002) have thus far precluded its routine implementation at the National Oceanic and Atmospheric Administration Forecast Systems Laboratory (NOAA/FSL). Since 1994, NOAA/FSL has concentrated on evaluating the scientific and engineering bases of ground-based GPS-Met and assessing its utility for operational weather forecasting, climate monitoring, satellite calibration and validation, and improved differential GPS positioning and navigation. The term “rapid” in the title of this paper is defined as “available in time to be used for a specific application.” The requirement for high accuracy GPS-Met retrievals with lower latency is primarily driven by two factors: the trend toward

  20. Overview of Space Weather, Space Weather Forecasts, and System Impacts During the High Activity from 19 October through 7 November 2003

    Science.gov (United States)

    Balch, C. C.; Crown, M. D.; Viereck, R.

    2004-05-01

    A remarkable interval of solar-geophysical activity occurred between 19 October and 7 November, 2003, including 17 major solar flares, six strong interplanetary shocks, six significant injections of energetic particles, and extreme levels of geomagnetic storming on 29 and 30 October. Among the more spectacular events were an X28 flare observed by the GOES XRS sensor on 4 November, which may be the highest ever recorded by GOES; a severe energetic particle event on 29 October, with peak fluxes making it the 4th highest observed in records going back to 1976; and an extreme geomagnetic storm which ranks as the 6th highest since 1932 based on the running Ap index. This interval provided unique challenges for space weather forecasting and nowcasting services and numerous system effects were reported. In this presentation we will provide some historical context for this activity, a discussion of the forecasts, warnings, and alerts that were issued, and we will cover some of the operational impacts of these space weather events.

  1. Weather Forecast Based Conditional Pest Management: A Stochastic Optimal Control Investigation

    OpenAIRE

    Lu, Liang; Elbakidze, Levan

    2011-01-01

    In this paper, we examine conditional, forecast-based dynamic pest management in agricultural crop production given stochastic pest infestations and stochastic climate dynamics throughout the growing season. Using stochastic optimal control we show that correlation between forecast error for climate prediction and forecast error for pest outbreaks can be used to improve pesticide application efficiency. In the general setting, we apply modified Hamiltonian approach to discuss the steady state...

  2. The very short-term rainfall forecasting for a mountainous watershed by means of an ensemble numerical weather prediction system in Taiwan

    Science.gov (United States)

    Wu, Ming-Chang; Lin, Gwo-Fong

    2017-03-01

    During typhoons, accurate forecasts of rainfall are always desired for various kinds of disaster warning systems to reduce the impact of rainfall-induced disasters. However, rainfall forecasting, especially the very short-term (hourly) rainfall, is one of the most difficult tasks in hydrology due to the high variability in space and time and the complex physical process. In this study, the purpose is to provide effective forecasts of very short-term rainfall by means of the ensemble numerical weather prediction system in Taiwan. To this end, the ensemble forecasts of hourly rainfall from this ensemble numerical weather prediction system are analyzed to evaluate the performance. Furthermore, a methodology, which is based on the principle of analogue prediction, is proposed to effectively process these ensemble forecasts for improving the performance on very short-term rainfall forecasting. To clearly demonstrate the advantage of the proposed methodology, actual application is conducted on a mountainous watershed to yield 1- to 6-h ahead forecasts during typhoon events. The results indicate that the proposed methodology is better performed and more flexible than the conventional one. Generally, the proposed methodology provides improved performance for very short-term rainfall forecasting, especially for 1- to 2-h ahead forecasting. The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems, such as flash-flood, landslide, and debris flow warning systems, during typhoons.

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

    Energy Technology Data Exchange (ETDEWEB)

    Kaiser, Mark J. [Center for Energy Studies, Louisiana State University, Energy Coast and Environment Building, Nicholson Extension Drive, Baton Rouge, LA 70803 (United States)]. E-mail: mkaiser@lsu.edu; Pulsipher, Allan G. [Center for Energy Studies, Louisiana State University, Energy Coast and Environment Building, Nicholson Extension Drive, Baton Rouge, LA 70803 (United States)

    2007-02-15

    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.

  4. Deep Space Ka-band Link Management and the MRO Demonstration: Long-term Weather Statistics Versus Forecasting

    Science.gov (United States)

    Davarian, Faramaz; Shambayati, Shervin; Slobin, Stephen

    2004-01-01

    During the last 40 years, deep space radio communication systems have experienced a move toward shorter wavelengths. In the 1960s a transition from L- to S-band occurred which was followed by a transition from S- to X-band in the 1970s. Both these transitions provided deep space links with wider bandwidths and improved radio metrics capability. Now, in the 2000s, a new change is taking place, namely a move to the Ka-band region of the radio frequency spectrum. Ka-band will soon replace X-band as the frequency of choice for deep space communications providing ample spectrum for the high data rate requirements of future missions. The low-noise receivers of deep space networks have a great need for link management techniques that can mitigate weather effects. In this paper, three approaches for managing Ka-band Earth-space links are investigated. The first approach uses aggregate annual statistics, the second one uses monthly statistics, and the third is based on the short-term forecasting of the local weather. An example of weather forecasting for Ka-band link performance prediction is presented. Furthermore, spacecraft commanding schemes suitable for Ka-band link management are investigated. Theses schemes will be demonstrated using NASA's Mars Reconnaissance Orbiter (MRO) spacecraft in the 2007 to 2008 time period, and the demonstration findings will be reported in a future publication.

  5. Ethno-meteorology and scientific weather forecasting: Small farmers and scientists’ perspectives on climate variability in the Okavango Delta, Botswana

    Directory of Open Access Journals (Sweden)

    Oluwatoyin Dare Kolawole

    2014-01-01

    Full Text Available Recent trends in abrupt weather changes continue to pose a challenge to agricultural production most especially in sub-Saharan Africa. The paper specifically addresses the questions on how local farmers read and predict the weather; and how they can collaborate with weather scientists in devising adaptation strategies for climate variability (CV in the Okavango Delta of Botswana. Recent trends in agriculture-related weather variables available from country’s climate services, as well as in freely available satellite rainfall products were analysed. The utility of a seasonal hydrological forecasting system for the study area in the context of supporting farmer’s information needs were assessed. Through a multi-stage sampling procedure, a total of 592 households heads in 8 rural communities in the Okavango Delta were selected and interviewed using open and close-ended interview schedules. Also, 19 scientists were purposively selected and interviewed using questionnaires. Key informant interviews, focus group and knowledge validation workshops were used to generate qualitative information from both farmers and scientists. Descriptive and inferential statistics were used in summarising the data. Analysis of satellite rainfall products indicated that there was a consistent increase in total annual rainfall throughout the region in the last 10 years, accompanied by an increase in number of rain days, and reduction of duration of dry spells. However, there is a progressive increase in the region’s temperatures leading to increase in potential evaporation. Findings from social surveys show that farmers’ age, education level, number of years engaged in farming, sources of weather information, knowledge of weather forecasting and decision on farming practices either had a significant relationship or correlation with their perceptions about the nature of both local [ethno-meteorological] and scientific weather knowledge. Nonetheless, there was a

  6. Extending to seasonal scales the current usage of short range weather forecasts and climate projections for water management in Spain

    Science.gov (United States)

    Rodriguez-Camino, Ernesto; Voces, José; Sánchez, Eroteida; Navascues, Beatriz; Pouget, Laurent; Roldan, Tamara; Gómez, Manuel; Cabello, Angels; Comas, Pau; Pastor, Fernando; Concepción García-Gómez, M.°; José Gil, Juan; Gil, Delfina; Galván, Rogelio; Solera, Abel

    2016-04-01

    This presentation, first, briefly describes the current use of weather forecasts and climate projections delivered by AEMET for water management in Spain. The potential use of seasonal climate predictions for water -in particular dams- management is then discussed more in-depth, using a pilot experience carried out by a multidisciplinary group coordinated by AEMET and DG for Water of Spain. This initiative is being developed in the framework of the national implementation of the GFCS and the European project, EUPORIAS. Among the main components of this experience there are meteorological and hydrological observations, and an empirical seasonal forecasting technique that provides an ensemble of water reservoir inflows. These forecasted inflows feed a prediction model for the dam state that has been adapted for this purpose. The full system is being tested retrospectively, over several decades, for selected water reservoirs located in different Spanish river basins. The assessment includes an objective verification of the probabilistic seasonal forecasts using standard metrics, and the evaluation of the potential social and economic benefits, with special attention to drought and flooding conditions. The methodology of implementation of these seasonal predictions in the decision making process is being developed in close collaboration with final users participating in this pilot experience.

