Global Ensemble Forecast System (GEFS) [1 Deg.

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

Global Forecast System (GFS) [1 Deg.

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and...

Statistical forecasting of met-ocean parameters in the Cochin estuarine system, southwest coast of India

Digital Repository Service at National Institute of Oceanography (India)

Srinivas, K.; Revichandran, C.; DineshKumar, P.K.

Three different statistical forecasting techniques - autoregressive, sinusoidal and exponentially weighted moving average (EWMA) were used to forecast monthly values of meteorological and oceanographic (met-ocean) parameters viz. sea surface...

Climate Prediction Center (CPC) NCEP-Global Forecast System (GFS) Precipitation Forecast Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Global Forecast System (GFS) forecast precipitation data at 37.5km resolution is created at the NOAA Climate Prediction Center for the purpose of near real-time...

Global Ensemble Forecast System (GEFS) [2.5 Deg.

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

Global Forecast System (GFS) [0.5 Deg.

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and...

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

Science.gov (United States)

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

2015-12-01

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

Climate Forecast System Version 2 (CFSv2) Operational Forecasts

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the...

Teaching ocean wave forecasting using computer-generated visualization and animation—Part 1: sea forecasting

Science.gov (United States)

Whitford, Dennis J.

2002-05-01

Ocean waves are the most recognized phenomena in oceanography. Unfortunately, undergraduate study of ocean wave dynamics and forecasting involves mathematics and physics and therefore can pose difficulties with some students because of the subject's interrelated dependence on time and space. Verbal descriptions and two-dimensional illustrations are often insufficient for student comprehension. Computer-generated visualization and animation offer a visually intuitive and pedagogically sound medium to present geoscience, yet there are very few oceanographic examples. A two-part article series is offered to explain ocean wave forecasting using computer-generated visualization and animation. This paper, Part 1, addresses forecasting of sea wave conditions and serves as the basis for the more difficult topic of swell wave forecasting addressed in Part 2. Computer-aided visualization and animation, accompanied by oral explanation, are a welcome pedagogical supplement to more traditional methods of instruction. In this article, several MATLAB ® software programs have been written to visualize and animate development and comparison of wave spectra, wave interference, and forecasting of sea conditions. These programs also set the stage for the more advanced and difficult animation topics in Part 2. The programs are user-friendly, interactive, easy to modify, and developed as instructional tools. By using these software programs, teachers can enhance their instruction of these topics with colorful visualizations and animation without requiring an extensive background in computer programming.

North American Mesoscale Forecast System (NAM) [12 km

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The North American Mesoscale Forecast System (NAM) is one of the major regional weather forecast models run by the National Centers for Environmental Prediction...

NOAA/NCEP Global Forecast System (GFS) Atmospheric Model

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — U.S. National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) numerical weather...

Ocean City, Maryland Tsunami Forecast Grids for MOST Model

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Ocean City, Maryland Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

Toward a Marine Ecological Forecasting System

Science.gov (United States)

2010-06-01

coral bleaching , living resource distribution, and pathogen progression). An operational ecological forecasting system depends upon the assimilation of...space scales (e.g., harmful algal blooms, dissolved oxygen concentration (hypoxia), water quality/beach closures, coral bleaching , living resource...advance. Two beaches in Lake Michigan have been selected for initial implementation. Forecasting Coral Bleaching in relation to Ocean Temperatures

Climate Forecast System Reforecast (CFSR), for 1981 to 2011

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The NCEP Climate Forecast System Reanalysis (CFSR) was designed and executed as a global, high resolution, coupled atmosphere-ocean-land surface-sea ice system to...

Multi-platform operational validation of the Western Mediterranean SOCIB forecasting system

Science.gov (United States)

Juza, Mélanie; Mourre, Baptiste; Renault, Lionel; Tintoré, Joaquin

2014-05-01

The development of science-based ocean forecasting systems at global, regional, and local scales can support a better management of the marine environment (maritime security, environmental and resources protection, maritime and commercial operations, tourism, ...). In this context, SOCIB (the Balearic Islands Coastal Observing and Forecasting System, www.socib.es) has developed an operational ocean forecasting system in the Western Mediterranean Sea (WMOP). WMOP uses a regional configuration of the Regional Ocean Modelling System (ROMS, Shchepetkin and McWilliams, 2005) nested in the larger scale Mediterranean Forecasting System (MFS) with a spatial resolution of 1.5-2km. WMOP aims at reproducing both the basin-scale ocean circulation and the mesoscale variability which is known to play a crucial role due to its strong interaction with the large scale circulation in this region. An operational validation system has been developed to systematically assess the model outputs at daily, monthly and seasonal time scales. Multi-platform observations are used for this validation, including satellite products (Sea Surface Temperature, Sea Level Anomaly), in situ measurements (from gliders, Argo floats, drifters and fixed moorings) and High-Frequency radar data. The validation procedures allow to monitor and certify the general realism of the daily production of the ocean forecasting system before its distribution to users. Additionally, different indicators (Sea Surface Temperature and Salinity, Eddy Kinetic Energy, Mixed Layer Depth, Heat Content, transports in key sections) are computed every day both at the basin-scale and in several sub-regions (Alboran Sea, Balearic Sea, Gulf of Lion). The daily forecasts, validation diagnostics and indicators from the operational model over the last months are available at www.socib.es.

Visual analysis of uncertainties in ocean forecasts for planning and operation of off-shore structures

KAUST Repository

Hollt, Thomas; Magdy, Ahmed; Chen, Guoning; Gopalakrishnan, Ganesh; Hoteit, Ibrahim; Hansen, Charles D.; Hadwiger, Markus

2013-01-01

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations used in ocean forecasting, i.e, simulations of sea surface elevation. Our system enables the interactive planning of both the placement and operation of off-shore structures. We illustrate this using a real-world simulation of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by strong loop currents. The oil and gas industry therefore relies on accurate ocean forecasting systems for planning their operations. Nowadays, these forecasts are based on multiple spatio-temporal simulations resulting in multidimensional, multivariate and multivalued data, so-called ensemble data. Changes in sea surface elevation are a good indicator for the movement of loop current eddies, and our visualization approach enables their interactive exploration and analysis. We enable analysis of the spatial domain, for planning the placement of structures, as well as detailed exploration of the temporal evolution at any chosen position, for the prediction of critical ocean states that require the shutdown of rig operations. © 2013 IEEE.

Visual analysis of uncertainties in ocean forecasts for planning and operation of off-shore structures

KAUST Repository

Hollt, Thomas

2013-02-01

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations used in ocean forecasting, i.e, simulations of sea surface elevation. Our system enables the interactive planning of both the placement and operation of off-shore structures. We illustrate this using a real-world simulation of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by strong loop currents. The oil and gas industry therefore relies on accurate ocean forecasting systems for planning their operations. Nowadays, these forecasts are based on multiple spatio-temporal simulations resulting in multidimensional, multivariate and multivalued data, so-called ensemble data. Changes in sea surface elevation are a good indicator for the movement of loop current eddies, and our visualization approach enables their interactive exploration and analysis. We enable analysis of the spatial domain, for planning the placement of structures, as well as detailed exploration of the temporal evolution at any chosen position, for the prediction of critical ocean states that require the shutdown of rig operations. © 2013 IEEE.

Assessment of Global Forecast Ocean Assimilation Model (FOAM) using new satellite SST data

Science.gov (United States)

Ascione Kenov, Isabella; Sykes, Peter; Fiedler, Emma; McConnell, Niall; Ryan, Andrew; Maksymczuk, Jan

2016-04-01

There is an increased demand for accurate ocean weather information for applications in the field of marine safety and navigation, water quality, offshore commercial operations, monitoring of oil spills and pollutants, among others. The Met Office, UK, provides ocean forecasts to customers from governmental, commercial and ecological sectors using the Global Forecast Ocean Assimilation Model (FOAM), an operational modelling system which covers the global ocean and runs daily, using the NEMO (Nucleus for European Modelling of the Ocean) ocean model with horizontal resolution of 1/4° and 75 vertical levels. The system assimilates salinity and temperature profiles, sea surface temperature (SST), sea surface height (SSH), and sea ice concentration observations on a daily basis. In this study, the FOAM system is updated to assimilate Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) SST data. Model results from one month trials are assessed against observations using verification tools which provide a quantitative description of model performance and error, based on statistical metrics, including mean error, root mean square error (RMSE), correlation coefficient, and Taylor diagrams. A series of hindcast experiments is used to run the FOAM system with AMSR2 and SEVIRI SST data, using a control run for comparison. Results show that all trials perform well on the global ocean and that largest SST mean errors were found in the Southern hemisphere. The geographic distribution of the model error for SST and temperature profiles are discussed using statistical metrics evaluated over sub-regions of the global ocean.

Regional Ocean Modeling System (ROMS): CNMI

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Regional Ocean Modeling System (ROMS) 7-day, 3-hourly forecast for the region surrounding the Commonwealth of the Northern Mariana Islands (CNMI) at approximately...

A data-assimilative ocean forecasting system for the Prince William sound and an evaluation of its performance during sound Predictions 2009

Science.gov (United States)

Farrara, John D.; Chao, Yi; Li, Zhijin; Wang, Xiaochun; Jin, Xin; Zhang, Hongchun; Li, Peggy; Vu, Quoc; Olsson, Peter Q.; Schoch, G. Carl; Halverson, Mark; Moline, Mark A.; Ohlmann, Carter; Johnson, Mark; McWilliams, James C.; Colas, Francois A.

2013-07-01

The development and implementation of a three-dimensional ocean modeling system for the Prince William Sound (PWS) is described. The system consists of a regional ocean model component (ROMS) forced by output from a regional atmospheric model component (the Weather Research and Forecasting Model, WRF). The ROMS ocean model component has a horizontal resolution of 1km within PWS and utilizes a recently-developed multi-scale 3DVAR data assimilation methodology along with freshwater runoff from land obtained via real-time execution of a digital elevation model. During the Sound Predictions Field Experiment (July 19-August 3, 2009) the system was run in real-time to support operations and incorporated all available real-time streams of data. Nowcasts were produced every 6h and a 48-h forecast was performed once a day. In addition, a sixteen-member ensemble of forecasts was executed on most days. All results were published at a web portal (http://ourocean.jpl.nasa.gov/PWS) in real time to support decision making.The performance of the system during Sound Predictions 2009 is evaluated. The ROMS results are first compared with the assimilated data as a consistency check. RMS differences of about 0.7°C were found between the ROMS temperatures and the observed vertical profiles of temperature that are assimilated. The ROMS salinities show greater discrepancies, tending to be too salty near the surface. The overall circulation patterns observed throughout the Sound are qualitatively reproduced, including the following evolution in time. During the first week of the experiment, the weather was quite stormy with strong southeasterly winds. This resulted in strong north to northwestward surface flow in much of the central PWS. Both the observed drifter trajectories and the ROMS nowcasts showed strong surface inflow into the Sound through the Hinchinbrook Entrance and strong generally northward to northwestward flow in the central Sound that was exiting through the Knight

Oceanic sources of predictability for MJO propagation across the Maritime Continent in a subset of S2S forecast models

Science.gov (United States)

DeMott, C. A.; Klingaman, N. P.

2017-12-01

Skillful prediction of the Madden-Julian oscillation (MJO) passage across the Maritime Continent (MC) has important implications for global forecasts of high-impact weather events, such as atmospheric rivers and heat waves. The North American teleconnection response to the MJO is strongest when MJO convection is located in the western Pacific Ocean, but many climate and forecast models are deficient in their simulation of MC-crossing MJO events. Compared to atmosphere-only general circulation models (AGCMs), MJO simulation skill generally improves with the addition of ocean feedbacks in coupled GCMs (CGCMs). Using observations, previous studies have noted that the degree of ocean coupling may vary considerably from one MJO event to the next. The coupling mechanisms may be linked to the presence of ocean Equatorial Rossby waves, the sign and amplitude of Equatorial surface currents, and the upper ocean temperature and salinity profiles. In this study, we assess the role of ocean feedbacks to MJO prediction skill using a subset of CGCMs participating in the Subseasonal-to-Seasonal (S2S) Project database. Oceanic observational and reanalysis datasets are used to characterize the upper ocean background state for observed MJO events that do and do not propagate beyond the MC. The ability of forecast models to capture the oceanic influence on the MJO is first assessed by quantifying SST forecast skill. Next, a set of previously developed air-sea interaction diagnostics is applied to model output to measure the role of SST perturbations on the forecast MJO. The "SST effect" in forecast MJO events is compared to that obtained from reanalysis data. Leveraging all ensemble members of a given forecast helps disentangle oceanic model biases from atmospheric model biases, both of which can influence the expression of ocean feedbacks in coupled forecast systems. Results of this study will help identify areas of needed model improvement for improved MJO forecasts.

Regional Ocean Modeling System (ROMS): Samoa

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Regional Ocean Modeling System (ROMS) 7-day, 3-hourly forecast for the region surrounding the islands of Samoa at approximately 3-km resolution. While considerable...

Regional Ocean Modeling System (ROMS): Guam

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Regional Ocean Modeling System (ROMS) 6-day, 3-hourly forecast for the region surrounding Guam at approximately 2-km resolution. While considerable effort has been...

Regional Ocean Modeling System (ROMS): Oahu

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Regional Ocean Modeling System (ROMS) 7-day, 3-hourly forecast for the region surrounding the island of Oahu at approximately 1-km resolution. While considerable...

Climate Prediction Center (CPC) NCEP-Global Forecast System (GFS) 0-10cm Soil-Moisture Forecast Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Global Forecast System (GFS) forecast 0-10cm soil-moisture data at 37.5km resolution is created at the NOAA Climate Prediction Center for the purpose of near...

Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

Science.gov (United States)

Onken, Reiner

2017-11-01

A relocatable ocean prediction system (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (western Mediterranean) in the framework of the REP14-MED experiment. The observational data, comprising more than 6000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS), which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy(1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 h, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available personal computer or a laptop.

Ocean wave forecasting using recurrent neural networks

Digital Repository Service at National Institute of Oceanography (India)

Mandal, S.; Prabaharan, N.

, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper describes an artificial neural network, namely recurrent neural network with rprop update algorithm and is applied for wave forecasting. Measured ocean waves off...

Impact of data assimilation on ocean current forecasts in the Angola Basin

Science.gov (United States)

Phillipson, Luke; Toumi, Ralf

2017-06-01

The ocean current predictability in the data limited Angola Basin was investigated using the Regional Ocean Modelling System (ROMS) with four-dimensional variational data assimilation. Six experiments were undertaken comprising a baseline case of the assimilation of salinity/temperature profiles and satellite sea surface temperature, with the subsequent addition of altimetry, OSCAR (satellite-derived sea surface currents), drifters, altimetry and drifters combined, and OSCAR and drifters combined. The addition of drifters significantly improves Lagrangian predictability in comparison to the baseline case as well as the addition of either altimetry or OSCAR. OSCAR assimilation only improves Lagrangian predictability as much as altimetry assimilation. On average the assimilation of either altimetry or OSCAR with drifter velocities does not significantly improve Lagrangian predictability compared to the drifter assimilation alone, even degrading predictability in some cases. When the forecast current speed is large, it is more likely that the combination improves trajectory forecasts. Conversely, when the currents are weaker, it is more likely that the combination degrades the trajectory forecast.

The Coastal Ocean Prediction Systems program: Understanding and managing our coastal ocean

International Nuclear Information System (INIS)

1990-01-01

This document is a compilation of summaries of papers presented at the Coastal Ocean Prediction Systems workshop. Topics include; marine forecasting, regulatory agencies and regulations, research and application models, research and operational observing, oceanic and atmospheric data assimilation, and coastal physical processes

Ocean Predictability and Uncertainty Forecasts Using Local Ensemble Transfer Kalman Filter (LETKF)

Science.gov (United States)

Wei, M.; Hogan, P. J.; Rowley, C. D.; Smedstad, O. M.; Wallcraft, A. J.; Penny, S. G.

2017-12-01

Ocean predictability and uncertainty are studied with an ensemble system that has been developed based on the US Navy's operational HYCOM using the Local Ensemble Transfer Kalman Filter (LETKF) technology. One of the advantages of this method is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates operational observations using ensemble method. The background covariance during this assimilation process is implicitly supplied with the ensemble avoiding the difficult task of developing tangent linear and adjoint models out of HYCOM with the complicated hybrid isopycnal vertical coordinate for 4D-VAR. The flow-dependent background covariance from the ensemble will be an indispensable part in the next generation hybrid 4D-Var/ensemble data assimilation system. The predictability and uncertainty for the ocean forecasts are studied initially for the Gulf of Mexico. The results are compared with another ensemble system using Ensemble Transfer (ET) method which has been used in the Navy's operational center. The advantages and disadvantages are discussed.

Surface wave effect on the upper ocean in marine forecast

Science.gov (United States)

Wang, Guansuo; Qiao, Fangli; Xia, Changshui; Zhao, Chang

2015-04-01

An Operational Coupled Forecast System for the seas off China and adjacent (OCFS-C) is constructed based on the paralleled wave-circulation coupled model, which is tested with comprehensive experiments and operational since November 1st, 2007. The main feature of the system is that the wave-induced mixing is considered in circulation model. Daily analyses and three day forecasts of three-dimensional temperature, salinity, currents and wave height are produced. Coverage is global at 1/2 degreed resolution with nested models up to 1/24 degree resolution in China Sea. Daily remote sensing sea surface temperatures (SST) are taken to relax to an analytical product as hot restarting fields for OCFS-C by the Nudging techniques. Forecasting-data inter-comparisons are performed to measure the effectiveness of OCFS-C in predicting upper-ocean quantities including SST, mixed layer depth (MLD) and subsurface temperature. The variety of performance with lead time and real-time is discussed as well using the daily statistic results for SST between forecast and satellite data. Several buoy observations and many Argo profiles are used for this validation. Except the conventional statistical metrics, non-dimension skill scores (SS) is taken to estimate forecast skill. Model SST comparisons with more one year-long SST time series from 2 buoys given a large SS value (more than 0.90). And skill in predicting the seasonal variability of SST is confirmed. Model subsurface temperature comparisons with that from a lot of Argo profiles indicated that OCFS-C has low skill in predicting subsurface temperatures between 80m and 120m. Inter-comparisons of MLD reveal that MLD from model is shallower than that from Argo profiles by about 12m. QCFS-C is successful and steady in predicting MLD. The daily statistic results for SST between 1-d, 2-d and 3-d forecast and data is adopted to describe variability of Skill in predicting SST with lead time or real time. In a word QCFS-C shows reasonable

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

Regional Ocean Modeling System (ROMS): Main Hawaiian Islands

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Regional Ocean Modeling System (ROMS) 7-day, 3-hourly forecast for the region surrounding the main Hawaiian islands at approximately 4-km resolution. While...

Forecasting the Ocean’s Optical Environment: Development of the BioCast System

Science.gov (United States)

2014-09-01

Gulf of Mexico (data not shown). Additional advancements in ocean optical forecasting will also result from the integration of BioCast with other...uniformed sailors and marines would have been rather conspicuous against the turbid gray waves marking the entryway to the riotous North Sea. Two...Detection of underwater mines with airborne, towed, or autonomous under- water platforms can be severely impeded by sustained water column turbidity

Evolution of Indian Ocean biases in the summer monsoon season hindcasts from the Met Office Global Seasonal Forecasting System GloSea5

Science.gov (United States)

Chevuturi, A.; Turner, A. G.; Woolnough, S. J.

2016-12-01

In this study we investigate the development of biases in the Indian Ocean region in summer hindcasts of the UK Met Office coupled initialised global seasonal forecasting system, GloSea5-GC2. Previous work has demonstrated the rapid evolution of strong monsoon circulation biases over India from seasonal forecasts initialised in early May, together with coupled strong easterly wind biases on the equator. We analyse a set of three springtime start dates for the 20-year hindcast period (1992-2011) and fifteen total ensemble members for each year. We use comparisons with a variety of observations to test the rate of evolving mean-state biases in the Arabian Sea, over India, and over the equatorial Indian Ocean. Biases are all shown to develop rapidly, particularly for the circulation bias over India that is connected to convection. These circulation biases later reach the surface and lead to responses in Arabian Sea SST in accordance with coastal and Ekman upwelling processes. We also assess the evolution of radiation and turbulent heat fluxes at the surface. Meanwhile at the equator, easterly biases in surface winds are shown to develop rapidly, consistent with an SST pattern that is consistent with positive-Indian Ocean dipole mean state conditions (warm western equatorial Indian Ocean, cold east). This bias develops consistent with coupled ocean-atmosphere exchanges and Bjerknes feedback. We hypothesize that lower tropospheric easterly wind biases developing in the equatorial region originate from the surface, and also that signals of the cold bias in the eastern equatorial Indian Ocean propagate to the Bay of Bengal via coastal Kelvin waves. Earlier work has shown the utility of wind-stress corrections in the Indian Ocean for correcting the easterly winds bias there and ultimately improving the evolution of the Indian Ocean Dipole. We identify and test this wind-stress correction technique in case study years from the hindcast period to see their impact on seasonal

Real-Time Ocean Prediction System for the East Coast of India

Science.gov (United States)

Warrior, H. V.

2016-02-01

The primary objective of the research work reported in this abstract was to develop a Realtime Environmental model for Ocean Dispersion and Impact (as part of an already in-place Decision Support System) for the purpose of radiological safety for the area along Kalpakkam (East Indian) coast. This system involves combining real-time ocean observations with numerical models of ocean processes to provide hindcasts, nowcasts and forecasts of currents, tides and waves. In this work we present the development of an Automated Coupled Atmospheric - Ocean Model (we call it IIT-CAOM) used to forecast the sea surface currents, sea surface temperature (SST) and salinity etc of the Bay of Bengal region under the influence of transient and unsteady atmospheric conditions. This method uses a coupling of Atmosphere and Ocean model. The models used here are the WRF for atmospheric simulations and POM for the ocean counterpart. It has a 3 km X 3 km resolution. This Coupled Model uses GFS (Global Forecast System) Data or FNL (Final Analyses) Data as initial conditions for jump-starting the atmospheric model. The Atmospheric model is run first thus extracting air temperature, wind speed and relative humidity. The heat flux subroutine computes the net heat flux, using above mentioned parameters data. The net heat flux feeds to the ocean model by simply adding net heat flux subroutine to the ocean model code without changing the model original structure. The online forecast of the IIT-CAOM is currently available in the web. The whole system has been automized and runs without any more manual support. The IIT-CAOM simulations have been carried out for Kalpakkam region, which is located on the East coast of India, about 70 km south of Chennai in Tamilnadu State and a three day forecast of sea surface currents, sea surface temperature (SST) and salinity, etc have been obtained.

Using the Regional Ocean Modelling System (ROMS to improve the sea surface temperature predictions of the MERCATOR Ocean System

Directory of Open Access Journals (Sweden)

Pedro Costa

2012-09-01

Full Text Available Global models are generally capable of reproducing the observed trends in the globally averaged sea surface temperature (SST. However, the global models do not perform as well on regional scales. Here, we present an ocean forecast system based on the Regional Ocean Modelling System (ROMS, the boundary conditions come from the MERCATOR ocean system for the North Atlantic (1/6° horizontal resolution. The system covers the region of the northwestern Iberian Peninsula with a horizontal resolution of 1/36°, forced with the Weather Research and Forecasting Model (WRF and the Soil Water Assessment Tool (SWAT. The ocean model results from the regional ocean model are validated using real-time SST and observations from the MeteoGalicia, INTECMAR and Puertos Del Estado real-time observational networks. The validation results reveal that over a one-year period the mean absolute error of the SST is less than 1°C, and several sources of measured data reveal that the errors decrease near the coast. This improvement is related to the inclusion of local forcing not present in the boundary condition model.

Regional Ocean Modeling System (ROMS): Oahu South Shore

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Regional Ocean Modeling System (ROMS) 2-day, 3-hourly forecast for the region surrounding the south shore of the island of Oahu at approximately 200-m resolution....

MyOcean Information System : achievements and perspectives

Science.gov (United States)

Loubrieu, T.; Dorandeu, J.; Claverie, V.; Cordier, K.; Barzic, Y.; Lauret, O.; Jolibois, T.; Blower, J.

2012-04-01

MyOcean system (http://www.myocean.eu) objective is to provide a Core Service for the Ocean. This means MyOcean is setting up an operational service for forecasts, analysis and expertise on ocean currents, temperature, salinity, sea level, primary ecosystems and ice coverage. The production of observation and forecasting data is distributed through 12 production centres. The interface with the external users (including web portal) and the coordination of the overall service is managed by a component called service desk. Besides, a transverse component called MIS (myOcean Information System) aims at connecting the production centres and service desk together, manage the shared information for the overall system and implement a standard Inspire interface for the external world. 2012 is a key year for the system. The MyOcean, 3-year project, which has set up the first versions of the system is ending. The MyOcean II, 2-year project, which will upgrade and consolidate the system is starting. Both projects are granted by the European commission within the GMES Program (7th Framework Program). At the end of the MyOcean project, the system has been designed and the 2 first versions have been implemented. The system now offers an integrated service composed with 237 ocean products. The ocean products are homogeneously described in a catalogue. They can be visualized and downloaded by the user (identified with a unique login) through a seamless web interface. The discovery and viewing interfaces are INSPIRE compliant. The data production, subsystems availability and audience are continuously monitored. The presentation will detail the implemented information system architecture and the chosen software solutions. Regarding the information system, MyOcean II is mainly aiming at consolidating the existing functions and promoting the operations cost-effectiveness. In addition, a specific effort will be done so that the less common data features of the system (ocean in

Assimilation scheme of the Mediterranean Forecasting System: operational implementation

Directory of Open Access Journals (Sweden)

E. Demirov

Full Text Available This paper describes the operational implementation of the data assimilation scheme for the Mediterranean Forecasting System Pilot Project (MFSPP. The assimilation scheme, System for Ocean Forecast and Analysis (SOFA, is a reduced order Optimal Interpolation (OI scheme. The order reduction is achieved by projection of the state vector into vertical Empirical Orthogonal Functions (EOF. The data assimilated are Sea Level Anomaly (SLA and temperature profiles from Expandable Bathy Termographs (XBT. The data collection, quality control, assimilation and forecast procedures are all done in Near Real Time (NRT. The OI is used intermittently with an assimilation cycle of one week so that an analysis is produced once a week. The forecast is then done for ten days following the analysis day. The root mean square (RMS between the model forecast and the analysis (the forecast RMS is below 0.7°C in the surface layers and below 0.2°C in the layers deeper than 200 m for all the ten forecast days. The RMS between forecast and initial condition (persistence RMS is higher than forecast RMS after the first day. This means that the model improves forecast with respect to persistence. The calculation of the misfit between the forecast and the satellite data suggests that the model solution represents well the main space and time variability of the SLA except for a relatively short period of three – four weeks during the summer when the data show a fast transition between the cyclonic winter and anti-cyclonic summer regimes. This occurs in the surface layers that are not corrected by our assimilation scheme hypothesis. On the basis of the forecast skill scores analysis, conclusions are drawn about future improvements.

