Sample records for weather forecasting
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Weather forecasting expert system study
NASA Technical Reports Server (NTRS)
1985-01-01
Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.
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Convective Weather Forecast Accuracy Analysis at Center and Sector Levels
NASA Technical Reports Server (NTRS)
Wang, Yao; Sridhar, Banavar
2010-01-01
This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in intensity and location for center forecast are relatively low. For example, 1-hour forecast intensity and horizontal location errors for ZDC center were about 0.12 and 0.13. However, the correlation between sector 1-hour forecast and actual weather coverage was weak, for sector ZDC32, about 32% of the total variation of observation weather intensity was unexplained by forecast; the sector horizontal location error was about 0.10. The paper also introduces an approach to estimate the sector three-dimensional actual weather coverage by using multiple sector forecasts, which turned out to produce better predictions. Using Multiple Linear Regression (MLR) model for this approach, the correlations between actual observation and the multiple sector forecast model prediction improved by several percents at 95% confidence level in comparison with single sector forecast.
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14 CFR 135.213 - Weather reports and forecasts.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 14 Aeronautics and Space 3 2011-01-01 2011-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that person...
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14 CFR 135.213 - Weather reports and forecasts.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 14 Aeronautics and Space 3 2014-01-01 2014-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that person...
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14 CFR 135.213 - Weather reports and forecasts.
Code of Federal Regulations, 2013 CFR
2013-01-01
... 14 Aeronautics and Space 3 2013-01-01 2013-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that person...
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14 CFR 135.213 - Weather reports and forecasts.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 14 Aeronautics and Space 3 2010-01-01 2010-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that person...
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14 CFR 135.213 - Weather reports and forecasts.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 14 Aeronautics and Space 3 2012-01-01 2012-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that person...
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Resolution of Probabilistic Weather Forecasts with Application in Disease Management.
Hughes, G; McRoberts, N; Burnett, F J
2017-02-01
Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.
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Economic Value of Weather and Climate Forecasts
NASA Astrophysics Data System (ADS)
Katz, Richard W.; Murphy, Allan H.
1997-06-01
Weather and climate extremes can significantly impact the economics of a region. This book examines how weather and climate forecasts can be used to mitigate the impact of the weather on the economy. Interdisciplinary in scope, it explores the meteorological, economic, psychological, and statistical aspects of weather prediction. Chapters by area specialists provide a comprehensive view of this timely topic. They encompass forecasts over a wide range of temporal scales, from weather over the next few hours to the climate months or seasons ahead, and address the impact of these forecasts on human behavior. Economic Value of Weather and Climate Forecasts seeks to determine the economic benefits of existing weather forecasting systems and the incremental benefits of improving these systems, and will be an interesting and essential text for economists, statisticians, and meteorologists.
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Medium-range fire weather forecasts
J.O. Roads; K. Ueyoshi; S.C. Chen; J. Alpert; F. Fujioka
1991-01-01
The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has...
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NASA Products to Enhance Energy Utility Load Forecasting
NASA Technical Reports Server (NTRS)
Lough, G.; Zell, E.; Engel-Cox, J.; Fungard, Y.; Jedlovec, G.; Stackhouse, P.; Homer, R.; Biley, S.
2012-01-01
Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts.
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NASA Astrophysics Data System (ADS)
Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.
2015-12-01
Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.
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National Weather Service Forecast Office - Honolulu, Hawai`i
Locations - Coastal Forecast Kauai Northwest Waters Kauai Windward Waters Kauai Leeward Waters Kauai Channel Oahu Forecast Oahu Surf Forecast Coastal Wind Observations Buoy Reports, and current weather conditions for selected locations tides, sunrise and sunset information Coastal Waters Forecast general weather
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High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of inco...
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NASA Astrophysics Data System (ADS)
Murray, S.; Guerra, J. A.
2017-12-01
One essential component of operational space weather forecasting is the prediction of solar flares. Early flare forecasting work focused on statistical methods based on historical flaring rates, but more complex machine learning methods have been developed in recent years. A multitude of flare forecasting methods are now available, however it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Current operational space weather centres cannot rely on automated methods, and generally use statistical forecasts with a little human intervention. Space weather researchers are increasingly looking towards methods used in terrestrial weather to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. It has proved useful in areas such as magnetospheric modelling and coronal mass ejection arrival analysis, however has not yet been implemented in operational flare forecasting. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASSA, ASAP, MAG4, MOSWOC, NOAA, and Solar Monitor). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. The results provide space weather forecasters with a set of parameters (combination weights, thresholds) that allow them to select the most appropriate values for constructing the 'best' ensemble forecast probability value, according to the performance metric of their choice. In this way different forecasts can be made to fit different end-user needs.
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LINKS to NATIONAL WEATHER SERVICE MARINE FORECAST OFFICES
Coastal Flooding Tsunamis 406 EPIRB's National Weather Service Marine Forecasts LINKS to NATIONAL WEATHER Marine Forecasts in text form ) Coastal NWS Forecast Offices have regionally focused marine webpages which are overflowing with information such as coastal forecasts, predicted tides, and buoy observations
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Flare forecasting at the Met Office Space Weather Operations Centre
NASA Astrophysics Data System (ADS)
Murray, S. A.; Bingham, S.; Sharpe, M.; Jackson, D. R.
2017-04-01
The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end-users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms: forecasts for each active region on the solar disk over the next 24 h and full-disk forecasts for the next 4 days. Here the forecasting process is described in detail, as well as first verification of archived forecasts using methods commonly used in operational weather prediction. Real-time verification available for operational flare forecasting use is also described. The influence of human forecasters is highlighted, with human-edited forecasts outperforming original model results and forecasting skill decreasing over longer forecast lead times.
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Flight Departure Delay and Rerouting Under Uncertainty in En Route Convective Weather
NASA Technical Reports Server (NTRS)
Mukherjee, Avijit; Grabbe, Shon; Sridhar, Banavar
2011-01-01
Delays caused by uncertainty in weather forecasts can be reduced by improving traffic flow management decisions. This paper presents a methodology for traffic flow management under uncertainty in convective weather forecasts. An algorithm for assigning departure delays and reroutes to aircraft is presented. Departure delay and route assignment are executed at multiple stages, during which, updated weather forecasts and flight schedules are used. At each stage, weather forecasts up to a certain look-ahead time are treated as deterministic and flight scheduling is done to mitigate the impact of weather on four-dimensional flight trajectories. Uncertainty in weather forecasts during departure scheduling results in tactical airborne holding of flights. The amount of airborne holding depends on the accuracy of forecasts as well as the look-ahead time included in the departure scheduling. The weather forecast look-ahead time is varied systematically within the experiments performed in this paper to analyze its effect on flight delays. Based on the results, longer look-ahead times cause higher departure delays and additional flying time due to reroutes. However, the amount of airborne holding necessary to prevent weather incursions reduces when the forecast look-ahead times are higher. For the chosen day of traffic and weather, setting the look-ahead time to 90 minutes yields the lowest total delay cost.
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The Weather Forecast Using Data Mining Research Based on Cloud Computing.
NASA Astrophysics Data System (ADS)
Wang, ZhanJie; Mazharul Mujib, A. B. M.
2017-10-01
Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.
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Evaluation and economic value of winter weather forecasts
NASA Astrophysics Data System (ADS)
Snyder, Derrick W.
State and local highway agencies spend millions of dollars each year to deploy winter operation teams to plow snow and de-ice roadways. Accurate and timely weather forecast information is critical for effective decision making. Students from Purdue University partnered with the Indiana Department of Transportation to create an experimental winter weather forecast service for the 2012-2013 winter season in Indiana to assist in achieving these goals. One forecast product, an hourly timeline of winter weather hazards produced daily, was evaluated for quality and economic value. Verification of the forecasts was performed with data from the Rapid Refresh numerical weather model. Two objective verification criteria were developed to evaluate the performance of the timeline forecasts. Using both criteria, the timeline forecasts had issues with reliability and discrimination, systematically over-forecasting the amount of winter weather that was observed while also missing significant winter weather events. Despite these quality issues, the forecasts still showed significant, but varied, economic value compared to climatology. Economic value of the forecasts was estimated to be 29.5 million or 4.1 million, depending on the verification criteria used. Limitations of this valuation system are discussed and a framework is developed for more thorough studies in the future.
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Weather Forecaster Understanding of Climate Models
NASA Astrophysics Data System (ADS)
Bol, A.; Kiehl, J. T.; Abshire, W. E.
2013-12-01
Weather forecasters, particularly those in broadcasting, are the primary conduit to the public for information on climate and climate change. However, many weather forecasters remain skeptical of model-based climate projections. To address this issue, The COMET Program developed an hour-long online lesson of how climate models work, targeting an audience of weather forecasters. The module draws on forecasters' pre-existing knowledge of weather, climate, and numerical weather prediction (NWP) models. In order to measure learning outcomes, quizzes were given before and after the lesson. Preliminary results show large learning gains. For all people that took both pre and post-tests (n=238), scores improved from 48% to 80%. Similar pre/post improvement occurred for National Weather Service employees (51% to 87%, n=22 ) and college faculty (50% to 90%, n=7). We believe these results indicate a fundamental misunderstanding among many weather forecasters of (1) the difference between weather and climate models, (2) how researchers use climate models, and (3) how they interpret model results. The quiz results indicate that efforts to educate the public about climate change need to include weather forecasters, a vital link between the research community and the general public.
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NASA Technical Reports Server (NTRS)
1975-01-01
This case study and generalization quantify benefits made possible through improved weather forecasting resulting from the integration of SEASAT data into local weather forecasts. The major source of avoidable economic losses to shipping from inadequate weather forecasting data is shown to be dependent on local precipitation forecasting. The ports of Philadelphia and Boston were selected for study.
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NASA Astrophysics Data System (ADS)
Schultz, David M.; Anderson, Stuart; Seo-Zindy, Ryo
2013-06-01
For students who major in meteorology, engaging in weather forecasting can motivate learning, develop critical-thinking skills, improve their written communication, and yield better forecasts. Whether such advances apply to students who are not meteorology majors has been less demonstrated. To test this idea, a weather discussion and an eLearning weather forecasting contest were devised for a meteorology course taken by third-year undergraduate earth- and environmental-science students. The discussion consisted of using the recent, present, and future weather to amplify the topics of the week's lectures. Then, students forecasted the next day's high temperature and the probability of precipitation for Woodford, the closest official observing site to Manchester, UK. The contest ran for 10 weeks, and the students received credit for participation. The top students at the end of the contest received bonus points on their final grade. A Web-based forecast contest application was developed to register the students, receive their forecasts, and calculate weekly standings. Students who were successful in the forecast contest were not necessarily those who achieved the highest scores on the tests, demonstrating that the contest was possibly testing different skills than traditional learning. Student evaluations indicate that the weather discussion and contest were reasonably successful in engaging students to learn about the weather outside of the classroom, synthesize their knowledge from the lectures, and improve their practical understanding of the weather. Therefore, students taking a meteorology class, but not majoring in meteorology, can derive academic benefits from weather discussions and forecast contests. Nevertheless, student evaluations also indicate that better integration of the lectures, weather discussions, and the forecasting contests is necessary.
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Weather Forecasting Systems and Methods
NASA Technical Reports Server (NTRS)
Mecikalski, John (Inventor); MacKenzie, Wayne M., Jr. (Inventor); Walker, John Robert (Inventor)
2014-01-01
A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.
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Weather assessment and forecasting
NASA Technical Reports Server (NTRS)
1977-01-01
Data management program activities centered around the analyses of selected far-term Office of Applications (OA) objectives, with the intent of determining if significant data-related problems would be encountered and if so what alternative solutions would be possible. Three far-term (1985 and beyond) OA objectives selected for analyses as having potential significant data problems were large-scale weather forecasting, local weather and severe storms forecasting, and global marine weather forecasting. An overview of general weather forecasting activities and their implications upon the ground based data system is provided. Selected topics were specifically oriented to the use of satellites.
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NASA Astrophysics Data System (ADS)
Ayscue, Emily P.
This study profiles the coastal tourism sector, a large and diverse consumer of climate and weather information. It is crucial to provide reliable, accurate and relevant resources for the climate and weather-sensitive portions of this stakeholder group in order to guide them in capitalizing on current climate and weather conditions and to prepare them for potential changes. An online survey of tourism business owners, managers and support specialists was conducted within the eight North Carolina oceanfront counties asking respondents about forecasts they use and for what purposes as well as why certain forecasts are not used. Respondents were also asked about their perceived dependency of their business on climate and weather as well as how valuable different forecasts are to their decision-making. Business types represented include: Agriculture, Outdoor Recreation, Accommodations, Food Services, Parks and Heritage, and Other. Weekly forecasts were the most popular forecasts with Monthly and Seasonal being the least used. MANOVA and ANOVA analyses revealed outdoor-oriented businesses (Agriculture and Outdoor Recreation) as perceiving themselves significantly more dependent on climate and weather than indoor-oriented ones (Food Services and Accommodations). Outdoor businesses also valued short-range forecasts significantly more than indoor businesses. This suggests a positive relationship between perceived climate and weather dependency and forecast value. The low perceived dependency and value of short-range forecasts of indoor businesses presents an opportunity to create climate and weather information resources directed at how they can capitalize on positive climate and weather forecasts and how to counter negative effects with forecasted adverse conditions. The low use of long-range forecasts among all business types can be related to the low value placed on these forecasts. However, these forecasts are still important in that they are used to make more financially risky decisions such as investment decisions.
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Road weather forecast quality analysis : project summary
DOT National Transportation Integrated Search
2006-03-01
The purpose of this research is to enhance the use of KDOTs Roadway Weather : Information System by improving the weather forecasts themselves and raising the level of : confidence in these forecasts.
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NASA Technical Reports Server (NTRS)
Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco
2012-01-01
Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.
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Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts
2013-12-01
ahead scheduling, Weather forecast , Wind power , Photovoltaic Power 15. NUMBER OF PAGES 107 16. PRICE CODE 17. SECURITY CLASSIFICATION OF...cost can be reached by accurately anticipating the future renewable power productions. This thesis suggests the use of weather forecasts to establish...reached by accurately anticipating the future renewable power productions. This thesis suggests the use of weather forecasts to establish day-ahead
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Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast
NASA Technical Reports Server (NTRS)
Zhu, Jiang; Stevens, E.; Zhang, X.; Zavodsky, B. T.; Heinrichs, T.; Broderson, D.
2014-01-01
A case study and monthly statistical analysis using sounder data assimilation to improve the Alaska regional weather forecast model are presented. Weather forecast in Alaska faces challenges as well as opportunities. Alaska has a large land with multiple types of topography and coastal area. Weather forecast models must be finely tuned in order to accurately predict weather in Alaska. Being in the high-latitudes provides Alaska greater coverage of polar orbiting satellites for integration into forecasting models than the lower 48. Forecasting marine low stratus clouds is critical to the Alaska aviation and oil industry and is the current focus of the case study. NASA AIRS/CrIS sounder profiles data are used to do data assimilation for the Alaska regional weather forecast model to improve Arctic marine stratus clouds forecast. Choosing physical options for the WRF model is discussed. Preprocess of AIRS/CrIS sounder data for data assimilation is described. Local observation data, satellite data, and global data assimilation data are used to verify and/or evaluate the forecast results by the MET tools Model Evaluation Tools (MET).
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NOAA's weather forecasts go hyper-local with next-generation weather
model NOAA HOME WEATHER OCEANS FISHERIES CHARTING SATELLITES CLIMATE RESEARCH COASTS CAREERS with next-generation weather model New model will help forecasters predict a storm's path, timing and intensity better than ever September 30, 2014 This is a comparison of two weather forecast models looking
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Finley, Cathy
2014-04-30
This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less
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Storm Prediction Center Fire Weather Forecasts
Archive NOAA Weather Radio Research Non-op. Products Forecast Tools Svr. Tstm. Events SPC Publications SPC Composite Maps Fire Weather Graphical Composite Maps Forecast and observational maps for various fire
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2006-06-28
KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral weather station in Florida, a member of the weather team looks over the weather balloons inside. The release of a Rawinsonde weather balloon was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton
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Verification of Space Weather Forecasts using Terrestrial Weather Approaches
NASA Astrophysics Data System (ADS)
Henley, E.; Murray, S.; Pope, E.; Stephenson, D.; Sharpe, M.; Bingham, S.; Jackson, D.
2015-12-01
The Met Office Space Weather Operations Centre (MOSWOC) provides a range of 24/7 operational space weather forecasts, alerts, and warnings, which provide valuable information on space weather that can degrade electricity grids, radio communications, and satellite electronics. Forecasts issued include arrival times of coronal mass ejections (CMEs), and probabilistic forecasts for flares, geomagnetic storm indices, and energetic particle fluxes and fluences. These forecasts are produced twice daily using a combination of output from models such as Enlil, near-real-time observations, and forecaster experience. Verification of forecasts is crucial for users, researchers, and forecasters to understand the strengths and limitations of forecasters, and to assess forecaster added value. To this end, the Met Office (in collaboration with Exeter University) has been adapting verification techniques from terrestrial weather, and has been working closely with the International Space Environment Service (ISES) to standardise verification procedures. We will present the results of part of this work, analysing forecast and observed CME arrival times, assessing skill using 2x2 contingency tables. These MOSWOC forecasts can be objectively compared to those produced by the NASA Community Coordinated Modelling Center - a useful benchmark. This approach cannot be taken for the other forecasts, as they are probabilistic and categorical (e.g., geomagnetic storm forecasts give probabilities of exceeding levels from minor to extreme). We will present appropriate verification techniques being developed to address these forecasts, such as rank probability skill score, and comparing forecasts against climatology and persistence benchmarks. As part of this, we will outline the use of discrete time Markov chains to assess and improve the performance of our geomagnetic storm forecasts. We will also discuss work to adapt a terrestrial verification visualisation system to space weather, to help MOSWOC forecasters view verification results in near real-time; plans to objectively assess flare forecasts under the EU Horizon 2020 FLARECAST project; and summarise ISES efforts to achieve consensus on verification.
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Flight Deck Weather Avoidance Decision Support: Implementation and Evaluation
NASA Technical Reports Server (NTRS)
Wu, Shu-Chieh; Luna, Rocio; Johnson, Walter W.
2013-01-01
Weather related disruptions account for seventy percent of the delays in the National Airspace System (NAS). A key component in the weather plan of the Next Generation of Air Transportation System (NextGen) is to assimilate observed weather information and probabilistic forecasts into the decision process of flight crews and air traffic controllers. In this research we explore supporting flight crew weather decision making through the development of a flight deck predicted weather display system that utilizes weather predictions generated by ground-based radar. This system integrates and presents this weather information, together with in-flight trajectory modification tools, within a cockpit display of traffic information (CDTI) prototype. that the CDTI features 2D and perspective 3D visualization models of weather. The weather forecast products that we implemented were the Corridor Integrated Weather System (CIWS) and the Convective Weather Avoidance Model (CWAM), both developed by MIT Lincoln Lab. We evaluated the use of CIWS and CWAM for flight deck weather avoidance in two part-task experiments. Experiment 1 compared pilots' en route weather avoidance performance in four weather information conditions that differed in the type and amount of predicted forecast (CIWS current weather only, CIWS current and historical weather, CIWS current and forecast weather, CIWS current and forecast weather and CWAM predictions). Experiment 2 compared the use of perspective 3D and 21/2D presentations of weather for flight deck weather avoidance. Results showed that pilots could take advantage of longer range predicted weather forecasts in performing en route weather avoidance but more research will be needed to determine what combinations of information are optimal and how best to present them.
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Verification of National Weather Service spot forecasts using surface observations
NASA Astrophysics Data System (ADS)
Lammers, Matthew Robert
Software has been developed to evaluate National Weather Service spot forecasts issued to support prescribed burns and early-stage wildfires. Fire management officials request spot forecasts from National Weather Service Weather Forecast Offices to provide detailed guidance as to atmospheric conditions in the vicinity of planned prescribed burns as well as wildfires that do not have incident meteorologists on site. This open source software with online display capabilities is used to examine an extensive set of spot forecasts of maximum temperature, minimum relative humidity, and maximum wind speed from April 2009 through November 2013 nationwide. The forecast values are compared to the closest available surface observations at stations installed primarily for fire weather and aviation applications. The accuracy of the spot forecasts is compared to those available from the National Digital Forecast Database (NDFD). Spot forecasts for selected prescribed burns and wildfires are used to illustrate issues associated with the verification procedures. Cumulative statistics for National Weather Service County Warning Areas and for the nation are presented. Basic error and accuracy metrics for all available spot forecasts and the entire nation indicate that the skill of the spot forecasts is higher than that available from the NDFD, with the greatest improvement for maximum temperature and the least improvement for maximum wind speed.
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Clarus : a clear solution for road weather information.
DOT National Transportation Integrated Search
2011-01-01
Weather forecast for tonight: Dark. Continued dark overnight, with widely scattered light by morning. These words by comedian George Carlin, while not a real weather forecast, demonstrate how insuffi cient forecasting can be. While the accuracy...
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Predicting Airspace Capacity Impacts Using the Consolidated Storm Prediction for Aviation
NASA Technical Reports Server (NTRS)
Russell, Carl
2010-01-01
Convective weather is currently the largest contributor to air traffic delays in the United States. In order to make effective traffic flow management decisions to mitigate these delays, weather forecasts must be made as early and as accurately as possible. A forecast product that could be used to mitigate convective weather impacts is the Consolidated Storm Prediction for Aviation. This product provides forecasts of cloud water content and convective top heights at 0- to 8-hour look-ahead times. The objective of this study was to examine a method of predicting the impact of convective weather on air traffic sector capacities using these forecasts. Polygons representing forecast convective weather were overlaid at multiple flight levels on a sector map to calculate the fraction of each sector covered by weather. The fractional volume coverage was used as the primary metric to determine convection s impact on sectors. Results reveal that the forecasts can be used to predict the probability and magnitude of weather impacts on sector capacity up to eight hours in advance.
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Weather monitoring and forecasting over eastern Attica (Greece) in the frame of FLIRE project
NASA Astrophysics Data System (ADS)
Kotroni, Vassiliki; Lagouvardos, Konstantinos; Chrysoulakis, Nektarios; Makropoulos, Christtos; Mimikou, Maria; Papathanasiou, Chrysoula; Poursanidis, Dimitris
2015-04-01
In the frame of FLIRE project a Decision Support System has been built with the aim to support decision making of Civil Protection Agencies and local stakeholders in the area of east Attica (Greece), in the cases of forest fires and floods. In this presentation we focus on a specific action that focuses on the provision of high resolution short-term weather forecasting data as well as of dense meteorological observations over the study area. Both weather forecasts and observations serve as an input in the Weather Information Management Tool (WIMT) of the Decision Support System. We focus on: (a) the description of the adopted strategy for setting-up the operational weather forecasting chain that provides the weather forecasts for the FLIRE project needs, (b) the presentation of the surface network station that provides real-time weather monitoring of the study area and (c) the strategy adopted for issuing smart alerts for thunderstorm forecasting based of real-time lightning observations as well as satellite observations.
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2006-06-28
KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral forecast facility in Florida, media were able to meet members of the weather team who review data used for forecasts as part of a tour of the facility. The team will play a role in the July 1 launch of Space Shuttle Discovery on mission STS-121. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton
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2006-06-28
KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral weather station in Florida, a member of the weather team prepares a Rawinsonde weather balloon for release. The release was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton
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NASA Astrophysics Data System (ADS)
Gelfan, Alexander; Moreydo, Vsevolod; Motovilov, Yury; Solomatine, Dimitri P.
2018-04-01
A long-term forecasting ensemble methodology, applied to water inflows into the Cheboksary Reservoir (Russia), is presented. The methodology is based on a version of the semi-distributed hydrological model ECOMAG (ECOlogical Model for Applied Geophysics) that allows for the calculation of an ensemble of inflow hydrographs using two different sets of weather ensembles for the lead time period: observed weather data, constructed on the basis of the Ensemble Streamflow Prediction methodology (ESP-based forecast), and synthetic weather data, simulated by a multi-site weather generator (WG-based forecast). We have studied the following: (1) whether there is any advantage of the developed ensemble forecasts in comparison with the currently issued operational forecasts of water inflow into the Cheboksary Reservoir, and (2) whether there is any noticeable improvement in probabilistic forecasts when using the WG-simulated ensemble compared to the ESP-based ensemble. We have found that for a 35-year period beginning from the reservoir filling in 1982, both continuous and binary model-based ensemble forecasts (issued in the deterministic form) outperform the operational forecasts of the April-June inflow volume actually used and, additionally, provide acceptable forecasts of additional water regime characteristics besides the inflow volume. We have also demonstrated that the model performance measures (in the verification period) obtained from the WG-based probabilistic forecasts, which are based on a large number of possible weather scenarios, appeared to be more statistically reliable than the corresponding measures calculated from the ESP-based forecasts based on the observed weather scenarios.
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Time Relevance of Convective Weather Forecast for Air Traffic Automation
NASA Technical Reports Server (NTRS)
Chan, William N.
2006-01-01
The Federal Aviation Administration (FAA) is handling nearly 120,000 flights a day through its Air Traffic Management (ATM) system and air traffic congestion is expected to increse substantially over the next 20 years. Weather-induced impacts to throughput and efficiency are the leading cause of flight delays accounting for 70% of all delays with convective weather accounting for 60% of all weather related delays. To support the Next Generation Air Traffic System goal of operating at 3X current capacity in the NAS, ATC decision support tools are being developed to create advisories to assist controllers in all weather constraints. Initial development of these decision support tools did not integrate information regarding weather constraints such as thunderstorms and relied on an additional system to provide that information. Future Decision Support Tools should move towards an integrated system where weather constraints are factored into the advisory of a Decision Support Tool (DST). Several groups such at NASA-Ames, Lincoln Laboratories, and MITRE are integrating convective weather data with DSTs. A survey of current convective weather forecast and observation data show they span a wide range of temporal and spatial resolutions. Short range convective observations can be obtained every 5 mins with longer range forecasts out to several days updated every 6 hrs. Today, the short range forecasts of less than 2 hours have a temporal resolution of 5 mins. Beyond 2 hours, forecasts have much lower temporal. resolution of typically 1 hour. Spatial resolutions vary from 1km for short range to 40km for longer range forecasts. Improving the accuracy of long range convective forecasts is a major challenge. A report published by the National Research Council states improvements for convective forecasts for the 2 to 6 hour time frame will only be achieved for a limited set of convective phenomena in the next 5 to 10 years. Improved longer range forecasts will be probabilistic as opposed to the deterministic shorter range forecasts. Despite the known low level of confidence with respect to long range convective forecasts, these data are still useful to a DST routing algorithm. It is better to develop an aircraft route using the best information available than no information. The temporally coarse long range forecast data needs to be interpolated to be useful to a DST. A DST uses aircraft trajectory predictions that need to be evaluated for impacts by convective storms. Each time-step of a trajectory prediction n&s to be checked against weather data. For the case of coarse temporal data, there needs to be a method fill in weather data where there is none. Simply using the coarse weather data without any interpolation can result in DST routes that are impacted by regions of strong convection. Increasing the temporal resolution of these data can be achieved but result in a large dataset that may prove to be an operational challenge in transmission and loading by a DST. Currently, it takes about 7mins retrieve a 7mb RUC2 forecast file from NOAA at NASA-Ames Research Center. A prototype NCWF6 1 hour forecast is about 3mb in size. A Six hour NCWFG forecast with a 1hr forecast time-step will be about l8mb (6 x 3mb). A 6 hour NCWF6 forecast with a l5min forecast time-step will be about 7mb (24 x 3mb). Based on the time it takes to retrieve a 7mb RUC2 forecast, it will take approximately 70mins to retrieve a 6 hour NCWF forecast with 15min time steps. Until those issues are addressed, there is a need to develop an algorithm that interpolates between these temporally coarse long range forecasts. This paper describes a method of how to use low temporal resolution probabilistic weather forecasts in a DST. The beginning of this paper is a description of some convective weather forecast and observation products followed by an example of how weather data are used by a DST. The subsequent sections will describe probabilistic forecasts followed by a descrtion of a method to use low temporal resolution probabilistic weather forecasts by providing a relevance value to these data outside of their valid times.
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General-aviation's view of progress in the aviation weather system
NASA Technical Reports Server (NTRS)
Lundgren, Douglas J.
1988-01-01
For all its activity statistics, general-aviation is the most vulnerable to hazardous weather. Of concern to the general aviation industry are: (1) the slow pace of getting units of the Automated Weather Observation System (AWOS) to the field; (2) the efforts of the National Weather Service to withdraw from both the observation and dissemination roles of the aviation weather system; (3) the need for more observation points to improve the accuracy of terminal and area forecasts; (4) the need for improvements in all area forecasts, terminal forecasts, and winds aloft forecasts; (5) slow progress in cockpit weather displays; (6) the erosion of transcribed weather broadcasts (TWEB) and other deficiencies in weather information dissemination; (7) the need to push to make the Direct User Access Terminal (DUAT) a reality; and (7) the need to improve severe weather (thunderstorm) warning systems.
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The pioneers of weather forecasting
NASA Astrophysics Data System (ADS)
Ballard, Susan
2016-01-01
In The Weather Experiment author Peter Moore takes us on a compelling journey through the early history of weather forecasting, bringing to life the personalities, lives and achievements of the men who put in place the building blocks required for forecasts to be possible.
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Seasonal Forecasting of Fire Weather Based on a New Global Fire Weather Database
NASA Technical Reports Server (NTRS)
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.
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ERIC Educational Resources Information Center
Schultz, David M.; Anderson, Stuart; Seo-Zindy, Ryo
2013-01-01
For students who major in meteorology, engaging in weather forecasting can motivate learning, develop critical-thinking skills, improve their written communication, and yield better forecasts. Whether such advances apply to students who are not meteorology majors has been less demonstrated. To test this idea, a weather discussion and an eLearning…
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2006-06-28
KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral forecast facility in Florida, Shuttle Weather Officer Kathy Winters briefs the media on how the launch weather forecast is developed. Attendees also were able to meet the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton
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Neural network based short-term load forecasting using weather compensation
DOE Office of Scientific and Technical Information (OSTI.GOV)
Chow, T.W.S.; Leung, C.T.
This paper presents a novel technique for electric load forecasting based on neural weather compensation. The proposed method is a nonlinear generalization of Box and Jenkins approach for nonstationary time-series prediction. A weather compensation neural network is implemented for one-day ahead electric load forecasting. The weather compensation neural network can accurately predict the change of actual electric load consumption from the previous day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing a more accurate load forecast with a 0.9% reduction in forecast error.
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About the National Forecast Chart
General Weather WPC Quantitative Precipitation Forecasts for coverage, and weather type from the NWS NDFD Weather Prediction Center 5830 University Research Court College Park, Maryland 20740 Weather Prediction
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2006-06-28
KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral weather station in Florida, workers release an upper-level weather balloon while several newscasters watch. The release of the balloon was part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. The radar-tracked balloon detects wind shears that can affect a shuttle launch. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton
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2006-06-28
KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral weather station in Florida, workers carry an upper-level weather balloon outside for release. The release was part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. The radar-tracked balloon detects wind shears that can affect a shuttle launch. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton
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2006-06-28
KENNEDY SPACE CENTER, FLA. - An upper-level weather balloon sails into the sky after release from the Cape Canaveral weather station in Florida. The release was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. The radar-tracked balloon detects wind shears that can affect a shuttle launch. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton
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NASA Astrophysics Data System (ADS)
Subramanian, Aneesh C.; Palmer, Tim N.
2017-06-01
Stochastic schemes to represent model uncertainty in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system has helped improve its probabilistic forecast skill over the past decade by both improving its reliability and reducing the ensemble mean error. The largest uncertainties in the model arise from the model physics parameterizations. In the tropics, the parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate. Superparameterization is a promising alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud-resolving model (CRM) embedded within a global climate model (GCM). In this paper, we compare the impact of initial random perturbations in embedded CRMs, within the ECMWF ensemble prediction system, with stochastically perturbed physical tendency (SPPT) scheme as a way to represent model uncertainty in medium-range tropical weather forecasts. We especially focus on forecasts of tropical convection and dynamics during MJO events in October-November 2011. These are well-studied events for MJO dynamics as they were also heavily observed during the DYNAMO field campaign. We show that a multiscale ensemble modeling approach helps improve forecasts of certain aspects of tropical convection during the MJO events, while it also tends to deteriorate certain large-scale dynamic fields with respect to stochastically perturbed physical tendencies approach that is used operationally at ECMWF.
Plain Language SummaryProbabilistic weather forecasts, especially for tropical weather, is still a significant challenge for global weather forecasting systems. Expressing uncertainty along with weather forecasts is important for informed decision making. Hence, we explore the use of a relatively new approach in using super-parameterization, where a cloud resolving model is embedded within a global model, in probabilistic tropical weather forecasts at medium range. We show that this approach helps improve modeling uncertainty in forecasts of certain features such as precipitation magnitude and location better, but forecasts of tropical winds are not necessarily improved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SpWea..15.1562H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SpWea..15.1562H"><span>Cost-Loss Analysis of Ensemble Solar Wind Forecasting: Space Weather Use of Terrestrial Weather Tools</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Henley, E. M.; Pope, E. C. D.</p> <p>2017-12-01</p> <p>This commentary concerns recent work on solar wind forecasting by Owens and Riley (2017). The approach taken makes effective use of tools commonly used in terrestrial weather—notably, via use of a simple model—generation of an "ensemble" forecast, and application of a "cost-loss" analysis to the resulting probabilistic information, to explore the benefit of this forecast to users with different risk appetites. This commentary aims to highlight these useful techniques to the wider space weather audience and to briefly discuss the general context of application of terrestrial weather approaches to space weather.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002memi.conf..106L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002memi.conf..106L"><span>Weather Forecasting From Woolly Art to Solid Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lynch, P.</p> <p></p> <p>THE PREHISTORY OF SCIENTIFIC FORECASTING Vilhelm Bjerknes Lewis Fry Richardson Richardson's Forecast THE BEGINNING OF MODERN NUMERICAL WEATHER PREDICTION John von Neumann and the Meteorology Project The ENIAC Integrations The Barotropic Model Primitive Equation Models NUMERICAL WEATHER PREDICTION TODAY ECMWF HIRLAM CONCLUSIONS REFERENCES</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1342861-epidemic-forecasting-messier-than-weather-forecasting-role-human-behavior-internet-data-streams-epidemic-forecast','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1342861-epidemic-forecasting-messier-than-weather-forecasting-role-human-behavior-internet-data-streams-epidemic-forecast"><span>Epidemic forecasting is messier than weather forecasting: The role of human behavior and internet data streams in epidemic forecast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Moran, Kelly Renee; Fairchild, Geoffrey; Generous, Nicholas; ...</p> <p>2016-11-14</p> <p>Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection andmore » Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. Here, we conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1342861','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1342861"><span>Epidemic forecasting is messier than weather forecasting: The role of human behavior and internet data streams in epidemic forecast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Moran, Kelly Renee; Fairchild, Geoffrey; Generous, Nicholas</p> <p></p> <p>Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection andmore » Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. Here, we conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5181546','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5181546"><span>Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Moran, Kelly R.; Fairchild, Geoffrey; Generous, Nicholas; Hickmann, Kyle; Osthus, Dave; Priedhorsky, Reid; Hyman, James; Del Valle, Sara Y.</p> <p>2016-01-01</p> <p>Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. We conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting. PMID:28830111</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.2892H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.2892H"><span>Monthly forecasting of agricultural pests in Switzerland</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hirschi, M.; Dubrovsky, M.; Spirig, C.; Samietz, J.; Calanca, P.; Weigel, A. P.; Fischer, A. M.; Rotach, M. W.</p> <p>2012-04-01</p> <p>Given the repercussions of pests and diseases on agricultural production, detailed forecasting tools have been developed to simulate the degree of infestation depending on actual weather conditions. The life cycle of pests is most successfully predicted if the micro-climate of the immediate environment (habitat) of the causative organisms can be simulated. Sub-seasonal pest forecasts therefore require weather information for the relevant habitats and the appropriate time scale. The pest forecasting system SOPRA (www.sopra.info) currently in operation in Switzerland relies on such detailed weather information, using hourly weather observations up to the day the forecast is issued, but only a climatology for the forecasting period. Here, we aim at improving the skill of SOPRA forecasts by transforming the weekly information provided by ECMWF monthly forecasts (MOFCs) into hourly weather series as required for the prediction of upcoming life phases of the codling moth, the major insect pest in apple orchards worldwide. Due to the probabilistic nature of operational monthly forecasts and the limited spatial and temporal resolution, their information needs to be post-processed for use in a pest model. In this study, we developed a statistical downscaling approach for MOFCs that includes the following steps: (i) application of a stochastic weather generator to generate a large pool of daily weather series consistent with the climate at a specific location, (ii) a subsequent re-sampling of weather series from this pool to optimally represent the evolution of the weekly MOFC anomalies, and (iii) a final extension to hourly weather series suitable for the pest forecasting model. Results show a clear improvement in the forecast skill of occurrences of upcoming codling moth life phases when incorporating MOFCs as compared to the operational pest forecasting system. This is true both in terms of root mean squared errors and of the continuous rank probability scores of the probabilistic forecasts vs. the mean absolute errors of the deterministic system. Also, the application of the climate conserving recalibration (CCR, Weigel et al. 2009) technique allows for successful correction of the under-confidence in the forecasted occurrences of codling moth life phases. Reference: Weigel, A. P.; Liniger, M. A. & Appenzeller, C. (2009). Seasonal Ensemble Forecasts: Are Recalibrated Single Models Better than Multimodels? Mon. Wea. Rev., 137, 1460-1479.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://images.nasa.gov/#/details-KSC-06pd1283.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-KSC-06pd1283.html"><span>KSC-06pd1283</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2006-06-28</p> <p>KENNEDY SPACE CENTER, FLA. - A Rawinsonde weather balloon sails into the sky after release from the Cape Canaveral forecast facility in Florida. The release was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. Rawinsonde balloons are GPS-tracked and can collect such data as atmospheric pressure, temperature, humidity and wind speed and direction up to 100,000 feet. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://images.nasa.gov/#/details-KSC-06pd1281.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-KSC-06pd1281.html"><span>KSC-06pd1281</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2006-06-28</p> <p>KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral forecast facility in Florida, a worker carries a Rawinsonde weather balloon outside for release. Rawinsonde balloons are GPS-tracked and can collect such data as atmospheric pressure, temperature, humidity and wind speed and direction up to 100,000 feet. The release was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://images.nasa.gov/#/details-KSC-06pd1282.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-KSC-06pd1282.html"><span>KSC-06pd1282</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2006-06-28</p> <p>KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral forecast facility in Florida, a worker releases a Rawinsonde weather balloon outside for release. Rawinsonde balloons are GPS-tracked and can collect such data as atmospheric pressure, temperature, humidity and wind speed and direction up to 100,000 feet. The release was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/26689','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/26689"><span>Using fire-weather forecasts and local weather observations in predicting burning index for individual fire-danger stations.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Owen P. Cramer</p> <p>1958-01-01</p> <p>Any agency engaged in forest-fire control needs accurate weather forecasts and systematic procedures for making the best use of predicted and reported weather information. This study explores the practicability of using several tabular and graphical aids for converting area forecasts and local observations of relative humidity and wind speed into predicted values for...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC33D1099S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC33D1099S"><span>Development of predictive weather scenarios for early prediction of rice yield in South Korea</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shin, Y.; Cho, J.; Jung, I.</p> <p>2017-12-01</p> <p>International grain prices are becoming unstable due to frequent occurrence of abnormal weather phenomena caused by climate change. Early prediction of grain yield using weather forecast data is important for stabilization of international grain prices. The APEC Climate Center (APCC) is providing seasonal forecast data based on monthly climate prediction models for global seasonal forecasting services. The 3-month and 6-month seasonal forecast data using the multi-model ensemble (MME) technique are provided in their own website, ADSS (APCC Data Service System, http://adss.apcc21.org/). The spatial resolution of seasonal forecast data for each individual model is 2.5°×2.5°(about 250km) and the time scale is created as monthly. In this study, we developed customized weather forecast scenarios that are combined seasonal forecast data and observational data apply to early rice yield prediction model. Statistical downscale method was applied to produce meteorological input data of crop model because field scale crop model (ORYZA2000) requires daily weather data. In order to determine whether the forecasting data is suitable for the crop model, we produced spatio-temporal downscaled weather scenarios and evaluated the predictability by comparison with observed weather data at 57 ASOS stations in South Korea. The customized weather forecast scenarios can be applied to various application fields not only early rice yield prediction. Acknowledgement This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No: PJ012855022017)" Rural Development Administration, Republic of Korea.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.prh.noaa.gov/pr/hnl','SCIGOVWS'); return false;" href="http://www.prh.noaa.gov/pr/hnl"><span>National Weather Service Forecast Office - Honolulu, Hawai`i</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Locations - <em>Coastal</em> Forecast Kauai Northwest Waters Kauai Windward Waters Kauai Leeward Waters Kauai Channel <em>Coastal</em> Wind Observations Buoy Reports, and current weather conditions for selected locations tides , sunrise and sunset information <em>Coastal</em> Waters Forecast general weather overview Tropical information</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMED31B0861A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMED31B0861A"><span>Using Flow Charts to Visualize the Decision-Making Process in Space Weather Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Aung, M. T. Y.; Myat, T.; Zheng, Y.; Mays, M. L.; Ngwira, C.; Damas, M. C.</p> <p>2016-12-01</p> <p>Our society today relies heavily on technological systems such as satellites, navigation systems, power grids and aviation. These systems are very sensitive to space weather disturbances. When Earth-directed space weather driven by the Sun arrives at the Earth, it causes changes to the Earth's radiation environment and the magnetosphere. Strong disturbances in the magnetosphere of the Earth are responsible for geomagnetic storms that can last from hours to days depending on strength of storms. Geomagnetic storms can severely impact critical infrastructure on Earth, such as the electric power grid, and Solar Energetic Particles that can endanger life in outer space. How can we lessen these adverse effects? They can be lessened through the early warning signals sent by space weather forecasters before CME or high-speed stream arrives. A space weather forecaster's duty is to send predicted notifications to high-tech industries and NASA missions so that they could take extra measures for protection. NASA space weather forecasters make prediction decisions by following certain steps and processes from the time an event occurs at the sun all the way to the impact locations. However, there has never been a tool that helps these forecasters visualize the decision process until now. A flow chart is created to help forecasters visualize the decision process. This flow chart provides basic knowledge of space weather and can be used to train future space weather forecasters. It also helps to cut down the training period and increase consistency in forecasting. The flow chart is also a great reference for people who are already familiar with space weather.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/31215','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/31215"><span>Probability fire weather forecasts .. show promise in 3-year trial</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Paul G. Scowcroft</p> <p>1970-01-01</p> <p>Probability fire weather forecasts were compared with categorical and climatological forecasts in a trial in southern California during the 1965-1967 fire seasons. Equations were developed to express the reliability of forecasts and degree of skill shown by the forecaster. Evaluation of 336 daily reports suggests that probability forecasts were more reliable. For...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/3355','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/3355"><span>Results of the Clarus demonstrations : evaluation of enhanced road weather forecasting enabled by Clarus.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2011-06-14</p> <p>This document is the final report of an evaluation of Clarus-enabled enhanced road weather forecasting used in the Clarus Demonstrations. This report examines the use of Clarus data to enhance four types of weather models and forecasts: The Local Ana...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2012-title46-vol2/pdf/CFR-2012-title46-vol2-sec45-191.pdf','CFR2012'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2012-title46-vol2/pdf/CFR-2012-title46-vol2-sec45-191.pdf"><span>46 CFR 45.191 - Pre-departure requirements.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2012&page.go=Go">Code of Federal Regulations, 2012 CFR</a></p> <p></p> <p>2012-10-01</p> <p>..., verification of mooring/docking space availability, and weather forecast checks were performed, and record the... voyage, the towing vessel master must conduct the following: (a) Weather forecast. Determine the marine weather forecast along the planned route, and contact the dock operator at the destination port to get an...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2014-title46-vol2/pdf/CFR-2014-title46-vol2-sec45-191.pdf','CFR2014'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2014-title46-vol2/pdf/CFR-2014-title46-vol2-sec45-191.pdf"><span>46 CFR 45.191 - Pre-departure requirements.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2014&page.go=Go">Code of Federal Regulations, 2014 CFR</a></p> <p></p> <p>2014-10-01</p> <p>..., verification of mooring/docking space availability, and weather forecast checks were performed, and record the... voyage, the towing vessel master must conduct the following: (a) Weather forecast. Determine the marine weather forecast along the planned route, and contact the dock operator at the destination port to get an...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2013-title46-vol2/pdf/CFR-2013-title46-vol2-sec45-191.pdf','CFR2013'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2013-title46-vol2/pdf/CFR-2013-title46-vol2-sec45-191.pdf"><span>46 CFR 45.191 - Pre-departure requirements.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2013&page.go=Go">Code of Federal Regulations, 2013 CFR</a></p> <p></p> <p>2013-10-01</p> <p>..., verification of mooring/docking space availability, and weather forecast checks were performed, and record the... voyage, the towing vessel master must conduct the following: (a) Weather forecast. Determine the marine weather forecast along the planned route, and contact the dock operator at the destination port to get an...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2011-title46-vol2/pdf/CFR-2011-title46-vol2-sec45-191.pdf','CFR2011'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2011-title46-vol2/pdf/CFR-2011-title46-vol2-sec45-191.pdf"><span>46 CFR 45.191 - Pre-departure requirements.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2011&page.go=Go">Code of Federal Regulations, 2011 CFR</a></p> <p></p> <p>2011-10-01</p> <p>..., verification of mooring/docking space availability, and weather forecast checks were performed, and record the... voyage, the towing vessel master must conduct the following: (a) Weather forecast. Determine the marine weather forecast along the planned route, and contact the dock operator at the destination port to get an...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017Ge%26Ae..57..769P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017Ge%26Ae..57..769P"><span>Space Weather Forecasting and Supporting Research in the USA</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pevtsov, A. A.</p> <p>2017-12-01</p> <p>In the United State, scientific research in space weather is funded by several Government Agencies including the National Science Foundation (NSF) and the National Aeronautics and Space Agency (NASA). For civilian and commercial purposes, space weather forecast is done by the Space Weather Prediction Center (SWPC) of the National Oceanic and Atmospheric Administration (NOAA). Observational data for modeling come from the network of groundbased observatories funded via various sources, as well as from the instruments on spacecraft. Numerical models used in forecast are developed in framework of individual research projects. The article provides a brief review of current state of space weather-related research and forecasting in the USA.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.1916G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.1916G"><span>How accurate are the weather forecasts for Bierun (southern Poland)?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gawor, J.</p> <p>2012-04-01</p> <p>Weather forecast accuracy has increased in recent times mainly thanks to significant development of numerical weather prediction models. Despite the improvements, the forecasts should be verified to control their quality. The evaluation of forecast accuracy can also be an interesting learning activity for students. It joins natural curiosity about everyday weather and scientific process skills: problem solving, database technologies, graph construction and graphical analysis. The examination of the weather forecasts has been taken by a group of 14-year-old students from Bierun (southern Poland). They participate in the GLOBE program to develop inquiry-based investigations of the local environment. For the atmospheric research the automatic weather station is used. The observed data were compared with corresponding forecasts produced by two numerical weather prediction models, i.e. COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) developed by Naval Research Laboratory Monterey, USA; it runs operationally at the Interdisciplinary Centre for Mathematical and Computational Modelling in Warsaw, Poland and COSMO (The Consortium for Small-scale Modelling) used by the Polish Institute of Meteorology and Water Management. The analysed data included air temperature, precipitation, wind speed, wind chill and sea level pressure. The prediction periods from 0 to 24 hours (Day 1) and from 24 to 48 hours (Day 2) were considered. The verification statistics that are commonly used in meteorology have been applied: mean error, also known as bias, for continuous data and a 2x2 contingency table to get the hit rate and false alarm ratio for a few precipitation thresholds. The results of the aforementioned activity became an interesting basis for discussion. The most important topics are: 1) to what extent can we rely on the weather forecasts? 2) How accurate are the forecasts for two considered time ranges? 3) Which precipitation threshold is the most predictable? 4) Why are some weather elements easier to verify than others? 5) What factors may contribute to the quality of the weather forecast?</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nco.ncep.noaa.gov/pmb/changes','SCIGOVWS'); return false;" href="http://www.nco.ncep.noaa.gov/pmb/changes"><span>NCO PMB - Upcoming Changes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>by Apr 12, 2018 Seeking public comments on the Hurricane Weather and <em>Research</em> Forecasting (HWRF) and Weather & <em>Research</em> Forecast No Changes on NOAAPORT NWS SCN 17-80 July 25, 2017 Upgrade GLW Upgrade June 9, 2015 HWRF Model Upgrade The Hurricane Weather and <em>Research</em> Forecast (HWRF) model will be</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/om/marine/wxradio.htm','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/om/marine/wxradio.htm"><span>NATIONAL WEATHER SERVICE MARINE PRODUCTS VIA NOAA WEATHER RADIO</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>! Boating Safety Beach Hazards Rip Currents Hypothermia Hurricanes Thunderstorms Lightning <em>Coastal</em> Flooding Radio network provides voice broadcasts of local and <em>coastal</em> marine forecasts on a continuous cycle. The forecasts are produced by local National Weather Service Forecast Offices. <em>Coastal</em> stations also broadcast</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017Ge%26Ae..57..869G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017Ge%26Ae..57..869G"><span>Space Weather Forecasting at IZMIRAN</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gaidash, S. P.; Belov, A. V.; Abunina, M. A.; Abunin, A. A.</p> <p>2017-12-01</p> <p>Since 1998, the Institute of Terrestrial Magnetism, Ionosphere, and Radio Wave Propagation (IZMIRAN) has had an operating heliogeophysical service—the Center for Space Weather Forecasts. This center transfers the results of basic research in solar-terrestrial physics into daily forecasting of various space weather parameters for various lead times. The forecasts are promptly available to interested consumers. This article describes the center and the main types of forecasts it provides: solar and geomagnetic activity, magnetospheric electron fluxes, and probabilities of proton increases. The challenges associated with the forecasting of effects of coronal mass ejections and coronal holes are discussed. Verification data are provided for the center's forecasts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110015843','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110015843"><span>Evaluating the Impact of Atmospheric Infrared Sounder (AIRS) Data On Convective Forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kozlowski, Danielle; Zavodsky, Bradley</p> <p>2011-01-01</p> <p>The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service (NWS) offices. SPoRT provides real-time NASA products and capabilities to its partners to address specific operational forecast challenges. The mission of SPoRT is to transition observations and research capabilities into operations to help improve short-term weather forecasts on a regional scale. Two areas of focus are data assimilation and modeling, which can to help accomplish SPoRT's programmatic goals of transitioning NASA data to operational users. Forecasting convective weather is one challenge that faces operational forecasters. Current numerical weather prediction (NWP) models that operational forecasters use struggle to properly forecast location, timing, intensity and/or mode of convection. Given the proper atmospheric conditions, convection can lead to severe weather. SPoRT's partners in the National Oceanic and Atmospheric Administration (NOAA) have a mission to protect the life and property of American citizens. This mission has been tested as recently as this 2011 severe weather season, which has seen more than 300 fatalities and injuries and total damages exceeding $10 billion. In fact, during the three day period from 25-27 April, 1,265 storms reports (362 tornado reports) were collected making this three day period one of most active in American history. To address the forecast challenge of convective weather, SPoRT produces a real-time NWP model called the SPoRT Weather Research and Forecasting (SPoRT-WRF), which incorporates unique NASA data sets. One of the NASA assets used in this unique model configuration is retrieved profiles from the Atmospheric Infrared Sounder (AIRS).The goal of this project is to determine the impact that these AIRS profiles have on the SPoRT-WRF forecasts by comparing to a current operational model and a control SPoRT-WRF model that does not contain AIRS profiles.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://images.nasa.gov/#/details-KSC-06pd1279.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-KSC-06pd1279.html"><span>KSC-06pd1279</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2006-06-28</p> <p>KENNEDY SPACE CENTER, FLA. - Under the watchful eyes of the media, an upper-level weather balloon begins its lift into the sky. The release of the balloon at the Cape Canaveral weather station in Florida was part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. The radar-tracked balloon detects wind shears that can affect a shuttle launch. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130003167','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130003167"><span>Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Nebula Cloud Computing Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Molthan, Andrew L.; Case, Jonathan L.; Venner, Jason; Moreno-Madrinan, Max. J.; Delgado, Francisco</p> <p>2012-01-01</p> <p>Over the past two years, scientists in the Earth Science Office at NASA fs Marshall Space Flight Center (MSFC) have explored opportunities to apply cloud computing concepts to support near real ]time weather forecast modeling via the Weather Research and Forecasting (WRF) model. Collaborators at NASA fs Short ]term Prediction Research and Transition (SPoRT) Center and the SERVIR project at Marshall Space Flight Center have established a framework that provides high resolution, daily weather forecasts over Mesoamerica through use of the NASA Nebula Cloud Computing Platform at Ames Research Center. Supported by experts at Ames, staff at SPoRT and SERVIR have established daily forecasts complete with web graphics and a user interface that allows SERVIR partners access to high resolution depictions of weather in the next 48 hours, useful for monitoring and mitigating meteorological hazards such as thunderstorms, heavy precipitation, and tropical weather that can lead to other disasters such as flooding and landslides. This presentation will describe the framework for establishing and providing WRF forecasts, example applications of output provided via the SERVIR web portal, and early results of forecast model verification against available surface ] and satellite ]based observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMIN43C1526M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMIN43C1526M"><span>Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Nebula Cloud Computing Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Molthan, A.; Case, J.; Venner, J.; Moreno-Madriñán, M. J.; Delgado, F.</p> <p>2012-12-01</p> <p>Over the past two years, scientists in the Earth Science Office at NASA's Marshall Space Flight Center (MSFC) have explored opportunities to apply cloud computing concepts to support near real-time weather forecast modeling via the Weather Research and Forecasting (WRF) model. Collaborators at NASA's Short-term Prediction Research and Transition (SPoRT) Center and the SERVIR project at Marshall Space Flight Center have established a framework that provides high resolution, daily weather forecasts over Mesoamerica through use of the NASA Nebula Cloud Computing Platform at Ames Research Center. Supported by experts at Ames, staff at SPoRT and SERVIR have established daily forecasts complete with web graphics and a user interface that allows SERVIR partners access to high resolution depictions of weather in the next 48 hours, useful for monitoring and mitigating meteorological hazards such as thunderstorms, heavy precipitation, and tropical weather that can lead to other disasters such as flooding and landslides. This presentation will describe the framework for establishing and providing WRF forecasts, example applications of output provided via the SERVIR web portal, and early results of forecast model verification against available surface- and satellite-based observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC51A1123T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC51A1123T"><span>Improving Seasonal Crop Monitoring and Forecasting for Soybean and Corn in Iowa</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Togliatti, K.; Archontoulis, S.; Dietzel, R.; VanLoocke, A.</p> <p>2016-12-01</p> <p>Accurately forecasting crop yield in advance of harvest could greatly benefit farmers, however few evaluations have been conducted to determine the effectiveness of forecasting methods. We tested one such method that used a combination of short-term weather forecasting from the Weather Research and Forecasting Model (WRF) to predict in season weather variables, such as, maximum and minimum temperature, precipitation and radiation at 4 different forecast lengths (2 weeks, 1 week, 3 days, and 0 days). This forecasted weather data along with the current and historic (previous 35 years) data from the Iowa Environmental Mesonet was combined to drive Agricultural Production Systems sIMulator (APSIM) simulations to forecast soybean and corn yields in 2015 and 2016. The goal of this study is to find the forecast length that reduces the variability of simulated yield predictions while also increasing the accuracy of those predictions. APSIM simulations of crop variables were evaluated against bi-weekly field measurements of phenology, biomass, and leaf area index from early and late planted soybean plots located at the Agricultural Engineering and Agronomy Research Farm in central Iowa as well as the Northwest Research Farm in northwestern Iowa. WRF model predictions were evaluated against observed weather data collected at the experimental fields. Maximum temperature was the most accurately predicted variable, followed by minimum temperature and radiation, and precipitation was least accurate according to RMSE values and the number of days that were forecasted within a 20% error of the observed weather. Our analysis indicated that for the majority of months in the growing season the 3 day forecast performed the best. The 1 week forecast came in second and the 2 week forecast was the least accurate for the majority of months. Preliminary results for yield indicate that the 2 week forecast is the least variable of the forecast lengths, however it also is the least accurate. The 3 day and 1 week forecast have a better accuracy, with an increase in variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.3058C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.3058C"><span>Training the next generation of scientists in Weather Forecasting: new approaches with real models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Carver, Glenn; Váňa, Filip; Siemen, Stephan; Kertesz, Sandor; Keeley, Sarah</p> <p>2014-05-01</p> <p>The European Centre for Medium Range Weather Forecasts operationally produce medium range forecasts using what is internationally acknowledged as the world leading global weather forecast model. Future development of this scientifically advanced model relies on a continued availability of experts in the field of meteorological science and with high-level software skills. ECMWF therefore has a vested interest in young scientists and University graduates developing the necessary skills in numerical weather prediction including both scientific and technical aspects. The OpenIFS project at ECMWF maintains a portable version of the ECMWF forecast model (known as IFS) for use in education and research at Universities, National Meteorological Services and other research and education organisations. OpenIFS models can be run on desktop or high performance computers to produce weather forecasts in a similar way to the operational forecasts at ECMWF. ECMWF also provide the Metview desktop application, a modern, graphical, and easy to use tool for analysing and visualising forecasts that is routinely used by scientists and forecasters at ECMWF and other institutions. The combination of Metview with the OpenIFS models has the potential to deliver classroom-friendly tools allowing students to apply their theoretical knowledge to real-world examples using a world-leading weather forecasting model. In this paper we will describe how the OpenIFS model has been used for teaching. We describe the use of Linux based 'virtual machines' pre-packaged on USB sticks that support a technically easy and safe way of providing 'classroom-on-a-stick' learning environments for advanced training in numerical weather prediction. We welcome discussions with interested parties.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=account+AND+information+AND+decision+AND+making&pg=7&id=EJ956136','ERIC'); return false;" href="https://eric.ed.gov/?q=account+AND+information+AND+decision+AND+making&pg=7&id=EJ956136"><span>Reducing Probabilistic Weather Forecasts to the Worst-Case Scenario: Anchoring Effects</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Joslyn, Susan; Savelli, Sonia; Nadav-Greenberg, Limor</p> <p>2011-01-01</p> <p>Many weather forecast providers believe that forecast uncertainty in the form of the worst-case scenario would be useful for general public end users. We tested this suggestion in 4 studies using realistic weather-related decision tasks involving high winds and low temperatures. College undergraduates, given the statistical equivalent of the…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=WEATHER&id=EJ1037438','ERIC'); return false;" href="https://eric.ed.gov/?q=WEATHER&id=EJ1037438"><span>Now, Here's the Weather Forecast...</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Richardson, Mathew</p> <p>2013-01-01</p> <p>The Met Office has a long history of weather forecasting, creating tailored weather forecasts for customers across the world. Based in Exeter, the Met Office is also home to the Met Office Hadley Centre, a world-leading centre for the study of climate change and its potential impacts. Climate information from the Met Office Hadley Centre is used…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMSH21F..01M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMSH21F..01M"><span>Current gaps in understanding and predicting space weather: An operations perspective</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Murtagh, W. J.</p> <p>2016-12-01</p> <p>The NOAA Space Weather Prediction Center (SWPC), one of the nine National Weather Service (NWS) National Centers for Environmental Prediction, is the Nation's official source for space weather alerts and warnings. Space weather effects the technology that forms the backbone of global economic vitality and national security, including satellite and airline operations, communications networks, and the electric power grid. Many of SWPC's over 48,000 subscribers rely on space weather forecasts for critical decision making. But extraordinary gaps still exist in our ability to meet customer needs for accurate and timely space weather forecasts and warnings. The 2015 National Space Weather Strategy recognizes that it is imperative that we improve the fundamental understanding of space weather and increase the accuracy, reliability, and timeliness of space-weather observations and forecasts in support of the growing demands. In this talk we provide a broad perspective of the key challenges that currently limit the forecaster's ability to better understand and predict space weather. We also examine the impact of these limitations on the end-user community.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110009907','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110009907"><span>Between the Rock and a Hard Place: The CCMC as a Transit Station Between Modelers and Forecasters</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hesse, Michael</p> <p>2009-01-01</p> <p>The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involved model evaluations, model transitions to operations, and the development of draft Space Weather forecasting tools. This presentation will focus on the latter element. Specifically, we will discuss the process of transition research models, or information generated by research models, to Space Weather Forecasting organizations. We will analyze successes as well as obstacles to further progress, and we will suggest avenues for increased transitioning success.