  7. On the contribution of lakes in predicting near-surface temperature in a global weather forecasting model

    Directory of Open Access Journals (Sweden)

    T. Stockdale

    2012-02-01

    Full Text Available The impact of lakes in numerical weather prediction is investigated in a set of global simulations performed with the ECMWF Integrated Forecasting System (IFS. A Fresh shallow-water Lake model (FLake is introduced allowing the coupling of both resolved and subgrid lakes (those that occupy less than 50% of a grid-box to the IFS atmospheric model. Global fields for the lake ancillary conditions (namely lake cover and lake depth, as well as initial conditions for the lake physical state, have been derived to initialise the forecast experiments. The procedure for initialising the lake variables is described and verified with particular emphasis on the importance of surface water temperature and freezing conditions. The response of short-range near surface temperature to the representation of lakes is examined in a set of forecast experiments covering one full year. It is shown that the impact of subgrid lakes is beneficial, reducing forecast error over the Northern territories of Canada and over Scandinavia particularly in spring and summer seasons. This is mainly attributed to the lake thermal effect, which delays the temperature response to seasonal radiation forcing.

  8. Climate indices over the last three decades in Tunisia using Weather Research and Forecasting Model:WRF

    Science.gov (United States)

    Deli, Meriem; Mkhinini, Nadia; Sadok Guellouz, Mohamed; Benjabrallah, Sadok

    2016-04-01

    Tunisia is a country situated in the south of the mediterannen basin. This region undergoes direct and indirect effects of climate change. Actually, we notice that summer temperatures have risen during the last decades. Nevertheless research on the tunisian climate are not well developed and are mainly based on observations; short and mid term forecast are not available for the tunisian case. In this context we have studied the climate properties of Tunisia over the last 30 years using Weather Research and Forecasting model WRF. Afterwards we compared our results to the observations that we have obteined on behalf of the National Institute of Meteorology. Results were then used to calculate different climate indices related to the air temperature such as extreme values during a specific period exceeding specific limits (Percentile), warm and cold spell duration and growing season length. We admit that we have created a reliable database for the Tunisian climate.

  9. Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information

    Science.gov (United States)

    H.K. Preisler; R.E. Burgan; J.C. Eidenshink; J.M. Klaver; R.W. Klaver

    2009-01-01

    The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i)...

  10. Multi-initial-conditions and Multi-physics Ensembles in the Weather Research and Forecasting Model to Improve Coastal Stratocumulus Forecasts for Solar Power Integration

    Science.gov (United States)

    Yang, H.

    2015-12-01

    In coastal Southern California, variation in solar energy production is predominantly due to the presence of stratocumulus clouds (Sc), as they greatly attenuate surface solar irradiance and cover most distributed photovoltaic systems on summer mornings. Correct prediction of the spatial coverage and lifetime of coastal Sc is therefore vital to the accuracy of solar energy forecasts in California. In Weather Research and Forecasting (WRF) model simulations, underprediction of Sc inherent in the initial conditions directly leads to an underprediction of Sc in the resulting forecasts. Hence, preprocessing methods were developed to create initial conditions more consistent with observational data and reduce spin-up time requirements. Mathiesen et al. (2014) previously developed a cloud data assimilation system to force WRF initial conditions to contain cloud liquid water based on CIMSS GOES Sounder cloud cover. The Well-mixed Preprocessor and Cloud Data Assimilation (WEMPPDA) package merges an initial guess of cloud liquid water content obtained from mixed-layer theory with assimilated CIMSS GOES Sounder cloud cover to more accurately represent the spatial coverage of Sc at initialization. The extent of Sc inland penetration is often constrained topographically; therefore, the low inversion base height (IBH) bias in NAM initial conditions decreases Sc inland penetration. The Inversion Base Height (IBH) package perturbs the initial IBH by the difference between model IBH and the 12Z radiosonde measurement. The performance of these multi-initial-condition configurations was evaluated over June, 2013 against SolarAnywhere satellite-derived surface irradiance data. Four configurations were run: 1) NAM initial conditions, 2) RAP initial conditions, 3) WEMPPDA applied to NAM, and 4) IBH applied to NAM. Both preprocessing methods showed significant improvement in the prediction of both spatial coverage and lifetime of coastal Sc. The best performing configuration was then

  11. Risk-based Operation and Maintenance Approach for Wave Energy Converters Taking Weather Forecast Uncertainties into Account

    DEFF Research Database (Denmark)

    Ambühl, Simon; Kramer, Morten Mejlhede; Sørensen, John Dalsgaard

    2016-01-01

    Inspection and maintenance costs are significant contributors to the cost of energy for wave energy converters. Maintenance can be performed after failure (corrective) or before a breakdown (preventive) occurs. Furthermore, helicopter and boat can be used to transport equipment and personnel...... to the device for operation and maintenance actions. This article focusses on a risk-based inspection and maintenance planning approach involving minimization of the overall repair costs including costs due to lost electricity production. The study includes real weather data and damage accumulation as well...... as uncertainties related with imperfect weather forecasts, costs, structural damage accumulation, inspection accuracy and the applied maintenance strategies. This article contains a case study where the risk-based maintenance strategy is applied for the Wavestar device....

  12. Great Historical Events That Were Significantly Affected by the Weather: Part 8, Germany's War on the Soviet Union, 1941-45. II. Some Important Weather Forecasts, 1942-45.

    Science.gov (United States)

    Neumann, J.; Flohn, H.

    1988-07-01

    Short- to medium-range weather forecasts were prepared by Soviet meteorologists for the Battle of Stalingrad. These included forecasts for days suitable for massing troops and equipment and for starting the Soviet offensive in November 1942 that resulted in the encirclement of the German 6th Army. Another forecast was connected with the operation of artificial thickening of the ice cover of the Volga River in the Stalingrad area that made it possible to drive tanks from the cast bank to the west bank of the river (width: about 1 km).In January 1943 a German Panzer army had to be withdrawn from the Caucasus. To accelerate the retreat, light elements of that army crossed some 42 km of the ice cover of the Gulf of Taganrog (Sea of Azov). The crossing was authorized after a meteorologist proved his estimate of the ice-cover thickness by landing in a light plane on the ice.In January 1945 weather forecasts played an important role in the major Soviet (2 200 000 troops and 5 000 warplanes) Oder-Vistula offensive. Marshal Koney writes with appreciation of the correct weather forecasts.In the Appendix, considerations that led German meteorologists to formulate a forecast for a minimum of five days of fog or low clouds from the Ardennes to southern England are reviewed. This forecast was used by the German High Command for the start of the Battle of the Bulge in December 1944.

  13. Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0

    Directory of Open Access Journals (Sweden)

    M. Rautenhaus

    2015-07-01

    Full Text Available We present "Met.3D", a new open-source tool for the interactive three-dimensional (3-D visualization of numerical ensemble weather predictions. The tool has been developed to support weather forecasting during aircraft-based atmospheric field campaigns; however, it is applicable to further forecasting, research and teaching activities. Our work approaches challenging topics related to the visual analysis of numerical atmospheric model output – 3-D visualization, ensemble visualization and how both can be used in a meaningful way suited to weather forecasting. Met.3D builds a bridge from proven 2-D visualization methods commonly used in meteorology to 3-D visualization by combining both visualization types in a 3-D context. We address the issue of spatial perception in the 3-D view and present approaches to using the ensemble to allow the user to assess forecast uncertainty. Interactivity is key to our approach. Met.3D uses modern graphics technology to achieve interactive visualization on standard consumer hardware. The tool supports forecast data from the European Centre for Medium Range Weather Forecasts (ECMWF and can operate directly on ECMWF hybrid sigma-pressure level grids. We describe the employed visualization algorithms, and analyse the impact of the ECMWF grid topology on computing 3-D ensemble statistical quantities. Our techniques are demonstrated with examples from the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign.

