scholarly journals The National Earth System Prediction Capability: Coordinating the Giant

2017 ◽  
Vol 98 (2) ◽  
pp. 239-252 ◽  
Author(s):  
Jessie C. Carman ◽  
Daniel P. Eleuterio ◽  
Timothy C. Gallaudet ◽  
Gerald L. Geernaert ◽  
Patrick A. Harr ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The United States ◽  
Department Of Energy ◽  
Earth System ◽  
Numerical Weather ◽  
Interagency Coordination ◽  
Research Technology ◽  
Prediction Capability ◽  
The U.S

Abstract The United States has had three operational numerical weather prediction centers since the Joint Numerical Weather Prediction Unit was closed in 1958. This led to separate paths for U.S. numerical weather prediction, research, technology, and operations, resulting in multiple community calls for better coordination. Since 2006, the three operational organizations—the U.S. Air Force, the U.S. Navy, and the National Weather Service—and, more recently, the Department of Energy, the National Aeronautics and Space Administration, the National Science Foundation, and the National Oceanic and Atmospheric Administration/Office of Oceanic and Atmospheric Research, have been working to increase coordination. This increasingly successful effort has resulted in the establishment of a National Earth System Prediction Capability (National ESPC) office with responsibility to further interagency coordination and collaboration. It has also resulted in sharing of data through an operational global ensemble, common software standards, and model components among the agencies. This article discusses the drivers, the progress, and the future of interagency collaboration.

50th Anniversary of Operational Numerical Weather Prediction

2007 ◽  
Vol 88 (5) ◽  
pp. 639-650 ◽  
Author(s):  
Kristine Harper ◽  
Louis W. Uccellini ◽  
Eugenia Kalnay ◽  
Kenneth Carey ◽  
Lauren Morone
Keyword(s):  
Air Force ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
50Th Anniversary ◽  
Well Being ◽  
The United States ◽  
Numerical Weather ◽  
American Meteorological Society ◽  
College Park ◽  
The U.S

The National Centers for Environmental Prediction (NCEP), Air Force Weather Agency (AFWA), Fleet Numerical Meteorology and Oceanography Center (FNMOC), National Weather Association, and American Meteorological Society (AMS) cosponsored a “Symposium on the 50th Anniversary of Operational Numerical Weather Prediction,” on 14–17 June 2004 at the University of Maryland, College Park in College Park, Maryland. Operational numerical weather prediction (NWP) in the United States started with the Joint Numerical Weather Prediction Unit (JNWPU) on 1 July 1954, staffed by members of the U.S. Weather Bureau, the U.S. Air Force and the U.S. Navy. The origins of NCEP, AFWA, and FNMOC can all be traced to the JNWPU. The symposium celebrated the pioneering developments in NWP and the remarkable improvements in forecast skill and support of the nation's economy, well being, and national defense achieved over the last 50 years. This essay was inspired by the presentations from that symposium.

The Common Community Physics Package (CCPP): bridging the gap between research and operations to improve U.S. numerical weather prediction capabilities

2020 ◽  
Author(s):  
Dom Heinzeller ◽  
Grant Firl ◽  
Ligia Bernardet ◽  
Laurie Carson ◽  
Man Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Modeling Framework ◽  
Initial Development ◽  
Numerical Weather ◽  
Computational Performance ◽  
The Common ◽  
The U.S ◽  
Physical Parameterizations