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040034245&hterms=time+series+forecasting&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dtime%2Bseries%2Bforecasting','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040034245&hterms=time+series+forecasting&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dtime%2Bseries%2Bforecasting"><span>Use of EOS Data in AWIPS for Weather Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jedlovec, Gary J.; Haines, Stephanie L.; Suggs, Ron J.; Bradshaw, Tom; Darden, Chris; Burks, Jason</p> <p>2003-01-01</p> <p>Operational weather forecasting relies heavily on real time data and modeling products for forecast preparation and dissemination of significant weather information to the public. The synthesis of this information (observations and model products) by the meteorologist is facilitated by a decision support system to display and integrate the information in a useful fashion. For the NWS this system is called Advanced Weather Interactive Processing System (AWIPS). Over the last few years NASA has launched a series of new Earth Observation Satellites (EOS) for climate monitoring that include several instruments that provide high-resolution measurements of atmospheric and surface features important for weather forecasting and analysis. The key to the utilization of these unique new measurements by the NWS is the real time integration of the EOS data into the AWIPS system. This is currently being done in the Huntsville and Birmingham NWS Forecast Offices under the NASA Short-term Prediction Research and Transition (SPORT) Program. This paper describes the use of near real time MODIS and AIRS data in AWIPS to improve the detection of clouds, moisture variations, atmospheric stability, and thermal signatures that can lead to significant weather development. The paper and the conference presentation will focus on several examples where MODIS and AIRS data have made a positive impact on forecast accuracy. The results of an assessment of the utility of these products for weather forecast improvement made at the Huntsville NWS Forecast Office will be presented.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120002991','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120002991"><span>Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Molthan, Andrew L.; Limaye, Ashutosh S.; Srikishen, Jayanthi</p> <p>2011-01-01</p> <p>Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula s "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA s National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA s SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT s experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2000GeoRL..27.4049T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2000GeoRL..27.4049T"><span>Evaluating space weather forecasts of geomagnetic activity from a user perspective</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thomson, A. W. P.</p> <p>2000-12-01</p> <p>Decision Theory can be used as a tool for discussing the relative costs of complacency and false alarms with users of space weather forecasts. We describe a new metric for the value of space weather forecasts, derived from Decision Theory. In particular we give equations for the level of accuracy that a forecast must exceed in order to be useful to a specific customer. The technique is illustrated by simplified example forecasts for global geomagnetic activity and for geophysical exploration and power grid management in the British Isles.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MS%26E..309a2054R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MS%26E..309a2054R"><span>Implementation of bayesian model averaging on the weather data forecasting applications utilizing open weather map</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rahmat, R. F.; Nasution, F. R.; Seniman; Syahputra, M. F.; Sitompul, O. S.</p> <p>2018-02-01</p> <p>Weather is condition of air in a certain region at a relatively short period of time, measured with various parameters such as; temperature, air preasure, wind velocity, humidity and another phenomenons in the atmosphere. In fact, extreme weather due to global warming would lead to drought, flood, hurricane and other forms of weather occasion, which directly affects social andeconomic activities. Hence, a forecasting technique is to predict weather with distinctive output, particullary mapping process based on GIS with information about current weather status in certain cordinates of each region with capability to forecast for seven days afterward. Data used in this research are retrieved in real time from the server openweathermap and BMKG. In order to obtain a low error rate and high accuracy of forecasting, the authors use Bayesian Model Averaging (BMA) method. The result shows that the BMA method has good accuracy. Forecasting error value is calculated by mean square error shows (MSE). The error value emerges at minumum temperature rated at 0.28 and maximum temperature rated at 0.15. Meanwhile, the error value of minimum humidity rates at 0.38 and the error value of maximum humidity rates at 0.04. Afterall, the forecasting error rate of wind speed is at 0.076. The lower the forecasting error rate, the more optimized the accuracy is.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2010-title15-vol3/pdf/CFR-2010-title15-vol3-sec946-4.pdf','CFR'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2010-title15-vol3/pdf/CFR-2010-title15-vol3-sec946-4.pdf"><span>15 CFR 946.4 - Menu of services.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2010&page.go=Go">Code of Federal Regulations, 2010 CFR</a></p> <p></p> <p>2010-01-01</p> <p>... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.4 Menu of services. The following are the basic weather...) Marine Forecasts, Statements, and Warnings (g) Hydrologic Forecasts and Warnings (h) Fire Weather...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2013-title15-vol3/pdf/CFR-2013-title15-vol3-sec946-4.pdf','CFR2013'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2013-title15-vol3/pdf/CFR-2013-title15-vol3-sec946-4.pdf"><span>15 CFR 946.4 - Menu of services.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2013&page.go=Go">Code of Federal Regulations, 2013 CFR</a></p> <p></p> <p>2013-01-01</p> <p>... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.4 Menu of services. The following are the basic weather...) Marine Forecasts, Statements, and Warnings (g) Hydrologic Forecasts and Warnings (h) Fire Weather...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20060009999','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20060009999"><span>Severe Weather Forecast Decision Aid</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bauman, William H., III; Wheeler, Mark M.; Short, David A.</p> <p>2005-01-01</p> <p>This report presents a 15-year climatological study of severe weather events and related severe weather atmospheric parameters. Data sources included local forecast rules, archived sounding data, Cloud-to-Ground Lightning Surveillance System (CGLSS) data, surface and upper air maps, and two severe weather event databases covering east-central Florida. The local forecast rules were used to set threat assessment thresholds for stability parameters that were derived from the sounding data. The severe weather events databases were used to identify days with reported severe weather and the CGLSS data was used to differentiate between lightning and non-lightning days. These data sets provided the foundation for analyzing the stability parameters and synoptic patterns that were used to develop an objective tool to aid in forecasting severe weather events. The period of record for the analysis was May - September, 1989 - 2003. The results indicate that there are certain synoptic patterns more prevalent on days with severe weather and some of the stability parameters are better predictors of severe weather days based on locally tuned threat values. The results also revealed the stability parameters that did not display any skill related to severe weather days. An interactive web-based Severe Weather Decision Aid was developed to assist the duty forecaster by providing a level of objective guidance based on the analysis of the stability parameters, CGLSS data, and synoptic-scale dynamics. The tool will be tested and evaluated during the 2005 warm season.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.crh.noaa.gov/greatlakes','SCIGOVWS'); return false;" href="http://www.crh.noaa.gov/greatlakes"><span>Great Lakes Maps - NOAA's National Weather Service</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p><em>Coastal</em> Forecast System) Waves (GLERL Great Lakes <em>Coastal</em> Forecast System) Ice Cover (GLERL Great Lakes <em>Coastal</em> Forecast System) NOAA's National Weather Service Central Region Headquarters Regional Office 7220</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28830111','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28830111"><span>Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Moran, Kelly R; Fairchild, Geoffrey; Generous, Nicholas; Hickmann, Kyle; Osthus, Dave; Priedhorsky, Reid; Hyman, James; Del Valle, Sara Y</p> <p>2016-12-01</p> <p>Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. We conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting. Published by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2014-title15-vol3/pdf/CFR-2014-title15-vol3-sec946-6.pdf','CFR2014'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2014-title15-vol3/pdf/CFR-2014-title15-vol3-sec946-6.pdf"><span>15 CFR 946.6 - Change in operations-transferring responsibility and moving field offices.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2014&page.go=Go">Code of Federal Regulations, 2014 CFR</a></p> <p></p> <p>2014-01-01</p> <p>... COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.6... Weather Service Forecast Office (NWSFO) or a NEXRAD Weather Service Office (NWSO) that is being established as a future Weather Forecast Office following commissioning of the NEXRAD at the new office; (2...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2010-title15-vol3/pdf/CFR-2010-title15-vol3-sec946-6.pdf','CFR'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2010-title15-vol3/pdf/CFR-2010-title15-vol3-sec946-6.pdf"><span>15 CFR 946.6 - Change in operations-transferring responsibility and moving field offices.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2010&page.go=Go">Code of Federal Regulations, 2010 CFR</a></p> <p></p> <p>2010-01-01</p> <p>... COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.6... Weather Service Forecast Office (NWSFO) or a NEXRAD Weather Service Office (NWSO) that is being established as a future Weather Forecast Office following commissioning of the NEXRAD at the new office; (2...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2011-title15-vol3/pdf/CFR-2011-title15-vol3-sec946-6.pdf','CFR2011'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2011-title15-vol3/pdf/CFR-2011-title15-vol3-sec946-6.pdf"><span>15 CFR 946.6 - Change in operations-transferring responsibility and moving field offices.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2011&page.go=Go">Code of Federal Regulations, 2011 CFR</a></p> <p></p> <p>2011-01-01</p> <p>... COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.6... Weather Service Forecast Office (NWSFO) or a NEXRAD Weather Service Office (NWSO) that is being established as a future Weather Forecast Office following commissioning of the NEXRAD at the new office; (2...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2012-title15-vol3/pdf/CFR-2012-title15-vol3-sec946-6.pdf','CFR2012'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2012-title15-vol3/pdf/CFR-2012-title15-vol3-sec946-6.pdf"><span>15 CFR 946.6 - Change in operations-transferring responsibility and moving field offices.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2012&page.go=Go">Code of Federal Regulations, 2012 CFR</a></p> <p></p> <p>2012-01-01</p> <p>... COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.6... Weather Service Forecast Office (NWSFO) or a NEXRAD Weather Service Office (NWSO) that is being established as a future Weather Forecast Office following commissioning of the NEXRAD at the new office; (2...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2013-title15-vol3/pdf/CFR-2013-title15-vol3-sec946-6.pdf','CFR2013'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2013-title15-vol3/pdf/CFR-2013-title15-vol3-sec946-6.pdf"><span>15 CFR 946.6 - Change in operations-transferring responsibility and moving field offices.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2013&page.go=Go">Code of Federal Regulations, 2013 CFR</a></p> <p></p> <p>2013-01-01</p> <p>... COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.6... Weather Service Forecast Office (NWSFO) or a NEXRAD Weather Service Office (NWSO) that is being established as a future Weather Forecast Office following commissioning of the NEXRAD at the new office; (2...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/om/marine/akvhfv.htm','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/om/marine/akvhfv.htm"><span>ALASKA MARINE VHF VOICE</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Tsunamis 406 EPIRB's National Weather Service Marine Forecasts ALASKA MARINE <em>VHF</em> VOICE Marine Forecast greater danger near shore or any shallow waters? NATIONAL WEATHER SERVICE PRODUCTS VIA ALASKA MARINE <em>VHF</em> VOICE NOAA broadcasts offshore forecasts, nearshore forecasts and storm warnings on marine <em>VHF</em> channels</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1612500L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1612500L"><span>Improved Weather and Power Forecasts for Energy Operations - the German Research Project EWeLiNE</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lundgren, Kristina; Siefert, Malte; Hagedorn, Renate; Majewski, Detlev</p> <p>2014-05-01</p> <p>The German energy system is going through a fundamental change. Based on the energy plans of the German federal government, the share of electrical power production from renewables should increase to 35% by 2020. This means that, in the near future at certain times renewable energies will provide a major part of Germany's power production. Operating a power supply system with a large share of weather-dependent power sources in a secure way requires improved power forecasts. One of the most promising strategies to improve the existing wind power and PV power forecasts is to optimize the underlying weather forecasts and to enhance the collaboration between the meteorology and energy sectors. Deutscher Wetterdienst addresses these challenges in collaboration with Fraunhofer IWES within the research project EWeLiNE. The overarching goal of the project is to improve the wind and PV power forecasts by combining improved power forecast models and optimized weather forecasts. During the project, the numerical weather prediction models COSMO-DE and COSMO-DE-EPS (Ensemble Prediction System) by Deutscher Wetterdienst will be generally optimized towards improved wind power and PV forecasts. For instance, it will be investigated whether the assimilation of new types of data, e.g. power production data, can lead to improved weather forecasts. With regard to the probabilistic forecasts, the focus is on the generation of ensembles and ensemble calibration. One important aspect of the project is to integrate the probabilistic information into decision making processes by developing user-specified products. In this paper we give an overview of the project and present first results.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/25995','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/25995"><span>Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Joseph J. Charney; Lesley A. Fusina</p> <p>2006-01-01</p> <p>This paper presents an assessment of fire weather and fire behavior predictions produced by a numerical weather prediction model similar to those used by operational weather forecasters when preparing their forecasts. The PSU/NCAR MM5 model is used to simulate the weather conditions associated with three fire episodes in June 2005. Extreme fire behavior was reported...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4851062','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4851062"><span>Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru</p> <p>2016-01-01</p> <p>This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons. PMID:27092508</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19850065097&hterms=statistics+levels&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dstatistics%2Blevels','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19850065097&hterms=statistics+levels&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dstatistics%2Blevels"><span>Use of observational and model-derived fields and regime model output statistics in mesoscale forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Forbes, G. S.; Pielke, R. A.</p> <p>1985-01-01</p> <p>Various empirical and statistical weather-forecasting studies which utilize stratification by weather regime are described. Objective classification was used to determine weather regime in some studies. In other cases the weather pattern was determined on the basis of a parameter representing the physical and dynamical processes relevant to the anticipated mesoscale phenomena, such as low level moisture convergence and convective precipitation, or the Froude number and the occurrence of cold-air damming. For mesoscale phenomena already in existence, new forecasting techniques were developed. The use of cloud models in operational forecasting is discussed. Models to calculate the spatial scales of forcings and resultant response for mesoscale systems are presented. The use of these models to represent the climatologically most prevalent systems, and to perform case-by-case simulations is reviewed. Operational implementation of mesoscale data into weather forecasts, using both actual simulation output and method-output statistics is discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27092508','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27092508"><span>Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru</p> <p>2016-04-15</p> <p>This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020080122','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020080122"><span>Smooth Sailing for Weather Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2002-01-01</p> <p>Through a cooperative venture with NASA's Stennis Space Center, WorldWinds, Inc., developed a unique weather and wave vector map using space-based radar satellite information and traditional weather observations. Called WorldWinds, the product provides accurate, near real-time, high-resolution weather forecasts. It was developed for commercial and scientific users. In addition to weather forecasting, the product's applications include maritime and terrestrial transportation, aviation operations, precision farming, offshore oil and gas operations, and coastal hazard response support. Target commercial markets include the operational maritime and aviation communities, oil and gas providers, and recreational yachting interests. Science applications include global long-term prediction and climate change, land-cover and land-use change, and natural hazard issues. Commercial airlines have expressed interest in the product, as it can provide forecasts over remote areas. WorldWinds, Inc., is currently providing its product to commercial weather outlets.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMSH32B..01B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMSH32B..01B"><span>NSF's Perspective on Space Weather Research for Building Forecasting Capabilities</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bisi, M. M.; Pulkkinen, A. A.; Bisi, M. M.; Pulkkinen, A. A.; Webb, D. F.; Oughton, E. J.; Azeem, S. I.</p> <p>2017-12-01</p> <p>Space weather research at the National Science Foundation (NSF) is focused on scientific discovery and on deepening knowledge of the Sun-Geospace system. The process of maturation of knowledge base is a requirement for the development of improved space weather forecast models and for the accurate assessment of potential mitigation strategies. Progress in space weather forecasting requires advancing in-depth understanding of the underlying physical processes, developing better instrumentation and measurement techniques, and capturing the advancements in understanding in large-scale physics based models that span the entire chain of events from the Sun to the Earth. This presentation will provide an overview of current and planned programs pertaining to space weather research at NSF and discuss the recommendations of the Geospace Section portfolio review panel within the context of space weather forecasting capabilities.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140002701','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140002701"><span>How MAG4 Improves Space Weather Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Falconer, David; Khazanov, Igor; Barghouty, Nasser</p> <p>2013-01-01</p> <p>Dangerous space weather is driven by solar flares and Coronal Mass Ejection (CMEs). Forecasting flares and CMEs is the first step to forecasting either dangerous space weather or All Clear. MAG4 (Magnetogram Forecast), developed originally for NASA/SRAG (Space Radiation Analysis Group), is an automated program that analyzes magnetograms from the HMI (Helioseismic and Magnetic Imager) instrument on NASA SDO (Solar Dynamics Observatory), and automatically converts the rate (or probability) of major flares (M- and X-class), Coronal Mass Ejections (CMEs), and Solar Energetic Particle Events.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1049659-value-medium-range-weather-forecasts-improvement-seasonal-hydrologic-prediction-skill','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1049659-value-medium-range-weather-forecasts-improvement-seasonal-hydrologic-prediction-skill"><span>Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Shukla, Shraddhanand; Voisin, Nathalie; Lettenmaier, D. P.</p> <p>2012-08-15</p> <p>We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980-2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980-2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 daysmore » of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMSM22D..02L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMSM22D..02L"><span>Space weather forecasting: Past, Present, Future</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lanzerotti, L. J.</p> <p>2012-12-01</p> <p>There have been revolutionary advances in electrical technologies over the last 160 years. The historical record demonstrates that space weather processes have often provided surprises in the implementation and operation of many of these technologies. The historical record also demonstrates that as the complexity of systems increase, including their interconnectedness and interoperability, they can become more susceptible to space weather effects. An engineering goal, beginning during the decades following the 1859 Carrington event, has been to attempt to forecast solar-produced disturbances that could affect technical systems, be they long grounded conductor-based or radio-based or required for exploration, or the increasingly complex systems immersed in the space environment itself. Forecasting of space weather events involves both frontier measurements and models to address engineering requirements, and industrial and governmental policies that encourage and permit creativity and entrepreneurship. While analogies of space weather forecasting to terrestrial weather forecasting are frequently made, and while many of the analogies are valid, there are also important differences. This presentation will provide some historical perspectives on the forecast problem, a personal assessment of current status of several areas including important policy issues, and a look into the not-too-distant future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMIN43C0091F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMIN43C0091F"><span>Forecasting Space Weather-Induced GPS Performance Degradation Using Random Forest</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Filjar, R.; Filic, M.; Milinkovic, F.</p> <p>2017-12-01</p> <p>Space weather and ionospheric dynamics have a profound effect on positioning performance of the Global Satellite Navigation System (GNSS). However, the quantification of that effect is still the subject of scientific activities around the world. In the latest contribution to the understanding of the space weather and ionospheric effects on satellite-based positioning performance, we conducted a study of several candidates for forecasting method for space weather-induced GPS positioning performance deterioration. First, a 5-days set of experimentally collected data was established, encompassing the space weather and ionospheric activity indices (including: the readings of the Sudden Ionospheric Disturbance (SID) monitors, components of geomagnetic field strength, global Kp index, Dst index, GPS-derived Total Electron Content (TEC) samples, standard deviation of TEC samples, and sunspot number) and observations of GPS positioning error components (northing, easting, and height positioning error) derived from the Adriatic Sea IGS reference stations' RINEX raw pseudorange files in quiet space weather periods. This data set was split into the training and test sub-sets. Then, a selected set of supervised machine learning methods based on Random Forest was applied to the experimentally collected data set in order to establish the appropriate regional (the Adriatic Sea) forecasting models for space weather-induced GPS positioning performance deterioration. The forecasting models were developed in the R/rattle statistical programming environment. The forecasting quality of the regional forecasting models developed was assessed, and the conclusions drawn on the advantages and shortcomings of the regional forecasting models for space weather-caused GNSS positioning performance deterioration.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.1940C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.1940C"><span>A Wind Forecasting System for Energy Application</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Courtney, Jennifer; Lynch, Peter; Sweeney, Conor</p> <p>2010-05-01</p> <p>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 probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A21G2240M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A21G2240M"><span>Quantitative impact of aerosols on numerical weather prediction. Part I: Direct radiative forcing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Marquis, J. W.; Zhang, J.; Reid, J. S.; Benedetti, A.; Christensen, M.</p> <p>2017-12-01</p> <p>While the effects of aerosols on climate have been extensively studied over the past two decades, the impacts of aerosols on operational weather forecasts have not been carefully quantified. Despite this lack of quantification, aerosol plumes can impact weather forecasts directly by reducing surface reaching solar radiation and indirectly through affecting remotely sensed data that are used for weather forecasts. In part I of this study, the direct impact of smoke aerosol plumes on surface temperature forecasts are quantified using a smoke aerosol event affecting the United States Upper-Midwest in 2015. NCEP, ECMWF and UKMO model forecast surface temperature uncertainties are studied with respect to aerosol loading. Smoke aerosol direct cooling efficiencies are derived and the potential of including aerosol particles in operational forecasts is discussed, with the consideration of aerosol trends, especially over regions with heavy aerosol loading.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760021021','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760021021"><span>A plan for the economic assessment of the benefits of improved meteorological forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bhattacharyya, R.; Greenberg, J.</p> <p>1975-01-01</p> <p>Benefit-cost relationships for the development of meteorological satellites are outlined. The weather forecast capabilities of the various weather satellites (Tiros, SEOS, Nimbus) are discussed, and the development of additional satellite systems is examined. A rational approach is development that leads to the establishment of the economic benefits which may result from the utilization of meteorological satellite data. The economic and social impacts of improved weather forecasting for industries and resources management are discussed, and significant weather sensitive industries are listed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170005447','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170005447"><span>Earth Remote Sensing for Weather Forecasting and Disaster Applications</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Molthan, Andrew; Bell, Jordan; Case, Jonathan; Cole, Tony; Elmer, Nicholas; McGrath, Kevin; Schultz, Lori; Zavodsky, Brad</p> <p>2016-01-01</p> <p>NASA's constellation of current missions provide several opportunities to apply satellite remote sensing observations to weather forecasting and disaster response applications. Examples include: Using NASA's Terra and Aqua MODIS, and the NASA/NOAA Suomi-NPP VIIRS missions to prepare weather forecasters for capabilities of GOES-R; Incorporating other NASA remote sensing assets for improving aspects of numerical weather prediction; Using NASA, NOAA, and international partner resources (e.g. ESA/Sentinel Series); and commercial platforms (high-res, or UAV) to support disaster mapping.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMSM51A1755F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMSM51A1755F"><span>The Ensemble Space Weather Modeling System (eSWMS): Status, Capabilities and Challenges</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fry, C. D.; Eccles, J. V.; Reich, J. P.</p> <p>2010-12-01</p> <p>Marking a milestone in space weather forecasting, the Space Weather Modeling System (SWMS) successfully completed validation testing in advance of operational testing at Air Force Weather Agency’s primary space weather production center. This is the first coupling of stand-alone, physics-based space weather models that are currently in operations at AFWA supporting the warfighter. Significant development effort went into ensuring the component models were portable and scalable while maintaining consistent results across diverse high performance computing platforms. Coupling was accomplished under the Earth System Modeling Framework (ESMF). The coupled space weather models are the Hakamada-Akasofu-Fry version 2 (HAFv2) solar wind model and GAIM1, the ionospheric forecast component of the Global Assimilation of Ionospheric Measurements (GAIM) model. The SWMS was developed by team members from AFWA, Explorations Physics International, Inc. (EXPI) and Space Environment Corporation (SEC). The successful development of the SWMS provides new capabilities beyond enabling extended lead-time, data-driven ionospheric forecasts. These include ingesting diverse data sets at higher resolution, incorporating denser computational grids at finer time steps, and performing probability-based ensemble forecasts. Work of the SWMS development team now focuses on implementing the ensemble-based probability forecast capability by feeding multiple scenarios of 5 days of solar wind forecasts to the GAIM1 model based on the variation of the input fields to the HAFv2 model. The ensemble SWMS (eSWMS) will provide the most-likely space weather scenario with uncertainty estimates for important forecast fields. The eSWMS will allow DoD mission planners to consider the effects of space weather on their systems with more advance warning than is currently possible. The payoff is enhanced, tailored support to the warfighter with improved capabilities, such as point-to-point HF propagation forecasts, single-frequency GPS error corrections, and high cadence, high-resolution Space Situational Awareness (SSA) products. We present the current status of eSWMS, its capabilities, limitations and path of transition to operational use.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMIN31F..02S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMIN31F..02S"><span>NCAR's Experimental Real-time Convection-allowing Ensemble Prediction System</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schwartz, C. S.; Romine, G. S.; Sobash, R.; Fossell, K.</p> <p>2016-12-01</p> <p>Since April 2015, the National Center for Atmospheric Research's (NCAR's) Mesoscale and Microscale Meteorology (MMM) Laboratory, in collaboration with NCAR's Computational Information Systems Laboratory (CISL), has been producing daily, real-time, 10-member, 48-hr ensemble forecasts with 3-km horizontal grid spacing over the conterminous United States (http://ensemble.ucar.edu). These computationally-intensive, next-generation forecasts are produced on the Yellowstone supercomputer, have been embraced by both amateur and professional weather forecasters, are widely used by NCAR and university researchers, and receive considerable attention on social media. Initial conditions are supplied by NCAR's Data Assimilation Research Testbed (DART) software and the forecast model is NCAR's Weather Research and Forecasting (WRF) model; both WRF and DART are community tools. This presentation will focus on cutting-edge research results leveraging the ensemble dataset, including winter weather predictability, severe weather forecasting, and power outage modeling. Additionally, the unique design of the real-time analysis and forecast system and computational challenges and solutions will be described.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060015720&hterms=art+science&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dart%2Bscience','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060015720&hterms=art+science&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dart%2Bscience"><span>The Art and Science of Long-Range Space Weather Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hathaway, David H.; Wilson, Robert M.</p> <p>2006-01-01</p> <p>Long-range space weather forecasts are akin to seasonal forecasts of terrestrial weather. We don t expect to forecast individual events but we do hope to forecast the underlying level of activity important for satellite operations and mission pl&g. Forecasting space weather conditions years or decades into the future has traditionally been based on empirical models of the solar cycle. Models for the shape of the cycle as a function of its amplitude become reliable once the amplitude is well determined - usually two to three years after minimum. Forecasting the amplitude of a cycle well before that time has been more of an art than a science - usually based on cycle statistics and trends. Recent developments in dynamo theory -the theory explaining the generation of the Sun s magnetic field and the solar activity cycle - have now produced models with predictive capabilities. Testing these models with historical sunspot cycle data indicates that these predictions may be highly reliable one, or even two, cycles into the future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006910','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006910"><span>Integration of Weather Data into Airspace and Traffic Operations Simulation (ATOS) for Trajectory- Based Operations Research</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Peters, Mark; Boisvert, Ben; Escala, Diego</p> <p>2009-01-01</p> <p>Explicit integration of aviation weather forecasts with the National Airspace System (NAS) structure is needed to improve the development and execution of operationally effective weather impact mitigation plans and has become increasingly important due to NAS congestion and associated increases in delay. This article considers several contemporary weather-air traffic management (ATM) integration applications: the use of probabilistic forecasts of visibility at San Francisco, the Route Availability Planning Tool to facilitate departures from the New York airports during thunderstorms, the estimation of en route capacity in convective weather, and the application of mixed-integer optimization techniques to air traffic management when the en route and terminal capacities are varying with time because of convective weather impacts. Our operational experience at San Francisco and New York coupled with very promising initial results of traffic flow optimizations suggests that weather-ATM integrated systems warrant significant research and development investment. However, they will need to be refined through rapid prototyping at facilities with supportive operational users We have discussed key elements of an emerging aviation weather research area: the explicit integration of aviation weather forecasts with NAS structure to improve the effectiveness and timeliness of weather impact mitigation plans. Our insights are based on operational experiences with Lincoln Laboratory-developed integrated weather sensing and processing systems, and derivative early prototypes of explicit ATM decision support tools such as the RAPT in New York City. The technical components of this effort involve improving meteorological forecast skill, tailoring the forecast outputs to the problem of estimating airspace impacts, developing models to quantify airspace impacts, and prototyping automated tools that assist in the development of objective broad-area ATM strategies, given probabilistic weather forecasts. Lincoln Laboratory studies and prototype demonstrations in this area are helping to define the weather-assimilated decision-making system that is envisioned as a key capability for the multi-agency Next Generation Air Transportation System [1]. The Laboratory's work in this area has involved continuing, operations-based evolution of both weather forecasts and models for weather impacts on the NAS. Our experience has been that the development of usable ATM technologies that address weather impacts must proceed via rapid prototyping at facilities whose users are highly motivated to participate in system evolution.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SpWea..15.1383S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SpWea..15.1383S"><span>Verification of Space Weather Forecasts Issued by the Met Office Space Weather Operations Centre</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sharpe, M. A.; Murray, S. A.</p> <p>2017-10-01</p> <p>The Met Office Space Weather Operations Centre was founded in 2014 and part of its remit is a daily Space Weather Technical Forecast to help the UK build resilience to space weather impacts; guidance includes 4 day geomagnetic storm forecasts (GMSF) and X-ray flare forecasts (XRFF). It is crucial for forecasters, users, modelers, and stakeholders to understand the strengths and weaknesses of these forecasts; therefore, it is important to verify against the most reliable truth data source available. The present study contains verification results for XRFFs using Geo-Orbiting Earth Satellite 15 satellite data and GMSF using planetary K-index (Kp) values from the GFZ Helmholtz Centre. To assess the value of the verification results, it is helpful to compare them against a reference forecast and the frequency of occurrence during a rolling prediction period is used for this purpose. An analysis of the rolling 12 month performance over a 19 month period suggests that both the XRFF and GMSF struggle to provide a better prediction than the reference. However, a relative operating characteristic and reliability analysis of the full 19 month period reveals that although the GMSF and XRFF possess discriminatory skill, events tend to be overforecast.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.3347A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.3347A"><span>Do location specific forecasts pose a new challenge for communicating uncertainty?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Abraham, Shyamali; Bartlett, Rachel; Standage, Matthew; Black, Alison; Charlton-Perez, Andrew; McCloy, Rachel</p> <p>2015-04-01</p> <p>In the last decade, the growth of local, site-specific weather forecasts delivered by mobile phone or website represents arguably the fastest change in forecast consumption since the beginning of Television weather forecasts 60 years ago. In this study, a street-interception survey of 274 members of the public a clear first preference for narrow weather forecasts above traditional broad weather forecasts is shown for the first time, with a clear bias towards this preference for users under 40. The impact of this change on the understanding of forecast probability and intensity information is explored. While the correct interpretation of the statement 'There is a 30% chance of rain tomorrow' is still low in the cohort, in common with previous studies, a clear impact of age and educational attainment on understanding is shown, with those under 40 and educated to degree level or above more likely to correctly interpret it. The interpretation of rainfall intensity descriptors ('Light', 'Moderate', 'Heavy') by the cohort is shown to be significantly different to official and expert assessment of the same descriptors and to have large variance amongst the cohort. However, despite these key uncertainties, members of the cohort generally seem to make appropriate decisions about rainfall forecasts. There is some evidence that the decisions made are different depending on the communication format used, and the cohort expressed a clear preference for tabular over graphical weather forecast presentation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=maintenance+AND+roads&id=EJ968865','ERIC'); return false;" href="https://eric.ed.gov/?q=maintenance+AND+roads&id=EJ968865"><span>Uncertainty Forecasts Improve Weather-Related Decisions and Attenuate the Effects of Forecast Error</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Joslyn, Susan L.; LeClerc, Jared E.</p> <p>2012-01-01</p> <p>Although uncertainty is inherent in weather forecasts, explicit numeric uncertainty estimates are rarely included in public forecasts for fear that they will be misunderstood. Of particular concern are situations in which precautionary action is required at low probabilities, often the case with severe events. At present, a categorical weather…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/3339','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/3339"><span>Enhanced road weather forecasting : Clarus regional demonstrations.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2011-01-01</p> <p>The quality of road weather forecasts has major impacts on users of surface transportation systems and managers of those systems. Improving the quality involves the ability to provide accurate, route-specific road weather information (e.g., timing an...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110011226','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011226"><span>Space Weather Models and Their Validation and Verification at the CCMC</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hesse, Michael</p> <p>2010-01-01</p> <p>The Community Coordinated l\\lodeling Center (CCMC) is a US multi-agency activity with a dual mission. With equal emphasis, CCMC strives to provide science support to the international space research community through the execution of advanced space plasma simulations, and it endeavors to support the space weather needs of the CS and partners. Space weather support involves a broad spectrum, from designing robust forecasting systems and transitioning them to forecasters, to providing space weather updates and forecasts to NASA's robotic mission operators. All of these activities have to rely on validation and verification of models and their products, so users and forecasters have the means to assign confidence levels to the space weather information. In this presentation, we provide an overview of space weather models resident at CCMC, as well as of validation and verification activities undertaken at CCMC or through the use of CCMC services.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/3685','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/3685"><span>Final report of the operation and demonstration test of short-range weather forecasting decision support within an advanced transportation weather information system (#Safe)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2006-04-01</p> <p>The purpose of the Advanced Transportation Weather Information System (ATWIS) was to provide en-route weather forecasts and road condition information to the traveling public across North Dakota and South Dakota. ATWIS was the first system to develop...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19880028072&hterms=artificial+intelligence+transportation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dartificial%2Bintelligence%2Btransportation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19880028072&hterms=artificial+intelligence+transportation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dartificial%2Bintelligence%2Btransportation"><span>Space Transportation System Meteorological Expert</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Beller, Arthur E.; Stafford, Sue P.</p> <p>1987-01-01</p> <p>The STS Meteorological Expert (STSMET) is a long-term project to acquire general Shuttle operational weather forecasting expertise specific to the launch locale, to apply it to Shuttle operational weather forecasting tasks at the Cape Canaveral Forecast Facility, and ultimately to provide an on-line real-time operational aid to the duty forecasters in performing their tasks. Particular attention is given to the development of an approach called scenario-based reasoning, with specific application to summer thunderstorms; this type of reasoning can also be applied to frontal weather phenomena, visibility including fog, and wind shear.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/26048','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/26048"><span>Test of wind predictions for peak fire-danger stations in Oregon and Washington.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Owen P. Cramer</p> <p>1957-01-01</p> <p>Relative accuracy of several wind-speed forecasting methods was tested during the forest fire seasons of 1950 and 1951. For the study, three fire-weather forecast centers of the U. S. Weather Bureau prepared individual station forecasts for 11 peak stations within the national. forests of Oregon and Washington. These spot forecasts were considered...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19790015713','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19790015713"><span>Atmospheric and oceanographic research review, 1978. [global weather, ocean/air interactions, and climate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1978-01-01</p> <p>Research activities related to global weather, ocean/air interactions, and climate are reported. The global weather research is aimed at improving the assimilation of satellite-derived data in weather forecast models, developing analysis/forecast models that can more fully utilize satellite data, and developing new measures of forecast skill to properly assess the impact of satellite data on weather forecasting. The oceanographic research goal is to understand and model the processes that determine the general circulation of the oceans, focusing on those processes that affect sea surface temperature and oceanic heat storage, which are the oceanographic variables with the greatest influence on climate. The climate research objective is to support the development and effective utilization of space-acquired data systems in climate forecast models and to conduct sensitivity studies to determine the affect of lower boundary conditions on climate and predictability studies to determine which global climate features can be modeled either deterministically or statistically.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1810693P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1810693P"><span>Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan</p> <p>2016-04-01</p> <p>Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more accurate measure of forecast uncertainty that could result in better decision-making. It offers different level of abstractions to help with the recalibration of the RAR method. It also has an inspection tool that displays the selected analogs, their observations and statistical data. It gives the users access to inner parts of the method, unveiling hidden information. References [GR05] GNEITING T., RAFTERY A. E.: Weather forecasting with ensemble methods. Science 310, 5746, 248-249, 2005. [KAL03] KALNAY E.: Atmospheric modeling, data assimilation and predictability. Cambridge University Press, 2003. [PH06] PALMER T., HAGEDORN R.: Predictability of weather and climate. Cambridge University Press, 2006. [HW06] HAMILL T. M., WHITAKER J. S.: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Monthly Weather Review 134, 11, 3209-3229, 2006. [DE06] DEITRICK S., EDSALL R.: The influence of uncertainty visualization on decision making: An empirical evaluation. Springer, 2006. [KMS08] KEIM D. A., MANSMANN F., SCHNEIDEWIND J., THOMAS J., ZIEGLER H.: Visual analytics: Scope and challenges. Springer, 2008.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMSM11E..06B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMSM11E..06B"><span>Verification of space weather forecasts at the UK Met Office</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bingham, S.; Sharpe, M.; Jackson, D.; Murray, S.</p> <p>2017-12-01</p> <p>The UK Met Office Space Weather Operations Centre (MOSWOC) has produced space weather guidance twice a day since its official opening in 2014. Guidance includes 4-day probabilistic forecasts of X-ray flares, geomagnetic storms, high-energy electron events and high-energy proton events. Evaluation of such forecasts is important to forecasters, stakeholders, model developers and users to understand the performance of these forecasts and also strengths and weaknesses to enable further development. Met Office terrestrial near real-time verification systems have been adapted to provide verification of X-ray flare and geomagnetic storm forecasts. Verification is updated daily to produce Relative Operating Characteristic (ROC) curves and Reliability diagrams, and rolling Ranked Probability Skill Scores (RPSSs) thus providing understanding of forecast performance and skill. Results suggest that the MOSWOC issued X-ray flare forecasts are usually not statistically significantly better than a benchmark climatological forecast (where the climatology is based on observations from the previous few months). By contrast, the issued geomagnetic storm activity forecast typically performs better against this climatological benchmark.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA550973','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA550973"><span>Development, Implementation, and Skill Assessment of the NOAA/NOS Great Lakes Operational Forecast System</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2011-01-01</p> <p>USA) 2011 Abstract The NOAA Great Lakes Operational Forecast System ( GLOFS ) uses near-real-time atmospheric observa- tions and numerical weather...Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS... GLOFS ) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMPA23A..04V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMPA23A..04V"><span>Presenting Critical Space Weather Information to Customers and Stakeholders (Invited)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Viereck, R. A.; Singer, H. J.; Murtagh, W. J.; Rutledge, B.</p> <p>2013-12-01</p> <p>Space weather involves changes in the near-Earth space environment that impact technological systems such as electric power, radio communication, satellite navigation (GPS), and satellite opeartions. As with terrestrial weather, there are several different kinds of space weather and each presents unique challenges to the impacted technologies and industries. But unlike terrestrial weather, many customers are not fully aware of space weather or how it impacts their systems. This issue is further complicated by the fact that the largest space weather events occur very infrequently with years going by without severe storms. Recent reports have estimated very large potential costs to the economy and to society if a geomagnetic storm were to cause major damage to the electric power transmission system. This issue has come to the attention of emergency managers and federal agencies including the office of the president. However, when considering space weather impacts, it is essential to also consider uncertainties in the frequency of events and the predicted impacts. The unique nature of space weather storms, the specialized technologies that are impacted by them, and the disparate groups and agencies that respond to space weather forecasts and alerts create many challenges to the task of communicating space weather information to the public. Many customers that receive forecasts and alerts are highly technical and knowledgeable about the subtleties of the space environment. Others know very little and require ongoing education and explanation about how a space weather storm will affect their systems. In addition, the current knowledge and understanding of the space environment that goes into forecasting storms is quite immature. It has only been within the last five years that physics-based models of the space environment have played important roles in predictions. Thus, the uncertainties in the forecasts are quite large. There is much that we don't know about space weather and this influences our forecasts. In this presentation, I will discuss the unique challenges that space weather forecasters face when explaining what we know and what we don't know about space weather events to customers and policy makers.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110009906','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110009906"><span>Space Weather Products at the Community Coordinated Modeling Center</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hesse, Michael; Kuznetsova, M.; Pulkkinen, A.; Maddox, M.; Rastaetter, L.; Berrios, D.; MacNeice, P.</p> <p>2010-01-01</p> <p>The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involves model evaluations, model transitions to operations, and the development of space weather forecasting tools. Owing to the pace of development in the science community, new model capabilities emerge frequently. Consequently, space weather products and tools involve not only increased validity, but often entirely new capabilities. This presentation will review the present state of space weather tools as well as point out emerging future capabilities.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMSH11C2240M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMSH11C2240M"><span>Comparative Studies of Prediction Strategies for Solar X-ray Time Series</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Muranushi, T.; Hattori, T.; Jin, Q.; Hishinuma, T.; Tominaga, M.; Nakagawa, K.; Fujiwara, Y.; Nakamura, T.; Sakaue, T.; Takahashi, T.; Seki, D.; Namekata, K.; Tei, A.; Ban, M.; Kawamura, A. D.; Hada-Muranushi, Y.; Asai, A.; Nemoto, S.; Shibata, K.</p> <p>2016-12-01</p> <p>Crucial virtues for operational space weather forecast are real-timeforecast ability, forecast precision and customizability to userneeds. The recent development of deep-learning makes it veryattractive to space weather, because (1) it learns gradually incomingdata, (2) it exhibits superior accuracy over conventional algorithmsin many fields, and (3) it makes the customization of the forecasteasier because it accepts raw images.However, the best deep-learning applications are only attainable bycareful human designers that understands both the mechanism of deeplearning and the application field. Therefore, we need to foster youngresearchers to enter the field of machine-learning aided forecast. So,we have held a seminar every Monday with undergraduate and graduatestudents from May to August 2016.We will review the current status of space weather science and theautomated real-time space weather forecast engine UFCORIN. Then, weintroduce the deep-learning space weather forecast environments wehave set up using Python and Chainer on students' laptop computers.We have started from simple image classification neural network, thenimplemented space-weather neural network that predicts future X-rayflux of the Sun based on the past X-ray lightcurve and magnetic fieldline-of-sight images.In order to perform each forecast faster, we have focused on simplelightcurve-to-lightcurve forecast, and performed comparative surveysby changing following parameters: The size and topology of the neural network Batchsize Neural network hyperparameters such as learning rates to optimize the preduction accuracy, and time for prediction.We have found how to design compact, fast but accurate neural networkto perform forecast. Our forecasters can perform predictionexperiment for four-year timespan in a few minutes, and achieveslog-scale errors of the order of 1. Our studies is ongoing, and inour talk we will review our progress till December.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.2792H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.2792H"><span>Discrete post-processing of total cloud cover ensemble forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hemri, Stephan; Haiden, Thomas; Pappenberger, Florian</p> <p>2017-04-01</p> <p>This contribution presents an approach to post-process ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical post-processing of ensemble predictions are tested. The first approach is based on multinomial logistic regression, the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on station-wise post-processing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. Reference Hemri, S., Haiden, T., & Pappenberger, F. (2016). Discrete post-processing of total cloud cover ensemble forecasts. Monthly Weather Review 144, 2565-2577.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19840019227','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19840019227"><span>The impact of satellite temperature soundings on the forecasts of a small national meteorological service</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wolfson, N.; Thomasell, A.; Alperson, Z.; Brodrick, H.; Chang, J. T.; Gruber, A.; Ohring, G.</p> <p>1984-01-01</p> <p>The impact of introducing satellite temperature sounding data on a numerical weather prediction model of a national weather service is evaluated. A dry five level, primitive equation model which covers most of the Northern Hemisphere, is used for these experiments. Series of parallel forecast runs out to 48 hours are made with three different sets of initial conditions: (1) NOSAT runs, only conventional surface and upper air observations are used; (2) SAT runs, satellite soundings are added to the conventional data over oceanic regions and North Africa; and (3) ALLSAT runs, the conventional upper air observations are replaced by satellite soundings over the entire model domain. The impact on the forecasts is evaluated by three verification methods: the RMS errors in sea level pressure forecasts, systematic errors in sea level pressure forecasts, and errors in subjective forecasts of significant weather elements for a selected portion of the model domain. For the relatively short range of the present forecasts, the major beneficial impacts on the sea level pressure forecasts are found precisely in those areas where the satellite sounding are inserted and where conventional upper air observations are sparse. The RMS and systematic errors are reduced in these regions. The subjective forecasts of significant weather elements are improved with the use of the satellite data. It is found that the ALLSAT forecasts are of a quality comparable to the SAR forecasts.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=248375','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=248375"><span>Forecast and virtual weather driven plant disease risk modeling system</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>We describe a system in use and development that leverages public weather station data, several spatialized weather forecast types, leaf wetness estimation, generic plant disease models, and online statistical evaluation. Convergent technological developments in all these areas allow, with funding f...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4154209','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4154209"><span>Using Forecast and Observed Weather Data to Assess Performance of Forecast Products in Identifying Heat Waves and Estimating Heat Wave Effects on Mortality</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Chen, Yeh-Hsin; Schwartz, Joel D.; Rood, Richard B.; O’Neill, Marie S.</p> <p>2014-01-01</p> <p>Background: Heat wave and health warning systems are activated based on forecasts of health-threatening hot weather. Objective: We estimated heat–mortality associations based on forecast and observed weather data in Detroit, Michigan, and compared the accuracy of forecast products for predicting heat waves. Methods: We derived and compared apparent temperature (AT) and heat wave days (with heat waves defined as ≥ 2 days of daily mean AT ≥ 95th percentile of warm-season average) from weather observations and six different forecast products. We used Poisson regression with and without adjustment for ozone and/or PM10 (particulate matter with aerodynamic diameter ≤ 10 μm) to estimate and compare associations of daily all-cause mortality with observed and predicted AT and heat wave days. Results: The 1-day-ahead forecast of a local operational product, Revised Digital Forecast, had about half the number of false positives compared with all other forecasts. On average, controlling for heat waves, days with observed AT = 25.3°C were associated with 3.5% higher mortality (95% CI: –1.6, 8.8%) than days with AT = 8.5°C. Observed heat wave days were associated with 6.2% higher mortality (95% CI: –0.4, 13.2%) than non–heat wave days. The accuracy of predictions varied, but associations between mortality and forecast heat generally tended to overestimate heat effects, whereas associations with forecast heat waves tended to underestimate heat wave effects, relative to associations based on observed weather metrics. Conclusions: Our findings suggest that incorporating knowledge of local conditions may improve the accuracy of predictions used to activate heat wave and health warning systems. Citation: Zhang K, Chen YH, Schwartz JD, Rood RB, O’Neill MS. 2014. Using forecast and observed weather data to assess performance of forecast products in identifying heat waves and estimating heat wave effects on mortality. Environ Health Perspect 122:912–918; http://dx.doi.org/10.1289/ehp.1306858 PMID:24833618</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.8563O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8563O"><span>Teaching weather and climate science in primary schools - a pilot project from the UK Met Office</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Orrell, Richard; Liggins, Felicity; Challenger, Lesley; Lethem, Dom; Campbell, Katy</p> <p>2017-04-01</p> <p>Wow Schools is a pilot project from the Met Office with an aim to inspire and educate the next generation of scientists and, uniquely, use the data collected by schools to improve weather forecasts and warnings across the UK. Wow Schools was launched in late 2015 with a competition open to primary schools across the UK. 74 schools entered the draw, all hoping to be picked as one of the ten lucky schools taking part in the pilot scheme. Each winning school received a fully automatic weather station (AWS), enabling them to transmit real-time local weather observations to the Met Office's Weather Observation Website (WOW - wow.metoffice.gov.uk), an award winning web portal for uploading and sharing a range of environmental observations. They were also given a package of materials designed to get students out of the classroom to observe the weather, get hands-on with the science underpinning weather forecasting, and analyse the data they are collecting. The curriculum-relevant materials were designed with the age group 7 to 11 in mind, but could be extended to support other age groups. Each school was offered a visit by a Wow Schools Ambassador (a Met Office employee) to bring the students' learning to life, and access to a dedicated forecast for its location generated by our new supercomputer. These forecasts are improved by the school's onsite AWS reinforcing the link between observations and forecast production. The Wow Schools pilot ran throughout 2016. Here, we present the initial findings of the project, examining the potential benefits and challenges of working with schools across the UK to: enrich students' understanding of the science of weather forecasting; to source an ongoing supply of weather observations and discover how these might be used in the forecasting process; and explore what materials and business model(s) would be most useful and affordable if a wider roll-out of the initiative was undertaken.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002BAMS...83..227H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002BAMS...83..227H"><span>Weather Support for the 2002 Winter Olympic and Paralympic Games.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Horel, J.; Potter, T.; Dunn, L.; Steenburgh, W. J.; Eubank, M.; Splitt, M.; Onton, D. J.</p> <p>2002-02-01</p> <p>The 2002 Winter Olympic and Paralympic Games will be hosted by Salt Lake City, Utah, during February-March 2002. Adverse weather during this period may delay sporting events, while snow and ice-covered streets and highways may impede access by the athletes and spectators to the venues. While winter snowstorms and other large-scale weather systems typically have widespread impacts throughout northern Utah, hazardous winter weather is often related to local terrain features (the Wasatch Mountains and Great Salt Lake are the most prominent ones). Examples of such hazardous weather include lake-effect snowstorms, ice fog, gap winds, downslope windstorms, and low visibility over mountain passes.A weather support system has been developed to provide weather information to the athletes, games officials, spectators, and the interested public around the world. This system is managed by the Salt Lake Olympic Committee and relies upon meteorologists from the public, private, and academic sectors of the atmospheric science community. Weather forecasting duties will be led by National Weather Service forecasters and a team of private, weather forecasters organized by KSL, the Salt Lake City NBC television affiliate. Other government agencies, commercial firms, and the University of Utah are providing specialized forecasts and support services for the Olympics. The weather support system developed for the 2002 Winter Olympics is expected to provide long-term benefits to the public through improved understanding,monitoring, and prediction of winter weather in the Intermountain West.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H11O..01R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H11O..01R"><span>A new precipitation and meteorological drought climatology based on weather patterns</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Richardson, D.; Fowler, H. J.; Kilsby, C. G.; Neal, R.</p> <p>2017-12-01</p> <p>Weather-pattern, or weather-type, classifications are a valuable tool in many applications as they characterise the broad-scale atmospheric circulation over a given region. An analysis of regional UK precipitation and meteorological drought climatology with respect to a set of objectively defined weather patterns is presented. This classification system, introduced last year, is currently being used by the Met Office in several probabilistic forecasting applications driven by ensemble forecasting systems. The classification consists of 30 daily patterns derived from North Atlantic Ocean and European mean sea level pressure data. Clustering these 30 patterns yields another set of eight patterns that are intended for use in longer-range applications. Weather pattern definitions and daily occurrences are mapped to the commonly-used Lamb Weather Types (LWTs), and parallels between the two classifications are drawn. Daily precipitation distributions are associated with each weather pattern and LWT. Drought index series are calculated for a range of aggregation periods and seasons. Monthly weather-pattern frequency anomalies are calculated for different drought index thresholds, representing dry, wet and drought conditions. The set of 30 weather patterns is shown to be adequate for precipitation-based analyses in the UK, although the smaller set of clustered patterns is not. Furthermore, intra-pattern precipitation variability is lower in the new classification compared to the LWTs, which is an advantage in the context of precipitation studies. Weather patterns associated with drought over the different UK regions are identified. This has potential forecasting application - if a model (e.g. a global seasonal forecast model) can predict weather pattern occurrences then regional drought outlooks may be derived from the forecasted weather patterns.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA609834','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA609834"><span>Impacts of Typhoon Megi (2010) on the South China Sea</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-06-01</p> <p>investigations. To obtain realistic typhoon-strength atmospheric forcing, the EASNFS applied typhoon-resolving Weather Research and Forecasting ( WRF ) model wind...EASNFS applied typhoon-resolving Weather Research and Forecasting ( WRF ) model wind field blended with global weather forecast winds from the U.S. Navy...only 1C. Sequential SST snapshots, of which only a Figure 1. The EASNFS model domain with topography and an inset covered by WRF model. Typhoon Megi’s</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA618215','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA618215"><span>Investigating Surface Bias Errors in the Weather Research and Forecasting (WRF) Model using a Geographic Information System (GIS)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2015-02-01</p> <p>WRF ) Model using a Geographic Information System (GIS) by Jeffrey A Smith, Theresa A Foley, John W Raby, and Brian Reen...ARL-TR-7212 ● FEB 2015 US Army Research Laboratory Investigating Surface Bias Errors in the Weather Research and Forecasting ( WRF ) Model...SUBTITLE Investigating surface bias errors in the Weather Research and Forecasting ( WRF ) Model using a Geographic Information System (GIS) 5a</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1349084-studies-regarding-quality-numerical-weather-forecasts-wrf-model-integrated-high-resolutions-romanian-territory','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1349084-studies-regarding-quality-numerical-weather-forecasts-wrf-model-integrated-high-resolutions-romanian-territory"><span>Studies regarding the quality of numerical weather forecasts of the WRF model integrated at high-resolutions for the Romanian territory</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Iriza, Amalia; Dumitrache, Rodica C.; Lupascu, Aurelia; ...</p> <p>2016-01-01</p> <p>Our paper aims to evaluate the quality of high-resolution weather forecasts from the Weather Research and Forecasting (WRF) numerical weather prediction model. The lateral and boundary conditions were obtained from the numerical output of the Consortium for Small-scale Modeling (COSMO) model at 7 km horizontal resolution. Furthermore, the WRF model was run for January and July 2013 at two horizontal resolutions (3 and 1 km). The numerical forecasts of the WRF model were evaluated using different statistical scores for 2 m temperature and 10 m wind speed. Our results showed a tendency of the WRF model to overestimate the valuesmore » of the analyzed parameters in comparison to observations.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1349084','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1349084"><span>Studies regarding the quality of numerical weather forecasts of the WRF model integrated at high-resolutions for the Romanian territory</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Iriza, Amalia; Dumitrache, Rodica C.; Lupascu, Aurelia</p> <p></p> <p>Our paper aims to evaluate the quality of high-resolution weather forecasts from the Weather Research and Forecasting (WRF) numerical weather prediction model. The lateral and boundary conditions were obtained from the numerical output of the Consortium for Small-scale Modeling (COSMO) model at 7 km horizontal resolution. Furthermore, the WRF model was run for January and July 2013 at two horizontal resolutions (3 and 1 km). The numerical forecasts of the WRF model were evaluated using different statistical scores for 2 m temperature and 10 m wind speed. Our results showed a tendency of the WRF model to overestimate the valuesmore » of the analyzed parameters in comparison to observations.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130012589','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130012589"><span>Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.</p> <p>2007-01-01</p> <p>Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation at the Shuttle Landing Facility is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAF5), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. This study specifically addresses the skill of different model configurations in forecasting warm season convective initiation. Numerous factors influence the development of convection over the Florida peninsula. These factors include sea breezes, river and lake breezes, the prevailing low-level flow, and convergent flow due to convex coastlines that enhance the sea breeze. The interaction of these processes produces the warm season convective patterns seen over the Florida peninsula. However, warm season convection remains one of the most poorly forecast meteorological parameters. To determine which configuration options are best to address this specific forecast concern, the Weather Research and Forecasting (WRF) model, which has two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM) was employed. In addition to the two dynamical cores, there are also two options for a "hot-start" initialization of the WRF model - the Local Analysis and Prediction System (LAPS; McGinley 1995) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS; Brewster 1996). Both LAPS and ADAS are 3- dimensional weather analysis systems that integrate multiple meteorological data sources into one consistent analysis over the user's domain of interest. This allows mesoscale models to benefit from the addition of highresolution data sources. Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and MLB with considerable flexibility as well as challenges. It is the goal of this study to assess the different configurations available and to determine which configuration will best predict warm season convective initiation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016WRR....52.4801P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016WRR....52.4801P"><span>Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara</p> <p>2016-06-01</p> <p>Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.wpc.ncep.noaa.gov','SCIGOVWS'); return false;" href="http://www.wpc.ncep.noaa.gov"><span>Weather Prediction Center (WPC) Home Page</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>grids, <em>quantitative</em> precipitation, and winter weather outlook probabilities can be found at: http Short Range Products » More Medium Range Products <em>Quantitative</em> Precipitation Forecasts Legacy Page Discussion (Day 1-3) <em>Quantitative</em> Precipitation Forecast Discussion NWS Weather Prediction Center College</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011tfa..confE..28B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011tfa..confE..28B"><span>The Mauna Kea Weather Center: Custom Atmospheric Forecasting Support for Mauna Kea</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Businger, Steven</p> <p>2011-03-01</p> <p>The success of operations at Mauna Kea Observatories is strongly influenced by weather conditions. The Mauna Kea Weather Center, an interdisciplinary research program, was established in 1999 to develop and provide custom weather support for Mauna Kea Observatories. The operational forecasting goals of the program are to facilitate the best possible use of favorable atmospheric conditions for scientific benefit and to ensure operational safety. During persistent clear periods, astronomical observing quality varies substantially due to changes in the vertical profiles of temperature, wind, moisture, and turbulence. Cloud and storm systems occasionally cause adverse or even hazardous conditions. A dedicated, daily, real-time mesoscale numerical modeling effort provides crucial forecast guidance in both cases. Several key atmospheric variables are forecast with sufficient skill to be of operational and scientific benefit to the telescopes on Mauna Kea. Summit temperature forecasts allow mirrors to be set to the ambient temperature to reduce image distortion. Precipitable water forecasts allow infrared observations to be prioritized according to atmospheric opacity. Forecasts of adverse and hazardous conditions protect the safety of personnel and allow for scheduling of maintenance when observing is impaired by cloud. The research component of the project continues to improve the accuracy and content of the forecasts. In particular, case studies have resulted in operational forecasts of astronomical observing quality, or seeing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21875244','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21875244"><span>Uncertainty forecasts improve weather-related decisions and attenuate the effects of forecast error.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Joslyn, Susan L; LeClerc, Jared E</p> <p>2012-03-01</p> <p>Although uncertainty is inherent in weather forecasts, explicit numeric uncertainty estimates are rarely included in public forecasts for fear that they will be misunderstood. Of particular concern are situations in which precautionary action is required at low probabilities, often the case with severe events. At present, a categorical weather warning system is used. The work reported here tested the relative benefits of several forecast formats, comparing decisions made with and without uncertainty forecasts. In three experiments, participants assumed the role of a manager of a road maintenance company in charge of deciding whether to pay to salt the roads and avoid a potential penalty associated with icy conditions. Participants used overnight low temperature forecasts accompanied in some conditions by uncertainty estimates and in others by decision advice comparable to categorical warnings. Results suggested that uncertainty information improved decision quality overall and increased trust in the forecast. Participants with uncertainty forecasts took appropriate precautionary action and withheld unnecessary action more often than did participants using deterministic forecasts. When error in the forecast increased, participants with conventional forecasts were reluctant to act. However, this effect was attenuated by uncertainty forecasts. Providing categorical decision advice alone did not improve decisions. However, combining decision advice with uncertainty estimates resulted in the best performance overall. The results reported here have important implications for the development of forecast formats to increase compliance with severe weather warnings as well as other domains in which one must act in the face of uncertainty. PsycINFO Database Record (c) 2012 APA, all rights reserved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012aogs...30..117H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012aogs...30..117H"><span>Recent Progress of Solar Weather Forecasting at Naoc</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>He, Han; Wang, Huaning; Du, Zhanle; Zhang, Liyun; Huang, Xin; Yan, Yan; Fan, Yuliang; Zhu, Xiaoshuai; Guo, Xiaobo; Dai, Xinghua</p> <p></p> <p>The history of solar weather forecasting services at National Astronomical Observatories, Chinese Academy of Sciences (NAOC) can be traced back to 1960s. Nowadays, NAOC is the headquarters of the Regional Warning Center of China (RWC-China), which is one of the members of the International Space Environment Service (ISES). NAOC is responsible for exchanging data, information and space weather forecasts of RWC-China with other RWCs. The solar weather forecasting services at NAOC cover short-term prediction (within two or three days), medium-term prediction (within several weeks), and long-term prediction (in time scale of solar cycle) of solar activities. Most efforts of the short-term prediction research are concentrated on the solar eruptive phenomena, such as flares, coronal mass ejections (CMEs) and solar proton events, which are the key driving sources of strong space weather disturbances. Based on the high quality observation data of the latest space-based and ground-based solar telescopes and with the help of artificial intelligence techniques, new numerical models with quantitative analyses and physical consideration are being developed for the predictions of solar eruptive events. The 3-D computer simulation technology is being introduced for the operational solar weather service platform to visualize the monitoring of solar activities, the running of the prediction models, as well as the presenting of the forecasting results. A new generation operational solar weather monitoring and forecasting system is expected to be constructed in the near future at NAOC.