scholarly journals Bridging Research to Operations Transitions: Status and Plans of Community GSI

2016 ◽  
Vol 97 (8) ◽  
pp. 1427-1440 ◽  
Author(s):  
Hui Shao ◽  
John Derber ◽  
Xiang-Yu Huang ◽  
Ming Hu ◽  
Kathryn Newman ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Models ◽  
Weather Prediction ◽  
The United States ◽  
Environmental Modeling ◽  
Research Community ◽  
Community Based ◽  
Management Framework ◽  
The Public ◽  
Environmental Prediction

Abstract With a goal of improving operational numerical weather prediction (NWP), the Developmental Testbed Center (DTC) has been working with operational centers, including, among others, the National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and the U.S. Air Force, to support numerical models/systems and their research, perform objective testing and evaluation of NWP methods, and facilitate research-to-operations transitions. This article introduces the first attempt of the DTC in the data assimilation area to help achieve this goal. Since 2009, the DTC, NCEP’s Environmental Modeling Center (EMC), and other developers have made significant progress in transitioning the operational Gridpoint Statistical Interpolation (GSI) data assimilation system into a community-based code management framework. Currently, GSI is provided to the public with user support and is open for contributions from internal developers as well as the broader research community, following the same code transition procedures. This article introduces measures and steps taken during this community GSI effort followed by discussions of encountered challenges and issues. The purpose of this article is to promote contributions from the research community to operational data assimilation capabilities and, furthermore, to seek potential solutions to stimulate such a transition and, eventually, improve the NWP capabilities in the United States.

The Emergence of Weather-Related Test Beds Linking Research and Forecasting Operations

2013 ◽  
Vol 94 (8) ◽  
pp. 1187-1211 ◽  
Author(s):  
F. Martin Ralph ◽  
Janet Intrieri ◽  
David Andra ◽  
Robert Atlas ◽  
Sid Boukabara ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Satellite Data ◽  
Numerical Models ◽  
Weather Prediction ◽  
Severe Weather ◽  
Test Bed ◽  
Current Test ◽  
Test Beds ◽  
Prediction Problems ◽  
Weather Research

Test beds have emerged as a critical mechanism linking weather research with forecasting operations. The U.S. Weather Research Program (USWRP) was formed in the 1990s to help identify key gaps in research related to major weather prediction problems and the role of observations and numerical models. This planning effort ultimately revealed the need for greater capacity and new approaches to improve the connectivity between the research and forecasting enterprise. Out of this developed the seeds for what is now termed “test beds.” While many individual projects, and even more broadly the NOAA/National Weather Service (NWS) Modernization, were successful in advancing weather prediction services, it was recognized that specific forecast problems warranted a more focused and elevated level of effort. The USWRP helped develop these concepts with science teams and provided seed funding for several of the test beds described. Based on the varying NOAA mission requirements for forecasting, differences in the organizational structure and methods used to provide those services, and differences in the state of the science related to those forecast challenges, test beds have taken on differing characteristics, strategies, and priorities. Current test bed efforts described have all emerged between 2000 and 2011 and focus on hurricanes (Joint Hurricane Testbed), precipitation (Hydrometeorology Testbed), satellite data assimilation (Joint Center for Satellite Data Assimilation), severe weather (Hazardous Weather Testbed), satellite data support for severe weather prediction (Short-Term Prediction Research and Transition Center), mesoscale modeling (Developmental Testbed Center), climate forecast products (Climate Testbed), testing and evaluation of satellite capabilities [Geostationary Operational Environmental Satellite-R Series (GOES-R) Proving Ground], aviation applications (Aviation Weather Testbed), and observing system experiments (OSSE Testbed).

scholarly journals Mesoscale Model Evaluation Testbed (MMET): A Resource for Transitioning NWP Innovations from Research to Operations (R2O)

2016 ◽  
Vol 97 (11) ◽  
pp. 2135-2147 ◽  
Author(s):  
Jamie K. Wolff ◽  
Michelle Harrold ◽  
Tracy Hertneky ◽  
Eric Aligo ◽  
Jacob R. Carley ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Multiscale Model ◽  
Environmental Modeling ◽  
Research Community ◽  
Weather Research And Forecasting ◽  
Forecast Performance ◽  
Testing Framework ◽  
Wide Range

Abstract A wide range of numerical weather prediction (NWP) innovations are under development in the research community that have the potential to positively impact operational models. The Developmental Testbed Center (DTC) helps facilitate the transition of these innovations from research to operations (R2O). With the large number of innovations available in the research community, it is critical to clearly define a testing protocol to streamline the R2O process. The DTC has defined such a process that relies on shared responsibilities of the researchers, the DTC, and operational centers to test promising new NWP advancements. As part of the first stage of this process, the DTC instituted the mesoscale model evaluation testbed (MMET), which established a common testing framework to assist the research community in demonstrating the merits of developments. The ability to compare performance across innovations for critical cases provides a mechanism for selecting the most promising capabilities for further testing. If the researcher demonstrates improved results using MMET, then the innovation may be considered for the second stage of comprehensive testing and evaluation (T&E) prior to entering the final stage of preimplementation T&E. MMET provides initialization and observation datasets for several case studies and multiday periods. In addition, the DTC provides baseline results for select operational configurations that use the Advanced Research version of Weather Research and Forecasting Model (ARW) or the National Oceanic and Atmospheric Administration (NOAA) Environmental Modeling System Nonhydrostatic Multiscale Model on the B grid (NEMS-NMMB). These baselines can be used for testing sensitivities to different model versions or configurations in order to improve forecast performance.