<p>Improving numerical weather prediction systems depends critically on the ability to transition innovations from research to operations (R2O) and to provide feedback from operations to research (O2R). This R2O2R cycle, sometimes referred to as "crossing the valley of death", has long been identified as a major challenge for the U.S. weather enterprise.</p><p>As part of a broader effort to bridge this gap and advance U.S. weather prediction capabilities, the Developmental Testbed Center (DTC) with staff at NOAA and NCAR has developed the Common Community Physics Package (CCPP) for application in NOAA's Unified Forecasting System (UFS). The CCPP consists of a library of physical parameterizations and a framework, which interfaces the physics with atmospheric models based on metadata information and standardized interfaces. The CCPP physics library contains physical parameterizations from the current operational U.S. global, mesoscale and high-resolution models, future implementation candidates, and additional physics from NOAA, NCAR and other organizations. The range of physics options in the CCPP physics library enables the application of the UFS - as well as every other model using the CCPP - across scales, from now-casting to seasonal and from high-resolution regional to global ensembles.</p><p>While the initial development of the CCPP was centered around the FV3 (Finite-Volume Cubed-Sphere) dynamical core of the UFS, its focus has since widened. The CCPP is also used by the DTC Single Column Model to support a hierarchical testing strategy, and by the next generation NEPTUNE (Navy Environmental Prediction sysTem Utilizing the Numa corE) model of the Naval Research Laboratory. Further, and most importantly, NOAA and NCAR recently signed an agreement to jointly develop the CCPP framework as a single, standardized way to interface physics with their models of the atmosphere (and other compartments of the Earth system). This places the CCPP in the heart of several of the U.S. flagship models and opens the door for bringing innovations from a large research community into operations.</p><p>In this contribution, we will present a brief overview of the concept of the CCPP, its technical design and the requirements for parameterizations to be considered as CCPP-compliant. We will describe the integration of CCPP in the UFS and touch upon the challenges in creating a flexible modeling framework while maintaining high computational performance. We will also provide information on how to obtain, use and contribute to the CCPP, as well as on the future development of the CCPP framework and upcoming additions to the CCPP physics library.</p>

Estimates of Cn2 from Numerical Weather Prediction Model Output and Comparison with Thermosonde Data

2010 ◽  
Vol 49 (8) ◽  
pp. 1742-1755 ◽  
Author(s):  
Rod Frehlich ◽  
Robert Sharman ◽  
Francois Vandenberghe ◽  
Wei Yu ◽  
Yubao Liu ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Structure Function ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The United States ◽  
Spatial Filter ◽  
Structure Functions ◽  
Model Output ◽  
Numerical Weather ◽  
Nwp Model

Abstract Area-averaged estimates of Cn2 from high-resolution numerical weather prediction (NWP) model output are produced from local estimates of the spatial structure functions of refractive index with corrections for the inherent smoothing and filtering effects of the underlying NWP model. The key assumptions are the existence of a universal statistical description of small-scale turbulence and a locally universal spatial filter for the NWP model variables. Under these assumptions, spatial structure functions of the NWP model variables can be related to the structure functions of the atmospheric variables and extended to the smaller underresolved scales. The shape of the universal spatial filter is determined by comparisons of model structure functions with the climatological spatial structure function determined from an archive of aircraft data collected in the upper troposphere and lower stratosphere. This method of computing Cn2 has an important advantage over more traditional methods that are based on vertical differences because the structure function–based estimates avoid reference to the turbulence outer length scale. To evaluate the technique, NWP model–derived structure-function estimates of Cn2 are compared with nighttime profiles of Cn2 derived from temperature structure-function sensors attached to a rawinsonde (thermosonde) near Holloman Air Force Base in the United States.

scholarly journals A Decade of Antarctic Science Support Through Amps

2012 ◽  
Vol 93 (11) ◽  
pp. 1699-1712 ◽  
Author(s):  
Jordan G. Powers ◽  
Kevin W. Manning ◽  
David H. Bromwich ◽  
John J. Cassano ◽  
Arthur M. Cayette
Keyword(s):  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Prediction System ◽  
Numerical Weather ◽  
The Antarctic ◽  
The U.S ◽  
Weather Research

The Antarctic Mesoscale Prediction System (AMPS) is a real-time numerical weather prediction (NWP) system covering Antarctica that has served a remarkable range of groups and activities for a decade. It employs the Weather Research and Forecasting model (WRF) on varying-resolution grids to generate numerical guidance in a variety of tailored products. While its priority mission has been to support the forecasters of the U.S. Antarctic Program, AMPS has evolved to assist a host of scientific and logistical needs for an international user base. The AMPS effort has advanced polar NWP and Antarctic science and looks to continue this into another decade. To inform those with Antarctic scientific and logistical interests and needs, the history, applications, and capabilities of AMPS are discussed.