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.prh.noaa.gov/hnl/pages/marinereports.php','SCIGOVWS'); return false;" href="http://www.prh.noaa.gov/hnl/pages/marinereports.php"><span>Hawaiian Marine Reports</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>(PHMO) Kohala (PHKM) South Point (PHWA) Forecasts Activity Planner Hawaii Marine Aviation <em>Fire</em> Weather (PHWA) Forecasts Activity Planner Hawaii Marine Aviation <em>Fire</em> Weather Local Graphics National Graphics</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20030061413&hterms=prospect&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dprospect','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20030061413&hterms=prospect&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dprospect"><span>Atmospheric Electrical Activity and the Prospects for Improving Short-Term, Weather Forcasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Goodman, Steven J.</p> <p>2003-01-01</p> <p>How might lightning measurements be used to improve short-term (0-24 hr) weather forecasting? We examine this question under two different prediction strategies. These include integration of lightning data into short-term forecasts (nowcasts) of convective (including severe) weather hazards and the assimilation of lightning data into cloud-resolving numerical weather prediction models. In each strategy we define specific metrics of forecast improvement and a progress assessment. We also address the conventional observing system deficiencies and potential gap-filling information that can be addressed through the use of the lightning measurement.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMED11D0147F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMED11D0147F"><span>Validation of the Kp Geomagnetic Index Forecast at CCMC</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Frechette, B. P.; Mays, M. L.</p> <p>2017-12-01</p> <p>The Community Coordinated Modeling Center (CCMC) Space Weather Research Center (SWRC) sub-team provides space weather services to NASA robotic mission operators and science campaigns and prototypes new models, forecasting techniques, and procedures. The Kp index is a measure of geomagnetic disturbances for space weather in the magnetosphere such as geomagnetic storms and substorms. In this study, we performed validation on the Newell et al. (2007) Kp prediction equation from December 2010 to July 2017. The purpose of this research is to understand the Kp forecast performance because it's critical for NASA missions to have confidence in the space weather forecast. This research was done by computing the Kp error for each forecast (average, minimum, maximum) and each synoptic period. Then to quantify forecast performance we computed the mean error, mean absolute error, root mean square error, multiplicative bias and correlation coefficient. A contingency table was made for each forecast and skill scores were computed. The results are compared to the perfect score and reference forecast skill score. In conclusion, the skill score and error results show that the minimum of the predicted Kp over each synoptic period from the Newell et al. (2007) Kp prediction equation performed better than the maximum or average of the prediction. However, persistence (reference forecast) outperformed all of the Kp forecasts (minimum, maximum, and average). Overall, the Newell Kp prediction still predicts within a range of 1, even though persistence beats it.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/FR-2012-11-19/pdf/2012-28190.pdf','FEDREG'); return false;" href="https://www.gpo.gov/fdsys/pkg/FR-2012-11-19/pdf/2012-28190.pdf"><span>77 FR 69436 - JPSS Polar Satellite-Gap Mitigation-Request for Public Comment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collection.action?collectionCode=FR">Federal Register 2010, 2011, 2012, 2013, 2014</a></p> <p></p> <p>2012-11-19</p> <p>... positive steps to mitigate the negative impacts to NOAA's numerical weather forecasts that could be...-satellite data, weather modeling, and data assimilation improvements. NOAA is convening teams of internal... of NOAA's numerical weather forecasts should we experience a loss of polar satellite environmental...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/om/marine/commsat.htm','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/om/marine/commsat.htm"><span>COMMERCIAL SERVICES PROVIDING MARINE FORECASTS VIA SATELLITE</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Tsunamis 406 EPIRB's National Weather Service Marine Forecasts <em>COMMERCIAL</em> SERVICES PROVIDING MARINE forecast seas? And may present an even greater danger near shore or any shallow waters? <em>COMMERCIAL</em> SERVICES <em>commercial</em> product or service does not imply any endorsement by the National Weather Service as to function</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA624125','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA624125"><span>Potential Technologies for Assessing Risk Associated with a Mesoscale Forecast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2015-10-01</p> <p>American GFS models, and informally applied on the Weather Research and Forecasting ( WRF ) model. The current CI equation is as follows...Reen B, Penc R. Investigating surface bias errors in the Weather Research and Forecasting ( WRF ) model using a Geographic Information System (GIS). J...Forecast model ( WRF -ARW) with extensions that might include finer terrain resolutions and more detailed representations of the underlying atmospheric</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMIN41A1387M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMIN41A1387M"><span>Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Molthan, A.; Limaye, A. S.</p> <p>2011-12-01</p> <p>Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula's "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA's National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA's SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT's experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula. This presentation will provide an overview of these activities from a scientific and cloud computing applications perspective, identifying the strengths and weaknesses for deploying each project within an IaaS environment, and ways to collaborate with the Nebula or other cloud-user communities to collaborate on projects as they go forward.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/oh/hrl/hag/empe_mpn/index.htm','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/oh/hrl/hag/empe_mpn/index.htm"><span>National Weather Service</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Forecast and Warning Services of the National Weather Service Introduction <em>Quantitative</em> precipitation future which is an active area of <em>research</em> currently. 2) Evaluate HPN performance for forecast periods</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140002373','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140002373"><span>Objective Lightning Probability Forecasts for East-Central Florida Airports</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Crawford, Winfred C.</p> <p>2013-01-01</p> <p>The forecasters at the National Weather Service in Melbourne, FL, (NWS MLB) identified a need to make more accurate lightning forecasts to help alleviate delays due to thunderstorms in the vicinity of several commercial airports in central Florida at which they are responsible for issuing terminal aerodrome forecasts. Such forecasts would also provide safer ground operations around terminals, and would be of value to Center Weather Service Units serving air traffic controllers in Florida. To improve the forecast, the AMU was tasked to develop an objective lightning probability forecast tool for the airports using data from the National Lightning Detection Network (NLDN). The resulting forecast tool is similar to that developed by the AMU to support space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) for use by the 45th Weather Squadron (45 WS) in previous tasks (Lambert and Wheeler 2005, Lambert 2007). The lightning probability forecasts are valid for the time periods and areas needed by the NWS MLB forecasters in the warm season months, defined in this task as May-September.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19930016427','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19930016427"><span>Interactive Forecasting with the National Weather Service River Forecast System</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Smith, George F.; Page, Donna</p> <p>1993-01-01</p> <p>The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMSM53D2235K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMSM53D2235K"><span>Community Coordinated Modeling Center: Paving the Way for Progress in Space Science Research to Operational Space Weather Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kuznetsova, M. M.; Maddox, M. M.; Mays, M. L.; Mullinix, R.; MacNeice, P. J.; Pulkkinen, A. A.; Rastaetter, L.; Shim, J.; Taktakishvili, A.; Zheng, Y.; Wiegand, C.</p> <p>2013-12-01</p> <p>Community Coordinated Modeling Center (CCMC) was established at the dawn of the millennium as an essential element on the National Space Weather Program. One of the CCMC goals was to pave the way for progress in space science research to operational space weather forecasting. Over the years the CCMC acquired the unique experience in preparing complex models and model chains for operational environment, in developing and maintaining powerful web-based tools and systems ready to be used by space weather service providers and decision makers as well as in space weather prediction capabilities assessments. The presentation will showcase latest innovative solutions for space weather research, analysis, forecasting and validation and review on-going community-wide initiatives enabled by CCMC applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940009202','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940009202"><span>Weather forecasting support for AASE-2</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Forbes, Gregory S.</p> <p>1992-01-01</p> <p>The AFEAS Contract and NASA Grant were awarded to Penn State in order to obtain real-time weather forecasting support for the NASA AASE-II Project, which was conducted between October 1991 and March 1992. Because of the special weather sensitivities of the NASA ER-2 aircraft, AASE-II planners felt that public weather forecasts issued by the National Weather Service would not be adequate for mission planning purposes. A likely consequence of resorting to that medium would have been that scientists would have had to be at work by 4 AM day after day in the hope that the aircraft could fly, only to be frustrated by a great number of 'scrubbed' missions. Thus, the Pennsylvania State University was contracted to provide real-time weather support to the AASE-II mission.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1616709F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1616709F"><span>Current problems in communication from the weather forecast in the prevention of hydraulic and hydrogeological risk</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fazzini, Massimiliano; Vaccaro, Carmela</p> <p>2014-05-01</p> <p>The Italian territory is one of the most fragile hydraulic and hydro geologic of the world, due to its complexity physiographic, lithological and above meteo-climatic too. Moreover, In recent years, the unhappy urbanization, the abandonment of mountain areas and countryside have fostered hydro geological instability, ever more devastating, in relation to the extremes of meteorological events. After the dramatic floods and landscapes of the last 24 months - in which more than 50 people died - it is actually open a public debate on the issues related to prevention, forecasting and management of hydro-meteorological risk. Aim of the correct weather forecasting at different spatial and temporal scales is to avoid or minimize the potential occurrence of damage or human losses resulting from the increasingly of frequent extreme weather events. In Italy, there are two major complex problems that do not allow for effective dissemination of the correct weather forecasting. First, the absence of a national meteorological service - which can ensure the quality of information. In this regard, it is at an advanced stage the establishment of a unified national weather service - formed by technicians to national and regional civil protection and the Meteorological Service of the Air Force, which will ensure the quality of the prediction, especially through exclusive processing of national and local weather forecasting and hydro geological weather alert. At present, however, this lack favors the increasing diffusion of meteorological sites more or less professional - often totally not "ethical" - which, at different spatial scales, tend to amplify the signals from the weather prediction models, describing them the users of the web such as exceptional or rare phenomena and often causing unjustified alarmism. This behavior is almost always aimed at the desire of give a forecast before other sites and therefore looking for new commercial sponsors, with easy profits. On the other hand, however, the almost complete absence of education to environmental risks - also from as primary school - does not allow the users to know to select the information ethically and technically correct, increasingly favoring the proliferation of most of the "weather-commercial" or private weather websites. It would seem therefore essential to implement the activities of specific information by the universities and public institutions responsible for forecasting and prevention-hydrological forecast.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2010-title46-vol2/pdf/CFR-2010-title46-vol2-sec45-191.pdf','CFR'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2010-title46-vol2/pdf/CFR-2010-title46-vol2-sec45-191.pdf"><span>46 CFR 45.191 - Pre-departure requirements.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2010&page.go=Go">Code of Federal Regulations, 2010 CFR</a></p> <p></p> <p>2010-10-01</p> <p>... voyage, the towing vessel master must conduct the following: (a) Weather forecast. Determine the marine weather forecast along the planned route, and contact the dock operator at the destination port to get an update on local weather conditions. (b) Inspection. Inspect each barge of the tow to ensure that they...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMPA41C..05P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMPA41C..05P"><span>Road Weather and Connected Vehicles</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pisano, P.; Boyce, B. C.</p> <p>2015-12-01</p> <p>On average, there are over 5.8 M vehicle crashes each year of which 23% are weather-related. Weather-related crashes are defined as those crashes that occur in adverse weather or on slick pavement. The vast majority of weather-related crashes happen on wet pavement (74%) and during rainfall (46%). Connected vehicle technologies hold the promise to transform road-weather management by providing improved road weather data in real time with greater temporal and geographic accuracy. This will dramatically expand the amount of data that can be used to assess, forecast, and address the impacts that weather has on roads, vehicles, and travelers. The use of vehicle-based measurements of the road and surrounding atmosphere with other, more traditional weather data sources, and create road and atmospheric hazard products for a variety of users. The broad availability of road weather data from mobile sources will vastly improve the ability to detect and forecast weather and road conditions, and will provide the capability to manage road-weather response on specific roadway links. The RWMP is currently demonstrating how weather, road conditions, and related vehicle data can be used for decision making through an innovative Integrated Mobile Observations project. FHWA is partnering with 3 DOTs (MN, MI, & NV) to pilot these applications. One is a mobile alerts application called the Motorists Advisories and Warnings (MAW) and a maintenance decision support application. These applications blend traditional weather information (e.g., radar, surface stations) with mobile vehicle data (e.g., temperature, brake status, wiper status) to determine current weather conditions. These weather conditions, and other road-travel-relevant information, are provided to users via web and phone applications. The MAW provides nowcasts and short-term forecasts out to 24 hours while the EMDSS application can provide forecasts up to 72 hours in advance. The three DOTs have placed readers and external road weather sensors on their maintenance fleet vehicles to collect vehicular and meteorological data. Data from all three states is sent to a processing system called the Pikalert® Vehicle Data Translator (VDT) that quality checks and uses the data to infer current and forecasted weather conditions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19800016231&hterms=forecasting+impact&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dforecasting%2Bimpact','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19800016231&hterms=forecasting+impact&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dforecasting%2Bimpact"><span>The impact of Sun-weather research on forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Larsen, M. F.</p> <p>1979-01-01</p> <p>The possible impact of Sun-weather research on forecasting is examined. The type of knowledge of the effect is evaluated to determine if it is in a form that can be used for forecasting purposes. It is concluded that the present understanding of the effect does not lend itself readily to applications for forecast purposes. The limits of present predictive skill are examined and it is found that skill is most lacking for prediction of the smallest scales of atmospheric motion. However, it is not expected that Sun-weather research will have any significant impact on forecasting the smaller scales since predictability at these scales is limited by the finite grid size resolution and the time scales of turbulent diffusion. The predictability limits for the largest scales are on the order of several weeks although presently only a one week forecast is achievable.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010008274','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010008274"><span>AWE: Aviation Weather Data Visualization Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Spirkovska, Lilly; Lodha, Suresh K.</p> <p>2000-01-01</p> <p>The two official sources for aviation weather reports both provide weather information to a pilot in a textual format. A number of systems have recently become available to help pilots with the visualization task by providing much of the data graphically. However, two types of aviation weather data are still not being presented graphically. These are airport-specific current weather reports (known as meteorological observations, or METARs) and forecast weather reports (known as terminal area forecasts, or TAFs). Our system, Aviation Weather Environment (AWE), presents intuitive graphical displays for both METARs and TAFs, as well as winds aloft forecasts. We start with a computer-generated textual aviation weather briefing. We map this briefing onto a cartographic grid specific to the pilot's area of interest. The pilot is able to obtain aviation-specific weather for the entire area or for his specific route. The route, altitude, true airspeed, and proposed departure time can each be modified in AWE. Integral visual display of these three elements of weather reports makes AWE a useful planning tool, as well as a weather briefing tool.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/41760','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/41760"><span>WRF-Fire: coupled weather-wildland fire modeling with the weather research and forecasting model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Janice L. Coen; Marques Cameron; John Michalakes; Edward G. Patton; Philip J. Riggan; Kara M. Yedinak</p> <p>2012-01-01</p> <p>A wildland fire behavior module (WRF-Fire) was integrated into the Weather Research and Forecasting (WRF) public domain numerical weather prediction model. The fire module is a surface fire behavior model that is two-way coupled with the atmospheric model. Near-surface winds from the atmospheric model are interpolated to a finer fire grid and used, with fuel properties...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMSM11E..04T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMSM11E..04T"><span>Assessing and Adapting Scientific Results for Space Weather Research to Operations (R2O)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thompson, B. J.; Friedl, L.; Halford, A. J.; Mays, M. L.; Pulkkinen, A. A.; Singer, H. J.; Stehr, J. W.</p> <p>2017-12-01</p> <p>Why doesn't a solid scientific paper necessarily result in a tangible improvement in space weather capability? A well-known challenge in space weather forecasting is investing effort to turn the results of basic scientific research into operational knowledge. This process is commonly known as "Research to Operations," abbreviated R2O. There are several aspects of this process: 1) How relevant is the scientific result to a particular space weather process? 2) If fully utilized, how much will that result improve the reliability of the forecast for the associated process? 3) How much effort will this transition require? Is it already in a relatively usable form, or will it require a great deal of adaptation? 4) How much burden will be placed on forecasters? Is it "plug-and-play" or will it require effort to operate? 5) How can robust space weather forecasting identify challenges for new research? This presentation will cover several approaches that have potential utility in assessing scientific results for use in space weather research. The demonstration of utility is the first step, relating to the establishment of metrics to ensure that there will be a clear benefit to the end user. The presentation will then move to means of determining cost vs. benefit, (where cost involves the full effort required to transition the science to forecasting, and benefit concerns the improvement of forecast reliability), and conclude with a discussion of the role of end users and forecasters in driving further innovation via "O2R."</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/898588','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/898588"><span>Weather-based forecasts of California crop yields</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Lobell, D B; Cahill, K N; Field, C B</p> <p>2005-09-26</p> <p>Crop yield forecasts provide useful information to a range of users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, while for many crops official forecasts do not exist. As broad-scale crop yields are largely dependent on weather, measurements from existing meteorological stations have the potential to provide a reliable, timely, and cost-effective means to anticipate crop yields. We developed weather-based models of state-wide yields for 12 major California crops (wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios), and tested their accuracy using cross-validation over themore » 1980-2003 period. Many crops were forecast with high accuracy, as judged by the percent of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, allowing for lead times that were longer than existing procedures in many cases.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130003215','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130003215"><span>Transition of Suomi National Polar-Orbiting Partnership (S-NPP) Data Products for Operational Weather Forecasting Applications</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Smith, Matthew R.; Molthan, Andrew L.; Fuell, Kevin K.; Jedlovec, Gary J.</p> <p>2012-01-01</p> <p>SPoRT is a team of NASA/NOAA scientists focused on demonstrating the utility of NASA and future NOAA data and derived products on improving short-term weather forecasts. Work collaboratively with a suite of unique products and selected WFOs in an end-to-end transition activity. Stable funding from NASA and NOAA. Recognized by the science community as the "go to" place for transitioning experimental and research data to the operational weather community. Endorsed by NWS ESSD/SSD chiefs. Proven paradigm for transitioning satellite observations and modeling capabilities to operations (R2O). SPoRT s transition of NASA satellite instruments provides unique or higher resolution data products to complement the baseline suite of geostationary data available to forecasters. SPoRT s partnership with NWS WFOs provides them with unique imagery to support disaster response and local forecast challenges. SPoRT has years of proven experience in developing and transitioning research products to the operational weather community. SPoRT has begun work with CONUS and OCONUS WFOs to determine the best products for maximum benefit to forecasters. VIIRS has already proven to be another extremely powerful tool, enhancing forecasters ability to handle difficult forecasting situations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SpWea..15.1222C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SpWea..15.1222C"><span>An abridged history of federal involvement in space weather forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caldwell, Becaja; McCarron, Eoin; Jonas, Seth</p> <p>2017-10-01</p> <p>Public awareness of space weather and its adverse effects on critical infrastructure systems, services, and technologies (e.g., the electric grid, telecommunications, and satellites) has grown through recent media coverage and scientific research. However, federal interest and involvement in space weather dates back to the decades between World War I and World War II when the National Bureau of Standards led efforts to observe, forecast, and provide warnings of space weather events that could interfere with high-frequency radio transmissions. The efforts to observe and predict space weather continued through the 1960s during the rise of the Cold War and into the present with U.S. government efforts to prepare the nation for space weather events. This paper provides a brief overview of the history of federal involvement in space weather forecasting from World War II, through the Apollo Program, and into the present.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA604708','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA604708"><span>Verification of a Non-Hydrostatic Dynamical Core Using Horizontally Spectral Element Vertically Finite Difference Method: 2D Aspects</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-04-01</p> <p>hydrostatic pressure vertical coordinate, which are the same as those used in the Weather Research and Forecasting ( WRF ) model, but a hybrid sigma...hydrostatic pressure vertical coordinate, which are the 33 same as those used in the Weather Research and Forecasting ( WRF ) model, but a hybrid 34 sigma...Weather Research and Forecasting 79 ( WRF ) Model. The Euler equations are in flux form based on the hydrostatic pressure vertical 80 coordinate. In</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA613335','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA613335"><span>Using the Random Nearest Neighbor Data Mining Method to Extract Maximum Information Content from Weather Forecasts from Multiple Predictors of Weather and One Predictand (Low-Level Turbulence)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-10-30</p> <p>Force Weather Agency (AFWA) WRF 15-km atmospheric model forecast data and low-level turbulence. Archives of historical model data forecast predictors...Relationships between WRF model predictors and PIREPS were developed using the new data mining methodology. The new methodology was inspired...convection. Predictors of turbulence were collected from the AFWA WRF 15km model, and corresponding PIREPS (the predictand) were collected between 2013</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/294','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/294"><span>Accuracy of National Weather Service wind-direction forecasts at Macon and Augusta, Georgia</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Leonidas G. Lavdas</p> <p>1997-01-01</p> <p>National Weather Service wind forecasts and observations over a nine-year period (1985 to 1993) were analyzed to determine the usefulness of these forecasts for forestry smoke management. Data from Macon, GA indicated that forecasts were accurate to within plus or minus 22.5E about 38 percent of the time. When a wider plus or minus 67.5E window was used, accuracy...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA600391','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA600391"><span>Statistical Analysis of Atmospheric Forecast Model Accuracy - A Focus on Multiple Atmospheric Variables and Location-Based Analysis</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-04-01</p> <p>WRF ) model is a numerical weather prediction system designed for operational forecasting and atmospheric research. This report examined WRF model... WRF , weather research and forecasting, atmospheric effects 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18. NUMBER OF...and Forecasting ( WRF ) model. The authors would also like to thank Ms. Sherry Larson, STS Systems Integration, LLC, ARL Technical Publishing Branch</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1036722','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1036722"><span>Verification of Weather Running Estimate-Nowcast (WRE-N) Forecasts Using a Spatial-Categorical Method</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2017-07-01</p> <p>forecasts and observations on a common grid, which enables the application a number of different spatial verification methods that reveal various...forecasts of continuous meteorological variables using categorical and object-based methods . White Sands Missile Range (NM): Army Research Laboratory (US... Research version of the Weather Research and Forecasting Model adapted for generating short-range nowcasts and gridded observations produced by the</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1375947','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1375947"><span>Utility usage forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hosking, Jonathan R. M.; Natarajan, Ramesh</p> <p></p> <p>The computer creates a utility demand forecast model for weather parameters by receiving a plurality of utility parameter values, wherein each received utility parameter value corresponds to a weather parameter value. Determining that a range of weather parameter values lacks a sufficient amount of corresponding received utility parameter values. Determining one or more utility parameter values that corresponds to the range of weather parameter values. Creating a model which correlates the received and the determined utility parameter values with the corresponding weather parameters values.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19730006912&hterms=journalism+studies&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Djournalism%2Bstudies','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19730006912&hterms=journalism+studies&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Djournalism%2Bstudies"><span>A study of comprehension and use of weather information by various agricultural groups in Wisconsin</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Smith, J. L.</p> <p>1972-01-01</p> <p>An attempt was made to determine whether current techniques are adequate for communicating improved weather forecasts to users. Primary concern was for agricultural users. Efforts were made to learn the preferred source of weather forecasts and the frequency of use. Attempts were also made to measure knowledge of specific terms having to do with weather and comprehension of terms less often used but critical to varying intensities of weather.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=wind&pg=7&id=EJ1064476','ERIC'); return false;" href="https://eric.ed.gov/?q=wind&pg=7&id=EJ1064476"><span>School Science Inspired by Improving Weather Forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Reid, Heather; Renfrew, Ian A.; Vaughan, Geraint</p> <p>2014-01-01</p> <p>High winds and heavy rain are regular features of the British weather, and forecasting these events accurately is a major priority for the Met Office and other forecast providers. This is the challenge facing DIAMET, a project involving university groups from Manchester, Leeds, Reading, and East Anglia, together with the Met Office. DIAMET is part…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=forecasting&pg=3&id=EJ748268','ERIC'); return false;" href="https://eric.ed.gov/?q=forecasting&pg=3&id=EJ748268"><span>Geography Department Weather Forecasting Contests in the 21st Century</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Skeeter, Brent R.</p> <p>2006-01-01</p> <p>The benefits of weather forecasting contests within geography departments are reaffirmed. The greatly increased ease of conducting such contests in the new millennium is stressed. Some of the specifics of the forecasting contest at Salisbury University are discussed. In addition, the advantages of a departmental contest over a national contest are…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4653624','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4653624"><span>Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Inoue, Jun; Yamazaki, Akira; Ono, Jun; Dethloff, Klaus; Maturilli, Marion; Neuber, Roland; Edwards, Patti; Yamaguchi, Hajime</p> <p>2015-01-01</p> <p>During ice-free periods, the Northern Sea Route (NSR) could be an attractive shipping route. The decline in Arctic sea-ice extent, however, could be associated with an increase in the frequency of the causes of severe weather phenomena, and high wind-driven waves and the advection of sea ice could make ship navigation along the NSR difficult. Accurate forecasts of weather and sea ice are desirable for safe navigation, but large uncertainties exist in current forecasts, partly owing to the sparse observational network over the Arctic Ocean. Here, we show that the incorporation of additional Arctic observations improves the initial analysis and enhances the skill of weather and sea-ice forecasts, the application of which has socioeconomic benefits. Comparison of 63-member ensemble atmospheric forecasts, using different initial data sets, revealed that additional Arctic radiosonde observations were useful for predicting a persistent strong wind event. The sea-ice forecast, initialised by the wind fields that included the effects of the observations, skilfully predicted rapid wind-driven sea-ice advection along the NSR. PMID:26585690</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1513883B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1513883B"><span>Nowcasting in the FROST-2014 Sochi Olympic project</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bica, Benedikt; Wang, Yong; Joe, Paul; Isaac, George; Kiktev, Dmitry; Bocharnikov, Nikolai</p> <p>2013-04-01</p> <p>FROST (Forecast and Research: the Olympic Sochi Testbed) 2014 is a WMO WWRP international project aimed at development, implementation, and demonstration of capabilities of short-range numerical weather prediction and nowcasting technologies for mountainous terrain in winter season. Sharp weather contrasts and high spatial and temporal variability are typical for the region of the Sochi-2014 Olympics. Steep mountainous terrain and an intricate mixture of maritime sub-tropical and Alpine environments make weather forecasting in this region extremely challenging. Goals of the FROST-2014 project: • To develop a comprehensive information resource of Alpine winter weather observations; • To improve and exploit: o Nowcasting systems of high impact weather phenomena (precipitation type and intensity, snow levels, visibility, wind speed, direction and gusts) in complex terrain; o High-resolution deterministic and ensemble mesoscale forecasts in winter complex terrain environment; • To improve the understanding of physics of high impact weather phenomena in the region; • To deliver forecasts (Nowcasts) to Olympic weather forecasters and decision makers and assess benefits of forecast improvement. 46 Automatic Meteorological Stations (AMS) were installed in the Olympic region by Roshydromet, by owners of sport venues and by the Megafon corporation, provider of mobile communication services. The time resolution of AMS observations does not exceed 10 minutes. For a subset of the stations it is even equal to 1 min. Data flow from the new dual polarization Doppler weather radar WRM200 in Sochi was organized at the end of 2012. Temperature/humidity and wind profilers and two Micro Rain Radars (MRR) will supplement the network. Nowcasting potential of NWP models participating in the project (COSMO, GEM, WRF, AROME, HARMONIE) is to be assessed for direct and post-processed (e.g. Kalman filter, 1-D model, MOS) model forecasts. Besides the meso-scale models, the specialized nowcasting systems are expected to be used in the project - ABOM, CARDS, INCA, INTW, STEPS, MeteoExpert. FROST-2014 is intended as an 'end-to-end' project. Its products will be used by local forecasters for meteorological support of the Olympics and preceding test sport events. The project is open for new interested participants. Additional information is available at http://frost2014.meteoinfo.ru.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.4469V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4469V"><span>Probabilistic forecasting of extreme weather events based on extreme value theory</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Van De Vyver, Hans; Van Schaeybroeck, Bert</p> <p>2016-04-01</p> <p>Extreme events in weather and climate such as high wind gusts, heavy precipitation or extreme temperatures are commonly associated with high impacts on both environment and society. Forecasting extreme weather events is difficult, and very high-resolution models are needed to describe explicitly extreme weather phenomena. A prediction system for such events should therefore preferably be probabilistic in nature. Probabilistic forecasts and state estimations are nowadays common in the numerical weather prediction community. In this work, we develop a new probabilistic framework based on extreme value theory that aims to provide early warnings up to several days in advance. We consider the combined events when an observation variable Y (for instance wind speed) exceeds a high threshold y and its corresponding deterministic forecasts X also exceeds a high forecast threshold y. More specifically two problems are addressed:} We consider pairs (X,Y) of extreme events where X represents a deterministic forecast, and Y the observation variable (for instance wind speed). More specifically two problems are addressed: Given a high forecast X=x_0, what is the probability that Y>y? In other words: provide inference on the conditional probability: [ Pr{Y>y|X=x_0}. ] Given a probabilistic model for Problem 1, what is the impact on the verification analysis of extreme events. These problems can be solved with bivariate extremes (Coles, 2001), and the verification analysis in (Ferro, 2007). We apply the Ramos and Ledford (2009) parametric model for bivariate tail estimation of the pair (X,Y). The model accommodates different types of extremal dependence and asymmetry within a parsimonious representation. Results are presented using the ensemble reforecast system of the European Centre of Weather Forecasts (Hagedorn, 2008). Coles, S. (2001) An Introduction to Statistical modelling of Extreme Values. Springer-Verlag.Ferro, C.A.T. (2007) A probability model for verifying deterministic forecasts of extreme events. Wea. Forecasting {22}, 1089-1100.Hagedorn, R. (2008) Using the ECMWF reforecast dataset to calibrate EPS forecasts. ECMWF Newsletter, {117}, 8-13.Ramos, A., Ledford, A. (2009) A new class of models for bivariate joint tails. J.R. Statist. Soc. B {71}, 219-241.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1714628G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1714628G"><span>Calls Forecast for the Moscow Ambulance Service. The Impact of Weather Forecast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gordin, Vladimir; Bykov, Philipp</p> <p>2015-04-01</p> <p>We use the known statistics of the calls for the current and previous days to predict them for tomorrow and for the following days. We assume that this algorithm will work operatively, will cyclically update the available information and will move the horizon of the forecast. Sure, the accuracy of such forecasts depends on their lead time, and from a choice of some group of diagnoses. For comparison we used the error of the inertial forecast (tomorrow there will be the same number of calls as today). Our technology has demonstrated accuracy that is approximately two times better compared to the inertial forecast. We obtained the following result: the number of calls depends on the actual weather in the city as well as on its rate of change. We were interested in the accuracy of the forecast for 12-hour sum of the calls in real situations. We evaluate the impact of the meteorological errors [1] on the forecast errors of the number of Ambulance calls. The weather and the Ambulance calls number both have seasonal tendencies. Therefore, if we have medical information from one city only, we should separate the impacts of such predictors as "annual variations in the number of calls" and "weather". We need to consider the seasonal tendencies (associated, e. g. with the seasonal migration of the population) and the impact of the air temperature simultaneously, rather than sequentially. We forecasted separately the number of calls with diagnoses of cardiovascular group, where it was demonstrated the advantage of the forecasting method, when we use the maximum daily air temperature as a predictor. We have a chance to evaluate statistically the influence of meteorological factors on the dynamics of medical problems. In some cases it may be useful for understanding of the physiology of disease and possible treatment options. We can assimilate some personal archives of medical parameters for the individuals with concrete diseases and the relative meteorological archive. As a result we hope to evaluate how weather can influence the intensity of the disease. Thus, the knowledge of the weather forecast for several days will help us to predict a state of health. The person will be able to take some proactive actions to avoid the anticipated worsening of his health. Literature 1. A. N. Bagrov, F. L. Bykov, V. A. Gordin. Complex Forecast of Surface Meteorological Parameters. Meteorology and Hydrology, 2014, N 5, 5-16 (Russian), 283-291 (English). 2. Bykov, Ph.L., Gordin, V.A., Objective Analysis of the Structure of Three-Dimensional Atmospheric Fronts. Izvestia of Russian Academy of Sciences. Ser. The Physics of Atmosphere and Ocean, 48 (2) (2012), 172-188 (Russian), 152-168 (English), http://dx.doi.org/10.1134/S0001433812020053 3. V.A.Gordin. Mathematical Problems and Methods in Hydrodynamical Weather Forecasting. Amsterdam etc.: Gordon & Breach Publ. House, 2000. 4. V.A.Gordin. Mathematics, Computer, Weather Forecasting, and Other Mathematical Physics' Scenarios. Moscow, Fizmatlit, 2010, 2012 (Russian).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018AAS...23115212H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018AAS...23115212H"><span>Improved Weather Forecasting for the Dynamic Scheduling System of the Green Bank Telescope</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Henry, Kari; Maddalena, Ronald</p> <p>2018-01-01</p> <p>The Robert C Byrd Green Bank Telescope (GBT) uses a software system that dynamically schedules observations based on models of vertical weather forecasts produced by the National Weather Service (NWS). The NWS provides hourly forecasted values for ~60 layers that extend into the stratosphere over the observatory. We use models, recommended by the Radiocommunication Sector of the International Telecommunications Union, to derive the absorption coefficient in each layer for each hour in the NWS forecasts and for all frequencies over which the GBT has receivers, 0.1 to 115 GHz. We apply radiative transfer models to derive the opacity and the atmospheric contributions to the system temperature, thereby deriving forecasts applicable to scheduling radio observations for up to 10 days into the future. Additionally, the algorithms embedded in the data processing pipeline use historical values of the forecasted opacity to calibrate observations. Until recently, we have concentrated on predictions for high frequency (> 15 GHz) observing, as these need to be scheduled carefully around bad weather. We have been using simple models for the contribution of rain and clouds since we only schedule low-frequency observations under these conditions. In this project, we wanted to improve the scheduling of the GBT and data calibration at low frequencies by deriving better algorithms for clouds and rain. To address the limitation at low frequency, the observatory acquired a Radiometrics Corporation MP-1500A radiometer, which operates in 27 channels between 22 and 30 GHz. By comparing 16 months of measurements from the radiometer against forecasted system temperatures, we have confirmed that forecasted system temperatures are indistinguishable from those measured under good weather conditions. Small miss-calibrations of the radiometer data dominate the comparison. By using recalibrated radiometer measurements, we looked at bad weather days to derive better models for forecasting the contribution of clouds to the opacity and system temperatures. We will show how these revised algorithms should help us improve both data calibration and the accuracy of scheduling low-frequency observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140002291','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140002291"><span>GPS Estimates of Integrated Precipitable Water Aid Weather Forecasters</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Moore, Angelyn W.; Gutman, Seth I.; Holub, Kirk; Bock, Yehuda; Danielson, David; Laber, Jayme; Small, Ivory</p> <p>2013-01-01</p> <p>Global Positioning System (GPS) meteorology provides enhanced density, low-latency (30-min resolution), integrated precipitable water (IPW) estimates to NOAA NWS (National Oceanic and Atmospheric Adminis tration Nat ional Weather Service) Weather Forecast Offices (WFOs) to provide improved model and satellite data verification capability and more accurate forecasts of extreme weather such as flooding. An early activity of this project was to increase the number of stations contributing to the NOAA Earth System Research Laboratory (ESRL) GPS meteorology observing network in Southern California by about 27 stations. Following this, the Los Angeles/Oxnard and San Diego WFOs began using the enhanced GPS-based IPW measurements provided by ESRL in the 2012 and 2013 monsoon seasons. Forecasters found GPS IPW to be an effective tool in evaluating model performance, and in monitoring monsoon development between weather model runs for improved flood forecasting. GPS stations are multi-purpose, and routine processing for position solutions also yields estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV (precipitable water vapor) using in situ pressure and temperature measurements, the basis for GPS meteorology. NOAA ESRL has implemented this concept with a nationwide distribution of more than 300 "GPSMet" stations providing IPW estimates at sub-hourly resolution currently used in operational weather models in the U.S.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19780003695','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19780003695"><span>Thunderstorm Research International Program (TRIP 77) report to management</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Taiani, A. J.</p> <p>1977-01-01</p> <p>A post analysis of the previous day's weather, followed by the day's forecast and an outlook on weather conditions for the following day is given. The normal NOAA weather charts were used, complemented by the latest GOES satellite pictures, the latest rawinsonde sounding, and the computer-derived thunderstorm probability forecasts associated with the sounding.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AIPC.1850c0015F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AIPC.1850c0015F"><span>Addressing forecast uncertainty impact on CSP annual performance</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ferretti, Fabio; Hogendijk, Christopher; Aga, Vipluv; Ehrsam, Andreas</p> <p>2017-06-01</p> <p>This work analyzes the impact of weather forecast uncertainty on the annual performance of a Concentrated Solar Power (CSP) plant. Forecast time series has been produced by a commercial forecast provider using the technique of hindcasting for the full year 2011 in hourly resolution for Ouarzazate, Morocco. Impact of forecast uncertainty has been measured on three case studies, representing typical tariff schemes observed in recent CSP projects plus a spot market price scenario. The analysis has been carried out using an annual performance model and a standard dispatch optimization algorithm based on dynamic programming. The dispatch optimizer has been demonstrated to be a key requisite to maximize the annual revenues depending on the price scenario, harvesting the maximum potential out of the CSP plant. Forecasting uncertainty affects the revenue enhancement outcome of a dispatch optimizer depending on the error level and the price function. Results show that forecasting accuracy of direct solar irradiance (DNI) is important to make best use of an optimized dispatch but also that a higher number of calculation updates can partially compensate this uncertainty. Improvement in revenues can be significant depending on the price profile and the optimal operation strategy. Pathways to achieve better performance are presented by having more updates both by repeatedly generating new optimized trajectories but also more often updating weather forecasts. This study shows the importance of working on DNI weather forecasting for revenue enhancement as well as selecting weather services that can provide multiple updates a day and probabilistic forecast information.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.329J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.329J"><span>Error discrimination of an operational hydrological forecasting system at a national scale</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jordan, F.; Brauchli, T.</p> <p>2010-09-01</p> <p>The use of operational hydrological forecasting systems is recommended for hydropower production as well as flood management. However, the forecast uncertainties can be important and lead to bad decisions such as false alarms and inappropriate reservoir management of hydropower plants. In order to improve the forecasting systems, it is important to discriminate the different sources of uncertainties. To achieve this task, reanalysis of past predictions can be realized and provide information about the structure of the global uncertainty. In order to discriminate between uncertainty due to the weather numerical model and uncertainty due to the rainfall-runoff model, simulations assuming perfect weather forecast must be realized. This contribution presents the spatial analysis of the weather uncertainties and their influence on the river discharge prediction of a few different river basins where an operational forecasting system exists. The forecast is based on the RS 3.0 system [1], [2], which is also running the open Internet platform www.swissrivers.ch [3]. The uncertainty related to the hydrological model is compared to the uncertainty related to the weather prediction. A comparison between numerous weather prediction models [4] at different lead times is also presented. The results highlight an important improving potential of both forecasting components: the hydrological rainfall-runoff model and the numerical weather prediction models. The hydrological processes must be accurately represented during the model calibration procedure, while weather prediction models suffer from a systematic spatial bias. REFERENCES [1] Garcia, J., Jordan, F., Dubois, J. & Boillat, J.-L. 2007. "Routing System II, Modélisation d'écoulements dans des systèmes hydrauliques", Communication LCH n° 32, Ed. Prof. A. Schleiss, Lausanne [2] Jordan, F. 2007. Modèle de prévision et de gestion des crues - optimisation des opérations des aménagements hydroélectriques à accumulation pour la réduction des débits de crue, thèse de doctorat n° 3711, Ecole Polytechnique Fédérale, Lausanne [3] Keller, R. 2009. "Le débit des rivières au peigne fin", Revue Technique Suisse, N°7/8 2009, Swiss engineering RTS, UTS SA, Lausanne, p. 11 [4] Kaufmann, P., Schubiger, F. & Binder, P. 2003. Precipitation forecasting by a mesoscale numerical weather prediction (NWP) model : eight years of experience, Hydrology and Earth System</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4214672','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4214672"><span>Forecasting Temporal Dynamics of Cutaneous Leishmaniasis in Northeast Brazil</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lewnard, Joseph A.; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R.; Glesby, Marshall J.; Ko, Albert I.; Carvalho, Edgar M.; Schriefer, Albert; Weinberger, Daniel M.</p> <p>2014-01-01</p> <p>Introduction Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. Methodology/Principal Findings We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. Significance These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets. PMID:25356734</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20050210110&hterms=hurricane+news&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dhurricane%2Bnews','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20050210110&hterms=hurricane+news&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dhurricane%2Bnews"><span>STS-114: Discovery L-3 Countdown Status Briefing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2005-01-01</p> <p>Bruce Buckingham of NASA Public Affair hosted this briefing. Jeff Spaulding, NASA Test Director; Scott Higgenbotham, STS-114 Payload-Mission Manager; Cathy Winters, Shuttle Weather Officer were present. Jeff specifically noted that the mission represents NASA's first step towards fulfilling the President's visions of returning to the Moon and then on to Mars and beyond. Scott reports that the 28,000 pounds of ISS hardware that is in the payload bay of the Discovery is ready to go, and completed final close outs. Cathy mentioned that Hurricane Dennis is not a threat, however, main threat of inland thunderstorms would result to 30% weather prohibiting launch. Cathy further gave current weather forecast supported with charts: the Launch Forecast, Tanking Forecast, SRB (Shuttle Solid Rocket Booster) Forecast, CONUS and TAL Launch Sites Forecast and with 24 hours and 48 hours turn around plan. Final inspections, ice formation, ice inspection, effect of weather conditions to the external tank, delays and contingencies were some of the topics covered with the News Media.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25356734','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25356734"><span>Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lewnard, Joseph A; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R; Glesby, Marshall J; Ko, Albert I; Carvalho, Edgar M; Schriefer, Albert; Weinberger, Daniel M</p> <p>2014-10-01</p> <p>Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.8493R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.8493R"><span>Weather forecasting with open source software</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rautenhaus, Marc; Dörnbrack, Andreas</p> <p>2013-04-01</p> <p>To forecast the weather situation during aircraft-based atmospheric field campaigns, we employ a tool chain of existing and self-developed open source software tools and open standards. Of particular value are the Python programming language with its extension libraries NumPy, SciPy, PyQt4, Matplotlib and the basemap toolkit, the NetCDF standard with the Climate and Forecast (CF) Metadata conventions, and the Open Geospatial Consortium Web Map Service standard. These open source libraries and open standards helped to implement the "Mission Support System", a Web Map Service based tool to support weather forecasting and flight planning during field campaigns. The tool has been implemented in Python and has also been released as open source (Rautenhaus et al., Geosci. Model Dev., 5, 55-71, 2012). In this presentation we discuss the usage of free and open source software for weather forecasting in the context of research flight planning, and highlight how the field campaign work benefits from using open source tools and open standards.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMSH21A2639N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMSH21A2639N"><span>An Early Prediction of Sunspot Cycle 25</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nandy, D.; Bhowmik, P.</p> <p>2017-12-01</p> <p>The Sun's magnetic activity governs our space environment, creates space weather and impacts our technologies and climate. With increasing reliance on space- and ground-based technologies that are subject to space weather, the need to be able to forecast the future activity of the Sun has assumed increasing importance. However, such long-range, decadal-scale space weather prediction has remained a great challenge as evident in the diverging forecasts for solar cycle 24. Based on recently acquired understanding of the physics of solar cycle predictability, we have devised a scheme to extend the forecasting window of solar cycles. Utilizing this we present an early forecast for sunspot cycle 25 which would be of use for space mission planning, satellite life-time estimates, and assessment of the long-term impacts of space weather on technological assets and planetary atmospheres.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://images.nasa.gov/#/details-KSC-2011-2976.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-KSC-2011-2976.html"><span>KSC-2011-2976</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2011-04-07</p> <p>CAPE CANAVERAL, Fla. - At the Cape Canaveral Air Force Station forecast facility in Florida, a member of the weather team demonstrates the effectiveness of the new weather radar display recently installed. The facility is operated by the U.S. Air Force 45th Weather Squadron and will generate a launch weather forecast for the scheduled July 8 lift off of space shuttle Atlantis on the STS-135 mission. Photo credit: NASA/Jack Pfaller</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://images.nasa.gov/#/details-KSC-2011-2975.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-KSC-2011-2975.html"><span>KSC-2011-2975</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2011-04-07</p> <p>CAPE CANAVERAL, Fla. - At the Cape Canaveral Air Force Station forecast facility in Florida, a member of the weather team demonstrates the effectiveness of the new weather radar display recently installed. The facility is operated by the U.S. Air Force 45th Weather Squadron and will generate a launch weather forecast for the scheduled July 8 lift off of space shuttle Atlantis on the STS-135 mission. Photo credit: NASA/Jack Pfaller</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMSH32B..05C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMSH32B..05C"><span>The effort to increase the space weather forecasting accuracy in KSWC</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Choi, J. S.</p> <p>2017-12-01</p> <p>The Korean Space Weather Center (KSWC) of the National Radio Research Agency (RRA) is a government agency which is the official source of space weather information for Korean Government and the primary action agency of emergency measure to severe space weather condition as the Regional Warning Center of the International Space Environment Service (ISES). KSWC's main role is providing alerts, watches, and forecasts in order to minimize the space weather impacts on both of public and commercial sectors of satellites, aviation, communications, navigations, power grids, and etc. KSWC is also in charge of monitoring the space weather condition and conducting research and development for its main role of space weather operation in Korea. Recently, KSWC are focusing on increasing the accuracy of space weather forecasting results and verifying the model generated results. The forecasting accuracy will be calculated based on the probability statistical estimation so that the results can be compared numerically. Regarding the cosmic radiation does, we are gathering the actual measured data of radiation does using the instrument by cooperation with the domestic airlines. Based on the measurement, we are going to verify the reliability of SAFE system which was developed by KSWC to provide the cosmic radiation does information with the airplane cabin crew and public users.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120007669','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120007669"><span>Using Science Data and Models for Space Weather Forecasting - Challenges and Opportunities</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hesse, Michael; Pulkkinen, Antti; Zheng, Yihua; Maddox, Marlo; Berrios, David; Taktakishvili, Sandro; Kuznetsova, Masha; Chulaki, Anna; Lee, Hyesook; Mullinix, Rick; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20120007669'); toggleEditAbsImage('author_20120007669_show'); toggleEditAbsImage('author_20120007669_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20120007669_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20120007669_hide"></p> <p>2012-01-01</p> <p>Space research, and, consequently, space weather forecasting are immature disciplines. Scientific knowledge is accumulated frequently, which changes our understanding or how solar eruptions occur, and of how they impact targets near or on the Earth, or targets throughout the heliosphere. Along with continuous progress in understanding, space research and forecasting models are advancing rapidly in capability, often providing substantially increases in space weather value over time scales of less than a year. Furthermore, the majority of space environment information available today is, particularly in the solar and heliospheric domains, derived from research missions. An optimal forecasting environment needs to be flexible enough to benefit from this rapid development, and flexible enough to adapt to evolving data sources, many of which may also stem from non-US entities. This presentation will analyze the experiences obtained by developing and operating both a forecasting service for NASA, and an experimental forecasting system for Geomagnetically Induced Currents.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1713741V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1713741V"><span>Using ensembles in water management: forecasting dry and wet episodes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>van het Schip-Haverkamp, Tessa; van den Berg, Wim; van de Beek, Remco</p> <p>2015-04-01</p> <p>Extreme weather situations as droughts and extensive precipitation are becoming more frequent, which makes it more important to obtain accurate weather forecasts for the short and long term. Ensembles can provide a solution in terms of scenario forecasts. MeteoGroup uses ensembles in a new forecasting technique which presents a number of weather scenarios for a dynamical water management project, called Water-Rijk, in which water storage and water retention plays a large role. The Water-Rijk is part of Park Lingezegen, which is located between Arnhem and Nijmegen in the Netherlands. In collaboration with the University of Wageningen, Alterra and Eijkelkamp a forecasting system is developed for this area which can provide water boards with a number of weather and hydrology scenarios in order to assist in the decision whether or not water retention or water storage is necessary in the near future. In order to make a forecast for drought and extensive precipitation, the difference 'precipitation- evaporation' is used as a measurement of drought in the weather forecasts. In case of an upcoming drought this difference will take larger negative values. In case of a wet episode, this difference will be positive. The Makkink potential evaporation is used which gives the most accurate potential evaporation values during the summer, when evaporation plays an important role in the availability of surface water. Scenarios are determined by reducing the large number of forecasts in the ensemble to a number of averaged members with each its own likelihood of occurrence. For the Water-Rijk project 5 scenario forecasts are calculated: extreme dry, dry, normal, wet and extreme wet. These scenarios are constructed for two forecasting periods, each using its own ensemble technique: up to 48 hours ahead and up to 15 days ahead. The 48-hour forecast uses an ensemble constructed from forecasts of multiple high-resolution regional models: UKMO's Euro4 model,the ECMWF model, WRF and Hirlam. Using multiple model runs and additional post processing, an ensemble can be created from non-ensemble models. The 15-day forecast uses the ECMWF Ensemble Prediction System forecast from which scenarios can be deduced directly. A combination of the ensembles from the two forecasting periods is used in order to have the highest possible resolution of the forecast for the first 48 hours followed by the lower resolution long term forecast.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFMED51B0430R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFMED51B0430R"><span>Promoting Interests in Atmospheric Science at a Liberal Arts Institution</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roussev, S.; Sherengos, P. M.; Limpasuvan, V.; Xue, M.</p> <p>2007-12-01</p> <p>Coastal Carolina University (CCU) students in Computer Science participated in a project to set up an operational weather forecast for the local community. The project involved the construction of two computing clusters and the automation of daily forecasting. Funded by NSF-MRI, two high-performance clusters were successfully established to run the University of Oklahoma's Advance Regional Prediction System (ARPS). Daily weather predictions are made over South Carolina and North Carolina at 3-km horizontal resolution (roughly 1.9 miles) using initial and boundary condition data provided by UNIDATA. At this high resolution, the model is cloud- resolving, thus providing detailed picture of heavy thunderstorms and precipitation. Forecast results are displayed on CCU's website (https://marc.coastal.edu/HPC) to complement observations at the National Weather Service in Wilmington N.C. Present efforts include providing forecasts at 1-km resolution (or finer), comparisons with other models like Weather Research and Forecasting (WRF) model, and the examination of local phenomena (like water spouts and tornadoes). Through these activities the students learn about shell scripting, cluster operating systems, and web design. More importantly, students are introduced to Atmospheric Science, the processes involved in making weather forecasts, and the interpretation of their forecasts. Simulations generated by the forecasts will be integrated into the contents of CCU's course like Fluid Dynamics, Atmospheric Sciences, Atmospheric Physics, and Remote Sensing. Operated jointly between the departments of Applied Physics and Computer Science, the clusters are expected to be used by CCU faculty and students for future research and inquiry-based projects in Computer Science, Applied Physics, and Marine Science.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA590768','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA590768"><span>Spacebuoy: A University Nanosat Space Weather Mission (III)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2013-10-11</p> <p>ionospheric forecasting models; specifically the operational Global Assimilation of Ionospheric Measurements (GAIM) model currently used by the Air Force... ionospheric forecasting models; specifically the operational Global Assimilation of Ionospheric Measurements (GAIM) model currently used by the Air...Mission Objectives • Provide critical space weather data for use in ionospheric forecasting efforts, particularly assimilated data used in the GAIM</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110011475','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011475"><span>Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dreher, Joseph G.; Crawford, Winifred; Lafosse, Richard; Hoeth, Brian; Burns, Kerry</p> <p>2009-01-01</p> <p>The peak winds near the surface are an important forecast element for space shuttle landings. As defined in the Flight Rules (FR), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings, and is required to issue surface average and 10-minute peak wind speed forecasts. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMU) developed a PC-based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center (KSC; Lambert 2003). However, the shuttle occasionally may land at Edwards Air Force Base (EAFB) in southern California when weather conditions at KSC in Florida are not acceptable, so SMG forecasters requested a similar tool be developed for EAFB.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110012914','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110012914"><span>Using Satellite Data in Weather Forecasting: I</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jedlovec, Gary J.; Suggs, Ronnie J.; Lecue, Juan M.</p> <p>2006-01-01</p> <p>The GOES Product Generation System (GPGS) is a set of computer codes and scripts that enable the assimilation of real-time Geostationary Operational Environmental Satellite (GOES) data into regional-weather-forecasting mathematical models. The GPGS can be used to derive such geophysical parameters as land surface temperature, the amount of precipitable water, the degree of cloud cover, the surface albedo, and the amount of insolation from satellite measurements of radiant energy emitted by the Earth and its atmosphere. GPGS incorporates a priori information (initial guesses of thermodynamic parameters of the atmosphere) and radiometric measurements from the geostationary operational environmental satellites along with mathematical models of physical principles that govern the transfer of energy in the atmosphere. GPGS solves the radiative-transfer equation and provides the resulting data products in formats suitable for use by weather-forecasting computer programs. The data-assimilation capability afforded by GPGS offers the potential to improve local weather forecasts ranging from 3 hours to 2 days - especially with respect to temperature, humidity, cloud cover, and the probability of precipitation. The improvements afforded by GPGS could be of interest to news media, utility companies, and other organizations that utilize regional weather forecasts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013CSR....63S.149S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013CSR....63S.149S"><span>Demonstrating the Alaska Ocean Observing System in Prince William Sound</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schoch, G. Carl; McCammon, Molly</p> <p>2013-07-01</p> <p>The Alaska Ocean Observing System and the Oil Spill Recovery Institute developed a demonstration project over a 5 year period in Prince William Sound. The primary goal was to develop a quasi-operational system that delivers weather and ocean information in near real time to diverse user communities. This observing system now consists of atmospheric and oceanic sensors, and a new generation of computer models to numerically simulate and forecast weather, waves, and ocean circulation. A state of the art data management system provides access to these products from one internet portal at http://www.aoos.org. The project culminated in a 2009 field experiment that evaluated the observing system and performance of the model forecasts. Observations from terrestrial weather stations and weather buoys validated atmospheric circulation forecasts. Observations from wave gages on weather buoys validated forecasts of significant wave heights and periods. There was an emphasis on validation of surface currents forecasted by the ocean circulation model for oil spill response and search and rescue applications. During the 18 day field experiment a radar array mapped surface currents and drifting buoys were deployed. Hydrographic profiles at fixed stations, and by autonomous vehicles along transects, were made to acquire measurements through the water column. Terrestrial weather stations were the most reliable and least costly to operate, and in situ ocean sensors were more costly and considerably less reliable. The radar surface current mappers were the least reliable and most costly but provided the assimilation and validation data that most improved ocean circulation forecasts. We describe the setting of Prince William Sound and the various observational platforms and forecast models of the observing system, and discuss recommendations for future development.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1910023G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1910023G"><span>Ensemble hydrological forecast efficiency evolution over various issue dates and lead-time: case study for the Cheboksary reservoir (Volga River)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gelfan, Alexander; Moreido, Vsevolod</p> <p>2017-04-01</p> <p>Ensemble hydrological forecasting allows for describing uncertainty caused by variability of meteorological conditions in the river basin for the forecast lead-time. At the same time, in snowmelt-dependent river basins another significant source of uncertainty relates to variability of initial conditions of the basin (snow water equivalent, soil moisture content, etc.) prior to forecast issue. Accurate long-term hydrological forecast is most crucial for large water management systems, such as the Cheboksary reservoir (the catchment area is 374 000 sq.km) located in the Middle Volga river in Russia. Accurate forecasts of water inflow volume, maximum discharge and other flow characteristics are of great value for this basin, especially before the beginning of the spring freshet season that lasts here from April to June. The semi-distributed hydrological model ECOMAG was used to develop long-term ensemble forecast of daily water inflow into the Cheboksary reservoir. To describe variability of the meteorological conditions and construct ensemble of possible weather scenarios for the lead-time of the forecast, two approaches were applied. The first one utilizes 50 weather scenarios observed in the previous years (similar to the ensemble streamflow prediction (ESP) procedure), the second one uses 1000 synthetic scenarios simulated by a stochastic weather generator. We investigated the evolution of forecast uncertainty reduction, expressed as forecast efficiency, over various consequent forecast issue dates and lead time. We analyzed the Nash-Sutcliffe efficiency of inflow hindcasts for the period 1982 to 2016 starting from 1st of March with 15 days frequency for lead-time of 1 to 6 months. This resulted in the forecast efficiency matrix with issue dates versus lead-time that allows for predictability identification of the basin. The matrix was constructed separately for observed and synthetic weather ensembles.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1711727H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1711727H"><span>Trends in the predictive performance of raw ensemble weather forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hemri, Stephan; Scheuerer, Michael; Pappenberger, Florian; Bogner, Konrad; Haiden, Thomas</p> <p>2015-04-01</p> <p>Over the last two decades the paradigm in weather forecasting has shifted from being deterministic to probabilistic. Accordingly, numerical weather prediction (NWP) models have been run increasingly as ensemble forecasting systems. The goal of such ensemble forecasts is to approximate the forecast probability distribution by a finite sample of scenarios. Global ensemble forecast systems, like the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, are prone to probabilistic biases, and are therefore not reliable. They particularly tend to be underdispersive for surface weather parameters. Hence, statistical post-processing is required in order to obtain reliable and sharp forecasts. In this study we apply statistical post-processing to ensemble forecasts of near-surface temperature, 24-hour precipitation totals, and near-surface wind speed from the global ECMWF model. Our main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the post-processed forecasts. The ECMWF ensemble is under continuous development, and hence its forecast skill improves over time. Parts of these improvements may be due to a reduction of probabilistic bias. Thus, we first hypothesize that the gain by post-processing decreases over time. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations we generate post-processed forecasts by ensemble model output statistics (EMOS) for each station and variable. Parameter estimates are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over rolling training periods that consist of the n days preceding the initialization dates. Given the higher average skill in terms of CRPS of the post-processed forecasts for all three variables, we analyze the evolution of the difference in skill between raw ensemble and EMOS forecasts. The fact that the gap in skill remains almost constant over time, especially for near-surface wind speed, suggests that improvements to the atmospheric model have an effect quite different from what calibration by statistical post-processing is doing. That is, they are increasing potential skill. Thus this study indicates that (a) further model development is important even if one is just interested in point forecasts, and (b) statistical post-processing is important because it will keep adding skill in the foreseeable future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/26550','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/26550"><span>Economic Impact of Fire Weather Forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Don Gunasekera; Graham Mills; Mark Williams</p> <p>2006-01-01</p> <p>Southeastern Australia, where the State of Victoria is located is regarded as one of the most fire prone areas in the world. The Australian Bureau of Meteorology provides fire weather services in Victoria as part of a national framework for the provision of such services. These services range from fire weather warnings to special forecasts for hazard reduction burns....</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110004366','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110004366"><span>Impact of AIRS Thermodynamic Profile on Regional Weather Forecast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chou, Shih-Hung; Zavodsky, Brad; Jedlovee, Gary</p> <p>2010-01-01</p> <p>Prudent assimilation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. AIRS-enhanced analysis has warmer and moister PBL. Forecasts with AIRS profiles are generally closer to NAM analyses than CNTL. Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecasts. Including AIRS profiles in assimilation process enhances the moist instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ApWS....7.3869S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ApWS....7.3869S"><span>Weather forecasting based on hybrid neural model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.</p> <p>2017-11-01</p> <p>Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AcMeS..27..199C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AcMeS..27..199C"><span>The meta-Gaussian Bayesian Processor of forecasts and associated preliminary experiments</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Fajing; Jiao, Meiyan; Chen, Jing</p> <p>2013-04-01</p> <p>Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast (BPF), a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast according to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting system in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hydrology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the deterministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AdAtS..32..967H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AdAtS..32..967H"><span>Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hou, Tuanjie; Kong, Fanyou; Chen, Xunlai; Lei, Hengchi; Hu, Zhaoxia</p> <p>2015-07-01</p> <p>To improve the accuracy of short-term (0-12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System (HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) three-dimensional variational data assimilation (3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station (AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting (QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6-9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score (FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PApGe.171...87D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PApGe.171...87D"><span>The Impact of Weather Forecasts of Various Lead Times on Snowmaking Decisions Made for the 2010 Vancouver Olympic Winter Games</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Doyle, Chris</p> <p>2014-01-01</p> <p>The Vancouver 2010 Winter Olympics were held from 12 to 28 February 2010, and the Paralympic events followed 2 weeks later. During the Games, the weather posed a grave threat to the viability of one venue and created significant complications for the event schedule at others. Forecasts of weather with lead times ranging from minutes to days helped organizers minimize disruptions to sporting events and helped ensure all medal events were successfully completed. Of comparable importance, however, were the scenarios and forecasts of probable weather for the winter in advance of the Games. Forecasts of mild conditions at the time of the Games helped the Games' organizers mitigate what would have been very serious potential consequences for at least one venue. Snowmaking was one strategy employed well in advance of the Games to prepare for the expected conditions. This short study will focus on how operational decisions were made by the Games' organizers on the basis of both climatological and snowmaking forecasts during the pre-Games winter. An attempt will be made to quantify, economically, the value of some of the snowmaking forecasts made for the Games' operators. The results obtained indicate that although the economic value of the snowmaking forecast was difficult to determine, the Games' organizers valued the forecast information greatly. This suggests that further development of probabilistic forecasts for applications like pre-Games snowmaking would be worthwhile.