Key words. Oceanography; general (marginal and semi-enclosed seas; numerical modeling; ocean prediction

Assimilation scheme of the Mediterranean Forecasting System: operational implementation

Directory of Open Access Journals (Sweden)

E. Demirov

2003-01-01

Full Text Available This paper describes the operational implementation of the data assimilation scheme for the Mediterranean Forecasting System Pilot Project (MFSPP. The assimilation scheme, System for Ocean Forecast and Analysis (SOFA, is a reduced order Optimal Interpolation (OI scheme. The order reduction is achieved by projection of the state vector into vertical Empirical Orthogonal Functions (EOF. The data assimilated are Sea Level Anomaly (SLA and temperature profiles from Expandable Bathy Termographs (XBT. The data collection, quality control, assimilation and forecast procedures are all done in Near Real Time (NRT. The OI is used intermittently with an assimilation cycle of one week so that an analysis is produced once a week. The forecast is then done for ten days following the analysis day. The root mean square (RMS between the model forecast and the analysis (the forecast RMS is below 0.7°C in the surface layers and below 0.2°C in the layers deeper than 200 m for all the ten forecast days. The RMS between forecast and initial condition (persistence RMS is higher than forecast RMS after the first day. This means that the model improves forecast with respect to persistence. The calculation of the misfit between the forecast and the satellite data suggests that the model solution represents well the main space and time variability of the SLA except for a relatively short period of three – four weeks during the summer when the data show a fast transition between the cyclonic winter and anti-cyclonic summer regimes. This occurs in the surface layers that are not corrected by our assimilation scheme hypothesis. On the basis of the forecast skill scores analysis, conclusions are drawn about future improvements. Key words. Oceanography; general (marginal and semi-enclosed seas; numerical modeling; ocean prediction

COAWST Forecast System : USGS : US East Coast and Gulf of Mexico (Experimental)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Experimental forecast model product from the USGS Coupled Ocean Atmosphere Wave Sediment-Transport (COAWST) modeling system. Data required to drive the modeling...

System of the Wind Wave Operational Forecast by the Black Sea Marine Forecast Center

Directory of Open Access Journals (Sweden)

Yu.B. Ratner

2017-10-01

Full Text Available System of the wind wave operational forecast in the Black Sea based on the SWAN (Simulating Waves Nearshore numerical spectral model is represented. In the course of the system development the SWAN model was adapted to take into account the features of its operation at the Black Sea Marine Forecast Center. The model input-output is agreed with the applied nomenclature and the data representation formats. The user interface for rapid access to simulation results was developed. The model adapted to wave forecast in the Black Sea in a quasi-operational mode, is validated for 2012–2015. Validation of the calculation results was carried out for all five forecasting terms based on the analysis of two-dimensional graphs of the wave height distribution derived from the data of prognostic calculations and remote measurements obtained with the altimeter installed on the Jason-2 satellite. Calculation of the statistical characteristics of the deviations between the wave height prognostic values and the data of their measurements from the Jason-2 satellite, as well as a regression analysis of the relationship between the forecasted and measured wave heights was additionally carried out. A comparison of the results obtained with the similar results reported in the works of other authors published in 2009–2016 showed their satisfactory compliance with each other. The forecasts carried out by the authors for the Black Sea as well as those obtained for the other World Ocean regions show that the current level of numerical methods for sea wave forecasting is in full compliance with the requirements of specialists engaged in studying and modeling the state of the ocean and the atmosphere, as well as the experts using these results for solving applied problems.

LiveOcean: A Daily Forecast Model of Ocean Acidification for Shellfish Growers

Science.gov (United States)

MacCready, P.; Siedlecki, S. A.; McCabe, R. M.

2016-12-01

The coastal estuaries of the NE Pacific host a highly productive shellfish industry, but in the past decade they have suffered from many years in which no natural set of oysters occurred. It appears that coastal waters with low Aragonite saturation state may be the cause. This "acidified" water is the result of (i) upwelling of NE Pacific water from near the shelf break that is already low in pH, and (ii) further acidification of that water by productivity and remineralization on the shelf, and (iii) increasing atmospheric CO2. As part of a coordinated research response to this issue, we have developed the LiveOcean modeling system, which creates daily three-day forecasts of circulation and biogeochemical properties in Oregon-Washington-British Columbia coastal and estuarine waters. The system includes realistic tides, atmospheric forcing (from a regional WRF model), ocean boundary conditions (from HYCOM), and rivers (from USGS and Environment Canada). The model is also used for Harmful Algal Bloom prediction. There has been extensive validation of hindcast runs for currents and hydrography, and more limited validation of biogeochemical variables. Model results are pushed daily to the cloud, and made available to the public through the NANOOS Visualization System (NVS). NVS also includes automated model-data comparisons with real-time NDBC and OOI moorings. Future work will focus on optimizing the utility of this system for regional shellfish growers.

Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles.

Science.gov (United States)

Höllt, Thomas; Magdy, Ahmed; Zhan, Peng; Chen, Guoning; Gopalakrishnan, Ganesh; Hoteit, Ibrahim; Hansen, Charles D; Hadwiger, Markus

2014-08-01

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea.

Forecasting of rainfall using ocean-atmospheric indices with a fuzzy neural technique

Science.gov (United States)

Srivastava, Gaurav; Panda, Sudhindra N.; Mondal, Pratap; Liu, Junguo

2010-12-01

SummaryForecasting of rainfall is imperative for rainfed agriculture of arid and semi-arid regions of the world where agriculture consumes nearly 80% of the total water demand. Fuzzy-Ranking Algorithm (FRA) is used to identify the significant input variables for rainfall forecast. A case study is carried out to forecast monthly rainfall in India with several ocean-atmospheric predictor variables. Three different scenarios of ocean-atmospheric predictor variables are used as a set of possible input variables for rainfall forecasting model: (1) two climate indices, i.e. Southern Oscillation Index (SOI) and Pacific Decadal Oscillation Index (PDOI); (2) Sea Surface Temperature anomalies (SSTa) in the 5° × 5° grid points in Indian Ocean; and (3) both the climate indices and SSTa. To generate a set of possible input variables for these scenarios, we use climatic indices and the SSTa data with different lags between 1 and 12 months. Nonlinear relationship between identified inputs and rainfall is captured with an Artificial Neural Network (ANN) technique. A new approach based on fuzzy c-mean clustering is proposed for dividing data into representative subsets for training, testing, and validation. The results show that this proposed approach overcomes the difficulty in determining optimal numbers of clusters associated with the data division technique of self-organized map. The ANN model developed with both the climate indices and SSTa shows the best performance for the forecast of the monthly August rainfall in India. Similar approach can be applied to forecast rainfall of any period at selected climatic regions of the world where significant relationship exists between the rainfall and climate indices.

Climate Forecast System Version 2 (CFSv2) Operational Analysis

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the...

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....

AIRS Impact on the Analysis and Forecast Track of Tropical Cyclone Nargis in a Global Data Assimilation and Forecasting System

Science.gov (United States)

Reale, O.; Lau, W.K.; Susskind, J.; Brin, E.; Liu, E.; Riishojgaard, L. P.; Rosenburg, R.; Fuentes, M.

2009-01-01

Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather forecasting systems, partly due to their shorter lifespan and more erratic track, compared to those in the Atlantic and the Pacific. Moreover, the automated analyses of cyclones over the northern Indian Ocean, produced by operational global data assimilation systems (DASs), are generally of inferior quality than in other basins. In this work it is shown that the assimilation of Atmospheric Infrared Sounder (AIRS) temperature retrievals under partial cloudy conditions can significantly impact the representation of the cyclone Nargis (which caused devastating loss of life in Myanmar in May 2008) in a global DAS. Forecasts produced from these improved analyses by a global model produce substantially smaller track errors. The impact of the assimilation of clear-sky radiances on the same DAS and forecasting system is positive, but smaller than the one obtained by ingestion of AIRS retrievals, possibly due to poorer coverage.

Climate Forecast System Reanalysis (CFSR), for 1979 to 2011

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The NCEP Climate Forecast System Reanalysis (CFSR) was initially completed for the 31-year period from 1979 to 2009, in January 2010. The CFSR was designed and...

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.

Ovis: A framework for visual analysis of ocean forecast ensembles

KAUST Repository

Hollt, Thomas; Magdy, Ahmed; Zhan, Peng; Chen, Guoning; Gopalakrishnan, Ganesh; Hoteit, Ibrahim; Hansen, Charles D.; Hadwiger, Markus

2014-01-01

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea. © 1995-2012 IEEE.

Ovis: A framework for visual analysis of ocean forecast ensembles

KAUST Repository

Hollt, Thomas

2014-08-01

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea. © 1995-2012 IEEE.

Wave energy potential: A forecasting system for the Mediterranean basin

International Nuclear Information System (INIS)

Carillo, Adriana; Sannino, Gianmaria; Lombardi, Emanuele

2015-01-01

ENEA is performing ocean wave modeling activities with the aim of both characterizing the Italian sea energy resource and providing the information necessary for the experimental at sea and operational phases of energy converters. Therefore a forecast system of sea waves and of the associated energy available has been developed and has been operatively running since June 2013. The forecasts are performed over the entire Mediterranean basin and, at a higher resolution, over ten sub-basins around the Italian coasts. The forecast system is here described along with the validation of the wave heights, performed by comparing them with the measurements from satellite sensors. [it

The Coastal Ocean Prediction Systems program: Understanding and managing our coastal ocean. Volume 2: Overview and invited papers

Energy Technology Data Exchange (ETDEWEB)

1990-05-15

This document is a compilation of summaries of papers presented at the Coastal Ocean Prediction Systems workshop. Topics include; marine forecasting, regulatory agencies and regulations, research and application models, research and operational observing, oceanic and atmospheric data assimilation, and coastal physical processes.

Development of Real-time Tsunami Inundation Forecast Using Ocean Bottom Tsunami Networks along the Japan Trench

Science.gov (United States)

Aoi, S.; Yamamoto, N.; Suzuki, W.; Hirata, K.; Nakamura, H.; Kunugi, T.; Kubo, T.; Maeda, T.

2015-12-01

In the 2011 Tohoku earthquake, in which huge tsunami claimed a great deal of lives, the initial tsunami forecast based on hypocenter information estimated using seismic data on land were greatly underestimated. From this lesson, NIED is now constructing S-net (Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench) which consists of 150 ocean bottom observatories with seismometers and pressure gauges (tsunamimeters) linked by fiber optic cables. To take full advantage of S-net, we develop a new methodology of real-time tsunami inundation forecast using ocean bottom observation data and construct a prototype system that implements the developed forecasting method for the Pacific coast of Chiba prefecture (Sotobo area). We employ a database-based approach because inundation is a strongly non-linear phenomenon and its calculation costs are rather heavy. We prepare tsunami scenario bank in advance, by constructing the possible tsunami sources, and calculating the tsunami waveforms at S-net stations, coastal tsunami heights and tsunami inundation on land. To calculate the inundation for target Sotobo area, we construct the 10-m-mesh precise elevation model with coastal structures. Based on the sensitivities analyses, we construct the tsunami scenario bank that efficiently covers possible tsunami scenarios affecting the Sotobo area. A real-time forecast is carried out by selecting several possible scenarios which can well explain real-time tsunami data observed at S-net from tsunami scenario bank. An advantage of our method is that tsunami inundations are estimated directly from the actual tsunami data without any source information, which may have large estimation errors. In addition to the forecast system, we develop Web services, APIs, and smartphone applications and brush them up through social experiments to provide the real-time tsunami observation and forecast information in easy way to understand toward urging people to evacuate.

The Met Office Coupled Atmosphere/Land/Ocean/Sea-Ice Data Assimilation System

Science.gov (United States)

Lea, Daniel; Mirouze, Isabelle; King, Robert; Martin, Matthew; Hines, Adrian

2015-04-01

The Met Office has developed a weakly-coupled data assimilation (DA) system using the global coupled model HadGEM3 (Hadley Centre Global Environment Model, version 3). At present the analysis from separate ocean and atmosphere DA systems are combined to produced coupled forecasts. The aim of coupled DA is to produce a more consistent analysis for coupled forecasts which may lead to less initialisation shock and improved forecast performance. The HadGEM3 coupled model combines the atmospheric model UM (Unified Model) at 60 km horizontal resolution on 85 vertical levels, the ocean model NEMO (Nucleus for European Modelling of the Ocean) at 25 km (at the equator) horizontal resolution on 75 vertical levels, and the sea-ice model CICE at the same resolution as NEMO. The atmosphere and the ocean/sea-ice fields are coupled every 1-hour using the OASIS coupler. The coupled model is corrected using two separate 6-hour window data assimilation systems: a 4D-Var for the atmosphere with associated soil moisture content nudging and snow analysis schemes on the one hand, and a 3D-Var FGAT for the ocean and sea-ice on the other hand. The background information in the DA systems comes from a previous 6-hour forecast of the coupled model. To isolate the impact of the coupled DA, 13-month experiments have been carried out, including 1) a full atmosphere/land/ocean/sea-ice coupled DA run, 2) an atmosphere-only run forced by OSTIA SSTs and sea-ice with atmosphere and land DA, and 3) an ocean-only run forced by atmospheric fields from run 2 with ocean and sea-ice DA. In addition, 5-day and 10-day forecast runs, have been produced from initial conditions generated by either run 1 or a combination of runs 2 and 3. The different results have been compared to each other and, whenever possible, to other references such as the Met Office atmosphere and ocean operational analyses or the OSTIA SST data. The performance of the coupled DA is similar to the existing separate ocean and atmosphere

ECMWF seasonal forecast system 3 and its prediction of sea surface temperature

Energy Technology Data Exchange (ETDEWEB)

Stockdale, Timothy N.; Anderson, David L.T.; Balmaseda, Magdalena A.; Ferranti, Laura; Mogensen, Kristian; Palmer, Timothy N.; Molteni, Franco; Vitart, Frederic [ECMWF, Reading (United Kingdom); Doblas-Reyes, Francisco [ECMWF, Reading (United Kingdom); Institut Catala de Ciencies del Clima (IC3), Barcelona (Spain)

2011-08-15

The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1 year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3-6 months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean. (orig.)

Using Combined Marine Spatial Planning Tools and Observing System Experiments to define Gaps in the Emerging European Ocean Observing System.

Science.gov (United States)

Nolan, G.; Pinardi, N.; Vukicevic, T.; Le Traon, P. Y.; Fernandez, V.

2016-02-01

Ocean observations are critical to providing accurate ocean forecasts that support operational decision making in European open and coastal seas. Observations are available in many forms from Fixed platforms e.g. Moored Buoys and tide gauges, underway measurements from Ferrybox systems, High Frequency radars and more recently from underwater Gliders and profiling floats. Observing System Simulation Experiments have been conducted to examine the relative contribution of each type of platform to an improvement in our ability to accurately forecast the future state of the ocean with HF radar and Gliders showing particular promise in improving model skill. There is considerable demand for ecosystem products and services from today's ocean observing system and biogeochemical observations are still relatively sparse particularly in coastal and shelf seas. There is a need to widen the techniques used to assess the fitness for purpose and gaps in the ocean observing system. As well as Observing System Simulation Experiments that quantify the effect of observations on the overall model skill we present a gap analysis based on (1) Examining where high model skill is required based on a marine spatial planning analysis of European seas i.e where does activity take place that requires more accurate forecasts? and (2) assessing gaps based on the capacity of the observing system to answer key societal challenges e.g. site suitability for aquaculture and ocean energy, oil spill response and contextual oceanographic products for fisheries and ecosystems. The broad based analysis will inform the development of the proposed European Ocean Observing System as a contribution to the Global Ocean Observing System (GOOS).

Assessment of the importance of the current-wave coupling in the shelf ocean forecasts

Directory of Open Access Journals (Sweden)

G. Jordà

2007-07-01

Full Text Available The effects of wave-current interactions on shelf ocean forecasts is investigated in the framework of the MFSTEP (Mediterranean Forecasting System Project Towards Enviromental Predictions project. A one way sequential coupling approach is adopted to link the wave model (WAM to the circulation model (SYMPHONIE. The coupling of waves and currents has been done considering four main processes: wave refraction due to currents, surface wind drag and bottom drag modifications due to waves, and the wave induced mass flux. The coupled modelling system is implemented in the southern Catalan shelf (NW Mediterranean, a region with characteristics similar to most of the Mediterranean shelves. The sensitivity experiments are run in a typical operational configuration. The wave refraction by currents seems to be not very relevant in a microtidal context such as the western Mediterranean. The main effect of waves on current forecasts is through the modification of the wind drag. The Stokes drift also plays a significant role due to its spatial and temporal characteristics. Finally, the enhanced bottom friction is just noticeable in the inner shelf.

Paradigm change in ocean studies: multi-platform observing and forecasting integrated approach in response to science and society needs

Science.gov (United States)

Tintoré, Joaquín

2017-04-01

The last 20 years of ocean research have allowed a description of the state of the large-scale ocean circulation. However, it is also well known that there is no such thing as an ocean state and that the ocean varies a wide range of spatial and temporal scales. More recently, in the last 10 years, new monitoring and modelling technologies have emerged allowing quasi real time observation and forecasting of the ocean at regional and local scales. Theses new technologies are key components of recent observing & forecasting systems being progressively implemented in many regional seas and coastal areas of the world oceans. As a result, new capabilities to characterise the ocean state and more important, its variability at small spatial and temporal scales, exists today in many cases in quasi-real time. Examples of relevance for society can be cited, among others our capabilities to detect and understand long-term climatic changes and also our capabilities to better constrain our forecasting capabilities of the coastal ocean circulation at temporal scales from sub-seasonal to inter-annual and spatial from regional to meso and submesoscale. The Mediterranean Sea is a well-known laboratory ocean where meso and submesoscale features can be ideally observed and studied as shown by the key contributions from projects such as Perseus, CMEMS, Jericonext, among others. The challenge for the next 10 years is the integration of theses technologies and multiplatform observing and forecasting systems to (a) monitor the variability at small scales mesoscale/weeks) in order (b) to resolve the sub-basin/seasonal and inter-annual variability and by this (c) establish the decadal variability, understand the associated biases and correct them. In other words, the new observing systems now allow a major change in our focus of ocean observation, now from small to large scales. Recent studies from SOCIB -www.socib.es- have shown the importance of this new small to large-scale multi

OceanNOMADS: Real-time and retrospective access to operational U.S. ocean prediction products

Science.gov (United States)

Harding, J. M.; Cross, S. L.; Bub, F.; Ji, M.

2011-12-01

The National Oceanic and Atmospheric Administration (NOAA) National Operational Model Archive and Distribution System (NOMADS) provides both real-time and archived atmospheric model output from servers at the National Centers for Environmental Prediction (NCEP) and National Climatic Data Center (NCDC) respectively (http://nomads.ncep.noaa.gov/txt_descriptions/marRutledge-1.pdf). The NOAA National Ocean Data Center (NODC) with NCEP is developing a complementary capability called OceanNOMADS for operational ocean prediction models. An NCEP ftp server currently provides real-time ocean forecast output (http://www.opc.ncep.noaa.gov/newNCOM/NCOM_currents.shtml) with retrospective access through NODC. A joint effort between the Northern Gulf Institute (NGI; a NOAA Cooperative Institute) and the NOAA National Coastal Data Development Center (NCDDC; a division of NODC) created the developmental version of the retrospective OceanNOMADS capability (http://www.northerngulfinstitute.org/edac/ocean_nomads.php) under the NGI Ecosystem Data Assembly Center (EDAC) project (http://www.northerngulfinstitute.org/edac/). Complementary funding support for the developmental OceanNOMADS from U.S. Integrated Ocean Observing System (IOOS) through the Southeastern University Research Association (SURA) Model Testbed (http://testbed.sura.org/) this past year provided NODC the analogue that facilitated the creation of an NCDDC production version of OceanNOMADS (http://www.ncddc.noaa.gov/ocean-nomads/). Access tool development and storage of initial archival data sets occur on the NGI/NCDDC developmental servers with transition to NODC/NCCDC production servers as the model archives mature and operational space and distribution capability grow. Navy operational global ocean forecast subsets for U.S waters comprise the initial ocean prediction fields resident on the NCDDC production server. The NGI/NCDDC developmental server currently includes the Naval Research Laboratory Inter-America Seas

Forecasting ocean wave energy: A Comparison of the ECMWF wave model with time series methods

DEFF Research Database (Denmark)

Reikard, Gordon; Pinson, Pierre; Bidlot, Jean

2011-01-01

Recently, the technology has been developed to make wave farms commercially viable. Since electricity is perishable, utilities will be interested in forecasting ocean wave energy. The horizons involved in short-term management of power grids range from as little as a few hours to as long as several...... days. In selecting a method, the forecaster has a choice between physics-based models and statistical techniques. A further idea is to combine both types of models. This paper analyzes the forecasting properties of a well-known physics-based model, the European Center for Medium-Range Weather Forecasts...... (ECMWF) Wave Model, and two statistical techniques, time-varying parameter regressions and neural networks. Thirteen data sets at locations in the Atlantic and Pacific Oceans and the Gulf of Mexico are tested. The quantities to be predicted are the significant wave height, the wave period, and the wave...

Evaluation of global monitoring and forecasting systems at Mercator Océan

Directory of Open Access Journals (Sweden)

J.-M. Lellouche

2013-01-01

Full Text Available Since December 2010, the MyOcean global analysis and forecasting system has consisted of the Mercator Océan NEMO global 1/4° configuration with a 1/12° nested model over the Atlantic and the Mediterranean. The open boundary data for the nested configuration come from the global 1/4° configuration at 20° S and 80° N.

The data are assimilated by means of a reduced-order Kalman filter with a 3-D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. A 3-D-Var scheme provides a correction for the slowly evolving large-scale biases in temperature and salinity. Altimeter data, satellite sea surface temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. In addition to the quality control performed by data producers, the system carries out a proper quality control on temperature and salinity vertical profiles in order to minimise the risk of erroneous observed profiles being assimilated in the model.

This paper describes the recent systems used by Mercator Océan and the validation procedure applied to current MyOcean systems as well as systems under development. The paper shows how refinements or adjustments to the system during the validation procedure affect its quality. Additionally, we show that quality checks (in situ, drifters and data sources (satellite sea surface temperature have as great an impact as the system design (model physics and assimilation parameters. The results of the scientific assessment are illustrated with diagnostics over the year 2010 mainly, assorted with time series over the 2007–2011 period. The validation procedure demonstrates the accuracy of MyOcean global products, whose quality is stable over time. All monitoring systems are close to altimetric observations with a forecast RMS difference of 7 cm. The update of the mean

Ocean forecasting in terrain-following coordinates: Formulation and skill assessment of the Regional Ocean Modeling System

Science.gov (United States)

Haidvogel, D.B.; Arango, H.; Budgell, W.P.; Cornuelle, B.D.; Curchitser, E.; Di, Lorenzo E.; Fennel, K.; Geyer, W.R.; Hermann, A.J.; Lanerolle, L.; Levin, J.; McWilliams, J.C.; Miller, A.J.; Moore, A.M.; Powell, T.M.; Shchepetkin, A.F.; Sherwood, C.R.; Signell, R.P.; Warner, J.C.; Wilkin, J.

2008-01-01

Systematic improvements in algorithmic design of regional ocean circulation models have led to significant enhancement in simulation ability across a wide range of space/time scales and marine system types. As an example, we briefly review the Regional Ocean Modeling System, a member of a general class of three-dimensional, free-surface, terrain-following numerical models. Noteworthy characteristics of the ROMS computational kernel include: consistent temporal averaging of the barotropic mode to guarantee both exact conservation and constancy preservation properties for tracers; redefined barotropic pressure-gradient terms to account for local variations in the density field; vertical interpolation performed using conservative parabolic splines; and higher-order, quasi-monotone advection algorithms. Examples of quantitative skill assessment are shown for a tidally driven estuary, an ice-covered high-latitude sea, a wind- and buoyancy-forced continental shelf, and a mid-latitude ocean basin. The combination of moderate-order spatial approximations, enhanced conservation properties, and quasi-monotone advection produces both more robust and accurate, and less diffusive, solutions than those produced in earlier terrain-following ocean models. Together with advanced methods of data assimilation and novel observing system technologies, these capabilities constitute the necessary ingredients for multi-purpose regional ocean prediction systems. 