  14. Thermal mapping as a valuable tool for road weather forecast and winter road maintenance: an example from the Italian Alps

    Science.gov (United States)

    Todeschini, Ilaria; Di Napoli, Claudia; Pretto, Ilaria; Merler, Giacomo; Cavaliere, Roberto; Apolloni, Roberto; Antonacci, Gianluca; Piazza, Andrea; Benedetti, Guido

    2016-08-01

    During the winter period ice is likely to form on roads, making pavement surfaces slippery and increasing accident risk. Road surface temperature (RST) is one of the most important parameters in ice formation. The LIFE+ "CLEANROADS" project aims to forecast RSTs in advance in order to support road maintenance services in the timely and effective preparation of preventive anti-icing measures. This support is provided through a novel MDSS (Maintenance Decision Support System). The final goal of the project is to quantitatively demonstrate that the implemented MDSS is capable to minimize the consumption of chemical anti-icing reagents (e.g. sodium chloride) and the associated environmental (water and air) impact while maintaining the current high levels of road safety. In the CLEAN-ROADS system RSTs have been forecast by applying the numerical model METRo (Model of the Environment and Temperature of Roads) to a network of RWIS (Road Weather Information System) stations installed on a test route in the Adige Valley (Italy). This forecast is however local and does not take into account typical peculiarities along road network, such as the presence of road sections that are particularly prone to ice formation. Thermal mapping, i.e. the acquisition of mobile RST measurements through infrared thermometry, permits to (i) identify and map those sections, and (ii) extend the forecast from a RWIS station to adjacent areas. The processing of thermal mapping signals is however challenging because of random variations in the road surface emissivity. To overcome this we have acquired several thermal mapping traces along the test route during winter seasons 2014-2015 and 2015-2016. We have then defined a "characteristic" thermal fingerprint as a function of all its historical thermal mapping signals, and used it to spatialize local METRo forecasts. Preliminary results suggest the high potential of such a technique for winter road applications.

  15. Impact of aerosols on the forecast accuracy of solar irradiance calculated by a numerical weather prediction model

    Science.gov (United States)

    Shimose, Ken-ichi; Ohtake, Hideaki; Fonseca, Joao Gari da Silva; Takashima, Takumi; Oozeki, Takashi; Yamada, Yoshinori

    2014-10-01

    The impact of aerosols on the forecast accuracy of solar irradiance calculated by a fine-scale, one day-ahead, and operational numerical weather prediction model (NWP) is investigated in this study. In order to investigate the impact of aerosols only, the clear sky period is chosen, which is defined as when there are no clouds in the observation data and in the forecast data at the same time. The evaluation of the forecast accuracy of the solar irradiance is done at a single observation point that is sometimes affected by aerosol events. The analysis period is one year from April 2010 to March 2011. During the clear sky period, the root mean square errors (RMSE) of the global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI) are 40.0 W m-2, 84.0 Wm-2, and 47.9 W m-2, respectively. During one extreme event, the RMSEs of the GHI, DNI, and DHI are 70.1 W m-2, 211.6 W m-2, and 141.7 W m-2, respectively. It is revealed that the extreme events were caused by aerosols such as dust or haze. In order to investigate the impact of the aerosols, the sensitivity experiments of the aerosol optical depth (AOD) for the extreme events are executed. The best result is obtained by changing the AOD to 2.5 times the original AOD. This changed AOD is consistent with the satellite observation. Thus, it is our conclusion that an accurate aerosol forecast is important for the forecast accuracy of the solar irradiance.

  16. The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

    Directory of Open Access Journals (Sweden)

    Simone Sperati

    2015-09-01

    Full Text Available A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE” with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.

  17. The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

    DEFF Research Database (Denmark)

    Sperati, Simone; Alessandrini, Stefano; Pinson, Pierre

    2015-01-01

    A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE”) with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting...... the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview...... and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field...

  18. A Survey on Forecasters’ View about Uncertainty in Weather Forecasts%天气预报中不确定性问题的调查分析

    Institute of Scientific and Technical Information of China (English)

    杜钧; 康志明

    2014-01-01

    围绕天气预报中的不确定性问题,对中国18个省(市、区)229名代表了国家、省和地市3级以及不同岗龄的预报员进行了问卷调查。具体调查内容包括了以下3个方面:(1)应用数值预报的情况;(2)对天气预报准确率的看法;(3)对天气预报(数值预报)中不确定性、集合预报产品以及预报发布形式等问题的看法。结果表明,数值预报产品目前已经成为预报员们制作日常天气预报的主要工具。因此,数值预报所固有的可预报性(不确定性)问题已是预报员不得不面对的一个科学问题。最后,介绍了一项新近关于美国预报员在日常天气预报中应用集合预报信息所遇到的一些挑战,这包括“资料不全面、分析工具不足、培训教育不够、缺乏检验、工作负荷重、模式分辨率低以及预报规范格式的限制”7方面。这些可供气象学科带头人和业务管理决策者思考和计划。%A nation-wide survey was conducted to 229 forecasters who came from 18 provinces representing both senior and junior forecasters at all three levels (national, provincial and regional) in China. Three issues are surveyed: (1) usage of numerical weather prediction (NWP) in daily operational weather forecasting; (2) opinion about weather forecast accuracy; and (3) view about uncertainty in forecasts including ensemble products and methods to convey forecast conifdence. The result shows that NWP has been applied by all forecasters to their daily preparations of weather forecasting. Therefore, forecast uncertainty or predictability issue associated with highly nonlinear NWP models is a reality which forecasters have to face day by day. Long-term education is needed to train forecasters in effectively using and correctly interpreting forecast uncertainty information. Regarding weather forecast accuracy, all forecasters admit that any forecast has errors and believe that it

  19. A New Weather Forecasting Technique using Back Propagation Neural Network with Modified Levenberg-Marquardt Algorithm for Learning

    Directory of Open Access Journals (Sweden)

    I.Kadar Shereef

    2011-11-01

    Full Text Available Temperature warnings are essential forecasts since they are utilized to guard life and property. Temperature forecasting is the kind of science and technology to approximate the temperature for a future time and for a given place. Temperature forecasts are performed by means of gathering quantitative data regarding the in progress state of the atmosphere. The author in this paper utilized a neural network-based technique for determining the temperature in future. The Neural Networks package consists of various kinds of training or learning techniques. One such technique is Back Propagation Neural Network (BPN technique. The main advantage of the Back Propagation Neural Network technique is that it can reasonably estimated a large class of functions. This technique is more efficient than numerical differentiation. The simple meaning of this term is that the proposed technique has ability to confine the complex relationships among several factors that contribute to assured temperature. The proposed idea is tested using the real time dataset. In order to further improve the prediction accuracy, this paper uses Modified Levenberg-Marquardt (LM Algorithm for Neural Network learning. In modified LM, the learning parameters are modified. The proposed algorithm has good convergence and also it reduces the amount of oscillation in learning procedure. The proposed technique is compare with the usage of BPN and the practical working of meteorological department. The experimental result shows that the proposed technique results in better accuracy of prediction when compared to the conventional technique of weather prediction.