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.H53H..01H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.H53H..01H"><span>NWS Operational Requirements for Ensemble-Based Hydrologic Forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hartman, R. K.</p> <p>2008-12-01</p> <p>Ensemble-based hydrologic forecasts have been developed and issued by National Weather Service (NWS) staff at River Forecast Centers (RFCs) for many years. Used principally for long-range water supply forecasts, only the uncertainty associated with weather and climate have been traditionally considered. As technology and societal expectations of resource managers increase, the use and desire for risk-based decision support tools has also increased. These tools require forecast information that includes reliable uncertainty estimates across all time and space domains. The development of reliable uncertainty estimates associated with hydrologic forecasts is being actively pursued within the United States and internationally. This presentation will describe the challenges, components, and requirements for operational hydrologic ensemble-based forecasts from the perspective of a NOAA/NWS River Forecast Center.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018LPICo2063.3188M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018LPICo2063.3188M"><span>Forecasting Space Weather Hazards for Astronauts in Deep Space</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Martens, P. C.</p> <p>2018-02-01</p> <p>Deep Space Gateway provides a unique platform to develop, calibrate, and test a space weather forecasting system for interplanetary travel in a real life setting. We will discuss requirements and design of such a system.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A11E0099L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A11E0099L"><span>Development of a WRF-RTFDDA-based high-resolution hybrid data-assimilation and forecasting system toward to operation in the Middle East</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Y.; Wu, W.; Zhang, Y.; Kucera, P. A.; Liu, Y.; Pan, L.</p> <p>2012-12-01</p> <p>Weather forecasting in the Middle East is challenging because of its complicated geographical nature including massive coastal area and heterogeneous land, and regional spare observational network. Strong air-land-sea interactions form multi-scale weather regimes in the area, which require a numerical weather prediction model capable of properly representing multi-scale atmospheric flow with appropriate initial conditions. The WRF-based Real-Time Four Dimensional Data Assimilation (RTFDDA) system is one of advanced multi-scale weather analysis and forecasting facilities developed at the Research Applications Laboratory (RAL) of NCAR. The forecasting system is applied for the Middle East with careful configuration. To overcome the limitation of the very sparsely available conventional observations in the region, we develop a hybrid data assimilation algorithm combining RTFDDA and WRF-3DVAR, which ingests remote sensing data from satellites and radar. This hybrid data assimilation blends Newtonian nudging FDDA and 3DVAR technology to effectively assimilate both conventional observations and remote sensing measurements and provide improved initial conditions for the forecasting system. For brevity, the forecasting system is called RTF3H (RTFDDA-3DVAR Hybrid). In this presentation, we will discuss the hybrid data assimilation algorithm, and its implementation, and the applications for high-impact weather events in the area. Sensitivity studies are conducted to understand the strength and limitations of this hybrid data assimilation algorithm.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMPA51B1808W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMPA51B1808W"><span>Weather uncertainty versus climate change uncertainty in a short television weather broadcast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Witte, J.; Ward, B.; Maibach, E.</p> <p>2011-12-01</p> <p>For TV meteorologists talking about uncertainty in a two-minute forecast can be a real challenge. It can quickly open the way to viewer confusion. TV meteorologists understand the uncertainties of short term weather models and have different methods to convey the degrees of confidence to the viewing public. Visual examples are seen in the 7-day forecasts and the hurricane track forecasts. But does the public really understand a 60 percent chance of rain or the hurricane cone? Communication of climate model uncertainty is even more daunting. The viewing public can quickly switch to denial of solid science. A short review of the latest national survey of TV meteorologists by George Mason University and lessons learned from a series of climate change workshops with TV broadcasters provide valuable insights into effectively using visualizations and invoking multimedia-learning theories in weather forecasts to improve public understanding of climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=solar+AND+radiation&pg=3&id=EJ944350','ERIC'); return false;" href="https://eric.ed.gov/?q=solar+AND+radiation&pg=3&id=EJ944350"><span>Fine Forecasts: Encouraging the Media to Include Ultraviolet Radiation Information in Summertime Weather Forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Richards, R.; Reeder, A. I.; Bulliard, J.-L.</p> <p>2004-01-01</p> <p>Melanoma and skin cancer are largely attributable to over-exposure to solar ultraviolet radiation (UVR). Reports of UVR levels within media weather forecasts appear to be well received by the public and have good potential to communicate the need for appropriate sun protection to a broad audience. This study describes provision of UVR messages by…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA620638','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA620638"><span>Optimizing Microgrid Architecture on Department of Defense Installations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-09-01</p> <p>PPA power purchase agreement PV photovoltaic QDR Quadrennial Defense Review SNL Sandia National Laboratory SPIDERS Smart Power Infrastructure...a MILP that dispatches fuel-based generators with consideration to an ensemble of forecasted inputs from renewable power sources, subject to physical...wind power project costs by region: 2012 projects, from [30]. 6. Weather Forecasts Weather forecasts are often presented as a single prediction</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/15200812','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/15200812"><span>Seasonal forecast of St. Louis encephalitis virus transmission, Florida.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Shaman, Jeffrey; Day, Jonathan F; Stieglitz, Marc; Zebiak, Stephen; Cane, Mark</p> <p>2004-05-01</p> <p>Disease transmission forecasts can help minimize human and domestic animal health risks by indicating where disease control and prevention efforts should be focused. For disease systems in which weather-related variables affect pathogen proliferation, dispersal, or transmission, the potential for disease forecasting exists. We present a seasonal forecast of St. Louis encephalitis virus transmission in Indian River County, Florida. We derive an empiric relationship between modeled land surface wetness and levels of SLEV transmission in humans. We then use these data to forecast SLEV transmission with a seasonal lead. Forecast skill is demonstrated, and a real-time seasonal forecast of epidemic SLEV transmission is presented. This study demonstrates how weather and climate forecast skill-verification analyses may be applied to test the predictability of an empiric disease forecast model.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3323226','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3323226"><span>Seasonal Forecast of St. Louis Encephalitis Virus Transmission, Florida</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Day, Jonathan F.; Stieglitz, Marc; Zebiak, Stephen; Cane, Mark</p> <p>2004-01-01</p> <p>Disease transmission forecasts can help minimize human and domestic animal health risks by indicating where disease control and prevention efforts should be focused. For disease systems in which weather-related variables affect pathogen proliferation, dispersal, or transmission, the potential for disease forecasting exists. We present a seasonal forecast of St. Louis encephalitis virus transmission in Indian River County, Florida. We derive an empirical relationship between modeled land surface wetness and levels of SLEV transmission in humans. We then use these data to forecast SLEV transmission with a seasonal lead. Forecast skill is demonstrated, and a real-time seasonal forecast of epidemic SLEV transmission is presented. This study demonstrates how weather and climate forecast skill verification analyses may be applied to test the predictability of an empirical disease forecast model. PMID:15200812</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100036761','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100036761"><span>Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela R.; Bauman, William H., III</p> <p>2008-01-01</p> <p>NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1916911R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1916911R"><span>Establishing NWP capabilities in African Small Island States (SIDs)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rögnvaldsson, Ólafur</p> <p>2017-04-01</p> <p>Íslenskar orkurannsóknir (ÍSOR), in collaboration with Belgingur Ltd. and the United Nations Economic Commission for Africa (UNECA) signed a Letter of Agreement in 2015 regarding collaboration in the "Establishing Operational Capacity for Building, Deploying and Using Numerical Weather and Seasonal Prediction Systems in Small Island States in Africa (SIDs)" project. The specific objectives of the collaboration were the following: - Build capacity of National Meteorological and Hydrology Services (NMHS) staff on the use of the WRF atmospheric model for weather and seasonal forecasting, interpretation of model results, and the use of observations to verify and improve model simulations. - Establish a platform for integrating short to medium range weather forecasts, as well as seasonal forecasts, into already existing infrastructure at NMHS and Regional Climate Centres. - Improve understanding of existing model results and forecast verification, for improving decision-making on the time scale of days to weeks. To meet these challenges the operational Weather On Demand (WOD) forecasting system, developed by Belgingur, is being installed in a number of SIDs countries (Cabo Verde, Guinea-Bissau, and Seychelles), as well as being deployed for the Pan-Africa region, with forecasts being disseminated to collaborating NMHSs.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H11J1336H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H11J1336H"><span>Balancing Flood Risk and Water Supply in California: Policy Search Combining Short-Term Forecast Ensembles and Groundwater Recharge</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Herman, J. D.; Steinschneider, S.; Nayak, M. A.</p> <p>2017-12-01</p> <p>Short-term weather forecasts are not codified into the operating policies of federal, multi-purpose reservoirs, despite their potential to improve service provision. This is particularly true for facilities that provide flood protection and water supply, since the potential flood damages are often too severe to accept the risk of inaccurate forecasts. Instead, operators must maintain empty storage capacity to mitigate flood risk, even if the system is currently in drought, as occurred in California from 2012-2016. This study investigates the potential for forecast-informed operating rules to improve water supply efficiency while maintaining flood protection, combining state-of-the-art weather hindcasts with a novel tree-based policy optimization framework. We hypothesize that forecasts need only accurately predict the occurrence of a storm, rather than its intensity, to be effective in regions like California where wintertime, synoptic-scale storms dominate the flood regime. We also investigate the potential for downstream groundwater injection to improve the utility of forecasts. These hypotheses are tested in a case study of Folsom Reservoir on the American River. Because available weather hindcasts are relatively short (10-20 years), we propose a new statistical framework to develop synthetic forecasts to assess the risk associated with inaccurate forecasts. The efficiency of operating policies is tested across a range of scenarios that include varying forecast skill and additional groundwater pumping capacity. Results suggest that the combined use of groundwater storage and short-term weather forecasts can substantially improve the tradeoff between water supply and flood control objectives in large, multi-purpose reservoirs in California.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006924','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006924"><span>Development and Implementation of Dynamic Scripts to Support Local Model Verification at National Weather Service Weather Forecast Offices</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zavordsky, Bradley; Case, Jonathan L.; Gotway, John H.; White, Kristopher; Medlin, Jeffrey; Wood, Lance; Radell, Dave</p> <p>2014-01-01</p> <p>Local modeling with a customized configuration is conducted at National Weather Service (NWS) Weather Forecast Offices (WFOs) to produce high-resolution numerical forecasts that can better simulate local weather phenomena and complement larger scale global and regional models. The advent of the Environmental Modeling System (EMS), which provides a pre-compiled version of the Weather Research and Forecasting (WRF) model and wrapper Perl scripts, has enabled forecasters to easily configure and execute the WRF model on local workstations. NWS WFOs often use EMS output to help in forecasting highly localized, mesoscale features such as convective initiation, the timing and inland extent of lake effect snow bands, lake and sea breezes, and topographically-modified winds. However, quantitatively evaluating model performance to determine errors and biases still proves to be one of the challenges in running a local model. Developed at the National Center for Atmospheric Research (NCAR), the Model Evaluation Tools (MET) verification software makes performing these types of quantitative analyses easier, but operational forecasters do not generally have time to familiarize themselves with navigating the sometimes complex configurations associated with the MET tools. To assist forecasters in running a subset of MET programs and capabilities, the Short-term Prediction Research and Transition (SPoRT) Center has developed and transitioned a set of dynamic, easily configurable Perl scripts to collaborating NWS WFOs. The objective of these scripts is to provide SPoRT collaborating partners in the NWS with the ability to evaluate the skill of their local EMS model runs in near real time with little prior knowledge of the MET package. The ultimate goal is to make these verification scripts available to the broader NWS community in a future version of the EMS software. This paper provides an overview of the SPoRT MET scripts, instructions for how the scripts are run, and example use cases.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC53G1293H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC53G1293H"><span>Some Advances in Downscaling Probabilistic Climate Forecasts for Agricultural Decision Support</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Han, E.; Ines, A.</p> <p>2015-12-01</p> <p>Seasonal climate forecasts, commonly provided in tercile-probabilities format (below-, near- and above-normal), need to be translated into more meaningful information for decision support of practitioners in agriculture. In this paper, we will present two new novel approaches to temporally downscale probabilistic seasonal climate forecasts: one non-parametric and another parametric method. First, the non-parametric downscaling approach called FResampler1 uses the concept of 'conditional block sampling' of weather data to create daily weather realizations of a tercile-based seasonal climate forecasts. FResampler1 randomly draws time series of daily weather parameters (e.g., rainfall, maximum and minimum temperature and solar radiation) from historical records, for the season of interest from years that belong to a certain rainfall tercile category (e.g., being below-, near- and above-normal). In this way, FResampler1 preserves the covariance between rainfall and other weather parameters as if conditionally sampling maximum and minimum temperature and solar radiation if that day is wet or dry. The second approach called predictWTD is a parametric method based on a conditional stochastic weather generator. The tercile-based seasonal climate forecast is converted into a theoretical forecast cumulative probability curve. Then the deviates for each percentile is converted into rainfall amount or frequency or intensity to downscale the 'full' distribution of probabilistic seasonal climate forecasts. Those seasonal deviates are then disaggregated on a monthly basis and used to constrain the downscaling of forecast realizations at different percentile values of the theoretical forecast curve. As well as the theoretical basis of the approaches we will discuss sensitivity analysis (length of data and size of samples) of them. In addition their potential applications for managing climate-related risks in agriculture will be shown through a couple of case studies based on actual seasonal climate forecasts for: rice cropping in the Philippines and maize cropping in India and Kenya.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1983steo....2......','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1983steo....2......"><span>Short-term energy outlook. Volume 2. Methodology</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p></p> <p>1983-05-01</p> <p>Recent changes in forecasting methodology for nonutility distillate fuel oil demand and for the near-term petroleum forecasts are discussed. The accuracy of previous short-term forecasts of most of the major energy sources published in the last 13 issues of the Outlook is evaluated. Macroeconomic and weather assumptions are included in this evaluation. Energy forecasts for 1983 are compared. Structural change in US petroleum consumption, the use of appropriate weather data in energy demand modeling, and petroleum inventories, imports, and refinery runs are discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA158010','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA158010"><span>A Conceptual Model of the Severe-Storm Environment for Inclusion into Air Weather Service Severe-Storm Analysis and Forecast Procedures.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1984-11-16</p> <p>thunderstorm forecasting , Bull. Am. Meteorol. Soc. 34:250-252. 19. Galway , J.G. (1956) The lifted index as a prediction of latent instability, Bull...downwind, which are geographically related and can be traced through time by a forecaster . In fact, a typical Great Plains severe-storm situation has...weather station setting, only one sounding can be plotted and anal- yzed because of time constraints. Appendix C contains two single-station forecast</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1016607','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1016607"><span>Change in Weather Research and Forecasting (WRF) Model Accuracy with Age of Input Data from the Global Forecast System (GFS)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2016-09-01</p> <p>Laboratory Change in Weather Research and Forecasting (WRF) Model Accuracy with Age of Input Data from the Global Forecast System (GFS) by JL Cogan...analysis. As expected, accuracy generally tended to decline as the large-scale data aged , but appeared to improve slightly as the age of the large...19 Table 7 Minimum and maximum mean RMDs for each WRF time (or GFS data age ) category. Minimum and</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=rain+AND+storm&pg=2&id=EJ522070','ERIC'); return false;" href="https://eric.ed.gov/?q=rain+AND+storm&pg=2&id=EJ522070"><span>Is It Going to Rain Today? Understanding the Weather Forecast.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Allsopp, Jim; And Others</p> <p>1996-01-01</p> <p>Presents a resource for science teachers to develop a better understanding of weather forecasts, including outlooks, watches, warnings, advisories, severe local storms, winter storms, floods, hurricanes, nonprecipitation hazards, precipitation probabilities, sky condition, and UV index. (MKR)</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=235875&keyword=Mathematical+AND+modeling&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=235875&keyword=Mathematical+AND+modeling&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Ensemble Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Ensemble forecasting has been used for operational numerical weather prediction in the United States and Europe since the early 1990s. An ensemble of weather or climate forecasts is used to characterize the two main sources of uncertainty in computer models of physical systems: ...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.3326M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.3326M"><span>Improving the health forecasting alert system for cold weather and heat-waves in England: a case-study approach using temperature-mortality relationships</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Masato, Giacomo; Cavany, Sean; Charlton-Perez, Andrew; Dacre, Helen; Bone, Angie; Carmicheal, Katie; Murray, Virginia; Danker, Rutger; Neal, Rob; Sarran, Christophe</p> <p>2015-04-01</p> <p>The health forecasting alert system for cold weather and heatwaves currently in use in the Cold Weather and Heatwave plans for England is based on 5 alert levels, with levels 2 and 3 dependent on a forecast or actual single temperature action trigger. Epidemiological evidence indicates that for both heat and cold, the impact on human health is gradual, with worsening impact for more extreme temperatures. The 60% risk of heat and cold forecasts used by the alerts is a rather crude probabilistic measure, which could be substantially improved thanks to the state-of-the-art forecast techniques. In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office's (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. The prototype health forecasting alert system introduces an "impact vs likelihood matrix" for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. The prototype shows some clear improvements over the current alert system. It allows for a much greater degree of flexibility, provides more detailed regional information about the health risks associated with periods of extreme temperatures, and is more coherent with other weather alerts which may make it easier for front line responders to use. It will require validation and engagement with stakeholders before it can be considered for use.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://mag.ncep.noaa.gov','SCIGOVWS'); return false;" href="http://mag.ncep.noaa.gov"><span>Model Analyses and Guidance</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Fire Weather <em>Sun</em>/Moon Long Range Forecasts Climate Prediction Past Weather Past Weather Heating/Cooling Space Weather <em>Sun</em> (Ultraviolet Radiation) Safety Campaigns Wind Drought Winter Weather Information</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1513210S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1513210S"><span>Identifying needs for streamflow forecasting in the Incomati basin, Southern Africa</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sunday, Robert; Werner, Micha; Masih, Ilyas; van der Zaag, Pieter</p> <p>2013-04-01</p> <p>Despite being widely recognised as an efficient tool in the operational management of water resources, rainfall and streamflow forecasts are currently not utilised in water management practice in the Incomati Basin in Southern Africa. Although, there have been initiatives for forecasting streamflow in the Sabie and Crocodile sub-basins, the outputs of these have found little use because of scepticism on the accuracy and reliability of the information, or the relevance of the information provided to the needs of the water managers. The process of improving these forecasts is underway, but as yet the actual needs of the forecasts are unclear and scope of the ongoing initiatives remains very limited. In this study questionnaires and focused group interviews were used to establish the need, potential use, benefit and required accuracy of rainfall and streamflow forecasts in the Incomati Basin. Thirty five interviews were conducted with professionals engaged in water sector and detailed discussions were held with water institutions, including the Inkomati Catchment Management Agency (ICMA), Komati Basin Water Authority (KOBWA), South African Weather Service (SAWS), water managers, dam operators, water experts, farmers and other water users in the Basin. Survey results show that about 97% of the respondents receive weather forecasts. In contrast to expectations, only 5% have access to the streamflow forecast. In the weather forecast, the most important variables were considered to be rainfall and temperature at daily and weekly time scales. Moreover, forecasts of global climatic indices such as El Niño or La Niña were neither received nor demanded. There was limited demand and/or awareness of flood and drought forecasts including the information on their linkages with global climatic indices. While the majority of respondents indicate the need and indeed use the weather forecast, the provision, communication and interpretation were in general found to be with too little detail and clarity. In some cases this was attributed to the short time and space allotted in media such as television and newspapers respectively. Major uses of the weather forecast were made in personal planning i.e., travelling (29%) and dressing (23%). The usefulness in water sector was reported for water allocation (23%), farming (11%) and flood monitoring (9%), but was considered as a factor having minor influence on the actual decision making in operational water management mainly due to uncertainty of the weather forecast, difference in the time scale and institutional arrangements. In the incidences where streamflow forecasts were received (5% of the cases), its application in decision making was not carried out due to high uncertainty. Moreover, dam operators indicated weekly streamflow forecast as very important in releasing water for agriculture but this was not the format in which forecasts were available to them. Generally, users affirmed the accuracy and benefits of weather forecasts and had no major concerns on the impacts of wrong forecasts. However, respondents indicated the need to improve the accuracy and accessibility of the forecast. Likewise, water managers expressed the need for both rainfall and flow forecasts but indicated that they face hindrances due to financial and human resource constraints. This shows that there is a need to strengthen water related forecasts and the consequent uses in the basin. This can be done through collaboration among forecasting and water organisations such as the SAWS, Research Institutions and users like ICMA, KOBWA and farmers. Collaboration between the meteorology and water resources sectors is important to establish consistent forecast information. The forecasts themselves should be detailed and user specific to ensure these are indeed used and can answer to the needs of the users.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100033126','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100033126"><span>A New Tool for Forecasting Solar Drivers of Severe Space Weather</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Adams, J. H.; Falconer, D.; Barghouty, A. F.; Khazanov, I.; Moore, R.</p> <p>2010-01-01</p> <p>This poster describes a tool that is designed to forecast solar drivers for severe space weather. Since most severe space weather is driven by Solar flares and Coronal Mass Ejections (CMEs) - the strongest of these originate in active regions and are driven by the release of coronal free magnetic energy and There is a positive correlation between an active region's free magnetic energy and the likelihood of flare and CME production therefore we can use this positive correlation as the basis of our empirical space weather forecasting tool. The new tool takes a full disk Michelson Doppler Imager (MDI) magnetogram, identifies strong magnetic field areas, identifies these with NOAA active regions, and measures a free-magnetic-energy proxy. It uses an empirically derived forecasting function to convert the free-magnetic-energy proxy to an expected event rate. It adds up the expected event rates from all active regions on the disk to forecast the expected rate and probability of each class of events -- X-class flares, X&M class flares, CMEs, fast CMEs, and solar particle events (SPEs).</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ThApC.tmp...76J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ThApC.tmp...76J"><span>Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal's Terai with the NCEP CFSv2</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jha, Prakash K.; Athanasiadis, Panos; Gualdi, Silvio; Trabucco, Antonio; Mereu, Valentina; Shelia, Vakhtang; Hoogenboom, Gerrit</p> <p>2018-03-01</p> <p>Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal's Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JHyd..562..502M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JHyd..562..502M"><span>Medium-range reference evapotranspiration forecasts for the contiguous United States based on multi-model numerical weather predictions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medina, Hanoi; Tian, Di; Srivastava, Puneet; Pelosi, Anna; Chirico, Giovanni B.</p> <p>2018-07-01</p> <p>Reference evapotranspiration (ET0) plays a fundamental role in agronomic, forestry, and water resources management. Estimating and forecasting ET0 have long been recognized as a major challenge for researchers and practitioners in these communities. This work explored the potential of multiple leading numerical weather predictions (NWPs) for estimating and forecasting summer ET0 at 101 U.S. Regional Climate Reference Network stations over nine climate regions across the contiguous United States (CONUS). Three leading global NWP model forecasts from THORPEX Interactive Grand Global Ensemble (TIGGE) dataset were used in this study, including the single model ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (EC), the National Centers for Environmental Prediction Global Forecast System (NCEP), and the United Kingdom Meteorological Office forecasts (MO), as well as multi-model ensemble forecasts from the combinations of these NWP models. A regression calibration was employed to bias correct the ET0 forecasts. Impact of individual forecast variables on ET0 forecasts were also evaluated. The results showed that the EC forecasts provided the least error and highest skill and reliability, followed by the MO and NCEP forecasts. The multi-model ensembles constructed from the combination of EC and MO forecasts provided slightly better performance than the single model EC forecasts. The regression process greatly improved ET0 forecast performances, particularly for the regions involving stations near the coast, or with a complex orography. The performance of EC forecasts was only slightly influenced by the size of the ensemble members, particularly at short lead times. Even with less ensemble members, EC still performed better than the other two NWPs. Errors in the radiation forecasts, followed by those in the wind, had the most detrimental effects on the ET0 forecast performances.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA496992','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA496992"><span>Potential Vorticity Analysis of Low Level Thunderstorm Dynamics in an Idealized Supercell Simulation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2009-03-01</p> <p>Severe Weather, Supercell, Weather Research and Forecasting Model , Advanced WRF 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT...27 A. ADVANCED RESEARCH WRF MODEL .................................................27 1. Data, Model Setup, and Methodology...03/11/2006 GFS model run. Top row: 11/12Z initialization. Middle row: 12 hour forecast valid at 12/00Z. Bottom row: 24 hour forecast valid at</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMPA23A4026P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMPA23A4026P"><span>Communicating Climate Change - Weather Forecast Need Assessment and Information Dissemination Mechanism to Farmers in Nepal</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Panthi, J., Sr.</p> <p>2014-12-01</p> <p>Climate Change is becoming one of the major threats to the fragile Himalayan ecosystem. It is affecting all sectors mainly fresh water, agriculture, forest, biodiversity and species. The subsistence agriculture system of Nepal is mainly rain-fed; therefore, climate change and climate extremes do have direct impacts on it. Weather extremes like droughts, floods and landslides long-lasting fog, hot and cold waves are affecting the agriculture sectors of Nepal. As human-induced climate change has already showing its impacts and it is going to be there for a long time to come, it is paramount importance to move towards the adaptation. Early warning system is an effective way for reducing the impacts of disasters. Forecasting of weather parameters (temperature, precipitation, and wind) helps farmers for their preparedness activities. With consultation with farmers and other relevant institutions, a research project was carried out, for the first time in Nepal, to identify the forecast information need to farmers and their dissemination mechanism. Community consultation workshops, key informant survey, and field observations were the techniques used for this research. Two ecological locations: Bageshwori VDC in Banke (plain) and Dhaibung VDC in Rasuwa (mountain) were taken as the pilot sites for this assessment. People in both the districts are dependent highly on agriculture and the weather extremes like hailstone, untimely rainfall; droughts are affecting their agriculture practices. They do not have confidence in the weather forecast information disseminated by the government of Nepal currently being done because it is a general forecast not done for a smaller domain and the forecast is valid only for 24 hours. The weather forecast need to the farmers in both the sites are: rainfall (intensity, duration and time), drought, and hailstone but in Banke, people wished to have the information of heat and cold waves too as they are affecting their wheat and tomato crops respectively the most. The mechanism of dissemination of the forecast information has been identified and agreed as local radio/FM, mobile telephoning to community leader and displaying and daily updating the forecast information in community hoarding boards.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC51C1177W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC51C1177W"><span>Predicting Near-surface Winds with WindNinja for Wind Energy Applications</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wagenbrenner, N. S.; Forthofer, J.; Shannon, K.; Butler, B.</p> <p>2016-12-01</p> <p>WindNinja is a high-resolution diagnostic wind model widely used by operational wildland fire managers to predict how near-surface winds may influence fire behavior. Many of the features which have made WindNinja successful for wildland fire are also important for wind energy applications. Some of these features include flexible runtime options which allow the user to initialize the model with coarser scale weather model forecasts, sparse weather station observations, or a simple domain-average wind for what-if scenarios; built-in data fetchers for required model inputs, including gridded terrain and vegetation data and operational weather model forecasts; relatively fast runtimes on simple hardware; an extremely user-friendly interface; and a number of output format options, including KMZ files for viewing in Google Earth and GeoPDFs which can be viewed in a GIS. The recent addition of a conservation of mass and momentum solver based on OpenFOAM libraries further increases the utility of WindNinja to modelers in the wind energy sector interested not just in mean wind predictions, but also in turbulence metrics. Here we provide an evaluation of WindNinja forecasts based on (1) operational weather model forecasts and (2) weather station observations provided by the MesoWest API. We also compare the high-resolution WindNinja forecasts to the coarser operational weather model forecasts. For this work we will use the High Resolution Rapid Refresh (HRRR) model and the North American Mesoscale (NAM) model. Forecasts will be evaluated with data collected in the Birch Creek valley of eastern Idaho, USA between June-October 2013. Near-surface wind, turbulence data, and vertical wind and temperature profiles were collected at very high spatial resolution during this field campaign specifically for use in evaluating high-resolution wind models like WindNinja. This work demonstrates the ability of WindNinja to generate very high-resolution wind forecasts for wind energy applications and evaluates the forecasts produced by two different initialization methods with data collected in a broad valley surrounded by complex terrain.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150002549','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150002549"><span>Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh</p> <p>2014-01-01</p> <p>Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100023328','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100023328"><span>Performance of Trajectory Models with Wind Uncertainty</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lee, Alan G.; Weygandt, Stephen S.; Schwartz, Barry; Murphy, James R.</p> <p>2009-01-01</p> <p>Typical aircraft trajectory predictors use wind forecasts but do not account for the forecast uncertainty. A method for generating estimates of wind prediction uncertainty is described and its effect on aircraft trajectory prediction uncertainty is investigated. The procedure for estimating the wind prediction uncertainty relies uses a time-lagged ensemble of weather model forecasts from the hourly updated Rapid Update Cycle (RUC) weather prediction system. Forecast uncertainty is estimated using measures of the spread amongst various RUC time-lagged ensemble forecasts. This proof of concept study illustrates the estimated uncertainty and the actual wind errors, and documents the validity of the assumed ensemble-forecast accuracy relationship. Aircraft trajectory predictions are made using RUC winds with provision for the estimated uncertainty. Results for a set of simulated flights indicate this simple approach effectively translates the wind uncertainty estimate into an aircraft trajectory uncertainty. A key strength of the method is the ability to relate uncertainty to specific weather phenomena (contained in the various ensemble members) allowing identification of regional variations in uncertainty.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4063030','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4063030"><span>A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tokumitsu, Masahiro; Ishida, Yoshiteru</p> <p>2014-01-01</p> <p>This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing. PMID:24803190</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24803190','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24803190"><span>A space weather forecasting system with multiple satellites based on a self-recognizing network.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tokumitsu, Masahiro; Ishida, Yoshiteru</p> <p>2014-05-05</p> <p>This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120007126','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120007126"><span>The SPoRT-WRF: Evaluating the Impact of NASA Datasets on Convective Forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zavodsky, Bradley; Kozlowski, Danielle; Case, Jonathan; Molthan, Andrew</p> <p>2012-01-01</p> <p>Short-term Prediction Research and Transition (SPoRT) seeks to improve short-term, regional weather forecasts using unique NASA products and capabilities SPoRT has developed a unique, real-time configuration of the NASA Unified Weather Research and Forecasting (WRF)WRF (ARW) that integrates all SPoRT modeling research data: (1) 2-km SPoRT Sea Surface Temperature (SST) Composite, (2) 3-km LIS with 1-km Greenness Vegetation Fraction (GVFs) (3) 45-km AIRS retrieved profiles. Transitioned this real-time forecast to NOAA's Hazardous Weather Testbed (HWT) as deterministic model at Experimental Forecast Program (EFP). Feedback from forecasters/participants and internal evaluation of SPoRT-WRF shows a cool, dry bias that appears to suppress convection likely related to methodology for assimilation of AIRS profiles Version 2 of the SPoRT-WRF will premier at the 2012 EFP and include NASA physics, cycling data assimilation methodology, better coverage of precipitation forcing, and new GVFs</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27875197','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27875197"><span>Time-Hierarchical Clustering and Visualization of Weather Forecast Ensembles.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ferstl, Florian; Kanzler, Mathias; Rautenhaus, Marc; Westermann, Rudiger</p> <p>2017-01-01</p> <p>We propose a new approach for analyzing the temporal growth of the uncertainty in ensembles of weather forecasts which are started from perturbed but similar initial conditions. As an alternative to traditional approaches in meteorology, which use juxtaposition and animation of spaghetti plots of iso-contours, we make use of contour clustering and provide means to encode forecast dynamics and spread in one single visualization. Based on a given ensemble clustering in a specified time window, we merge clusters in time-reversed order to indicate when and where forecast trajectories start to diverge. We present and compare different visualizations of the resulting time-hierarchical grouping, including space-time surfaces built by connecting cluster representatives over time, and stacked contour variability plots. We demonstrate the effectiveness of our visual encodings with forecast examples of the European Centre for Medium-Range Weather Forecasts, which convey the evolution of specific features in the data as well as the temporally increasing spatial variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.spc.noaa.gov/products/exper/fire_wx','SCIGOVWS'); return false;" href="http://www.spc.noaa.gov/products/exper/fire_wx"><span>Storm Prediction Center Day 3-8 Fire Weather Forecast Issued on May 27,</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>National RADAR Product Archive NOAA Weather Radio Research <em>Non</em>-op. Products Forecast Tools Svr. Tstm information in MS-Word or PDF. Note: Through September 29, 2015 the SPC will issue <em>Experimental</em> Probabilistic</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19880015721','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19880015721"><span>Meteorological and Environmental Inputs to Aviation Systems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Camp, Dennis W. (Editor); Frost, Walter (Editor)</p> <p>1988-01-01</p> <p>Reports on aviation meteorology, most of them informal, are presented by representatives of the National Weather Service, the Bracknell (England) Meteorological Office, the NOAA Wave Propagation Lab., the Fleet Numerical Oceanography Center, and the Aircraft Owners and Pilots Association. Additional presentations are included on aircraft/lidar turbulence comparison, lightning detection and locating systems, objective detection and forecasting of clear air turbulence, comparative verification between the Generalized Exponential Markov (GEM) Model and official aviation terminal forecasts, the evaluation of the Prototype Regional Observation and Forecast System (PROFS) mesoscale weather products, and the FAA/MIT Lincoln Lab. Doppler Weather Radar Program.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMSH51E..07B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMSH51E..07B"><span>A NOAA/SWPC Perspective on Space Weather Forecasts That Fail</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Biesecker, D. A.</p> <p>2014-12-01</p> <p>The Space Weather Prediction Center (SWPC) at NOAA is the Official US source for space weather watches, warning and alerts. These alerts are provided to a breadth of customers covering a range of industries, including electric utilities, airlines, emergency managers, and users of precision GPS to name a few. This talk will review the current tools used by SWPC to forecast geomagnetic storms, solar flares, and solar energetic particle events and present the SWPC performance in each of these areas. We will include a discussion of the current limitations and examples of events that proved difficult to forecast.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140008767','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140008767"><span>Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zhu, Jiang; Stevens, E.; Zavodsky, B. T.; Zhang, X.; Heinrichs, T.; Broderson, D.</p> <p>2014-01-01</p> <p>Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA605125','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA605125"><span>Coordination, Data Management and Enhancement of the International Arctic Buoy Programme (IABP) a US Interagency Arctic Buoy Programme \\201USIABP\\202 contribution to the IABP</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2013-09-30</p> <p>data from the IABP ); 2.) Forecasting weather and sea ice conditions; 3.) Forcing, assimilation and validation of global weather and climate models ...International Arctic Buoy Programme ( IABP ) A US Interagency Arctic Buoy Programme (USIABP) contribution to the IABP Dr. Ignatius G. Rigor Polar...ice motion. These observations are assimilated into Numerical Weather Prediction (NWP) models that are used to forecast weather on synoptic time</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.emc.ncep.noaa.gov','SCIGOVWS'); return false;" href="http://www.emc.ncep.noaa.gov"><span>National Centers for Environmental Prediction</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>SYSTEM CFS CLIMATE FORECAST SYSTEM NAQFC NAQFC MODEL GEFS GLOBAL ENSEMBLE FORECAST SYSTEM HWRF <em>HURRICANE</em> WEATHER RESEARCH and FORECASTING HMON HMON - OPERATIONAL <em>HURRICANE</em> FORECASTING WAVEWATCH III WAVEWATCH III</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010cosp...38.4169J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010cosp...38.4169J"><span>Operational Numerical Weather Prediction at the Met Office and potential ways forward for operational space weather prediction systems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jackson, David</p> <p></p> <p>NICT (National Institute of Information and Communications Technology) has been in charge of space weather forecast service in Japan for more than 20 years. The main target region of the space weather is the geo-space in the vicinity of the Earth where human activities are dominant. In the geo-space, serious damages of satellites, international space stations and astronauts take place caused by energetic particles or electromagnetic disturbances: the origin of the causes is dynamically changing of solar activities. Positioning systems via GPS satellites are also im-portant recently. Since the most significant effect of positioning error comes from disturbances of the ionosphere, it is crucial to estimate time-dependent modulation of the electron density profiles in the ionosphere. NICT is one of the 13 members of the ISES (International Space Environment Service), which is an international assembly of space weather forecast centers under the UNESCO. With help of geo-space environment data exchanging among the member nations, NICT operates daily space weather forecast service every day to provide informa-tion on forecasts of solar flare, geomagnetic disturbances, solar proton event, and radio-wave propagation conditions in the ionosphere. The space weather forecast at NICT is conducted based on the three methodologies: observations, simulations and informatics (OSI model). For real-time or quasi real-time reporting of space weather, we conduct our original observations: Hiraiso solar observatory to monitor the solar activity (solar flare, coronal mass ejection, and so on), domestic ionosonde network, magnetometer HF radar observations in far-east Siberia, and south-east Asia low-latitude ionosonde network (SEALION). Real-time observation data to monitor solar and solar-wind activities are obtained through antennae at NICT from ACE and STEREO satellites. We have a middle-class super-computer (NEC SX-8R) to maintain real-time computer simulations for solar and solar-wind, magnetosphere and ionosphere. The three simulations are directly or indirectly connected each other based on real-time observa-tion data to reproduce a virtual geo-space region on the super-computer. Informatics is a new methodology to make precise forecast of space weather. Based on new information and communication technologies (ICT), it provides more information in both quality and quantity. At NICT, we have been developing a cloud-computing system named "space weather cloud" based on a high-speed network system (JGN2+). Huge-scale distributed storage (1PB), clus-ter computers, visualization systems and other resources are expected to derive new findings and services of space weather forecasting. The final goal of NICT space weather service is to predict near-future space weather conditions and disturbances which will be causes of satellite malfunctions, tele-communication problems, and error of GPS navigations. In the present talk, we introduce our recent activities on the space weather services and discuss how we are going to develop the services from the view points of space science and practical uses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002AGUFMGC21A0152F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002AGUFMGC21A0152F"><span>Communicating Uncertainties in Weather and Climate Information: Results of a National Academies Workshop</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Friday, E.; Barron, E. J.; Elfring, C.; Geller, L.</p> <p>2002-12-01</p> <p>When a major East Coast snowstorm was forecast during the winter of 2001, people began preparing - both the public and the decision-makers responsible for public services. There was an air of urgency, heightened because just the previous year the region had been hit hard by a storm of unpredicted strength. But this time, the storm never materialized and people were left wondering what went "wrong" with the forecast. Did something go wrong or did forecasters just fail to communicate their information in an effective way? Did they convey a sense of the likelihood of the event and keep people up to date as information changed? In the summer of 2001, the National Academies' Board on Atmospheric Sciences and Climate hosted a workshop designed to explore the communication of uncertainty in weather and climate information. Workshop participants examined five case studies that were chosen to illustrate a range of forecast timescales and certainty levels. The cases were: Red River Flood, Grand Forks, April 1997; East Coast Winter Storm, March 2001; Oklahoma-Kansas Tornado Outbreak, May 3, 1999; El Nino 1997-1998, and Climate Change Science, a report issued in 2001. In each of these cases, participants examined who said what, when, to whom, how, and with what effect. The last two cases specifically address climate-related topics. This paper summarizes the final workshop report (Communicating Uncertainties in Weather and Climate Information: Summary of a Workshop, NRC 2002), including an overview of the five cases and lessons learned about communicating uncertainties in weather and climate forecasts. Among other findings, the report stresses that communication and appropriate dissemination of information, including information about uncertainty in the forecasts and the forecaster's confidence in the product, should be an integral, ongoing part of the forecasting process, not an afterthought. Explaining uncertainty should be an integral part of what weather and climate forecasters do and is essential to delivering accurate and useful information.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.emc.ncep.noaa.gov/monsoondesk/modup.php','SCIGOVWS'); return false;" href="http://www.emc.ncep.noaa.gov/monsoondesk/modup.php"><span>National Centers for Environmental Prediction</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Statistics <em>Observational</em> Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar Hurricane Weather <em>Research</em> and Forecast System ANALYSIS FORECAST MODEL GSI Gridpoint Statistical Weather and Climate Prediction (NCWCP) 5830 University <em>Research</em> Court College Park, MD 20740 Page Author</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.emc.ncep.noaa.gov/index.php','SCIGOVWS'); return false;" href="http://www.emc.ncep.noaa.gov/index.php"><span>National Centers for Environmental Prediction</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Statistics <em>Observational</em> Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar WEATHER <em>RESEARCH</em> and FORECASTING HMON HMON - OPERATIONAL HURRICANE FORECASTING WAVEWATCH III WAVEWATCH III Modeling Center NOAA Center for Weather and Climate Prediction (NCWCP) 5830 University <em>Research</em> Court</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1812607K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812607K"><span>Developing a robust methodology for assessing the value of weather/climate services</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Krijnen, Justin; Golding, Nicola; Buontempo, Carlo</p> <p>2016-04-01</p> <p>Increasingly, scientists involved in providing weather and climate services are expected to demonstrate the value of their work for end users in order to justify the costs of developing and delivering these services. This talk will outline different approaches that can be used to assess the socio-economic benefits of weather and climate services, including, among others, willingness to pay and avoided costs. The advantages and limitations of these methods will be discussed and relevant case-studies will be used to illustrate each approach. The choice of valuation method may be influenced by different factors, such as resource and time constraints and the end purposes of the study. In addition, there are important methodological differences which will affect the value assessed. For instance the ultimate value of a weather/climate forecast to a decision-maker will not only depend on forecast accuracy but also on other factors, such as how the forecast is communicated to and consequently interpreted by the end-user. Thus, excluding these additional factors may result in inaccurate socio-economic value estimates. In order to reduce the inaccuracies in this valuation process we propose an approach that assesses how the initial weather/climate forecast information can be incorporated within the value chain of a given sector, taking into account value gains and losses at each stage of the delivery process. By this we aim to more accurately depict the socio-economic benefits of a weather/climate forecast to decision-makers.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110024156','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110024156"><span>Adaptation of Mesoscale Weather Models to Local Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Manobianco, John T.; Taylor, Gregory E.; Case, Jonathan L.; Dianic, Allan V.; Wheeler, Mark W.; Zack, John W.; Nutter, Paul A.</p> <p>2003-01-01</p> <p>Methodologies have been developed for (1) configuring mesoscale numerical weather-prediction models for execution on high-performance computer workstations to make short-range weather forecasts for the vicinity of the Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS) and (2) evaluating the performances of the models as configured. These methodologies have been implemented as part of a continuing effort to improve weather forecasting in support of operations of the U.S. space program. The models, methodologies, and results of the evaluations also have potential value for commercial users who could benefit from tailoring their operations and/or marketing strategies based on accurate predictions of local weather. More specifically, the purpose of developing the methodologies for configuring the models to run on computers at KSC and CCAFS is to provide accurate forecasts of winds, temperature, and such specific thunderstorm-related phenomena as lightning and precipitation. The purpose of developing the evaluation methodologies is to maximize the utility of the models by providing users with assessments of the capabilities and limitations of the models. The models used in this effort thus far include the Mesoscale Atmospheric Simulation System (MASS), the Regional Atmospheric Modeling System (RAMS), and the National Centers for Environmental Prediction Eta Model ( Eta for short). The configuration of the MASS and RAMS is designed to run the models at very high spatial resolution and incorporate local data to resolve fine-scale weather features. Model preprocessors were modified to incorporate surface, ship, buoy, and rawinsonde data as well as data from local wind towers, wind profilers, and conventional or Doppler radars. The overall evaluation of the MASS, Eta, and RAMS was designed to assess the utility of these mesoscale models for satisfying the weather-forecasting needs of the U.S. space program. The evaluation methodology includes objective and subjective verification methodologies. Objective (e.g., statistical) verification of point forecasts is a stringent measure of model performance, but when used alone, it is not usually sufficient for quantifying the value of the overall contribution of the model to the weather-forecasting process. This is especially true for mesoscale models with enhanced spatial and temporal resolution that may be capable of predicting meteorologically consistent, though not necessarily accurate, fine-scale weather phenomena. Therefore, subjective (phenomenological) evaluation, focusing on selected case studies and specific weather features, such as sea breezes and precipitation, has been performed to help quantify the added value that cannot be inferred solely from objective evaluation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ThApC.tmp..147N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ThApC.tmp..147N"><span>The role of atmospheric internal variability on the prediction skill of interannual North Pacific sea-surface temperatures</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Narapusetty, Balachandrudu</p> <p>2017-06-01</p> <p>The sensitivity of the sea-surface temperature (SST) prediction skill to the atmospheric internal variability (weather noise) in the North Pacific (20∘-60∘N;120∘E-80∘W) on decadal timescales is examined using state-of-the-art Climate Forecasting System model version 2 (CFS) and a variation of CFS in an Interactive Ensemble approach (CFSIE), wherein six copies of atmospheric components with different perturbed initial states of CFS are coupled with the same ocean model by exchanging heat, momentum and fresh water fluxes dynamically at the air-sea interface throughout the model integrations. The CFSIE experiments are designed to reduce weather noise and using a few ten-year long forecasts this study shows that reduction in weather noise leads to lower SST forecast skill. To understand the pathways that cause the reduced SST prediction skill, two twenty-year long forecasts produced with CFS and CFSIE for 1980-2000 are analyzed for the ocean subsurface characteristics that influence SST due to the reduction in weather noise in the North Pacific. The heat budget analysis in the oceanic mixed layer across the North Pacific reveals that weather noise significantly impacts the heat transport in the oceanic mixed layer. In the CFSIE forecasts, the reduced weather noise leads to increased variations in heat content due to shallower mixed layer, diminished heat storage and enhanced horizontal heat advection. The enhancement of the heat advection spans from the active Kuroshio regions of the east coast of Japan to the west coast of continental United States and significantly diffuses the basin-wide SST anomaly (SSTA) contrasts and leads to reduction in the SST prediction skill in decadal forecasts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110011476','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011476"><span>Anvil Forecast Tool in the Advanced Weather Interactive Processing System (AWIPS)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barrett, Joe H., III; Hood, Doris</p> <p>2009-01-01</p> <p>Launch Weather Officers (LWOs) from the 45th Weather Squadron (45 WS) and forecasters from the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violating the Lightning Launch Commit Criteria (LLCC) (Krider et al. 2006; Space Shuttle Flight Rules (FR), NASA/JSC 2004)). As a result, the Applied Meteorology Unit (AMU) developed a tool that creates an anvil threat corridor graphic that can be overlaid on satellite imagery using the Meteorological Interactive Data Display System (MIDDS, Short and Wheeler, 2002). The tool helps forecasters estimate the locations of thunderstorm anvils at one, two, and three hours into the future. It has been used extensively in launch and landing operations by both the 45 WS and SMG. The Advanced Weather Interactive Processing System (AWIPS) is now used along with MIDDS for weather analysis and display at SMG. In Phase I of this task, SMG tasked the AMU to transition the tool from MIDDS to AWIPS (Barrett et aI., 2007). For Phase II, SMG requested the AMU make the Anvil Forecast Tool in AWIPS more configurable by creating the capability to read model gridded data from user-defined model files instead of hard-coded files. An NWS local AWIPS application called AGRID was used to accomplish this. In addition, SMG needed to be able to define the pressure levels for the model data, instead of hard-coding the bottom level as 300 mb and the top level as 150 mb. This paper describes the initial development of the Anvil Forecast Tool for MIDDS, followed by the migration of the tool to AWIPS in Phase I. It then gives a detailed presentation of the Phase II improvements to the AWIPS tool.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19740014155&hterms=fruits+vegetables&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dfruits%2Bvegetables','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19740014155&hterms=fruits+vegetables&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dfruits%2Bvegetables"><span>Impact of nowcasting on the production and processing of agricultural crops. [in the US</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dancer, W. S.; Tibbitts, T. W.</p> <p>1973-01-01</p> <p>The value was studied of improved weather information and weather forecasting to farmers, growers, and agricultural processing industries in the United States. The study was undertaken to identify the production and processing operations that could be improved with accurate and timely information on changing weather patterns. Estimates were then made of the potential savings that could be realized with accurate information about the prevailing weather and short term forecasts for up to 12 hours. This weather information has been termed nowcasting. The growing, marketing, and processing operations of the twenty most valuable crops in the United States were studied to determine those operations that are sensitive to short-term weather forecasting. Agricultural extension specialists, research scientists, growers, and representatives of processing industries were consulted and interviewed. The value of the crops included in this survey and their production levels are given. The total value for crops surveyed exceeds 24 billion dollars and represents more than 92 percent of total U.S. crop value.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060052398&hterms=benefit+decision+making&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dbenefit%2Bdecision%2Bmaking','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060052398&hterms=benefit+decision+making&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dbenefit%2Bdecision%2Bmaking"><span>Impact of Probabilistic Weather on Flight Routing Decisions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Sheth, Kapil; Sridhar, Banavar; Mulfinger, Daniel</p> <p>2006-01-01</p> <p>Flight delays in the United States have been found to increase year after year, along with the increase in air traffic. During the four-month period from May through August of 2005, weather related delays accounted for roughly 70% of all reported delays, The current weather prediction in tactical (within 2 hours) timeframe is at manageable levels, however, the state of forecasting weather for strategic (2-6 hours) timeframe is still not dependable for long-term planning. In the absence of reliable severe weather forecasts, the decision-making for flights longer than two hours is challenging. This paper deals with an approach of using probabilistic weather prediction for Traffic Flow Management use, and a general method using this prediction for estimating expected values of flight length and delays in the National Airspace System (NAS). The current state-of-the-art convective weather forecasting is employed to aid the decision makers in arriving at decisions for traffic flow and flight planing. The six-agency effort working on the Next Generation Air Transportation System (NGATS) have considered weather-assimilated decision-making as one of the principal foci out of a list of eight. The weather Integrated Product Team has considered integrated weather information and improved aviation weather forecasts as two of the main efforts (Ref. 1, 2). Recently, research has focused on the concept of operations for strategic traffic flow management (Ref. 3) and how weather data can be integrated for improved decision-making for efficient traffic management initiatives (Ref. 4, 5). An overview of the weather data needs and benefits of various participants in the air traffic system along with available products can be found in Ref. 6. Previous work related to use of weather data in identifying and categorizing pilot intrusions into severe weather regions (Ref. 7, 8) has demonstrated a need for better forecasting in the strategic planning timeframes and moving towards a probabilistic description of weather (Ref. 9). This paper focuses on. specified probability in a local region for flight intrusion/deviation decision-making. The process uses a probabilistic weather description, implements that in a air traffic assessment system to study trajectories of aircraft crossing a cut-off probability contour. This value would be useful for meteorologists in creating optimum distribution profiles for severe weather, Once available, the expected values of flight path and aggregate delays are calculated for efficient operations. The current research, however, does not deal with the issue of multiple cell encounters, as well as echo tops, and will be a topic of future work.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PolSc..13....1G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PolSc..13....1G"><span>Assessment of marine weather forecasts over the Indian sector of Southern Ocean</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gera, Anitha; Mahapatra, D. K.; Sharma, Kuldeep; Prakash, Satya; Mitra, A. K.; Iyengar, G. R.; Rajagopal, E. N.; Anilkumar, N.</p> <p>2017-09-01</p> <p>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.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70162660','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70162660"><span>System designed for issuing landslide alerts in the San Francisco Bay area</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Finley, D.</p> <p>1987-01-01</p> <p>A system for forecasting landslides during major storms has been developed for the San Francisco Bay area by the U.S Geological Survey and was successfully tested during heavy storms in the bay area during February 1986. Based on the forecasts provided by the USGS, the National Weather Service (NWS) included landslide warnings in its regular weather forecasts or in special weather statements transmitted to local radio and television stations and other news media. USGS scientists said the landslide forecasting and warning system for the San Francisco Bay area can be used as a prototype in developing similar systems for other parts of the Nation susceptible to landsliding. Studies show damage from landslides in the United States averages an estimated $1.5 billion per year. </p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20040161135','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20040161135"><span>Tropospheric Airborne Meteorological Data Reporting (TAMDAR) Sensor Development</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Daniels, Taumi S.; Tsoucalas, George; Anderson, Mark; Mulally, Daniel; Moninger, William; Mamrosh, Richard</p> <p>2004-01-01</p> <p>One of the recommendations of the National Aviation Weather Program Council was to expand and institutionalize the generation, dissemination, and use of automated pilot reports (PIREPS) to the full spectrum of the aviation community, including general aviation. In response to this and other similar recommendations, NASA initiated cooperative research into the development of an electronic pilot reporting capability (Daniels 2002). The ultimate goal is to develop a small low-cost sensor, collect useful meteorological observations below 25,000 ft., downlink the data in near real time, and use the data to improve weather forecasts. Primary users of the data include pilots, who are one targeted audience for the improved weather information that will result from the TAMDAR data. The weather data will be disseminated and used to improve aviation safety by providing pilots with enhanced weather situational awareness. In addition, the data will be used to improve the accuracy and timeliness of weather forecasts. Other users include air traffic controllers, flight service stations, and airline weather centers. Additionally, the meteorological data collected by TAMDAR is expected to have a significant positive impact on forecast accuracy for ground based applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMIN33B1816B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMIN33B1816B"><span>Transitioning the Rice Realtime Forecast Models to DSCOVR</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bala, R.; Reiff, P. H.</p> <p>2016-12-01</p> <p>The Rice realtime forecast models of global magnetospheric indices Kp, Dst and AE have been actively running at mms.rice.edu/realtime/forecast.html for nearly a decade now. These neural network models were trained using the ACE archival solar wind data while the near-realtime forecasts are provided using instantaneous upwind solar wind data stream measured at the L1 point through ACE. Additionally, the webpage also provide status of the current space weather condition as an additional resource, updating every ten minutes. Furthermore, the subscribers of our space weather alert system, called `spacalrt', have been receiving email notices based on predefined thresholds. One of the gaps that is currently seen in the Rice neural network models lies in the density dependent models using variants of the solar wind pressure. The anomalous behavior in reporting densities in ACE has been a common issue for some time now. Often such behavior is observed when the solar energetic particle that are associated with solar flares or CMEs are Earth directed. Therefore, it is understood that the subsequent measures of the density reported by ACE will be either very low or, at a minimum, contaminated. Under these circumstances, the density-based Rice models typically underpredict. However, the newly launched DSCOVR satellite will help enhance our prediction models with high-quality data; it has real time space weather data available through the NOAA's Space Weather Prediction Center as of July, 2016. We are in the process of transitioning our forecast operations to include data from DSCOVR while running the original ACE data stream in parallel until it lasts. This paper will compare and contrast the forecasted values from the two satellites. Finally, we will discuss our efforts in providing the forecast products for the Rice space weather website that will be a part of the book on "Machine Learning Techniques for Space Weather" to be published by Elsiever.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120003993','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120003993"><span>Early Transition and Use of VIIRS and GOES-R Products by NWS Forecast Offices</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Fuell, Kevin K.; Smith, Mathew; Jedlovec, Gary</p> <p>2012-01-01</p> <p>The Visible Infrared Imaging Radiometer Suite (VIIRS) on the NPOESS Preparatory Project (NPP) satellite, part of the Joint Polar Satellite System (JPSS), and the ABI and GLM sensors scheduled for the GOES-R geostationary satellite will bring advanced observing capabilities to the operational weather community. The NASA Short-term Prediction Research and Transition (SPoRT) project at Marshall Space Flight Center has been facilitating the use of real-time experimental and research satellite data by NWS Weather Forecast Offices (WFOs) for a number of years to demonstrate the planned capabilities of future sensors to address particular forecast challenges through improve situational awareness and short-term weather forecasts. For the NOAA GOES-R Proving Ground (PG) activity, SPoRT is developing and disseminating selected GOES-R proxy products to collaborating WFOs and National Centers. SPoRT developed the a pseudo-Geostationary Lightning Mapper product and helped in the transition of the Algorithm Working Group (AWG) Convective Initiation (CI) proxy product for the Hazardous Weather Testbed (HWT) Spring Experiment,. Along with its partner WFOs, SPoRT is evaluating MODIS/GOES Hybrid products, which brings ABI-like data sets from existing NASA instrumentation in front of the forecaster for everyday use. The Hybrid uses near real-time MODIS imagery to demonstrate future ABI capabilities, while utilizing standard GOES imagery to provide the temporal frequency of geostationary imagery expected by operational forecasters. In addition, SPoRT is collaborating with the GOES-R hydrology AWG to transition a baseline proxy product for rainfall rate / quantitative precipitation estimate (QPE) to the OCONUS regions. For VIIRS, SPoRT is demonstrating multispectral observing capabilities and the utility of low-light channels not previously available on operational weather satellites to address a variety of weather forecast challenges. This presentation will discuss the results of transitioning these products to collaborating WFOs throughout the country.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMSH44A..02H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMSH44A..02H"><span>The scientific challenges to forecasting and nowcasting the magnetospheric response to space weather (Invited)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hesse, M.; Kuznetsova, M. M.; Birn, J.; Pulkkinen, A. A.</p> <p>2013-12-01</p> <p>Space weather is different from terrestrial weather in an essential way. Terrestrial weather has benefitted from a long history of research, which has led to a deep and detailed level of understanding. In comparison, space weather is scientifically in its infancy. Many key processes in the causal chains from processes on the Sun to space weather effects in various locations in the heliosphere remain either poorly understood or not understood at all. Space weather is therefore, and will remain in the foreseeable future, primarily a research field. Extensive further research efforts are needed before we can reasonably expect the precision and fidelity of weather forecasts. For space weather within the Earth's magnetosphere, the coupling between solar wind and magnetosphere is of crucial importance. While past research has provided answers, often on qualitative levels, to some of the most fundamental questions, answers to some of the latter and the ability to predict quantitatively remain elusive. This presentation will provide an overview of pertinent aspects of solar wind-magnetospheric coupling, its importance for space weather near the Earth, and it will analyze the state of our ability to describe and predict its efficiency. It will conclude with a discussion of research activities, which are aimed at improving our ability to quantitatively forecast coupling processes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://polar.ncep.noaa.gov/seaice/Forecasts.html','SCIGOVWS'); return false;" href="http://polar.ncep.noaa.gov/seaice/Forecasts.html"><span>MMAB Sea Ice Forecast Page</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>verification statistics Grumbine, R. W., <em>Virtual</em> Floe Ice Drift Forecast Model Intercomparison, Weather and Forecasting, 13, 886-890, 1998. MMAB Note: <em>Virtual</em> Floe Ice Drift Forecast Model Intercomparison 1996 pdf ~47</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/12793730','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/12793730"><span>Activities of the Japanese space weather forecast center at Communications Research Laboratory.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Watari, Shinichi; Tomita, Fumihiko</p> <p>2002-12-01</p> <p>The International Space Environment Service (ISES) is an international organization for space weather forecasts and belongs to the International Union of Radio Science (URSI). There are eleven ISES forecast centers in the world, and Communications Research Laboratory (CRL) runs the Japanese one. We make forecasts on the space environment and deliver them over the phones and through the Internet. Our forecasts could be useful for human activities in space. Currently solar activity is near maximum phase of the solar cycle 23. We report the several large disturbances of space environment occurred in 2001, during which low-latitude auroras were observed several times in Japan.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ClDy...40.3089D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ClDy...40.3089D"><span>Calibration and combination of dynamical seasonal forecasts to enhance the value of predicted probabilities for managing risk</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dutton, John A.; James, Richard P.; Ross, Jeremy D.</p> <p>2013-06-01</p> <p>Seasonal probability forecasts produced with numerical dynamics on supercomputers offer great potential value in managing risk and opportunity created by seasonal variability. The skill and reliability of contemporary forecast systems can be increased by calibration methods that use the historical performance of the forecast system to improve the ongoing real-time forecasts. Two calibration methods are applied to seasonal surface temperature forecasts of the US National Weather Service, the European Centre for Medium Range Weather Forecasts, and to a World Climate Service multi-model ensemble created by combining those two forecasts with Bayesian methods. As expected, the multi-model is somewhat more skillful and more reliable than the original models taken alone. The potential value of the multimodel in decision making is illustrated with the profits achieved in simulated trading of a weather derivative. In addition to examining the seasonal models, the article demonstrates that calibrated probability forecasts of weekly average temperatures for leads of 2-4 weeks are also skillful and reliable. The conversion of ensemble forecasts into probability distributions of impact variables is illustrated with degree days derived from the temperature forecasts. Some issues related to loss of stationarity owing to long-term warming are considered. The main conclusion of the article is that properly calibrated probabilistic forecasts possess sufficient skill and reliability to contribute to effective decisions in government and business activities that are sensitive to intraseasonal and seasonal climate variability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016cosp...41E.180B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016cosp...41E.180B"><span>Operational Space Weather Activities in the US</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Berger, Thomas; Singer, Howard; Onsager, Terrance; Viereck, Rodney; Murtagh, William; Rutledge, Robert</p> <p>2016-07-01</p> <p>We review the current activities in the civil operational space weather forecasting enterprise of the United States. The NOAA/Space Weather Prediction Center is the nation's official source of space weather watches, warnings, and alerts, working with partners in the Air Force as well as international operational forecast services to provide predictions, data, and products on a large variety of space weather phenomena and impacts. In October 2015, the White House Office of Science and Technology Policy released the National Space Weather Strategy (NSWS) and associated Space Weather Action Plan (SWAP) that define how the nation will better forecast, mitigate, and respond to an extreme space weather event. The SWAP defines actions involving multiple federal agencies and mandates coordination and collaboration with academia, the private sector, and international bodies to, among other things, develop and sustain an operational space weather observing system; develop and deploy new models of space weather impacts to critical infrastructure systems; define new mechanisms for the transition of research models to operations and to ensure that the research community is supported for, and has access to, operational model upgrade paths; and to enhance fundamental understanding of space weather through support of research models and observations. The SWAP will guide significant aspects of space weather operational and research activities for the next decade, with opportunities to revisit the strategy in the coming years through the auspices of the National Science and Technology Council.