Mediterranea Forecasting System: a focus on wave-current coupling

Science.gov (United States)

Clementi, Emanuela; Delrosso, Damiano; Pistoia, Jenny; Drudi, Massimiliano; Fratianni, Claudia; Grandi, Alessandro; Pinardi, Nadia; Oddo, Paolo; Tonani, Marina

2016-04-01

The Mediterranean Forecasting System (MFS) is a numerical ocean prediction system that produces analyses, reanalyses and short term forecasts for the entire Mediterranean Sea and its Atlantic Ocean adjacent areas. MFS became operational in the late 90's and has been developed and continuously improved in the framework of a series of EU and National funded programs and is now part of the Copernicus Marine Service. The MFS is composed by the hydrodynamic model NEMO (Nucleus for European Modelling of the Ocean) 2-way coupled with the third generation wave model WW3 (WaveWatchIII) implemented in the Mediterranean Sea with 1/16 horizontal resolution and forced by ECMWF atmospheric fields. The model solutions are corrected by the data assimilation system (3D variational scheme adapted to the oceanic assimilation problem) with a daily assimilation cycle, using a background error correlation matrix varying seasonally and in different sub-regions of the Mediterranean Sea. The focus of this work is to present the latest modelling system upgrades and the related achieved improvements. In order to evaluate the performance of the coupled system a set of experiments has been built by coupling the wave and circulation models that hourly exchange the following fields: the sea surface currents and air-sea temperature difference are transferred from NEMO model to WW3 model modifying respectively the mean momentum transfer of waves and the wind speed stability parameter; while the neutral drag coefficient computed by WW3 model is passed to NEMO that computes the turbulent component. In order to validate the modelling system, numerical results have been compared with in-situ and remote sensing data. This work suggests that a coupled model might be capable of a better description of wave-current interactions, in particular feedback from the ocean to the waves might assess an improvement on the prediction capability of wave characteristics, while suggests to proceed toward a fully

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

International Nuclear Information System (INIS)

Kaiser, Mark J.; Pulsipher, Allan G.

2007-01-01

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

Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2): atmosphere-land-ocean-sea ice coupled prediction system for operational seasonal forecasting

Science.gov (United States)

Takaya, Yuhei; Hirahara, Shoji; Yasuda, Tamaki; Matsueda, Satoko; Toyoda, Takahiro; Fujii, Yosuke; Sugimoto, Hiroyuki; Matsukawa, Chihiro; Ishikawa, Ichiro; Mori, Hirotoshi; Nagasawa, Ryoji; Kubo, Yutaro; Adachi, Noriyuki; Yamanaka, Goro; Kuragano, Tsurane; Shimpo, Akihiko; Maeda, Shuhei; Ose, Tomoaki

2018-02-01

This paper describes the Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2), which was put into operation in June 2015 for the purpose of performing seasonal predictions. JMA/MRI-CPS2 has various upgrades from its predecessor, JMA/MRI-CPS1, including improved resolution and physics in its atmospheric and oceanic components, introduction of an interactive sea-ice model and realistic initialization of its land component. Verification of extensive re-forecasts covering a 30-year period (1981-2010) demonstrates that JMA/MRI-CPS2 possesses improved seasonal predictive skills for both atmospheric and oceanic interannual variability as well as key coupled variability such as the El Niño-Southern Oscillation (ENSO). For ENSO prediction, the new system better represents the forecast uncertainty and transition/duration of ENSO phases. Our analysis suggests that the enhanced predictive skills are attributable to incremental improvements resulting from all of the changes, as is apparent in the beneficial effects of sea-ice coupling and land initialization on 2-m temperature predictions. JMA/MRI-CPS2 is capable of reasonably representing the seasonal cycle and secular trends of sea ice. The sea-ice coupling remarkably enhances the predictive capability for the Arctic 2-m temperature, indicating the importance of this factor, particularly for seasonal predictions in the Arctic region.

Noise Reduction of Ocean-Bottom Pressure Data Toward Real-Time Tsunami Forecasting

Science.gov (United States)

Tsushima, H.; Hino, R.

2008-12-01

We discuss a method of noise reduction of ocean-bottom pressure data to be fed into the near-field tsunami forecasting scheme proposed by Tsushima et al. [2008a]. In their scheme, the pressure data is processed in real time as follows: (1) removing ocean tide components by subtracting the sea-level variation computed from a theoretical tide model, (2) applying low-pass digital filter to remove high-frequency fluctuation due to seismic waves, and (3) removing DC-offset and linear-trend component to determine a baseline of relative sea level. However, it turns out this simple method is not always successful in extracting tsunami waveforms from the data, when the observed amplitude is ~1cm. For disaster mitigation, accurate forecasting of small tsunamis is important as well as large tsunamis. Since small tsunami events occur frequently, successful tsunami forecasting of those events are critical to obtain public reliance upon tsunami warnings. As a test case, we applied the data-processing described above to the bottom pressure records containing tsunami with amplitude less than 1 cm which was generated by the 2003 Off-Fukushima earthquake occurring in the Japan Trench subduction zone. The observed pressure variation due to the ocean tide is well explained by the calculated tide signals from NAO99Jb model [Matsumoto et al., 2000]. However, the tide components estimated by BAYTAP-G [Tamura et al., 1991] from the pressure data is more appropriate for predicting and removing the ocean tide signals. In the pressure data after removing the tide variations, there remain pressure fluctuations with frequencies ranging from about 0.1 to 1 mHz and with amplitudes around ~10 cm. These fluctuations distort the estimation of zero-level and linear trend to define relative sea-level variation, which is treated as tsunami waveform in the subsequent analysis. Since the linear trend is estimated from the data prior to the origin time of the earthquake, an artificial linear trend is

Ocean-Atmosphere Coupling Processes Affecting Predictability in the Climate System

Science.gov (United States)

Miller, A. J.; Subramanian, A. C.; Seo, H.; Eliashiv, J. D.

2017-12-01

Predictions of the ocean and atmosphere are often sensitive to coupling at the air-sea interface in ways that depend on the temporal and spatial scales of the target fields. We will discuss several aspects of these types of coupled interactions including oceanic and atmospheric forecast applications. For oceanic mesoscale eddies, the coupling can influence the energetics of the oceanic flow itself. For Madden-Julian Oscillation onset, the coupling timestep should resolve the diurnal cycle to properly raise time-mean SST and latent heat flux prior to deep convection. For Atmospheric River events, the evolving SST field can alter the trajectory and intensity of precipitation anomalies along the California coast. Improvements in predictions will also rely on identifying and alleviating sources of biases in the climate states of the coupled system. Surprisingly, forecast skill can also be improved by enhancing stochastic variability in the atmospheric component of coupled models as found in a multiscale ensemble modeling approach.

Tsunami simulation method initiated from waveforms observed by ocean bottom pressure sensors for real-time tsunami forecast; Applied for 2011 Tohoku Tsunami

Science.gov (United States)

Tanioka, Yuichiro

2017-04-01

After tsunami disaster due to the 2011 Tohoku-oki great earthquake, improvement of the tsunami forecast has been an urgent issue in Japan. National Institute of Disaster Prevention is installing a cable network system of earthquake and tsunami observation (S-NET) at the ocean bottom along the Japan and Kurile trench. This cable system includes 125 pressure sensors (tsunami meters) which are separated by 30 km. Along the Nankai trough, JAMSTEC already installed and operated the cable network system of seismometers and pressure sensors (DONET and DONET2). Those systems are the most dense observation network systems on top of source areas of great underthrust earthquakes in the world. Real-time tsunami forecast has depended on estimation of earthquake parameters, such as epicenter, depth, and magnitude of earthquakes. Recently, tsunami forecast method has been developed using the estimation of tsunami source from tsunami waveforms observed at the ocean bottom pressure sensors. However, when we have many pressure sensors separated by 30km on top of the source area, we do not need to estimate the tsunami source or earthquake source to compute tsunami. Instead, we can initiate a tsunami simulation from those dense tsunami observed data. Observed tsunami height differences with a time interval at the ocean bottom pressure sensors separated by 30 km were used to estimate tsunami height distribution at a particular time. In our new method, tsunami numerical simulation was initiated from those estimated tsunami height distribution. In this paper, the above method is improved and applied for the tsunami generated by the 2011 Tohoku-oki great earthquake. Tsunami source model of the 2011 Tohoku-oki great earthquake estimated using observed tsunami waveforms, coseimic deformation observed by GPS and ocean bottom sensors by Gusman et al. (2012) is used in this study. The ocean surface deformation is computed from the source model and used as an initial condition of tsunami

Improving estimations of greenhouse gas transfer velocities by atmosphere-ocean couplers in Earth-System and regional models

Science.gov (United States)

Vieira, V. M. N. C. S.; Sahlée, E.; Jurus, P.; Clementi, E.; Pettersson, H.; Mateus, M.

2015-09-01

Earth-System and regional models, forecasting climate change and its impacts, simulate atmosphere-ocean gas exchanges using classical yet too simple generalizations relying on wind speed as the sole mediator while neglecting factors as sea-surface agitation, atmospheric stability, current drag with the bottom, rain and surfactants. These were proved fundamental for accurate estimates, particularly in the coastal ocean, where a significant part of the atmosphere-ocean greenhouse gas exchanges occurs. We include several of these factors in a customizable algorithm proposed for the basis of novel couplers of the atmospheric and oceanographic model components. We tested performances with measured and simulated data from the European coastal ocean, having found our algorithm to forecast greenhouse gas exchanges largely different from the forecasted by the generalization currently in use. Our algorithm allows calculus vectorization and parallel processing, improving computational speed roughly 12× in a single cpu core, an essential feature for Earth-System models applications.

Operational Forecasting and Warning systems for Coastal hazards in Korea

Science.gov (United States)

Park, Kwang-Soon; Kwon, Jae-Il; Kim, Jin-Ah; Heo, Ki-Young; Jun, Kicheon

2017-04-01

Coastal hazards caused by both Mother Nature and humans cost tremendous social, economic and environmental damages. To mitigate these damages many countries have been running the operational forecasting or warning systems. Since 2009 Korea Operational Oceanographic System (KOOS) has been developed by the leading of Korea Institute of Ocean Science and Technology (KIOST) in Korea and KOOS has been operated in 2012. KOOS is consists of several operational modules of numerical models and real-time observations and produces the basic forecasting variables such as winds, tides, waves, currents, temperature and salinity and so on. In practical application systems include storm surges, oil spills, and search and rescue prediction models. In particular, abnormal high waves (swell-like high-height waves) have occurred in the East coast of Korea peninsula during winter season owing to the local meteorological condition over the East Sea, causing property damages and the loss of human lives. In order to improve wave forecast accuracy even very local wave characteristics, numerical wave modeling system using SWAN is established with data assimilation module using 4D-EnKF and sensitivity test has been conducted. During the typhoon period for the prediction of sever waves and the decision making support system for evacuation of the ships, a high-resolution wave forecasting system has been established and calibrated.

Remote sensing for global change, climate change and atmosphere and ocean forecasting. Volume 1

International Nuclear Information System (INIS)

1992-01-01

This volume is separated in three sessions. First part is on remote sensing for global change (with global modelling, land cover change on global scale, ocean colour studies of marine biosphere, biological and hydrological interactions and large scale experiments). Second part is on remote sensing for climate change (with earth radiation and clouds, sea ice, global climate research programme). Third part is on remote sensing for atmosphere and ocean forecasting (with temperatures and humidity, winds, data assimilation, cloud imagery, sea surface temperature, ocean waves and topography). (A.B.). refs., figs., tabs

MyOcean Central Information System - Achievements and Perspectives

Science.gov (United States)

Claverie, Vincent; Loubrieu, Thomas; Jolibois, Tony; de Dianous, Rémi; Blower, Jon; Romero, Laia; Griffiths, Guy

2013-04-01

Since 2009, MyOcean (http://www.myocean.eu) is providing an operational service, for forecasts, analysis and expertise on ocean currents, temperature, salinity, sea level, primary ecosystems and ice coverage. The production of observation and forecasting data is done by 42 Production Units (PU). Product download and visualisation are hosted by 25 Dissemination Units (DU). All these products and associated services are gathered in a single catalogue hiding the intricate distributed organization of PUs and DUs. Besides applying INSPIRE directive and OGC recommendations, MyOcean overcomes technical choices and challenges. This presentation focuses on 3 specific issues met by MyOcean and relevant for many Spatial Data Infrastructures: user's transaction accounting, large volume download and stream line the catalogue maintenance. Transaction Accounting: Set up powerful means to get detailed knowledge of system usage in order to subsequently improve the products (ocean observations, analysis and forecast dataset) and services (view, download) offer. This subject drives the following ones: Central authentication management for the distributed web services implementations: add-on to THREDDS Data Server for WMS and NETCDF sub-setting service, specific FTP. Share user management with co-funding projects. In addition to MyOcean, alternate projects also need consolidated information about the use of the cofunded products. Provide a central facility for the user management. This central facility provides users' rights to geographically distributed services and gathers transaction accounting history from these distributed services. Propose a user-friendly web interface to download large volume of data (several GigaBytes) as robust as basic FTP but intuitive and file/directory independent. This should rely on a web service drafting the INSPIRE to-be specification and OGC recommendations for download taking into account that FTP server is not enough friendly (need to know

Tidal simulation using regional ocean modeling systems (ROMS)

Science.gov (United States)

Wang, Xiaochun; Chao, Yi; Li, Zhijin; Dong, Changming; Farrara, John; McWilliams, James C.; Shum, C. K.; Wang, Yu; Matsumoto, Koji; Rosenfeld, Leslie K.;

The Red Sea Modeling and Forecasting System

KAUST Repository

Hoteit, Ibrahim

2015-04-01

Despite its importance for a variety of socio-economical and political reasons and the presence of extensive coral reef gardens along its shores, the Red Sea remains one of the most under-studied large marine physical and biological systems in the global ocean. This contribution will present our efforts to build advanced modeling and forecasting capabilities for the Red Sea, which is part of the newly established Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). Our Red Sea modeling system compromises both regional and nested costal MIT general circulation models (MITgcm) with resolutions varying between 8 km and 250 m to simulate the general circulation and mesoscale dynamics at various spatial scales, a 10-km resolution Weather Research Forecasting (WRF) model to simulate the atmospheric conditions, a 4-km resolution European Regional Seas Ecosystem Model (ERSEM) to simulate the Red Sea ecosystem, and a 1-km resolution WAVEWATCH-III model to simulate the wind driven surface waves conditions. We have also implemented an oil spill model, and a probabilistic dispersion and larval connectivity modeling system (CMS) based on a stochastic Lagrangian framework and incorporating biological attributes. We are using the models outputs together with available observational data to study all aspects of the Red Sea circulations. Advanced monitoring capabilities are being deployed in the Red Sea as part of the SAMERCK, comprising multiple gliders equipped with hydrographical and biological sensors, high frequency (HF) surface current/wave mapping, buoys/ moorings, etc, complementing the available satellite ocean and atmospheric observations and Automatic Weather Stations (AWS). The Red Sea models have also been equipped with advanced data assimilation capabilities. Fully parallel ensemble-based Kalman filtering (EnKF) algorithms have been implemented with the MITgcm and ERSEM for assimilating all available multivariate satellite and in-situ data sets. We

The Red Sea Modeling and Forecasting System

KAUST Repository

Hoteit, Ibrahim; Gopalakrishnan, Ganesh; Latif, Hatem; Toye, Habib; Zhan, Peng; Kartadikaria, Aditya R.; Viswanadhapalli, Yesubabu; Yao, Fengchao; Triantafyllou, George; Langodan, Sabique; Cavaleri, Luigi; Guo, Daquan; Johns, Burt

2015-01-01

Despite its importance for a variety of socio-economical and political reasons and the presence of extensive coral reef gardens along its shores, the Red Sea remains one of the most under-studied large marine physical and biological systems in the global ocean. This contribution will present our efforts to build advanced modeling and forecasting capabilities for the Red Sea, which is part of the newly established Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). Our Red Sea modeling system compromises both regional and nested costal MIT general circulation models (MITgcm) with resolutions varying between 8 km and 250 m to simulate the general circulation and mesoscale dynamics at various spatial scales, a 10-km resolution Weather Research Forecasting (WRF) model to simulate the atmospheric conditions, a 4-km resolution European Regional Seas Ecosystem Model (ERSEM) to simulate the Red Sea ecosystem, and a 1-km resolution WAVEWATCH-III model to simulate the wind driven surface waves conditions. We have also implemented an oil spill model, and a probabilistic dispersion and larval connectivity modeling system (CMS) based on a stochastic Lagrangian framework and incorporating biological attributes. We are using the models outputs together with available observational data to study all aspects of the Red Sea circulations. Advanced monitoring capabilities are being deployed in the Red Sea as part of the SAMERCK, comprising multiple gliders equipped with hydrographical and biological sensors, high frequency (HF) surface current/wave mapping, buoys/ moorings, etc, complementing the available satellite ocean and atmospheric observations and Automatic Weather Stations (AWS). The Red Sea models have also been equipped with advanced data assimilation capabilities. Fully parallel ensemble-based Kalman filtering (EnKF) algorithms have been implemented with the MITgcm and ERSEM for assimilating all available multivariate satellite and in-situ data sets. We

Decadal Prediction Skill in the GEOS-5 Forecast System

Science.gov (United States)

Ham, Yoo-Geun; Rienecker, Michele M.; Suarez, Max J.; Vikhliaev, Yury; Zhao, Bin; Marshak, Jelena; Vernieres, Guillaume; Schubert, Siegfried D.

2013-01-01

A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office's (GMAO's) GEOS-5 Atmosphere-Ocean general circulation model. The hind casts are initialized every December 1st from 1959 to 2010, following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multivariate ensemble optimal interpolation ocean and sea-ice reanalysis, and from GMAO's atmospheric reanalysis, the modern-era retrospective analysis for research and applications. The mean forecast skill of a three-member-ensemble is compared to that of an experiment without initialization but also forced with observed greenhouse gases. The results show that initialization increases the forecast skill of North Atlantic sea surface temperature compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. On the other hand, the initialization reduces the skill in predicting the warming trend over some regions outside the Atlantic. The annual-mean Atlantic meridional overturning circulation index, which is defined here as the maximum of the zonally-integrated overturning stream function at mid-latitude, is predictable up to a 4-year lead time, consistent with the predictable signal in upper ocean heat content over the North Atlantic. While the 6- to 9-year forecast skill measured by mean squared skill score shows 50 percent improvement in the upper ocean heat content over the subtropical and mid-latitude Atlantic, prediction skill is relatively low in the sub-polar gyre. This low skill is due in part to features in the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region that differ from observations. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.

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

Science.gov (United States)

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

2016-02-01

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

Forecast products from the Gulf of Mexico created by the NOAA Harmful Algal Bloom Operational Forecast System (HAB-OFS) from 2007-09-10 to the present

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This collection contains outputs from the NOAA Harmful Algal Bloom Operational Forecast System (HAB-OFS) in the form of bulletin documents beginning on 2007-09-10....

NYHOPS Forecast Model Results

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — 3D Marine Nowcast/Forecast System for the New York Bight NYHOPS subdomain. Currents, waves, surface meteorology, and water conditions.

Toward Sub-seasonal to Seasonal Arctic Sea Ice Forecasting Using the Regional Arctic System Model (RASM)

Science.gov (United States)

Kamal, S.; Maslowski, W.; Roberts, A.; Osinski, R.; Cassano, J. J.; Seefeldt, M. W.

2017-12-01

The Regional Arctic system model has been developed and used to advance the current state of Arctic modeling and increase the skill of sea ice forecast. RASM is a fully coupled, limited-area model that includes the atmosphere, ocean, sea ice, land hydrology and runoff routing components and the flux coupler to exchange information among them. Boundary conditions are derived from NCEP Climate Forecasting System Reanalyses (CFSR) or Era Iterim (ERA-I) for hindcast simulations or from NCEP Coupled Forecast System Model version 2 (CFSv2) for seasonal forecasts. We have used RASM to produce sea ice forecasts for September 2016 and 2017, in contribution to the Sea Ice Outlook (SIO) of the Sea Ice Prediction Network (SIPN). Each year, we produced three SIOs for the September minimum, initialized on June 1, July 1 and August 1. In 2016, predictions used a simple linear regression model to correct for systematic biases and included the mean September sea ice extent, the daily minimum and the week of the minimum. In 2017, we produced a 12-member ensemble on June 1 and July 1, and 28-member ensemble August 1. The predictions of September 2017 included the pan-Arctic and regional Alaskan sea ice extent, daily and monthly mean pan-Arctic maps of sea ice probability, concentration and thickness. No bias correction was applied to the 2017 forecasts. Finally, we will also discuss future plans for RASM forecasts, which include increased resolution for model components, ecosystem predictions with marine biogeochemistry extensions (mBGC) to the ocean and sea ice components, and feasibility of optional boundary conditions using the Navy Global Environmental Model (NAVGEM).

Gulf of Mexico Coastal Ocean Observing System: The Gulf Component of the U.S. Integrated Ocean Observing System

Science.gov (United States)

Bernard, L. J.; Moersdorf, P. F.

2005-05-01

The United States is developing an Integrated Ocean Observing System (IOOS) as the U.S. component of the international Global Ocean Observing System (GOOS). IOOS consists of: (1) a coastal observing system for the U.S. EEZ, estuaries, and Great Lakes; and (2) a contribution to the global component of GOOS focused on climate and maritime services. The coastal component will consist of: (1) a National Backbone of observations and products from our coastal ocean supported by federal agencies; and (2) contributions of Regional Coastal Ocean Observing Systems (RCOOS). The Gulf of Mexico Coastal Ocean Observing System (GCOOS) is one of eleven RCOOS. This paper describes how GCOOS is progressing as a system of systems to carry out data collection, analysis, product generation, dissemination of information, and data archival. These elements are provided by federal, state, and local government agencies, academic institutions, non-government organization, and the private sector. This end-to-end system supports the seven societal goals of the IOOS, as provided by the U.S. Commission on Ocean Policy: detect and forecast oceanic components of climate variability, facilitate safe and efficient marine operations, ensure national security, manage marine resources, preserve and restore healthy marine ecosystems, mitigate natural hazards, and ensure public health. The initial building blocks for GCOOS include continuing in situ observations, satellite products, models, and other information supported by federal and state government, private industry, and academia. GCOOS has compiled an inventory of such activities, together with descriptions, costs, sources of support, and possible out-year budgets. These activities provide information that will have broader use as they are integrated and enhanced. GCOOS has begun that process by several approaches. First, GCOOS has established a web site (www.gcoos.org) which is a portal to such activities and contains pertinent information

The Impact of Ocean Data Assimilation on Seasonal-to-Interannual Forecasts: A Case Study of the 2006 El Nino Event

Science.gov (United States)

Yang, Shu-Chih; Rienecker, Michele; Keppenne, Christian

2010-01-01

This study investigates the impact of four different ocean analyses on coupled forecasts of the 2006 El Nino event. Forecasts initialized in June 2006 using ocean analyses from an assimilation that uses flow-dependent background error covariances are compared with those using static error covariances that are not flow dependent. The flow-dependent error covariances reflect the error structures related to the background ENSO instability and are generated by the coupled breeding method. The ocean analyses used in this study result from the assimilation of temperature and salinity, with the salinity data available from Argo floats. Of the analyses, the one using information from the coupled bred vectors (BV) replicates the observed equatorial long wave propagation best and exhibits more warming features leading to the 2006 El Nino event. The forecasts initialized from the BV-based analysis agree best with the observations in terms of the growth of the warm anomaly through two warming phases. This better performance is related to the impact of the salinity analysis on the state evolution in the equatorial thermocline. The early warming is traced back to salinity differences in the upper ocean of the equatorial central Pacific, while the second warming, corresponding to the mature phase, is associated with the effect of the salinity assimilation on the depth of the thermocline in the western equatorial Pacific. The series of forecast experiments conducted here show that the structure of the salinity in the initial conditions is important to the forecasts of the extension of the warm pool and the evolution of the 2006 El Ni o event.

Ocean model system for radionuclides - validation and application to the Rokkasho coastal area

International Nuclear Information System (INIS)

Kobayashi, Takuya

2010-01-01

Coastal areas have complex environmental systems and often a high influence from the atmosphere, rivers and the open sea. A nuclear fuel reprocessing plant in Japan releases liquid radioactive waste from a discharge pipe to such a complex coastal area. Consequently, the development of radionuclide migration forecast system in the ocean plays an important rule for assessing the behavior of radionuclides in the coastal area. The development of ocean model systems will be presented and model application will also be described. (author)

Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system

Science.gov (United States)

Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic

2014-01-01

The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid

Summer monsoon circulation and precipitation over the tropical Indian Ocean during ENSO in the NCEP climate forecast system

Science.gov (United States)

Chowdary, J. S.; Chaudhari, H. S.; Gnanaseelan, C.; Parekh, Anant; Suryachandra Rao, A.; Sreenivas, P.; Pokhrel, S.; Singh, P.

2014-04-01

This study investigates the El Niño Southern Oscillation (ENSO) teleconnections to tropical Indian Ocean (TIO) and their relationship with the Indian summer monsoon in the coupled general circulation model climate forecast system (CFS). The model shows good skill in simulating the impact of El Niño over the Indian Oceanic rim during its decay phase (the summer following peak phase of El Niño). Summer surface circulation patterns during the developing phase of El Niño are more influenced by local Sea Surface Temperature (SST) anomalies in the model unlike in observations. Eastern TIO cooling similar to that of Indian Ocean Dipole (IOD) is a dominant model feature in summer. This anomalous SST pattern therefore is attributed to the tendency of the model to simulate more frequent IOD events. On the other hand, in the model baroclinic response to the diabatic heating anomalies induced by the El Niño related warm SSTs is weak, resulting in reduced zonal extension of the Rossby wave response. This is mostly due to weak eastern Pacific summer time SST anomalies in the model during the developing phase of El Niño as compared to observations. Both eastern TIO cooling and weak SST warming in El Niño region combined together undermine the ENSO teleconnections to the TIO and south Asia regions. The model is able to capture the spatial patterns of SST, circulation and precipitation well during the decay phase of El Niño over the Indo-western Pacific including the typical spring asymmetric mode and summer basin-wide warming in TIO. The model simulated El Niño decay one or two seasons later, resulting long persistent warm SST and circulation anomalies mainly over the southwest TIO. In response to the late decay of El Niño, Ekman pumping shows two maxima over the southern TIO. In conjunction with this unrealistic Ekman pumping, westward propagating Rossby waves display two peaks, which play key role in the long-persistence of the TIO warming in the model (for more than a

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

Directory of Open Access Journals (Sweden)

Chengshi Tian

2018-03-01

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

The application of hybrid artificial intelligence systems for forecasting

Science.gov (United States)

Lees, Brian; Corchado, Juan

1999-03-01

The results to date are presented from an ongoing investigation, in which the aim is to combine the strengths of different artificial intelligence methods into a single problem solving system. The premise underlying this research is that a system which embodies several cooperating problem solving methods will be capable of achieving better performance than if only a single method were employed. The work has so far concentrated on the combination of case-based reasoning and artificial neural networks. The relative merits of artificial neural networks and case-based reasoning problem solving paradigms, and their combination are discussed. The integration of these two AI problem solving methods in a hybrid systems architecture, such that the neural network provides support for learning from past experience in the case-based reasoning cycle, is then presented. The approach has been applied to the task of forecasting the variation of physical parameters of the ocean. Results obtained so far from tests carried out in the dynamic oceanic environment are presented.