The Accuracy of Solar Irradiance Calculations Used in Mesoscale Numerical Weather Prediction

2005 ◽  
Vol 133 (4) ◽  
pp. 783-792 ◽  
Author(s):  
Robert J. Zamora ◽  
Ellsworth G. Dutton ◽  
Michael Trainer ◽  
Stuart A. McKeen ◽  
James M. Wilczak ◽  
...  
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
The United States ◽  
Model Errors ◽  
Numerical Weather ◽  
Mesoscale Numerical Weather Prediction

In this paper, solar irradiance forecasts made by mesoscale numerical weather prediction models are compared with observations taken during three air-quality experiments in various parts of the United States. The authors evaluated the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and the National Centers for Environmental Prediction (NCEP) Eta Model. The observations were taken during the 2000 Texas Air Quality Experiment (TexAQS), the 2000 Central California Ozone Study (CCOS), and the New England Air Quality Study (NEAQS) 2002. The accuracy of the model forecast irradiances show a strong dependence on the aerosol optical depth. Model errors on the order of 100 W m−2 are possible when the aerosol optical depth exceeds 0.1. For smaller aerosol optical depths, the climatological attenuation used in the models yields solar irradiance estimates that are in good agreement with the observations.

scholarly journals Evaluating the Economic Impacts of Improvements to the High-Resolution Rapid Refresh (HRRR) Numerical Weather Prediction Model

2021 ◽  
pp. 1-36
Author(s):  
David D. Turner ◽  
Harvey Cutler ◽  
Martin Shields ◽  
Rebecca Hill ◽  
Brad Hartman ◽  
...  
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Economic Impacts ◽  
Weather Prediction ◽  
Critical Role ◽  
Weather Conditions ◽  
Numerical Weather Prediction Model ◽  
Economic Gain ◽  
Numerical Weather ◽  
The U.S

AbstractForecasts from numerical weather prediction (NWP) models play a critical role in many sectors of the American economy. Improvements to operational NWP model forecasts are generally assumed to provide significant economic savings through better decision making. But is this true? Since 2014, several new versions of the High-Resolution Rapid Refresh (HRRR) model were released into operation within the National Weather Service. Practically, forecasts have an economic impact only if they lead to a different action than what would be taken under an alternative information set. And in many sectors, these decisions only need to be considered during certain weather conditions. We estimate the economic impacts of improvements made to the HRRR, using 12-hour wind, precipitation, and temperature forecasts in several cases where they can have “economically meaningful” behavioral consequences. We examine three different components of the U.S. economy where such information matters: 1) better integration of wind energy resources into the electric grid, 2) increased worker output due to better precipitation forecasts that allow workers to arrive to their jobs on time, and 3) better decisions by agricultural producers in preparing for freezing conditions. These applications demonstrate some of the challenges in ascertaining the economic impacts of improved weather forecasts, including highlighting key assumptions that must be made to make the problem tractable. For these sectors, we demonstrate that there was a marked economic gain for the U.S. between HRRR versions 1 and 2, and a smaller, but still appreciable economic gain between versions 2 and 3.

scholarly journals Using a Coevolutionary Postprocessor to Improve Skill for Both Forecasts of Surface Temperature and Nowcasts of Convection Occurrence

2019 ◽  
Vol 147 (11) ◽  
pp. 4241-4259 ◽  
Author(s):  
Paul J. Roebber ◽  
John Crockett
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Evolutionary Programming ◽  
The United States ◽  
Training Data ◽  
New Approach ◽  
Numerical Weather ◽  
Probabilistic Forecasts