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1113083S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1113083S"><span>Wintertime component of the THORPEX Pacific-Asian Regional Campaign (T-PARC)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Song, Y.; Toth, Z.; Asuma, Y.; Reynolds, C.; Lngland, R.; Szunyogh, I.; Colle, B.; Chang, E.; Doyle, C.; Kats, A.</p> <p>2009-04-01</p> <p>The winter component of the T-PARC is an international field project that aims at improving high impact weather event forecasts for North America. The main objective is to understand how perturbations from the tropics, Eurasia and polar fronts travel through waveguide and turn into high impact weather events. Through adaptive observations by using manned aircrafts (NOAA G-IV and US Air force C-130s) and Russian rawinsonde network over data sparse regions, it is expected that accurate initial conditions will improve the numerical weather forecasts. Non-adaptive aircraft measurements over the Pacific Rim and part of India are also deployed through E-AMDAR program, which is expected to improve the background field over Asia where perturbations are initiated. The campaign is led by NOAA and joined by agencies and universities from US, Canada, Mexico, Japan, ECWMF, and Russia. While most observational data will be assimilated by operational centers to improve real time numerical weather predictions, post field studies will focus on aspects such as: data impact on forecast and analysis, dry and moist processes that affect the formation and propagation of perturbations, meso-scale storm structure, error growth, forecast "busts" under certain atmospheric regimes, and socio-economic applications such as costs and benefits of improved forecasts and their use by the public for high impact weather events. In particular, a Winter Olympics demonstration project (February 12 - February 28) is expected to be a test bed during winter T-PARC for real user outreach and application purposes. Effectiveness of existing targeting methods as well as new targeting methods in the 3-5 day lead time range will be pursued and other aspects related to data assimilation and numerical forecasts (both deterministic and ensemble forecasts) will be investigated within this project as well.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1914150G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1914150G"><span>Forecasting of wet snow avalanche activity: Proof of concept and operational implementation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gobiet, Andreas; Jöbstl, Lisa; Rieder, Hannes; Bellaire, Sascha; Mitterer, Christoph</p> <p>2017-04-01</p> <p>State-of-the-art tools for the operational assessment of avalanche danger include field observations, recordings from automatic weather stations, meteorological analyses and forecasts, and recently also indices derived from snowpack models. In particular, an index for identifying the onset of wet-snow avalanche cycles (LWCindex), has been demonstrated to be useful. However, its value for operational avalanche forecasting is currently limited, since detailed, physically based snowpack models are usually driven by meteorological data from automatic weather stations only and have therefore no prognostic ability. Since avalanche risk management heavily relies on timely information and early warnings, many avalanche services in Europe nowadays start issuing forecasts for the following days, instead of the traditional assessment of the current avalanche danger. In this context, the prognostic operation of detailed snowpack models has recently been objective of extensive research. In this study a new, observationally constrained setup for forecasting the onset of wet-snow avalanche cycles with the detailed snow cover model SNOWPACK is presented and evaluated. Based on data from weather stations and different numerical weather prediction models, we demonstrate that forecasts of the LWCindex as indicator for wet-snow avalanche cycles can be useful for operational warning services, but is so far not reliable enough to be used as single warning tool without considering other factors. Therefore, further development currently focuses on the improvement of the forecasts by applying ensemble techniques and suitable post processing approaches to the output of numerical weather prediction models. In parallel, the prognostic meteo-snow model chain is operationally used by two regional avalanche warning services in Austria since winter 2016/2017 for the first time. Experiences from the first operational season and first results from current model developments will be reported.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA589390','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA589390"><span>An Automated Weather Research and Forecasting (WRF)-Based Nowcasting System: Software Description</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2013-10-01</p> <p>14. ABSTRACT A Web service /Web interface software package has been engineered to address the need for an automated means to run the Weather Research...An Automated Weather Research and Forecasting (WRF)- Based Nowcasting System: Software Description by Stephen F. Kirby, Brian P. Reen, and...Based Nowcasting System: Software Description Stephen F. Kirby, Brian P. Reen, and Robert E. Dumais Jr. Computational and Information Sciences</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_14 --> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1513709R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1513709R"><span>GEOSS interoperability for Weather, Ocean and Water</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Richardson, David; Nyenhuis, Michael; Zsoter, Ervin; Pappenberger, Florian</p> <p>2013-04-01</p> <p>"Understanding the Earth system — its weather, climate, oceans, atmosphere, water, land, geodynamics, natural resources, ecosystems, and natural and human-induced hazards — is crucial to enhancing human health, safety and welfare, alleviating human suffering including poverty, protecting the global environment, reducing disaster losses, and achieving sustainable development. Observations of the Earth system constitute critical input for advancing this understanding." With this in mind, the Group on Earth Observations (GEO) started implementing the Global Earth Observation System of Systems (GEOSS). GEOWOW, short for "GEOSS interoperability for Weather, Ocean and Water", is supporting this objective. GEOWOW's main challenge is to improve Earth observation data discovery, accessibility and exploitability, and to evolve GEOSS in terms of interoperability, standardization and functionality. One of the main goals behind the GEOWOW project is to demonstrate the value of the TIGGE archive in interdisciplinary applications, providing a vast amount of useful and easily accessible information to the users through the GEO Common Infrastructure (GCI). GEOWOW aims at developing funcionalities that will allow easy discovery, access and use of TIGGE archive data and of in-situ observations, e.g. from the Global Runoff Data Centre (GRDC), to support applications such as river discharge forecasting.TIGGE (THORPEX Interactive Grand Global Ensemble) is a key component of THORPEX: a World Weather Research Programme to accelerate the improvements in the accuracy of 1-day to 2 week high-impact weather forecasts for the benefit of humanity. The TIGGE archive consists of ensemble weather forecast data from ten global NWP centres, starting from October 2006, which has been made available for scientific research. The TIGGE archive has been used to analyse hydro-meteorological forecasts of flooding in Europe as well as in China. In general the analysis has been favourable in terms of forecast skill and concluded that the use of a multi-model forecast is beneficial. Long term analysis of individual centres, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), has been conducted in the past. However, no long term and large scale study has been performed so far with inclusion of different global numerical models. Here we present some initial results from such a study.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1614739W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1614739W"><span>Sensitivity of a Simulated Derecho Event to Model Initial Conditions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Wei</p> <p>2014-05-01</p> <p>Since 2003, the MMM division at NCAR has been experimenting cloud-permitting scale weather forecasting using Weather Research and Forecasting (WRF) model. Over the years, we've tested different model physics, and tried different initial and boundary conditions. Not surprisingly, we found that the model's forecasts are more sensitive to the initial conditions than model physics. In 2012 real-time experiment, WRF-DART (Data Assimilation Research Testbed) at 15 km was employed to produce initial conditions for twice-a-day forecast at 3 km. On June 29, this forecast system captured one of the most destructive derecho event on record. In this presentation, we will examine forecast sensitivity to different model initial conditions, and try to understand the important features that may contribute to the success of the forecast.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.U34A..02L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.U34A..02L"><span>How Satellites Have Contributed to Building a Weather Ready Nation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lapenta, W.</p> <p>2017-12-01</p> <p>NOAA's primary mission since its inception has been to reduce the loss of life and property, as well as disruptions from, high impact weather and water-related events. In recent years, significant societal losses resulting even from well forecast extreme events have shifted attention from the forecast alone toward ensuring societal response is equal to the risks that exist for communities, businesses and the public. The responses relate to decisions ranging from coastal communities planning years in advance to mitigate impacts from rising sea level, to immediate lifesaving decisions such as a family seeking adequate shelter during a tornado warning. NOAA is committed to building a "Weather-Ready Nation" where communities are prepared for and respond appropriately to these events. The Weather-Ready Nation (WRN) strategic priority is building community resilience in the face of increasing vulnerability to extreme weather, water, climate and environmental threats. To build a Weather-Ready Nation, NOAA is enhancing Impact-Based Decision Support Services (IDSS), transitioning science and technology advances into forecast operations, applying social science research to improve the communication and usefulness of information, and expanding its dissemination efforts to achieve far-reaching readiness, responsiveness and resilience. These four components of Weather-Ready Nation are helping ensure NOAA data, products and services are fully utilized to minimize societal impacts from extreme events. Satellite data and satellite products have been important elements of the national Weather Service (NWS) operations for more than 40 years. When one examines the uses of satellite data specific to the internal forecast and warning operations of NWS, two main applications are evident. The first is the use of satellite data in numerical weather prediction models; the second is the use of satellite imagery and derived products for mesoscale and short-range weather warning and prediction. The purpose of this paper is to highlight the value of the satellite component of the global observing system to NWS operational weather forecasting and emphasize how these data form a critical component of the NWS ability to protect life and property and ensure economic well-being.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010cosp...38.4168M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010cosp...38.4168M"><span>Activities of NICT space weather project</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Murata, Ken T.; Nagatsuma, Tsutomu; Watari, Shinichi; Shinagawa, Hiroyuki; Ishii, Mamoru</p> <p></p> <p>NICT (National Institute of Information and Communications Technology) has been in charge of space weather forecast service in Japan for more than 20 years. The main target region of the space weather is the geo-space in the vicinity of the Earth where human activities are dominant. In the geo-space, serious damages of satellites, international space stations and astronauts take place caused by energetic particles or electromagnetic disturbances: the origin of the causes is dynamically changing of solar activities. Positioning systems via GPS satellites are also im-portant recently. Since the most significant effect of positioning error comes from disturbances of the ionosphere, it is crucial to estimate time-dependent modulation of the electron density profiles in the ionosphere. NICT is one of the 13 members of the ISES (International Space Environment Service), which is an international assembly of space weather forecast centers under the UNESCO. With help of geo-space environment data exchanging among the member nations, NICT operates daily space weather forecast service every day to provide informa-tion on forecasts of solar flare, geomagnetic disturbances, solar proton event, and radio-wave propagation conditions in the ionosphere. The space weather forecast at NICT is conducted based on the three methodologies: observations, simulations and informatics (OSI model). For real-time or quasi real-time reporting of space weather, we conduct our original observations: Hiraiso solar observatory to monitor the solar activity (solar flare, coronal mass ejection, and so on), domestic ionosonde network, magnetometer HF radar observations in far-east Siberia, and south-east Asia low-latitude ionosonde network (SEALION). Real-time observation data to monitor solar and solar-wind activities are obtained through antennae at NICT from ACE and STEREO satellites. We have a middle-class super-computer (NEC SX-8R) to maintain real-time computer simulations for solar and solar-wind, magnetosphere and ionosphere. The three simulations are directly or indirectly connected each other based on real-time observa-tion data to reproduce a virtual geo-space region on the super-computer. Informatics is a new methodology to make precise forecast of space weather. Based on new information and communication technologies (ICT), it provides more information in both quality and quantity. At NICT, we have been developing a cloud-computing system named "space weather cloud" based on a high-speed network system (JGN2+). Huge-scale distributed storage (1PB), clus-ter computers, visualization systems and other resources are expected to derive new findings and services of space weather forecasting. The final goal of NICT space weather service is to predict near-future space weather conditions and disturbances which will be causes of satellite malfunctions, tele-communication problems, and error of GPS navigations. In the present talk, we introduce our recent activities on the space weather services and discuss how we are going to develop the services from the view points of space science and practical uses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20060053300','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20060053300"><span>Convective Weather Forecast Quality Metrics for Air Traffic Management Decision-Making</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chatterji, Gano B.; Gyarfas, Brett; Chan, William N.; Meyn, Larry A.</p> <p>2006-01-01</p> <p>Since numerical weather prediction models are unable to accurately forecast the severity and the location of the storm cells several hours into the future when compared with observation data, there has been a growing interest in probabilistic description of convective weather. The classical approach for generating uncertainty bounds consists of integrating the state equations and covariance propagation equations forward in time. This step is readily recognized as the process update step of the Kalman Filter algorithm. The second well known method, known as the Monte Carlo method, consists of generating output samples by driving the forecast algorithm with input samples selected from distributions. The statistical properties of the distributions of the output samples are then used for defining the uncertainty bounds of the output variables. This method is computationally expensive for a complex model compared to the covariance propagation method. The main advantage of the Monte Carlo method is that a complex non-linear model can be easily handled. Recently, a few different methods for probabilistic forecasting have appeared in the literature. A method for computing probability of convection in a region using forecast data is described in Ref. 5. Probability at a grid location is computed as the fraction of grid points, within a box of specified dimensions around the grid location, with forecast convection precipitation exceeding a specified threshold. The main limitation of this method is that the results are dependent on the chosen dimensions of the box. The examples presented Ref. 5 show that this process is equivalent to low-pass filtering of the forecast data with a finite support spatial filter. References 6 and 7 describe the technique for computing percentage coverage within a 92 x 92 square-kilometer box and assigning the value to the center 4 x 4 square-kilometer box. This technique is same as that described in Ref. 5. Characterizing the forecast, following the process described in Refs. 5 through 7, in terms of percentage coverage or confidence level is notionally sound compared to characterizing in terms of probabilities because the probability of the forecast being correct can only be determined using actual observations. References 5 through 7 only use the forecast data and not the observations. The method for computing the probability of detection, false alarm ratio and several forecast quality metrics (Skill Scores) using both the forecast and observation data are given in Ref. 2. This paper extends the statistical verification method in Ref. 2 to determine co-occurrence probabilities. The method consists of computing the probability that a severe weather cell (grid location) is detected in the observation data in the neighborhood of the severe weather cell in the forecast data. Probabilities of occurrence at the grid location and in its neighborhood with higher severity, and with lower severity in the observation data compared to that in the forecast data are examined. The method proposed in Refs. 5 through 7 is used for computing the probability that a certain number of cells in the neighborhood of severe weather cells in the forecast data are seen as severe weather cells in the observation data. Finally, the probability of existence of gaps in the observation data in the neighborhood of severe weather cells in forecast data is computed. Gaps are defined as openings between severe weather cells through which an aircraft can safely fly to its intended destination. The rest of the paper is organized as follows. Section II summarizes the statistical verification method described in Ref. 2. The extension of this method for computing the co-occurrence probabilities in discussed in Section HI. Numerical examples using NCWF forecast data and NCWD observation data are presented in Section III to elucidate the characteristics of the co-occurrence probabilities. This section also discusses the procedure for computing throbabilities that the severity of convection in the observation data will be higher or lower in the neighborhood of grid locations compared to that indicated at the grid locations in the forecast data. The probability of coverage of neighborhood grid cells is also described via examples in this section. Section IV discusses the gap detection algorithm and presents a numerical example to illustrate the method. The locations of the detected gaps in the observation data are used along with the locations of convective weather cells in the forecast data to determine the probability of existence of gaps in the neighborhood of these cells. Finally, the paper is concluded in Section V.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110012804','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110012804"><span>4-D Cloud Water Content Fields Derived from Operational Satellite Data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Smith, William L., Jr.; Minnis, Patrick</p> <p>2010-01-01</p> <p>In order to improve operational safety and efficiency, the transportation industry, including aviation, has an urgent need for accurate diagnoses and predictions of clouds and associated weather conditions. Adverse weather accounts for 70% of all air traffic delays within the U.S. National Airspace System. The Federal Aviation Administration has determined that as much as two thirds of weather-related delays are potentially avoidable with better weather information and roughly 20% of all aviation accidents are weather related. Thus, it is recognized that an important factor in meeting the goals of the Next Generation Transportation System (NexGen) vision is the improved integration of weather information. The concept of a 4-D weather cube is being developed to address that need by integrating observed and forecasted weather information into a shared 4-D database, providing an integrated and nationally consistent weather picture for a variety of users and to support operational decision support systems. Weather analyses and forecasts derived using Numerical Weather Prediction (NWP) models are a critical tool that forecasters rely on for guidance and also an important element in current and future decision support systems. For example, the Rapid Update Cycle (RUC) and the recently implemented Rapid Refresh (RR) Weather Research and Forecast (WRF) models provide high frequency forecasts and are key elements of the FAA Aviation Weather Research Program. Because clouds play a crucial role in the dynamics and thermodynamics of the atmosphere, they must be adequately accounted for in NWP models. The RUC, for example, cycles at full resolution five cloud microphysical species (cloud water, cloud ice, rain, snow, and graupel) and has the capability of updating these fields from observations. In order to improve the models initial state and subsequent forecasts, cloud top altitude (or temperature, T(sub c)) derived from operational satellite data, surface observations of cloud base altitude, radar reflectivity, and lightning data are used to help build and remove clouds in the models assimilation system. Despite this advance and the many recent advances made in our understanding of cloud physical processes and radiative effects, many problems remain in adequately representing clouds in models. While the assimilation of cloud top information derived from operational satellite data has merit, other information is available that has not yet been exploited. For example, the vertically integrated cloud water content (CWC) or cloud water path (CWP) and cloud geometric thickness (delta Z) are standard products being derived routinely from operational satellite data. These and other cloud products have been validated under a variety of conditions. Since the uncertainties have generally been found to be less than those found in model analyses and forecasts, the satellite products should be suitable for data assimilation, provided an appropriate strategy can be developed that links the satellite-derived cloud parameters with cloud parameters specified in the model. In this paper, we briefly outline such a strategy and describe a methodology to retrieve cloud water content profiles from operational satellite data. Initial results and future plans are presented. It is expected that the direct assimilation of this new product will provide the most accurate depiction of the vertical distribution of cloud water ever produced at the high spatial and temporal resolution needed for short term weather analyses and forecasts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MAP...130..265T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MAP...130..265T"><span>Fuzzy logic-based analogue forecasting and hybrid modelling of horizontal visibility</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tuba, Zoltán; Bottyán, Zsolt</p> <p>2018-04-01</p> <p>Forecasting visibility is one of the greatest challenges in aviation meteorology. At the same time, high accuracy visibility forecasts can significantly reduce or make avoidable weather-related risk in aviation as well. To improve forecasting visibility, this research links fuzzy logic-based analogue forecasting and post-processed numerical weather prediction model outputs in hybrid forecast. Performance of analogue forecasting model was improved by the application of Analytic Hierarchy Process. Then, linear combination of the mentioned outputs was applied to create ultra-short term hybrid visibility prediction which gradually shifts the focus from statistical to numerical products taking their advantages during the forecast period. It gives the opportunity to bring closer the numerical visibility forecast to the observations even it is wrong initially. Complete verification of categorical forecasts was carried out; results are available for persistence and terminal aerodrome forecasts (TAF) as well in order to compare. The average value of Heidke Skill Score (HSS) of examined airports of analogue and hybrid forecasts shows very similar results even at the end of forecast period where the rate of analogue prediction in the final hybrid output is 0.1-0.2 only. However, in case of poor visibility (1000-2500 m), hybrid (0.65) and analogue forecasts (0.64) have similar average of HSS in the first 6 h of forecast period, and have better performance than persistence (0.60) or TAF (0.56). Important achievement that hybrid model takes into consideration physics and dynamics of the atmosphere due to the increasing part of the numerical weather prediction. In spite of this, its performance is similar to the most effective visibility forecasting methods and does not follow the poor verification results of clearly numerical outputs.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EOSTr..93R.153M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EOSTr..93R.153M"><span>Transforming community access to space science models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>MacNeice, Peter; Hesse, Michael; Kuznetsova, Maria; Maddox, Marlo; Rastaetter, Lutz; Berrios, David; Pulkkinen, Antti</p> <p>2012-04-01</p> <p>Researching and forecasting the ever changing space environment (often referred to as space weather) and its influence on humans and their activities are model-intensive disciplines. This is true because the physical processes involved are complex, but, in contrast to terrestrial weather, the supporting observations are typically sparse. Models play a vital role in establishing a physically meaningful context for interpreting limited observations, testing theory, and producing both nowcasts and forecasts. For example, with accurate forecasting of hazardous space weather conditions, spacecraft operators can place sensitive systems in safe modes, and power utilities can protect critical network components from damage caused by large currents induced in transmission lines by geomagnetic storms.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140000907','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140000907"><span>Transforming Community Access to Space Science Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>MacNeice, Peter; Heese, Michael; Kunetsova, Maria; Maddox, Marlo; Rastaetter, Lutz; Berrios, David; Pulkkinen, Antti</p> <p>2012-01-01</p> <p>Researching and forecasting the ever changing space environment (often referred to as space weather) and its influence on humans and their activities are model-intensive disciplines. This is true because the physical processes involved are complex, but, in contrast to terrestrial weather, the supporting observations are typically sparse. Models play a vital role in establishing a physically meaningful context for interpreting limited observations, testing theory, and producing both nowcasts and forecasts. For example, with accurate forecasting of hazardous space weather conditions, spacecraft operators can place sensitive systems in safe modes, and power utilities can protect critical network components from damage caused by large currents induced in transmission lines by geomagnetic storms.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://images.nasa.gov/#/details-NHQ_2017_1118_NASA+Launches+NOAA+Weather+Satellite+to+Improve+Forecasts.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-NHQ_2017_1118_NASA+Launches+NOAA+Weather+Satellite+to+Improve+Forecasts.html"><span>NASA Launches NOAA Weather Satellite to Improve Forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2017-11-18</p> <p>Early on the morning of Saturday, Nov. 18, NASA successfully launched for the National Oceanic and Atmospheric Administration (NOAA) the first in a series of four advanced polar-orbiting satellites, equipped with next-generation technology and designed to improve the accuracy of U.S. weather forecasts out to seven days. The Joint Polar Satellite System-1 (JPSS-1) lifted off on a United Launch Alliance Delta II rocket from Vandenberg Air Force Base on California’s central coast. JPSS-1 data will improve weather forecasting and help agencies involved with post-storm recovery by visualizing storm damage and the geographic extent of power outages.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=apple+AND+IBM&id=EJ384548','ERIC'); return false;" href="https://eric.ed.gov/?q=apple+AND+IBM&id=EJ384548"><span>Software Reviews: Programs Worth a Second Look.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Olds, Henry F., Jr.; And Others</p> <p>1988-01-01</p> <p>Examines four software packages: (1) "Wordbench"--writing and word processing, grades 9-12 (IBM and Apple); (2) "Muppet Slate"--language arts, grades K-2 (Apple); (3) "Accu-Weather Forecaster"--weather analysis and forecasting, grades 3-12 (modem with IBM or Mac); and (4) "The Ripple That Changed American…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/om/marine/media.htm','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/om/marine/media.htm"><span>MASS MEDIA</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Office Marine, Tropical, and Tsunami Services Branch Items of Interest Marine Forecasts <em>Text</em>, Graphic , either directly or as a Secondary Audio Program (SAP). Scrolling of NWS <em>text</em> forecasts via specialized cable TV weather channels is becoming increasingly commonplace. In the case of severe weather, <em>text</em> is</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018LPICo2063.3050D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018LPICo2063.3050D"><span>Using DSG to Build the Capability of Space Weather Forecasting in Deep Space</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>DeLuca, E. E.; Golub, L.; Korreck, K.; Savage, S.; McKenzie, D. D.; Rachmeler, L.; Winebarger, A.; Martens, P.</p> <p>2018-02-01</p> <p>The prospect of astronaut missions to deep space and off the Sun-Earth line raises new challenges for space weather awareness and forecasting. We need to identify the requirements and pathways that will allow us to protect human life and equipment.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110024052','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110024052"><span>Severe Weather Forecast Decision Aid</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bauman, William H., III; Wheeler, Mark</p> <p>2005-01-01</p> <p>The Applied Meteorology Unit developed a forecast tool that provides an assessment of the likelihood of local convective severe weather for the day in order to enhance protection of personnel and material assets of the 45th Space Wing Cape Canaveral Air Force Station (CCAFS), and Kennedy Space Center (KSC).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/om/marine/inmarsat.htm','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/om/marine/inmarsat.htm"><span>INMARSAT-C SafetyNET</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Tsunamis 406 EPIRB's National Weather Service Marine Forecasts INMARSAT-<em>C</em> SafetyNET Marine Forecast Offices greater danger near shore or any shallow waters? NATIONAL WEATHER SERVICE PRODUCTS VIA INMARSAT-<em>C</em> SafetyNET Inmarsat-<em>C</em> SafetyNET is an internationally adopted, automated satellite system for promulgating</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.prh.noaa.gov/guam','SCIGOVWS'); return false;" href="http://www.prh.noaa.gov/guam"><span>National Weather Service Forecast Office Guam Home</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>National Alerts Text Current Conditions Observations Satellite Hydrology River & Lake AHPS <em>Radar</em> Imagery AAFB (Guam) AAFB (Guam) Dial up CONUS <em>Radar</em> Forecasts Activity Planner Guam Public Marine Aviation ; Weather Topics: Local Alerts, Current Conditions, <em>Radar</em>, Satellite, Climate, W-GUM.Webmaster@noaa.gov</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16..278L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16..278L"><span>Use of medium-range weather forecasts for drought mitigation and adaptation under a Mediterranean area</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lahlou, Ouiam; Imani, Yasmina; Bennasser Alaoui, Si; Dutra, Emanuel; DiGiuseppe, Francesca; Pappenberger, Florian; Wetterhall, Fredrik</p> <p>2014-05-01</p> <p>Use of medium-range weather forecasts for drought mitigation and adaptation under a Mediterranean area Authors: Ouiam Lahlou1, Yasmina Imani1, Si Bennasser Alaoui1, Emmanuel Dutra 2, Francesca Di Guiseppe2, Florian Pappenberger2, Fredrik Wetterhall2 1: Institut Agronomique et Vétérinaire Hassan II (IAV Hassan II) 2: European Center for Medium-Range Weather Forecasts (ECMWF) The main pillar of economic development in Morocco is the agricultural sector employing 40% of the active workforce. Agriculture is still mainly dominated by rainfed agriculture which is vulnerable to an increasing frequency and severity of drought events. In rainfed agriculture, there are few interventions possible once crops are planted. Medium to long range weather forecasts could therefore provide valid information for crop selection and sowing time at the onset of the yield season and later to plan mitigation measures during dry-spell episodes. More than 600 daily forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasting system were analyzed in terms of probabilistic skills scores. Results show that, while daily and weekly accumulated precipitation are poorly predicted there is good skill in the forecast of occurrence and extent of dry periods. The availability of this information to decision makers in the agricultural sector would mean moving from a reactive drought management plan to a proactive one. This is very important, especially for the remote areas where often the needed help comes late. A simulation case-study involving farmers who were made aware of the availability of forecasts for the next seasons, show that medium-range forecasts will allow i) governments and relief agencies to position themselves for more effective and cost-efficient drought interventions, ii) producers to be more aware of their production options and insure their payment rate, iii) Herders, to cope with higher food costs for their cattle iv) farmers to better plan the pre-season agronomic corrections, to schedule the most appropriate timing for the unique complementary irrigation that they can provide to cereals, and to better schedule the harvesting date. Since failing on these mitigation actions due to a lack of forecast availability would be highly priced for the rural Marocco economy, we stress that forecasting drought onset, especially under the high variability of the Mediterranean climate, is of a paramount importance.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100036254','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100036254"><span>Maintaining a Local Data Integration System in Support of Weather Forecast Operations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian</p> <p>2010-01-01</p> <p>Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) at Johnson Space Center in Houston, TX have used a local data integration system (LDIS) as part of their forecast and warning operations. The original LDIS was developed by NASA's Applied Meteorology Unit (AMU; Bauman et ai, 2004) in 1998 (Manobianco and Case 1998) and has undergone subsequent improvements. Each has benefited from three-dimensional (3-D) analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive understanding of evolving fine-scale weather features</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011TellA..63..550G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011TellA..63..550G"><span>Predictability of short-range forecasting: a multimodel approach</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>García-Moya, Jose-Antonio; Callado, Alfons; Escribà, Pau; Santos, Carlos; Santos-Muñoz, Daniel; Simarro, Juan</p> <p>2011-05-01</p> <p>Numerical weather prediction (NWP) models (including mesoscale) have limitations when it comes to dealing with severe weather events because extreme weather is highly unpredictable, even in the short range. A probabilistic forecast based on an ensemble of slightly different model runs may help to address this issue. Among other ensemble techniques, Multimodel ensemble prediction systems (EPSs) are proving to be useful for adding probabilistic value to mesoscale deterministic models. A Multimodel Short Range Ensemble Prediction System (SREPS) focused on forecasting the weather up to 72 h has been developed at the Spanish Meteorological Service (AEMET). The system uses five different limited area models (LAMs), namely HIRLAM (HIRLAM Consortium), HRM (DWD), the UM (UKMO), MM5 (PSU/NCAR) and COSMO (COSMO Consortium). These models run with initial and boundary conditions provided by five different global deterministic models, namely IFS (ECMWF), UM (UKMO), GME (DWD), GFS (NCEP) and CMC (MSC). AEMET-SREPS (AE) validation on the large-scale flow, using ECMWF analysis, shows a consistent and slightly underdispersive system. For surface parameters, the system shows high skill forecasting binary events. 24-h precipitation probabilistic forecasts are verified using an up-scaling grid of observations from European high-resolution precipitation networks, and compared with ECMWF-EPS (EC).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26213516','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26213516"><span>The main pillar: Assessment of space weather observational asset performance supporting nowcasting, forecasting, and research to operations.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Posner, A; Hesse, M; St Cyr, O C</p> <p>2014-04-01</p> <p>Space weather forecasting critically depends upon availability of timely and reliable observational data. It is therefore particularly important to understand how existing and newly planned observational assets perform during periods of severe space weather. Extreme space weather creates challenging conditions under which instrumentation and spacecraft may be impeded or in which parameters reach values that are outside the nominal observational range. This paper analyzes existing and upcoming observational capabilities for forecasting, and discusses how the findings may impact space weather research and its transition to operations. A single limitation to the assessment is lack of information provided to us on radiation monitor performance, which caused us not to fully assess (i.e., not assess short term) radiation storm forecasting. The assessment finds that at least two widely spaced coronagraphs including L4 would provide reliability for Earth-bound CMEs. Furthermore, all magnetic field measurements assessed fully meet requirements. However, with current or even with near term new assets in place, in the worst-case scenario there could be a near-complete lack of key near-real-time solar wind plasma data of severe disturbances heading toward and impacting Earth's magnetosphere. Models that attempt to simulate the effects of these disturbances in near real time or with archival data require solar wind plasma observations as input. Moreover, the study finds that near-future observational assets will be less capable of advancing the understanding of extreme geomagnetic disturbances at Earth, which might make the resulting space weather models unsuitable for transition to operations. Manuscript assesses current and near-future space weather assetsCurrent assets unreliable for forecasting of severe geomagnetic stormsNear-future assets will not improve the situation.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4508931','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4508931"><span>The main pillar: Assessment of space weather observational asset performance supporting nowcasting, forecasting, and research to operations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Posner, A; Hesse, M; St Cyr, O C</p> <p>2014-01-01</p> <p>Space weather forecasting critically depends upon availability of timely and reliable observational data. It is therefore particularly important to understand how existing and newly planned observational assets perform during periods of severe space weather. Extreme space weather creates challenging conditions under which instrumentation and spacecraft may be impeded or in which parameters reach values that are outside the nominal observational range. This paper analyzes existing and upcoming observational capabilities for forecasting, and discusses how the findings may impact space weather research and its transition to operations. A single limitation to the assessment is lack of information provided to us on radiation monitor performance, which caused us not to fully assess (i.e., not assess short term) radiation storm forecasting. The assessment finds that at least two widely spaced coronagraphs including L4 would provide reliability for Earth-bound CMEs. Furthermore, all magnetic field measurements assessed fully meet requirements. However, with current or even with near term new assets in place, in the worst-case scenario there could be a near-complete lack of key near-real-time solar wind plasma data of severe disturbances heading toward and impacting Earth's magnetosphere. Models that attempt to simulate the effects of these disturbances in near real time or with archival data require solar wind plasma observations as input. Moreover, the study finds that near-future observational assets will be less capable of advancing the understanding of extreme geomagnetic disturbances at Earth, which might make the resulting space weather models unsuitable for transition to operations. Key Points Manuscript assesses current and near-future space weather assets Current assets unreliable for forecasting of severe geomagnetic storms Near-future assets will not improve the situation PMID:26213516</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A11B0014Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A11B0014Z"><span>An evaluation of the impact of biomass burning smoke aerosol particles on near surface temperature forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, J.; Reid, J. S.; Benedetti, A.; Christensen, M.; Marquis, J. W.</p> <p>2016-12-01</p> <p>Currently, with the improvements in aerosol forecast accuracies through aerosol data assimilation, the community is unavoidably facing a scientific question: is it worth the computational time to insert real-time aerosol analyses into numerical models for weather forecasts? In this study, by analyzing a significant biomass burning aerosol event that occurred in 2015 over the Northern part of the Central US, the impact of aerosol particles on near-surface temperature forecasts is evaluated. The aerosol direct surface cooling efficiency, which links surface temperature changes to aerosol loading, is derived from observational-based data for the first time. The potential of including real-time aerosol analyses into weather forecasting models for near surface temperature forecasts is also investigated.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23209852','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23209852"><span>Forecast of dengue incidence using temperature and rainfall.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hii, Yien Ling; Zhu, Huaiping; Ng, Nawi; Ng, Lee Ching; Rocklöv, Joacim</p> <p>2012-01-01</p> <p>An accurate early warning system to predict impending epidemics enhances the effectiveness of preventive measures against dengue fever. The aim of this study was to develop and validate a forecasting model that could predict dengue cases and provide timely early warning in Singapore. We developed a time series Poisson multivariate regression model using weekly mean temperature and cumulative rainfall over the period 2000-2010. Weather data were modeled using piecewise linear spline functions. We analyzed various lag times between dengue and weather variables to identify the optimal dengue forecasting period. Autoregression, seasonality and trend were considered in the model. We validated the model by forecasting dengue cases for week 1 of 2011 up to week 16 of 2012 using weather data alone. Model selection and validation were based on Akaike's Information Criterion, standardized Root Mean Square Error, and residuals diagnoses. A Receiver Operating Characteristics curve was used to analyze the sensitivity of the forecast of epidemics. The optimal period for dengue forecast was 16 weeks. Our model forecasted correctly with errors of 0.3 and 0.32 of the standard deviation of reported cases during the model training and validation periods, respectively. It was sensitive enough to distinguish between outbreak and non-outbreak to a 96% (CI = 93-98%) in 2004-2010 and 98% (CI = 95%-100%) in 2011. The model predicted the outbreak in 2011 accurately with less than 3% possibility of false alarm. We have developed a weather-based dengue forecasting model that allows warning 16 weeks in advance of dengue epidemics with high sensitivity and specificity. We demonstrate that models using temperature and rainfall could be simple, precise, and low cost tools for dengue forecasting which could be used to enhance decision making on the timing, scale of vector control operations, and utilization of limited resources.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.262J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.262J"><span>A new forecast presentation tool for offshore contractors</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jørgensen, M.</p> <p>2009-09-01</p> <p>Contractors working off shore are often very sensitive to both sea and weather conditions, and it's essential that they have easy access to reliable information on coming conditions to enable planning of when to start or shut down offshore operations to avoid loss of life and materials. Danish Meteorological Institute, DMI, recently, in cooperation with business partners in the field, developed a new application to accommodate that need. The "Marine Forecast Service” is a browser based forecast presentation tool. It provides an interface for the user to enable easy and quick access to all relevant meteorological and oceanographic forecasts and observations for a given area of interest. Each customer gains access to the application via a standard login/password procedure. Once logged in, the user can inspect animated forecast maps of parameters like wind, gust, wave height, swell and current among others. Supplementing the general maps, the user can choose to look at forecast graphs for each of the locations where the user is running operations. These forecast graphs can also be overlaid with the user's own in situ observations, if such exist. Furthermore, the data from the graphs can be exported as data files that the customer can use in his own applications as he desires. As part of the application, a forecaster's view on the current and near future weather situation is presented to the user as well, adding further value to the information presented through maps and graphs. Among other features of the product, animated radar and satellite images could be mentioned. And finally the application provides the possibility of a "second opinion” through traditional weather charts from another recognized provider of weather forecasts. The presentation will provide more detailed insights into the contents of the applications as well as some of the experiences with the product.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012stsp.book.....F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012stsp.book.....F"><span>Storms in Space</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Freeman, John W.</p> <p>2012-11-01</p> <p>Introduction; The cast of characters; Vignettes of the storm; 1. Two kinds of weather; 2. The saga of the storm; 3. Weather stations in space; 4. Lights in the night: the signature of the storm; 5. A walking tour of the magnetosphere; 6. The sun: where it all begins; 7. Nowcasting and forecasting storms in space; 8. Technology and the risks from storms in space; 9. A conversation with Joe Allen; 10. Manned exploration and space weather hazards; 11. The present and future of space weather forecasting; Mathematical appendix. A closer look; Glossary; Figure captions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/15350206','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/15350206"><span>A weather-driven model of malaria transmission.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hoshen, Moshe B; Morse, Andrew P</p> <p>2004-09-06</p> <p>Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission. This paper describes a mathematical-biological model of the parasite dynamics, comprising both the weather-dependent within-vector stages and the weather-independent within-host stages. Numerical evaluations of the model in both time and space show that it qualitatively reconstructs the prevalence of infection. A process-based modelling structure has been developed that may be suitable for the simulation of malaria forecasts based on seasonal weather forecasts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29706974','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29706974"><span>A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Puntel, Laila A; Sawyer, John E; Barker, Daniel W; Thorburn, Peter J; Castellano, Michael J; Moore, Kenneth J; VanLoocke, Andrew; Heaton, Emily A; Archontoulis, Sotirios V</p> <p>2018-01-01</p> <p>Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time ( R 2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity ( R 2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined ( n = 31) with an average error range of ±38 kg N ha -1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5909184','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5909184"><span>A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Puntel, Laila A.; Sawyer, John E.; Barker, Daniel W.; Thorburn, Peter J.; Castellano, Michael J.; Moore, Kenneth J.; VanLoocke, Andrew; Heaton, Emily A.; Archontoulis, Sotirios V.</p> <p>2018-01-01</p> <p>Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha−1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost. PMID:29706974</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130009987','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130009987"><span>Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.</p> <p>2007-01-01</p> <p>Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision at the Shuttle Landing Facility. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAFs), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. Both the SMG and the MLB are currently implementing the Weather Research and Forecasting Environmental Modeling System (WRF EMS) software into their operations. The WRF EMS software allows users to employ both dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model- the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and NWS MLB with a lot of flexibility. It also creates challenges, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and to determine which configuration will best predict warm season convective initiation in East-Central Florida. Four different combinations of WRF initializations will be run (ADAS-ARW, ADAS-NMM, LAPS-ARW, and LAPS-NMM) at a 4-km resolution over the Florida peninsula and adjacent coastal waters. Five candidate convective initiation days using three different flow regimes over East-Central Florida will be examined, as well as two null cases (non-convection days). Each model run will be integrated 12 hours with three runs per day, at 0900, 1200, and 1500 UTe. ADAS analyses will be generated every 30 minutes using Level II Weather Surveillance Radar-1988 Doppler (WSR-88D) data from all Florida radars to verify the convection forecast. These analyses will be run on the same domain as the four model configurations. To quantify model performance, model output will be subjectively compared to the ADAS analyses of convection to determine forecast accuracy. In addition, a subjective comparison of the performance of the ARW using a high-resolution local grid with 2-way nesting, I-way nesting, and no nesting will be made for select convective initiation cases. The inner grid will cover the East-Central Florida region at a resolution of 1.33 km. The authors will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for predicting warm season convective initiation over East-Central Florida.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMSH43C..02J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMSH43C..02J"><span>Modeling AWSoM CMEs with EEGGL: A New Approach for Space Weather Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jin, M.; Manchester, W.; van der Holst, B.; Sokolov, I.; Toth, G.; Vourlidas, A.; de Koning, C. A.; Gombosi, T. I.</p> <p>2015-12-01</p> <p>The major source of destructive space weather is coronal mass ejections (CMEs). However, our understanding of CMEs and their propagation in the heliosphere is limited by the insufficient observations. Therefore, the development of first-principals numerical models plays a vital role in both theoretical investigation and providing space weather forecasts. Here, we present results of the simulation of CME propagation from the Sun to 1AU by combining the analytical Gibson & Low (GL) flux rope model with the state-of-art solar wind model AWSoM. We also provide an approach for transferring this research model to a space weather forecasting tool by demonstrating how the free parameters of the GL flux rope can be prescribed based on remote observations via the new Eruptive Event Generator by Gibson-Low (EEGGL) toolkit. This capability allows us to predict the long-term evolution of the CME in interplanetary space. We perform proof-of-concept case studies to show the capability of the model to capture physical processes that determine CME evolution while also reproducing many observed features both in the corona and at 1 AU. We discuss the potential and limitations of this model as a future space weather forecasting tool.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.128H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.128H"><span>WIRE: Weather Intelligence for Renewable Energies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heimo, A.; Cattin, R.; Calpini, B.</p> <p>2010-09-01</p> <p>Renewable energies such as wind and solar energy will play an important, even decisive role in order to mitigate and adapt to the projected dramatic consequences to our society and environment due to climate change. Due to shrinking fossil resources, the transition to more and more renewable energy shares is unavoidable. But, as wind and solar energy are strongly dependent on highly variable weather processes, increased penetration rates will also lead to strong fluctuations in the electricity grid which need to be balanced. Proper and specific forecasting of ‘energy weather' is a key component for this. Therefore, it is today appropriate to scientifically address the requirements to provide the best possible specific weather information for forecasting the energy production of wind and solar power plants within the next minutes up to several days. Towards such aims, Weather Intelligence will first include developing dedicated post-processing algorithms coupled with weather prediction models and with past and/or online measurement data especially remote sensing observations. Second, it will contribute to investigate the difficult relationship between the highly intermittent weather dependent power production and concurrent capacities such as transport and distribution of this energy to the end users. Selecting, resp. developing surface-based and satellite remote sensing techniques well adapted to supply relevant information to the specific post-processing algorithms for solar and wind energy production short-term forecasts is a major task with big potential. It will lead to improved energy forecasts and help to increase the efficiency of the renewable energy productions while contributing to improve the management and presumably the design of the energy grids. The second goal will raise new challenges as this will require first from the energy producers and distributors definitions of the requested input data and new technologies dedicated to the management of power plants and electricity grids and second from the meteorological measurement community to deliver suitable, short term high quality forecasts to fulfill these requests with emphasis on highly variable weather conditions and spatially distributed energy productions often located in complex terrain. This topic has been submitted for a new COST Action under the title "Short-Term High Resolution Wind and Solar Energy Production Forecasts".</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29595302','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29595302"><span>Communicating weather forecast uncertainty: Do individual differences matter?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Grounds, Margaret A; Joslyn, Susan L</p> <p>2018-03-01</p> <p>Research suggests that people make better weather-related decisions when they are given numeric probabilities for critical outcomes (Joslyn & Leclerc, 2012, 2013). However, it is unclear whether all users can take advantage of probabilistic forecasts to the same extent. The research reported here assessed key cognitive and demographic factors to determine their relationship to the use of probabilistic forecasts to improve decision quality. In two studies, participants decided between spending resources to prevent icy conditions on roadways or risk a larger penalty when freezing temperatures occurred. Several forecast formats were tested, including a control condition with the night-time low temperature alone and experimental conditions that also included the probability of freezing and advice based on expected value. All but those with extremely low numeracy scores made better decisions with probabilistic forecasts. Importantly, no groups made worse decisions when probabilities were included. Moreover, numeracy was the best predictor of decision quality, regardless of forecast format, suggesting that the advantage may extend beyond understanding the forecast to general decision strategy issues. This research adds to a growing body of evidence that numerical uncertainty estimates may be an effective way to communicate weather danger to general public end users. (PsycINFO Database Record (c) 2018 APA, all rights reserved).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1182264','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1182264"><span>Application of global weather and climate model output to the design and operation of wind-energy systems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Curry, Judith</p> <p></p> <p>This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatorymore » environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140012856','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140012856"><span>The GOES-R Geostationary Lightning Mapper (GLM) and the Global Observing System for Total Lightning</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Goodman, Steven J.; Blakeslee, R. J.; Koshak, W.; Buechler, D.; Carey, L.; Chronis, T.; Mach, D.; Bateman, M.; Peterson, H.; McCaul, E. W., Jr.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20140012856'); toggleEditAbsImage('author_20140012856_show'); toggleEditAbsImage('author_20140012856_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20140012856_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20140012856_hide"></p> <p>2014-01-01</p> <p>for the existing GOES system currently operating over the Western Hemisphere. New and improved instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved temporal, spatial, and spectral resolution for the next generation Advanced Baseline Imager (ABI). The GLM will map total lightning continuously day and night with near-uniform spatial resolution of 8 km with a product latency of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency among a number of potential applications. The GLM will help address the National Weather Service requirement for total lightning observations globally to support warning decision-making and forecast services. Science and application development along with pre-operational product demonstrations and evaluations at NWS national centers, forecast offices, and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and check-out of GOES-R in 2016. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A23C2371H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A23C2371H"><span>Verifying Operational and Developmental Air Force Weather Cloud Analysis and Forecast Products Using Lidar Data from Department of Energy Atmospheric Radiation Measurement (ARM) Sites</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hildebrand, E. P.</p> <p>2017-12-01</p> <p>Air Force Weather has developed various cloud analysis and forecast products designed to support global Department of Defense (DoD) missions. A World-Wide Merged Cloud Analysis (WWMCA) and short term Advected Cloud (ADVCLD) forecast is generated hourly using data from 16 geostationary and polar-orbiting satellites. Additionally, WWMCA and Numerical Weather Prediction (NWP) data are used in a statistical long-term (out to five days) cloud forecast model known as the Diagnostic Cloud Forecast (DCF). The WWMCA and ADVCLD are generated on the same polar stereographic 24 km grid for each hemisphere, whereas the DCF is generated on the same grid as its parent NWP model. When verifying the cloud forecast models, the goal is to understand not only the ability to detect cloud, but also the ability to assign it to the correct vertical layer. ADVCLD and DCF forecasts traditionally have been verified using WWMCA data as truth, but this might over-inflate the performance of those models because WWMCA also is a primary input dataset for those models. Because of this, in recent years, a WWMCA Reanalysis product has been developed, but this too is not a fully independent dataset. This year, work has been done to incorporate data from external, independent sources to verify not only the cloud forecast products, but the WWMCA data itself. One such dataset that has been useful for examining the 3-D performance of the cloud analysis and forecast models is Atmospheric Radiation Measurement (ARM) data from various sites around the globe. This presentation will focus on the use of the Department of Energy (DoE) ARM data to verify Air Force Weather cloud analysis and forecast products. Results will be presented to show relative strengths and weaknesses of the analyses and forecasts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.4737B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.4737B"><span>Analysis of Numerical Weather Predictions of Reference Evapotranspiration and Precipitation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bughici, Theodor; Lazarovitch, Naftali; Fredj, Erick; Tas, Eran</p> <p>2017-04-01</p> <p>This study attempts to improve the forecast skill of the evapotranspiration (ET0) and Precipitation for the purpose of crop irrigation management over Israel using the Weather Research and Forecasting (WRF) Model. Optimized crop irrigation, in term of timing and quantities, decreases water and agrochemicals demand. Crop water demands depend on evapotranspiration and precipitation. The common method for computing reference evapotranspiration, for agricultural needs, ET0, is according to the FAO Penman-Monteith equation. The weather variables required for ET0 calculation (air temperature, relative humidity, wind speed and solar irradiance) are estimated by the WRF model. The WRF Model with two-way interacting domains at horizontal resolutions of 27, 9 and 3 km is used in the study. The model prediction was performed in an hourly time resolution and a 3 km spatial resolution, with forecast lead-time of up to four days. The WRF prediction of these variables have been compared against measurements from 29 meteorological stations across Israel for the year 2013. The studied area is small but with strong climatic gradient, diverse topography and variety of synoptic conditions. The forecast skill that was used for forecast validation takes into account the prediction bias, mean absolute error and root mean squared error. The forecast skill of the variables was almost robust to lead time, except for precipitation. The forecast skill was tested across stations with respect to topography and geographic location and for all stations with respect to seasonality and synoptic weather system determined by employing a semi-objective synoptic systems classification to the forecasted days. It was noticeable that forecast skill of some of the variables was deteriorated by seasonality and topography. However, larger impacts in the ET0 skill scores on the forecasted day are achieved by a synoptic based forecast. These results set the basis for increasing the robustness of ET0 to synoptic effects and for more precise crop irrigation over Israel.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1917891R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1917891R"><span>WOD - Weather On Demand forecasting system</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina</p> <p>2017-04-01</p> <p>The backbone of the Belgingur forecasting system (called WOD - Weather On Demand) is the WRF-Chem atmospheric model, with a number of in-house customisations. Initial and boundary data are taken from the Global Forecasting System, operated by the National Oceanic and Atmospheric Administration (NOAA). Operational forecasts use cycling of a number of parameters, mainly deep soil and surface fields. This is done to minimise spin-up effects and to ensure proper book-keeping of hydrological fields such as snow accumulation and runoff, as well as the constituents of various chemical parameters. The WOD system can be used to create conventional short- to medium-range weather forecasts for any location on the globe. The WOD system can also be used for air quality purposes (e.g. dispersion forecasts from volcanic eruptions) and as a tool to provide input to other modelling systems, such as hydrological models. A wide variety of post-processing options are also available, making WOD an ideal tool for creating highly customised output that can be tailored to the specific needs of individual end-users. The most recent addition to the WOD system is an integrated verification system where forecasts can be compared to surface observations from chosen locations. Forecast visualisation, such as weather charts, meteograms, weather icons and tables, is done via number of web components that can be configured to serve the varying needs of different end-users. The WOD system itself can be installed in an automatic way on hardware running a range of Linux based OS. System upgrades can also be done in semi-automatic fashion, i.e. upgrades and/or bug-fixes can be pushed to the end-user hardware without system downtime. Importantly, the WOD system requires only rudimentary knowledge of the WRF modelling, and the Linux operating systems on behalf of the end-user, making it an ideal NWP tool in locations with limited IT infrastructure.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMIN51D0035T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMIN51D0035T"><span>JPSS Products, Applications and Training</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Torres, J. R.; Connell, B. H.; Miller, S. D.</p> <p>2017-12-01</p> <p>The Joint Polar Satellite System (JPSS) is a new generation polar-orbiting operational environmental satellite system that will monitor the weather and environment around the globe. JPSS will provide technological and scientific improvements in environmental monitoring via high resolution satellite imagery and derived products that stand to improve weather forecasting capabilities for National Weather Service (NWS) forecasters and complement operational Geostationary satellites. JPSS will consist of four satellites, JPSS-1 through JPSS-4, where JPSS-1 is due to launch in Fall 2017. A predecessor, prototype and operational risk-reduction for JPSS is the Suomi-National Polar-orbiting Partnership (S-NPP) satellite, launched on 28 October 2011. The following instruments on-board S-NPP will also be hosted on JPSS-1: Visible Infrared Imaging Radiometer Suite (VIIRS), Cross-track Infrared Sounder (CrIS), Advanced Technology Microwave Sounder (ATMS), Ozone Mapping and Profiler Suite (OMPS) and the Clouds and Earth's Radiant Energy System (CERES). JPSS-1 instruments will provide satellite imagery, products and applications to users. The applications include detecting water and ice clouds, snow, sea surface temperatures, fog, fire, severe weather, vegetation health, aerosols, and sensing reflected lunar and emitted visible-wavelength light during the nighttime via the Day/Night Band (DNB) sensor included on VIIRS. Currently, there are only a few polar products that are operational for forecasters, however, more products will become available in the near future via Advanced Weather Interactive Processing System-II (AWIPS-II)-a forecasting analysis software package that forecasters can use to analyze meteorological data. To complement the polar products an wealth of training materials are currently in development. Denoted as the Satellite Foundational Course for JPSS (SatFC-J), this training will benefit NWS forecasters to utilize satellite data in their forecasts and daily operations as they discover their operational value in the NWS forecast process. As JPSS-1 launch nears, training materials will be produced in the form of modules, videos, quick guides, fact sheets, and hands-on exercises.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/25652','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/25652"><span>Guidelines for disseminating road weather messages.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2010-06-01</p> <p>The tremendous growth in the amount of available weather and road condition informationincluding devices that gather weather information, models and forecasting tools for predicting weather conditions, and electronic devices used by travelersha...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA609711','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA609711"><span>Tropical Cyclone Prediction Using COAMPS-TC</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-09-01</p> <p>landfalling hurricanes with the advanced hurricane WRF model. Monthly Weather Review 136:1,990–2,005, http://dx.doi.org/10.1175/2007MWR2085.1. DeMaria, M...Weisman. 2004. The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecast ( WRF ) Model. Atmospheric Science</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/om/marine/faq.htm','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/om/marine/faq.htm"><span>National Weather Service Marine Forecasts - FAQ</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>! Boating Safety Beach Hazards Rip Currents Hypothermia Hurricanes Thunderstorms Lightning <em>Coastal</em> Flooding marine <em>coastal</em> areas may be found in Appendix B of the National Ocean Service's Coast Pilot's, volumes 1 Advisory (SCA): An advisory issued by <em>coastal</em> and Great Lakes Weather Forecast Offices (WFO) for areas</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1419925','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1419925"><span>Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Optis, Michael; Scott, George N.; Draxl, Caroline</p> <p></p> <p>The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present.more » Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008EOSTr..89..544S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008EOSTr..89..544S"><span>In Brief: Forecasting meningitis threats</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Showstack, Randy</p> <p>2008-12-01</p> <p>The University Corporation for Atmospheric Research (UCAR), in conjunction with a team of health and weather organizations, has launched a project to provide weather forecasts to medical officials in Africa to help reduce outbreaks of meningitis. The forecasts will enable local health care providers to target vaccination programs more effectively. In 2009, meteorologists with the National Center for Atmospheric Research, which is managed by UCAR, will begin issuing 14-day forecasts of atmospheric conditions in Ghana. Later, UCAR plans to work closely with health experts from several African countries to design and test a decision support system to provide health officials with useful meteorological information. ``By targeting forecasts in regions where meningitis is a threat, we may be able to help vulnerable populations. Ultimately, we hope to build on this project and provide information to public health programs battling weather-related diseases in other parts of the world,'' said Rajul Pandya, director of UCAR's Community Building Program. Funding for the project comes from a $900,000 grant from Google.org, the philanthropic arm of the Internet search company.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19850004215','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19850004215"><span>Recent examples of mesoscale numerical forecasts of severe weather events along the east coast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kocin, P. J.; Uccellini, L. W.; Zack, J. W.; Kaplan, M. L.</p> <p>1984-01-01</p> <p>Mesoscale numerical forecasts utilizing the Mesoscale Atmospheric Simulation System (MASS) are documented for two East Coast severe weather events. The two events are the thunderstorm and heavy snow bursts in the Washington, D.C. - Baltimore, MD region on 8 March 1984 and the devastating tornado outbreak across North and South Carolina on 28 March 1984. The forecasts are presented to demonstrate the ability of the model to simulate dynamical interactions and diabatic processes and to note some of the problems encountered when using mesoscale models for day-to-day forecasting.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016cosp...41E.240B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016cosp...41E.240B"><span>Research activities at the Australian Bureau of Meteorology for the regional ionospheric specification and forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bouya, Zahra; Terkildsen, Michael</p> <p>2016-07-01</p> <p>The Australian Space Forecast Centre (ASFC) provides space weather forecasts to a diverse group of customers. Space Weather Services (SWS) within the Australian Bureau of Meteorology is focussed both on developing tailored products and services for the key customer groups, and supporting ASFC operations. Research in SWS is largely centred on the development of data-driven models using a range of solar-terrestrial data. This paper will cover some data requirements , approaches and recent SWS activities for data driven modelling with a focus on the regional Ionospheric specification and forecasting.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018IJBm..tmp...32G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018IJBm..tmp...32G"><span>Operational forecasting of human-biometeorological conditions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Giannaros, T. M.; Lagouvardos, K.; Kotroni, V.; Matzarakis, A.</p> <p>2018-03-01</p> <p>This paper presents the development of an operational forecasting service focusing on human-biometeorological conditions. The service is based on the coupling of numerical weather prediction models with an advanced human-biometeorological model. Human thermal perception and stress forecasts are issued on a daily basis for Greece, in both point and gridded format. A user-friendly presentation approach is adopted for communicating the forecasts to the public via the worldwide web. The development of the presented service highlights the feasibility of replacing standard meteorological parameters and/or indices used in operational weather forecasting activities for assessing the thermal environment. This is of particular significance for providing effective, human-biometeorology-oriented, warnings for both heat waves and cold outbreaks.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19770028064&hterms=Getting+yes&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DGetting%2Byes','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19770028064&hterms=Getting+yes&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DGetting%2Byes"><span>Economic benefits of improved meteorological forecasts - The construction industry</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bhattacharyya, R. K.; Greenberg, J. S.</p> <p>1976-01-01</p> <p>Estimates are made of the potential economic benefits accruing to particular industries from timely utilization of satellite-derived six-hour weather forecasts, and of economic penalties resulting from failure to utilize such forecasts in day-to-day planning. The cost estimate study is centered on the U.S. construction industry, with results simplified to yes/no 6-hr forecasts on thunderstorm activity and work/no work decisions. Effects of weather elements (thunderstorms, snow and sleet) on various construction operations are indicated. Potential dollar benefits for other industries, including air transportation and other forms of transportation, are diagrammed for comparison. Geosynchronous satellites such as STORMSAT, SEOS, and SMS/GOES are considered as sources of the forecast data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27812298','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27812298"><span>Mixture EMOS model for calibrating ensemble forecasts of wind speed.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Baran, S; Lerch, S</p> <p>2016-03-01</p> <p>Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble forecasts. We propose an EMOS model for calibrating wind speed forecasts based on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions where model parameters and component weights are estimated by optimizing the values of proper scoring rules over a rolling training period. The new model is tested on wind speed forecasts of the 50 member European Centre for Medium-range Weather Forecasts ensemble, the 11 member Aire Limitée Adaptation dynamique Développement International-Hungary Ensemble Prediction System ensemble of the Hungarian Meteorological Service, and the eight-member University of Washington mesoscale ensemble, and its predictive performance is compared with that of various benchmark EMOS models based on single parametric families and combinations thereof. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. The mixture EMOS model significantly outperforms the TN and LN EMOS methods; moreover, it provides better calibrated forecasts than the TN-LN combination model and offers an increased flexibility while avoiding covariate selection problems. © 2016 The Authors Environmetrics Published by JohnWiley & Sons Ltd.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017E%26ES...57a2002L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017E%26ES...57a2002L"><span>Testing efficacy of monthly forecast application in agrometeorology: Winter wheat phenology dynamic</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lalic, B.; Jankovic, D.; Dekic, Lj; Eitzinger, J.; Firanj Sremac, A.</p> <p>2017-02-01</p> <p>Use of monthly weather forecast as input meteorological data for agrometeorological forecasting, crop modelling and plant protection can foster promising applications in agricultural production. Operational use of monthly or seasonal weather forecast can help farmers to optimize field operations (fertilizing, irrigation) and protection measures against plant diseases and pests by taking full advantage of monthly forecast information in predicting plant development, pest and disease risks and yield potentials few weeks in advance. It can help producers to obtain stable or higher yield with the same inputs and to minimise losses caused by weather. In Central and South-Eastern Europe ongoing climate change lead to shifts of crops phenology dynamics (i.e. in Serbia 4-8 weeks earlier in 2016 than in previous years) and brings this subject in the front of agronomy science and practice. Objective of this study is to test efficacy of monthly forecast in predicting phenology dynamics of different winter wheat varieties, using phenological model developed by Forecasting and Warning Service of Serbia in plant protection. For that purpose, historical monthly forecast for four months (March 1, 2005 - June 30, 2005) was assimilated from ECMWF MARS archive for 50 ensemble members and control run. Impact of different agroecological conditions is tested by using observed and forecasted data for two locations - Rimski Sancevi (Serbia) and Groß-Enzersdorf (Austria).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMPA13A0222S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMPA13A0222S"><span>Major Risks, Uncertain Outcomes: Making Ensemble Forecasts Work for Multiple Audiences</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Semmens, K. A.; Montz, B.; Carr, R. H.; Maxfield, K.; Ahnert, P.; Shedd, R.; Elliott, J.