An operational coupled wave-current forecasting system for the northern Adriatic Sea

Science.gov (United States)

Russo, A.; Coluccelli, A.; Deserti, M.; Valentini, A.; Benetazzo, A.; Carniel, S.

2012-04-01

Since 2005 an Adriatic implementation of the Regional Ocean Modeling System (AdriaROMS) is being producing operational short-term forecasts (72 hours) of some hydrodynamic properties (currents, sea level, temperature, salinity) of the Adriatic Sea at 2 km horizontal resolution and 20 vertical s-levels, on a daily basis. The main objective of AdriaROMS, which is managed by the Hydro-Meteo-Clima Service (SIMC) of ARPA Emilia Romagna, is to provide useful products for civil protection purposes (sea level forecasts, outputs to run other forecasting models as for saline wedge, oil spills and coastal erosion). In order to improve the forecasts in the coastal area, where most of the attention is focused, a higher resolution model (0.5 km, again with 20 vertical s-levels) has been implemented for the northern Adriatic domain. The new implementation is based on the Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modeling System (COAWST)and adopts ROMS for the hydrodynamic and Simulating WAve Nearshore (SWAN) for the wave module, respectively. Air-sea fluxes are computed using forecasts produced by the COSMO-I7 operational atmospheric model. At the open boundary of the high resolution model, temperature, salinity and velocity fields are provided by AdriaROMS while the wave characteristics are provided by an operational SWAN implementation (also managed by SIMC). Main tidal components are imposed as well, derived from a tidal model. Work in progress is oriented now on the validation of model results by means of extensive comparisons with acquired hydrographic measurements (such as CTDs or XBTs from sea-truth campaigns), currents and waves acquired at observational sites (including those of SIMC, CNR-ISMAR network and its oceanographic tower, located off the Venice littoral) and satellite-derived wave-heights data. Preliminary results on the forecast waves denote how, especially during intense storms, the effect of coupling can lead to significant variations in the wave

A nowcast-forecast information system for PWS

International Nuclear Information System (INIS)

Thomas, G.L.; Cox, W.

2000-01-01

The development of the Prince William Sound Oil Spill Recovery Institute's (ORI) nowcast-forecast information system was discussed. OSRI addresses oil spill response and prevention research and development in the Arctic and subArctic. A realistic electronic model of the ecosystem was a much needed tool for efficient prioritization of oil spill technologies. The OSRI Sound Ecosystem Assessment (SEA) research program focused on developing a physical-biological model that consisted of static and biological resources that change over long time periods. This includes bathymetry, shoreline type, and substrate-dependent vegetation. It also focused on developing a model of dynamic properties such as wind, weather, plankton, and wildlife populations that undergo significant changes on annual or shorter time scales. The nowcast information system is a long-term development project which uses the Princeton ocean model (POM), a static runoff model, a network of weather and water observation stations, an Intranet which allows the observational data to run in near-real time and an Internet home page. It will contribute to sustaining the natural resources of coastal areas. It was concluded that the nowcast-forecast information system has short-term applications to oil spill prevention and response and long-term applications to the natural resources at risk to spills. 33 refs

Design and skill assessment of an Operational Forecasting System for currents and sea level variability to the Santos Estuarine System - Brazil

Science.gov (United States)

Godoi Rezende Costa, C.; Castro, B. M.; Blumberg, A. F.; Leite, J. R. B., Sr.

2017-12-01

Santos City is subject to an average of 12 storm tide events per year. Such events bring coastal flooding able to threat human life and damage coastal infrastructure. Severe events have forced the interruption of ferry boat services and ship traffic through Santos Harbor, causing great impacts to Santos Port, the largest in South America, activities. Several studies have focused on the hydrodynamics of storm tide events but only a few of those studies have pursued an operational initiative to predict short term (operational forecasting system built to predict sea surface elevation and currents in the Santos Estuarine System and (ii) to evaluate model performance in simulating observed sea surface elevation. The Santos Operational Forecasting System (SOFS) hydrodynamic module is based on the Stevens Institute Estuarine and Coastal Ocean Model (sECOM). The fully automated SOFS is designed to provide up to 71 h forecast of sea surface elevations and currents every day. The system automatically collects results from global models to run the SOFS nested into another sECOM based model for the South Brazil Bight (SBB). Global forecasting results used to force both models come from Mercator Ocean, released by Copernicus Marine Service, and from the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) stablished by the Center for Weather Forecasts and Climate Studies (with Portuguese acronym CPTEC). The complete routines task take about 8 hours of run time to finish. SOFS was able to hindcast a severe storm tide event that took place in Santos on August 21-22, 2016. Comparisons with observed sea level provided skills of 0.92 and maximum root mean square errors of 25 cm. The good agreement with observed data shows the potential of the designed system to predict storm tides and to support both human and assets protection.

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

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

2017-12-01

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

Scientific management of Mediterranean coastal zone: a hybrid ocean forecasting system for oil spill and search and rescue operations.

Science.gov (United States)

Jordi, A; Ferrer, M I; Vizoso, G; Orfila, A; Basterretxea, G; Casas, B; Alvarez, A; Roig, D; Garau, B; Martínez, M; Fernández, V; Fornés, A; Ruiz, M; Fornós, J J; Balaguer, P; Duarte, C M; Rodríguez, I; Alvarez, E; Onken, R; Orfila, P; Tintoré, J

2006-01-01

The oil spill from Prestige tanker showed the importance of scientifically based protocols to minimize the impacts on the environment. In this work, we describe a new forecasting system to predict oil spill trajectories and their potential impacts on the coastal zone. The system is formed of three main interconnected modules that address different capabilities: (1) an operational circulation sub-system that includes nested models at different scales, data collection with near-real time assimilation, new tools for initialization or assimilation based on genetic algorithms and feature-oriented strategic sampling; (2) an oil spill coastal sub-system that allows simulation of the trajectories and fate of spilled oil together with evaluation of coastal zone vulnerability using environmental sensitivity indexes; (3) a risk management sub-system for decision support based on GIS technology. The system is applied to the Mediterranean Sea where surface currents are highly variable in space and time, and interactions between local, sub-basin and basin scale increase the non-linear interactions effects which need to be adequately resolved at each one of the intervening scales. Besides the Mediterranean Sea is a complex reduced scale ocean representing a real scientific and technological challenge for operational oceanography and particularly for oil spill response and search and rescue operations.

Ocean modelling aspects for drift applications

Science.gov (United States)

Stephane, L.; Pierre, D.

2010-12-01

Nowadays, many authorities in charge of rescue-at-sea operations lean on operational oceanography products to outline research perimeters. Moreover, current fields estimated with sophisticated ocean forecasting systems can be used as input data for oil spill/ adrift object fate models. This emphasises the necessity of an accurate sea state forecast, with a mastered level of reliability. This work focuses on several problems inherent to drift modeling, dealing in the first place with the efficiency of the oceanic current field representation. As we want to discriminate the relevance of a particular physical process or modeling option, the idea is to generate series of current fields of different characteristics and then qualify them in term of drift prediction efficiency. Benchmarked drift scenarios were set up from real surface drifters data, collected in the Mediterranean sea and off the coasts of Angola. The time and space scales that we are interested in are about 72 hr forecasts (typical timescale communicated in case of crisis), for distance errors that we hope about a few dozen of km around the forecast (acceptable for reconnaissance by aircrafts) For the ocean prediction, we used some regional oceanic configurations based on the NEMO 2.3 code, nested into Mercator 1/12° operational system. Drift forecasts were computed offline with Mothy (Météo France oil spill modeling system) and Ariane (B. Blanke, 1997), a Lagrangian diagnostic tool. We were particularly interested in the importance of the horizontal resolution, vertical mixing schemes, and any processes that may impact the surface layer. The aim of the study is to ultimately point at the most suitable set of parameters for drift forecast use inside operational oceanic systems. We are also motivated in assessing the relevancy of ensemble forecasts regarding determinist predictions. Several tests showed that mis-described observed trajectories can finally be modelled statistically by using uncertainties

Comparative Analysis of Upper Ocean Heat Content Variability from Ensemble Operational Ocean Analyses

Science.gov (United States)

Xue, Yan; Balmaseda, Magdalena A.; Boyer, Tim; Ferry, Nicolas; Good, Simon; Ishikawa, Ichiro; Rienecker, Michele; Rosati, Tony; Yin, Yonghong; Kumar, Arun

2012-01-01

Upper ocean heat content (HC) is one of the key indicators of climate variability on many time-scales extending from seasonal to interannual to long-term climate trends. For example, HC in the tropical Pacific provides information on thermocline anomalies that is critical for the longlead forecast skill of ENSO. Since HC variability is also associated with SST variability, a better understanding and monitoring of HC variability can help us understand and forecast SST variability associated with ENSO and other modes such as Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), Tropical Atlantic Variability (TAV) and Atlantic Multidecadal Oscillation (AMO). An accurate ocean initialization of HC anomalies in coupled climate models could also contribute to skill in decadal climate prediction. Errors, and/or uncertainties, in the estimation of HC variability can be affected by many factors including uncertainties in surface forcings, ocean model biases, and deficiencies in data assimilation schemes. Changes in observing systems can also leave an imprint on the estimated variability. The availability of multiple operational ocean analyses (ORA) that are routinely produced by operational and research centers around the world provides an opportunity to assess uncertainties in HC analyses, to help identify gaps in observing systems as they impact the quality of ORAs and therefore climate model forecasts. A comparison of ORAs also gives an opportunity to identify deficiencies in data assimilation schemes, and can be used as a basis for development of real-time multi-model ensemble HC monitoring products. The OceanObs09 Conference called for an intercomparison of ORAs and use of ORAs for global ocean monitoring. As a follow up, we intercompared HC variations from ten ORAs -- two objective analyses based on in-situ data only and eight model analyses based on ocean data assimilation systems. The mean, annual cycle, interannual variability and longterm trend of HC have

klax 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...

kprc 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...

katl 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...

kmcn 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...

kogb 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...

kama 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...

ptkk 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...

kiwa 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...

kavp 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...

kdca 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...

kbwg 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...

kdfw 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...

kssi 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...

pahn 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...

ksrq 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...

kpvd 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...

kisp 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...

kttd 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...

pmdy 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...

kmgm 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...

khib 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...

pavd 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...

kfar 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...

kluk 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...

kwwr 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...

klse 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...

ksts 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...

koth 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...

kbfl 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...

ksgf 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...

klch 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...

kpkb 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...

krog 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...

kbjc 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...

ksea 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...

kbwi 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...

kftw 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...

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...

kabq 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...

ksny 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...

khio 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...

klaf 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...

kfoe 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...

ksmx 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...

kipt 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...

ktrk 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...

kwmc 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...

katy 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...

tjmz 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...

kdet 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...

kcxp 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...

kbur 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...

krkd 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...

pawg 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...

kloz 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...

kcec 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...

kdec 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...

paor 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...

kavl 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...

kdrt 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...

kstl 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...

kbfi 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...

khsv 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...

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...

kekn 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...

tncm 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...

kith 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...

kgnv 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...

ktoi 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...

kgso 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...

nstu 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...

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...

kgdv 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...

kcmx 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...

kdls 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...

koaj 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...

krhi 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...

kbpk 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...

khuf 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...

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...

kyng 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...

kcwa 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...

kflg 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...

ktup 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...

ktop 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...

kink 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...

krut 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...

kbli 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...

kaoo 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...

klit 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...

panc 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...

ktcl 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...

pgwt 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...

kpsp 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...

kbih 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...

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...

kart 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...

kilm 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...

kpne 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...

kabi 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...

ptpn 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...

kblf 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...

kosh 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...

kpdt 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...

kewr 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...

kiso 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...

kpga 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...

kbkw 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...

kmyl 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...

krbg 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...

kril 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...

ksus 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...

padq 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...

kbil 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...

krfd 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...

kdug 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...

ktix 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...

kcod 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...

kslk 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...

kgfl 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...

kguc 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...

kmlu 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...

kbff 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...

ksmn 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...

kdro 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...

kmce 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...

ktpa 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...

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...

kcre 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...

klws 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...

kotm 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...

khqm 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...

kabr 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...

klal 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...

kelp 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...

kecg 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...

khbg 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...

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...

konp 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...

pkwa 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...

ktvf 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...

paga 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...

khks 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...

kdsm 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...

kpsm 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...

kgrb 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...

kgmu 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...

papg 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...

kbgm 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...

pamc 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...

klrd 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...

ksan 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...

patk 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...

kowb 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...

klru 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...

kfxe 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...

kjct 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...

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...

paaq 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...

kaex 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...

klbx 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...

kmia 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...

kpit 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...

kcrw 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...

paen 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...

kast 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...

kuin 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...

kmht 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...

krsw 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...

kbpi 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...

kcys 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...

kflo 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...

kphx 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...

pakn 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...

pabt 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...

krdg 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...

khdn 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...

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...

Prediction of summer monsoon rainfall over India using the NCEP climate forecast system

Energy Technology Data Exchange (ETDEWEB)

Pattanaik, D.R. [India Meteorological Department (IMD), New Delhi (India); Kumar, Arun [Climate Prediction Center, National Centre for Environmental Prediction (NCEP)/NWS/NOAA, Camp Springs, MD (United States)

2010-03-15

The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June to September (JJAS). The evaluation is based on the National Centre for Environmental Prediction's (NCEP) climate forecast system (CFS) initialized during March, April and May and integrated for a period of 9 months with a 15 ensemble members for 25 years period from 1981 to 2005. The CFS's hindcast climatology during JJAS of March (lag-3), April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of verification climatology with the rainfall maxima (one over the west-coast of India and the other over the head Bay of Bengal region) well simulated. The pattern correlation between verification and forecast climatology over the global tropics and Indian monsoon region (IMR) bounded by 50 E-110 E and 10 S-35 N shows significant correlation coefficient (CCs). The skill of simulation of broad scale monsoon circulation index (Webster and Yang; WY index) is quite good in the CFS with highly significant CC between the observed and predicted by the CFS from the March, April and May forecasts. High skill in forecasting El Nino event is also noted for the CFS March, April and May initial conditions, whereas, the skill of the simulation of Indian Ocean Dipole is poor and is basically due to the poor skill of prediction of sea surface temperature (SST) anomalies over the eastern equatorial Indian Ocean. Over the IMR the skill of monsoon rainfall forecast during JJAS as measured by the spatial Anomaly CC between forecast rainfall anomaly and the observed rainfall anomaly during 1991, 1994, 1997 and 1998 is high (almost of the order of 0.6), whereas, during the year 1982, 1984, 1985, 1987 and 1989 the ACC is only around 0.3. By using lower and upper tropospheric forecast winds during JJAS over the regions of significant CCs as predictors for the All India Summer Monsoon

Dynamic reusable workflows for ocean science

Science.gov (United States)

Signell, Richard; Fernandez, Filipe; Wilcox, Kyle

2016-01-01

Digital catalogs of ocean data have been available for decades, but advances in standardized services and software for catalog search and data access make it now possible to create catalog-driven workflows that automate — end-to-end — data search, analysis and visualization of data from multiple distributed sources. Further, these workflows may be shared, reused and adapted with ease. Here we describe a workflow developed within the US Integrated Ocean Observing System (IOOS) which automates the skill-assessment of water temperature forecasts from multiple ocean forecast models, allowing improved forecast products to be delivered for an open water swim event. A series of Jupyter Notebooks are used to capture and document the end-to-end workflow using a collection of Python tools that facilitate working with standardized catalog and data services. The workflow first searches a catalog of metadata using the Open Geospatial Consortium (OGC) Catalog Service for the Web (CSW), then accesses data service endpoints found in the metadata records using the OGC Sensor Observation Service (SOS) for in situ sensor data and OPeNDAP services for remotely-sensed and model data. Skill metrics are computed and time series comparisons of forecast model and observed data are displayed interactively, leveraging the capabilities of modern web browsers. The resulting workflow not only solves a challenging specific problem, but highlights the benefits of dynamic, reusable workflows in general. These workflows adapt as new data enters the data system, facilitate reproducible science, provide templates from which new scientific workflows can be developed, and encourage data providers to use standardized services. As applied to the ocean swim event, the workflow exposed problems with two of the ocean forecast products which led to improved regional forecasts once errors were corrected. While the example is specific, the approach is general, and we hope to see increased use of dynamic

Dynamic Reusable Workflows for Ocean Science

Directory of Open Access Journals (Sweden)

Richard P. Signell

2016-10-01

Full Text Available Digital catalogs of ocean data have been available for decades, but advances in standardized services and software for catalog searches and data access now make it possible to create catalog-driven workflows that automate—end-to-end—data search, analysis, and visualization of data from multiple distributed sources. Further, these workflows may be shared, reused, and adapted with ease. Here we describe a workflow developed within the US Integrated Ocean Observing System (IOOS which automates the skill assessment of water temperature forecasts from multiple ocean forecast models, allowing improved forecast products to be delivered for an open water swim event. A series of Jupyter Notebooks are used to capture and document the end-to-end workflow using a collection of Python tools that facilitate working with standardized catalog and data services. The workflow first searches a catalog of metadata using the Open Geospatial Consortium (OGC Catalog Service for the Web (CSW, then accesses data service endpoints found in the metadata records using the OGC Sensor Observation Service (SOS for in situ sensor data and OPeNDAP services for remotely-sensed and model data. Skill metrics are computed and time series comparisons of forecast model and observed data are displayed interactively, leveraging the capabilities of modern web browsers. The resulting workflow not only solves a challenging specific problem, but highlights the benefits of dynamic, reusable workflows in general. These workflows adapt as new data enter the data system, facilitate reproducible science, provide templates from which new scientific workflows can be developed, and encourage data providers to use standardized services. As applied to the ocean swim event, the workflow exposed problems with two of the ocean forecast products which led to improved regional forecasts once errors were corrected. While the example is specific, the approach is general, and we hope to see increased

Potential predictability and forecast skill in ensemble climate forecast: the skill-persistence rule

Science.gov (United States)

Jin, Y.; Rong, X.; Liu, Z.

2017-12-01

This study investigates the factors that impact the forecast skill for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill of sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further examined using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but can be distorted by the sampling error and non-AR1 processes.

Model-Aided Altimeter-Based Water Level Forecasting System in Mekong River

Science.gov (United States)

Chang, C. H.; Lee, H.; Hossain, F.; Okeowo, M. A.; Basnayake, S. B.; Jayasinghe, S.; Saah, D. S.; Anderson, E.; Hwang, E.

2017-12-01

Mekong River, one of the massive river systems in the world, has drainage area of about 795,000 km2 covering six countries. People living in its drainage area highly rely on resources given by the river in terms of agriculture, fishery, and hydropower. Monitoring and forecasting the water level in a timely manner, is urgently needed over the Mekong River. Recently, using TOPEX/Poseidon (T/P) altimetry water level measurements in India, Biancamaria et al. [2011] has demonstrated the capability of an altimeter-based flood forecasting system in Bangladesh, with RMSE from 0.6 - 0.8 m for lead times up to 5 days on 10-day basis due to T/P's repeat period. Hossain et al. [2013] further established a daily water level forecasting system in Bangladesh using observations from Jason-2 in India and HEC-RAS hydraulic model, with RMSE from 0.5 - 1.5 m and an underestimating mean bias of 0.25 - 1.25 m. However, such daily forecasting system relies on a collection of Jason-2 virtual stations (VSs) to ensure frequent sampling and data availability. Since the Mekong River is a meridional river with few number of VSs, the direct application of this system to the Mekong River becomes challenging. To address this problem, we propose a model-aided altimeter-based forecasting system. The discharge output by Variable Infiltration Capacity hydrologic model is used to reconstruct a daily water level product at upstream Jason-2 VSs based on the discharge-to-level rating curve. The reconstructed daily water level is then used to perform regression analysis with downstream in-situ water level to build regression models, which are used to forecast a daily water level. In the middle reach of the Mekong River from Nakhon Phanom to Kratie, a 3-day lead time forecasting can reach RMSE about 0.7 - 1.3 m with correlation coefficient around 0.95. For the lower reach of the Mekong River, the water flow becomes more complicated due to the reversal flow between the Tonle Sap Lake and the Mekong River

A global flash flood forecasting system

Science.gov (United States)

Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin

2016-04-01

The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial

An Operational Coastal Forecasting System in Galicia (NW Spain)

Science.gov (United States)

Balseiro, C. F.; Carracedo, P.; Pérez, E.; Pérez, V.; Taboada, J.; Venacio, A.; Vilasa, L.

2009-09-01

The Galician coast (NW Iberian Peninsula coast) and mainly the Rias Baixas (southern Galician rias) are one of the most productive ecosystems in the world, supporting a very active fishing and aquiculture industry. This high productivity lives together with a high human pressure and an intense maritime traffic, which means an important environmental risk. Besides that, Harmful Algae Blooms (HAB) are common in this area, producing important economical losses in aquiculture. In this context, the development of an Operational Hydrodynamic Ocean Forecast System is the first step to the development of a more sophisticated Ocean Integrated Decision Support Tool. A regional oceanographic forecasting system in the Galician Coast has been developed by MeteoGalicia (the Galician regional meteorological agency) inside ESEOO project to provide forecasts on currents, sea level, water temperature and salinity. This system is based on hydrodynamic model MOHID, forced with the operational meteorological model WRF, supported daily at MeteoGalicia . Two grid meshes are running nested at different scales, one of ~2km at the shelf scale and the other one with a resolution of 500 m at the rias scale. ESEOAT (Puertos del Estado) model provide salinity and temperature fields which are relaxed at all depth along the open boundary of the regional model (~6km). Temperature and salinity initial fields are also obtained from this application. Freshwater input from main rivers are included as forcing in MOHID model. Monthly mean discharge data from gauge station have been provided by Aguas de Galicia. Nowadays a coupling between an hydrological model (SWAT) and the hydrodynamic one are in development with the aim to verify the impact of the rivers discharges. The system runs operationally daily, providing two days of forecast. First model verifications had been performed against Puertos del Estado buoys and Xunta de Galicia buoys network along the Galician coast. High resolution model results

Evaluation of precipitation forecasts from 3D-Var and hybrid GSI-based system during Indian summer monsoon 2015

Science.gov (United States)

Singh, Sanjeev Kumar; Prasad, V. S.

2018-02-01

This paper presents a systematic investigation of medium-range rainfall forecasts from two versions of the National Centre for Medium Range Weather Forecasting (NCMRWF)-Global Forecast System based on three-dimensional variational (3D-Var) and hybrid analysis system namely, NGFS and HNGFS, respectively, during Indian summer monsoon (June-September) 2015. The NGFS uses gridpoint statistical interpolation (GSI) 3D-Var data assimilation system, whereas HNGFS uses hybrid 3D ensemble-variational scheme. The analysis includes the evaluation of rainfall fields and comparisons of rainfall using statistical score such as mean precipitation, bias, correlation coefficient, root mean square error and forecast improvement factor. In addition to these, categorical scores like Peirce skill score and bias score are also computed to describe particular aspects of forecasts performance. The comparison results of mean precipitation reveal that both the versions of model produced similar large-scale feature of Indian summer monsoon rainfall for day-1 through day-5 forecasts. The inclusion of fully flow-dependent background error covariance significantly improved the wet biases in HNGFS over the Indian Ocean. The forecast improvement factor and Peirce skill score in the HNGFS have also found better than NGFS for day-1 through day-5 forecasts.

Role of Ocean Initial Conditions to Diminish Dry Bias in the Seasonal Prediction of Indian Summer Monsoon Rainfall: A Case Study Using Climate Forecast System

Science.gov (United States)

Koul, Vimal; Parekh, Anant; Srinivas, G.; Kakatkar, Rashmi; Chowdary, Jasti S.; Gnanaseelan, C.

2018-03-01

Coupled models tend to underestimate Indian summer monsoon (ISM) rainfall over most of the Indian subcontinent. Present study demonstrates that a part of dry bias is arising from the discrepancies in Oceanic Initial Conditions (OICs). Two hindcast experiments are carried out using Climate Forecast System (CFSv2) for summer monsoons of 2012-2014 in which two different OICs are utilized. With respect to first experiment (CTRL), second experiment (AcSAL) differs by two aspects: usage of high-resolution atmospheric forcing and assimilation of only ARGO observed temperature and salinity profiles for OICs. Assessment of OICs indicates that the quality of OICs is enhanced due to assimilation of actual salinity profiles. Analysis reveals that AcSAL experiment showed 10% reduction in the dry bias over the Indian land region during the ISM compared to CTRL. This improvement is consistently apparent in each month and is highest for June. The better representation of upper ocean thermal structure of tropical oceans at initial stage supports realistic upper ocean stability and mixing. Which in fact reduced the dominant cold bias over the ocean, feedback to air-sea interactions and land sea thermal contrast resulting better representation of monsoon circulation and moisture transport. This reduced bias of tropospheric moisture and temperature over the Indian land mass and also produced better tropospheric temperature gradient over land as well as ocean. These feedback processes reduced the dry bias in the ISM rainfall. Study concludes that initializing the coupled models with realistic OICs can reduce the underestimation of ISM rainfall prediction.