Abstract An evolutionary programming postprocessor, using coevolution in a predator–prey ecosystem model, is developed and applied both to 72-h, 2-m temperature forecasts for the conterminous United States and southern Canada and to 60-min nowcasts of convection occurrence for the United States east of 94°W. The new approach improves deterministic and probabilistic forecasts of surface temperature relative to bias-corrected numerical weather prediction forecasts and to an earlier version of evolutionary programming forecasts for these same data. The new method also improves deterministic performance for an artificial neural network trained and evaluated for these same data. Additionally, the new approach substantially improves these forecasts’ reliability, as evidenced by reductions in the occurrence of excessive outliers in the rank histogram. The coevolutionary postprocessor also improves deterministic nowcasts of convection occurrence when compared to those produced by the National Weather Service’s AutoNowCaster system and to those obtained using multiple logistic regression. Notably, the degree of improvement relative to traditional methods appears to be problem dependent, while the training and implementation of such a system requires additional effort. However, the coevolutionary system is shown to be robust to imbalances between the frequency of positive and null events in the training data, unlike many postprocessing methods; to be implementable and effective in an adaptive mode, removing the need for retraining as inputs (such as numerical weather prediction model data) change; and to provide a useful, alternative perspective on the likelihood of event occurrence when used in combination with other methods.

The irreplaceable utility of sequential data assimilation for numerical weather prediction system development: Lessons learned from an experimental HWRF system

2021 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Ghassan J. Alaka ◽  
Henry R. Winterbottom
Keyword(s):  
United States ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The United States ◽  
Sequential Data ◽  
Prediction System ◽  
Numerical Weather ◽  
Hwrf Model ◽  
Sequential Data Assimilation

AbstractLimited-area numerical weather prediction models currently run operationally in the United States follow a “partially-cycled” schedule, where sequential data assimilation is periodically interrupted by replacing model states with solutions interpolated from a global model. While this strategy helps overcome several practical challenges associated with real-time regional forecasting, it is no substitute for a robust sequential data assimilation approach for research-to-operations purposes. Partial cycling can mask systematic errors in weather models, data assimilation systems, and data pre-processing techniques, since it introduces information from a different prediction system. It also adds extra heuristics to the model initialization steps outside the general Bayesian filtering framework from which data assimilation methods are derived. This study uses a research-oriented modeling system, which is self-contained in the operational Hurricane Weather Research and Forecasting (HWRF) model package, to illustrate why next-generation modeling systems should prioritize sequential data assimilation at early stages of development. This framework permits the rigorous examination of all model system components—in a manner that has never been done for the HWRF model. Examples presented in this manuscript show how sequential data assimilation capabilities can accelerate model advancements and increase academic involvement in operational forecasting systems at a time when the United States is developing a new hurricane forecasting system.

scholarly journals How Well Do Operational Numerical Weather Prediction Configurations Represent Hydrology?

2019 ◽  
Vol 20 (8) ◽  
pp. 1533-1552 ◽  
Author(s):  
Ervin Zsoter ◽  
Hannah Cloke ◽  
Elisabeth Stephens ◽  
Patricia de Rosnay ◽  
Joaquin Muñoz-Sabater ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Water Budget ◽  
Snow Depth ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Earth System ◽  
Numerical Weather

Abstract Land surface models (LSMs) have traditionally been designed to focus on providing lower-boundary conditions to the atmosphere with less focus on hydrological processes. State-of-the-art application of LSMs includes a land data assimilation system (LDAS), which incorporates available land surface observations to provide an improved realism of surface conditions. While improved representations of the surface variables (such as soil moisture and snow depth) make LDAS an essential component of any numerical weather prediction (NWP) system, the related increments remove or add water, potentially having a negative impact on the simulated hydrological cycle by opening the water budget. This paper focuses on evaluating how well global NWP configurations are able to support hydrological applications, in addition to the traditional weather forecasting. River discharge simulations from two climatological reanalyses are compared: one “online” set, which includes land–atmosphere coupling and LDAS with an open water budget, and an “offline” set with a closed water budget and no LDAS. It was found that while the online version of the model largely improves temperature and snow depth conditions, it causes poorer representation of peak river flow, particularly in snowmelt-dominated areas in the high latitudes. Without addressing such issues there will never be confidence in using LSMs for hydrological forecasting applications across the globe. This type of analysis should be used to diagnose where improvements need to be made; considering the whole Earth system in the data assimilation and coupling developments is critical for moving toward the goal of holistic Earth system approaches.

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