</p> <p>2017-12-01</p> <p>When extreme river levels are possible in a community, effective communication of weather and hydrologic forecasts is critical to protect life and property. Residents, emergency personnel, and water resource managers need to make timely decisions about how and when to prepare. Uncertainty in forecasting is a critical component of this decision-making, but often poses a confounding factor for public and professional understanding of forecast products. In 2016 and 2017, building on previous research about the use of uncertainty forecast products, and with funding from NOAA's CSTAR program, East Carolina University and Nurture Nature Center (a non-profit organization with a focus on flooding issues, based in Easton, PA) conducted a research project to understand how various audiences use and interpret ensemble forecasts showing a range of hydrologic forecast possibilities. These audiences include community residents, emergency managers and water resource managers. The research team held focus groups in Jefferson County, WV and Frederick County, MD, to test a new suite of products from the National Weather Service's Hydrologic Ensemble Forecast System (HEFS). HEFS is an ensemble system that provides short and long-range forecasts, ranging from 6 hours to 1 year, showing uncertainty in hydrologic forecasts. The goal of the study was to assess the utility of the HEFS products, identify the barriers to proper understanding of the products, and suggest modifications to product design that could improve the understandability and accessibility for residential, emergency managers, and water resource managers. The research team worked with the Sterling, VA Weather Forecast Office and the Middle Atlantic River Forecast center to develop a weather scenario as the basis of the focus group discussions, which also included pre and post session surveys. This presentation shares the findings from those focus group discussions and surveys, including recommendations for revisions to HEFS products to improve accessibility of the forecast tools for various audiences. The presentation will provide a broad perspective on the range of graphic design considerations that affected how the public responded to products and will provide an overview of lessons learned about how product design can influence decision-making by users.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1991BAMS...72..491M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1991BAMS...72..491M"><span>Sources and Uses of Weather Information for Agricultural Decision Makers.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McNew, Kevin P.; Mapp, Harry P.; Duchon, Claude E.; Merritt, Earl S.</p> <p>1991-04-01</p> <p>Numerous studies have examined the importance of weather information to farmers and ranchers across the U.S. This study is focused on the kinds of weather information received by farmers and ranchers, the sources of that information, and its use in production and marketing decisions. Our results are based on a survey of 292 producers from the principal agricultural areas of Oklahoma. Producers were classified into five categories related to their source of income from crop and livestock sales.Among temperature, precipitation, relative humility, and wind speed, temperature information was most widely received. Forecast lengths of highest interest were 24-h and 5-day forecasts. Precipitation information was used by many respondents for planting and harvesting decisions. Weather data and forecasts seem to be of greater value to diversified crop and livestock operators than specialized crop and livestock, perhaps due to more frequent timing decisions. Relative humility and wind information appear to be important especially during specific times of the growing season, for example, at harvest time and time of pesticide application. Television is the primary source of weather information for more than 60% of the producers.It appears that there may be a role for both public and private entities in transforming weather data and forecasts into recommendations to crop and livestock producers. Further research is needed to determine the potential value of weather information for alternative production, marketing and livestock decisions, different categories of producers, and different geographic regions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PApGe.171....1I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PApGe.171....1I"><span>Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10): a World Weather Research Programme Project</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Isaac, G. A.; Joe, P. I.; Mailhot, J.; Bailey, M.; Bélair, S.; Boudala, F. S.; Brugman, M.; Campos, E.; Carpenter, R. L.; Crawford, R. W.; Cober, S. G.; Denis, B.; Doyle, C.; Reeves, H. D.; Gultepe, I.; Haiden, T.; Heckman, I.; Huang, L. X.; Milbrandt, J. A.; Mo, R.; Rasmussen, R. M.; Smith, T.; Stewart, R. E.; Wang, D.; Wilson, L. J.</p> <p>2014-01-01</p> <p>A World Weather Research Programme (WWRP) project entitled the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) was developed to be associated with the Vancouver 2010 Olympic and Paralympic Winter Games conducted between 12 February and 21 March 2010. The SNOW-V10 international team augmented the instrumentation associated with the Winter Games and several new numerical weather forecasting and nowcasting models were added. Both the additional observational and model data were available to the forecasters in real time. This was an excellent opportunity to demonstrate existing capability in nowcasting and to develop better techniques for short term (0-6 h) nowcasts of winter weather in complex terrain. Better techniques to forecast visibility, low cloud, wind gusts, precipitation rate and type were evaluated. The weather during the games was exceptionally variable with many periods of low visibility, low ceilings and precipitation in the form of both snow and rain. The data collected should improve our understanding of many physical phenomena such as the diabatic effects due to melting snow, wind flow around and over terrain, diurnal flow reversal in valleys associated with daytime heating, and precipitation reductions and increases due to local terrain. Many studies related to these phenomena are described in the Special Issue on SNOW-V10 for which this paper was written. Numerical weather prediction and nowcast models have been evaluated against the unique observational data set now available. It is anticipated that the data set and the knowledge learned as a result of SNOW-V10 will become a resource for other World Meteorological Organization member states who are interested in improving forecasts of winter weather.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/3362','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/3362"><span>Guidelines for disseminating road weather advisory & control information.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2012-06-01</p> <p>The tremendous growth in the amount of available weather and road condition informationincluding devices that gather weather information, models and forecasting tools for predicting weather conditions, and electronic devices used by travelersha...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMPA51A2198S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMPA51A2198S"><span>Incorporating Medium-Range Weather Forecasts in Seasonal Crop Scenarios over the Greater Horn of Africa to Support National/Regional/Local Decision Makers</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shukla, S.; Husak, G. J.; Funk, C. C.; Verdin, J. P.</p> <p>2015-12-01</p> <p>The USAID's Famine Early Warning Systems Network (FEWS NET) provides seasonal assessments of crop conditions over the Greater Horn of Africa (GHA) and other food insecure regions. These assessments and current livelihood, nutrition, market conditions and conflicts are used to generate food security scenarios that help national, regional and local decision makers target their resources and mitigate socio-economic losses. Among the various tools that FEWS NET uses is the FAO's Water Requirement Satisfaction Index (WRSI). The WRSI is a simple yet powerful crop assessment model that incorporates current moisture conditions (at the time of the issuance of forecast), precipitation scenarios, potential evapotranspiration and crop parameters to categorize crop conditions into different classes ranging from "failure" to "very good". The WRSI tool has been shown to have a good agreement with local crop yields in the GHA region. At present, the precipitation scenarios used to drive the WRSI are based on either a climatological forecast (that assigns equal chances of occurrence to all possible scenarios and has no skill over the forecast period) or a sea-surface temperature anomaly based scenario (which at best have skill at the seasonal scale). In both cases, the scenarios fail to capture the skill that can be attained by initial atmospheric conditions (i.e., medium-range weather forecasts). During the middle of a cropping season, when a week or two of poor rains can have a devastating effect, two weeks worth of skillful precipitation forecasts could improve the skill of the crop scenarios. With this working hypothesis, we examine the value of incorporating medium-range weather forecasts in improving the skill of crop scenarios in the GHA region. We use the NCEP's Global Ensemble Forecast system (GEFS) weather forecasts and examine the skill of crop scenarios generated using the GEFS weather forecasts with respect to the scenarios based solely on the climatological forecast. The period of analysis is from 1985-2010 (over which the reforecasts of GEFS is available) and the focus season is October-November-December. We examine the improvement (if any) in long-term skill, and present results for several recent drought events in the region.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMIN42A..06B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMIN42A..06B"><span>Near Real Time Data for Operational Space Weather Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Berger, T. E.</p> <p>2014-12-01</p> <p>Space weather operations presents unique challenges for data systems and providers. Space weather events evolve more quickly than terrestrial weather events. While terrestrial weather occurs on timescales of minutes to hours, space weather storms evolve on timescales of seconds to minutes. For example, the degradation of the High Frequency Radio communications between the ground and commercial airlines is nearly instantaneous when a solar flare occurs. Thus the customer is observing impacts at the same time that the operational forecast center is seeing the event unfold. The diversity and spatial scale of the space weather system is such that no single observation can capture the salient features. The vast space that encompasses space weather and the scarcity of observations further exacerbates the situation and make each observation even more valuable. The physics of interplanetary space, through which many major storms propagate, is very different from the physics of the ionosphere where most of the impacts are felt. And while some observations can be made from ground-based observatories, many of the most critical data comes from satellites, often in unique orbits far from Earth. In this presentation, I will describe some of the more important sources and types of data that feed into the operational alerts, watches, and warnings of space weather storms. Included will be a discussion of some of the new space weather forecast models and the data challenges that they bring forward.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMNH41C..06W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMNH41C..06W"><span>Remote Sensing and River Discharge Forecasting for Major Rivers in South Asia (Invited)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Webster, P. J.; Hopson, T. M.; Hirpa, F. A.; Brakenridge, G. R.; De-Groeve, T.; Shrestha, K.; Gebremichael, M.; Restrepo, P. J.</p> <p>2013-12-01</p> <p>The South Asia is a flashpoint for natural disasters particularly flooding of the Indus, Ganges, and Brahmaputra has profound societal impacts for the region and globally. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion of the Kosi River in India, the 2010 flooding of the Indus River in Pakistan and the 2013 Uttarakhand exemplify disasters on scales almost inconceivable elsewhere. Their frequent occurrence of floods combined with large and rapidly growing populations, high levels of poverty and low resilience, exacerbate the impact of the hazards. Mitigation of these devastating hazards are compounded by limited flood forecast capability, lack of rain/gauge measuring stations and forecast use within and outside the country, and transboundary data sharing on natural hazards. Here, we demonstrate the utility of remotely-derived hydrologic and weather products in producing skillful flood forecasting information without reliance on vulnerable in situ data sources. Over the last decade a forecast system has been providing operational probabilistic forecasts of severe flooding of the Brahmaputra and Ganges Rivers in Bangldesh was developed (Hopson and Webster 2010). The system utilizes ECMWF weather forecast uncertainty information and ensemble weather forecasts, rain gauge and satellite-derived precipitation estimates, together with the limited near-real-time river stage observations from Bangladesh. This system has been expanded to Pakistan and has successfully forecast the 2010-2012 flooding (Shrestha and Webster 2013). To overcome the in situ hydrological data problem, recent efforts in parallel with the numerical modeling have utilized microwave satellite remote sensing of river widths to generate operational discharge advective-based forecasts for the Ganges and Brahmaputra. More than twenty remotely locations upstream of Bangldesh were used to produce stand-alone river flow nowcasts and forecasts at 1-15 days lead time. showing that satellite-based flow estimates are a useful source of dynamical surface water information in data-scarce regions and that they could be used for model calibration and data assimilation purposes in near-time hydrologic forecast applications (Hirpa et al. 2013). More recent efforts during this year's monsoon season are optimally combining these different independent sources of river forecast information along with archived flood inundation imagery of the Dartmouth Flood Observatory to improve the visualization and overall skill of the ongoing CFAB ensemble weather forecast-based flood forecasting system within the unique context of the ongoing flood forecasting efforts for Bangladesh.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4638344','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4638344"><span>Local Climate Experts: The Influence of Local TV Weather Information on Climate Change Perceptions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Bloodhart, Brittany; Maibach, Edward; Myers, Teresa; Zhao, Xiaoquan</p> <p>2015-01-01</p> <p>Individuals who identify changes in their local climate are also more likely to report that they have personally experienced global climate change. One way that people may come to recognize that their local climate is changing is through information provided by local TV weather forecasters. Using random digit dialing, 2,000 adult local TV news viewers in Virginia were surveyed to determine whether routine exposure to local TV weather forecasts influences their perceptions of extreme weather in Virginia, and their perceptions about climate change more generally. Results indicate that paying attention to TV weather forecasts is associated with beliefs that extreme weather is becoming more frequent in Virginia, which in turn is associated with stronger beliefs and concerns about climate change. These associations were strongest for individuals who trust their local TV weathercaster as a source of information about climate change, and for those who identify as politically conservative or moderate. The findings add support to the literature suggesting that TV weathercasters can play an important role in educating the public about climate change. PMID:26551357</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26551357','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26551357"><span>Local Climate Experts: The Influence of Local TV Weather Information on Climate Change Perceptions.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bloodhart, Brittany; Maibach, Edward; Myers, Teresa; Zhao, Xiaoquan</p> <p>2015-01-01</p> <p>Individuals who identify changes in their local climate are also more likely to report that they have personally experienced global climate change. One way that people may come to recognize that their local climate is changing is through information provided by local TV weather forecasters. Using random digit dialing, 2,000 adult local TV news viewers in Virginia were surveyed to determine whether routine exposure to local TV weather forecasts influences their perceptions of extreme weather in Virginia, and their perceptions about climate change more generally. Results indicate that paying attention to TV weather forecasts is associated with beliefs that extreme weather is becoming more frequent in Virginia, which in turn is associated with stronger beliefs and concerns about climate change. These associations were strongest for individuals who trust their local TV weathercaster as a source of information about climate change, and for those who identify as politically conservative or moderate. The findings add support to the literature suggesting that TV weathercasters can play an important role in educating the public about climate change.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A21G2241M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A21G2241M"><span>Quantitative impact of aerosols on numerical weather prediction. Part II: Impacts to IR radiance assimilation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Marquis, J. W.; Campbell, J. R.; Oyola, M. I.; Ruston, B. C.; Zhang, J.</p> <p>2017-12-01</p> <p>This is part II of a two-part series examining the impacts of aerosol particles on weather forecasts. In this study, the aerosol indirect effects on weather forecasts are explored by examining the temperature and moisture analysis associated with assimilating dust contaminated hyperspectral infrared radiances. The dust induced temperature and moisture biases are quantified for different aerosol vertical distribution and loading scenarios. The overall impacts of dust contamination on temperature and moisture forecasts are quantified over the west coast of Africa, with the assistance of aerosol retrievals from AERONET, MPL, and CALIOP. At last, methods for improving hyperspectral infrared data assimilation in dust contaminated regions are proposed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA595323','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA595323"><span>Assessing High-Resolution Weather Research and Forecasting (WRF) Forecasts Using an Object-Based Diagnostic Evaluation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-02-01</p> <p>Operational Model Archive and Distribution System ( NOMADS ). The RTMA product was generated using a 2-D variational method to assimilate point weather...observations and satellite-derived measurements (National Weather Service, 2013). The products were downloaded using the NOMADS General Regularly...of the completed WRF run" read Start_Date echo $Start_Date echo " " echo "Enter 2- digit , zulu, observation hour (HH) for remapping" read oHH</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://images.nasa.gov/#/details-KSC-20180301-PH_SWW01_0017.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-KSC-20180301-PH_SWW01_0017.html"><span>GOES-S Liftoff</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2018-03-01</p> <p>A United Launch Alliance Atlas V rocket lifts off from Space Launch Complex 41 at Cape Canaveral Air Force Station carrying the NOAA Geostationary Operational Environmental Satellite, or GOES-S. Liftoff was at 5:02 p.m. EST. GOES-S is the second satellite in a series of next-generation weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1511752B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1511752B"><span>AFFECTS - Advanced Forecast For Ensuring Communications Through Space</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bothmer, Volker</p> <p>2013-04-01</p> <p>Through the AFFECTS project funded by the European Union's 7th Framework Programme, European and US scientists develop an advanced proto-type space weather warning system to safeguard the operation of telecommunication and navigation systems on Earth to the threat of solar storms. The project is led by the University of Göttingen's Institute for Astrophysics and comprises worldwide leading research and academic institutions and industrial enterprises from Germany, Belgium, Ukraine, Norway and the United States. The key objectives of the AFFECTS project are: State-of-the-art analysis and modelling of the Sun-Earth chain of effects on the Earth's ionosphere and their subsequent impacts on communication systems based on multipoint space observations and complementary ground-based data. Development of a prototype space weather early warning system and reliable space weather forecasts, with specific emphasis on ionospheric applications. Dissemination of new space weather products and services to end users, the scientific community and general public. The presentation summarizes the project highlights, with special emphasis on the developed space weather forecast tools.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=312594&Lab=NERL&keyword=dependency&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=312594&Lab=NERL&keyword=dependency&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Improving High-Resolution Weather Forecasts Using the Weather Research and Forecasting (WRF) Model with an Updated Kain–Fritsch Scheme</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Efforts to improve the prediction accuracy of high-resolution (1–10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at ...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1024473','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1024473"><span>Software Tools for Stochastic Simulations of Turbulence</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2015-08-28</p> <p>client interface to FTI. Specefic client programs using this interface include the weather forecasting code WRF ; the high energy physics code, FLASH...client programs using this interface include the weather forecasting code WRF ; the high energy physics code, FLASH; and two locally constructed fluid...45 4.4.2.2 FLASH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.4.2.3 WRF</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=314150&Lab=NERL&keyword=simulation+AND+processes&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=314150&Lab=NERL&keyword=simulation+AND+processes&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>How reliable is the offline linkage of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The aim for this research is to evaluate the ability of the offline linkage of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological, e.g. evaporation (ET), soil moisture (SM), runoff, and baseflow. First, the VIC mo...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130010469','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130010469"><span>Creating Interactive Graphical Overlays in the Advanced Weather Interactive Processing System (AWIPS) Using Shapefiles and DGM Files</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barrett, Joe H., III; Lafosse, Richard; Hood, Doris; Hoeth, Brian</p> <p>2007-01-01</p> <p>Graphical overlays can be created in real-time in the Advanced Weather Interactive Processing System (AWIPS) using shapefiles or DARE Graphics Metafile (DGM) files. This presentation describes how to create graphical overlays on-the-fly for AWIPS, by using two examples of AWIPS applications that were created by the Applied Meteorology Unit (AMU). The first example is the Anvil Threat Corridor Forecast Tool, which produces a shapefile that depicts a graphical threat corridor of the forecast movement of thunderstorm anvil clouds, based on the observed or forecast upper-level winds. This tool is used by the Spaceflight Meteorology Group (SMG) and 45th Weather Squadron (45 WS) to analyze the threat of natural or space vehicle-triggered lightning over a location. The second example is a launch and landing trajectory tool that produces a DGM file that plots the ground track of space vehicles during launch or landing. The trajectory tool can be used by SMG and the 45 WS forecasters to analyze weather radar imagery along a launch or landing trajectory. Advantages of both file types will be listed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC43A0694K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC43A0694K"><span>Towards a More Accurate Solar Power Forecast By Improving NWP Model Physics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Köhler, C.; Lee, D.; Steiner, A.; Ritter, B.</p> <p>2014-12-01</p> <p>The growing importance and successive expansion of renewable energies raise new challenges for decision makers, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the uncertainties associated with the large share of weather-dependent power sources. Precise power forecast, well-timed energy trading on the stock market, and electrical grid stability can be maintained. The research project EWeLiNE is a collaboration of the German Weather Service (DWD), the Fraunhofer Institute (IWES) and three German transmission system operators (TSOs). Together, wind and photovoltaic (PV) power forecasts shall be improved by combining optimized NWP and enhanced power forecast models. The conducted work focuses on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. Not only the representation of the model cloud characteristics, but also special events like Sahara dust over Germany and the solar eclipse in 2015 are treated and their effect on solar power accounted for. An overview of the EWeLiNE project and results of the ongoing research will be presented.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMSH21F..04K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMSH21F..04K"><span>Physics-based Space Weather Forecasting in the Project for Solar-Terrestrial Environment Prediction (PSTEP) in Japan</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kusano, K.</p> <p>2016-12-01</p> <p>Project for Solar-Terrestrial Environment Prediction (PSTEP) is a Japanese nation-wide research collaboration, which was recently launched. PSTEP aims to develop a synergistic interaction between predictive and scientific studies of the solar-terrestrial environment and to establish the basis for next-generation space weather forecasting using the state-of-the-art observation systems and the physics-based models. For this project, we coordinate the four research groups, which develop (1) the integration of space weather forecast system, (2) the physics-based solar storm prediction, (3) the predictive models of magnetosphere and ionosphere dynamics, and (4) the model of solar cycle activity and its impact on climate, respectively. In this project, we will build the coordinated physics-based model to answer the fundamental questions concerning the onset of solar eruptions and the mechanism for radiation belt dynamics in the Earth's magnetosphere. In this paper, we will show the strategy of PSTEP, and discuss about the role and prospect of the physics-based space weather forecasting system being developed by PSTEP.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003EAEJA.....5049T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003EAEJA.....5049T"><span>Development of a European Ensemble System for Seasonal Prediction: Application to crop yield</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Terres, J. M.; Cantelaube, P.</p> <p>2003-04-01</p> <p>Western European agriculture is highly intensive and the weather is the main source of uncertainty for crop yield assessment and for crop management. In the current system, at the time when a crop yield forecast is issued, the weather conditions leading up to harvest time are unknown and are therefore a major source of uncertainty. The use of seasonal weather forecast would bring additional information for the remaining crop season and has valuable benefit for improving the management of agricultural markets and environmentally sustainable farm practices. An innovative method for supplying seasonal forecast information to crop simulation models has been developed in the frame of the EU funded research project DEMETER. It consists in running a crop model on each individual member of the seasonal hindcasts to derive a probability distribution of crop yield. Preliminary results of cumulative probability function of wheat yield provides information on both the yield anomaly and the reliability of the forecast. Based on the spread of the probability distribution, the end-user can directly quantify the benefits and risks of taking weather-sensitive decisions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ACP....1713391R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ACP....1713391R"><span>Impact of the 4 April 2014 Saharan dust outbreak on the photovoltaic power generation in Germany</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rieger, Daniel; Steiner, Andrea; Bachmann, Vanessa; Gasch, Philipp; Förstner, Jochen; Deetz, Konrad; Vogel, Bernhard; Vogel, Heike</p> <p>2017-11-01</p> <p>The importance for reliable forecasts of incoming solar radiation is growing rapidly, especially for those countries with an increasing share in photovoltaic (PV) power production. The reliability of solar radiation forecasts depends mainly on the representation of clouds and aerosol particles absorbing and scattering radiation. Especially under extreme aerosol conditions, numerical weather prediction has a systematic bias in the solar radiation forecast. This is caused by the design of numerical weather prediction models, which typically account for the direct impact of aerosol particles on radiation using climatological mean values and the impact on cloud formation assuming spatially and temporally homogeneous aerosol concentrations. These model deficiencies in turn can lead to significant economic losses under extreme aerosol conditions. For Germany, Saharan dust outbreaks occurring 5 to 15 times per year for several days each are prominent examples for conditions, under which numerical weather prediction struggles to forecast solar radiation adequately. We investigate the impact of mineral dust on the PV-power generation during a Saharan dust outbreak over Germany on 4 April 2014 using ICON-ART, which is the current German numerical weather prediction model extended by modules accounting for trace substances and related feedback processes. We find an overall improvement of the PV-power forecast for 65 % of the pyranometer stations in Germany. Of the nine stations with very high differences between forecast and measurement, eight stations show an improvement. Furthermore, we quantify the direct radiative effects and indirect radiative effects of mineral dust. For our study, direct effects account for 64 %, indirect effects for 20 % and synergistic interaction effects for 16 % of the differences between the forecast including mineral dust radiative effects and the forecast neglecting mineral dust.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1439549-solar-time-based-analog-ensemble-method-regional-solar-power-forecasting','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1439549-solar-time-based-analog-ensemble-method-regional-solar-power-forecasting"><span>A Solar Time-Based Analog Ensemble Method for Regional Solar Power Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hodge, Brian S; Zhang, Xinmin; Li, Yuan</p> <p></p> <p>This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, formore » both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. The normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150002754','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150002754"><span>Applications of LANCE Data at SPoRT</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Molthan, Andrew</p> <p>2014-01-01</p> <p>Short term Prediction Research and Transition (SPoRT) Center: Mission: Apply NASA and NOAA measurement systems and unique Earth science research to improve the accuracy of short term weather prediction at the regional/local scale. Goals: Evaluate and assess the utility of NASA and NOAA Earth science data and products and unique research capabilities to address operational weather forecast problems; Provide an environment which enables the development and testing of new capabilities to improve short term weather forecasts on a regional scale; Help ensure successful transition of new capabilities to operational weather entities for the benefit of society</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=520827','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=520827"><span>A weather-driven model of malaria transmission</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Hoshen, Moshe B; Morse, Andrew P</p> <p>2004-01-01</p> <p>Background Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission. Methods This paper describes a mathematical-biological model of the parasite dynamics, comprising both the weather-dependent within-vector stages and the weather-independent within-host stages. Results Numerical evaluations of the model in both time and space show that it qualitatively reconstructs the prevalence of infection. Conclusion A process-based modelling structure has been developed that may be suitable for the simulation of malaria forecasts based on seasonal weather forecasts. PMID:15350206</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMSH21B2413D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMSH21B2413D"><span>An overview on the Space Weather in Latin America: from Space Research to Space Weather and its Forecast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>De Nardin, C. M.; Gonzalez-Esparza, A.; Dasso, S.</p> <p>2015-12-01</p> <p>We present an overview on the Space Weather in Latin America, highlighting the main findings from our review the recent advances in the space science investigations in Latin America focusing in the solar-terrestrial interactions, modernly named space weather, which leaded to the creation of forecast centers. Despite recognizing advances in the space research over the whole Latin America, this review is restricted to the evolution observed in three countries (Argentina, Brazil and Mexico) only, due to the fact that these countries have recently developed operational center for monitoring the space weather. The work starts with briefly mentioning the first groups that started the space science in Latin America. The current status and research interest of such groups are then described together with the most referenced works and the challenges for the next decade to solve space weather puzzles. A small inventory of the networks and collaborations being built is also described. Finally, the decision process for spinning off the space weather prediction centers from the space science groups is reported with an interpretation of the reason/opportunities that lead to it. Lastly, the constraints for the progress in the space weather monitoring, research, and forecast are listed with recommendations to overcome them.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080042406','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080042406"><span>Anvil Forecast Tool in the Advanced Weather Interactive Processing System, Phase II</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barrett, Joe H., III</p> <p>2008-01-01</p> <p>Meteorologists from the 45th Weather Squadron (45 WS) and Spaceflight Meteorology Group have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Light Rules. As a result, the Applied Meteorology Unit (AMU) created a graphical overlay tool for the Meteorological Interactive Data Display Systems (MIDDS) to indicate the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24665600','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24665600"><span>[50 years of the methodology of weather forecasting for medicine].</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Grigor'ev, K I; Povazhnaia, E L</p> <p>2014-01-01</p> <p>The materials reported in the present article illustrate the possibility of weather forecasting for the medical purposes in the historical aspect. The main characteristics of the relevant organizational and methodological approaches to meteoprophylaxis based of the standard medical forecasts are presented. The emphasis is laid on the priority of the domestic medical school in the development of the principles of diagnostics and treatment of meteosensitivity and meteotropic complications in the patients presenting with various diseases with special reference to their age-related characteristics.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20050210112&hterms=retraction&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dretraction','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20050210112&hterms=retraction&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dretraction"><span>STS-114: Discovery L-2 Countdown Status Briefing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2005-01-01</p> <p>George Diller of NASA Public Affairs hosted this briefing. Pete Nickolenko, NASA Test Director; Scott Higgenbotham, STS-114 Payload-Mission Manager; Cathy Winters, Shuttle Weather Officer were present. Pete reports his team has completed the avionics system check ups, servicing of the cryogenic tanks will take about seven hours that day, and will perform engine system checks and pad close outs come evening. Pete also summarized other standard close out activities: check ups of the Orbiter and ground communications network, rotary service, structure retraction, and external tank load (ETL). Pete reported that the mission will be 12 days with two weather contingency days, and end of mission landing scheduled at Kennedy Space Center (KSC) at approximately 11:00 in the morning, Eastern time on July 25th. Scott briefly reported that all hardware is on board Discovery, closed out, and ready to fly. Cathy reported that hurricane Dennis moved to the North and looking forward to launch. She mentioned of a new hurricane looming and will be named Emily, spotted some crosswinds which will migrate to the west, there is 30% probability weather prohibiting launch. Cathy further gave current weather forecast supported with charts: the Launch Forecast, Tanking Forecast, SRB (Shuttle Solid Rocket Booster) Forecast, CONUS and TAL Launch Sites Forecast, and with 24 hours and 48 hours turn around plan. Launch constraints, weather, crosswinds, cloud cover, ground imagery system, launch countdown, launch crews, mission management simulations, launch team simulations were topics covered with the News Media.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AtmRe.123....2S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AtmRe.123....2S"><span>Progress and challenges with Warn-on-Forecast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stensrud, David J.; Wicker, Louis J.; Xue, Ming; Dawson, Daniel T.; Yussouf, Nusrat; Wheatley, Dustan M.; Thompson, Therese E.; Snook, Nathan A.; Smith, Travis M.; Schenkman, Alexander D.; Potvin, Corey K.; Mansell, Edward R.; Lei, Ting; Kuhlman, Kristin M.; Jung, Youngsun; Jones, Thomas A.; Gao, Jidong; Coniglio, Michael C.; Brooks, Harold E.; Brewster, Keith A.</p> <p>2013-04-01</p> <p>The current status and challenges associated with two aspects of Warn-on-Forecast-a National Oceanic and Atmospheric Administration research project exploring the use of a convective-scale ensemble analysis and forecast system to support hazardous weather warning operations-are outlined. These two project aspects are the production of a rapidly-updating assimilation system to incorporate data from multiple radars into a single analysis, and the ability of short-range ensemble forecasts of hazardous convective weather events to provide guidance that could be used to extend warning lead times for tornadoes, hailstorms, damaging windstorms and flash floods. Results indicate that a three-dimensional variational assimilation system, that blends observations from multiple radars into a single analysis, shows utility when evaluated by forecasters in the Hazardous Weather Testbed and may help increase confidence in a warning decision. The ability of short-range convective-scale ensemble forecasts to provide guidance that could be used in warning operations is explored for five events: two tornadic supercell thunderstorms, a macroburst, a damaging windstorm and a flash flood. Results show that the ensemble forecasts of the three individual severe thunderstorm events are very good, while the forecasts from the damaging windstorm and flash flood events, associated with mesoscale convective systems, are mixed. Important interactions between mesoscale and convective-scale features occur for the mesoscale convective system events that strongly influence the quality of the convective-scale forecasts. The development of a successful Warn-on-Forecast system will take many years and require the collaborative efforts of researchers and operational forecasters to succeed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100042585','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100042585"><span>ADAS Update and Maintainability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela R.</p> <p>2010-01-01</p> <p>Since 2000, both the National Weather Service Melbourne (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LOIS) as part of their forecast and warning operations. The original LOIS was developed by the Applied Meteorology Unit (AMU) in 1998 (Manobianco and Case 1998) and has undergone subsequent improvements. Each has benefited from three-dimensional (3-D) analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (AD AS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive understanding of evolving fine-scale weather features. Over the years, the LDIS has become problematic to maintain since it depends on AMU-developed shell scripts that were written for an earlier version of the ADAS software. The goals of this task were to update the NWS MLB/SMG LDIS with the latest version of ADAS, incorporate new sources of observational data, and upgrade and modify the AMU-developed shell scripts written to govern the system. In addition, the previously developed ADAS graphical user interface (GUI) was updated. Operationally, these upgrades will result in more accurate depictions of the current local environment to help with short-range weather forecasting applications, while also offering an improved initialization for local versions of the Weather Research and Forecasting (WRF) model used by both groups.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1913515D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1913515D"><span>The potential predictability of fire danger provided by ECMWF forecast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Di Giuseppe, Francesca</p> <p>2017-04-01</p> <p>The European Forest Fire Information System (EFFIS), is currently being developed in the framework of the Copernicus Emergency Management Services to monitor and forecast fire danger in Europe. The system provides timely information to civil protection authorities in 38 nations across Europe and mostly concentrates on flagging regions which might be at high danger of spontaneous ignition due to persistent drought. The daily predictions of fire danger conditions are based on the US Forest Service National Fire Danger Rating System (NFDRS), the Canadian forest service Fire Weather Index Rating System (FWI) and the Australian McArthur (MARK-5) rating systems. Weather forcings are provided in real time by the European Centre for Medium range Weather Forecasts (ECMWF) forecasting system. The global system's potential predictability is assessed using re-analysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 years of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire and low values correspond to non observed events. A more quantitative skill evaluation was performed using the Extremal Dependency Index which is a skill score specifically designed for rare events. It revealed that the three indices were more skilful on a global scale than the random forecast to detect large fires. The performance peaks in the boreal forests, in the Mediterranean, the Amazon rain-forests and southeast Asia. The skill-scores were then aggregated at country level to reveal which nations could potentiallty benefit from the system information in aid of decision making and fire control support. Overall we found that fire danger modelling based on weather forecasts, can provide reasonable predictability over large parts of the global landmass.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMIN33B1550A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMIN33B1550A"><span>Aggregation of Environmental Model Data for Decision Support</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alpert, J. C.</p> <p>2013-12-01</p> <p>Weather forecasts and warnings must be prepared and then delivered so as to reach their intended audience in good time to enable effective decision-making. An effort to mitigate these difficulties was studied at a Workshop, 'Sustaining National Meteorological Services - Strengthening WMO Regional and Global Centers' convened, June , 2013, by the World Bank, WMO and the US National Weather Service (NWS). The skill and accuracy of atmospheric forecasts from deterministic models have increased and there are now ensembles of such models that improve decisions to protect life, property and commerce. The NWS production of numerical weather prediction products result in model output from global and high resolution regional ensemble forecasts. Ensembles are constructed by changing the initial conditions to make a 'cloud' of forecasts that attempt to span the space of possible atmospheric realizations which can quantify not only the most likely forecast, but also the uncertainty. This has led to an unprecedented increase in data production and information content from higher resolution, multi-model output and secondary calculations. One difficulty is to obtain the needed subset of data required to estimate the probability of events, and report the information. The calibration required to reliably estimate the probability of events, and honing of threshold adjustments to reduce false alarms for decision makers is also needed. To meet the future needs of the ever-broadening user community and address these issues on a national and international basis, the weather service implemented the NOAA Operational Model Archive and Distribution System (NOMADS). NOMADS provides real-time and retrospective format independent access to climate, ocean and weather model data and delivers high availability content services as part of NOAA's official real time data dissemination at its new NCWCP web operations center. An important aspect of the server's abilities is to aggregate the matrix of model output offering access to probability and calibrating information for real time decision making. The aggregation content server reports over ensemble component and forecast time in addition to the other data dimensions of vertical layer and position for each variable. The unpacking, organization and reading of many binary packed files is accomplished most efficiently on the server while weather element event probability calculations, the thresholds for more accurate decision support, or display remain for the client. Our goal is to reduce uncertainty for variables of interest, e.g, agricultural importance. The weather service operational GFS model ensemble and short range ensemble forecasts can make skillful probability forecasts to alert users if and when their selected weather events will occur. A description of how this framework operates and how it can be implemented using existing NOMADS content services and applications is described.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol3/pdf/CFR-2011-title14-vol3-sec135-219.pdf','CFR2011'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol3/pdf/CFR-2011-title14-vol3-sec135-219.pdf"><span>14 CFR 135.219 - IFR: Destination airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2011&page.go=Go">Code of Federal Regulations, 2011 CFR</a></p> <p></p> <p>2011-01-01</p> <p>... 14 Aeronautics and Space 3 2011-01-01 2011-01-01 false IFR: Destination airport weather minimums... VFR/IFR Operating Limitations and Weather Requirements § 135.219 IFR: Destination airport weather... latest weather reports or forecasts, or any combination of them, indicate that weather conditions at the...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2012-title14-vol3/pdf/CFR-2012-title14-vol3-sec135-219.pdf','CFR2012'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2012-title14-vol3/pdf/CFR-2012-title14-vol3-sec135-219.pdf"><span>14 CFR 135.219 - IFR: Destination airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2012&page.go=Go">Code of Federal Regulations, 2012 CFR</a></p> <p></p> <p>2012-01-01</p> <p>... 14 Aeronautics and Space 3 2012-01-01 2012-01-01 false IFR: Destination airport weather minimums... VFR/IFR Operating Limitations and Weather Requirements § 135.219 IFR: Destination airport weather... latest weather reports or forecasts, or any combination of them, indicate that weather conditions at the...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2014-title14-vol3/pdf/CFR-2014-title14-vol3-sec135-219.pdf','CFR2014'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2014-title14-vol3/pdf/CFR-2014-title14-vol3-sec135-219.pdf"><span>14 CFR 135.219 - IFR: Destination airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2014&page.go=Go">Code of Federal Regulations, 2014 CFR</a></p> <p></p> <p>2014-01-01</p> <p>... 14 Aeronautics and Space 3 2014-01-01 2014-01-01 false IFR: Destination airport weather minimums... VFR/IFR Operating Limitations and Weather Requirements § 135.219 IFR: Destination airport weather... latest weather reports or forecasts, or any combination of them, indicate that weather conditions at the...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2013-title14-vol3/pdf/CFR-2013-title14-vol3-sec135-219.pdf','CFR2013'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2013-title14-vol3/pdf/CFR-2013-title14-vol3-sec135-219.pdf"><span>14 CFR 135.219 - IFR: Destination airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2013&page.go=Go">Code of Federal Regulations, 2013 CFR</a></p> <p></p> <p>2013-01-01</p> <p>... 14 Aeronautics and Space 3 2013-01-01 2013-01-01 false IFR: Destination airport weather minimums... VFR/IFR Operating Limitations and Weather Requirements § 135.219 IFR: Destination airport weather... latest weather reports or forecasts, or any combination of them, indicate that weather conditions at the...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2010-title14-vol3/pdf/CFR-2010-title14-vol3-sec135-219.pdf','CFR'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2010-title14-vol3/pdf/CFR-2010-title14-vol3-sec135-219.pdf"><span>14 CFR 135.219 - IFR: Destination airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2010&page.go=Go">Code of Federal Regulations, 2010 CFR</a></p> <p></p> <p>2010-01-01</p> <p>... 14 Aeronautics and Space 3 2010-01-01 2010-01-01 false IFR: Destination airport weather minimums... VFR/IFR Operating Limitations and Weather Requirements § 135.219 IFR: Destination airport weather... latest weather reports or forecasts, or any combination of them, indicate that weather conditions at the...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29080010','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29080010"><span>Future sea ice conditions and weather forecasts in the Arctic: Implications for Arctic shipping.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gascard, Jean-Claude; Riemann-Campe, Kathrin; Gerdes, Rüdiger; Schyberg, Harald; Randriamampianina, Roger; Karcher, Michael; Zhang, Jinlun; Rafizadeh, Mehrad</p> <p>2017-12-01</p> <p>The ability to forecast sea ice (both extent and thickness) and weather conditions are the major factors when it comes to safe marine transportation in the Arctic Ocean. This paper presents findings focusing on sea ice and weather prediction in the Arctic Ocean for navigation purposes, in particular along the Northeast Passage. Based on comparison with the observed sea ice concentrations for validation, the best performing Earth system models from the Intergovernmental Panel on Climate Change (IPCC) program (CMIP5-Coupled Model Intercomparison Project phase 5) were selected to provide ranges of potential future sea ice conditions. Our results showed that, despite a general tendency toward less sea ice cover in summer, internal variability will still be large and shipping along the Northeast Passage might still be hampered by sea ice blocking narrow passages. This will make sea ice forecasts on shorter time and space scales and Arctic weather prediction even more important.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018PApGe.tmp.1252R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018PApGe.tmp.1252R"><span>Waterspout Forecasting Method Over the Eastern Adriatic Using a High-Resolution Numerical Weather Model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Renko, Tanja; Ivušić, Sarah; Telišman Prtenjak, Maja; Šoljan, Vinko; Horvat, Igor</p> <p>2018-03-01</p> <p>In this study, a synoptic and mesoscale analysis was performed and Szilagyi's waterspout forecasting method was tested on ten waterspout events in the period of 2013-2016. Data regarding waterspout occurrences were collected from weather stations, an online survey at the official website of the National Meteorological and Hydrological Service of Croatia and eyewitness reports from newspapers and the internet. Synoptic weather conditions were analyzed using surface pressure fields, 500 hPa level synoptic charts, SYNOP reports and atmospheric soundings. For all observed waterspout events, a synoptic type was determined using the 500 hPa geopotential height chart. The occurrence of lightning activity was determined from the LINET lightning database, and waterspouts were divided into thunderstorm-related and "fair weather" ones. Mesoscale characteristics (with a focus on thermodynamic instability indices) were determined using the high-resolution (500 m grid length) mesoscale numerical weather model and model results were compared with the available observations. Because thermodynamic instability indices are usually insufficient for forecasting waterspout activity, the performance of the Szilagyi Waterspout Index (SWI) was tested using vertical atmospheric profiles provided by the mesoscale numerical model. The SWI successfully forecasted all waterspout events, even the winter events. This indicates that the Szilagyi's waterspout prognostic method could be used as a valid prognostic tool for the eastern Adriatic.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.423L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.423L"><span>Verification of FLYSAFE Clear Air Turbulence (CAT) objects against aircraft turbulence measurements</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lunnon, R.; Gill, P.; Reid, L.; Mirza, A.</p> <p>2009-09-01</p> <p>Prediction of gridded CAT fields The main causes of CAT are (a) Vertical wind shear - low Richardson Number (b) Mountain waves (c) Convection. All three causes contribute roughly equally to CAT occurrences, globally Prediction of shear induced CAT The predictions of shear induced CAT has a longer history than either mountain-wave induced CAT or convectively induced CAT. Both Global Aviation Forecasting Centres are currently using the Ellrod TI1 algorithm (Ellrod and Knapp, 1992). This predictor is the scalar product of deformation [akm1]and vertical wind shear. More sophisticated algorithms can amplify errors in non-linear, differentiated quantities so it is very likely that Ellrod will out-perform other algorithms when verified globally. Prediction of mountain wave CAT The Global Aviation Forecasting Centre in the UK has been generating automated forecasts of mountain wave CAT since the late 1990s, based on the diagnosis of gravity wave drag. Generation of CAT objects In the FLYSAFE project it was decided at an early stage that short range forecasts of meteorological hazards, i.e. icing, Clear Air Turbulence, Cumulonimbus Clouds, should be represented as weather objects, that is, descriptions of individual hazardous volumes of airspace. For CAT, the forecast information on which the weather objects were based was gridded, that comprised a representation of a hazard level for all points in a pre-defined 3-D grid, for a range of forecast times. A "grid-to-objects" capability was generated. This is discussed further in Mirza and Drouin (this conference). Verification of CAT forecasts Verification was performed using digital accelerometer data from aircraft in the British Airways Boeing 747 fleet. A preliminary processing of the aircraft data were performed to generate a truth field on a scale similar to that used to provide gridded forecasts to airlines. This truth field was binary, i.e. each flight segment was characterised as being either "turbulent" or "benign". A gridded forecast field is a continuously changing variable. In contrast, a simple weather object must be characterised by a specific threshold. For a gridded forecast and a binary truth measure it is possible to generate Relative Operating Characteristic (ROC) curves. For weather objects, a single point in the hit-rate/false-alarm-rate space can be generated. If this point is plotted on a ROC curve graph then the skill of the forecast using weather objects can be compared with the skill of the gridded forecast.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140008768','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140008768"><span>Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Case, Jonathan L.; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.</p> <p>2014-01-01</p> <p>SPoRT/SERVIR/RCMRD/KMS Collaboration: Builds off strengths of each organization. SPoRT: Transition of satellite, modeling and verification capabilities; SERVIR-Africa/RCMRD: International capacity-building expertise; KMS: Operational organization with regional weather forecasting expertise in East Africa. Hypothesis: Improved land-surface initialization over Eastern Africa can lead to better temperature, moisture, and ultimately precipitation forecasts in NWP models. KMS currently initializes Weather Research and Forecasting (WRF) model with NCEP/Global Forecast System (GFS) model 0.5-deg initial / boundary condition data. LIS will provide much higher-resolution land-surface data at a scale more representative to regional WRF configuration. Future implementation of real-time NESDIS/VIIRS vegetation fraction to further improve land surface representativeness.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20170006548&hterms=tourism&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dtourism','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170006548&hterms=tourism&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dtourism"><span>Space Weather Products and Tools Used in Auroral Monitoring and Forecasting at CCMC/SWRC</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zheng, Yihua; Rastaetter, Lutz</p> <p>2015-01-01</p> <p>Key points discussed in this chapter are (1) the importance of aurora research to scientific advances and space weather applications, (2) space weather products at CCMC that are relevant to aurora monitoring and forecasting, and (3) the need for more effort from the whole community to achieve a better and long-lead-time forecast of auroral activity. Aurora, as manifestations of solar wind-magnetosphere-ionosphere coupling that occurs in a region of space that is relatively easy to access for sounding rockets, satellites, and other types of observational platforms, serves as a natural laboratory for studying the underlying physics of the complex system. From a space weather application perspective, auroras can cause surface charging of technological assets passing through the region, result in scintillation effects affecting communication and navigation, and cause radar cluttering that hinders military and civilian applications. Indirectly, an aurora and its currents can induce geomagnetically induced currents (GIC) on the ground, which poses major concerns for the wellbeing and operation of power grids, particularly during periods of intense geomagnetic activity. In addition, accurate auroral forecasting is desired for auroral tourism. In this chapter, we first review some of the existing auroral models and discuss past validation efforts. Such efforts are crucial in transitioning a model(s) from research to operations and for further model improvement and development that also benefits scientific endeavors. Then we will focus on products and tools that are used for auroral monitoring and forecasting at the Space Weather Research Center (SWRC). As part of the CCMC (Community Coordinated Modeling Center), SWRC has been providing space weather services since 2010.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19860033237&hterms=heinemann&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dheinemann','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19860033237&hterms=heinemann&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dheinemann"><span>Rapid weather information dissemination in Florida</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Martsolf, J. D.; Heinemann, P. H.; Gerber, J. F.; Crosby, F. L.; Smith, D. L.</p> <p>1984-01-01</p> <p>The development of the Florida Agricultural Services and Technology (FAST) plan to provide ports for users to call for weather information is described. FAST is based on the Satellite Frost Forecast System, which makes a broad base of weather data available to its users. The methods used for acquisition and dissemination of data from various networks under the FAST plan are examined. The system provides color coded IR or thermal maps, precipitation maps, and textural forecast information. A diagram of the system is provided.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018EPJWC.17602008K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018EPJWC.17602008K"><span>Scientific motivation for ADM/Aeolus mission</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Källén, Erland</p> <p>2018-04-01</p> <p>The ADM/Aeolus wind lidar mission will provide a global coverage of atmospheric wind profiles. Atmospheric wind observations are required for initiating weather forecast models and for predicting and monitoring long term climate change. Improved knowledge of the global wind field is widely recognised as fundamental to advancing the understanding and prediction of weather and climate. In particular over tropical areas there is a need for better wind data leading to improved medium range (3-10 days) weather forecasts over the whole globe.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://images.nasa.gov/#/details-KSC-20180301-VP-CDC01_0001-GOES_S_Launch_Commentary-3182524.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-KSC-20180301-VP-CDC01_0001-GOES_S_Launch_Commentary-3182524.html"><span>KSC-20180301-VP-CDC01_0001-GOES_S_Launch_Commentary-3182524</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2018-03-01</p> <p>A United Launch Alliance Atlas V rocket lifts off from Space Launch Complex 41 at Cape Canaveral Air Force Station carrying the NOAA Geostationary Operational Environmental Satellite, or GOES-S. Liftoff was at 5:02 p.m. EST. GOES-S is the second satellite in a series of next-generation weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110007286','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110007286"><span>Projected Applications of a "Weather in a Box" Computing System at the NASA Short-Term Prediction Research and Transition (SPoRT) Center</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jedlovec, Gary J.; Molthan, Andrew; Zavodsky, Bradley T.; Case, Jonathan L.; LaFontaine, Frank J.; Srikishen, Jayanthi</p> <p>2010-01-01</p> <p>The NASA Short-term Prediction Research and Transition Center (SPoRT)'s new "Weather in a Box" resources will provide weather research and forecast modeling capabilities for real-time application. Model output will provide additional forecast guidance and research into the impacts of new NASA satellite data sets and software capabilities. By combining several research tools and satellite products, SPoRT can generate model guidance that is strongly influenced by unique NASA contributions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002EGSGA..27.1435B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27.1435B"><span>A Data Assimilation System For Operational Weather Forecast In Galicia Region (nw Spain)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Balseiro, C. F.; Souto, M. J.; Pérez-Muñuzuri, V.; Brewster, K.; Xue, M.</p> <p></p> <p>Regional weather forecast models, such as the Advanced Regional Prediction System (ARPS), over complex environments with varying local influences require an accurate meteorological analysis that should include all local meteorological measurements available. In this work, the ARPS Data Analysis System (ADAS) (Xue et al. 2001) is applied as a three-dimensional weather analysis tool to include surface station and rawinsonde data with the NCEP AVN forecasts as the analysis background. Currently in ADAS, a set of five meteorological variables are considered during the analysis: horizontal grid-relative wind components, pressure, potential temperature and spe- cific humidity. The analysis is used for high resolution numerical weather prediction for the Galicia region. The analysis method used in ADAS is based on the successive corrective scheme of Bratseth (1986), which asymptotically approaches the result of a statistical (optimal) interpolation, but at lower computational cost. As in the optimal interpolation scheme, the Bratseth interpolation method can take into account the rel- ative error between background and observational data, therefore they are relatively insensitive to large variations in data density and can integrate data of mixed accuracy. This method can be applied economically in an operational setting, providing signifi- cant improvement over the background model forecast as well as any analysis without high-resolution local observations. A one-way nesting is applied for weather forecast in Galicia region, and the use of this assimilation system in both domains shows better results not only in initial conditions but also in all forecast periods. Bratseth, A.M. (1986): "Statistical interpolation by means of successive corrections." Tellus, 38A, 439-447. Souto, M. J., Balseiro, C. F., Pérez-Muñuzuri, V., Xue, M. Brewster, K., (2001): "Im- pact of cloud analysis on numerical weather prediction in the galician region of Spain". Submitted to Journal of Applied Meteorology. Xue, M., Wang. D., Gao, J., Brewster, K, Droegemeier, K. K., (2001): "The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation". Meteor. Atmos Physics. Accepted</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMIN11B1530T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMIN11B1530T"><span>Operational specification and forecasting advances for Dst, LEO thermospheric densities, and aviation radiation dose and dose rate</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tobiska, W.; Knipp, D. J.; Burke, W. J.; Bouwer, D.; Bailey, J. J.; Hagan, M. P.; Didkovsky, L. V.; Garrett, H. B.; Bowman, B. R.; Gannon, J. L.; Atwell, W.; Blake, J. B.; Crain, W.; Rice, D.; Schunk, R. W.; Fulgham, J.; Bell, D.; Gersey, B.; Wilkins, R.; Fuschino, R.; Flynn, C.; Cecil, K.; Mertens, C. J.; Xu, X.; Crowley, G.; Reynolds, A.; Azeem, S. I.; Wiley, S.; Holland, M.; Malone, K.</p> <p>2013-12-01</p> <p>Space weather's effects upon the near-Earth environment are due to dynamic changes in the energy transfer processes from the Sun's photons, particles, and fields. Of the space environment domains that are affected by space weather, the magnetosphere, thermosphere, and even troposphere are key regions that are affected. Space Environment Technologies (SET) has developed and is producing innovative space weather applications. Key operational systems for providing timely information about the effects of space weather on these domains are SET's Magnetosphere Alert and Prediction System (MAPS), LEO Alert and Prediction System (LAPS), and Automated Radiation Measurements for Aviation Safety (ARMAS) system. MAPS provides a forecast Dst index out to 6 days through the data-driven, redundant data stream Anemomilos algorithm. Anemomilos uses observational proxies for the magnitude, location, and velocity of solar ejecta events. This forecast index is used by satellite operations to characterize upcoming geomagnetic storms, for example. LAPS is the SET fully redundant operational system providing recent history, current epoch, and forecast solar and geomagnetic indices for use in operational versions of the JB2008 thermospheric density model. The thermospheric densities produced by that system, driven by the LAPS data, are forecast to 72-hours to provide the global mass densities for satellite operators. ARMAS is a project that has successfully demonstrated the operation of a micro dosimeter on aircraft to capture the real-time radiation environment due to Galactic Cosmic Rays and Solar Energetic Particles. The dose and dose-rates are captured on aircraft, downlinked in real-time via the Iridium satellites, processed on the ground, incorporated into the most recent NAIRAS global radiation climatology data runs, and made available to end users via the web and smart phone apps. ARMAS provides the 'weather' of the radiation environment to improve air-crew and passenger safety. Many of the data products from MAPS, LAPS, and ARMAS are available on the SpaceWx smartphone app for iPhone, iPad, iPod, and Android professional users and public space weather education. We describe recent forecasting advances for moving the space weather information from these automated systems into operational, derivative products for communications, aviation, and satellite operations uses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EOSTr..95..205A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EOSTr..95..205A"><span>Norway and Cuba Continue Collaborating to Build Capacity to Improve Weather Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Antuña, Juan Carlos; Kalnay, Eugenia; Mesquita, Michel D. S.</p> <p>2014-06-01</p> <p>The Future of Climate Extremes in the Caribbean Extreme Cuban Climate (XCUBE) project, which is funded by the Norwegian Directorate for Civil Protection as part of an assignment for the Norwegian Ministry of Foreign Affairs to support scientific cooperation between Norway and Cuba, carried out a training workshop on seasonal forecasting, reanalysis data, and weather research and forecasting (WRF). The workshop was a follow-up to the XCUBE workshop conducted in Havana in 2013 and provided Cuban scientists with access to expertise on seasonal forecasting, the WRF model developed by the National Center for Atmospheric Research (NCAR) and the community, data assimilation, and reanalysis.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC43A0686N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC43A0686N"><span>Spatial Pattern Classification for More Accurate Forecasting of Variable Energy Resources</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Novakovskaia, E.; Hayes, C.; Collier, C.</p> <p>2014-12-01</p> <p>The accuracy of solar and wind forecasts is becoming increasingly essential as grid operators continue to integrate additional renewable generation onto the electric grid. Forecast errors affect rate payers, grid operators, wind and solar plant maintenance crews and energy traders through increases in prices, project down time or lost revenue. While extensive and beneficial efforts were undertaken in recent years to improve physical weather models for a broad spectrum of applications these improvements have generally not been sufficient to meet the accuracy demands of system planners. For renewables, these models are often used in conjunction with additional statistical models utilizing both meteorological observations and the power generation data. Forecast accuracy can be dependent on specific weather regimes for a given location. To account for these dependencies it is important that parameterizations used in statistical models change as the regime changes. An automated tool, based on an artificial neural network model, has been developed to identify different weather regimes as they impact power output forecast accuracy at wind or solar farms. In this study, improvements in forecast accuracy were analyzed for varying time horizons for wind farms and utility-scale PV plants located in different geographical regions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19850027224','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19850027224"><span>Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 4</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Keitz, J. F.</p> <p>1982-01-01</p> <p>The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 4 of the four major tasks included in the study. Task 4 uses flight plan segment wind and temperature differences as indicators of dates and geographic areas for which significant forecast errors may have occurred. An in-depth analysis is then conducted for the days identified. The analysis show that significant errors occur in the operational forecast on 15 of the 33 arbitrarily selected days included in the study. Wind speeds in an area of maximum winds are underestimated by at least 20 to 25 kts. on 14 of these days. The analysis also show that there is a tendency to repeat the same forecast errors from prog to prog. Also, some perceived forecast errors from the flight plan comparisons could not be verified by visual inspection of the corresponding National Meteorological Center forecast and analyses charts, and it is likely that they are the result of weather data interpolation techniques or some other data processing procedure in the airlines' flight planning systems.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SoPh..292...69O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SoPh..292...69O"><span>Probabilistic Solar Wind and Geomagnetic Forecasting Using an Analogue Ensemble or "Similar Day" Approach</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Owens, M. J.; Riley, P.; Horbury, T. S.</p> <p>2017-05-01</p> <p>Effective space-weather prediction and mitigation requires accurate forecasting of near-Earth solar-wind conditions. Numerical magnetohydrodynamic models of the solar wind, driven by remote solar observations, are gaining skill at forecasting the large-scale solar-wind features that give rise to near-Earth variations over days and weeks. There remains a need for accurate short-term (hours to days) solar-wind forecasts, however. In this study we investigate the analogue ensemble (AnEn), or "similar day", approach that was developed for atmospheric weather forecasting. The central premise of the AnEn is that past variations that are analogous or similar to current conditions can be used to provide a good estimate of future variations. By considering an ensemble of past analogues, the AnEn forecast is inherently probabilistic and provides a measure of the forecast uncertainty. We show that forecasts of solar-wind speed can be improved by considering both speed and density when determining past analogues, whereas forecasts of the out-of-ecliptic magnetic field [BN] are improved by also considering the in-ecliptic magnetic-field components. In general, the best forecasts are found by considering only the previous 6 - 12 hours of observations. Using these parameters, the AnEn provides a valuable probabilistic forecast for solar-wind speed, density, and in-ecliptic magnetic field over lead times from a few hours to around four days. For BN, which is central to space-weather disturbance, the AnEn only provides a valuable forecast out to around six to seven hours. As the inherent predictability of this parameter is low, this is still likely a marked improvement over other forecast methods. We also investigate the use of the AnEn in forecasting geomagnetic indices Dst and Kp. The AnEn provides a valuable probabilistic forecast of both indices out to around four days. We outline a number of future improvements to AnEn forecasts of near-Earth solar-wind and geomagnetic conditions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070013861&hterms=regional+impacts+climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dregional%2Bimpacts%2Bclimate%2Bchange','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070013861&hterms=regional+impacts+climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dregional%2Bimpacts%2Bclimate%2Bchange"><span>The Impact of Atmospheric InfraRed Sounder (AIRS) Profiles on Short-term Weather Forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.; Lapenta, William</p> <p>2007-01-01</p> <p>The Atmospheric Infrared Sounder (AIRS), together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced spacebased atmospheric sounding systems. The combined AlRS/AMSU system provides radiance measurements used to retrieve temperature profiles with an accuracy of 1 K over 1 km layers under both clear and partly cloudy conditions, while the accuracy of the derived humidity profiles is 15% in 2 km layers. Critical to the successful use of AIRS profiles for weather and climate studies is the use of profile quality indicators and error estimates provided with each profile Aside form monitoring changes in Earth's climate, one of the objectives of AIRS is to provide sounding information of sufficient accuracy such that the assimilation of the new observations, especially in data sparse region, will lead to an improvement in weather forecasts. The purpose of this paper is to describe a procedure to optimally assimilate highresolution AIRS profile data in a regional analysis/forecast model. The paper will focus on the impact of AIRS profiles on a rapidly developing east coast storm and will also discuss preliminary results for a 30-day forecast period, simulating a quasi-operation environment. Temperature and moisture profiles were obtained from the prototype version 5.0 EOS science team retrieval algorithm which includes explicit error information for each profile. The error profile information was used to select the highest quality temperature and moisture data for every profile location and pressure level for assimilation into the ARPS Data Analysis System (ADAS). The AIRS-enhanced analyses were used as initial fields for the Weather Research and Forecast (WRF) system used by the SPORT project for regional weather forecast studies. The ADASWRF system will be run on CONUS domain with an emphasis on the east coast. The preliminary assessment of the impact of the AIRS profiles will focus on quality control issues associated with AIRS, intelligent use of the quality indicators, and forecast verification.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1615738W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1615738W"><span>Improvements in approaches to forecasting and evaluation techniques</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weatherhead, Elizabeth</p> <p>2014-05-01</p> <p>The US is embarking on an experiment to make significant and sustained improvements in weather forecasting. The effort stems from a series of community conversations that recognized the rapid advancements in observations, modeling and computing techniques in the academic, governmental and private sectors. The new directions and initial efforts will be summarized, including information on possibilities for international collaboration. Most new projects are scheduled to start in the last half of 2014. Several advancements include ensemble forecasting with global models, and new sharing of computing resources. Newly developed techniques for evaluating weather forecast models will be presented in detail. The approaches use statistical techniques that incorporate pair-wise comparisons of forecasts with observations and account for daily auto-correlation to assess appropriate uncertainty in forecast changes. Some of the new projects allow for international collaboration, particularly on the research components of the projects.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1438336-short-term-global-horizontal-irradiance-forecasting-based-sky-imaging-pattern-recognition','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1438336-short-term-global-horizontal-irradiance-forecasting-based-sky-imaging-pattern-recognition"><span>Short-Term Global Horizontal Irradiance Forecasting Based on Sky Imaging and Pattern Recognition</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hodge, Brian S; Feng, Cong; Cui, Mingjian</p> <p></p> <p>Accurate short-term forecasting is crucial for solar integration in the power grid. In this paper, a classification forecasting framework based on pattern recognition is developed for 1-hour-ahead global horizontal irradiance (GHI) forecasting. Three sets of models in the forecasting framework are trained by the data partitioned from the preprocessing analysis. The first two sets of models forecast GHI for the first four daylight hours of each day. Then the GHI values in the remaining hours are forecasted by an optimal machine learning model determined based on a weather pattern classification model in the third model set. The weather pattern ismore » determined by a support vector machine (SVM) classifier. The developed framework is validated by the GHI and sky imaging data from the National Renewable Energy Laboratory (NREL). Results show that the developed short-term forecasting framework outperforms the persistence benchmark by 16% in terms of the normalized mean absolute error and 25% in terms of the normalized root mean square error.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002AGUFM.A71E..05B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002AGUFM.A71E..05B"><span>Forecasting Dust Storms Using the CARMA-Dust Model and MM5 Weather Data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barnum, B. H.; Winstead, N. S.; Wesely, J.; Hakola, A.; Colarco, P.; Toon, O. B.; Ginoux, P.; Brooks, G.; Hasselbarth, L. M.; Toth, B.; Sterner, R.