  20. A centralized real-time controller for the reservoir's management on the Seine River using ensemble weather forecasting

    Science.gov (United States)

    Ficchi, Andrea; Raso, Luciano; Jay-Allemand, Maxime; Dorchies, David; Malaterre, Pierre-Olivier; Pianosi, Francesca; Van Overloop, Peter-Jules

    2013-04-01

    The reservoirs on the Seine River, upstream of Paris, are regulated with the objective of reducing floods and supporting low flows. The current management of these reservoirs is empirical, reactive, and decentralized, mainly based on filling curves, constructed from an analysis of historical floods and low flows. When inflows are significantly different from their seasonal average, this management strategy proves inefficient. Climate change is also a challenge, for the possible modification of future hydrologic conditions. To improve such management strategy, in this study we investigate the use of Tree-Based Model Predictive Control (TB-MPC), a proactive and centralized method that uses all the information available in real-time, including ensemble weather forecasting. In TB-MPC, a tree is generated from an ensemble of weather forecast. The tree structure summarizes the information contained in the ensemble, specifying the time, along the optimization horizon, when forecast trajectories diverge and thus uncertainty is expected to be resolved. This information is then used in the model predictive control framework. The TB-MPC controller is implemented in combination with the integrated model of the water system, including a semi-distributed hydrologic model of the watershed, a simplified hydraulic model of the river network, and the four reservoir models. Optimization takes into account the cost associated to floods and low-flows, and a penalty cost based on the final reservoir storages. The performances of the TB-MPC controller will be simulated and compared with those of deterministic MPC and with the actual management performances. This work is part of the Climaware European project (2010-2013) set up to develop and to assess measures for sustainable water resources management regarding adaptation to climate change.

  1. Simulation of Flash-Flood-Producing Storm Events in Saudi Arabia Using the Weather Research and Forecasting Model

    KAUST Repository

    Deng, Liping

    2015-05-01

    The challenges of monitoring and forecasting flash-flood-producing storm events in data-sparse and arid regions are explored using the Weather Research and Forecasting (WRF) Model (version 3.5) in conjunction with a range of available satellite, in situ, and reanalysis data. Here, we focus on characterizing the initial synoptic features and examining the impact of model parameterization and resolution on the reproduction of a number of flood-producing rainfall events that occurred over the western Saudi Arabian city of Jeddah. Analysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data suggests that mesoscale convective systems associated with strong moisture convergence ahead of a trough were the major initial features for the occurrence of these intense rain events. The WRF Model was able to simulate the heavy rainfall, with driving convective processes well characterized by a high-resolution cloud-resolving model. The use of higher (1 km vs 5 km) resolution along the Jeddah coastline favors the simulation of local convective systems and adds value to the simulation of heavy rainfall, especially for deep-convection-related extreme values. At the 5-km resolution, corresponding to an intermediate study domain, simulation without a cumulus scheme led to the formation of deeper convective systems and enhanced rainfall around Jeddah, illustrating the need for careful model scheme selection in this transition resolution. In analysis of multiple nested WRF simulations (25, 5, and 1 km), localized volume and intensity of heavy rainfall together with the duration of rainstorms within the Jeddah catchment area were captured reasonably well, although there was evidence of some displacements of rainstorm events.

  2. Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model.

    Science.gov (United States)

    Olatinwo, Rabiu O; Prabha, Thara V; Paz, Joel O; Hoogenboom, Gerrit

    2012-03-01

    Early leaf spot of peanut (Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

  3. Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model

    Science.gov (United States)

    Olatinwo, Rabiu O.; Prabha, Thara V.; Paz, Joel O.; Hoogenboom, Gerrit

    2012-03-01

    Early leaf spot of peanut ( Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

  4. Ensemble-based analysis of Front Range severe convection on 6-7 June 2012: Forecast uncertainty and communication of weather information to Front Range decision-makers

    Science.gov (United States)

    Vincente, Vanessa

    The variation of topography in Colorado not only adds to the beauty of its landscape, but also tests our ability to predict warm season severe convection. Deficient radar coverage and limited observations make quantitative precipitation forecasting quite a challenge. Past studies have suggested that greater forecast skill of mesoscale convection initiation and precipitation characteristics are achievable considering an ensemble with explicitly predicted convection compared to one that has parameterized convection. The range of uncertainty and probabilities in these forecasts can help forecasters in their precipitation predictions and communication of weather information to emergency managers (EMs). EMs serve an integral role in informing and protecting communities in anticipation of hazardous weather. An example of such an event occurred on the evening of 6 June 2012, where areas to the lee of the Rocky Mountain Front Range were impacted by flash-flood-producing severe convection that included heavy rain and copious amounts of hail. Despite the discrepancy in the timing, location and evolution of convection, the convection-allowing ensemble forecasts generally outperformed those of the convection-parameterized ensemble in representing the mesoscale processes responsible for the 6-7 June severe convective event. Key features sufficiently reproduced by several of the convection-allowing ensemble members resembled the observations: 1) general location of a convergence boundary east of Denver, 2) convective initiation along the boundary, 3) general location of a weak cold front near the Wyoming/Nebraska border, and 4) cold pools and moist upslope characteristics that contributed to the backbuilding of convection. Members from the convection-parameterized ensemble that failed to reproduce these results displaced the convergence boundary, produced a cold front that moved southeast too quickly, and used the cold front for convective initiation. The convection

  5. Assimilation of Sentinel-1 estimates of Precipitable Water Vapor (PWV) into a Numerical Weather Model for a more accurate forecast of extreme weather events

    Science.gov (United States)

    Mateus, Pedro; Nico, Giovanni; Catalao, Joao

    2017-04-01

    In the last two decades, SAR interferometry has been used to obtain maps of Precipitable Water Vapor (PWV).This maps are characterized by their high spatial resolution when compared to the currently available PWV measurements (e.g. GNSS, radiometers or radiosondes). Several previous works have shown that assimilating PWV values, mainly derived from GNSS observations, into Numerical Weather Models (NWMs) can significantly improve rainfall predictions.It is noteworthy that the PWV-derived from GNSS observations have a high temporal resolution but a low spatialone. In addition, there are many regions without any GNSS stations, where temporal and spatial distribution of PWV areonly available through satellite measurements. The first attempt to assimilate InSAR-derived maps of PWV (InSAR-PWV) into a NWM was made by Pichelli et al. [1].They used InSAR-PWV maps obtained from ENVISAT-ASAR images and the mesoscale weather prediction model MM5 over the city of Rome, Italy. The statistical indices show that the InSAR-PWVdata assimilation improves the forecast of weak to moderateprecipitation (model over the city of Lisbon, Portugal, during a light rain event not forecast by the model.Results showed that after data assimilation, there is a bias correction of the PWV field and an improvement in the forecast of the weakto moderate rainfall up to 9 h after the assimilation time. We used, for the first time, the Weather Research and Forecast Data Assimilation (WRFDA) model, at micro-scale resolutions (3 km), over the Iberian Peninsula (focusing on the southern region of Spain) and during a convective cell associated with a local heavy rainfall event, to study the impact of assimilation PWV maps obtained from SAR interferometric phase calculated using images acquired by the Sentinel-1 satellite. It's worth noting that, in this case, the model without assimilation PWV maps fails to reproduce the amount and the region of heavy rainfall. The assimilation of InSAR-PWV maps with high

  6. Developing a Global, Short-Term Fire Weather Forecasting Tool Using NWP Input Meteorology and Satellite Fire Data

    Science.gov (United States)

    Peterson, D. A.; Hyer, E. J.; Wang, J.

    2011-12-01

    In order to meet the emerging need for better estimates of biomass burning emissions in air quality and climate models, a statistical model is developed to characterize the effect of a given set of meteorological conditions on the following day's fire activity, including ignition and spread potential. Preliminary tests are conducted within several spatial domains of the North American boreal forest by investigating a wide range of meteorological information, including operational fire weather forecasting indices, such as the Canadian Forest Fire Danger Rating System (CFFDRS). However, rather than using local noon surface station data, the six components of the CFFDRS are modified to use inputs from the North America Regional Reanalysis (NARR) and the Navy's Operational Global Atmospheric Prediction System Model (NOGAPS). The Initial Spread Index (ISI) and the Fire Weather Index (FWI) are shown to be the most relevant components of the CFFDRS for short-term changes in fire activity. However, both components are found to be highly sensitive to variations in relative humidity and wind speed input data. Several variables related to fire ignition from dry lighting, such as instability and the synoptic pattern, are also incorporated. Cases of fire ignition, growth, decay, and extinction are stratified using satellite fire observations from the Geostationary Operational Environmental Satellites (GOES) and the MODerate Resolution Imaging Spectroradiometer (MODIS) and compared to the available suite of meteorological information. These comparisons reveal that combinations of meteorological variables, such as the FWI, ISI, and additional indices developed for this study, produce the greatest separability between major fire growth and decay cases, which are defined by the observed change in fire counts and fire radiative power. This information is used to derive statistical relationships affecting the short-term changes in fire activity and subsequently applied to other