Evaporation-precipitation variability over Indian Ocean and its assessment in NCEP Climate Forecast System (CFSv2)

Energy Technology Data Exchange (ETDEWEB)

Pokhrel, Samir; Parekh, Anant; Saha, Subodh Kumar; Dhakate, Ashish; Chaudhari, Hemantkumar S. [Indian Institute of Tropical Meteorology, Pune (India); Rahaman, Hasibur [Indian National Centre for Ocean Information Services, Hyderabad (India); Gairola, Rakesh Mohan [Space Applications Centre, ISRO, Ahmedabad (India)

2012-11-15

An attempt has been made to explore all the facets of Evaporation-Precipitation (E-P) distribution and variability over the Indian Ocean (IO) basin using Objectively Analyzed air-sea Fluxes (OAFlux) data and subsequently a thorough assessment of the latest version of National Centers for Environment Prediction (NCEP) Climate Forecast System (CFS) version-2 is done. This study primarily focuses on two fundamental issues, first, the core issue of pervasive cold SST bias in the CFS simulation in the context of moisture flux exchange between the atmosphere and the ocean and second, the fidelity of the model in simulating mean and variability of E-P and its elemental components associated with the climatic anomalies occurring over the Indian and the Pacific ocean basin. Valuation of evaporation and precipitation, the two integral component of E-P, along with the similar details of wind speed, air-sea humidity difference ({Delta}Q) and Sea Surface Temperature (SST) are performed. CFS simulation is vitiated by the presence of basin wide systematic positive bias in evaporation, {Delta}Q and similar negative bias in wind speed and SST. Bifurcation of the evaporation bias into its components reveals that bias in air humidity (Q{sub a}) is basically responsible for the presence of pervasive positive evaporation bias. The regions where CFS does not adhere to the observed wind-evaporation and Q{sub a} -evaporation relation was found to lie over the northern Arabian Sea (AS), the western Bay of Bengal (BoB) and the western Equatorial IO. Evaporation bias is found to control a significant quantum of cold SST bias over most of the basin owing to its intimate association with SST in a coupled feedback system. This area is stretched over the almost entire north IO, north of 15 {sup circle} S excluding a small equatorial strip, where the evaporation bias may essentially explain 20-100 % of cold SST bias. This percentage is maximum over the western IO, central AS and BoB. The CFS

NWS Marine Forecast Areas

Science.gov (United States)

of Commerce Ocean Prediction Center National Oceanic and Atmospheric Administration Analysis & Unified Surface Analysis Ocean Ocean Products Ice & Icebergs NIC Ice Products NAIS Iceberg Analysis Social Media Facebook Twitter YouTube Search Search For Go NWS All NOAA NWS Marine Forecast Areas

The Stevens Integrated Maritime Surveillance Forecast System: Expansion and Enhancement

National Research Council Canada - National Science Library

Bruno, Michael S; Blumberg, Alan F

2006-01-01

... for the real-time assessment of ocean, weather, environmental, and vessel traffic conditions throughout the New York Harbor region, and the forecast of conditions in the near and long-term and under specific threat scenarios...

Real Time In Situ data management system for EuroGOOS: A ROOSes-MyOcean joint effort

Science.gov (United States)

Pouliquen, S.; Carval, T.; Loubrieu, T.; von Schuckmann, K.; Wehde, H.; SjurRingheim, L.; Hammarklint, T.; Harman, A.; Soetje, K.; Gies, T.; de Alfonso, M.; Perivoliotis, L.; Kassis, D.; Marinova, V.

2012-04-01

MyOcean is the implementation project of the GMES Marine Core Service, that develop the first concerted and integrated pan-European capacity for Ocean Monitoring and Forecasting. Within this project, the in-situ Thematic Assembly Centre (in-situ TAC) of MyOcean is a distributed service integrating data from different sources for operational oceanography needs. The MyOcean in-situ TAC is collecting and carrying out quality control in a homogeneous manner on data from outside MyOcean data providers, especially EuroGOOSparners, to fit the needs of internal and external users. It provides access to integrated datasets of core parameters for initialization, forcing, assimilation and validation of ocean numerical models. Since the primary objective of MyOcean is to forecast ocean state, the initial focus is on observations from automatic observatories at sea (e.g. floats, buoys, gliders, ferrybox, drifters, SOOP) which are transmitted in real-time to the shore. The second objective is to set up a system for re-analysis purposes that integrate data over the past 20 years. The global and regional portals set up by the INS-TAC are extended by the EuroGOOS ROOSES to integrate additionnal parameters important for downstream and national applications.

A critical evaluation of the upper ocean heat budget in the Climate Forecast System Reanalysis data for the south central equatorial Pacific

Energy Technology Data Exchange (ETDEWEB)

Liu Hailong; Liu Xiangcui [State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing (China); Zhang Minghua [Institute for Terrestrial and Planetary Atmospheres, Stony Brook University, State University of New York, Stony Brook, NY (United States); Lin Wuyin, E-mail: lhl@lasg.iap.ac.cn [Atmospheric Sciences Division, Brookhaven National Laboratory, Upton, NY (United States)

2011-07-15

Coupled ocean-atmospheric models suffer from the common bias of a spurious rain belt south of the central equatorial Pacific throughout the year. Observational constraints on key processes responsible for this bias are scarce. The recently available reanalysis from a coupled model system for the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) data is a potential benchmark for climate models in this region. Its suitability for model evaluation and validation, however, needs to be established. This paper examines the mixed layer heat budget and the ocean surface currents-key factors for the sea surface temperature control in the double Inter-Tropical Convergence Zone in the central Pacific-from 5 deg. S to 10 deg. S and 170 deg. E to 150 deg. W. Two independent approaches are used. The first approach is through comparison of CFSR data with collocated station observations from field experiments; the second is through the residual analysis of the heat budget of the mixed layer. We show that the CFSR overestimates the net surface flux in this region by 23 W m{sup -2}. The overestimated net surface flux is mainly due to an even larger overestimation of shortwave radiation by 44 W m{sup -2}, which is compensated by a surface latent heat flux overestimated by 14 W m{sup -2}. However, the quality of surface currents and the associated oceanic heat transport in CFSR are not compromised by the surface flux biases, and they agree with the best available estimates. The uncertainties of the observational data from field experiments are also briefly discussed in the present study.

ATOP - The Advanced Taiwan Ocean Prediction System Based on the mpiPOM. Part 1: Model Descriptions, Analyses and Results

Directory of Open Access Journals (Sweden)

Leo Oey

2013-01-01

Full Text Available A data-assimilated Taiwan Ocean Prediction (ATOP system is being developed at the National Central University, Taiwan. The model simulates sea-surface height, three-dimensional currents, temperature and salinity and turbulent mixing. The model has options for tracer and particle-tracking algorithms, as well as for wave-induced Stokes drift and wave-enhanced mixing and bottom drag. Two different forecast domains have been tested: a large-grid domain that encompasses the entire North Pacific Ocean at 0.1° × 0.1° horizontal resolution and 41 vertical sigma levels, and a smaller western North Pacific domain which at present also has the same horizontal resolution. In both domains, 25-year spin-up runs from 1988 - 2011 were first conducted, forced by six-hourly Cross-Calibrated Multi-Platform (CCMP and NCEP reanalysis Global Forecast System (GSF winds. The results are then used as initial conditions to conduct ocean analyses from January 2012 through February 2012, when updated hindcasts and real-time forecasts begin using the GFS winds. This paper describes the ATOP system and compares the forecast results against satellite altimetry data for assessing model skills. The model results are also shown to compare well with observations of (i the Kuroshio intrusion in the northern South China Sea, and (ii subtropical counter current. Review and comparison with other models in the literature of ¡§(i¡¨ are also given.

Black Sea coastal forecasting system

Directory of Open Access Journals (Sweden)

A. I. Kubryakov

2012-03-01

Full Text Available The Black Sea coastal nowcasting and forecasting system was built within the framework of EU FP6 ECOOP (European COastalshelf sea OPerational observing and forecasting system project for five regions: the south-western basin along the coasts of Bulgaria and Turkey, the north-western shelf along the Romanian and Ukrainian coasts, coastal zone around of the Crimea peninsula, the north-eastern Russian coastal zone and the coastal zone of Georgia. The system operates in the real-time mode during the ECOOP project and afterwards. The forecasts include temperature, salinity and current velocity fields. Ecosystem model operates in the off-line mode near the Crimea coast.

The Impact of Ocean Observations in Seasonal Climate Prediction

Science.gov (United States)

Rienecker, Michele; Keppenne, Christian; Kovach, Robin; Marshak, Jelena

2010-01-01

The ocean provides the most significant memory for the climate system. Hence, a critical element in climate forecasting with coupled models is the initialization of the ocean with states from an ocean data assimilation system. Remotely-sensed ocean surface fields (e.g., sea surface topography, SST, winds) are now available for extensive periods and have been used to constrain ocean models to provide a record of climate variations. Since the ocean is virtually opaque to electromagnetic radiation, the assimilation of these satellite data is essential to extracting the maximum information content. More recently, the Argo drifters have provided unprecedented sampling of the subsurface temperature and salinity. Although the duration of this observation set has been too short to provide solid statistical evidence of its impact, there are indications that Argo improves the forecast skill of coupled systems. This presentation will address the impact these different observations have had on seasonal climate predictions with the GMAO's coupled model.

Decadal-Scale Forecasting of Climate Drivers for Marine Applications.

Science.gov (United States)

Salinger, J; Hobday, A J; Matear, R J; O'Kane, T J; Risbey, J S; Dunstan, P; Eveson, J P; Fulton, E A; Feng, M; Plagányi, É E; Poloczanska, E S; Marshall, A G; Thompson, P A

Climate influences marine ecosystems on a range of time scales, from weather-scale (days) through to climate-scale (hundreds of years). Understanding of interannual to decadal climate variability and impacts on marine industries has received less attention. Predictability up to 10 years ahead may come from large-scale climate modes in the ocean that can persist over these time scales. In Australia the key drivers of climate variability affecting the marine environment are the Southern Annular Mode, the Indian Ocean Dipole, the El Niño/Southern Oscillation, and the Interdecadal Pacific Oscillation, each has phases that are associated with different ocean circulation patterns and regional environmental variables. The roles of these drivers are illustrated with three case studies of extreme events-a marine heatwave in Western Australia, a coral bleaching of the Great Barrier Reef, and flooding in Queensland. Statistical and dynamical approaches are described to generate forecasts of climate drivers that can subsequently be translated to useful information for marine end users making decisions at these time scales. Considerable investment is still needed to support decadal forecasting including improvement of ocean-atmosphere models, enhancement of observing systems on all scales to support initiation of forecasting models, collection of important biological data, and integration of forecasts into decision support tools. Collaboration between forecast developers and marine resource sectors-fisheries, aquaculture, tourism, biodiversity management, infrastructure-is needed to support forecast-based tactical and strategic decisions that reduce environmental risk over annual to decadal time scales. © 2016 Elsevier Ltd. All rights reserved.

Impact of scatterometer wind (ASCAT-A/B) data assimilation on semi real-time forecast system at KIAPS

Science.gov (United States)

Han, H. J.; Kang, J. H.

2016-12-01

Since Jul. 2015, KIAPS (Korea Institute of Atmospheric Prediction Systems) has been performing the semi real-time forecast system to assess the performance of their forecast system as a NWP model. KPOP (KIAPS Protocol for Observation Processing) is a part of KIAPS data assimilation system and has been performing well in KIAPS semi real-time forecast system. In this study, due to the fact that KPOP would be able to treat the scatterometer wind data, we analyze the effect of scatterometer wind (ASCAT-A/B) on KIAPS semi real-time forecast system. O-B global distribution and statistics of scatterometer wind give use two information which are the difference between background field and observation is not too large and KPOP processed the scatterometer wind data well. The changes of analysis increment because of O-B global distribution appear remarkably at the bottom of atmospheric field. It also shows that scatterometer wind data cover wide ocean where data would be able to short. Performance of scatterometer wind data can be checked through the vertical error reduction against IFS between background and analysis field and vertical statistics of O-A. By these analysis result, we can notice that scatterometer wind data will influence the positive effect on lower level performance of semi real-time forecast system at KIAPS. After, long-term result based on effect of scatterometer wind data will be analyzed.

Test operation of a real-time tsunami inundation forecast system using actual data observed by S-net

Science.gov (United States)

Suzuki, W.; Yamamoto, N.; Miyoshi, T.; Aoi, S.

2017-12-01

If the tsunami inundation information can be rapidly and stably forecast before the large tsunami attacks, the information would have effectively people realize the impeding danger and necessity of evacuation. Toward that goal, we have developed a prototype system to perform the real-time tsunami inundation forecast for Chiba prefecture, eastern Japan, using off-shore ocean bottom pressure data observed by the seafloor observation network for earthquakes and tsunamis along the Japan Trench (S-net) (Aoi et al., 2015, AGU). Because tsunami inundation simulation requires a large computation cost, we employ a database approach searching the pre-calculated tsunami scenarios that reasonably explain the observed S-net pressure data based on the multi-index method (Yamamoto et al., 2016, EPS). The scenario search is regularly repeated, not triggered by the occurrence of the tsunami event, and the forecast information is generated from the selected scenarios that meet the criterion. Test operation of the prototype system using the actual observation data started in April, 2017 and the performance and behavior of the system during non-tsunami event periods have been examined. It is found that the treatment of the noises affecting the observed data is the main issue to be solved toward the improvement of the system. Even if the observed pressure data are filtered to extract the tsunami signals, the noises in ordinary times or unusually large noises like high ocean waves due to storm affect the comparison between the observed and scenario data. Due to the noises, the tsunami scenarios are selected and the tsunami is forecast although any tsunami event does not actually occur. In most cases, the selected scenarios due to the noises have the fault models in the region along the Kurile or Izu-Bonin Trenches, far from the S-net region, or the fault models below the land. Based on the parallel operation of the forecast system with a different scenario search condition and

Operational aerosol and dust storm forecasting

International Nuclear Information System (INIS)

Westphal, D L; Curtis, C A; Liu, M; Walker, A L

2009-01-01

The U. S. Navy now conducts operational forecasting of aerosols and dust storms on global and regional scales. The Navy Aerosol Analysis and Prediction System (NAAPS) is run four times per day and produces 6-day forecasts of sulfate, smoke, dust and sea salt aerosol concentrations and visibility for the entire globe. The Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS (registered) ) is run twice daily for Southwest Asia and produces 3-day forecasts of dust, smoke, and visibility. The graphical output from these models is available on the Internet (www.nrlmry.navy.mil/aerosol/). The aerosol optical properties are calculated for each specie for each forecast output time and used for sea surface temperature (SST) retrieval corrections, regional electro-optical (EO) propagation assessments, and the development of satellite algorithms. NAAPS daily aerosol optical depth (AOD) values are compared with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) AOD values. Visibility forecasts are compared quantitatively with surface synoptic reports.

A Deep Learning Algorithm of Neural Network for the Parameterization of Typhoon-Ocean Feedback in Typhoon Forecast Models

Science.gov (United States)

Jiang, Guo-Qing; Xu, Jing; Wei, Jun

2018-04-01

Two algorithms based on machine learning neural networks are proposed—the shallow learning (S-L) and deep learning (D-L) algorithms—that can potentially be used in atmosphere-only typhoon forecast models to provide flow-dependent typhoon-induced sea surface temperature cooling (SSTC) for improving typhoon predictions. The major challenge of existing SSTC algorithms in forecast models is how to accurately predict SSTC induced by an upcoming typhoon, which requires information not only from historical data but more importantly also from the target typhoon itself. The S-L algorithm composes of a single layer of neurons with mixed atmospheric and oceanic factors. Such a structure is found to be unable to represent correctly the physical typhoon-ocean interaction. It tends to produce an unstable SSTC distribution, for which any perturbations may lead to changes in both SSTC pattern and strength. The D-L algorithm extends the neural network to a 4 × 5 neuron matrix with atmospheric and oceanic factors being separated in different layers of neurons, so that the machine learning can determine the roles of atmospheric and oceanic factors in shaping the SSTC. Therefore, it produces a stable crescent-shaped SSTC distribution, with its large-scale pattern determined mainly by atmospheric factors (e.g., winds) and small-scale features by oceanic factors (e.g., eddies). Sensitivity experiments reveal that the D-L algorithms improve maximum wind intensity errors by 60-70% for four case study simulations, compared to their atmosphere-only model runs.

Forecasting Tools Point to Fishing Hotspots

Science.gov (United States)

2009-01-01

Private weather forecaster WorldWinds Inc. of Slidell, Louisiana has employed satellite-gathered oceanic data from Marshall Space Flight Center to create a service that is every fishing enthusiast s dream. The company's FishBytes system uses information about sea surface temperature and chlorophyll levels to forecast favorable conditions for certain fish populations. Transmitting the data to satellite radio subscribers, FishBytes provides maps that guide anglers to the areas they are most likely to make their favorite catch.

Integrated forecast system atmospheric - hydrologic - hydraulic for the Urubamba river basin

Energy Technology Data Exchange (ETDEWEB)

Metzger, L [Peruvian National Weather Service, Lima (Peru); Carrillo, M; Diaz, A; Coronado, J; Fano, G [Peruvian National Weather Service, Lima (Peru)

2004-07-01

climate model a statistical forecast was developed using Empirical Orthogonal Functions (EOF), this methodology uses the Long Wave Radiation as a predictor for the precipitation occurrence in the study area. This model is based on an atmospheric-ocean teleconnection El NINO 3 region in the central tropical pacific and the observed rainfall over the Andes. The information generated by the atmospheric model was used as input for the Sacramento hydrologic model originally developed by the National Weather Service River Forecast System (NWSRFS) which considers all the historical data (precipitation, flows and evapotranspiration), the model considers a perturbation in the form of a random variable which depends on the standard deviation and the mean, this algorithm allows to have not only one precipitation time series but the double or triple. This is the basis on the hydrologic ensemble forecasting where each precipitation time series generates a flow time series and then using post processing codes we find the probabilistic forecasts of non excedance for different percentage of probability. Finally the hydraulic model used was the HEC-RAS V.3.1 developed by the U.S Army Corps of Engineering which used all the cross sections available in the zone, manning values, contraction and expansion coefficients to convert the forecasted flow data into water level of the Urubamba river in four check points requested by the user: Malvinas, Nuevo Mundo, Sepahua and Maldonadillo. SENAMHI provided of useful information for 2 years and was the result of a multidisciplinary systemic work that joined meteorologists, hydrologists, climatologists and system engineers. The information used by the Regional numerical model RAMS was assimilated from geostationary satellite GOES 8 and automatic stations located in strategic points considering the topography, accessibility, security, extreme rainfall conditions and consequent variability in the levels of the Urubamba river. As a conclusion the work

Integrated forecast system atmospheric-hydrologic-hydraulic for the Urubamba River Basin

Energy Technology Data Exchange (ETDEWEB)

Metzger, L; Carrillo, M; Diaz, A; Coronado, J; Fano, G [Peruvian National Weather Service, Lima (Peru)

2006-02-15

climate model a statistical forecast was developed using Empirical Orthogonal Functions (EOF), this methodology uses the Long Wave Radiation as a predictor for the precipitation occurrence in the study area. This model is based on an atmospheric-ocean teleconnection El Nino 3 region in the central tropical pacific and the observed rainfall over the Andes. The information generated by the atmospheric model was used as input for the Sacramento hydrologic model originally developed by the National Weather Service River Forecast System (NWSRFS) which considers all the historical data (precipitation, flows and evapotranspiration), the model considers a perturbation in the form of a random variable which depends on the standard deviation and the mean, this algorithm allows to have not only one precipitation time series but the double or triple. This is the basis on the hydrologic ensemble forecasting where each precipitation time series generates a flow time series and then using post processing codes we find the probabilistic forecasts of non excedance for different percentage of probability. Finally the hydraulic model used was the HEC-RAS V.3.1 developed by the U.S Army Corps of Engineering which used all the cross sections available in the zone, manning values, contraction and expansion coefficients to convert the forecasted flow data into water level of the Urubamba river in four check points requested by the user: Malvinas, Nuevo Mundo, Sepahua and Maldonadillo. SENAMHI provided of useful information for 2 years and was the result of a multidisciplinary systemic work that joined meteorologists, hydrologists, climatologists and system engineers. The information used by the Regional numerical model RAMS was assimilated from geostationary satellite GOES 8 and automatic stations located in strategic points considering the topography, accessibility, security, extreme rainfall conditions and consequent variability in the levels of the Urubamba river. As a conclusion the work

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

Science.gov (United States)

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

2017-12-01

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

Forecasting in Complex Systems

Science.gov (United States)

Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Turcotte, D. L.; Donnellan, A.

2014-12-01

Complex nonlinear systems are typically characterized by many degrees of freedom, as well as interactions between the elements. Interesting examples can be found in the areas of earthquakes and finance. In these two systems, fat tails play an important role in the statistical dynamics. For earthquake systems, the Gutenberg-Richter magnitude-frequency is applicable, whereas for daily returns for the securities in the financial markets are known to be characterized by leptokurtotic statistics in which the tails are power law. Very large fluctuations are present in both systems. In earthquake systems, one has the example of great earthquakes such as the M9.1, March 11, 2011 Tohoku event. In financial systems, one has the example of the market crash of October 19, 1987. Both were largely unexpected events that severely impacted the earth and financial systems systemically. Other examples include the M9.3 Andaman earthquake of December 26, 2004, and the Great Recession which began with the fall of Lehman Brothers investment bank on September 12, 2013. Forecasting the occurrence of these damaging events has great societal importance. In recent years, national funding agencies in a variety of countries have emphasized the importance of societal relevance in research, and in particular, the goal of improved forecasting technology. Previous work has shown that both earthquakes and financial crashes can be described by a common Landau-Ginzburg-type free energy model. These metastable systems are characterized by fat tail statistics near the classical spinodal. Correlations in these systems can grow and recede, but do not imply causation, a common source of misunderstanding. In both systems, a common set of techniques can be used to compute the probabilities of future earthquakes or crashes. In this talk, we describe the basic phenomenology of these systems and emphasize their similarities and differences. We also consider the problem of forecast validation and verification

Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction

Science.gov (United States)

Li, Zhijin; Chao, Yi; Li, P. Peggy

2012-01-01

A multi-scale three-dimensional variational data assimilation system (MS-3DVAR) has been formulated and the associated software system has been developed for improving high-resolution coastal ocean prediction. This system helps improve coastal ocean prediction skill, and has been used in support of operational coastal ocean forecasting systems and field experiments. The system has been developed to improve the capability of data assimilation for assimilating, simultaneously and effectively, sparse vertical profiles and high-resolution remote sensing surface measurements into coastal ocean models, as well as constraining model biases. In this system, the cost function is decomposed into two separate units for the large- and small-scale components, respectively. As such, data assimilation is implemented sequentially from large to small scales, the background error covariance is constructed to be scale-dependent, and a scale-dependent dynamic balance is incorporated. This scheme then allows effective constraining large scales and model bias through assimilating sparse vertical profiles, and small scales through assimilating high-resolution surface measurements. This MS-3DVAR enhances the capability of the traditional 3DVAR for assimilating highly heterogeneously distributed observations, such as along-track satellite altimetry data, and particularly maximizing the extraction of information from limited numbers of vertical profile observations.

Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule

Science.gov (United States)

Jin, Yishuai; Rong, Xinyao; Liu, Zhengyu

2017-12-01

This study investigates the factors relationship between the forecast skills for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill for sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further proved using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but could be distorted by sampling errors and non-AR1 processes. This study suggests that the so called "perfect skill" is model dependent and cannot serve as an accurate estimate of the true upper limit of real world prediction skill, unless the model can capture at least the persistence property of the observation.

Near Real Time Operational Satellite Ocean Color Products From NOAA OSPO CoastWatch Okeanos System:: Status and Challenges

Science.gov (United States)

Banghua Yan, B.

2016-02-01

Near real-time (NRT) ocean color (OC) satellite operation products are generated and distributed in NOAA Okeanos Operational Product System, by using the CWAPS including the Multi-Sensor Level (MSL) 12 and the chlorophyll-a frontal algorithms. Current OC operational products include daily chlorophyll concentration (anomaly), water turbidity, remote sensing reflectance and chlorophyll frontal products from Moderate-resolution Imaging Spectroradiometer (MODIS)/Aqua. The products have been widely applied to USA local and state ecosystem research, ecosystem observations, and fisheries managements for coastal and regional forecasting of ocean water quality, phytoplankton concentrations, and primary production. Users of the products have the National Ocean Service, National Marine Fisheries Service, National Weather Service, and Oceanic and Atmospheric Research. Recently, the OC products are being extended to S-NPP VIIRS to provide global NRT ocean color products to user community suh as National Weatrher Service for application for Global Data Assimilation System and Real-Time Ocean Forecast System. However, there remain some challenges in application of the products due to certain product quality and coverage issues. Recent efforts were made to provide a comprehensive web-based Quality Assurance (QA) tool for monitoring OC products quality in near real time mode, referring to http://www.ospo.noaa.gov/Products/ocean/color_new/color.htm. The new QA monitoring tool includes but not limited to the following advanced features applicable for MODIS/Aqua and NPP/VIIRS OC products: 1) Monitoring product quality in NRT mode; 2) Monitoring the availability and quality of OC products with time; 3) Detecting anomalous OC products due to low valid pixels and other quality issues. As an example, potential application and challenges of the ocean color products to oceanic oil spill detection are investigated. It is thus expected that the Okeanos ocean color operational system in

An Improved Ocean Observing System for Coastal Louisiana: WAVCIS (WAVE-CURRENT-SURGE Information System )

Science.gov (United States)

Zhang, X.; Stone, G. W.; Gibson, W. J.; Braud, D.