</p> <p>2002-12-01</p> <p>An operational model for the forecast of dust storms in Northern Africa, the Middle East and Southwest Asia has been developed for the United States Air Force Weather Agency (AFWA). The dust forecast model uses the 5th generation Penn State Mesoscale Meteorology Model (MM5), and a modified version of the Colorado Aerosol and Radiation Model for Atmospheres (CARMA). AFWA conducted a 60 day evaluation of the dust model to look at the model's ability to forecast dust storms for short, medium and long range (72 hour) forecast periods. The study used satellite and ground observations of dust storms to verify the model's effectiveness. Each of the main mesoscale forecast theaters was broken down into smaller sub-regions for detailed analysis. The study found the forecast model was able to forecast dust storms in Saharan Africa and the Sahel region with an average Probability of Detection (POD)exceeding 68%, with a 16% False Alarm Rate (FAR). The Southwest Asian theater had average POD's of 61% with FAR's averaging 10%.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/3370','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/3370"><span>Testing and Evaluation of Preliminary Design Guidelines for Disseminating Road Weather Advisory & Control Information</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2012-06-01</p> <p>The tremendous growth in the amount of available weather and road condition informationincluding devices that gather weather information, models and forecasting tools for predicting weather conditions, and electronic devices used by travelersha...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050137673','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050137673"><span>A Case Study of the Impact of AIRS Temperature Retrievals on Numerical Weather Prediction</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Reale, O.; Atlas, R.; Jusem, J. C.</p> <p>2004-01-01</p> <p>Large errors in numerical weather prediction are often associated with explosive cyclogenesis. Most studes focus on the under-forecasting error, i.e. cases of rapidly developing cyclones which are poorly predicted in numerical models. However, the over-forecasting error (i.e., to predict an explosively developing cyclone which does not occur in reality) is a very common error that severely impacts the forecasting skill of all models and may also present economic costs if associated with operational forecasting. Unnecessary precautions taken by marine activities can result in severe economic loss. Moreover, frequent occurrence of over-forecasting can undermine the reliance on operational weather forecasting. Therefore, it is important to understand and reduce the prdctions of extreme weather associated with explosive cyclones which do not actually develop. In this study we choose a very prominent case of over-forecasting error in the northwestern Pacific. A 960 hPa cyclone develops in less than 24 hour in the 5-day forecast, with a deepening rate of about 30 hPa in one day. The cyclone is not versed in the analyses and is thus a case of severe over-forecasting. By assimilating AIRS data, the error is largely eliminated. By following the propagation of the anomaly that generates the spurious cyclone, it is found that a small mid-tropospheric geopotential height negative anomaly over the northern part of the Indian subcontinent in the initial conditions, propagates westward, is amplified by orography, and generates a very intense jet streak in the subtropical jet stream, with consequent explosive cyclogenesis over the Pacific. The AIRS assimilation eliminates this anomaly that may have been caused by erroneous upper-air data, and represents the jet stream more correctly. The energy associated with the jet is distributed over a much broader area and as a consequence a multiple, but much more moderate cyclogenesis is observed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A11E1926W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A11E1926W"><span>Forecast Verification: Identification of small changes in weather forecasting skill</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weatherhead, E. C.; Jensen, T. L.</p> <p>2017-12-01</p> <p>Global and regonal weather forecasts have improved over the past seven decades most often because of small, incrmental improvements. The identificaiton and verification of forecast improvement due to proposed small changes in forecasting can be expensive and, if not carried out efficiently, can slow progress in forecasting development. This presentation will look at the skill of commonly used verification techniques and show how the ability to detect improvements can depend on the magnitude of the improvement, the number of runs used to test the improvement, the location on the Earth and the statistical techniques used. For continuous variables, such as temperture, wind and humidity, the skill of a forecast can be directly compared using a pair-wise statistical test that accommodates the natural autocorrelation and magnitude of variability. For discrete variables, such as tornado outbreaks, or icing events, the challenges is to reduce the false alarm rate while improving the rate of correctly identifying th discrete event. For both continuus and discrete verification results, proper statistical approaches can reduce the number of runs needed to identify a small improvement in forecasting skill. Verification within the Next Generation Global Prediction System is an important component to the many small decisions needed to make stat-of-the-art improvements to weather forecasting capabilities. The comparison of multiple skill scores with often conflicting results requires not only appropriate testing, but also scientific judgment to assure that the choices are appropriate not only for improvements in today's forecasting capabilities, but allow improvements that will come in the future.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130012521','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130012521"><span>An Objective Verification of the North American Mesoscale Model for Kennedy Space Center and Cape Canaveral Air Force Station</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bauman, William H., III</p> <p>2010-01-01</p> <p>The 45th Weather Squadron (45 WS) Launch Weather Officers use the 12-km resolution North American Mesoscale (NAM) model (MesoNAM) text and graphical product forecasts extensively to support launch weather operations. However, the actual performance of the model at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) has not been measured objectively. In order to have tangible evidence of model performance, the 45 WS tasked the Applied Meteorology Unit to conduct a detailed statistical analysis of model output compared to observed values. The model products are provided to the 45 WS by ACTA, Inc. and include hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The objective analysis compared the MesoNAM forecast winds, temperature and dew point, as well as the changes in these parameters over time, to the observed values from the sensors in the KSC/CCAFS wind tower network. Objective statistics will give the forecasters knowledge of the model's strength and weaknesses, which will result in improved forecasts for operations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JPhCS.749a2006S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JPhCS.749a2006S"><span>Short time ahead wind power production forecast</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sapronova, Alla; Meissner, Catherine; Mana, Matteo</p> <p>2016-09-01</p> <p>An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.weather.gov/','NIH-MEDLINEPLUS'); return false;" href="http://www.weather.gov/"><span>National Weather Service</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://medlineplus.gov/">MedlinePlus</a></p> <p></p> <p></p> <p>... Data SAFETY Floods Tsunami Beach Hazards Wildfire Cold Tornadoes Fog Air Quality Heat Hurricanes Lightning Safe Boating ... Winter Weather Forecasts River Flooding Latest Warnings Thunderstorm/Tornado Outlook Hurricanes Fire Weather Outlooks UV Alerts Drought ...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140010095','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140010095"><span>The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve</p> <p>2014-01-01</p> <p>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 ExREF in preparing their rainfall forecasts. Preliminary results will be presented.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.2489S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.2489S"><span>Multivariate Statistical Postprocessing of Ensemble Forcasts of Precipitation and Temperature over four River Basins in California</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Scheuerer, Michael; Hamill, Thomas M.; Whitin, Brett; He, Minxue; Henkel, Arthur</p> <p>2017-04-01</p> <p>Hydrological forecasts strongly rely on predictions of precipitation amounts and temperature as meteorological inputs to hydrological models. Ensemble weather predictions provide a number of different scenarios that reflect the uncertainty about these meteorological inputs, but are often biased and underdispersive, and therefore require statistical postprocessing. In hydrological applications it is crucial that spatial and temporal (i.e. between different forecast lead times) dependencies as well as dependence between the two weather variables is adequately represented by the recalibrated forecasts. We present a study with temperature and precipitation forecasts over four river basins over California that are postprocessed with a variant of the nonhomogeneous Gaussian regression method (Gneiting et al., 2005) and the censored, shifted gamma distribution approach (Scheuerer and Hamill, 2015) respectively. For modelling spatial, temporal and inter-variable dependence we propose a variant of the Schaake Shuffle (Clark et al., 2005) that uses spatio-temporal trajectories of observed temperture and precipitation as a dependence template, and chooses the historic dates in such a way that the divergence between the marginal distributions of these trajectories and the univariate forecast distributions is minimized. For the four river basins considered in our study, this new multivariate modelling technique consistently improves upon the Schaake Shuffle and yields reliable spatio-temporal forecast trajectories of temperature and precipitation that can be used to force hydrological forecast systems. References: Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., Wilby, R., 2004. The Schaake Shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields. Journal of Hydrometeorology, 5, pp.243-262. Gneiting, T., Raftery, A.E., Westveld, A.H., Goldman, T., 2005. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS. Monthly Weather Review, 133, pp.1098-1118. Scheuerer, M., Hamill, T.M., 2015. Statistical postprocessing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Monthly Weather Review, 143, pp.4578-4596. Scheuerer, M., Hamill, T.M., Whitin, B., He, M., and Henkel, A., 2016: A method for preferential selection of dates in the Schaake shuffle approach to constructing spatio-temporal forecast fields of temperature and precipitation. Water Resources Research, submitted.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1913441D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1913441D"><span>ICE CONTROL - Towards optimizing wind energy production during icing events</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dorninger, Manfred; Strauss, Lukas; Serafin, Stefano; Beck, Alexander; Wittmann, Christoph; Weidle, Florian; Meier, Florian; Bourgeois, Saskia; Cattin, René; Burchhart, Thomas; Fink, Martin</p> <p>2017-04-01</p> <p>Forecasts of wind power production loss caused by icing weather conditions are produced by a chain of physical models. The model chain consists of a numerical weather prediction model, an icing model and a production loss model. Each element of the model chain is affected by significant uncertainty, which can be quantified using targeted observations and a probabilistic forecasting approach. In this contribution, we present preliminary results from the recently launched project ICE CONTROL, an Austrian research initiative on measurements, probabilistic forecasting, and verification of icing on wind turbine blades. ICE CONTROL includes an experimental field phase, consisting of measurement campaigns in a wind park in Rhineland-Palatinate, Germany, in the winters 2016/17 and 2017/18. Instruments deployed during the campaigns consist of a conventional icing detector on the turbine hub and newly devised ice sensors (eologix Sensor System) on the turbine blades, as well as meteorological sensors for wind, temperature, humidity, visibility, and precipitation type and spectra. Liquid water content and spectral characteristics of super-cooled water droplets are measured using a Fog Monitor FM-120. Three cameras document the icing conditions on the instruments and on the blades. Different modelling approaches are used to quantify the components of the model-chain uncertainties. The uncertainty related to the initial conditions of the weather prediction is evaluated using the existing global ensemble prediction system (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Furthermore, observation system experiments are conducted with the AROME model and its 3D-Var data assimilation to investigate the impact of additional observations (such as Mode-S aircraft data, SCADA data and MSG cloud mask initialization) on the numerical icing forecast. The uncertainty related to model formulation is estimated from multi-physics ensembles based on the Weather Research and Forecasting model (WRF) by perturbing parameters in the physical parameterization schemes. In addition, uncertainties of the icing model and of its adaptations to the rotating turbine blade are addressed. The model forecasts combined with the suite of instruments and their measurements make it possible to conduct a step-wise verification of all the components of the model chain - a novel aspect compared to similar ongoing and completed forecasting projects.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JGRC..122..775S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRC..122..775S"><span>Improved forecasts of winter weather extremes over midlatitudes with extra Arctic observations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sato, Kazutoshi; Inoue, Jun; Yamazaki, Akira; Kim, Joo-Hong; Maturilli, Marion; Dethloff, Klaus; Hudson, Stephen R.; Granskog, Mats A.</p> <p>2017-02-01</p> <p>Recent cold winter extremes over Eurasia and North America have been considered to be a consequence of a warming Arctic. More accurate weather forecasts are required to reduce human and socioeconomic damages associated with severe winters. However, the sparse observing network over the Arctic brings errors in initializing a weather prediction model, which might impact accuracy of prediction results at midlatitudes. Here we show that additional Arctic radiosonde observations from the Norwegian young sea ICE expedition (N-ICE2015) drifting ice camps and existing land stations during winter improved forecast skill and reduced uncertainties of weather extremes at midlatitudes of the Northern Hemisphere. For two winter storms over East Asia and North America in February 2015, ensemble forecast experiments were performed with initial conditions taken from an ensemble atmospheric reanalysis in which the observation data were assimilated. The observations reduced errors in initial conditions in the upper troposphere over the Arctic region, yielding more precise prediction of the locations and strengths of upper troughs and surface synoptic disturbances. Errors and uncertainties of predicted upper troughs at midlatitudes would be brought with upper level high potential vorticity (PV) intruding southward from the observed Arctic region. This is because the PV contained a "signal" of the additional Arctic observations as it moved along an isentropic surface. This suggests that a coordinated sustainable Arctic observing network would be effective not only for regional weather services but also for reducing weather risks in locations distant from the Arctic.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1376542-stochastic-parameterization-toward-new-view-weather-climate-models','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1376542-stochastic-parameterization-toward-new-view-weather-climate-models"><span>Stochastic Parameterization: Toward a New View of Weather and Climate Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Berner, Judith; Achatz, Ulrich; Batté, Lauriane; ...</p> <p>2017-03-31</p> <p>The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans,more » land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1376542','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1376542"><span>Stochastic Parameterization: Toward a New View of Weather and Climate Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Berner, Judith; Achatz, Ulrich; Batté, Lauriane</p> <p></p> <p>The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans,more » land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JSWSC...8A..32F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JSWSC...8A..32F"><span>Climate, weather, space weather: model development in an operational context</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Folini, Doris</p> <p>2018-05-01</p> <p>Aspects of operational modeling for climate, weather, and space weather forecasts are contrasted, with a particular focus on the somewhat conflicting demands of "operational stability" versus "dynamic development" of the involved models. Some common key elements are identified, indicating potential for fruitful exchange across communities. Operational model development is compelling, driven by factors that broadly fall into four categories: model skill, basic physics, advances in computer architecture, and new aspects to be covered, from costumer needs over physics to observational data. Evaluation of model skill as part of the operational chain goes beyond an automated skill score. Permanent interaction between "pure research" and "operational forecast" people is beneficial to both sides. This includes joint model development projects, although ultimate responsibility for the operational code remains with the forecast provider. The pace of model development reflects operational lead times. The points are illustrated with selected examples, many of which reflect the author's background and personal contacts, notably with the Swiss Weather Service and the Max Planck Institute for Meteorology, Hamburg, Germany. In view of current and future challenges, large collaborations covering a range of expertise are a must - within and across climate, weather, and space weather. To profit from and cope with the rapid progress of computer architectures, supercompute centers must form part of the team.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012cosp...39.1955T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012cosp...39.1955T"><span>A regressive storm model for extreme space weather</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Terkildsen, Michael; Steward, Graham; Neudegg, Dave; Marshall, Richard</p> <p>2012-07-01</p> <p>Extreme space weather events, while rare, pose significant risk to society in the form of impacts on critical infrastructure such as power grids, and the disruption of high end technological systems such as satellites and precision navigation and timing systems. There has been an increased focus on modelling the effects of extreme space weather, as well as improving the ability of space weather forecast centres to identify, with sufficient lead time, solar activity with the potential to produce extreme events. This paper describes the development of a data-based model for predicting the occurrence of extreme space weather events from solar observation. The motivation for this work was to develop a tool to assist space weather forecasters in early identification of solar activity conditions with the potential to produce extreme space weather, and with sufficient lead time to notify relevant customer groups. Data-based modelling techniques were used to construct the model, and an extensive archive of solar observation data used to train, optimise and test the model. The optimisation of the base model aimed to eliminate false negatives (missed events) at the expense of a tolerable increase in false positives, under the assumption of an iterative improvement in forecast accuracy during progression of the solar disturbance, as subsequent data becomes available.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H13J1545M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H13J1545M"><span>Statistical and Hydrological evaluation of precipitation forecasts from IMD MME and ECMWF numerical weather forecasts for Indian River basins</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mohite, A. R.; Beria, H.; Behera, A. K.; Chatterjee, C.; Singh, R.</p> <p>2016-12-01</p> <p>Flood forecasting using hydrological models is an important and cost-effective non-structural flood management measure. For forecasting at short lead times, empirical models using real-time precipitation estimates have proven to be reliable. However, their skill depreciates with increasing lead time. Coupling a hydrologic model with real-time rainfall forecasts issued from numerical weather prediction (NWP) systems could increase the lead time substantially. In this study, we compared 1-5 days precipitation forecasts from India Meteorological Department (IMD) Multi-Model Ensemble (MME) with European Center for Medium Weather forecast (ECMWF) NWP forecasts for over 86 major river basins in India. We then evaluated the hydrologic utility of these forecasts over Basantpur catchment (approx. 59,000 km2) of the Mahanadi River basin. Coupled MIKE 11 RR (NAM) and MIKE 11 hydrodynamic (HD) models were used for the development of flood forecast system (FFS). RR model was calibrated using IMD station rainfall data. Cross-sections extracted from SRTM 30 were used as input to the MIKE 11 HD model. IMD started issuing operational MME forecasts from the year 2008, and hence, both the statistical and hydrologic evaluation were carried out from 2008-2014. The performance of FFS was evaluated using both the NWP datasets separately for the year 2011, which was a large flood year in Mahanadi River basin. We will present figures and metrics for statistical (threshold based statistics, skill in terms of correlation and bias) and hydrologic (Nash Sutcliffe efficiency, mean and peak error statistics) evaluation. The statistical evaluation will be at pan-India scale for all the major river basins and the hydrologic evaluation will be for the Basantpur catchment of the Mahanadi River basin.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1110921B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1110921B"><span>Metric optimisation for analogue forecasting by simulated annealing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bliefernicht, J.; Bárdossy, A.</p> <p>2009-04-01</p> <p>It is well known that weather patterns tend to recur from time to time. This property of the atmosphere is used by analogue forecasting techniques. They have a long history in weather forecasting and there are many applications predicting hydrological variables at the local scale for different lead times. The basic idea of the technique is to identify past weather situations which are similar (analogue) to the predicted one and to take the local conditions of the analogues as forecast. But the forecast performance of the analogue method depends on user-defined criteria like the choice of the distance function and the size of the predictor domain. In this study we propose a new methodology of optimising both criteria by minimising the forecast error with simulated annealing. The performance of the methodology is demonstrated for the probability forecast of daily areal precipitation. It is compared with a traditional analogue forecasting algorithm, which is used operational as an element of a hydrological forecasting system. The study is performed for several meso-scale catchments located in the Rhine basin in Germany. The methodology is validated by a jack-knife method in a perfect prognosis framework for a period of 48 years (1958-2005). The predictor variables are derived from the NCEP/NCAR reanalysis data set. The Brier skill score and the economic value are determined to evaluate the forecast skill and value of the technique. In this presentation we will present the concept of the optimisation algorithm and the outcome of the comparison. It will be also demonstrated how a decision maker should apply a probability forecast to maximise the economic benefit from it.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100026666','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100026666"><span>P.88 Regional Precipitation Forecast with Atmospheric Infrared Sounder (AIRS) Profiles</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chou, Shih-Hung; Zavodsky, Bradley; Jedlovec, Gary</p> <p>2010-01-01</p> <p>Prudent assimulation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. In general, AIRS-enhanced analysis more closely resembles radiosondes than the CNTL; forecasts with AIRS profiles are generally closer to NAM analyses than CNTL for sensible weather parameters (not shown here). Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecases. Including AIRS profiles in assimilation process enhances the low-level instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150010107','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150010107"><span>Forecasting Propagation and Evolution of CMEs in an Operational Setting: What Has Been Learned</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zheng, Yihua; Macneice, Peter; Odstrcil, Dusan; Mays, M. L.; Rastaetter, Lutz; Pulkkinen, Antti; Taktakishvili, Aleksandre; Hesse, Michael; Kuznetsova, M. Masha; Lee, Hyesook; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20150010107'); toggleEditAbsImage('author_20150010107_show'); toggleEditAbsImage('author_20150010107_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20150010107_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20150010107_hide"></p> <p>2013-01-01</p> <p>One of the major types of solar eruption, coronal mass ejections (CMEs) not only impact space weather, but also can have significant societal consequences. CMEs cause intense geomagnetic storms and drive fast mode shocks that accelerate charged particles, potentially resulting in enhanced radiation levels both in ions and electrons. Human and technological assets in space can be endangered as a result. CMEs are also the major contributor to generating large amplitude Geomagnetically Induced Currents (GICs), which are a source of concern for power grid safety. Due to their space weather significance, forecasting the evolution and impacts of CMEs has become a much desired capability for space weather operations worldwide. Based on our operational experience at Space Weather Research Center at NASA Goddard Space Flight Center (http://swrc.gsfc.nasa.gov), we present here some of the insights gained about accurately predicting CME impacts, particularly in relation to space weather operations. These include: 1. The need to maximize information to get an accurate handle of three-dimensional (3-D) CME kinetic parameters and therefore improve CME forecast; 2. The potential use of CME simulation results for qualitative prediction of regions of space where solar energetic particles (SEPs) may be found; 3. The need to include all CMEs occurring within a 24 h period for a better representation of the CME interactions; 4. Various other important parameters in forecasting CME evolution in interplanetary space, with special emphasis on the CME propagation direction. It is noted that a future direction for our CME forecasting is to employ the ensemble modeling approach.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013SpWea..11..557Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013SpWea..11..557Z"><span>Forecasting propagation and evolution of CMEs in an operational setting: What has been learned</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zheng, Yihua; Macneice, Peter; Odstrcil, Dusan; Mays, M. L.; Rastaetter, Lutz; Pulkkinen, Antti; Taktakishvili, Aleksandre; Hesse, Michael; Masha Kuznetsova, M.; Lee, Hyesook; Chulaki, Anna</p> <p>2013-10-01</p> <p>of the major types of solar eruption, coronal mass ejections (CMEs) not only impact space weather, but also can have significant societal consequences. CMEs cause intense geomagnetic storms and drive fast mode shocks that accelerate charged particles, potentially resulting in enhanced radiation levels both in ions and electrons. Human and technological assets in space can be endangered as a result. CMEs are also the major contributor to generating large amplitude Geomagnetically Induced Currents (GICs), which are a source of concern for power grid safety. Due to their space weather significance, forecasting the evolution and impacts of CMEs has become a much desired capability for space weather operations worldwide. Based on our operational experience at Space Weather Research Center at NASA Goddard Space Flight Center (http://swrc.gsfc.nasa.gov), we present here some of the insights gained about accurately predicting CME impacts, particularly in relation to space weather operations. These include: 1. The need to maximize information to get an accurate handle of three-dimensional (3-D) CME kinetic parameters and therefore improve CME forecast; 2. The potential use of CME simulation results for qualitative prediction of regions of space where solar energetic particles (SEPs) may be found; 3. The need to include all CMEs occurring within a 24 h period for a better representation of the CME interactions; 4. Various other important parameters in forecasting CME evolution in interplanetary space, with special emphasis on the CME propagation direction. It is noted that a future direction for our CME forecasting is to employ the ensemble modeling approach.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140008304','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140008304"><span>Prediction of Weather Impacted Airport Capacity using Ensemble Learning</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wang, Yao Xun</p> <p>2011-01-01</p> <p>Ensemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.6575B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.6575B"><span>Bayesian quantitative precipitation forecasts in terms of quantiles</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bentzien, Sabrina; Friederichs, Petra</p> <p>2014-05-01</p> <p>Ensemble prediction systems (EPS) for numerical weather predictions on the mesoscale are particularly developed to obtain probabilistic guidance for high impact weather. An EPS not only issues a deterministic future state of the atmosphere but a sample of possible future states. Ensemble postprocessing then translates such a sample of forecasts into probabilistic measures. This study focus on probabilistic quantitative precipitation forecasts in terms of quantiles. Quantiles are particular suitable to describe precipitation at various locations, since no assumption is required on the distribution of precipitation. The focus is on the prediction during high-impact events and related to the Volkswagen Stiftung funded project WEX-MOP (Mesoscale Weather Extremes - Theory, Spatial Modeling and Prediction). Quantile forecasts are derived from the raw ensemble and via quantile regression. Neighborhood method and time-lagging are effective tools to inexpensively increase the ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Since an EPS provides a large amount of potentially informative predictors, a variable selection is required in order to obtain a stable statistical model. A Bayesian formulation of quantile regression allows for inference about the selection of predictive covariates by the use of appropriate prior distributions. Moreover, the implementation of an additional process layer for the regression parameters accounts for spatial variations of the parameters. Bayesian quantile regression and its spatially adaptive extension is illustrated for the German-focused mesoscale weather prediction ensemble COSMO-DE-EPS, which runs (pre)operationally since December 2010 at the German Meteorological Service (DWD). Objective out-of-sample verification uses the quantile score (QS), a weighted absolute error between quantile forecasts and observations. The QS is a proper scoring function and can be decomposed into reliability, resolutions and uncertainty parts. A quantile reliability plot gives detailed insights in the predictive performance of the quantile forecasts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100040487','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100040487"><span>Applied Meteorology Unit (AMU) Quarterly Report - Fourth Quarter FY-10</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bauman, William; Crawford, Winifred; Barrett, Joe; Watson, Leela; Wheeler, Mark</p> <p>2010-01-01</p> <p>Three AMU tasks were completed in this Quarter, each resulting in a forecast tool now being used in operations and a final report documenting how the work was done. AMU personnel completed the following tasks (1) Phase II of the Peak Wind Tool for General Forecasting task by delivering an improved wind forecasting tool to operations and providing training on its use; (2) a graphical user interface (GUI) she updated with new scripts to complete the ADAS Update and Maintainability task, and delivered the scripts to the Spaceflight Meteorology Group on Johnson Space Center, Texas and National Weather Service in Melbourne, Fla.; and (3) the Verify MesoNAM Performance task after we created and delivered a GUI that forecasters will use to determine the performance of the operational MesoNAM weather model forecast.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/om/marine/spacewx.htm','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/om/marine/spacewx.htm"><span>Space Weather Impacts to Mariners</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Tsunamis 406 EPIRB's National Weather Service Marine Forecasts SPACE WEATHER <em>IMPACTS</em> TO MARINERS Marine present an even greater danger near shore or any shallow waters? Space Weather <em>Impacts</em> to Mariners Don't ), Notices to Mariners, Special Paragraphs: "(73) SPACE WEATHER <em>IMPACTS</em>. There is a growing potential</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2010-title14-vol3/pdf/CFR-2010-title14-vol3-sec135-221.pdf','CFR'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2010-title14-vol3/pdf/CFR-2010-title14-vol3-sec135-221.pdf"><span>14 CFR 135.221 - IFR: Alternate airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2010&page.go=Go">Code of Federal Regulations, 2010 CFR</a></p> <p></p> <p>2010-01-01</p> <p>... 14 Aeronautics and Space 3 2010-01-01 2010-01-01 false IFR: Alternate airport weather minimums... VFR/IFR Operating Limitations and Weather Requirements § 135.221 IFR: Alternate airport weather minimums. No person may designate an alternate airport unless the weather reports or forecasts, or any...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2013-title14-vol3/pdf/CFR-2013-title14-vol3-sec135-221.pdf','CFR2013'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2013-title14-vol3/pdf/CFR-2013-title14-vol3-sec135-221.pdf"><span>14 CFR 135.221 - IFR: Alternate airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2013&page.go=Go">Code of Federal Regulations, 2013 CFR</a></p> <p></p> <p>2013-01-01</p> <p>... 14 Aeronautics and Space 3 2013-01-01 2013-01-01 false IFR: Alternate airport weather minimums... VFR/IFR Operating Limitations and Weather Requirements § 135.221 IFR: Alternate airport weather minimums. No person may designate an alternate airport unless the weather reports or forecasts, or any...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol3/pdf/CFR-2011-title14-vol3-sec135-221.pdf','CFR2011'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol3/pdf/CFR-2011-title14-vol3-sec135-221.pdf"><span>14 CFR 135.221 - IFR: Alternate airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2011&page.go=Go">Code of Federal Regulations, 2011 CFR</a></p> <p></p> <p>2011-01-01</p> <p>... 14 Aeronautics and Space 3 2011-01-01 2011-01-01 false IFR: Alternate airport weather minimums... VFR/IFR Operating Limitations and Weather Requirements § 135.221 IFR: Alternate airport weather minimums. No person may designate an alternate airport unless the weather reports or forecasts, or any...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2014-title14-vol3/pdf/CFR-2014-title14-vol3-sec135-221.pdf','CFR2014'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2014-title14-vol3/pdf/CFR-2014-title14-vol3-sec135-221.pdf"><span>14 CFR 135.221 - IFR: Alternate airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2014&page.go=Go">Code of Federal Regulations, 2014 CFR</a></p> <p></p> <p>2014-01-01</p> <p>... 14 Aeronautics and Space 3 2014-01-01 2014-01-01 false IFR: Alternate airport weather minimums... VFR/IFR Operating Limitations and Weather Requirements § 135.221 IFR: Alternate airport weather minimums. No person may designate an alternate airport unless the weather reports or forecasts, or any...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2012-title14-vol3/pdf/CFR-2012-title14-vol3-sec135-221.pdf','CFR2012'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2012-title14-vol3/pdf/CFR-2012-title14-vol3-sec135-221.pdf"><span>14 CFR 135.221 - IFR: Alternate airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2012&page.go=Go">Code of Federal Regulations, 2012 CFR</a></p> <p></p> <p>2012-01-01</p> <p>... 14 Aeronautics and Space 3 2012-01-01 2012-01-01 false IFR: Alternate airport weather minimums... VFR/IFR Operating Limitations and Weather Requirements § 135.221 IFR: Alternate airport weather minimums. No person may designate an alternate airport unless the weather reports or forecasts, or any...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://kidshealth.org/en/parents/frostbite.html','NIH-MEDLINEPLUS'); return false;" href="https://kidshealth.org/en/parents/frostbite.html"><span>Frostbite</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://medlineplus.gov/">MedlinePlus</a></p> <p></p> <p></p> <p>... Preventing Frostbite To help prevent frostbite in cold weather: Stay updated on weather forecasts. If it's extremely cold, even brief exposure ... Medical History Cold, Ice, and Snow Safety Cold-Weather Sports and Your Family First-Aid Kit What ...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/outlook_ak_tab.php','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/outlook_ak_tab.php"><span>National Maps - Alaska - NOAA's National Weather Service</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>select <em>the</em> go button to submit request City, St Go Sign-up <em>for</em> Email Alerts RSS Feeds RSS Feeds Warnings current Forecast <em>for</em> Alaska is produced by <em>the</em> NWS Anchorage Forecast Office. It is updated daily Skip Navigation Links weather.gov NOAA logo-Select to go to <em>the</em> NOAA homepage National Oceanic and</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=temperature+AND+classes&pg=3&id=EJ722372','ERIC'); return false;" href="https://eric.ed.gov/?q=temperature+AND+classes&pg=3&id=EJ722372"><span>The Quest for the Perfect Weather Forecaster</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Kahl, Jonathan; Horwitz, Kevin; Berg, Craig; Gruhl, Mary</p> <p>2004-01-01</p> <p>It is said that meteorology is the only profession where a person can be wrong half the time and still keep his or her job. The truth is not quite so bleak, but one can still ask, "Just how accurate are weather forecasters, anyway?" This article presents two projects for middle level students to investigate this issue in a hands-on,…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760004817','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760004817"><span>Multidisciplinary studies of the social, economic and political impact resulting from recent advances in satellite meteorology. Volume 6: Executive summary. [technological forecasting spacecraft control/attitude (inclination) -classical mechanics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1975-01-01</p> <p>An assessment of the technological impact of modern satellite weather forecasting for the United States is presented. Topics discussed are: (1) television broadcasting of weather; (2) agriculture (crop production); (3) water resources; (4) urban development; (5) recreation; and (6) transportation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140009603','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140009603"><span>A Comparison of Perturbed Initial Conditions and Multiphysics Ensembles in a Severe Weather Episode in Spain</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tapiador, Francisco; Tao, Wei-Kuo; Angelis, Carlos F.; Martinez, Miguel A.; Cecilia Marcos; Antonio Rodriguez; Hou, Arthur; Jong Shi, Jain</p> <p>2012-01-01</p> <p>Ensembles of numerical model forecasts are of interest to operational early warning forecasters as the spread of the ensemble provides an indication of the uncertainty of the alerts, and the mean value is deemed to outperform the forecasts of the individual models. This paper explores two ensembles on a severe weather episode in Spain, aiming to ascertain the relative usefulness of each one. One ensemble uses sensible choices of physical parameterizations (precipitation microphysics, land surface physics, and cumulus physics) while the other follows a perturbed initial conditions approach. The results show that, depending on the parameterizations, large differences can be expected in terms of storm location, spatial structure of the precipitation field, and rain intensity. It is also found that the spread of the perturbed initial conditions ensemble is smaller than the dispersion due to physical parameterizations. This confirms that in severe weather situations operational forecasts should address moist physics deficiencies to realize the full benefits of the ensemble approach, in addition to optimizing initial conditions. The results also provide insights into differences in simulations arising from ensembles of weather models using several combinations of different physical parameterizations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA607725','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA607725"><span>An Analysis of Numerical Weather Prediction of the Diabatic Rossby Vortex</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-06-01</p> <p>Forecast SLP Mean and Spread ...............................................................................................148 2. DRV02 72 Hour...ECMWF Ensemble Forecast SLP Mean and Spread ...............................................................................................149 3...DRV03 72 Hour ECMWF Ensemble Forecast SLP Mean and Spread</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110008800','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110008800"><span>Demonstrating the Operational Value of Thermodynamic Hyperspectral Profiles in the Pre-Convective Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kozlowski, Danielle; Zavodsky, Bradley T.; Jedlovec, Gary J.</p> <p>2011-01-01</p> <p>The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service (NWS) Weather Forecasting Offices (WFO). As a part of the transition to operations process, SPoRT attempts to identify possible limitations in satellite observations and provide operational forecasters a product that will result in the most impact on their forecasts. One operational forecast challenge that some NWS offices face, is forecasting convection in data-void regions such as large bodies of water. The Atmospheric Infrared Sounder (AIRS) is a sounding instrument aboard NASA's Aqua satellite that provides temperature and moisture profiles of the atmosphere. This paper will demonstrate an approach to assimilate AIRS profile data into a regional configuration of the WRF model using its three-dimensional variational (3DVAR) assimilation component to be used as a proxy for the individual profiles.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015TESS....120004Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015TESS....120004Z"><span>CCMC: Serving research and space weather communities with unique space weather services, innovative tools and resources</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zheng, Yihua; Kuznetsova, Maria M.; Pulkkinen, Antti; Maddox, Marlo</p> <p>2015-04-01</p> <p>With the addition of Space Weather Research Center (a sub-team within CCMC) in 2010 to address NASA’s own space weather needs, CCMC has become a unique entity that not only facilitates research through providing access to the state-of-the-art space science and space weather models, but also plays a critical role in providing unique space weather services to NASA robotic missions, developing innovative tools and transitioning research to operations via user feedback. With scientists, forecasters and software developers working together within one team, through close and direct connection with space weather customers and trusted relationship with model developers, CCMC is flexible, nimble and effective to meet customer needs. In this presentation, we highlight a few unique aspects of CCMC/SWRC’s space weather services, such as addressing space weather throughout the solar system, pushing the frontier of space weather forecasting via the ensemble approach, providing direct personnel and tool support for spacecraft anomaly resolution, prompting development of multi-purpose tools and knowledge bases, and educating and engaging the next generation of space weather scientists.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUSM.A53C..04H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUSM.A53C..04H"><span>Weather modeling for hazard and consequence assessment operations during the 2006 Winter Olympic Games</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hayes, P.; Trigg, J. L.; Stauffer, D.; Hunter, G.; McQueen, J.</p> <p>2006-05-01</p> <p>Consequence assessment (CA) operations are those processes that attempt to mitigate negative impacts of incidents involving hazardous materials such as chemical, biological, radiological, nuclear, and high explosive (CBRNE) agents, facilities, weapons, or transportation. Incident types range from accidental spillage of chemicals at/en route to/from a manufacturing plant, to the deliberate use of radiological or chemical material as a weapon in a crowded city. The impacts of these incidents are highly variable, from little or no impact to catastrophic loss of life and property. Local and regional scale atmospheric conditions strongly influence atmospheric transport and dispersion processes in the boundary layer, and the extent and scope of the spread of dangerous materials in the lower levels of the atmosphere. Therefore, CA personnel charged with managing the consequences of CBRNE incidents must have detailed knowledge of current and future weather conditions to accurately model potential effects. A meteorology team was established at the U.S. Defense Threat Reduction Agency (DTRA) to provide weather support to CA personnel operating DTRA's CA tools, such as the Hazard Prediction and Assessment Capability (HPAC) tool. The meteorology team performs three main functions: 1) regular provision of meteorological data for use by personnel using HPAC, 2) determination of the best performing medium-range model forecast for the 12 - 48 hour timeframe and 3) provision of real-time help-desk support to users regarding acquisition and use of weather in HPAC CA applications. The normal meteorology team operations were expanded during a recent modeling project which took place during the 2006 Winter Olympic Games. The meteorology team took advantage of special weather observation datasets available in the domain of the Winter Olympic venues and undertook a project to improve weather modeling at high resolution. The varied and complex terrain provided a special challenge to the modelers on the meteorology team. Some of the Olympic venues were located in the mountains to the west of Torino, while the rest were located on the relatively flat plain in and around the cities of Pinerolo and Torino to the east. DTRA partners at Pennsylvania State University (PSU) and the U.S. National Center for Atmospheric Research (NCAR) established data collection and assimilation, and forecast modeling processes that used special weather station observations provided by the Area Previsione e Monitoraggio Ambientale of Italy's ARPA Piemonte. At PSU a version of the MM5 was especially prepared to use observation data to forecast weather in a four-nest configuration. Two other DTRA partners provided independent weather forecast models against which the PSU model data were compared. The U.S. Air Force Weather Agency provided its MM5 forecast model data and the U.S. National Oceanic and Atmospheric Administration's National Centers for Environmental Prediction provided data from a special version of their WRF model. The project produced many opportunities to improve the modeling and forecasting capability at DTRA. DTRA and its partners plan to expand upon this experience during upcoming field tests, and to further improve and expand the capability to provide accurate high-resolution weather forecast information to hazard and consequence assessment operations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1814979G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1814979G"><span>Tracking Cloud Motion and Deformation for Short-Term Photovoltaic Power Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Good, Garrett; Siefert, Malte; Fritz, Rafael; Saint-Drenan, Yves-Marie; Dobschinski, Jan</p> <p>2016-04-01</p> <p>With the increasing role of photovoltaic power production, the need to accurately forecast and anticipate weather-driven elements like cloud cover has become ever more important. Of particular concern is forecasting on the short-term (up to several hours), for which the most recent full weather simulation may no longer provide the most accurate information in light of real-time satellite measurements. We discuss the application of the image correlation velocimetry technique described by Tokumaru & Dimotakis (1995) (for calculating flow fields from images) to measure deformations of various orders based on recent satellite imagery, with the goal of not only more accurately forecasting the advection of cloud structures, but their continued deformation as well.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H31A1498G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H31A1498G"><span>Extreme precipitation forecasting in the Chilean Andean region with complex topography using the Weather Research and Forecasting (WRF) model</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gironás, J.; Yáñez Morroni, G.; Caneo, M.; Delgado, R.</p> <p>2017-12-01</p> <p>The Weather Research and Forecasting (WRF) model is broadly used for weather forecasting, hindcasting and researching due to its good performance. However, the atmospheric conditions for simulating are not always optimal when it includes complex topographies: affecting WRF mathematical stability and convergence, therefore, its performance. As Chile is a country strongly characterized by a complex topography and high gradients of elevation, WRF is ineffective resolving Chilean mountainous terrain and foothills. The need to own an effective weather forecasting tool relies on that Chile's main cities are located in these regions. Furthermore, the most intense rainfall events take place here, commonly caused by the presence of cutoff lows. This work analyzes a microphysics scheme ensemble to enhance initial forecasts made by the Chilean Weather Agency (DMC). These forecasts were made over the Santiago piedmont, in Quebrada de Ramón watershed, located upstream an urban area highly populated. In this region a non-existing planning increases the potential damage of a flash flood. An initial testing was made over different vertical levels resolution (39 and 50 levels), and subsequently testing with land use and surface models, and finally with the initial and boundary condition data (GFS/FNL). Our task made emphasis in analyzing microphysics and lead time (3 to 5 days before the storm peak) in the computational simulations over three extreme rainfall events between 2015 and 2017. WRF shortcoming are also related to the complex configuration of the synoptic events, even when the steep topography difficult the rainfall event peak amount, and to a lesser degree, the exact rainfall event beginning prediction. No evident trend was found in the lead time, but as expected, better results in rainfall and zero isotherm height are obtained with smaller anticipation. We found that WRF do predict properly the N-hours with the biggest amount of rainfall (5 hours corresponding to Quebrada de Ramón's time of concentration) and the temperatures during the event. This is a fundamental input to a hydrological model that could forecast flash floods. Finally, WSM-6Class microphysics was chosen as the one with best performance, but a geostatistical approach to countervail WRF forecasts' shortcomings over Andean piedmont is required.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1611854A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1611854A"><span>Improving GEFS Weather Forecasts for Indian Monsoon with Statistical Downscaling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Agrawal, Ankita; Salvi, Kaustubh; Ghosh, Subimal</p> <p>2014-05-01</p> <p>Weather forecast has always been a challenging research problem, yet of a paramount importance as it serves the role of 'key input' in formulating modus operandi for immediate future. Short range rainfall forecasts influence a wide range of entities, right from agricultural industry to a common man. Accurate forecasts actually help in minimizing the possible damage by implementing pre-decided plan of action and hence it is necessary to gauge the quality of forecasts which might vary with the complexity of weather state and regional parameters. Indian Summer Monsoon Rainfall (ISMR) is one such perfect arena to check the quality of weather forecast not only because of the level of intricacy in spatial and temporal patterns associated with it, but also the amount of damage it can cause (because of poor forecasts) to the Indian economy by affecting agriculture Industry. The present study is undertaken with the rationales of assessing, the ability of Global Ensemble Forecast System (GEFS) in predicting ISMR over central India and the skill of statistical downscaling technique in adding value to the predictions by taking them closer to evidentiary target dataset. GEFS is a global numerical weather prediction system providing the forecast results of different climate variables at a fine resolution (0.5 degree and 1 degree). GEFS shows good skills in predicting different climatic variables but fails miserably over rainfall predictions for Indian summer monsoon rainfall, which is evident from a very low to negative correlation values between predicted and observed rainfall. Towards the fulfilment of second rationale, the statistical relationship is established between the reasonably well predicted climate variables (GEFS) and observed rainfall. The GEFS predictors are treated with multicollinearity and dimensionality reduction techniques, such as principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Statistical relationship is established between the principal components and observed rainfall over training period and predictions are obtained for testing period. The validations show high improvements in correlation coefficient between observed and predicted data (0.25 to 0.55). The results speak in favour of statistical downscaling methodology which shows the capability to reduce the gap between observed data and predictions. A detailed study is required to be carried out by applying different downscaling techniques to quantify the improvements in predictions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/om/marine/marinewxi.htm','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/om/marine/marinewxi.htm"><span>National Weather Service Marine Text Forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Offshore NAVTEX Bay and <em>Coastal</em> Surf Zone Recreational Marine Weather Statements Special Marine Warnings Marine Weather Messages Alaska Bay and <em>Coastal</em> Great Lakes Open Lake Great Lakes Nearshore Great Lakes</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160007687','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160007687"><span>Real-Time Kennedy Space Center and Cape Canaveral Air Force Station High-Resolution Model Implementation and Verification</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shafer, Jaclyn A.; Watson, Leela R.</p> <p>2015-01-01</p> <p>Customer: NASA's Launch Services Program (LSP), Ground Systems Development and Operations (GSDO), and Space Launch System (SLS) programs. NASA's LSP, GSDO, SLS and other programs at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) use the daily and weekly weather forecasts issued by the 45th Weather Squadron (45 WS) as decision tools for their day-to-day and launch operations on the Eastern Range (ER). For example, to determine if they need to limit activities such as vehicle transport to the launch pad, protect people, structures or exposed launch vehicles given a threat of severe weather, or reschedule other critical operations. The 45 WS uses numerical weather prediction models as a guide for these weather forecasts, particularly the Air Force Weather Agency (AFWA) 1.67 kilometer Weather Research and Forecasting (WRF) model. Considering the 45 WS forecasters' and Launch Weather Officers' (LWO) extensive use of the AFWA model, the 45 WS proposed a task at the September 2013 Applied Meteorology Unit (AMU) Tasking Meeting requesting the AMU verify this model. Due to the lack of archived model data available from AFWA, verification is not yet possible. Instead, the AMU proposed to implement and verify the performance of an ER version of the AMU high-resolution WRF Environmental Modeling System (EMS) model (Watson 2013) in real-time. The tasking group agreed to this proposal; therefore the AMU implemented the WRF-EMS model on the second of two NASA AMU modeling clusters. The model was set up with a triple-nested grid configuration over KSC/CCAFS based on previous AMU work (Watson 2013). The outer domain (D01) has 12-kilometer grid spacing, the middle domain (D02) has 4-kilometer grid spacing, and the inner domain (D03) has 1.33-kilometer grid spacing. The model runs a 12-hour forecast every hour, D01 and D02 domain outputs are available once an hour and D03 is every 15 minutes during the forecast period. The AMU assessed the WRF-EMS 1.33-kilometer domain model performance for the 2014 warm season (May-September). Verification statistics were computed using the Model Evaluation Tools, which compared the model forecasts to observations. The mean error values were close to 0 and the root mean square error values were less than 1.8 for mean sea-level pressure (millibars), temperature (degrees Kelvin), dewpoint temperature (degrees Kelvin), and wind speed (per millisecond), all very small differences between the forecast and observations considering the normal magnitudes of the parameters. The precipitation forecast verification results showed consistent under-forecasting of the precipitation object size. This could be an artifact of calculating the statistics for each hour rather than for the entire 12-hour period. The AMU will continue to generate verification statistics for the 1.33-kilometer WRF-EMS domain as data become available in future cool and warm seasons. More data will produce more robust statistics and reveal a more accurate assessment of model performance. Once the formal task was complete, the AMU conducted additional work to better understand the wind direction results. The results were stratified diurnally and by wind speed to determine what effects the stratifications would have on the model wind direction verification statistics. The results are summarized in the addendum at the end of this report. In addition to verifying the model's performance, the AMU also made the output available in the Advanced Weather Interactive Processing System II (AWIPS II). This allows the 45 WS and AMU staff to customize the model output display on the AMU and Range Weather Operations AWIPS II client computers and conduct real-time subjective analyses. In the future, the AMU will implement an updated version of the WRF-EMS model that incorporates local data assimilation. This model will also run in real-time and be made available in AWIPS II.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MNRAS.459..419P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MNRAS.459..419P"><span>Validation of WRF forecasts for the Chajnantor region</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pozo, Diana; Marín, J. C.; Illanes, L.; Curé, M.; Rabanus, D.</p> <p>2016-06-01</p> <p>This study assesses the performance of the Weather Research and Forecasting (WRF) model to represent the near-surface weather conditions and the precipitable water vapour (PWV) in the Chajnantor plateau, in the north of Chile, from 2007 April to December. The WRF model shows a very good performance forecasting the near-surface temperature and zonal wind component, although it overestimates the 2 m water vapour mixing ratio and underestimates the 10 m meridional wind component. The model represents very well the seasonal, intraseasonal and the diurnal variation of PWV. However, the PWV errors increase after the 12 h of simulation. Errors in the simulations are larger than 1.5 mm only during 10 per cent of the study period, they do not exceed 0.5 mm during 65 per cent of the time and they are below 0.25 mm more than 45 per cent of the time, which emphasizes the good performance of the model to forecast the PWV over the region. The misrepresentation of the near-surface humidity in the region by the WRF model may have a negative impact on the PWV forecasts. Thus, having accurate forecasts of humidity near the surface may result in more accurate PWV forecasts. Overall, results from this, as well as recent studies, supports the use of the WRF model to provide accurate weather forecasts for the region, particularly for the PWV, which can be of great benefit for astronomers in the planning of their scientific operations and observing time.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AdSpR..47.2073R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AdSpR..47.2073R"><span>Forecasting space weather: Can new econometric methods improve accuracy?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reikard, Gordon</p> <p>2011-06-01</p> <p>Space weather forecasts are currently used in areas ranging from navigation and communication to electric power system operations. The relevant forecast horizons can range from as little as 24 h to several days. This paper analyzes the predictability of two major space weather measures using new time series methods, many of them derived from econometrics. The data sets are the A p geomagnetic index and the solar radio flux at 10.7 cm. The methods tested include nonlinear regressions, neural networks, frequency domain algorithms, GARCH models (which utilize the residual variance), state transition models, and models that combine elements of several techniques. While combined models are complex, they can be programmed using modern statistical software. The data frequency is daily, and forecasting experiments are run over horizons ranging from 1 to 7 days. Two major conclusions stand out. First, the frequency domain method forecasts the A p index more accurately than any time domain model, including both regressions and neural networks. This finding is very robust, and holds for all forecast horizons. Combining the frequency domain method with other techniques yields a further small improvement in accuracy. Second, the neural network forecasts the solar flux more accurately than any other method, although at short horizons (2 days or less) the regression and net yield similar results. The neural net does best when it includes measures of the long-term component in the data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20170004871&hterms=Lagrangian&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DLagrangian','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170004871&hterms=Lagrangian&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DLagrangian"><span>What If We Had A Magnetograph at Lagrangian L5?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Pevtsov, Alexei A.; Bertello, Luca; MacNeice, Peter; Petrie, Gordon</p> <p>2016-01-01</p> <p>Synoptic Carrington charts of magnetic field are routinely used as an input for modelings of solar wind and other aspects of space weather forecast. However, these maps are constructed using only the observations from the solar hemisphere facing Earth. The evolution of magnetic flux on the "farside" of the Sun, which may affect the topology of coronal field in the "nearside," is largely ignored. It is commonly accepted that placing a magnetograph in Lagrangian L5 point would improve the space weather forecast. However, the quantitative estimates of anticipated improvements have been lacking. We use longitudinal magnetograms from the Synoptic Optical Long-term Investigations of the Sun (SOLIS) to investigate how adding data from L5 point would affect the outcome of two major models used in space weather forecast.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19850027225','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19850027225"><span>Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning: Summary report</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Keitz, J. F.</p> <p>1982-01-01</p> <p>The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This summary report discusses the results of each of the four major tasks of the study. Task 1 compared airline flight plans based on operational forecasts to plans based on the verifying analyses and found that average fuel savings of 1.2 to 2.5 percent are possible with improved forecasts. Task 2 consisted of similar comparisons but used a model developed for the FAA by SRI International that simulated the impact of ATc diversions on the flight plans. While parts of Task 2 confirm the Task I findings, inconsistency with other data and the known impact of ATC suggests that other Task 2 findings are the result of errors in the model. Task 3 compares segment weather data from operational flight plans with the weather actually observed by the aircraft and finds the average error could result in fuel burn penalties (or savings) of up to 3.6 percent for the average 8747 flight. In Task 4 an in-depth analysis of the weather forecast for the 33 days included in the study finds that significant errors exist on 15 days. Wind speeds in the area of maximum winds are underestimated by 20 to 50 kts., a finding confirmed in the other three tasks.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1715278C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1715278C"><span>Linking seasonal climate forecasts with crop models in Iberian Peninsula</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Capa, Mirian; Ines, Amor; Baethgen, Walter; Rodriguez-Fonseca, Belen; Han, Eunjin; Ruiz-Ramos, Margarita</p> <p>2015-04-01</p> <p>Translating seasonal climate forecasts into agricultural production forecasts could help to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. In this study, we use seasonal rainfall forecasts and crop models to improve predictability of wheat yield in the Iberian Peninsula (IP). Additionally, we estimate economic margins and production risks associated with extreme scenarios of seasonal rainfall forecast. This study evaluates two methods for disaggregating seasonal climate forecasts into daily weather data: 1) a stochastic weather generator (CondWG), and 2) a forecast tercile resampler (FResampler). Both methods were used to generate 100 (with FResampler) and 110 (with CondWG) weather series/sequences for three scenarios of seasonal rainfall forecasts. Simulated wheat yield is computed with the crop model CERES-wheat (Ritchie and Otter, 1985), which is included in Decision Support System for Agrotechnology Transfer (DSSAT v.4.5, Hoogenboom et al., 2010). Simulations were run at two locations in northeastern Spain where the crop model was calibrated and validated with independent field data. Once simulated yields were obtained, an assessment of farmer's gross margin for different seasonal climate forecasts was accomplished to estimate production risks under different climate scenarios. This methodology allows farmers to assess the benefits and risks of a seasonal weather forecast in IP prior to the crop growing season. The results of this study may have important implications on both, public (agricultural planning) and private (decision support to farmers, insurance companies) sectors. Acknowledgements Research by M. Capa-Morocho has been partly supported by a PICATA predoctoral fellowship of the Moncloa Campus of International Excellence (UCM-UPM) and MULCLIVAR project (CGL2012-38923-C02-02) References Hoogenboom, G. et al., 2010. The Decision Support System for Agrotechnology Transfer (DSSAT).Version 4.5 [CD-ROM].University of Hawaii, Honolulu, Hawaii. Ritchie, J.T., Otter, S., 1985. Description and performanceof CERES-Wheat: a user-oriented wheat yield model. In: ARS Wheat Yield Project. ARS-38.Natl Tech Info Serv, Springfield, Missouri, pp. 159-175.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1712188O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1712188O"><span>The total probabilities from high-resolution ensemble forecasting of floods</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian</p> <p>2015-04-01</p> <p>Ensemble forecasting has for a long time been used in meteorological modelling, to give an indication of the uncertainty of the forecasts. As meteorological ensemble forecasts often show some bias and dispersion errors, there is a need for calibration and post-processing of the ensembles. Typical methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). To make optimal predictions of floods along the stream network in hydrology, we can easily use the ensemble members as input to the hydrological models. However, some of the post-processing methods will need modifications when regionalizing the forecasts outside the calibration locations, as done by Hemri et al. (2013). We present a method for spatial regionalization of the post-processed forecasts based on EMOS and top-kriging (Skøien et al., 2006). We will also look into different methods for handling the non-normality of runoff and the effect on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133(5), 1155-1174, doi:10.1175/MWR2906.1, 2005. Skøien, J. O., Merz, R. and Blöschl, G.: Top-kriging - Geostatistics on stream networks, Hydrol. Earth Syst. Sci., 10(2), 277-287, 2006.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMSH32B..06B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMSH32B..06B"><span>Space Weather Forecasting Operational Needs: A view from NOAA/SWPC</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Biesecker, D. A.; Onsager, T. G.; Rutledge, R.</p> <p>2017-12-01</p> <p>The gaps in space weather forecasting are many. From long lead time forecasts, to accurate warnings with lead time to take action, there is plenty of room for improvement. Significant numbers of new observations would improve this picture, but it's also important to recognize the value of numerical modeling. The obvious interplanetary mission concepts that would be ideal would be 1) to measure the in-situ solar wind along the entire Sun-Earth line from as near to the Sun as possible all the way to Earth 2) a string of spacecraft in 1 AU heliocentric orbits making in-situ measurements as well as remote-sensing observations of the Sun, corona, and heliosphere. Even partially achieving these ideals would benefit space weather services, improving lead time and providing greater accuracy further into the future. The observations alone would improve forecasting. However, integrating these data into numerical models, as boundary conditions or via data assimilation, would provide the greatest improvements.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JGRD..123.3341M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JGRD..123.3341M"><span>Assimilating InSAR Maps of Water Vapor to Improve Heavy Rainfall Forecasts: A Case Study With Two Successive Storms</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mateus, Pedro; Miranda, Pedro M. A.; Nico, Giovanni; Catalão, João.; Pinto, Paulo; Tomé, Ricardo</p> <p>2018-04-01</p> <p>Very high resolution precipitable water vapor maps obtained by the Sentinel-1 A synthetic aperture radar (SAR), using the SAR interferometry (InSAR) technique, are here shown to have a positive impact on the performance of severe weather forecasts. A case study of deep convection which affected the city of Adra, Spain, on 6-7 September 2015, is successfully forecasted by the Weather Research and Forecasting model initialized with InSAR data assimilated by the three-dimensional variational technique, with improved space and time distributions of precipitation, as observed by the local weather radar and rain gauge. This case study is exceptional because it consisted of two severe events 12 hr apart, with a timing that allows for the assimilation of both the ascending and descending satellite images, each for the initialization of each event. The same methodology applied to the network of Global Navigation Satellite System observations in Iberia, at the same times, failed to reproduce observed precipitation, although it also improved, in a more modest way, the forecast skill. The impact of precipitable water vapor data is shown to result from a direct increment of convective available potential energy, associated with important adjustments in the low-level wind field, favoring its release in deep convection. It is suggested that InSAR images, complemented by dense Global Navigation Satellite System data, may provide a new source of water vapor data for weather forecasting, since their sampling frequency could reach the subdaily scale by merging different SAR platforms, or when future geosynchronous radar missions become operational.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/894417','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/894417"><span>AMMA 2005 Dakar International Conference to be Held November 28-December 2, 2005</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Lamb, Peter</p> <p>2006-05-09</p> <p>Consistent with the original proposal (dated April 14, 2005), the grant supported the participation in the above conference of a number of West African meteorologists, the majority of whom will be supporting the ARM Mobile Facility deployment in Niamey in various ways during 2006. The following seven individuals were fully funded (complete airfare, accommodation, registration, meals) to participate in the Conference –Yerima Ladan (Head, ASECNA Forecast Office, Niamey Airport); Dr. Ousmane Manga Adamou (University of Niamey); Abdou Adam Abdoul-Aziz Abebe (Forecasater, ASECNA Forecast Office, Niamey Airport); Hassane Abdou (Forecaster, ASECNA Forecast Office, Niamey Airport); Saley Diori (Forecaster, ASECNA Forecast Office,more » Niamey Airport); Alhassane Diallo (Meteorological Engineer, Burkina Faso Weather Service). The following three individuals were partly funded (for some of their airfare, accommodation, registration, meals) to participate in the Conference – Katiellou Lawan (International Relations, Niger Weather Service); Mamoutou Kouressy (Institute of Rural Economics, Niger Department of Agriculture); and Francis Dide (Benin Weather Service). I am confident that the participation of the above individuals in the Conference will facilitate both the smooth operation of the ARM Mobile Facility in Niamey during 2006 and the involvement of University of Niamey scientists in analysis of the data collected. We appreciate greatly this support from the ARM Program.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AcMeS..26...93B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AcMeS..26...93B"><span>Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bao, Hongjun; Zhao, Linna</p> <p>2012-02-01</p> <p>A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110008779','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110008779"><span>Demonstrating the Operational Value of Atmospheric Infrared Sounder (AIRS) Retrieved Profiles in the Pre-Convective Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kozlowski, Danielle M.; Zavodsky, T.; Jedloved, Gary J.</p> <p>2011-01-01</p> <p>The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service offices. SPoRT provides real-time NASA products and capabilities to its partners to address specific operational forecast challenges. One operational forecast challenge is forecasting convective weather in data-void regions such as large bodies of water (e.g. Gulf of Mexico). To address this forecast challenge, SPoRT produces a twice-daily three-dimensional analysis that blends a model first-guess from the Advanced Research Weather Research and Forecasting (WRF-ARW) model with retrieved profiles from the Atmospheric Infrared Sounder (AIRS) -- a hyperspectral sounding instrument aboard NASA's Aqua satellite that provides temperature and moisture profiles of the atmosphere. AIRS profiles are unique in that they give a three dimensional view of the atmosphere that is not available through the current rawinsonde network. AIRS has two overpass swaths across North America each day, one valid in the 0700-0900 UTC timeframe and the other in the 1900-2100 UTC timeframe. This is helpful because the rawinsonde network only has data from 0000 UTC and 1200 UTC at specific land-based locations. Comparing the AIRS analysis product with control analyses that include no AIRS data demonstrates the value of the retrieved profiles to situational awareness for the pre-convective (and convective) environment. In an attempt to verify that the AIRS analysis was a good representation of the vertical structure of the atmosphere, both the AIRS and control analyses are compared to a Rapid Update Cycle (RUC) analysis used by operational forecasters. Using guidance from operational forecasters, convective available potential energy (CAPE) was determined to be a vital variable in making convective forecasts and is used herein to demonstrate the utility of the AIRS profiles in changing the vertical thermodynamic structure of the atmosphere in the pre-convective and convective environment. CAPE is an important metric because of it is a quantitative measure of atmospheric stability, which is necessary information when forecasting for convective weather. Case studies from the summer of 2010 were examined, and most impact from the AIRS retrieved profiles occurred over the data-void Gulf of Mexico with fields of convective potential closer to the RUC than the CNTL. Mixed results were found when AIRS retrieved profiles were used over land, so more cases need to be examined to determine whether AIRS would be an effective tool over land. Additional analyses of problematic convective forecasts over the Gulf Coast will be needed to determine the operational impact of AIRS. SPoRT eventually plans to transition the AIRS product to select Weather Forecast Office (WFO) partners, pending the outcome of these additional analyses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1612099S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1612099S"><span>Towards more accurate wind and solar power prediction by improving NWP model physics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Steiner, Andrea; Köhler, Carmen; von Schumann, Jonas; Ritter, Bodo</p> <p>2014-05-01</p> <p>The growing importance and successive expansion of renewable energies raise new challenges for decision makers, economists, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the errors and provide an a priori estimate of remaining uncertainties associated with the large share of weather-dependent power sources. For this purpose it is essential to optimize NWP model forecasts with respect to those prognostic variables which are relevant for wind and solar power plants. An improved weather forecast serves as the basis for a sophisticated power forecasts. Consequently, a well-timed energy trading on the stock market, and electrical grid stability can be maintained. The German Weather Service (DWD) currently is involved with two projects concerning research in the field of renewable energy, namely ORKA*) and EWeLiNE**). Whereas the latter is in collaboration with the Fraunhofer Institute (IWES), the project ORKA is led by energy & meteo systems (emsys). Both cooperate with German transmission system operators. The goal of the projects is to improve wind and photovoltaic (PV) power forecasts by combining optimized NWP and enhanced power forecast models. In this context, the German Weather Service aims to improve its model system, including the ensemble forecasting system, by working on data assimilation, model physics and statistical post processing. This presentation is focused on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. First steps leading to improved physical parameterization schemes within the NWP-model are presented. Wind mast measurements reaching up to 200 m height above ground are used for the estimation of the (NWP) wind forecast error at heights relevant for wind energy plants. One particular problem is the daily cycle in wind speed. The transition from stable stratification during nighttime to well mixed conditions during the day presents a big challenge to NWP models. Fast decrease and successive increase in hub-height wind speed after sunrise, and the formation of nocturnal low level jets will be discussed. For PV, the life cycle of low stratus clouds and fog is crucial. Capturing these processes correctly depends on the accurate simulation of diffusion or vertical momentum transport and the interaction with other atmospheric and soil processes within the numerical weather model. Results from Single Column Model simulations and 3d case studies will be presented. Emphasis is placed on wind forecasts; however, some references to highlights concerning the PV-developments will also be given. *) ORKA: Optimierung von Ensembleprognosen regenerativer Einspeisung für den Kürzestfristbereich am Anwendungsbeispiel der Netzsicherheitsrechnungen **) EWeLiNE: Erstellung innovativer Wetter- und Leistungsprognosemodelle für die Netzintegration wetterabhängiger Energieträger, www.projekt-eweline.de</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160008033','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160008033"><span>Magnetogram Forecast: An All-Clear Space Weather Forecasting System</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barghouty, Nasser; Falconer, David</p> <p>2015-01-01</p> <p>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</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130012522','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130012522"><span>Maintaining a Local Data Integration System in Support of Weather Forecast Operations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian</p> <p>2010-01-01</p> <p>Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LDIS) as part of their forecast and warning operations. Each has benefited from 3-dimensional analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive and complete understanding of evolving fine-scale weather features. Recent efforts have been undertaken to update the LDIS through the formal tasking process of NASA's Applied Meteorology Unit. The goals include upgrading LDIS with the latest version of ADAS, incorporating new sources of observational data, and making adjustments to shell scripts written to govern the system. A series of scripts run a complete modeling system consisting of the preprocessing step, the main model integration, and the post-processing step. The preprocessing step prepares the terrain, surface characteristics data sets, and the objective analysis for model initialization. Data ingested through ADAS include (but are not limited to) Level II Weather Surveillance Radar- 1988 Doppler (WSR-88D) data from six Florida radars, Geostationary Operational Environmental Satellites (GOES) visible and infrared satellite imagery, surface and upper air observations throughout Florida from NOAA's Earth System Research Laboratory/Global Systems Division/Meteorological Assimilation Data Ingest System (MADIS), as well as the Kennedy Space Center ICape Canaveral Air Force Station wind tower network. The scripts provide NWS MLB and SMG with several options for setting a desirable runtime configuration of the LDIS to account for adjustments in grid spacing, domain location, choice of observational data sources, and selection of background model fields, among others. The utility of an improved LDIS will be demonstrated through postanalysis warm and cool season case studies that compare high-resolution model output with and without the ADAS analyses. Operationally, these upgrades will result in more accurate depictions of the current local environment to help with short-range weather forecasting applications, while also offering an improved initialization for local versions of the Weather Research and Forecasting model.