  7. Space Weather - Current Capabilities, Future Requirements, and the Path to Improved Forecasting

    Science.gov (United States)

    Mann, Ian

    2016-07-01

    We present an overview of Space Weather activities and future opportunities including assessments of current status and capabilities, knowledge gaps, and future directions in relation to both observations and modeling. The review includes input from the scientific community including from SCOSTEP scientific discipline representatives (SDRs), COSPAR Main Scientific Organizers (MSOs), and SCOSTEP/VarSITI leaders. The presentation also draws on results from the recent activities related to the production of the COSPAR-ILWS Space Weather Roadmap "Understanding Space Weather to Shield Society" [Schrijver et al., Advances in Space Research 55, 2745 (2015) http://dx.doi.org/10.1016/j.asr.2015.03.023], from the activities related to the United Nations (UN) Committee on the Peaceful Uses of Outer Space (COPUOS) actions in relation to the Long-term Sustainability of Outer Space (LTS), and most recently from the newly formed and ongoing efforts of the UN COPUOS Expert Group on Space Weather.

  8. Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information

    Science.gov (United States)

    Preisler, H.K.; Burgan, R.E.; Eidenshink, J.C.; Klaver, Jacqueline M.; Klaver, R.W.

    2009-01-01

    The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i) number of ignitions; (ii) number of fires above a given size; (iii) conditional probabilities of fires greater than a specified size, given ignition. As an illustration, we used the methods to study the skill of the Fire Potential Index an index that incorporates satellite and surface observations to map fire potential at a national scale in forecasting distributions of large fires. ?? 2009 IAWF.

  9. Assessing the value of post-processed state-of-the-art long-term weather forecast ensembles for agricultural water management mediated by farmers' behaviours

    Science.gov (United States)

    Li, Yu; Giuliani, Matteo; Castelletti, Andrea

    2016-04-01

    Recent advances in modelling of coupled ocean-atmosphere dynamics significantly improved skills of long-term climate forecast from global circulation models (GCMs). These more accurate weather predictions are supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping and watering time) and for more effectively coping with the adverse impacts of climate variability. Yet, assessing how actually valuable this information can be to a farmer is not straightforward and farmers' response must be taken into consideration. Indeed, in the context of agricultural systems potentially useful forecast information should alter stakeholders' expectation, modify their decisions, and ultimately produce an impact on their performance. Nevertheless, long-term forecast are mostly evaluated in terms of accuracy (i.e., forecast quality) by comparing hindcast and observed values and only few studies investigated the operational value of forecast looking at the gain of utility within the decision-making context, e.g. by considering the derivative of forecast information, such as simulated crop yields or simulated soil moisture, which are essential to farmers' decision-making process. In this study, we contribute a step further in the assessment of the operational value of long-term weather forecasts products by embedding these latter into farmers' behavioral models. This allows a more critical assessment of the forecast value mediated by the end-users' perspective, including farmers' risk attitudes and behavioral patterns. Specifically, we evaluate the operational value of thirteen state-of-the-art long-range forecast products against climatology forecast and empirical prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of the farmers' decision-making process. Raw ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in

  10. Experiments in Objective Aviation Weather Forecasting Using Upper-Level Steering.

    Science.gov (United States)

    1983-12-13

    Estoque 2 and Reed 3 developed graphical 1000- mb forecast techniques, applying baroclinic theory, and these techniques steered thermal patterns using upper...none. 42 References 1. Fjbrtoft. R. (1952) On a numerical method of integrating the barotropic vorti- city equation. Tellus 4:179-194. 2. Estoque . M.A

  11. Probabilistic online runoff forecasting for urban catchments using inputs from rain gauges as well as statically and dynamically adjusted weather radar

    DEFF Research Database (Denmark)

    Löwe, Roland; Thorndahl, Søren; Mikkelsen, Peter Steen

    2014-01-01

    are considered that account for the spatial distribution of rainfall in different degrees of detail. Considering two urban example catchments, we show that statically adjusted radar rainfall input improves the quality of probabilistic runoff forecasts as compared to input based on rain gauge observations......We investigate the application of rainfall observations and forecasts from rain gauges and weather radar as input to operational urban runoff forecasting models. We apply lumped rainfall runoff models implemented in a stochastic grey-box modelling framework. Different model structures...... runoff forecasts compared to the structures considering mean areal rainfall only. A time-dynamic adjustment of the radar data to rain gauge data provides improved rainfall forecasts when compared with rainfall observations on the ground. However, dynamic adjustment reduces the potential for creating...

  12. Forecasting of Severe Weather in Austria and Hungary Using High-Resolution Ensemble Prediction System

    Science.gov (United States)

    Szucs, Mihaly; Simon, Andre; Szintai, Balazs; Suklitsch, Martin; Wang, Yong; Wastl, Clemens; Boloni, Gergely

    2015-04-01

    The study presents and compares several approaches in EPS (ensemble prediction system) forecasting based on the non-hydrostatic, high resolution AROME model. The PEARP (global ARPEGE model EPS) was used for coupling. Besides, AROME-EPS was also generated upon hydrostatic ALADIN-EPS forecasts (LAEF), which were used as initial and lateral boundary conditions for each AROME-EPS run. The horizontal resolution of the AROME model is 2.5km and it uses 60 vertical levels for the vertical discretization. In most of the tests, the AROME-EPS run with 10+1 members in Hungarian and 16 members in Austrian implementation. The forecast length was usually set to 30-36 hours. The use of high-resolution EPS has advantages in almost all situations with severe convection (mostly in forecasting intense multicell thunderstorms or mesoscale convective systems of non-frontal origin). The possibility of severe thunderstorm was indicated by several EPS runs even if the deterministic (reference) AROME model failed to forecast the event. Similarly, it could be shown that the AROME-EPS can perform better than hydrostatic global or ALADIN-EPS models in situations with strong wind or heavy precipitation induced by large-scale circulation (mainly in mountain regions). Both EDA (Ensemble of Data Assimilation) and SPPT (Stochastically Perturbed Parameterized Tendencies) methods were tested as a potential perturbation generation method on limited area. The EDA method was able to improve the accuracy of single members through the reduction of the analysis error by applying local data assimilation. It was also able to increase the spread of the system in the early hours due to the additional analysis perturbations. The impact of the SPPT scheme was proven to be smaller in comparison to the impact of this method in global ensemble systems. Further possibilities of improving the assimilation methods and the setup of the AROME-EPS are also discussed.

  13. Weather impact forecasting using MOGREPS with socially- and geographically-derived vulnerability and exposure datasets

    Science.gov (United States)

    Robbins, Joanne

    2010-05-01

    Hazardous weather has a major impact on health and safety and on the economy in the UK and other countries. This has renewed efforts within the Met Office to develop a system that can predict weather related impacts, extending the Office's ability to warn the public and business sectors of potential weather related hazards. Ultimately the prediction of both an impending weather hazard and its potential for impact will allow concise decision making to implement mitigation strategies. A diagnostic tool has been developed for the UK transport network, which focuses on quantifying the risk of specific vehicle types overturning in strong winds or being disrupted due to heavy snow. The impacts to those using the network are then quantified in terms of journey delay times and number of people affected. For this tool to be effective it requires vulnerability and exposure datasets to be combined with the probability of hazardous weather occurring. Probabilities are obtained from MOGREPS (Met Office Global and Regional Ensemble Prediction System), while vulnerability is a combined field which determines the varying structural and geographical attributes of each kilometre segment of motorway route. A segment's attributes influence the likelihood of that location having weather related impacts, and the severity of these impacts. The exposure field accounts for the changes in traffic flow by vehicle type, day of the week and time of the day, and increases risk of disruption if the route has high traffic flows while reducing the risk in low traffic flow areas. The model has been shown to be useful in the Met Office Operations Centre where it is used as a support tool for issuing weather warnings. However, verification of the output has been difficult due to the lack of severe wind gust events over the trial period and the difficulty in obtaining accurate impact verification information. The Highways Agency is now on board, both receiving the model's output in their Operations

  14. Can Weather Radars Help Monitoring and Forecasting Wind Power Fluctuations at Large Offshore Wind Farms?

    DEFF Research Database (Denmark)

    Trombe, Pierre-Julien; Pinson, Pierre; Madsen, Henrik

    2011-01-01

    The substantial impact of wind power fluctuations at large offshore wind farms calls for the development of dedicated monitoring and prediction approaches. Based on recent findings, a Local Area Weather Radar (LAWR) was installed at Horns Rev with the aim of improving predictability, controlability...... and potentially maintenance planning. Additional images are available from a Doppler radar covering the same area. The parallel analysis of rain events detection and of regime sequences in wind (and power) fluctuations demonstrates the interest of employing weather radars for a better operation and management...