2005-05-01

WAVCIS is a regional ocean observing and forecasting system. It was designed to measure, process, forecast, and distribute oceanographic and meteorological information. WAVCIS was developed and is maintained by the Coastal Studies Institute at Louisiana State University. The in-situ observing stations are distributed along the central Louisiana and Mississippi coast. The forecast region covers the entire Gulf of Mexico with emphasis on offshore Louisiana. By using state-of-the-art instrumentation, WAVCIS measures directional waves, currents, temperature, water level, conductivity, turbidity, salinity, dissolved oxygen, chlorophyll, Meteorological parameters include wind speed and direction, air pressure and temperature visibility and humidity. Through satellite communication links, the measured data are transmitted to the WAVCIS laboratory. After processing, they are available to the public via the internet on a near real-time basis. WAVCIS also includes a forecasting capability. Waves, tides, currents, and winds are forecast daily for up to 80 hours in advance. There are a number of numerical wave and surge models that can be used for forecasts. WAM and SWAN are used for operational purposes to forecast sea state. Tides at each station are predicted based on the harmonic constants calculated from past in-situ observations at respective sites. Interpolated winds from the ETA model are used as input forcing for waves. Both in-situ and forecast information are available online to the users through WWW. Interactive GIS web mapping is implemented on the WAVCIS webpage to visualize the model output and in-situ observational data. WAVCIS data can be queried, retrieved, downloaded, and analyzed through the web page. Near real-time numerical model skill assessment can also be performed by using the data from in-situ observing stations.

Using Seasonal Forecasting Data for Vessel Routing

Science.gov (United States)

Bell, Ray; Kirtman, Ben

2017-04-01

We present an assessment of seasonal forecasting of surface wind speed, significant wave height and ocean surface current speed in the North Pacific for potential use of vessel routing from Singapore to San Diego. WaveWatchIII is forced with surface winds and ocean surface currents from the Community Climate System Model 4 (CCSM4) retrospective forecasts for the period of 1982-2015. Several lead time forecasts are used from zero months to six months resulting in 2,720 model years, ensuring the findings from this study are robust. July surface wind speed and significant wave height can be skillfully forecast with a one month lead time, with the western North Pacific being the most predictable region. Beyond May initial conditions (lead time of two months) the El Niño Southern Oscillation (ENSO) Spring predictability barrier limits skill of significant wave height but there is skill for surface wind speed with January initial conditions (lead time of six months). In a separate study of vessel routing between Norfolk, Virginia and Gibraltar we demonstrate the benefit of a multimodel approach using the North American Multimodel Ensemble (NMME). In collaboration with Charles River Analytics an all-encompassing forecast is presented by using machine learning on the various ensembles which can be using used for industry applications.

Tsunami Forecasting: The 10 August 2009 Andaman tsunami Demonstrates Progress

Science.gov (United States)

Titov, Vasily; Moore, Christopher; Uslu, Burak; Kanoglu, Utku

2010-05-01

The 10 August 2009 Andaman non-destructive tsunami in the Indian Ocean demonstrated advances in creating a tsunami-resilient global society. Following the Indian Ocean tsunami on 26 December 2004, scientists at the National Oceanic and Atmospheric Administration Center for Tsunami Research (NCTR) at the Pacific Marine Environmental Laboratory (PMEL) developed an interface for its validated and verified tsunami numerical model Method of Splitting Tsunamis (MOST). MOST has been benchmarked substantially through analytical solutions, experimental results and field measurements (Synolakis et al., 2008). MOST and its interface the Community Model Interface for Tsunami (ComMIT) are distributed through extensive capacity-building sessions for the Indian Ocean nations using UNESCO/Intergovernmental Oceanographic Commission (IOC), AusAID, and USAID funding. Over one hundred-sixty scientists have been trained in tsunami inundation mapping, leading to the first generation of inundation models for many Indian Ocean shorelines. During the 10 August 2009 Andaman tsunami event, NCTR scientists exercised the forecast system in research mode using the first generation inundation models developed during ComMIT trainings. Assimilating key data from a Kingdom of Thailand tsunameter, coastal tsunami amplitudes were predicted in Indonesia, Thailand, and India coastlines, before the first tsunami arrival, using models developed by ComMIT trainees. Since its first test in 2003, one more time, NCTR's forecasting methodology proved the effectiveness of operational tsunami forecasting using real-time deep-ocean data assimilated into forecast models (Wei et al., 2008 and Titov, 2009). The 2009 Andaman tsunami demonstrated that operational tsunami forecasting tools are now available and coupled with inundation mapping tools can be effective and can reduce false alarms. International collaboration is required to fully utilize this technology's potential. Enhanced educational efforts both at

The GOCF/AWAP system - forecasting temperature extremes

International Nuclear Information System (INIS)

Fawcett, Robert; Hume, Timothy

2010-01-01

Gridded hourly temperature forecasts from the Bureau of Meteorology's Gridded Operational Consensus Forecasting (GOCF) system are combined in real time with the Australian Water Availability Project (AWAP) gridded daily temperature analyses to produce gridded daily maximum and minimum temperature forecasts with lead times from one to five days. These forecasts are compared against the historical record of AWAP daily temperature analyses (1911 to present), to identify regions where record or near-record temperatures are predicted to occur. This paper describes the GOCF/AWAP system, showing how the daily maximum and minimum temperature forecasts are prepared from the hourly forecasts, and how they are bias-corrected in real time using the AWAP analyses, against which they are subsequently verified. Using monthly climatologies of long-term daily mean, standard deviation and all-time highest and lowest on record, derived forecast products (for both maximum and minimum temperature) include ordinary and standardised anomalies, 'forecast - highest on record' and 'forecast - lowest on record'. Compensation for the climatological variation across the country is achieved in these last two products, which provide the necessary guidance as to whether or not record-breaking temperatures are expected, by expressing the forecast departure from the previous record in both 0 C and standard deviations.

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

Directory of Open Access Journals (Sweden)

Hugo Carrão

2018-06-01

Full Text Available Meaningful seasonal prediction of drought conditions is key information for end-users and water managers, particularly in Latin America where crop and livestock production are key for many regional economies. However, there are still not many studies of the feasibility of such a forecasts at continental level in the region. In this study, precipitation predictions from the European Centre for Medium Range Weather (ECMWF seasonal forecast system S4 are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI for Latin America, and their skill is evaluated over the hindcast period 1981–2010. The value-added utility in using the ensemble S4 forecast to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on their climatological characteristics. As expected, skill of the S4-generated SPI forecasts depends on the season, location, and the specific aggregation period considered (the 3- and 6-month SPI were evaluated. Added skill from the S4 for lead times equaling the SPI accumulation periods is primarily present in regions with high intra-annual precipitation variability, and is found mostly for the months at the end of the dry seasons for 3-month SPI, and half-yearly periods for 6-month SPI. The ECMWF forecast system behaves better than the climatology for clustered grid points in the North of South America, the Northeast of Argentina, Uruguay, southern Brazil and Mexico. The skillful regions are similar for the SPI3 and -6, but become reduced in extent for the severest SPI categories. Forecasting different magnitudes of meteorological drought intensity on a seasonal time scale still remains a challenge. However, the ECMWF S4 forecasting system does capture the occurrence of drought events for the aforementioned regions and seasons reasonably well. In the near term, the largest advances in the prediction of meteorological drought for Latin

CDM Convective Forecast Planning guidance

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The CDM Convective Forecast Planning (CCFP) guidance product provides a foreast of en-route aviation convective hazards. The forecasts are updated every 2 hours and...

Hybrid Intrusion Forecasting Framework for Early Warning System

Science.gov (United States)

Kim, Sehun; Shin, Seong-Jun; Kim, Hyunwoo; Kwon, Ki Hoon; Han, Younggoo

Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.

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...

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...

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...

The distribution of wind power forecast errors from operational systems

Energy Technology Data Exchange (ETDEWEB)

Hodge, Bri-Mathias; Ela, Erik; Milligan, Michael

2011-07-01

Wind power forecasting is one important tool in the integration of large amounts of renewable generation into the electricity system. Wind power forecasts from operational systems are not perfect, and thus, an understanding of the forecast error distributions can be important in system operations. In this work, we examine the errors from operational wind power forecasting systems, both for a single wind plant and for an entire interconnection. The resulting error distributions are compared with the normal distribution and the distribution obtained from the persistence forecasting model at multiple timescales. A model distribution is fit to the operational system forecast errors and the potential impact on system operations highlighted through the generation of forecast confidence intervals. (orig.)

Ocean Prediction Center

Science.gov (United States)

Social Media Facebook Twitter YouTube Search Search For Go NWS All NOAA Weather Analysis & Forecasts of Commerce Ocean Prediction Center National Oceanic and Atmospheric Administration Analysis & Unified Surface Analysis Ocean Ocean Products Ice & Icebergs NIC Ice Products NAIS Iceberg Analysis

iFLOOD: A Real Time Flood Forecast System for Total Water Modeling in the National Capital Region

Science.gov (United States)

Sumi, S. J.; Ferreira, C.

2017-12-01

Extreme flood events are the costliest natural hazards impacting the US and frequently cause extensive damages to infrastructure, disruption to economy and loss of lives. In 2016, Hurricane Matthew brought severe damage to South Carolina and demonstrated the importance of accurate flood hazard predictions that requires the integration of riverine and coastal model forecasts for total water prediction in coastal and tidal areas. The National Weather Service (NWS) and the National Ocean Service (NOS) provide flood forecasts for almost the entire US, still there are service-gap areas in tidal regions where no official flood forecast is available. The National capital region is vulnerable to multi-flood hazards including high flows from annual inland precipitation events and surge driven coastal inundation along the tidal Potomac River. Predicting flood levels on such tidal areas in river-estuarine zone is extremely challenging. The main objective of this study is to develop the next generation of flood forecast systems capable of providing accurate and timely information to support emergency management and response in areas impacted by multi-flood hazards. This forecast system is capable of simulating flood levels in the Potomac and Anacostia River incorporating the effects of riverine flooding from the upstream basins, urban storm water and tidal oscillations from the Chesapeake Bay. Flood forecast models developed so far have been using riverine data to simulate water levels for Potomac River. Therefore, the idea is to use forecasted storm surge data from a coastal model as boundary condition of this system. Final output of this validated model will capture the water behavior in river-estuary transition zone far better than the one with riverine data only. The challenge for this iFLOOD forecast system is to understand the complex dynamics of multi-flood hazards caused by storm surges, riverine flow, tidal oscillation and urban storm water. Automated system

Ocean Chlorophyll as a Precursor of ENSO: An Earth System Modeling Study

Science.gov (United States)

Park, Jong-Yeon; Dunne, John P.; Stock, Charles A.

2018-02-01

Ocean chlorophyll concentration, a proxy for phytoplankton, is strongly influenced by internal ocean dynamics such as those associated with El Niño-Southern Oscillation (ENSO). Observations show that ocean chlorophyll responses to ENSO generally lead sea surface temperature (SST) responses in the equatorial Pacific. A long-term global Earth system model simulation incorporating marine biogeochemical processes also exhibits a preceding chlorophyll response. In contrast to simulated SST anomalies, which significantly lag the wind-driven subsurface heat response to ENSO, chlorophyll anomalies respond rapidly. Iron was found to be the key factor connecting the simulated surface chlorophyll anomalies to the subsurface ocean response. Westerly wind bursts decrease central Pacific chlorophyll by reducing iron supply through wind-driven thermocline deepening but increase western Pacific chlorophyll by enhancing the influx of coastal iron from the maritime continent. Our results mechanistically support the potential for chlorophyll-based indices to inform seasonal ENSO forecasts beyond previously identified SST-based indices.

Factors Reducing Efficiency of the Operational Oceanographic Forecast Systems in the Arctic Basin

Directory of Open Access Journals (Sweden)

V.N. Belokopytov

2017-04-01

Full Text Available Reliability of the forecasted fields in the Arctic Basin is limited by a number of problems resulting, in the first turn, from lack of operational information. Due to the ice cover, satellite data on the sea level and the sea surface temperature is either completely not available or partially accessible in summer. The amount of CTD measuring systems functioning in the operational mode (3 – 5 probes is not sufficient. The number of the temperature-profiling buoys the probing depth of which is limited to 60 m, is not enough for the Arctic as well. Lack of spatial resolution of the available altimetry information (14 km, as compared to the Rossby radius in the Arctic Ocean (2 – 12 km, requires a thorough analysis of the forecasting system practical goals. The basic factor enhancing reliability of the oceanographic forecast consists in the fact that the key oceanographic regions, namely the eastern parts of the Norwegian and Greenland seas, the Barents Sea and the Chukchi Sea including the Bering Strait (where the Atlantic and Pacific waters flow in and transform, and the halocline structure is formed are partially or completely free of ice and significantly better provided with operational information.

Assessing the Skill of Chlorophyll Forecasts: Latest Development and Challenges Ahead Using the Case of the Equatorial Pacific

Science.gov (United States)

Rousseaux, Cecile S.; Gregg, Watson W.

2018-01-01

Using a global ocean biogeochemical model combined with a forecast of physical oceanic and atmospheric variables from the NASA Global Modeling and Assimilation Office, we assess the skill of a chlorophyll concentrations forecast in the Equatorial Pacific for the period 2012-2015 with a focus on the forecast of the onset of the 2015 El Nino event. Using a series of retrospective 9-month hindcasts, we assess the uncertainties of the forecasted chlorophyll by comparing the monthly total chlorophyll concentration from the forecast with the corresponding monthly ocean chlorophyll data from the Suomi-National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-NPP VIIRS) satellite. The forecast was able to reproduce the phasing of the variability in chlorophyll concentration in the Equatorial Pacific, including the beginning of the 2015-2016 El Nino. The anomaly correlation coefficient (ACC) was significant (p less than 0.05) for forecast at 1-month (R=0.33), 8-month (R=0.42) and 9-month (R=0.41) lead times. The root mean square error (RMSE) increased from 0.0399 microgram chl L(exp -1) for the 1-month lead forecast to a maximum of 0.0472 microgram chl L(exp -1) for the 9-month lead forecast indicating that the forecast of the amplitude of chlorophyll concentration variability was getting worse. Forecasts with a 3-month lead time were on average the closest to the S-NPP VIIRS data (23% or 0.033 microgram chl L(exp -1)) while the forecast with a 9-month lead time were the furthest (31% or 0.042 microgram chl L(exp -1)). These results indicate the potential for forecasting chlorophyll concentration in this region but also highlights various deficiencies and suggestions for improvements to the current biogeochemical forecasting system. This system provides an initial basis for future applications including the effects of El Nino events on fisheries and other ocean resources given improvements identified in the analysis of these results.

Decadal climate predictions improved by ocean ensemble dispersion filtering

Science.gov (United States)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its

Towards uncertainty estimation for operational forecast products - a multi-model-ensemble approach for the North Sea and the Baltic Sea

Science.gov (United States)

Golbeck, Inga; Li, Xin; Janssen, Frank

2014-05-01

Several independent operational ocean models provide forecasts of the ocean state (e.g. sea level, temperature, salinity and ice cover) in the North Sea and the Baltic Sea on a daily basis. These forecasts are the primary source of information for a variety of information and emergency response systems used e.g. to issue sea level warnings or carry out oil drift forecast. The forecasts are of course highly valuable as such, but often suffer from a lack of information on their uncertainty. With the aim of augmenting the existing operational ocean forecasts in the North Sea and the Baltic Sea by a measure of uncertainty a multi-model-ensemble (MME) system for sea surface temperature (SST), sea surface salinity (SSS) and water transports has been set up in the framework of the MyOcean-2 project. Members of MyOcean-2, the NOOS² and HIROMB/BOOS³ communities provide 48h-forecasts serving as inputs. Different variables are processed separately due to their different physical characteristics. Based on the so far collected daily MME products of SST and SSS, a statistical method, Empirical Orthogonal Function (EOF) analysis is applied to assess their spatial and temporal variability. For sea surface currents, progressive vector diagrams at specific points are consulted to estimate the performance of the circulation models especially in hydrodynamic important areas, e.g. inflow/outflow of the Baltic Sea, Norwegian trench and English Channel. For further versions of the MME system, it is planned to extend the MME to other variables like e.g. sea level, ocean currents or ice cover based on the needs of the model providers and their customers. It is also planned to include in-situ data to augment the uncertainty information and for validation purposes. Additionally, weighting methods will be implemented into the MME system to develop more complex uncertainty measures. The methodology used to create the MME will be outlined and different ensemble products will be presented. In

Flood Forecasting in River System Using ANFIS

International Nuclear Information System (INIS)

Ullah, Nazrin; Choudhury, P.

2010-01-01

The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

An Electrical Energy Consumption Monitoring and Forecasting System

Directory of Open Access Journals (Sweden)

J. L. Rojas-Renteria

2016-10-01

Full Text Available Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecasting system is investigated. The monitoring system was installed in an actual building and the recordings were used to design and evaluate the forecasting system, based on an artificial neural network. Results show that the system can provide detailed monitoring and also an accurate forecast for a building’s consumption.

Automated sensor networks to advance ocean science

Science.gov (United States)

Schofield, O.; Orcutt, J. A.; Arrott, M.; Vernon, F. L.; Peach, C. L.; Meisinger, M.; Krueger, I.; Kleinert, J.; Chao, Y.; Chien, S.; Thompson, D. R.; Chave, A. D.; Balasuriya, A.

2010-12-01

The National Science Foundation has funded the Ocean Observatories Initiative (OOI), which over the next five years will deploy infrastructure to expand scientist’s ability to remotely study the ocean. The deployed infrastructure will be linked by a robust cyberinfrastructure (CI) that will integrate marine observatories into a coherent system-of-systems. OOI is committed to engaging the ocean sciences community during the construction pahse. For the CI, this is being enabled by using a “spiral design strategy” allowing for input throughout the construction phase. In Fall 2009, the OOI CI development team used an existing ocean observing network in the Mid-Atlantic Bight (MAB) to test OOI CI software. The objective of this CI test was to aggregate data from ships, autonomous underwater vehicles (AUVs), shore-based radars, and satellites and make it available to five different data-assimilating ocean forecast models. Scientists used these multi-model forecasts to automate future glider missions in order to demonstrate the feasibility of two-way interactivity between the sensor web and predictive models. The CI software coordinated and prioritized the shared resources that allowed for the semi-automated reconfiguration of assett-tasking, and thus enabled an autonomous execution of observation plans for the fixed and mobile observation platforms. Efforts were coordinated through a web portal that provided an access point for the observational data and model forecasts. Researchers could use the CI software in tandem with the web data portal to assess the performance of individual numerical model results, or multi-model ensembles, through real-time comparisons with satellite, shore-based radar, and in situ robotic measurements. The resulting sensor net will enable a new means to explore and study the world’s oceans by providing scientists a responsive network in the world’s oceans that can be accessed via any wireless network.

An independent system operator's perspective on operational ramp forecasting

Energy Technology Data Exchange (ETDEWEB)

Porter, G. [New Brunswick System Operator, Fredericton, NB (Canada)

2010-07-01

One of the principal roles of the power system operator is to select the most economical resources to reliably supply electric system power needs. Operational wind power production forecasts are required by system operators in order to understand the impact of ramp event forecasting on dispatch functions. A centralized dispatch approach can contribute to a more efficient use of resources that traditional economic dispatch methods. Wind ramping events can have a significant impact on system reliability. Power systems can have constrained or robust transmission systems, and may also be islanded or have large connections to neighbouring systems. Power resources can include both flexible and inflexible generation resources. Wind integration tools must be used by system operators to improve communications and connections with wind power plants. Improved wind forecasting techniques are also needed. Sensitivity to forecast errors is dependent on current system conditions. System operators require basic production forecasts, probabilistic forecasts, and event forecasts. Forecasting errors were presented as well as charts outlining the implications of various forecasts. tabs., figs.

High Resolution Forecasts in the Florida Straits: Predicting the Modulations of the Florida Current and Connectivity Around South Florida and Cuba

Science.gov (United States)

Kourafalou, V.; Kang, H.; Perlin, N.; Le Henaff, M.; Lamkin, J. T.

2016-02-01

Connectivity around the South Florida coastal regions and between South Florida and Cuba are largely influenced by a) local coastal processes and b) circulation in the Florida Straits, which is controlled by the larger scale Florida Current variability. Prediction of the physical connectivity is a necessary component for several activities that require ocean forecasts, such as oil spills, fisheries research, search and rescue. This requires a predictive system that can accommodate the intense coastal to offshore interactions and the linkages to the complex regional circulation. The Florida Straits, South Florida and Florida Keys Hybrid Coordinate Ocean Model is such a regional ocean predictive system, covering a large area over the Florida Straits and the adjacent land areas, representing both coastal and oceanic processes. The real-time ocean forecast system is high resolution ( 900m), embedded in larger scale predictive models. It includes detailed coastal bathymetry, high resolution/high frequency atmospheric forcing and provides 7-day forecasts, updated daily (see: http://coastalmodeling.rsmas.miami.edu/). The unprecedented high resolution and coastal details of this system provide value added on global forecasts through downscaling and allow a variety of applications. Examples will be presented, focusing on the period of a 2015 fisheries cruise around the coastal areas of Cuba, where model predictions helped guide the measurements on biophysical connectivity, under intense variability of the mesoscale eddy field and subsequent Florida Current meandering.

Coastal observing and forecasting system for the German Bight – estimates of hydrophysical states

Directory of Open Access Journals (Sweden)

W. Petersen

2011-09-01

Full Text Available A coastal observing system for Northern and Arctic Seas (COSYNA aims at construction of a long-term observatory for the German part of the North Sea, elements of which will be deployed as prototype modules in Arctic coastal waters. At present a coastal prediction system deployed in the area of the German Bight integrates near real-time measurements with numerical models in a pre-operational way and provides continuously state estimates and forecasts of coastal ocean state. The measurement suite contributing to the pre-operational set up includes in situ time series from stationary stations, a High-Frequency (HF radar system measuring surface currents, a FerryBox system and remote sensing data from satellites. The forecasting suite includes nested 3-D hydrodynamic models running in a data-assimilation mode, which are forced with up-to-date meteorological forecast data. This paper reviews the present status of the system and its recent upgrades focusing on developments in the field of coastal data assimilation. Model supported data analysis and state estimates are illustrated using HF radar and FerryBox observations as examples. A new method combining radial surface current measurements from a single HF radar with a priori information from a hydrodynamic model is presented, which optimally relates tidal ellipses parameters of the 2-D current field and the M2 phase and magnitude of the radials. The method presents a robust and helpful first step towards the implementation of a more sophisticated assimilation system and demonstrates that even using only radials from one station can substantially benefit state estimates for surface currents. Assimilation of FerryBox data based on an optimal interpolation approach using a Kalman filter with a stationary background covariance matrix derived from a preliminary model run which was validated against remote sensing and in situ data demonstrated the capabilities of the pre-operational system. Data

Short-term load forecasting of power system

Science.gov (United States)

Xu, Xiaobin

2017-05-01

In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

Climate Forecast System

Science.gov (United States)

Weather Service NWS logo - Click to go to the NWS home page Climate Forecast System Home News Organization Web portal to all Federal, state and local government Web resources and services. The NCEP Climate when using the CFS Reanalysis (CFSR) data. Saha, Suranjana, and Coauthors, 2010: The NCEP Climate

Interpretations of systematic errors in the NCEP Climate Forecast System at lead times of 2, 4, 8, ..., 256 days

Directory of Open Access Journals (Sweden)

Siwon Song

2012-09-01

Full Text Available The climatology of mean bias errors (relative to 1-day forecasts was examined in a 20-year hindcast set from version 1 of the Climate Forecast System (CFS, for forecast lead times of 2, 4, 8, 16, ... 256 days, verifying in different seasons. Results mostly confirm the simple expectation that atmospheric model biases should be evident at short lead (2–4 days, while soil moisture errors develop over days-weeks and ocean errors emerge over months. A further simplification is also evident: surface temperature bias patterns have nearly fixed geographical structure, growing with different time scales over land and ocean. The geographical pattern has mostly warm and dry biases over land and cool bias over the oceans, with two main exceptions: (1 deficient stratocumulus clouds cause warm biases in eastern subtropical oceans, and (2 high latitude land is too cold in boreal winter. Further study of the east Pacific cold tongue-Intertropical Convergence Zone (ITCZ complex shows a possible interaction between a rapidly-expressed atmospheric model bias (poleward shift of deep convection beginning at day 2 and slow ocean dynamics (erroneously cold upwelling along the equator in leads > 1 month. Further study of the high latitude land cold bias shows that it is a thermal wind balance aspect of the deep polar vortex, not just a near-surface temperature error under the wintertime inversion, suggesting that its development time scale of weeks to months may involve long timescale processes in the atmosphere, not necessarily in the land model. Winter zonal wind errors are small in magnitude, but a refractive index map shows that this can cause modest errors in Rossby wave ducting. Finally, as a counterpoint to our initial expectations about error growth, a case of non-monotonic error growth is shown: velocity potential bias grows with lead on a time scale of weeks, then decays over months. It is hypothesized that compensations between land and ocean errors may

Sea Level Forecasts Aggregated from Established Operational Systems

Directory of Open Access Journals (Sweden)

Andy Taylor

2017-08-01

Full Text Available A system for providing routine seven-day forecasts of sea level observable at tide gauge locations is described and evaluated. Forecast time series are aggregated from well-established operational systems of the Australian Bureau of Meteorology; although following some adjustments these systems are only quasi-complimentary. Target applications are routine coastal decision processes under non-extreme conditions. The configuration aims to be relatively robust to operational realities such as version upgrades, data gaps and metadata ambiguities. Forecast skill is evaluated against hourly tide gauge observations. Characteristics of the bias correction term are demonstrated to be primarily static in time, with time varying signals showing regional coherence. This simple approach to exploiting existing complex systems can offer valuable levels of skill at a range of Australian locations. The prospect of interpolation between observation sites and exploitation of lagged-ensemble uncertainty estimates could be meaningfully pursued. Skill characteristics define a benchmark against which new operational sea level forecasting systems can be measured. More generally, an aggregation approach may prove to be optimal for routine sea level forecast services given the physically inhomogeneous processes involved and ability to incorporate ongoing improvements and extensions of source systems.