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/os/aviation.shtml','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/os/aviation.shtml"><span>OS Aviation Information</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Aviation Weather Program is to couple the art and science of meteorology to enhance the <em>safe</em> and efficient significant weather forecasts crossing international boundaries. Keeping Our National Airspace System <em>Safe</em> The System Newsletter Aviation Weather Center (AWC) Alaska Aviation Weather Unit (AAWU) Space <em>Environment</em></p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11..299H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11..299H"><span>Probabilistic flood warning using grand ensemble weather forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>He, Y.; Wetterhall, F.; Cloke, H.; Pappenberger, F.; Wilson, M.; Freer, J.; McGregor, G.</p> <p>2009-04-01</p> <p>As the severity of floods increases, possibly due to climate and landuse change, there is urgent need for more effective and reliable warning systems. The incorporation of numerical weather predictions (NWP) into a flood warning system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and can lead to a high number of false or missed warnings. An ensemble of weather forecasts from one Ensemble Prediction System (EPS), when used on catchment hydrology, can provide improved early flood warning as some of the uncertainties can be quantified. EPS forecasts from a single weather centre only account for part of the uncertainties originating from initial conditions and stochastic physics. Other sources of uncertainties, including numerical implementations and/or data assimilation, can only be assessed if a grand ensemble of EPSs from different weather centres is used. When various models that produce EPS from different weather centres are aggregated, the probabilistic nature of the ensemble precipitation forecasts can be better retained and accounted for. The availability of twelve global EPSs through the 'THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for the design of an improved probabilistic flood forecasting framework. This work presents a case study using the TIGGE database for flood warning on a meso-scale catchment. The upper reach of the River Severn catchment located in the Midlands Region of England is selected due to its abundant data for investigation and its relatively small size (4062 km2) (compared to the resolution of the NWPs). This choice was deliberate as we hypothesize that the uncertainty in the forcing of smaller catchments cannot be represented by a single EPS with a very limited number of ensemble members, but only through the variance given by a large number ensembles and ensemble system. A coupled atmospheric-hydrologic-hydraulic cascade system driven by the TIGGE ensemble forecasts is set up to study the potential benefits of using the TIGGE database in early flood warning. Physically based and fully distributed LISFLOOD suite of models is selected to simulate discharge and flood inundation consecutively. The results show the TIGGE database is a promising tool to produce forecasts of discharge and flood inundation comparable with the observed discharge and simulated inundation driven by the observed discharge. The spread of discharge forecasts varies from centre to centre, but it is generally large, implying a significant level of uncertainties. Precipitation input uncertainties dominate and propagate through the cascade chain. The current NWPs fall short of representing the spatial variability of precipitation on a comparatively small catchment. This perhaps indicates the need to improve NWPs resolution and/or disaggregation techniques to narrow down the spatial gap between meteorology and hydrology. It is not necessarily true that early flood warning becomes more reliable when more ensemble forecasts are employed. It is difficult to identify the best forecast centre(s), but in general the chance of detecting floods is increased by using the TIGGE database. Only one flood event was studied because most of the TIGGE data became available after October 2007. It is necessary to test the TIGGE ensemble forecasts with other flood events in other catchments with different hydrological and climatic regimes before general conclusions can be made on its robustness and applicability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20040047190','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20040047190"><span>The Effect of NEXRAD Image Looping and National Convective Weather Forecast Product on Pilot Decision Making in the Use of a Cockpit Weather Information Display</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Burgess, Malcolm A.; Thomas, Rickey P.</p> <p>2004-01-01</p> <p>This experiment investigated improvements to cockpit weather displays to better support the hazardous weather avoidance decision-making of general aviation pilots. Forty-eight general aviation pilots were divided into three equal groups and presented with a simulated flight scenario involving embedded convective activity. The control group had access to conventional sources of pre-flight and in-flight weather products. The two treatment groups were provided with a weather display that presented NEXRAD mosaic images, graphic depiction of METARs, and text METARs. One treatment group used a NEXRAD image looping feature and the second group used the National Convective Weather Forecast (NCWF) product overlaid on the NEXRAD display. Both of the treatment displays provided a significant increase in situation awareness but, they provided incomplete information required to deal with hazardous convective weather conditions, and would require substantial pilot training to permit their safe and effective use.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMIN33C1562M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMIN33C1562M"><span>Innovative Near Real-Time Data Dissemination Tools Developed by the Space Weather Research Center</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mullinix, R.; Maddox, M. M.; Berrios, D.; Kuznetsova, M.; Pulkkinen, A.; Rastaetter, L.; Zheng, Y.</p> <p>2012-12-01</p> <p>Space weather affects virtually all of NASA's endeavors, from robotic missions to human exploration. Knowledge and prediction of space weather conditions are therefore essential to NASA operations. The diverse nature of currently available space environment measurements and modeling products compels the need for a single access point to such information. The Integrated Space Weather Analysis (iSWA) System provides this single point access along with the capability to collect and catalog a vast range of sources including both observational and model data. NASA Goddard Space Weather Research Center heavily utilizes the iSWA System daily for research, space weather model validation, and forecasting for NASA missions. iSWA provides the capabilities to view and analyze near real-time space weather data from any where in the world. This presentation will describe the technology behind the iSWA system and describe how to use the system for space weather research, forecasting, training, education, and sharing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2010-title15-vol3/pdf/CFR-2010-title15-vol3-sec946-10.pdf','CFR'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2010-title15-vol3/pdf/CFR-2010-title15-vol3-sec946-10.pdf"><span>15 CFR 946.10 - Liaison officer.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2010&page.go=Go">Code of Federal Regulations, 2010 CFR</a></p> <p></p> <p>2010-01-01</p> <p>... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.10 Liaison officer. Prior to restructuring a field office... the provision of timely weather warnings and forecasts. ...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2012-title15-vol3/pdf/CFR-2012-title15-vol3-sec946-10.pdf','CFR2012'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2012-title15-vol3/pdf/CFR-2012-title15-vol3-sec946-10.pdf"><span>15 CFR 946.10 - Liaison officer.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2012&page.go=Go">Code of Federal Regulations, 2012 CFR</a></p> <p></p> <p>2012-01-01</p> <p>... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.10 Liaison officer. Prior to restructuring a field office... the provision of timely weather warnings and forecasts. ...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2014-title15-vol3/pdf/CFR-2014-title15-vol3-sec946-10.pdf','CFR2014'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2014-title15-vol3/pdf/CFR-2014-title15-vol3-sec946-10.pdf"><span>15 CFR 946.10 - Liaison officer.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2014&page.go=Go">Code of Federal Regulations, 2014 CFR</a></p> <p></p> <p>2014-01-01</p> <p>... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.10 Liaison officer. Prior to restructuring a field office... the provision of timely weather warnings and forecasts. ...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2011-title15-vol3/pdf/CFR-2011-title15-vol3-sec946-10.pdf','CFR2011'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2011-title15-vol3/pdf/CFR-2011-title15-vol3-sec946-10.pdf"><span>15 CFR 946.10 - Liaison officer.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2011&page.go=Go">Code of Federal Regulations, 2011 CFR</a></p> <p></p> <p>2011-01-01</p> <p>... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.10 Liaison officer. Prior to restructuring a field office... the provision of timely weather warnings and forecasts. ...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2013-title15-vol3/pdf/CFR-2013-title15-vol3-sec946-10.pdf','CFR2013'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2013-title15-vol3/pdf/CFR-2013-title15-vol3-sec946-10.pdf"><span>15 CFR 946.10 - Liaison officer.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2013&page.go=Go">Code of Federal Regulations, 2013 CFR</a></p> <p></p> <p>2013-01-01</p> <p>... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.10 Liaison officer. Prior to restructuring a field office... the provision of timely weather warnings and forecasts. ...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/3343','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/3343"><span>Enhanced road weather content for travel advisories : Clarus regional demonstrations.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2011-01-01</p> <p>Statewide transportation information systems need road weather and road condition forecasts to improve safety and mobility for transportation users. Under the Clarus Initiatives regional demonstrations enhanced road weather content was developed f...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMIN13A1099J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMIN13A1099J"><span>Projected Applications of a ``Climate in a Box'' Computing System at the NASA Short-term Prediction Research and Transition (SPoRT) Center</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jedlovec, G.; Molthan, A.; Zavodsky, B.; Case, J.; Lafontaine, F.</p> <p>2010-12-01</p> <p>The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to “Climate in a Box” systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the “Climate in a Box” system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA’s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the “Climate in a Box” system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110007276','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110007276"><span>Projected Applications of a "Climate in a Box" Computing System at the NASA Short-Term Prediction Research and Transition (SPoRT) Center</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jedlovec, Gary J.; Molthan, Andrew L.; Zavodsky, Bradley; Case, Jonathan L.; LaFontaine, Frank J.</p> <p>2010-01-01</p> <p>The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to "Climate in a Box" systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the "Climate in a Box" system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the "Climate in a Box" system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED198015.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED198015.pdf"><span>The Chesapeake: A Boating Guide to Weather. Educational Series Number 25.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Lucy, Jon; And Others</p> <p></p> <p>The purpose of this publication is to promote a better understanding of how basic weather features develop on Chesapeake Bay and enable boaters to enjoy the Bay's unique waterways. Sections include: (1) Chesapeake Bay climate; (2) general weather features; (3) seasonal trends; (4) sources of weather information and forecasts; (5) weather service…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/predictions.php','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/predictions.php"><span>Climate Prediction - NOAA's National Weather Service</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Statistical Models... MOS Prod GFS-LAMP Prod <em>Climate</em> Past Weather Predictions Weather Safety Weather Radio National Weather Service on FaceBook NWS on Facebook NWS Director Home > <em>Climate</em> > Predictions <em>Climate</em> Prediction Long range forecasts across the U.S. <em>Climate</em> Prediction Web Sites <em>Climate</em> Prediction</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017WRR....5310085B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017WRR....5310085B"><span>Using Meteorological Analogues for Reordering Postprocessed Precipitation Ensembles in Hydrological Forecasting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bellier, Joseph; Bontron, Guillaume; Zin, Isabella</p> <p>2017-12-01</p> <p>Meteorological ensemble forecasts are nowadays widely used as input of hydrological models for probabilistic streamflow forecasting. These forcings are frequently biased and have to be statistically postprocessed, using most of the time univariate techniques that apply independently to individual locations, lead times and weather variables. Postprocessed ensemble forecasts therefore need to be reordered so as to reconstruct suitable multivariate dependence structures. The Schaake shuffle and ensemble copula coupling are the two most popular methods for this purpose. This paper proposes two adaptations of them that make use of meteorological analogues for reconstructing spatiotemporal dependence structures of precipitation forecasts. Performances of the original and adapted techniques are compared through a multistep verification experiment using real forecasts from the European Centre for Medium-Range Weather Forecasts. This experiment evaluates not only multivariate precipitation forecasts but also the corresponding streamflow forecasts that derive from hydrological modeling. Results show that the relative performances of the different reordering methods vary depending on the verification step. In particular, the standard Schaake shuffle is found to perform poorly when evaluated on streamflow. This emphasizes the crucial role of the precipitation spatiotemporal dependence structure in hydrological ensemble forecasting.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19740014156&hterms=Social+media&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DSocial%2Bmedia','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19740014156&hterms=Social+media&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DSocial%2Bmedia"><span>Broadcast media and the dissemination of weather information</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Byrnes, J.</p> <p>1973-01-01</p> <p>Although television is the public's most preferred source of weather information, it fails to provide weather reports to those groups who seek the information early in the day and during the day. The result is that many people most often use radio as a source of information, yet preferring the medium of television. The public actively seeks weather information from both radio and TV stations, usually seeking information on current conditions and short range forecasts. forecasts. Nearly all broadcast stations surveyed were eager to air severe weather bulletins quickly and often. Interest in Nowcasting was high among radio and TV broadcasters, with a significant portion indicating a willingness to pay something for the service. However, interest among TV stations in increasing the number of daily reports was small.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890004487','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890004487"><span>Meteorological support for space operations: Review and recommendations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1988-01-01</p> <p>The current meteorological support provided to NASA by NOAA, Air Weather Service, and other contractors is reviewed and suggestions are offered for its improvement. These recommendations include improvement in NASA's internal management organizational structure that would accommodate continued improvement in operational weather support, installation of new observing systems, improvement in analysis and forecasting procedures, and the establishment of an Applied Research and Forecasting Facility.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JPhCS.753c2042G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JPhCS.753c2042G"><span>Wind power forecasting: IEA Wind Task 36 & future research issues</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Giebel, G.; Cline, J.; Frank, H.; Shaw, W.; Pinson, P.; Hodge, B.-M.; Kariniotakis, G.; Madsen, J.; Möhrlen, C.</p> <p>2016-09-01</p> <p>This paper presents the new International Energy Agency Wind Task 36 on Forecasting, and invites to collaborate within the group. Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, MetOffice, met.no, DMI,...), operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://images.nasa.gov/#/details-KSC-20180227-PH_KLS02_0023.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-KSC-20180227-PH_KLS02_0023.html"><span>GOES-S Mission Science Briefing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2018-02-27</p> <p>In the Kennedy Space Center's Press Site auditorium, Louis Uccellini, director of the National Weather Service for NOAA, speaks to members of the media at a mission briefing on National Oceanic and Atmospheric Administration's, or NOAA's, Geostationary Operational Environmental Satellite, or GOES-S. The spacecraft is the second satellite in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_23 --> <div id="page_24" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="461"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://images.nasa.gov/#/details-KSC-20180228-PH_KLS01_0110.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-KSC-20180228-PH_KLS01_0110.html"><span>GOES-S NASA Social</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2018-02-28</p> <p>Joe Pica, director of the Office of Observations for the National Oceanic and Atmospheric Administration's, or NOAA’s, National Weather Service, speaks to members of social media in the Kennedy Space Center’s Press Site auditorium. The briefing focused on the Geostationary Operational Environmental Satellite, or GOES-S, the second spacecraft in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130001925','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130001925"><span>Applications of NASA and NOAA Satellite Observations by NASA's Short-term Prediction Research and Transition (SPoRT) Center in Response to Natural Disasters</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Molthan, Andrew L.; Burks, Jason E.; McGrath, Kevin M.; Jedlovec, Gary J.</p> <p>2012-01-01</p> <p>NASA s Short-term Prediction Research and Transition (SPoRT) Center supports the transition of unique NASA and NOAA research activities to the operational weather forecasting community. SPoRT emphasizes real-time analysis and prediction out to 48 hours. SPoRT partners with NOAA s National Weather Service (NWS) Weather Forecast Offices (WFOs) and National Centers to improve current products, demonstrate future satellite capabilities and explore new data assimilation techniques. Recently, the SPoRT Center has been involved in several activities related to disaster response, in collaboration with NOAA s National Weather Service, NASA s Applied Sciences Disasters Program, and other partners.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1919378C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1919378C"><span>The GOES-R/JPSS Approach for Identifying Hazardous Low Clouds: Overview and Operational Impacts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Calvert, Corey; Pavolonis, Michael; Lindstrom, Scott; Gravelle, Chad; Terborg, Amanda</p> <p>2017-04-01</p> <p>Low ceiling and visibility is a weather hazard that nearly every forecaster, in nearly every National Weather Service (NWS) Weather Forecast Office (WFO), must regularly address. In addition, national forecast centers such as the Aviation Weather Center (AWC), Alaska Aviation Weather Unit (AAWU) and the Ocean Prediction Center (OPC) are responsible for issuing low ceiling and visibility related products. As such, reliable methods for detecting and characterizing hazardous low clouds are needed. Traditionally, hazardous areas of Fog/Low Stratus (FLS) are identified using a simple stand-alone satellite product that is constructed by subtracting the 3.9 and 11 μm brightness temperatures. However, the 3.9-11 μm brightness temperature difference (BTD) has several major limitations. In an effort to address the limitations of the BTD product, the GOES-R Algorithm Working Group (AWG) developed an approach that fuses satellite, Numerical Weather Prediction (NWP) model, Sea Surface Temperature (SST) analyses, and other data sets (e.g. digital surface elevation maps, surface emissivity maps, and surface type maps) to determine the probability that hazardous low clouds are present using a naïve Bayesian classifier. In addition, recent research has focused on blending geostationary (e.g. GOES-R) and low earth orbit (e.g. JPSS) satellite data to further improve the products. The FLS algorithm has adopted an enterprise approach in that it can utilize satellite data from a variety of current and future operational sensors and NWP data from a variety of models. The FLS products are available in AWIPS/N-AWIPS/AWIPS-II and have been evaluated within NWS operations over the last four years as part of the Satellite Proving Ground. Forecaster feedback has been predominantly positive and references to these products within Area Forecast Discussions (AFD's) indicate that the products are influencing operational forecasts. At the request of the NWS, the FLS products are currently being transitioned to NOAA/NESDIS operations, which will ensure that users have long-term access to these products. This paper will provide an overview of the FLS products and illustrate how they are being used to improve transportation safety and efficiency.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1995BAMS...76..711C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1995BAMS...76..711C"><span>Uses and Applications of Climate Forecasts for Power Utilities.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Changnon, Stanley A.; Changnon, Joyce M.; Changnon, David</p> <p>1995-05-01</p> <p>The uses and potential applications of climate forecasts for electric and gas utilities were assessed 1) to discern needs for improving climate forecasts and guiding future research, and 2) to assist utilities in making wise use of forecasts. In-depth structured interviews were conducted with 56 decision makers in six utilities to assess existing and potential uses of climate forecasts. Only 3 of the 56 use forecasts. Eighty percent of those sampled envisioned applications of climate forecasts, given certain changes and additional information. Primary applications exist in power trading, load forecasting, fuel acquisition, and systems planning, with slight differences in interests between utilities. Utility staff understand probability-based forecasts but desire climatological information related to forecasted outcomes, including analogs similar to the forecasts, and explanations of the forecasts. Desired lead times vary from a week to three months, along with forecasts of up to four seasons ahead. The new NOAA forecasts initiated in 1995 provide the lead times and longer-term forecasts desired. Major hindrances to use of forecasts are hard-to-understand formats, lack of corporate acceptance, and lack of access to expertise. Recent changes in government regulations altered the utility industry, leading to a more competitive world wherein information about future weather conditions assumes much more value. Outreach efforts by government forecast agencies appear valuable to help achieve the appropriate and enhanced use of climate forecasts by the utility industry. An opportunity for service exists also for the private weather sector.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140013005','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140013005"><span>The Main Pillar: Assessment of Space Weather Observational Asset Performance Supporting Nowcasting, Forecasting and Research to Operations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Posner, Arik; Hesse, Michael; SaintCyr, Chris</p> <p>2014-01-01</p> <p>Space weather forecasting critically depends upon availability of timely and reliable observational data. It is therefore particularly important to understand how existing and newly planned observational assets perform during periods of severe space weather. Extreme space weather creates challenging conditions under which instrumentation and spacecraft may be impeded or in which parameters reach values that are outside the nominal observational range. This paper analyzes existing and upcoming observational capabilities for forecasting, and discusses how the findings may impact space weather research and its transition to operations. A single limitation to the assessment is lack of information provided to us on radiation monitor performance, which caused us not to fully assess (i.e., not assess short term) radiation storm forecasting. The assessment finds that at least two widely spaced coronagraphs including L4 would provide reliability for Earth-bound CMEs. Furthermore, all magnetic field measurements assessed fully meet requirements. However, with current or even with near term new assets in place, in the worst-case scenario there could be a near-complete lack of key near-real-time solar wind plasma data of severe disturbances heading toward and impacting Earth's magnetosphere. Models that attempt to simulate the effects of these disturbances in near real time or with archival data require solar wind plasma observations as input. Moreover, the study finds that near-future observational assets will be less capable of advancing the understanding of extreme geomagnetic disturbances at Earth, which might make the resulting space weather models unsuitable for transition to operations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMSM22D..08S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMSM22D..08S"><span>Space Weather Forecasting: An Enigma</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sojka, J. J.</p> <p>2012-12-01</p> <p>The space age began in earnest on October 4, 1957 with the launch of Sputnik 1 and was fuelled for over a decade by very strong national societal concerns. Prior to this single event the adverse effects of space weather had been registered on telegraph lines as well as interference on early WWII radar systems, while for countless eons the beauty of space weather as mid-latitude auroral displays were much appreciated. These prior space weather impacts were in themselves only a low-level science puzzle pursued by a few dedicated researchers. The technology boost and innovation that the post Sputnik era generated has almost single handedly defined our present day societal technology infrastructure. During the decade following Neil's walk on the moon on July 21, 1969 an international thrust to understand the science of space, and its weather, was in progress. However, the search for scientific understand was parsed into independent "stove pipe" categories: The ionosphere-aeronomy, the magnetosphere, the heliosphere-sun. The present day scientific infrastructure of funding agencies, learned societies, and international organizations are still hampered by these 1960's logical divisions which today are outdated in the pursuit of understanding space weather. As this era of intensive and well funded scientific research progressed so did societies innovative uses for space technologies and space "spin-offs". Well over a decade ago leaders in technology, science, and the military realized that there was indeed an adverse side to space weather that with each passing year became more severe. In 1994 several U.S. agencies established the National Space Weather Program (NSWP) to focus scientific attention on the system wide issue of the adverse effects of space weather on society and its technologies. Indeed for the past two decades a significant fraction of the scientific community has actively engaged in understanding space weather and hence crossing the "stove-pipe" disciplines. The perceived progress in space weather understanding differs significantly depending upon which community (scientific, technology, forecaster, society) is addressing the question. Even more divergent are these thoughts when the question is how valuable is the scientific capability of forecasting space weather. This talk will discuss present day as well as future potential for forecasting space weather for a few selected examples. The author will attempt to straddle the divergent community opinions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170002549','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170002549"><span>Evaluation of NU-WRF Rainfall Forecasts for IFloodS</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wu, Di; Peters-Lidard, Christa; Tao, Wei-Kuo; Petersen, Walter</p> <p>2016-01-01</p> <p>The Iowa Flood Studies (IFloodS) campaign was conducted in eastern Iowa as a pre- GPM-launch campaign from 1 May to 15 June 2013. During the campaign period, real time forecasts are conducted utilizing NASA-Unified Weather Research and Forecasting (NU-WRF) model to support the everyday weather briefing. In this study, two sets of the NU-WRF rainfall forecasts are evaluated with Stage IV and Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE), with the objective to understand the impact of Land Surface initialization on the predicted precipitation. NU-WRF is also compared with North American Mesoscale Forecast System (NAM) 12 kilometer forecast. In general, NU-WRF did a good job at capturing individual precipitation events. NU-WRF is also able to replicate a better rainfall spatial distribution compare with NAM. Further sensitivity tests show that the high-resolution makes a positive impact on rainfall forecast. The two sets of NU-WRF simulations produce very close rainfall characteristics. The Land surface initialization do not show significant impact on short term rainfall forecast, and it is largely due to the soil conditions during the field campaign period.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H44B..05R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H44B..05R"><span>An Ensemble-Based Forecasting Framework to Optimize Reservoir Releases</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ramaswamy, V.; Saleh, F.</p> <p>2017-12-01</p> <p>Increasing frequency of extreme precipitation events are stressing the need to manage water resources on shorter timescales. Short-term management of water resources becomes proactive when inflow forecasts are available and this information can be effectively used in the control strategy. This work investigates the utility of short term hydrological ensemble forecasts for operational decision making during extreme weather events. An advanced automated hydrologic prediction framework integrating a regional scale hydrologic model, GIS datasets and the meteorological ensemble predictions from the European Center for Medium Range Weather Forecasting (ECMWF) was coupled to an implicit multi-objective dynamic programming model to optimize releases from a water supply reservoir. The proposed methodology was evaluated by retrospectively forecasting the inflows to the Oradell reservoir in the Hackensack River basin in New Jersey during the extreme hydrologic event, Hurricane Irene. Additionally, the flexibility of the forecasting framework was investigated by forecasting the inflows from a moderate rainfall event to provide important perspectives on using the framework to assist reservoir operations during moderate events. The proposed forecasting framework seeks to provide a flexible, assistive tool to alleviate the complexity of operational decision-making.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1997BAMS...78.2851V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1997BAMS...78.2851V"><span>Performance of an Advanced MOS System in the 1996-97 National Collegiate Weather Forecasting Contest.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vislocky, Robert L.; Fritsch, J. Michael</p> <p>1997-12-01</p> <p>A prototype advanced model output statistics (MOS) forecast system that was entered in the 1996-97 National Collegiate Weather Forecast Contest is described and its performance compared to that of widely available objective guidance and to contest participants. The prototype system uses an optimal blend of aviation (AVN) and nested grid model (NGM) MOS forecasts, explicit output from the NGM and Eta guidance, and the latest surface weather observations from the forecast site. The forecasts are totally objective and can be generated quickly on a personal computer. Other "objective" forms of guidance tracked in the contest are 1) the consensus forecast (i.e., the average of the forecasts from all of the human participants), 2) the combination of NGM raw output (for precipitation forecasts) and NGM MOS guidance (for temperature forecasts), and 3) the combination of Eta Model raw output (for precipitation forecasts) and AVN MOS guidance (for temperature forecasts).Results show that the advanced MOS system finished in 20th place out of 737 original entrants, or better than approximately 97% of the human forecasters who entered the contest. Moreover, the advanced MOS system was slightly better than consensus (23d place). The fact that an objective forecast system finished ahead of consensus is a significant accomplishment since consensus is traditionally a very formidable "opponent" in forecast competitions. Equally significant is that the advanced MOS system was superior to the traditional guidance products available from the National Centers for Environmental Prediction (NCEP). Specifically, the combination of NGM raw output and NGM MOS guidance finished in 175th place, and the combination of Eta Model raw output and AVN MOS guidance finished in 266th place. The latter result is most intriguing since the proposed elimination of all NGM products would likely result in a serious degradation of objective products disseminated by NCEP, unless they are replaced with equal or better substitutes. On the other hand, the positive performance of the prototype advanced MOS system shows that it is possible to create a single objective product that is not only superior to currently available objective guidance products, but is also on par with some of the better human forecasters.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMSH21A2635C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMSH21A2635C"><span>CCMC: bringing space weather awareness to the next generation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chulaki, A.; Muglach, K.; Zheng, Y.; Mays, M. L.; Kuznetsova, M. M.; Taktakishvili, A.; Collado-Vega, Y. M.; Rastaetter, L.; Mendoza, A. M. M.; Thompson, B. J.; Pulkkinen, A. A.; Pembroke, A. D.</p> <p>2017-12-01</p> <p>Making space weather an element of core education is critical for the future of the young field of space weather. Community Coordinated Modeling Center (CCMC) is an interagency partnership established to aid the transition of modern space science models into space weather forecasting while supporting space science research. Additionally, over the past ten years it has established itself as a global space science education resource supporting undergraduate and graduate education and research, and spreading space weather awareness worldwide. A unique combination of assets, capabilities and close ties to the scientific and educational communities enable our small group to serve as a hub for rising generations of young space scientists and engineers. CCMC offers a variety of educational tools and resources publicly available online and providing access to the largest collection of modern space science models developed by the international research community. CCMC has revolutionized the way these simulations are utilized in classrooms settings, student projects, and scientific labs. Every year, this online system serves hundreds of students, educators and researchers worldwide. Another major CCMC asset is an expert space weather prototyping team primarily serving NASA's interplanetary space weather needs. Capitalizing on its unique capabilities and experiences, the team also provides in-depth space weather training to hundreds of students and professionals. One training module offers undergraduates an opportunity to actively engage in real-time space weather monitoring, analysis, forecasting, tools development and research, eventually serving remotely as NASA space weather forecasters. In yet another project, CCMC is collaborating with Hayden Planetarium and Linkoping University on creating a visualization platform for planetariums (and classrooms) to provide simulations of dynamic processes in the large domain stretching from the solar corona to the Earth's upper atmosphere, for near real-time and historical space weather events.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A33J0398N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A33J0398N"><span>Sub-seasonal Predictability of Heavy Precipitation Events: Implication for Real-time Flood Management in Iran</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Najafi, H.; Shahbazi, A.; Zohrabi, N.; Robertson, A. W.; Mofidi, A.; Massah Bavani, A. R.</p> <p>2016-12-01</p> <p>Each year, a number of high impact weather events occur worldwide. Since any level of predictability at sub-seasonal to seasonal timescale is highly beneficial to society, international efforts is now on progress to promote reliable Ensemble Prediction Systems for monthly forecasts within the WWRP/WCRP initiative (S2S) project and North American Multi Model Ensemble (NMME). For water resources managers in the face of extreme events, not only can reliable forecasts of high impact weather events prevent catastrophic losses caused by floods but also contribute to benefits gained from hydropower generation and water markets. The aim of this paper is to analyze the predictability of recent severe weather events over Iran. Two recent heavy precipitations are considered as an illustration to examine whether S2S forecasts can be used for developing flood alert systems especially where large cascade of dams are in operation. Both events have caused major damages to cities and infrastructures. The first severe precipitation was is in the early November 2015 when heavy precipitation (more than 50 mm) occurred in 2 days. More recently, up to 300 mm of precipitation is observed within less than a week in April 2016 causing a consequent flash flood. Over some stations, the observed precipitation was even more than the total annual mean precipitation. To analyze the predictive capability, ensemble forecasts from several operational centers including (European Centre for Medium-Range Weather Forecasts (ECMWF) system, Climate Forecast System Version 2 (CFSv2) and Chinese Meteorological Center (CMA) are evaluated. It has been observed that significant changes in precipitation anomalies were likely to be predicted days in advance. The next step will be to conduct thorough analysis based on comparing multi-model outputs over the full hindcast dataset developing real-time high impact weather prediction systems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19970023066','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19970023066"><span>Workstation-Based Real-Time Mesoscale Modeling Designed for Weather Support to Operations at the Kennedy Space Center and Cape Canaveral Air Station</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Manobianco, John; Zack, John W.; Taylor, Gregory E.</p> <p>1996-01-01</p> <p>This paper describes the capabilities and operational utility of a version of the Mesoscale Atmospheric Simulation System (MASS) that has been developed to support operational weather forecasting at the Kennedy Space Center (KSC) and Cape Canaveral Air Station (CCAS). The implementation of local, mesoscale modeling systems at KSC/CCAS is designed to provide detailed short-range (less than 24 h) forecasts of winds, clouds, and hazardous weather such as thunderstorms. Short-range forecasting is a challenge for daily operations, and manned and unmanned launches since KSC/CCAS is located in central Florida where the weather during the warm season is dominated by mesoscale circulations like the sea breeze. For this application, MASS has been modified to run on a Stardent 3000 workstation. Workstation-based, real-time numerical modeling requires a compromise between the requirement to run the system fast enough so that the output can be used before expiration balanced against the desire to improve the simulations by increasing resolution and using more detailed physical parameterizations. It is now feasible to run high-resolution mesoscale models such as MASS on local workstations to provide timely forecasts at a fraction of the cost required to run these models on mainframe supercomputers. MASS has been running in the Applied Meteorology Unit (AMU) at KSC/CCAS since January 1994 for the purpose of system evaluation. In March 1995, the AMU began sending real-time MASS output to the forecasters and meteorologists at CCAS, Spaceflight Meteorology Group (Johnson Space Center, Houston, Texas), and the National Weather Service (Melbourne, Florida). However, MASS is not yet an operational system. The final decision whether to transition MASS for operational use will depend on a combination of forecaster feedback, the AMU's final evaluation results, and the life-cycle costs of the operational system.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.spc.noaa.gov','SCIGOVWS'); return false;" href="http://www.spc.noaa.gov"><span>NOAA/NWS Storm Prediction Center</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Thunderstorm/Tornado Watches Mesoscale Discussions Convective Outlooks Thunderstorm Outlook <em>Fire</em> Weather Analysis Sounding Climatology Upper-Air Maps HREF HRRR Browser SREF SREF Plumes <em>Fire</em> Weather Composite Maps Convective Outlook. Critical <em>fire</em> weather conditions are forecast today. See details... Critical <em>fire</em> weather</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/48889','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/48889"><span>Using weather forecasts for predicting forest-fire danger</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>H. T. Gisborne</p> <p>1925-01-01</p> <p>Three kinds of weather control the fluctuations of forest-fire danger-wet weather, dry weather, and windy weather. Two other conditions also contribute to the fluctuation of fire danger. These are the occurrence of lightning and the activities of man. But neither of these fire-starting agencies is fully effective unless the weather has dried out the forest materials so...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100036464','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100036464"><span>An Objective Verification of the North American Mesoscale Model for Kennedy Space Center and Cape Canaveral Air Force Station</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bauman, William H., III</p> <p>2010-01-01</p> <p>The 45th Weather Squadron (45 WS) Launch Weather Officers (LWO's) use the 12-km resolution North American Mesoscale (NAM) model (MesoNAM) text and graphical product forecasts extensively to support launch weather operations. However, the actual performance of the model at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) has not been measured objectively. In order to have tangible evidence of model performance, the 45 WS tasked the Applied Meteorology Unit (AMU; Bauman et ai, 2004) to conduct a detailed statistical analysis of model output compared to observed values. The model products are provided to the 45 WS by ACTA, Inc. and include hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The objective analysis compared the MesoNAM forecast winds, temperature (T) and dew pOint (T d), as well as the changes in these parameters over time, to the observed values from the sensors in the KSC/CCAFS wind tower network shown in Table 1. These objective statistics give the forecasters knowledge of the model's strengths and weaknesses, which will result in improved forecasts for operations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018PNAS..115...59O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018PNAS..115...59O"><span>Observational evidence of European summer weather patterns predictable from spring</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ossó, Albert; Sutton, Rowan; Shaffrey, Len; Dong, Buwen</p> <p>2018-01-01</p> <p>Forecasts of summer weather patterns months in advance would be of great value for a wide range of applications. However, seasonal dynamical model forecasts for European summers have very little skill, particularly for rainfall. It has not been clear whether this low skill reflects inherent unpredictability of summer weather or, alternatively, is a consequence of weaknesses in current forecast systems. Here we analyze atmosphere and ocean observations and identify evidence that a specific pattern of summertime atmospheric circulation––the summer East Atlantic (SEA) pattern––is predictable from the previous spring. An index of North Atlantic sea-surface temperatures in March–April can predict the SEA pattern in July–August with a cross-validated correlation skill above 0.6. Our analyses show that the sea-surface temperatures influence atmospheric circulation and the position of the jet stream over the North Atlantic. The SEA pattern has a particularly strong influence on rainfall in the British Isles, which we find can also be predicted months ahead with a significant skill of 0.56. Our results have immediate application to empirical forecasts of summer rainfall for the United Kingdom, Ireland, and northern France and also suggest that current dynamical model forecast systems have large potential for improvement.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AMTD....4..599F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AMTD....4..599F"><span>Space and ground segment performance of the FORMOSAT-3/COSMIC mission: four years in orbit</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fong, C.-J.; Whiteley, D.; Yang, E.; Cook, K.; Chu, V.; Schreiner, B.; Ector, D.; Wilczynski, P.; Liu, T.-Y.; Yen, N.</p> <p>2011-01-01</p> <p>The FORMOSAT-3/COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) mission consisting of six Low-Earth-Orbit (LEO) satellites is the world's first demonstration constellation using radio occultation signals from Global Positioning System (GPS) satellites. The radio occultation signals are retrieved in near real-time for global weather/climate monitoring, numerical weather prediction, and space weather research. The mission has processed on average 1400 to 1800 high-quality atmospheric sounding profiles per day. The atmospheric radio occultation soundings data are assimilated into operational numerical weather prediction models for global weather prediction, including typhoon/hurricane/cyclone forecasts. The radio occultation data has shown a positive impact on weather predictions at many national weather forecast centers. A proposed follow-on mission transitions the program from the current experimental research system to a significantly improved real-time operational system, which will reliably provide 8000 radio occultation soundings per day. The follow-on mission as planned will consist of 12 satellites with a data latency of 45 min, which will provide greatly enhanced opportunities for operational forecasts and scientific research. This paper will address the FORMOSAT-3/COSMIC system and mission overview, the spacecraft and ground system performance after four years in orbit, the lessons learned from the encountered technical challenges and observations, and the expected design improvements for the new spacecraft and ground system.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1998BAMS...79..845R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1998BAMS...79..845R"><span>An Overview of NWS Weather Support for the XXVI Olympiad.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rothfusz, Lans P.; McLaughlin, Melvin R.; Rinard, Stephen K.</p> <p>1998-05-01</p> <p>The 1996 Centennial Olympic Games in Atlanta, Georgia, received weather support from the National Weather Service (NWS). The mandate to provide this support gave the NWS an unprecedented opportunity to employ in an operational setting several tools and practices similar to those planned for the "modernized" era of the NWS. The project also provided a glimpse of technology and practices not planned for the NWS modernization, but that might be valuable in the future. The underlying purpose of the project was to protect the life and property of the two million spectators, athletes, volunteers, and officials visiting and/or participating in the games. While there is no way to accurately account for lives and property that were protected by the NWS support, the absence of weather-related deaths, significant injuries, and damaged property during the games despite an almost daily occurrence of thunderstorms, high temperatures, and/or rain indicates that the project was a success. In fact, popular perception held that weather had no effect on the games. The 2000+ weather bulletins issued during the 6-week support period suggest otherwise. The authors describe the many facets of this demanding and successful project, with special attention given to aspects related to operational forecasting. A postproject survey completed by the Olympics forecasters, feedback provided by weather support customers, and experiences of the management team provide the bases for project observations and recommendations for future operational forecasting activities.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC43A0679A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC43A0679A"><span>Probabilistic Weather Information Tailored to the Needs of Transmission System Operators</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alberts, I.; Stauch, V.; Lee, D.; Hagedorn, R.</p> <p>2014-12-01</p> <p>Reliable and accurate forecasts for wind and photovoltaic (PV) power production are essential for stable transmission systems. A high potential for improving the wind and PV power forecasts lies in optimizing the weather forecasts, since these energy sources are highly weather dependent. For this reason the main objective of the German research project EWeLiNE is to improve the quality the underlying numerical weather predictions towards energy operations. In this project, the German Meteorological Service (DWD), the Fraunhofer Institute for Wind Energy and Energy System Technology, and three of the German transmission system operators (TSOs) are working together to improve the weather and power forecasts. Probabilistic predictions are of particular interest, as the quantification of uncertainties provides an important tool for risk management. Theoretical considerations suggest that it can be advantageous to use probabilistic information to represent and respond to the remaining uncertainties in the forecasts. However, it remains a challenge to integrate this information into the decision making processes related to market participation and power systems operations. The project is planned and carried out in close cooperation with the involved TSOs in order to ensure the usability of the products developed. It will conclude with a demonstration phase, in which the improved models and newly developed products are combined into a process chain and used to provide information to TSOs in a real-time decision support tool. The use of a web-based development platform enables short development cycles and agile adaptation to evolving user needs. This contribution will present the EWeLiNE project and discuss ideas on how to incorporate probabilistic information into the users' current decision making processes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110014504','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110014504"><span>Upgrade Summer Severe Weather Tool</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela</p> <p>2011-01-01</p> <p>The goal of this task was to upgrade to the existing severe weather database by adding observations from the 2010 warm season, update the verification dataset with results from the 2010 warm season, use statistical logistic regression analysis on the database and develop a new forecast tool. The AMU analyzed 7 stability parameters that showed the possibility of providing guidance in forecasting severe weather, calculated verification statistics for the Total Threat Score (TTS), and calculated warm season verification statistics for the 2010 season. The AMU also performed statistical logistic regression analysis on the 22-year severe weather database. The results indicated that the logistic regression equation did not show an increase in skill over the previously developed TTS. The equation showed less accuracy than TTS at predicting severe weather, little ability to distinguish between severe and non-severe weather days, and worse standard categorical accuracy measures and skill scores over TTS.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_24 --> <div id="page_25" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="481"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19870010632','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19870010632"><span>Predicting the magnetospheric plasma of weather</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dawson, John M.</p> <p>1986-01-01</p> <p>The prediction of the plasma environment in time, the plasma weather, is discussed. It is important to be able to predict when large magnetic storms will produce auroras, which will affect the space station operating in low orbit, and what precautions to take both for personnel and sensitive control (computer) equipment onboard. It is also important to start to establish a set of plasma weather records and a record of the ability to predict this weather. A successful forecasting system requires a set of satellite weather stations to provide data from which predictions can be made and a set of plasma weather codes capable of accurately forecasting the status of the Earth's magnetosphere. A numerical magnetohydrodynamic fluid model which is used to model the flow in the magnetosphere, the currents flowing into and out of the auroral regions, the magnetopause, the bow shock location and the magnetotail of the Earth is discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010pcms.confE..62C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010pcms.confE..62C"><span>Severe rainfall prediction systems for civil protection purposes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Comellas, A.; Llasat, M. C.; Molini, L.; Parodi, A.; Siccardi, F.</p> <p>2010-09-01</p> <p>One of the most common natural hazards impending on Mediterranean regions is the occurrence of severe weather structures able to produce heavy rainfall. Floods have killed about 1000 people across all Europe in last 10 years. With the aim of mitigating this kind of risk, quantitative precipitation forecasts (QPF) and rain probability forecasts are two tools nowadays available for national meteorological services and institutions responsible for weather forecasting in order to and predict rainfall, by using either the deterministic or the probabilistic approach. This study provides an insight of the different approaches used by Italian (DPC) and Catalonian (SMC) Civil Protection and the results they achieved with their peculiar issuing-system for early warnings. For the former, the analysis considers the period between 2006-2009 in which the predictive ability of the forecasting system, based on the numerical weather prediction model COSMO-I7, has been put into comparison with ground based observations (composed by more than 2000 raingauge stations, Molini et al., 2009). Italian system is mainly focused on regional-scale warnings providing forecasts for periods never shorter than 18 hours and very often have a 36-hour maximum duration . The information contained in severe weather bulletins is not quantitative and usually is referred to a specific meteorological phenomena (thunderstorms, wind gales et c.). Updates and refining have a usual refresh time of 24 hours. SMC operates within the Catalonian boundaries and uses a warning system that mixes both quantitative and probabilistic information. For each administrative region ("comarca") Catalonia is divided into, forecasters give an approximate value of the average predicted rainfall and the probability of overcoming that threshold. Usually warnings are re-issued every 6 hours and their duration depends on the predicted time extent of the storm. In order to provide a comprehensive QPF verification, the rainfall predicted by Mesoscale Model 5 (MM5), the SMC forecast operational model, is compared with the local rain gauge network for year 2008 (Comellas et al., 2010). This study presents benefits and drawbacks of both Italian and Catalonian systems. Moreover, a particular attention is paid on the link between system's predictive ability and the predicted severe weather type as a function of its space-time development.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.1906D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.1906D"><span>An industry perspective on the use of seasonal forecasts and weather information for evaluating sensitivities in traded commodity supply chains</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Domeisen, Daniela; Slavov, Georgi</p> <p>2015-04-01</p> <p>Weather information on seasonal timescales is crucial to various end users, from the level of subsistence farming to the government level. Also the financial industry is ever more aware of and interested in the benefits that early and correctly interpreted forecast information provides. Straight forward and often cited applications include the estimation of rainfall and temperature anomalies for drought - prone agricultural areas producing traded commodities, as well as some of the rather direct impacts of weather on energy production. Governments, weather services, as well as both academia and private companies are working on tailoring climate and weather information to a growing number of customers. However, also other large markets, such as coal, iron ore, and gas, are crucially dependent on seasonal weather information and forecasts, while the needs are again very dependent on the direction of the predicted signal. So far, relatively few providers in climate services address these industries. All of these commodities show a strong seasonal and weather dependence, and an unusual winter or summer can crucially impact their demand and supply. To name a few impacts, gas is crucially driven by heating demand, iron ore excavation is dependent on the available water resources, and coal mining is dependent on winter temperatures and rainfall. This contribution will illustrate and provide an inside view of the type of climate and weather information needed for the various large commodity industries.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1986PhDT.......138S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1986PhDT.......138S"><span>a Weather Monitoring System for Application to Apple and Corn Production</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stirm, Walter Leroy</p> <p></p> <p>Many crop management decisions are based on weather -crop development relationships. Daily weather data is currently used in most crop development research and applied models. Present weather and computer technology now makes possible monitoring of crop development on a realtime basis. This research tests a method of computing crop sensitive temperatures for corn and apple using standard hourly meteorological data. The method also makes use of detailed plant physiological stage measurements to determine timing of vital cultural operations tied to the observed weather conditions. The sensitive temperature method incorporates very short term weather variability accounting for changes in the cloud cover, radiation rates, evaporative cooling and other factors involved in the plant's energy balance. The relationship of plant and weather measurements are also used to determine corn emergence, corn grain drydown rate and fruit harvest duration. The monitoring system also incorporates a crop growth unit forecast technique employing short and medium range temperature forecasts of the National Weather Service. The projections of growth units are made for five and ten days into the future. Predicted growth unit accumulations are compared to historical growth unit accumulations to determine the forecast stage. The sensitive temperature crop monitoring system removes some of the error involved in evaluation of growth units by average daily temperature. Carry over maximum and minimums, extended duration of warm or cool periods within the day and disruption of diurnal temperature curve by passage of fronts are eliminated.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018AdSR...15...99E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018AdSR...15...99E"><span>Mapping users' expectations regarding extended-range forecasts</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ervasti, Tiina; Gregow, Hilppa; Vajda, Andrea; Laurila, Terhi K.; Mäkelä, Antti</p> <p>2018-05-01</p> <p>An online survey was used to map the needs and preferences of the Finnish general public concerning extended-range forecasts and their presentation. First analyses of the survey were used to guide the co-design process of novel extended-range forecasts to be developed and tested during the project. In addition, the survey was used to engage the respondents from the general public to participate in a one year piloting phase that started in June 2017. The respondents considered that the tailored extended-range forecasts would be beneficial in planning activities, preparing for the weather risks and scheduling the everyday life. The respondents also perceived the information about the impacts of weather conditions more important than advice on how to prepare for the impacts.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150002156','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150002156"><span>Real-Time Kennedy Space Center and Cape Canaveral Air Force Station High-Resolution Model Implementation and Verification</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shafer, Jaclyn; Watson, Leela R.</p> <p>2015-01-01</p> <p>NASA's Launch Services Program, Ground Systems Development and Operations, Space Launch System and other programs at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) use the daily and weekly weather forecasts issued by the 45th Weather Squadron (45 WS) as decision tools for their day-to-day and launch operations on the Eastern Range (ER). Examples include determining if they need to limit activities such as vehicle transport to the launch pad, protect people, structures or exposed launch vehicles given a threat of severe weather, or reschedule other critical operations. The 45 WS uses numerical weather prediction models as a guide for these weather forecasts, particularly the Air Force Weather Agency (AFWA) 1.67 km Weather Research and Forecasting (WRF) model. Considering the 45 WS forecasters' and Launch Weather Officers' (LWO) extensive use of the AFWA model, the 45 WS proposed a task at the September 2013 Applied Meteorology Unit (AMU) Tasking Meeting requesting the AMU verify this model. Due to the lack of archived model data available from AFWA, verification is not yet possible. Instead, the AMU proposed to implement and verify the performance of an ER version of the high-resolution WRF Environmental Modeling System (EMS) model configured by the AMU (Watson 2013) in real time. Implementing a real-time version of the ER WRF-EMS would generate a larger database of model output than in the previous AMU task for determining model performance, and allows the AMU more control over and access to the model output archive. The tasking group agreed to this proposal; therefore the AMU implemented the WRF-EMS model on the second of two NASA AMU modeling clusters. The AMU also calculated verification statistics to determine model performance compared to observational data. Finally, the AMU made the model output available on the AMU Advanced Weather Interactive Processing System II (AWIPS II) servers, which allows the 45 WS and AMU staff to customize the model output display on the AMU and Range Weather Operations (RWO) AWIPS II client computers and conduct real-time subjective analyses.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20020082964&hterms=chaos&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dchaos','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20020082964&hterms=chaos&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dchaos"><span>Climate Prediction Sees Future Despite Chaos: Researchers Outside NASA use NCCS Resources for Studies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2002-01-01</p> <p>The air on this mostly sunny January day is crisp and the wind is blustery. The morning's National Weather Service 6-hour forecast had accurately predicted these conditions for the Baltimore-Washington area and the 2-3 day extended outlook was almost perfect. The previous week, the National Center for Environmental Prediction's (NCEP) 6-10 day temperature and precipitation outlook for the general trends for the' region was correct as well. However, no forecast could have predicted specific details about this day. It is 28.5 F in the sunshine bright enough for dark sunglasses, and windy enough to blow off a hat. Such details are impossible to foresee with any accuracy and are outside the scope of routine weather prediction. Equally difficult is accurately forecasting weather beyond about 2 weeks.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1335576','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1335576"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Giebel, G.; Cline, J.; Frank, H.</p> <p></p> <p>Here, this paper presents the new International Energy Agency Wind Task 36 on Forecasting, and invites to collaborate within the group. Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, MetOffice, met.no, DMI,...), operational forecaster and forecast users. The Taskmore » is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010092181','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010092181"><span>AWE: Aviation Weather Data Visualization Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Spirkovska, Lilly; Lodha, Suresh K.; Norvig, Peter (Technical Monitor)</p> <p>2000-01-01</p> <p>Weather is one of the major causes of aviation accidents. General aviation (GA) flights account for 92% of all the aviation accidents, In spite of all the official and unofficial sources of weather visualization tools available to pilots, there is an urgent need for visualizing several weather related data tailored for general aviation pilots. Our system, Aviation Weather Data Visualization Environment AWE), presents graphical displays of meteorological observations, terminal area forecasts, and winds aloft forecasts onto a cartographic grid specific to the pilot's area of interest. Decisions regarding the graphical display and design are made based on careful consideration of user needs. Integral visual display of these elements of weather reports is designed for the use of GA pilots as a weather briefing and route selection tool. AWE provides linking of the weather information to the flight's path and schedule. The pilot can interact with the system to obtain aviation-specific weather for the entire area or for his specific route to explore what-if scenarios and make "go/no-go" decisions. The system, as evaluated by some pilots at NASA Ames Research Center, was found to be useful.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUSMIN33A..05D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUSMIN33A..05D"><span>Benefits of Sharing Information: Supermodel Ensemble and Applications in South America</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dias, P. L.</p> <p>2006-05-01</p> <p>A model intercomparison program involving a large number of academic and operational institutions has been implemented in South America since 2003, motivated by the SALLJEX Intercomparison Program in 2003 (a research program focused on the identification of the role of the Andes low level jet moisture transport from the Amazon to the Plata basin) and the WMO/THORPEX (www.wmo.int/thorpex) goals to improve predictability through the proper combination of numerical weather forecasts. This program also explores the potential predictability associated with the combination of a large number of possible scenarios in the time scale of a few days to up to 15 days. Five academic institutions and five operational forecasting centers in several countries in South America, 1 academic institution in the USA, and the main global forecasting centers (NCEP, UKMO, ECMWF) agreed to provide numerical products based on operational and experimental models. The metric for model validation is concentrated on the fit of the forecast to surface observations. Meteorological data from airports, synoptic stations operated by national weather services, automatic data platforms maintained by different institutions, the PIRATA buoys etc are all collected through LDM/NCAR or direct transmission. Approximately 40 models outputs are available on a daily basis, twice a day. A simple procedure based on data assimilation principles was quite successful in combining the available forecasts in order to produce temperature, dew point, wind, pressure and precipitation forecasts at station points in S. America. The procedure is based on removing each model bias at the observational point and a weighted average based on the mean square error of the forecasts. The base period for estimating the bias and mean square error is of the order of 15 to 30 days. Products of the intercomparison model program and the optimal statistical combination of the available forecasts are public and available in real time (www.master.iag.usp.br/). Monitoring of the use of the products reveal a growing trend in the last year (reaching about 10.000 accesses per day in recent months). The intercomparison program provides a rich data set for educational products (real time use in Synoptic Meteorology and Numerical Weather Forecasting lectures), operational weather forecasts in national or regional weather centers and for research purposes. During the first phase of the program it was difficult to convince potential participants to share the information in the public homepage. However, as the system evolved, more and more institutions became associated with the program. The general opinion of the participants is that the system provides an unified metric for evaluation, a forum for discussion of the physical origin of the model forecast differences and therefore improvement of the quality of the numerical guidance.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/om/contact.shtml','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/om/contact.shtml"><span>Contact the NWS Office of Services</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Publications Assessments, Aware, Brochures... Get Weather Info Forecasts, <em>Past</em> Weather, Weather Radio... Other <em>question</em>, share a story, complaint or compliment. Get information on reprinting or using photos, videos and transmitters for your area. Find <em>Past</em> Weather: rainfall, wind and other data prior to today. Get Certified</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.nws.noaa.gov/om/marine/commar.htm','SCIGOVWS'); return false;" href="http://www.nws.noaa.gov/om/marine/commar.htm"><span>COMMERCIAL MARITIME COAST STATIONS and WEATHER NETS</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>Tsunamis 406 EPIRB's National Weather Service Marine Forecasts <em>COMMERCIAL</em> MARITIME COAST STATIONS and PRODUCTS VIA <em>COMMERCIAL</em> MARITIME COAST STATIONS and WEATHER NETS <em>Commercial</em> maritime coast stations, which ;NETS" operating on <em>commercial</em> marine VHF, MF and HF frequencies, where weather information is</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2010-title14-vol3/pdf/CFR-2010-title14-vol3-sec125-369.pdf','CFR'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2010-title14-vol3/pdf/CFR-2010-title14-vol3-sec125-369.pdf"><span>14 CFR 125.369 - Alternate airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2010&page.go=Go">Code of Federal Regulations, 2010 CFR</a></p> <p></p> <p>2010-01-01</p> <p>... 14 Aeronautics and Space 3 2010-01-01 2010-01-01 false Alternate airport weather minimums. 125.369... § 125.369 Alternate airport weather minimums. No person may list an airport as an alternate airport in the flight release unless the appropriate weather reports or forecasts, or any combination thereof...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol3/pdf/CFR-2011-title14-vol3-sec121-599.pdf','CFR2011'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol3/pdf/CFR-2011-title14-vol3-sec121-599.pdf"><span>14 CFR 121.599 - Familiarity with weather conditions.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2011&page.go=Go">Code of Federal Regulations, 2011 CFR</a></p> <p></p> <p>2011-01-01</p> <p>... 14 Aeronautics and Space 3 2011-01-01 2011-01-01 false Familiarity with weather conditions. 121... § 121.599 Familiarity with weather conditions. (a) Domestic and flag operations. No aircraft dispatcher may release a flight unless he is thoroughly familiar with reported and forecast weather conditions on...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2013-title14-vol3/pdf/CFR-2013-title14-vol3-sec125-369.pdf','CFR2013'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2013-title14-vol3/pdf/CFR-2013-title14-vol3-sec125-369.pdf"><span>14 CFR 125.369 - Alternate airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2013&page.go=Go">Code of Federal Regulations, 2013 CFR</a></p> <p></p> <p>2013-01-01</p> <p>... 14 Aeronautics and Space 3 2013-01-01 2013-01-01 false Alternate airport weather minimums. 125.369... § 125.369 Alternate airport weather minimums. No person may list an airport as an alternate airport in the flight release unless the appropriate weather reports or forecasts, or any combination thereof...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol3/pdf/CFR-2011-title14-vol3-sec125-369.pdf','CFR2011'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2011-title14-vol3/pdf/CFR-2011-title14-vol3-sec125-369.pdf"><span>14 CFR 125.369 - Alternate airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2011&page.go=Go">Code of Federal Regulations, 2011 CFR</a></p> <p></p> <p>2011-01-01</p> <p>... 14 Aeronautics and Space 3 2011-01-01 2011-01-01 false Alternate airport weather minimums. 125.369... § 125.369 Alternate airport weather minimums. No person may list an airport as an alternate airport in the flight release unless the appropriate weather reports or forecasts, or any combination thereof...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2012-title14-vol3/pdf/CFR-2012-title14-vol3-sec125-369.pdf','CFR2012'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2012-title14-vol3/pdf/CFR-2012-title14-vol3-sec125-369.pdf"><span>14 CFR 125.369 - Alternate airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2012&page.go=Go">Code of Federal Regulations, 2012 CFR</a></p> <p></p> <p>2012-01-01</p> <p>... 14 Aeronautics and Space 3 2012-01-01 2012-01-01 false Alternate airport weather minimums. 125.369... § 125.369 Alternate airport weather minimums. No person may list an airport as an alternate airport in the flight release unless the appropriate weather reports or forecasts, or any combination thereof...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2014-title14-vol3/pdf/CFR-2014-title14-vol3-sec125-369.pdf','CFR2014'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2014-title14-vol3/pdf/CFR-2014-title14-vol3-sec125-369.pdf"><span>14 CFR 125.369 - Alternate airport weather minimums.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2014&page.go=Go">Code of Federal Regulations, 2014 CFR</a></p> <p></p> <p>2014-01-01</p> <p>... 14 Aeronautics and Space 3 2014-01-01 2014-01-01 false Alternate airport weather minimums. 125.369... § 125.369 Alternate airport weather minimums. No person may list an airport as an alternate airport in the flight release unless the appropriate weather reports or forecasts, or any combination thereof...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2014-title14-vol3/pdf/CFR-2014-title14-vol3-sec121-599.pdf','CFR2014'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2014-title14-vol3/pdf/CFR-2014-title14-vol3-sec121-599.pdf"><span>14 CFR 121.599 - Familiarity with weather conditions.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2014&page.go=Go">Code of Federal Regulations, 2014 CFR</a></p> <p></p> <p>2014-01-01</p> <p>... 14 Aeronautics and Space 3 2014-01-01 2014-01-01 false Familiarity with weather conditions. 121... § 121.599 Familiarity with weather conditions. (a) Domestic and flag operations. No aircraft dispatcher may release a flight unless he is thoroughly familiar with reported and forecast weather conditions on...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2012-title14-vol3/pdf/CFR-2012-title14-vol3-sec121-599.pdf','CFR2012'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2012-title14-vol3/pdf/CFR-2012-title14-vol3-sec121-599.pdf"><span>14 CFR 121.599 - Familiarity with weather conditions.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2012&page.go=Go">Code of Federal Regulations, 2012 CFR</a></p> <p></p> <p>2012-01-01</p> <p>... 14 Aeronautics and Space 3 2012-01-01 2012-01-01 false Familiarity with weather conditions. 121... § 121.599 Familiarity with weather conditions. (a) Domestic and flag operations. No aircraft dispatcher may release a flight unless he is thoroughly familiar with reported and forecast weather conditions on...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_25 --> <div class="footer-extlink text-muted" style="margin-bottom:1rem; text-align:center;">Some links on this page may take you to non-federal websites. 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