  15. Global chemical weather forecasts for field campaign planning: predictions and observations of large-scale features during MINOS, CONTRACE, and INDOEX

    Directory of Open Access Journals (Sweden)

    M. G. Lawrence

    2003-01-01

    Full Text Available The first global tropospheric forecasts of O3 and its precursors have been used in the daily flight planning of field measurement campaigns. The 3-D chemistry-transport model MATCH-MPIC is driven by meteorological data from a weather center (NCEP to produce daily 3-day forecasts of the global distributions of O3 and related gases, as well as regional CO tracers. This paper describes the forecast system and its use in three field campaigns, MINOS, CONTRACE and INDOEX. An overview is given of the forecasts by MATCH-MPIC and by three other chemical weather forecast models (EURAD, ECHAM, and FLEXPART, focusing on O3 and CO. Total CO and regional CO tracers were found to be the most valuable gases for flight planning, due to their relatively well-defined anthropogenic source regions and lifetimes of one to a few months. CO was in good agreement with the observations on nearly all the flights (generally  r > 0.7, and the relative RMS differences for the deviations from the means was less than 20%. In every case in which the chemical weather forecasts were primarily responsible for the flight plans, the targeted features were observed. Three forecasted phenomena are discussed in detail: outflow from Asia observed in the Mediterranean upper troposphere during MINOS, outflow from North America observed in the middle troposphere over northern Europe during CONTRACE, and the location of the "chemical ITCZ'' over the Indian Ocean during INDOEX. In particular it is shown that although intercontinental pollution plumes such as those observed during MINOS and CONTRACE occur repeatedly during the months around the campaigns, their frequency is sufficiently low (~10--30% of the time that global chemical weather forecasts are important for enabling them to be observed during limited-duration field campaigns. The MATCH-MPIC chemical weather forecasts, including an interface for making customized figures from the output, are available for community use via http://www.mpch-mainz.mpg.de/~lawrence/forecasts.html.

  16. Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models: COST Action ES0905 Final Report

    Directory of Open Access Journals (Sweden)

    Jun–Ichi Yano

    2014-12-01

    Full Text Available The research network “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” was organized with European funding (COST Action ES0905 for the period of 2010–2014. Its extensive brainstorming suggests how the subgrid-scale parameterization problem in atmospheric modeling, especially for convection, can be examined and developed from the point of view of a robust theoretical basis. Our main cautions are current emphasis on massive observational data analyses and process studies. The closure and the entrainment–detrainment problems are identified as the two highest priorities for convection parameterization under the mass–flux formulation. The need for a drastic change of the current European research culture as concerns policies and funding in order not to further deplete the visions of the European researchers focusing on those basic issues is emphasized.

  17. Urban fine-scale forecasting reveals weather conditions with unprecedented detail

    NARCIS (Netherlands)

    Ronda, R.J.; Steeneveld, G.J.; Heusinkveld, B.G.; Attema, Jisk; Holtslag, A.A.M.

    2017-01-01

    Feasibility of Numerical Weather Prediction at urban neighborhood and street scales demonstrated for summer conditions in the Amsterdam metropolitan region (Netherlands). As the number of urban dwellers increases from an estimated 4 billion in 2014 to an expected 6.5 billion by 2050 (UN 2014),

  18. Forecasting severe ice storms using numerical weather prediction: the March 2010 Newfoundland event

    OpenAIRE

    Hosek, J.; P. Musilek; E. Lozowski; Pytlak, P.

    2011-01-01

    The northeast coast of North America is frequently hit by severe ice storms. These freezing rain events can produce large ice accretions that damage structures, frequently power transmission and distribution infrastructure. For this reason, it is highly desirable to model and forecast such icing events, so that the consequent damages can be prevented or mitigated. The case study presented in this paper focuses on the March 2010 ice storm event that took place in eastern Newfoundland. We apply...

  19. Enhancing Cloud Radiative Processes and Radiation Efficiency in the Advanced Research Weather Research and Forecasting (WRF) Model

    Energy Technology Data Exchange (ETDEWEB)

    Iacono, Michael J. [Atmospheric and Environmental Research, Lexington, MA (United States)

    2015-03-09

    The objective of this research has been to evaluate and implement enhancements to the computational performance of the RRTMG radiative transfer option in the Advanced Research version of the Weather Research and Forecasting (WRF) model. Efficiency is as essential as accuracy for effective numerical weather prediction, and radiative transfer is a relatively time-consuming component of dynamical models, taking up to 30-50 percent of the total model simulation time. To address this concern, this research has implemented and tested a version of RRTMG that utilizes graphics processing unit (GPU) technology (hereinafter RRTMGPU) to greatly improve its computational performance; thereby permitting either more frequent simulation of radiative effects or other model enhancements. During the early stages of this project the development of RRTMGPU was completed at AER under separate NASA funding to accelerate the code for use in the Goddard Space Flight Center (GSFC) Goddard Earth Observing System GEOS-5 global model. It should be noted that this final report describes results related to the funded portion of the originally proposed work concerning the acceleration of RRTMG with GPUs in WRF. As a k-distribution model, RRTMG is especially well suited to this modification due to its relatively large internal pseudo-spectral (g-point) dimension that, when combined with the horizontal grid vector in the dynamical model, can take great advantage of the GPU capability. Thorough testing under several model configurations has been performed to ensure that RRTMGPU improves WRF model run time while having no significant impact on calculated radiative fluxes and heating rates or on dynamical model fields relative to the RRTMG radiation. The RRTMGPU codes have been provided to NCAR for possible application to the next public release of the WRF forecast model.

  20. Combined use of weather forecasting and satellite remote sensing information for fire risk, fire and fire impact monitoring

    Directory of Open Access Journals (Sweden)

    Wolfgang Knorr

    2011-06-01

    Full Text Available The restoration of fire-affected forest areas needs to be combined with their future protection from renewed catastrophic fires, such as those that occurred in Greece during the 2007 summer season. The present work demonstrates that the use of various sources of satellite data in conjunction with weather forecast information is capable of providing valuable information for the characterization of fire danger with the purpose of protecting the Greek national forest areas. This study shows that favourable meteorological conditions have contributed to the fire outbreak during the days of the unusually damaging fires in Peloponnese as well as Euboia (modern Greek: Evia at the end of August 2007. During those days, Greece was located between an extended high pressure system in Central Europe and a low pressure system in the Middle East. Their combination resulted in strong north-northeasterly winds in the Aegean Sea. As a consequence, strong winds were also observed in the regions of Evia and Peloponnese, especially in mountainous areas. The analysis of satellite images showing smoke emitted from the fires corroborates the results from the weather forecasts. A further analysis using the Fraction of Absorbed Photosyntetically Active Radiation (FAPAR as an indicator of active vegetation shows the extent of the destruction caused by the fire. The position of the burned areas coincides with that of the active fires detected in the earlier satellite image. Using the annual maximum FAPAR as an indicator of regional vegetation density, it was found that only regions with relatively high FAPAR were burned.

  1. Great Historical Events That Were Significantly Affected by the Weather: Part 8, Germany's War on the Soviet Union, 1941-45. I. Long-range Weather Forecasts for 1941-42 and Climatological Studies.