A Wind Forecasting System for Energy Application

Science.gov (United States)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2010-05-01

Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated

An operational wave forecasting system for the east coast of India

Science.gov (United States)

Sandhya, K. G.; Murty, P. L. N.; Deshmukh, Aditya N.; Balakrishnan Nair, T. M.; Shenoi, S. S. C.

2018-03-01

Demand for operational ocean state forecasting is increasing, owing to the ever-increasing marine activities in the context of blue economy. In the present study, an operational wave forecasting system for the east coast of India is proposed using unstructured Simulating WAves Nearshore model (UNSWAN). This modelling system uses very high resolution mesh near the Indian east coast and coarse resolution offshore, and thus avoids the necessity of nesting with a global wave model. The model is forced with European Centre for Medium-Range Weather Forecasts (ECMWF) winds and simulates wave parameters and wave spectra for the next 3 days. The spatial pictures of satellite data overlaid on simulated wave height show that the model is capable of simulating the significant wave heights and their gradients realistically. Spectral validation has been done using the available data to prove the reliability of the model. To further evaluate the model performance, the wave forecast for the entire year 2014 is evaluated against buoy measurements over the region at 4 waverider buoy locations. Seasonal analysis of significant wave height (Hs) at the four locations showed that the correlation between the modelled and observed was the highest (in the range 0.78-0.96) during the post-monsoon season. The variability of Hs was also the highest during this season at all locations. The error statistics showed clear seasonal and geographical location dependence. The root mean square error at Visakhapatnam was the same (0.25) for all seasons, but it was the smallest for pre-monsoon season (0.12 m and 0.17 m) for Puducherry and Gopalpur. The wind sea component showed higher variability compared to the corresponding swell component in all locations and for all seasons. The variability was picked by the model to a reasonable level in most of the cases. The results of statistical analysis show that the modelling system is suitable for use in the operational scenario.

Comparison of two new short-term wind-power forecasting systems

Energy Technology Data Exchange (ETDEWEB)

Ramirez-Rosado, Ignacio J. [Department of Electrical Engineering, University of Zaragoza, Zaragoza (Spain); Fernandez-Jimenez, L. Alfredo [Department of Electrical Engineering, University of La Rioja, Logrono (Spain); Monteiro, Claudio; Sousa, Joao; Bessa, Ricardo [FEUP, Fac. Engenharia Univ. Porto (Portugal)]|[INESC - Instituto de Engenharia de Sistemas e Computadores do Porto, Porto (Portugal)

2009-07-15

This paper presents a comparison of two new advanced statistical short-term wind-power forecasting systems developed by two independent research teams. The input variables used in both systems were the same: forecasted meteorological variable values obtained from a numerical weather prediction model; and electric power-generation registers from the SCADA system of the wind farm. Both systems are described in detail and the forecasting results compared, revealing great similarities, although the proposed structures of the two systems are different. The forecast horizon for both systems is 72 h, allowing the use of the forecasted values in electric market operations, as diary and intra-diary power generation bid offers, and in wind-farm maintenance planning. (author)

Seasonal Forecast Skill And Teleconnections Over East Africa

Science.gov (United States)

MacLeod, D.; Palmer, T.

2017-12-01

Many people living in East Africa are significantly exposed to risks arising from climate variability. The region experiences two rainy seasons and poor performance of either or both of these (such as seen recently in 2016/17) reduces agricultural productivity and threatens food security. In combination with other factors this can lead to famine. By utilizing seasonal climate forecasts, preparatory actions can be taken in order to mitigate the risks arising from such climate variability. As part of the project ForPAc: "Towards forecast-based preparedness action", we are working with humanitarian agencies in Kenya to build such early warning systems on subseasonal-to-seasonal timescales. Here, the seasonal predictability and forecast skill of the two East African rainy seasons will be presented. Results from the new ECMWF operational forecasting system SEAS5 will be shown and compared to the previous System 4. Analysis of a new 110 year long atmosphere-only simulation will also be discussed, demonstrating impacts of atmosphere-ocean coupling as well as putting operational forecast skill in a long-term context. Particular focus will be given to the model representation of teleconnections of seasonal climate with global sea surface temperatures; highlighting sources of forecast error and informing future model development.

Investigating the Potential Impact of the Surface Water and Ocean Topography (SWOT) Altimeter on Ocean Mesoscale Prediction

Science.gov (United States)

Carrier, M.; Ngodock, H.; Smith, S. R.; Souopgui, I.

2016-02-01

NASA's Surface Water and Ocean Topography (SWOT) satellite, scheduled for launch in 2020, will provide sea surface height anomaly (SSHA) observations with a wider swath width and higher spatial resolution than current satellite altimeters. It is expected that this will help to further constrain ocean models in terms of the mesoscale circulation. In this work, this expectation is investigated by way of twin data assimilation experiments using the Navy Coastal Ocean Model Four Dimensional Variational (NCOM-4DVAR) data assimilation system using a weak constraint formulation. Here, a nature run is created from which SWOT observations are sampled, as well as along-track SSHA observations from simulated Jason-2 tracks. The simulated SWOT data has appropriate spatial coverage, resolution, and noise characteristics based on an observation-simulator program provided by the SWOT science team. The experiment is run for a three-month period during which the analysis is updated every 24 hours and each analysis is used to initialize a 96 hour forecast. The forecasts in each experiment are compared to the available nature run to determine the impact of the assimilated data. It is demonstrated here that the SWOT observations help to constrain the model mesoscale in a more consistent manner than traditional altimeter observations. The findings of this study suggest that data from SWOT may have a substantial impact on improving the ocean model analysis and forecast of mesoscale features and surface ocean transport.

A hierarchical spatiotemporal analog forecasting model for count data.

Science.gov (United States)

McDermott, Patrick L; Wikle, Christopher K; Millspaugh, Joshua

2018-01-01

Analog forecasting is a mechanism-free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous applications of analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes, but it has typically been presented in an empirical or heuristic procedure, rather than as a formal statistical model. The methodology presented here extends the model-based analog method of McDermott and Wikle (Environmetrics, 27, 2016, 70) by placing analog forecasting within a fully hierarchical statistical framework that can accommodate count observations. Using a Bayesian approach, the hierarchical analog model is able to quantify rigorously the uncertainty associated with forecasts. Forecasting waterfowl settling patterns in the northwestern United States and Canada is conducted by applying the hierarchical analog model to a breeding population survey dataset. Sea surface temperature (SST) in the Pacific Ocean is used to help identify potential analogs for the waterfowl settling patterns.

Long-term simulation of 137Cs in the Irish Sea by using ocean environment assessment system

International Nuclear Information System (INIS)

Kobayashi, Takuya; Otosaka, Shigeyoshi; Togawa, Orihiko; Hayashi, Keisuke

2007-01-01

An ocean environment assessment system that forecasts the detailed migration processes of radionuclides in the ocean has been developed. This system consists of an ocean current model and a particle random-walk model. The ocean current model is the modified Princeton Ocean Model (POM). The POM calculates tidal currents by giving tide levels at the open boundary. The particle random-walk model, SEA-GEARN, calculates the radionuclides migration in the ocean. Radionuclides that exist in the ocean are modeled in three phases, such as dissolved in seawater, adsorbed with large particulate matter (LPM) and adsorbed with active bottom sediment. The adsorption and desorption processes between the dissolved and solid phases are solved with the kinetic transfer coefficients by the stochastic method. Deposition of LPM and resuspension from bottom sediment are also considered. As model validation, the system has been applied to the Irish Sea to simulate the long-term dispersion of 137 Cs actually released from the BNFL spent nuclear fuel reprocessing plant at Sellafield in UK. Calculation results of surface dissolved 137 Cs have been compared with the observed data. The observed data are extracted from a database, Marine Information System (MARIS), which is released from the International Atomic Energy Agency's Marine Environment Laboratory (IAEA-MEL) in Monaco. The calculation well reproduces the main characteristics of migration of surface dissolved 137 Cs concentration in the Irish Sea. (author)

Human-model hybrid Korean air quality forecasting system.

Science.gov (United States)

Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

2016-09-01

The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the

Towards an integrated forecasting system for fisheries on habitat-bound stocks

DEFF Research Database (Denmark)

Christensen, Asbjørn; Butenschön, M.; Gürkan, Z.

2013-01-01

First results of a coupled modelling and forecasting system for fisheries on habitat-bound stocks are being presented. The system consists currently of three mathematically, fundamentally different model subsystems coupled offline: POLCOMS providing the physical environment implemented...... in the domain of the north-west European shelf, the SPAM model which describes sandeel stocks in the North Sea, and the third component, the SLAM model, which connects POLCOMS and SPAM by computing the physical– biological interaction. Our major experience by the coupling model subsystems is that well......-defined and generic model interfaces are very important for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows for analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate...

Online updating procedures for a real-time hydrological forecasting system

International Nuclear Information System (INIS)

Kahl, B; Nachtnebel, H P

2008-01-01

Rainfall-runoff-models can explain major parts of the natural runoff pattern but never simulate the observed hydrograph exactly. Reasons for errors are various sources of uncertainties embedded in the model forecasting system. Errors are due to measurement errors, the selected time period for calibration and validation, the parametric uncertainty and the model imprecision. In on-line forecasting systems forecasted input data is used which additionally generates a major uncertainty for the hydrological forecasting system. Techniques for partially compensating these uncertainties are investigated in the recent study in a medium sized catchment in the Austrian part of the Danube basin. The catchment area is about 1000 km2. The forecasting system consists of a semi-distributed continuous rainfall-runoff model that uses quantitative precipitation and temperature forecasts. To provide adequate system states at the beginning of the forecasting period continuous simulation is required, especially in winter. In this study two online updating methods are used and combined for enhancing the runoff forecasts. The first method is used for updating the system states at the beginning of the forecasting period by changing the precipitation input. The second method is an autoregressive error model, which is used to eliminate systematic errors in the model output. In combination those two methods work together well as each method is more effective in different runoff situations.

Streamflow Forecasting Using Nuero-Fuzzy Inference System

Science.gov (United States)

Nanduri, U. V.; Swain, P. C.

2005-12-01

The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A

Real-time emergency forecasting technique for situation management systems

Science.gov (United States)

Kopytov, V. V.; Kharechkin, P. V.; Naumenko, V. V.; Tretyak, R. S.; Tebueva, F. B.

2018-05-01

The article describes the real-time emergency forecasting technique that allows increasing accuracy and reliability of forecasting results of any emergency computational model applied for decision making in situation management systems. Computational models are improved by the Improved Brown’s method applying fractal dimension to forecast short time series data being received from sensors and control systems. Reliability of emergency forecasting results is ensured by the invalid sensed data filtering according to the methods of correlation analysis.

Forecast Icing Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Forecast Icing Product (FIP) is an automatically-generated index suitable for depicting areas of potentially hazardous airframe icing. The FIP algorithm uses...

A preliminary study of the impact of the ERS 1 C band scatterometer wind data on the European Centre for Medium-Range Weather Forecasts global data assimilation system

Science.gov (United States)

Hoffman, Ross N.

1993-01-01

A preliminary assessment of the impact of the ERS 1 scatterometer wind data on the current European Centre for Medium-Range Weather Forecasts analysis and forecast system has been carried out. Although the scatterometer data results in changes to the analyses and forecasts, there is no consistent improvement or degradation. Our results are based on comparing analyses and forecasts from assimilation cycles. The two sets of analyses are very similar except for the low level wind fields over the ocean. Impacts on the analyzed wind fields are greater over the southern ocean, where other data are scarce. For the most part the mass field increments are too small to balance the wind increments. The effect of the nonlinear normal mode initialization on the analysis differences is quite small, but we observe that the differences tend to wash out in the subsequent 6-hour forecast. In the Northern Hemisphere, analysis differences are very small, except directly at the scatterometer locations. Forecast comparisons reveal large differences in the Southern Hemisphere after 72 hours. Notable differences in the Northern Hemisphere do not appear until late in the forecast. Overall, however, the Southern Hemisphere impacts are neutral. The experiments described are preliminary in several respects. We expect these data to ultimately prove useful for global data assimilation.

Towards the Olympic Games: Guanabara Bay Forecasting System and its Application on the Floating Debris Cleaning Actions.

Science.gov (United States)

Pimentel, F. P.; Marques Da Cruz, L.; Cabral, M. M.; Miranda, T. C.; Garção, H. F.; Oliveira, A. L. S. C.; Carvalho, G. V.; Soares, F.; São Tiago, P. M.; Barmak, R. B.; Rinaldi, F.; dos Santos, F. A.; Da Rocha Fragoso, M.; Pellegrini, J. C.

2016-02-01

Marine debris is a widespread pollution issue that affects almost all water bodies and is remarkably relevant in estuaries and bays. Rio de Janeiro city will host the 2016 Olympic Games and Guanabara Bay will be the venue for the sailing competitions. Historically serving as deposit for all types of waste, this water body suffers with major environmental problems, one of them being the massive presence of floating garbage. Therefore, it is of great importance to count on effective contingency actions to address this issue. In this sense, an operational ocean forecasting system was designed and it is presently being used by the Rio de Janeiro State Government to manage and control the cleaning actions on the bay. The forecasting system makes use of high resolution hydrodynamic and atmospheric models and a lagragian particle transport model, in order to provide probabilistic forecasts maps of the areas where the debris are most probably accumulating. All the results are displayed on an interactive GIS web platform along with the tracks of the boats that make the garbage collection, so the decision makers can easily command the actions, enhancing its efficiency. The integration of in situ data and advanced techniques such as Lyapunov exponent analysis are also being developed in the system, so to increase its forecast reliability. Additionally, the system also gathers and compiles on its database all the information on the debris collection, including quantity, type, locations, accumulation areas and their correlation with the environmental factors that drive the runoff and surface drift. Combining probabilistic, deterministic and statistical approaches, the forecasting system of Guanabara Bay has been proving to be a powerful tool for the environmental management and will be of great importance on helping securing the safety and fairness of the Olympic sailing competitions. The system design, its components and main results are presented in this paper.

Calibration of Ocean Forcing with satellite Flux Estimates (COFFEE)

Science.gov (United States)

Barron, Charlie; Jan, Dastugue; Jackie, May; Rowley, Clark; Smith, Scott; Spence, Peter; Gremes-Cordero, Silvia

2016-04-01

Predicting the evolution of ocean temperature in regional ocean models depends on estimates of surface heat fluxes and upper-ocean processes over the forecast period. Within the COFFEE project (Calibration of Ocean Forcing with satellite Flux Estimates, real-time satellite observations are used to estimate shortwave, longwave, sensible, and latent air-sea heat flux corrections to a background estimate from the prior day's regional or global model forecast. These satellite-corrected fluxes are used to prepare a corrected ocean hindcast and to estimate flux error covariances to project the heat flux corrections for a 3-5 day forecast. In this way, satellite remote sensing is applied to not only inform the initial ocean state but also to mitigate errors in surface heat flux and model representations affecting the distribution of heat in the upper ocean. While traditional assimilation of sea surface temperature (SST) observations re-centers ocean models at the start of each forecast cycle, COFFEE endeavors to appropriately partition and reduce among various surface heat flux and ocean dynamics sources. A suite of experiments in the southern California Current demonstrates a range of COFFEE capabilities, showing the impact on forecast error relative to a baseline three-dimensional variational (3DVAR) assimilation using operational global or regional atmospheric forcing. Experiment cases combine different levels of flux calibration with assimilation alternatives. The cases use the original fluxes, apply full satellite corrections during the forecast period, or extend hindcast corrections into the forecast period. Assimilation is either baseline 3DVAR or standard strong-constraint 4DVAR, with work proceeding to add a 4DVAR expanded to include a weak constraint treatment of the surface flux errors. Covariance of flux errors is estimated from the recent time series of forecast and calibrated flux terms. While the California Current examples are shown, the approach is

Challenges and potential solutions for European coastal ocean modelling

Science.gov (United States)

She, Jun; Stanev, Emil

2017-04-01

Coastal operational oceanography is a science and technological platform to integrate and transform the outcomes in marine monitoring, new knowledge generation and innovative technologies into operational information products and services in the coastal ocean. It has been identified as one of the four research priorities by EuroGOOS (She et al. 2016). Coastal modelling plays a central role in such an integration and transformation. A next generation coastal ocean forecasting system should have following features: i) being able to fully exploit benefits from future observations, ii) generate meaningful products in finer scales e.g., sub-mesoscale and in estuary-coast-sea continuum, iii) efficient parallel computing and model grid structure, iv) provide high quality forecasts as forcing to NWP and coastal climate models, v) resolving correctly inter-basin and inter-sub-basin water exchange, vi) resolving synoptic variability and predictability in marine ecosystems, e.g., for algae bloom, vi) being able to address critical and relevant issues in coastal applications, e.g., marine spatial planning, maritime safety, marine pollution protection, disaster prevention, offshore wind energy, climate change adaptation and mitigation, ICZM (integrated coastal zone management), the WFD (Water Framework Directive), and the MSFD (Marine Strategy Framework Directive), especially on habitat, eutrophication, and hydrographic condition descriptors. This presentation will address above challenges, identify limits of current models and propose correspondent research needed. The proposed roadmap will address an integrated monitoring-modelling approach and developing Unified European Coastal Ocean Models. In the coming years, a few new developments in European Sea observations can expected, e.g., more near real time delivering on profile observations made by research vessels, more shallow water Argo floats and bio-Argo floats deployed, much more high resolution sea level data from SWOT

Ensemble hydromoeteorological forecasting in Denmark

DEFF Research Database (Denmark)

Lucatero Villasenor, Diana

forecasts where a dampening of the differences of precipitation quality occurs. Seasonal meteorological forecasts are possible due to changes of large scale patterns of the ocean and land, such as el Niño, that evolve at a much slower pace than the atmosphere, which can have an impact on its evolution later...

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

Science.gov (United States)

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

2017-12-01

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

Waste Information Management System with Integrated Transportation Forecast Data

International Nuclear Information System (INIS)

Upadhyay, H.; Quintero, W.; Shoffner, P.; Lagos, L.

2009-01-01

The Waste Information Management System with Integrated Transportation Forecast Data was developed to support the Department of Energy (DOE) mandated accelerated cleanup program. The schedule compression required close coordination and a comprehensive review and prioritization of the barriers that impeded treatment and disposition of the waste streams at each site. Many issues related to site waste treatment and disposal were potential critical path issues under the accelerated schedules. In order to facilitate accelerated cleanup initiatives, waste managers at DOE field sites and at DOE Headquarters in Washington, D.C., needed timely waste forecast and transportation information regarding the volumes and types of waste that would be generated by the DOE sites over the next 40 years. Each local DOE site has historically collected, organized, and displayed site waste forecast information in separate and unique systems. However, waste and shipment information from all sites needed a common application to allow interested parties to understand and view the complete complex-wide picture. The Waste Information Management System with Integrated Transportation Forecast Data allows identification of total forecasted waste volumes, material classes, disposition sites, choke points, technological or regulatory barriers to treatment and disposal, along with forecasted waste transportation information by rail, truck and inter-modal shipments. The Applied Research Center (ARC) at Florida International University (FIU) in Miami, Florida, has deployed the web-based forecast and transportation system and is responsible for updating the waste forecast and transportation data on a regular basis to ensure the long-term viability and value of this system. (authors)

Predicting Typhoon Induced Storm Surges Using the Operational Ocean Forecast System

Directory of Open Access Journals (Sweden)

Sung Hyup You

2010-01-01

Full Text Available This study was performed to compare storm surges simulated by the operational storm surges/tide forecast system (STORM : Storm surges/Tide Operational Model of the Korea Meteorological Administration (KMA with observations from 30 coastal tidal stations during nine typhoons that occurred between 2005 and 2007. The results (bias showed that for cases of overestimation (or underestimation, storm surges tended to be overestimated (as well as underestimated at all coastal stations. The maximum positive bias was approximately 6.92 cm for Typhoon Ewiniar (2006, while the maximum negative bias was approximately -12.06 cm for Typhoon Khanun (2005. The maximum and minimum root mean square errors (RMSEs were 14.61 and 6.78 cm, which occurred for Typhoons Khanun (2005 and Usagi (2007, respectively. For all nine typhoons, total averaged RMSE was approximately 10.2 cm. Large differences between modeled and observed storm surges occurred in two cases. In the first, a very weak typhoon, such as Typhoon Khanun (2005, caused low storm surges. In the other, exemplified by Typhoon Nari (2007, there were errors in the predicted typhoon strength used as input data for the storm surge model.

Evaluation of CFSV2 Forecast Skill for Indian Summer Monsoon Sub-Seasonal Characteristics

Science.gov (United States)

S, S. A.; Ghosh, S.

2015-12-01

Prediction of sub seasonal monsoon characteristics of Indian Summer Monsoon (ISM) is highly crucial for agricultural planning and water resource management. The Climate forecast System version 2 (CFS V2), the state of the art coupled climate model developed by NCEP, is currently being employed for the seasonal and extended range forecasts of ISM. Even though CFSV2 is a fully coupled ocean- atmosphere- land model with advanced physics, increased resolution and refined initialisation, its ISM forecasts, in terms of seasonal mean and variability needs improvement. Numerous works have been done for verifying the CFSV2 forecasts in terms of the seasonal mean, its mean and variability, active and break spells, and El Nino Southern Oscillation (ENSO) - monsoon interactions. Most of these works are based on either rain fall strength or rainfall based indices. Here we evaluate the skill of CFS v2 model in forecasting the various sub seasonal features of ISM, viz., the onset and withdrawal days of monsoon that are determined using circulation based indices, the Monsoon Intra Seasonal Oscillations (MISO), and Indian Ocean and Pacific Ocean sea surface temperatures. The MISO index, we use here, is based on zonal wind at 850 hPa and Outgoing Long wave Radiation (OLR) anomalies. With this work, we aim at assessing the skill of the model in simulating the large scale circulation patterns and their variabilities within the monsoon season. Variabilities in these large scale circulation patterns are primarily responsible for the variabilities in the seasonal monsoon strength and its temporal distribution across the season. We find that the model can better forecast the large scale circulation and than the actual precipitation. Hence we suggest that seasonal rainfall forecasts can be improved by the statistical downscaling of CFSV2 forecasts by incorporating the established relationships between the well forecasted large scale variables and monsoon precipitation.

Energy operations and planning decision support for systems using weather forecast information

International Nuclear Information System (INIS)

Altalo, M.G.

2004-01-01

Hydroelectric utilities deal with uncertainties on a regular basis. These include uncertainties in weather, policy and markets. This presentation outlined regional studies to define uncertainty, sources of uncertainty and their affect on power managers, power marketers, power insurers and end users. Solutions to minimize uncertainties include better forecasting and better business processes to mobilize action. The main causes of uncertainty in energy operations and planning include uncaptured wind, precipitation and wind events. Load model errors also contribute to uncertainty in energy operations. This presentation presented the results of a 2002-2003 study conducted by the National Oceanic and Atmospheric Administration (NOAA) on the impact uncertainties in northeast energy weather forecasts. The study demonstrated the cost of seabreeze error on transmission and distribution. The impact of climate scale events were also presented along with energy demand implications. It was suggested that energy planners should incorporate climate change parameters into planning, and that models should include probability distribution forecasts and ensemble forecasting methods that incorporate microclimate estimates. It was also suggested that seabreeze, lake effect, fog, afternoon thunderstorms and frontal passage should be incorporated into forecasts. tabs., figs

Analyzing Effect of System Inertia on Grid Frequency Forecasting Usnig Two Stage Neuro-Fuzzy System

Science.gov (United States)

Chourey, Divyansh R.; Gupta, Himanshu; Kumar, Amit; Kumar, Jitesh; Kumar, Anand; Mishra, Anup

2018-04-01

Frequency forecasting is an important aspect of power system operation. The system frequency varies with load-generation imbalance. Frequency variation depends upon various parameters including system inertia. System inertia determines the rate of fall of frequency after the disturbance in the grid. Though, inertia of the system is not considered while forecasting the frequency of power system during planning and operation. This leads to significant errors in forecasting. In this paper, the effect of inertia on frequency forecasting is analysed for a particular grid system. In this paper, a parameter equivalent to system inertia is introduced. This parameter is used to forecast the frequency of a typical power grid for any instant of time. The system gives appreciable result with reduced error.

Hybrid ellipsoidal fuzzy systems in forecasting regional electricity loads

Energy Technology Data Exchange (ETDEWEB)

Pai, Ping-Feng [Department of Information Management, National Chi Nan University, 1 University Road, Puli, Nantou 545, Taiwan (China)

2006-09-15

Because of the privatization of electricity in many countries, load forecasting has become one of the most crucial issues in the planning and operations of electric utilities. In addition, accurate regional load forecasting can provide the transmission and distribution operators with more information. The hybrid ellipsoidal fuzzy system was originally designed to solve control and pattern recognition problems. The main objective of this investigation is to develop a hybrid ellipsoidal fuzzy system for time series forecasting (HEFST) and apply the proposed model to forecast regional electricity loads in Taiwan. Additionally, a scaled conjugate gradient learning method is employed in the supervised learning phase of the HEFST model. Subsequently, numerical data taken from the existing literature is used to demonstrate the forecasting performance of the HEFST model. Simulation results reveal that the proposed model has better forecasting performance than the artificial neural network model and the regression model. Thus, the HEFST model is a valid and promising alternative for forecasting regional electricity loads. (author)

The Impact of the Assimilation of Aquarius Sea Surface Salinity Data in the GEOS Ocean Data Assimilation System

Science.gov (United States)

Vernieres, Guillaume Rene Jean; Kovach, Robin M.; Keppenne, Christian L.; Akella, Santharam; Brucker, Ludovic; Dinnat, Emmanuel Phillippe

2014-01-01

Ocean salinity and temperature differences drive thermohaline circulations. These properties also play a key role in the ocean-atmosphere coupling. With the availability of L-band space-borne observations, it becomes possible to provide global scale sea surface salinity (SSS) distribution. This study analyzes globally the along-track (Level 2) Aquarius SSS retrievals obtained using both passive and active L-band observations. Aquarius alongtrack retrieved SSS are assimilated into the ocean data assimilation component of Version 5 of the Goddard Earth Observing System (GEOS-5) assimilation and forecast model. We present a methodology to correct the large biases and errors apparent in Version 2.0 of the Aquarius SSS retrieval algorithm and map the observed Aquarius SSS retrieval into the ocean models bulk salinity in the topmost layer. The impact of the assimilation of the corrected SSS on the salinity analysis is evaluated by comparisons with insitu salinity observations from Argo. The results show a significant reduction of the global biases and RMS of observations-minus-forecast differences at in-situ locations. The most striking results are found in the tropics and southern latitudes. Our results highlight the complementary role and problems that arise during the assimilation of salinity information from in-situ (Argo) and space-borne surface (SSS) observations

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

Science.gov (United States)

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

2017-12-01

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

NeXOS, developing and evaluating a new generation of insitu ocean observation systems.