    Science.gov (United States)

    Neumann, J.; Flohn, H.

    1987-06-01

    A brief account is given of Baur's long-range weather forecast prepared in the autumn of 1941 for the 1941-42 winter in Eastern Europe. Baur's forecast called for a normal' or mild winter but the winter turned out to be one of the most severe winters on record. The cold, the icy winds and blizzards gravely hit the German armies and coincided with the first major Soviet counteroffensive of the war. A Soviet weather forecast for January 1942, also called for a mild month.A review of the climatological studies prepared for the war indicates that the occurrence of mud periods of considerable intensity in autumn was not considered. The autumn 1941 mud period immobilized most of the German armies for a month and caused the attempted final German assault on Moscow to take place in an early and severe winter.Hitter would not tolerate the mention of winter and still less the mention of the retreat of Napoleon's Grande Armée from Russia.The support given by Soviet meteorologists and hydrologists to the Red Army is sketched. For the 1941-42 winter the more-important short- to medium-range forecasts included a forecast for 7 November (anniversary of the October Revolution) at Moscow and a forecast for the start of Zhukov's counteroffensive in the Battle of Moscow in December 1941.

  2. Knowing Their Place: The Blue Hill Observatory and the Value of Local Knowledge in an Era of Synoptic Weather Forecasting, 1884-1894.

    Science.gov (United States)

    Bergman, James

    2016-09-01

    Argument The history of meteorology has focused a great deal on the "scaling up" of knowledge infrastructures through the development of national and global observation networks. This article argues that such efforts to scale up were paralleled by efforts to define a place for local knowledge. By examining efforts of the Blue Hill Meteorological Observatory, near Boston, Massachusetts, to issue local weather forecasts that competed with the centralized forecasts of the U.S. Signal Service, this article finds that Blue Hill, as a user of the Signal Service's observation network, developed a new understanding of local knowledge by combining local observations of the weather with the synoptic maps afforded by the nationwide telegraph network of the U.S. Signal Service. Blue Hill used these forecasts not only as a service, but also as evidence of the superiority of its model of local forecasting over the Signal Service's model, and in the process opened up larger questions about the value of a weather forecast and the value of different kinds of knowledge in meteorology.

  3. Thinking of the Teaching of Weather Situation Forecast Equation at High Altitudes%高空形势预报方程的教学实践思考

    Institute of Scientific and Technical Information of China (English)

    蔡佳熙

    2015-01-01

    Along with the development of modern weather forecast ,it needs the forecasters to improve their skills. Traditional methodologies of weather forecast have important effects in revising the results of numerical forecast. Based on these discussed above,when explaining theoretical knowledge,it is important to investigate their use in practical work. This paper discusses how to use the weather situation forecast equation in synoptic chart analysis and where it is suitable to use.%随着现代天气预报的发展,对预报员的能力提出了更高的要求.传统的天气学预报方法在订正数值预报结果方面的作用不容忽视.基于此,在课堂教学实践中讲解理论知识的同时,也要注意讲授理论知识的实际应用.文章重点讨论了高空形势预报方程在实际天气图分析中的应用及其适用.

  4. 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

  5. Energy conservation by means of weather forecast. Adjusting climate control by the weather; Energie besparen dankzij weersvoorspelling. Klimaatregeling afstemmen op het weer

    Energy Technology Data Exchange (ETDEWEB)

    Van der Wagt, L. (ed.)

    2003-09-01

    This article is based on the results of a feasibility study on energy conservation by means of improved control of building climatization on the basis of data from weather forecasts that can be found through Internet. Use has been made of a building simulation model to estimate the effects of indoor climate control. [Dutch] Niets veranderlijker dan het Nederlandse weer, wordt vaak gezegd. Dat is best vervelend als je wilt weten of je morgen met de fiets naar het werk kan. Of als je van plan bent een dagje naar het strand te gaan. Het levert echter wel een onuitputtelijke voorraad gespreksstof op: het weer is immers een van die onderwerpen waar iedereen iets over te vertellen heeft. Een recente studie van TNO voegt daar een interessant nieuw onderwerp aan toe. Deze studie laat zien dat weersvoorspellingen op internet kunnen worden gebruikt om de klirnaatregeling van gebouwen dusdanig te verbeteren dat 20-30% aan energie kan worden bespaard. De weerman als redder van het milieu.

  6. Incorporating Medium-Range Weather Forecasts in Seasonal Crop Scenarios over the Greater Horn of Africa to Support National/Regional/Local Decision Makers

    Science.gov (United States)

    Shukla, S.; Husak, G. J.; Funk, C. C.; Verdin, J. P.

    2015-12-01

    The USAID's Famine Early Warning Systems Network (FEWS NET) provides seasonal assessments of crop conditions over the Greater Horn of Africa (GHA) and other food insecure regions. These assessments and current livelihood, nutrition, market conditions and conflicts are used to generate food security scenarios that help national, regional and local decision makers target their resources and mitigate socio-economic losses. Among the various tools that FEWS NET uses is the FAO's Water Requirement Satisfaction Index (WRSI). The WRSI is a simple yet powerful crop assessment model that incorporates current moisture conditions (at the time of the issuance of forecast), precipitation scenarios, potential evapotranspiration and crop parameters to categorize crop conditions into different classes ranging from "failure" to "very good". The WRSI tool has been shown to have a good agreement with local crop yields in the GHA region. At present, the precipitation scenarios used to drive the WRSI are based on either a climatological forecast (that assigns equal chances of occurrence to all possible scenarios and has no skill over the forecast period) or a sea-surface temperature anomaly based scenario (which at best have skill at the seasonal scale). In both cases, the scenarios fail to capture the skill that can be attained by initial atmospheric conditions (i.e., medium-range weather forecasts). During the middle of a cropping season, when a week or two of poor rains can have a devastating effect, two weeks worth of skillful precipitation forecasts could improve the skill of the crop scenarios. With this working hypothesis, we examine the value of incorporating medium-range weather forecasts in improving the skill of crop scenarios in the GHA region. We use the NCEP's Global Ensemble Forecast system (GEFS) weather forecasts and examine the skill of crop scenarios generated using the GEFS weather forecasts with respect to the scenarios based solely on the climatological forecast

  7. Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

    Science.gov (United States)

    Bell, Jordan R.; Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.

    2012-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and

  8. Forecasting the Solar Drivers of Severe Space Weather from Active-Region Magnetograms

    Science.gov (United States)

    Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F.; Khazanov, Igor

    2012-01-01

    Solar drivers of severe space weather can be predicted from line-of-sight magnetograms, via a free-energy proxy measured from the neutral lines. This can be done in near real time. In addition to depending strongly on the free magnetic energy, an active region's chance of having a major eruption depends strongly on other aspects of the evolving magnetic field (e.g., its complexity and flux emergence).

  9. An Assessment of Interactive Graphics Processing in Short-Range Terminal Weather Forecasting.

    Science.gov (United States)

    1984-01-18

    hlei ght Ansi lsis for 1200 GMIT’, :3 F’ebrushy 1983 7 - A(,. Gene rstl Wesit her, Si? usition for-’\\ MV 1: (si se No. It (si) Sur-fsmre *Pressure...IT, 1; J irruzi ry 19 83 77 A8. Gene ruil Wesit her. Sirui t ion for. IMFL Case No. 18: (A d Sirfswe Pr-essure An; vs is for- 16iGO GM’T,. 2:3 1’ebrust...is a "menu-driven interaictive sYsteni designed to ingest individual forecasts into a data file through the use of fornratted interrogation

  10. Integrating interannual climate variability forecasts into weather-indexed crop insurance. The case of Malawi, Kenya and Tanzania

    Science.gov (United States)

    Vicarelli, M.; Giannini, A.; Osgood, D.

    2009-12-01

    In this study we explore the potential for re-insurance schemes built on regional climatic forecasts. We focus on micro-insurance contracts indexed on precipitation in 9 villages in Kenya, Tanzania (Eastern Africa) and Malawi (Southern Africa), and analyze the precipitation patterns and payouts resulting from El Niño Southern Oscillation (ENSO). The inability to manage future climate risk represents a “poverty trap” for several African regions. Weather shocks can potentially destabilize not only household, but also entire countries. Governments in drought-prone countries, donors and relief agencies are becoming aware of the importance to develop an ex-ante risk management framework for weather risk. Joint efforts to develop innovative mechanisms to spread and pool risk such as microinsurance and microcredit are currently being designed in several developing countries. While ENSO is an important component in modulating the rainfall regime in tropical Africa, the micro-insurance experiments currently under development to address drought risk among smallholder farmers in this region do not take into account ENSO monitoring or forecasting yet. ENSO forecasts could be integrated in the contracts and reinsurance schemes could be designed at the continental scale taking advantage of the different impact of ENSO on different regions. ENSO is associated to a bipolar precipitation pattern in Southern and Eastern Africa. La Niña years (i.e. Cold ENSO Episodes) are characterized by dry climate in Eastern Africa and wet climate in Southern Africa. During El Niño (or Warm Episode) the precipitation dipole is inverted, and Eastern Africa experiences increased probability for above normal rainfall (Halpert and Ropelewski, 1992, Journal of Climate). Our study represents the first exercise in trying to include ENSO forecasts in micro weather index insurance contract design. We analyzed the contracts payouts with respect to climate variability. In particular (i) we simulated

  11. Evaluation of Enhanced High Resolution MODIS/AMSR-E SSTs and the Impact on Regional Weather Forecast

    Science.gov (United States)

    Schiferl, Luke D.; Fuell, Kevin K.; Case, Jonathan L.; Jedlovec, Gary J.

    2010-01-01

    Over the last few years, the NASA Short-term Prediction Research and Transition (SPoRT) Center has been generating a 1-km sea surface temperature (SST) composite derived from retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) for use in operational diagnostics and regional model initialization. With the assumption that the day-to-day variation in the SST is nominal, individual MODIS passes aboard the Earth Observing System (EOS) Aqua and Terra satellites are used to create and update four composite SST products each day at 0400, 0700, 1600, and 1900 UTC, valid over the western Atlantic and Caribbean waters. A six month study from February to August 2007 over the marine areas surrounding southern Florida was conducted to compare the use of the MODIS SST composite versus the Real-Time Global SST analysis to initialize the Weather Research and Forecasting (WRF) model. Substantial changes in the forecast heat fluxes were seen at times in the marine boundary layer, but relatively little overall improvement was measured in the sensible weather elements. The limited improvement in the WRF model forecasts could be attributed to the diurnal changes in SST seen in the MODIS SST composites but not accounted for by the model. Furthermore, cloud contamination caused extended periods when individual passes of MODIS were unable to update the SSTs, leading to substantial SST latency and a cool bias during the early summer months. In order to alleviate the latency problems, the SPoRT Center recently enhanced its MODIS SST composite by incorporating information from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) instruments as well as the Operational Sea Surface Temperature and Sea Ice Analysis. These enhancements substantially decreased the latency due to cloud cover and improved the bias and correlation of the composites at available marine point observations. While these enhancements improved upon the modeled cold bias using the original MODIS SSTs

  12. Impacts of Amazonia biomass burning aerosols assessed from short-range weather forecasts

    Directory of Open Access Journals (Sweden)

    S. R. Kolusu

    2015-11-01

    Full Text Available The direct radiative impacts of biomass burning aerosols (BBA on meteorology are investigated using short-range forecasts from the Met Office Unified Model (MetUM over South America during the South American Biomass Burning Analysis (SAMBBA. The impacts are evaluated using a set of three simulations: (i no aerosols, (ii with monthly mean aerosol climatologies and (iii with prognostic aerosols modelled using the Coupled Large-scale Aerosol Simulator for Studies In Climate (CLASSIC scheme. Comparison with observations show that the prognostic CLASSIC scheme provides the best representation of BBA. The impacts of BBA are quantified over central and southern Amazonia from the first and second day of 2-day forecasts during 14 September–3 October 2012. On average, during the first day of the forecast, including prognostic BBA reduces the clear-sky net radiation at the surface by 15 ± 1 W m−2 and reduces net top-of-atmosphere (TOA radiation by 8 ± 1 W m−2, with a direct atmospheric warming of 7 ± 1 W m−2. BBA-induced reductions in all-sky radiation are smaller in magnitude: 9.0 ± 1 W m−2 at the surface and 4.0 ± 1 W m−2 at TOA. In this modelling study the BBA therefore exert an overall cooling influence on the Earth–atmosphere system, although some levels of the atmosphere are directly warmed by the absorption of solar radiation. Due to the reduction of net radiative flux at the surface, the mean 2 m air temperature is reduced by around 0.1 ± 0.02 °C. The BBA also cools the boundary layer (BL but warms air above by around 0.2 °C due to the absorption of shortwave radiation. The overall impact is to reduce the BL depth by around 19 ± 8 m. These differences in heating lead to a more anticyclonic circulation at 700 hPa, with winds changing by around 0.6 m s−1. Inclusion of climatological or prognostic BBA in the MetUM makes a small but significant improvement in forecasts of temperature and relative humidity, but improvements were

  13. Forecasting, Forecasting

    Science.gov (United States)

    Michael A. Fosberg

    1987-01-01

    Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.

  14. Data Assimilation of SMAP Observations and the Impact on Weather Forecasts and Heat Stress

    Science.gov (United States)

    Zavodsky, Bradley; Case, Jonathan; Blankenship, Clay; Crosson, William; White, Khristopher

    2014-01-01

    SPoRT produces real-time LIS soil moisture products for situational awareness and local numerical weather prediction over CONUS, Mesoamerica, and East Africa ?Currently interact/collaborate with operational partners on evaluation of soil moisture products ?Drought/fire ?Extreme heat ?Convective initiation ?Flood and water borne diseases ?Initial efforts to assimilate L2 soil moisture observations from SMOS (as a precursor for SMAP) have been successful ?Active/passive blended product from SMAP will be assimilated similarly and higher spatial resolution should improve on local-scale processes

  15. Further Discussion the Development on New Weather Forecasting Technology System%再论气象台天气预报技术新体系的发展

    Institute of Scientific and Technical Information of China (English)

    徐羹慧

    2012-01-01

    在前期对气象台天气预报技术发展的现状分析、发展趋势预测的基础上,从气象台天气预报技术的基本属性分析出发,进一步探讨气象台天气预报技术体系的基本框架和内容,并就推进技术发展的若干重要问题进行讨论。本文认为,如同“临床医学”一样,建筑在多种优秀成果基础上气象台天气预报技术的发展已自成体系,要象重视数值天气预报那样重视气象台预报技术体系的发展,发挥气象科技进步的综合效益,加快气象台一线预报服务水平的提高。%Based on the previous analysis upon the progress of weather forecasting system and the prediction of its developing tendency, the fundamental frame and content of weather forecasting technology system and several important issues regarding advancing technology were further discussed in this paper through analyzing the basic attribute of weather prediction skills. It was advocated that, just as "Clinical Medicine" ,the weather prediction technology founded upon various excellent researching outcomes has already had its own system. Just like numerical weather forecast ,the development of weather prediction technology system should be emphasized. Not only the benefit of the advanced meteorological science and technology should be exerted ,but also the improving process of weather prediction should be speeded up.

  16. kwmc Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. kont Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. kcrg Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. kjac Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. krdu Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  1. kiwd Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  2. krbl Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  3. kssf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  4. ksaw Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  5. kmot Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  6. kiso Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  7. kgck Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  8. kcvg Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  9. pafa Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  10. kcrq Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  11. ksun Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  12. kpia Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. krow Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. kbtv Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  15. kbke Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. kbpt Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. kact Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. kavl Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. kbur Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. krsw Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  1. klnd Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  2. kpuw Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  3. kbis Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  4. kcmx Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  5. kipt Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  6. kteb Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  7. kely Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  8. kfat Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  9. phny Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  10. kdsm Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  11. kbos Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  12. kpdx Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. tjsj Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. kpae Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  15. kpbi Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. kalb Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. paga Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. kjax Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. kdnl Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. kfwa Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  1. khts Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  2. kpbf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  3. kosh Terminal Aerodrome Forecast

    Data.gov (United States)

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