Science.gov (United States)

Delory, Eric; del Rio, Joaquin; Golmen, Lars; Roar Hareide, Nils; Pearlman, Jay; Rolin, Jean-Francois; Waldmann, Christoph; Zielinski, Oliver

2017-04-01

Ocean biological, chemical or physical processes occur over widely varying scales in space and time: from micro- to kilometer scales, from less than seconds to centuries. While space systems supply important data and information, insitu data is necessary for comprehensive modeling and forecasting of ocean dynamics. Yet, collection of in-situ observation on these scales is inherently challenging and remains generally difficult and costly in time and resources. This paper address the innovations and significant developments for a new generation of insitu sensors in FP7 European Union project "Next generation, Cost- effective, Compact, Multifunctional Web Enabled Ocean Sensor Systems Empowering Marine, Maritime and Fisheries Management" or "NeXOS" for short. Optical and acoustics sensors are the focus of NeXOS but NeXOS moves beyond just sensors as systems that simultaneously address multiple objectives and applications are becoming increasingly important. Thus NeXOS takes a perspective of both sensors and sensor systems with significant advantages over existing observing capabilities via the implementation of innovations such as multiplatform integration, greater reliability through better antifouling management and greater sensor and data interoperability through use of OGC standards. This presentation will address the sensor system development and field-testing of the new NeXOS sensor systems. This is being done on multiple platforms including profiling floats, gliders, ships, buoys and subsea stations. The implementation of a data system based on SWE and PUCK furthers interoperability across measurements and platforms. This presentation will review the sensor system capabilities, the status of field tests and recommendations for long-term ocean monitoring.

The Global Ocean Observing System (GOOS): New developments

International Nuclear Information System (INIS)

Summerhayes, C.P.

1999-01-01

GOOS will provide information about the present and future states of seas and oceans and their living resources, and on the role of the oceans in climate change. Among other things, it will include monitoring the extent to which the sea is polluted, and applying models enabling the behaviour of polluted environments to be forecast given a variety of forcing conditions including anthropogenic and natural changes. Implementation has begun through integration of previously separate existing observing systems into a GOOS Initial Observing System, and through the development of Pilot Projects, most notably in the coastal seas of Europe and North-east Asia. Although the present emphasis is on the measurement of physical properties, plans are underway for increasing the observation of chemical and biological parameters. The main biological thrust at present comes through the Global Coral Reef Monitoring Network (GCRMN). Consideration needs to be given to incorporation into the GOOS Initial Observing System of present national, international and global chemical and biological monitoring systems, and the development and implementation of new chemical and biological monitoring subsystems, especially in coastal seas for monitoring the health of those environments. GOOS will offer marine scientists and other users a scheme of continuing measurements on a scale larger in time and space than can be accomplished by individuals for their own applications, and a vastly improved store of basic marine environmental data for a multitude of purposes. For GOOS news see the GOOS Homepage at http://ioc.unesco.org/GOOS/. (author)

MOCASSIM - an operational forecast system for the Portuguese coastal waters.

Science.gov (United States)

Vitorino, J.; Soares, C.; Almeida, S.; Rusu, E.; Pinto, J.

2003-04-01

An operational system for the forecast of oceanographic conditions off the Portuguese coast is presently being implemented at Instituto Hidrográfico (IH), in the framework of project MOCASSIM. The system is planned to use a broad range of observations provided both from IH observational networks (wave buoys, tidal gauges) and programs (hydrographic surveys, moorings) as well as from external sources. The MOCASSIM system integrates several numerical models which, combined, are intended to cover the relevant physical processes observed in the geographical areas of interest. At the present stage of development the system integrates a circulation module and a wave module. The circulation module is based on the Harvard Ocean Prediction System (HOPS), a primitive equation model formulated under the rigid lid assumption, which includes a data assimilation module. The wave module is based on the WaveWatch3 (WW3) model, which provides wave conditions in the North Atlantic basin, and on the SWAN model which is used to improve the wave forecasts on coastal or other specific areas of interest. The models use the meteorological forcing fields of a limited area model (ALADIN model) covering the Portuguese area, which are being provided in the framework of a close colaboration with Instituto de Meteorologia. Although still under devellopment, the MOCASSIM system has already been used in several operationnal contexts. These included the operational environmental assessment during both national and NATO navy exercises and, more recently, the monitoring of the oceanographic conditions in the NW Iberian area affected by the oil spill of MV "Prestige". The system is also a key component of ongoing research on the oceanography of the Portuguese continental margin, which is presently being conducted at IH in the framework of national and European funded projects.

The impact of covariance localization on the performance of an ocean EnKF system assimilating glider data in the Ligurian Sea

Science.gov (United States)

Falchetti, Silvia; Alvarez, Alberto

2018-04-01

Data assimilation through an ensemble Kalman filter (EnKF) is not exempt from deficiencies, including the generation of long-range unphysical correlations that degrade its performance. The covariance localization technique has been proposed and used in previous research to mitigate this effect. However, an evaluation of its performance is usually hindered by the sparseness and unsustained collection of independent observations. This article assesses the performance of an ocean prediction system composed of a multivariate EnKF coupled with a regional configuration of the Regional Ocean Model System (ROMS) with a covariance localization solution and data assimilation from an ocean glider that operated over a limited region of the Ligurian Sea. Simultaneous with the operation of the forecast system, a high-quality data set was repeatedly collected with a CTD sensor, i.e., every day during the period from 5 to 20 August 2013 (approximately 4 to 5 times the synoptic time scale of the area), located on board the NR/V Alliance for model validation. Comparisons between the validation data set and the forecasts provide evidence that the performance of the prediction system with covariance localization is superior to that observed using only EnKF assimilation without localization or using a free run ensemble. Furthermore, it is shown that covariance localization also increases the robustness of the model to the location of the assimilated data. Our analysis reveals that improvements are detected with regard to not only preventing the occurrence of spurious correlations but also preserving the spatial coherence in the updated covariance matrix. Covariance localization has been shown to be relevant in operational frameworks where short-term forecasts (on the order of days) are required.

Can decadal climate predictions be improved by ocean ensemble dispersion filtering?

Science.gov (United States)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-12-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years inadvance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-termweather forecasts represent an initial value problem and long-term climate projections represent a boundarycondition problem, the decadal climate prediction falls in-between these two time scales. The ocean memorydue to its heat capacity holds big potential skill on the decadal scale. In recent years, more precise initializationtechniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions.Ensembles are another important aspect. Applying slightly perturbed predictions results in an ensemble. Insteadof using and evaluating one prediction, but the whole ensemble or its ensemble average, improves a predictionsystem. However, climate models in general start losing the initialized signal and its predictive skill from oneforecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improvedby a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. Wefound that this procedure, called ensemble dispersion filter, results in more accurate results than the standarddecadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions showan increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with largerensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from oceanensemble dispersion filtering toward the ensemble mean. This study is part of MiKlip (fona-miklip.de) - a major project on decadal climate prediction in Germany.We focus on the Max-Planck-Institute Earth System Model using the low-resolution version (MPI-ESM-LR) andMiKlip's basic initialization strategy as in 2017 published decadal climate forecast: http://www.fona-miklip.de/decadal-forecast

Satellite-based Calibration of Heat Flux at the Ocean Surface

Science.gov (United States)

Barron, C. N.; Dastugue, J. M.; May, J. C.; Rowley, C. D.; Smith, S. R.; Spence, P. L.; Gremes-Cordero, S.

2016-02-01

Model forecasts of upper ocean heat content and variability on diurnal to daily scales are highly dependent on estimates of heat flux through the air-sea interface. Satellite remote sensing is applied to not only inform the initial ocean state but also to mitigate errors in surface heat flux and model representations affecting the distribution of heat in the upper ocean. Traditional assimilation of sea surface temperature (SST) observations re-centers ocean models at the start of each forecast cycle. Subsequent evolution depends on estimates of surface heat fluxes and upper-ocean processes over the forecast period. The COFFEE project (Calibration of Ocean Forcing with satellite Flux Estimates) endeavors to correct ocean forecast bias through a responsive error partition among surface heat flux and ocean dynamics sources. A suite of experiments in the southern California Current demonstrates a range of COFFEE capabilities, showing the impact on forecast error relative to a baseline three-dimensional variational (3DVAR) assimilation using Navy operational global or regional atmospheric forcing. COFFEE addresses satellite-calibration of surface fluxes to estimate surface error covariances and links these to the ocean interior. Experiment cases combine different levels of flux calibration with different assimilation alternatives. The cases may use the original fluxes, apply full satellite corrections during the forecast period, or extend hindcast corrections into the forecast period. Assimilation is either baseline 3DVAR or standard strong-constraint 4DVAR, with work proceeding to add a 4DVAR expanded to include a weak constraint treatment of the surface flux errors. Covariance of flux errors is estimated from the recent time series of forecast and calibrated flux terms. While the California Current examples are shown, the approach is equally applicable to other regions. These approaches within a 3DVAR application are anticipated to be useful for global and larger

A short-term ensemble wind speed forecasting system for wind power applications

Science.gov (United States)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

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.

Towards operational modeling and forecasting of the Iberian shelves ecosystem.

Directory of Open Access Journals (Sweden)

Martinho Marta-Almeida

Full Text Available There is a growing interest on physical and biogeochemical oceanic hindcasts and forecasts from a wide range of users and businesses. In this contribution we present an operational biogeochemical forecast system for the Portuguese and Galician oceanographic regions, where atmospheric, hydrodynamic and biogeochemical variables are integrated. The ocean model ROMS, with a horizontal resolution of 3 km, is forced by the atmospheric model WRF and includes a Nutrients-Phytoplankton-Zooplankton-Detritus biogeochemical module (NPZD. In addition to oceanographic variables, the system predicts the concentration of nitrate, phytoplankton, zooplankton and detritus (mmol N m(-3. Model results are compared against radar currents and remote sensed SST and chlorophyll. Quantitative skill assessment during a summer upwelling period shows that our modelling system adequately represents the surface circulation over the shelf including the observed spatial variability and trends of temperature and chlorophyll concentration. Additionally, the skill assessment also shows some deficiencies like the overestimation of upwelling circulation and consequently, of the duration and intensity of the phytoplankton blooms. These and other departures from the observations are discussed, their origins identified and future improvements suggested. The forecast system is the first of its kind in the region and provides free online distribution of model input and output, as well as comparisons of model results with satellite imagery for qualitative operational assessment of model skill.

Impact of GFZ's Effective Angular Momentum Forecasts on Polar Motion Prediction

Science.gov (United States)

Dill, Robert; Dobslaw, Henryk

2017-04-01

The Earth System Modelling group at GeoForschungsZentrum (GFZ) Potsdam offers now 6-day forecasts of Earth rotation excitation due to atmospheric, oceanic, and hydrologic angular momentum changes that are consistent with its 40 years-long EAM series. Those EAM forecasts are characterized by an improved long-term consistency due to the introduction of a time-invariant high-resolution reference topography into the AAM processing that accounts for occasional NWP model changes. In addition, all tidal signals from both atmosphere and ocean have been separated, and the temporal resolution of both AAM and OAM has been increased to 3 hours. Analysis of an extended set of EAM short-term hindcasts revealed positive prediction skills for up to 6 days into the future when compared to a persistent forecast. Whereas UT1 predictions in particular rely on an accurate AAM forecast, skillfull polar motion prediction requires high-quality OAM forecasts as well. We will present in this contribution the results from a multi-year hindcast experiment, demonstrating that the polar motion prediction as currently available from Bulletin A can be improved in particular for lead-times between 2 and 5 days by incorporating OAM forecasts. We will also report about early results obtained at Observatoire de Paris to predict polar motion from the integration of GFZ's 6-day EAM forecasts into the Liouville equation in a routine setting, that fully takes into account the operational latencies of all required input products.

Use of INSAT-3D sounder and imager radiances in the 4D-VAR data assimilation system and its implications in the analyses and forecasts

Science.gov (United States)

Indira Rani, S.; Taylor, Ruth; George, John P.; Rajagopal, E. N.

2016-05-01

INSAT-3D, the first Indian geostationary satellite with sounding capability, provides valuable information over India and the surrounding oceanic regions which are pivotal to Numerical Weather Prediction. In collaboration with UK Met Office, NCMRWF developed the assimilation capability of INSAT-3D Clear Sky Brightness Temperature (CSBT), both from the sounder and imager, in the 4D-Var assimilation system being used at NCMRWF. Out of the 18 sounder channels, radiances from 9 channels are selected for assimilation depending on relevance of the information in each channel. The first three high peaking channels, the CO2 absorption channels and the three water vapor channels (channel no. 10, 11, and 12) are assimilated both over land and Ocean, whereas the window channels (channel no. 6, 7, and 8) are assimilated only over the Ocean. Measured satellite radiances are compared with that from short range forecasts to monitor the data quality. This is based on the assumption that the observed satellite radiances are free from calibration errors and the short range forecast provided by NWP model is free from systematic errors. Innovations (Observation - Forecast) before and after the bias correction are indicative of how well the bias correction works. Since the biases vary with air-masses, time, scan angle and also due to instrument degradation, an accurate bias correction algorithm for the assimilation of INSAT-3D sounder radiance is important. This paper discusses the bias correction methods and other quality controls used for the selected INSAT-3D sounder channels and the impact of bias corrected radiance in the data assimilation system particularly over India and surrounding oceanic regions.

The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

Science.gov (United States)

Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve

2014-01-01

The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global Systems Division (GSD) of the NOAA Earth System Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9-km horizontal grid spacing. The ensemble has eight members, all employing WRF-ARW. The ensemble uses a variety of initial conditions from LAPS and the Global Forecasting System (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in forecasting extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 2012-2013 and 2013-2014 winters. A smaller domain covering just the West Coast was created to minimize band-width consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Short-term Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River Forecast Center's 4 km gridded QPE to compare with all operational NWP models 6-hr QPF along with the ExREF mean 6-hr QPF so the forecasters can build confidence in the use of the

Coastal and Riverine Flood Forecast Model powered by ADCIRC

Science.gov (United States)

Khalid, A.; Ferreira, C.

2017-12-01

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

Forecasting of Processes in Complex Systems for Real-World Problems

Czech Academy of Sciences Publication Activity Database

Pelikán, Emil

2014-01-01

Roč. 24, č. 6 (2014), s. 567-589 ISSN 1210-0552 Institutional support: RVO:67985807 Keywords : complex systems * data assimilation * ensemble forecasting * forecasting * global solar radiation * judgmental forecasting * multimodel forecasting * pollution Subject RIV: IN - Informatics, Computer Science Impact factor: 0.479, year: 2014

Long-range forecasting of intermittent streamflow

Science.gov (United States)

van Ogtrop, F. F.; Vervoort, R. W.; Heller, G. Z.; Stasinopoulos, D. M.; Rigby, R. A.

2011-11-01

Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a statistical model to forecast streamflow up to 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS) to determine the probability of flow occurring in any of the systems. We then use the same regression framework in combination with a right-skewed distribution, the Box-Cox t distribution, to model the intensity (depth) of the non-zero streamflows. Time, seasonality and climate indices, describing the Pacific and Indian Ocean sea surface temperatures, are tested as covariates in the GAMLSS model to make probabilistic 6 and 12-month forecasts of the occurrence and intensity of streamflow. The output reveals that in the study region the occurrence and variability of flow is driven by sea surface temperatures and therefore forecasts can be made with some skill.

Long-range forecasting of intermittent streamflow

Directory of Open Access Journals (Sweden)

F. F. van Ogtrop

2011-11-01

Full Text Available Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a statistical model to forecast streamflow up to 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS to determine the probability of flow occurring in any of the systems. We then use the same regression framework in combination with a right-skewed distribution, the Box-Cox t distribution, to model the intensity (depth of the non-zero streamflows. Time, seasonality and climate indices, describing the Pacific and Indian Ocean sea surface temperatures, are tested as covariates in the GAMLSS model to make probabilistic 6 and 12-month forecasts of the occurrence and intensity of streamflow. The output reveals that in the study region the occurrence and variability of flow is driven by sea surface temperatures and therefore forecasts can be made with some skill.

Radar Based Flow and Water Level Forecasting in Sewer Systems

DEFF Research Database (Denmark)

Thorndahl, Søren; Rasmussen, Michael R.; Grum, M.

2009-01-01

This paper describes the first radar based forecast of flow and/or water level in sewer systems in Denmark. The rainfall is successfully forecasted with a lead time of 1-2 hours, and flow/levels are forecasted an additional ½-1½ hours using models describing the behaviour of the sewer system. Bot...

Real-time forecasting of the April 11, 2012 Sumatra tsunami

Science.gov (United States)

Wang, Dailin; Becker, Nathan C.; Walsh, David; Fryer, Gerard J.; Weinstein, Stuart A.; McCreery, Charles S.; ,

2012-01-01

The April 11, 2012, magnitude 8.6 earthquake off the northern coast of Sumatra generated a tsunami that was recorded at sea-level stations as far as 4800 km from the epicenter and at four ocean bottom pressure sensors (DARTs) in the Indian Ocean. The governments of India, Indonesia, Sri Lanka, Thailand, and Maldives issued tsunami warnings for their coastlines. The United States' Pacific Tsunami Warning Center (PTWC) issued an Indian Ocean-wide Tsunami Watch Bulletin in its role as an Interim Service Provider for the region. Using an experimental real-time tsunami forecast model (RIFT), PTWC produced a series of tsunami forecasts during the event that were based on rapidly derived earthquake parameters, including initial location and Mwp magnitude estimates and the W-phase centroid moment tensor solutions (W-phase CMTs) obtained at PTWC and at the U. S. Geological Survey (USGS). We discuss the real-time forecast methodology and how successive, real-time tsunami forecasts using the latest W-phase CMT solutions improved the accuracy of the forecast.

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

Science.gov (United States)

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

2017-04-01

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

Advances in electric power and energy systems load and price forecasting

CERN Document Server

2017-01-01

A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...

Sea surface temperature predictions using a multi-ocean analysis ensemble scheme

Science.gov (United States)

Zhang, Ying; Zhu, Jieshun; Li, Zhongxian; Chen, Haishan; Zeng, Gang

2017-08-01

This study examined the global sea surface temperature (SST) predictions by a so-called multiple-ocean analysis ensemble (MAE) initialization method which was applied in the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). Different from most operational climate prediction practices which are initialized by a specific ocean analysis system, the MAE method is based on multiple ocean analyses. In the paper, the MAE method was first justified by analyzing the ocean temperature variability in four ocean analyses which all are/were applied for operational climate predictions either at the European Centre for Medium-range Weather Forecasts or at NCEP. It was found that these systems exhibit substantial uncertainties in estimating the ocean states, especially at the deep layers. Further, a set of MAE hindcasts was conducted based on the four ocean analyses with CFSv2, starting from each April during 1982-2007. The MAE hindcasts were verified against a subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) Project. Comparisons suggested that MAE shows better SST predictions than CFSRR over most regions where ocean dynamics plays a vital role in SST evolutions, such as the El Niño and Atlantic Niño regions. Furthermore, significant improvements were also found in summer precipitation predictions over the equatorial eastern Pacific and Atlantic oceans, for which the local SST prediction improvements should be responsible. The prediction improvements by MAE imply a problem for most current climate predictions which are based on a specific ocean analysis system. That is, their predictions would drift towards states biased by errors inherent in their ocean initialization system, and thus have large prediction errors. In contrast, MAE arguably has an advantage by sampling such structural uncertainties, and could efficiently cancel these errors out in their predictions.

Coastal Ocean Observing Network - Open Source Architecture for Data Management and Web-Based Data Services

Science.gov (United States)

Pattabhi Rama Rao, E.; Venkat Shesu, R.; Udaya Bhaskar, T. V. S.

2012-07-01

The observations from the oceans are the backbone for any kind of operational services, viz. potential fishing zone advisory services, ocean state forecast, storm surges, cyclones, monsoon variability, tsunami, etc. Though it is important to monitor open Ocean, it is equally important to acquire sufficient data in the coastal ocean through coastal ocean observing systems for re-analysis, analysis and forecast of coastal ocean by assimilating different ocean variables, especially sub-surface information; validation of remote sensing data, ocean and atmosphere model/analysis and to understand the processes related to air-sea interaction and ocean physics. Accurate information and forecast of the state of the coastal ocean at different time scales is vital for the wellbeing of the coastal population as well as for the socio-economic development of the country through shipping, offshore oil and energy etc. Considering the importance of ocean observations in terms of understanding our ocean environment and utilize them for operational oceanography, a large number of platforms were deployed in the Indian Ocean including coastal observatories, to acquire data on ocean variables in and around Indian Seas. The coastal observation network includes HF Radars, wave rider buoys, sea level gauges, etc. The surface meteorological and oceanographic data generated by these observing networks are being translated into ocean information services through analysis and modelling. Centralized data management system is a critical component in providing timely delivery of Ocean information and advisory services. In this paper, we describe about the development of open-source architecture for real-time data reception from the coastal observation network, processing, quality control, database generation and web-based data services that includes on-line data visualization and data downloads by various means.

Road icing forecasting and detecting system

Science.gov (United States)

Xu, Hongke; Zheng, Jinnan; Li, Peiqi; Wang, Qiucai

2017-05-01

Regard for the facts that the low accuracy and low real-time of the artificial observation to determine the road icing condition, and it is difficult to forecast icing situation, according to the main factors influencing the road-icing, and the electrical characteristics reflected by the pavement ice layer, this paper presents an innovative system, that is, ice-forecasting of the highway's dangerous section. The system bases on road surface water salinity measurements and pavement temperature measurement to calculate the freezing point of water and temperature change trend, and then predicts the occurrence time of road icing; using capacitance measurements to verdict the road surface is frozen or not; This paper expounds the method of using single chip microcomputer as the core of the control system and described the business process of the system.

HYbrid Coordinate Ocean Model (HYCOM): Global

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Global HYbrid Coordinate Ocean Model (HYCOM) and U.S. Navy Coupled Ocean Data Assimilation (NCODA) 3-day, daily forecast at approximately 9-km (1/12-degree)...

Results on SSH neural network forecasting in the Mediterranean Sea

Science.gov (United States)

Rixen, Michel; Beckers, Jean-Marie; Alvarez, Alberto; Tintore, Joaquim

2002-01-01

Nowadays, satellites are the only monitoring systems that cover almost continuously all possible ocean areas and are now an essential part of operational oceanography. A novel approach based on artificial intelligence (AI) concepts, exploits pasts time series of satellite images to infer near future ocean conditions at the surface by neural networks and genetic algorithms. The size of the AI problem is drastically reduced by splitting the spatio-temporal variability contained in the remote sensing data by using empirical orthogonal function (EOF) decomposition. The problem of forecasting the dynamics of a 2D surface field can thus be reduced by selecting the most relevant empirical modes, and non-linear time series predictors are then applied on the amplitudes only. In the present case study, we use altimetric maps of the Mediterranean Sea, combining TOPEX-POSEIDON and ERS-1/2 data for the period 1992 to 1997. The learning procedure is applied to each mode individually. The final forecast is then reconstructed form the EOFs and the forecasted amplitudes and compared to the real observed field for validation of the method.

An assessment of the surface climate in the NCEP climate forecast system reanalysis

Energy Technology Data Exchange (ETDEWEB)

Wang, Wanqiu; Xie, Pingping; Yoo, Soo-Hyun; Xue, Yan; Kumar, Arun [Climate Prediction Center, NCEP/NWS/NOAA, Camp Springs, MD (United States); Wu, Xingren [Environmental Modeling Center, NCEP/NWS/NOAA, Camp Springs, MD (United States)

2011-10-15

This paper analyzes surface climate variability in the climate forecast system reanalysis (CFSR) recently completed at the National Centers for Environmental Prediction (NCEP). The CFSR represents a new generation of reanalysis effort with first guess from a coupled atmosphere-ocean-sea ice-land forecast system. This study focuses on the analysis of climate variability for a set of surface variables including precipitation, surface air 2-m temperature (T2m), and surface heat fluxes. None of these quantities are assimilated directly and thus an assessment of their variability provides an independent measure of the accuracy. The CFSR is compared with observational estimates and three previous reanalyses (the NCEP/NCAR reanalysis or R1, the NCEP/DOE reanalysis or R2, and the ERA40 produced by the European Centre for Medium-Range Weather Forecasts). The CFSR has improved time-mean precipitation distribution over various regions compared to the three previous reanalyses, leading to a better representation of freshwater flux (evaporation minus precipitation). For interannual variability, the CFSR shows improved precipitation correlation with observations over the Indian Ocean, Maritime Continent, and western Pacific. The T2m of the CFSR is superior to R1 and R2 with more realistic interannual variability and long-term trend. On the other hand, the CFSR overestimates downward solar radiation flux over the tropical Western Hemisphere warm pool, consistent with a negative cloudiness bias and a positive sea surface temperature bias. Meanwhile, the evaporative latent heat flux in CFSR appears to be larger than other observational estimates over most of the globe. A few deficiencies in the long-term variations are identified in the CFSR. Firstly, dramatic changes are found around 1998-2001 in the global average of a number of variables, possibly related to the changes in the assimilated satellite observations. Secondly, the use of multiple streams for the CFSR induces spurious

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