Development of a Real-Time Regional Ocean Forecast System with Application to a Domain off the U.S. East Coast

2004 ◽  
Vol 38 (1) ◽  
pp. 61-79 ◽  
Author(s):  
Laurence C. Breaker ◽  
Desiraju B. Rao ◽  
John G.W. Kelley ◽  
Ilya Rivin
Keyword(s):  
Real Time ◽  
East Coast ◽  
Weather Prediction ◽  
Forecast Model ◽  
The United States ◽  
Ocean Model ◽  
Sea Surface ◽  
Forecast System ◽  
Environmental Prediction ◽  
The U.S

This paper discusses the needs to establish a capability to provide real-time regional ocean forecasts and the feasibility of producing them on an operational basis. Specifically, the development of a Regional Ocean Forecast System using the Princeton Ocean Model (POM) as a prototype and its application to the East Coast of the U.S. are presented. The ocean forecasts are produced using surface forcing from the Eta model, the operational mesoscale weather prediction model at the National Centers for Environmental Prediction (NCEP). At present, the ocean forecast model, called the East Coast-Regional Ocean Forecast System (EC-ROFS) includes assimilation of sea surface temperatures from in situ and satellite data and sea surface height anomalies from satellite altimeters. Examples of forecast products, their evaluation, problems that arose during the development of the system, and solutions to some of those problems are also discussed. Even though work is still in progress to improve the performance of EC-ROFS, it became clear that the forecast products which are generated can be used by marine forecasters if allowances for known model deficiencies are taken into account. The EC-ROFS became fully operational at NCEP in March 2002, and is the first forecast system of its type to become operational in the civil sector of the United States.

scholarly journals Regulation of “Classic” Community-Based Charitable Organizations

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Putnam Barber ◽  
Steven Rathgeb Smith
Keyword(s):  
The United States ◽  
Regulatory Agencies ◽  
Charitable Organizations ◽  
Community Based ◽  
The Public ◽  
Significant Burden ◽  
Tax Exempt ◽  
Available Information ◽  
Source Of Information

AbstractTax-exempt organizations in the United States are, in general, required to file regular reports on their operations with the Internal Revenue Service (IRS). These filings represent a significant burden for the filing organizations, are a critical source of information for many observers, and are relied upon by regulators and donors as indicators of the organizations’ commitment to achieving the wide variety of purposes for which the exemptions are granted. This paper recommends that the resources of the IRS be refocused so that greater attention can be paid to complex organizations, while the burden of preparing annual filings by simpler organization is also reduced. More generally, this paper also recommends that new attention be paid to the information collected and to its publication. It argues that the needs of the public and other regulatory agencies are not well-served by the current information and that the limitations on currently available information contribute to misunderstanding of and cynicism about the role of charitable nonprofits in American life.

scholarly journals Comparisons of Performance Using Data Assimilation and Data Fusion Approaches in Acquiring Precipitable Water Vapor: A Case Study of a Western United States of America Area

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2943
Author(s):  
Zhaohui Xiong ◽  
Jizhang Sang ◽  
Xiaogong Sun ◽  
Bao Zhang ◽  
Junyu Li
Keyword(s):  
United States ◽  
Water Vapor ◽  
Data Assimilation ◽  
United States Of America ◽  
Weather Prediction ◽  
Wrf Model ◽  
Precipitable Water ◽  
The United States ◽  
Satellite System ◽  
Precipitable Water Vapor

There are two main types of methods available to obtain precipitable water vapor (PWV) with high accuracy. One is to assimilate observations into a numerical weather prediction (NWP) model, for example, the Weather Research and Forecasting (WRF) model, to improve the accuracy of meteorological parameters, and then obtain the PWV with improved accuracy. The other is the direct fusion of multi-source PWV products. Regarding the two approaches, we conduct a comparison experiment on the West Coast of the United States of America with the data from May 2018, in which the WRF data assimilation (DA) system is used to assimilate the Global Navigation Satellite System (GNSS) PWV, while the method by Zhang et al. to fuse the GNSS PWV, ERA5 PWV and MODIS (moderate-resolution imaging spectroradiometer) PWV. As a result, four groups of PWV products are generated: the assimilated GNSS PWV, the unassimilated GNSS PWV, PWV from the fusion of the GNSS PWV and ECWMF (European Centre for Medium-Range Weather Forecasts) ERA5 (ECWMF Reanalysis 5) PWV, and PWV from the fusion of the GNSS PWV, ERA5 PWV and MODIS PWV. Experiments show that the data assimilation based on the WRF model (WRFDA) and adopted fusion method can generate PWV products with similar accuracy (1.47 mm vs. 1.52 mm). Assimilating the GNSS PWV into the WRF model slightly improves the accuracy of the inverted PWV by 0.18 mm. The fusion of the MODIS PWV, GNSS PWV and ERA5 PWV results in a higher accuracy than the fusion of GNSS PWV and ERA5 PWV by a margin of 0.35 mm. In addition, the inland canyon topography appears to have an influence on the inversion accuracy of both the methods.

scholarly journals The Impact of Dropwindsonde and Supplemental Rawinsonde Observations on Track Forecasts for Hurricane Irene (2011)

2013 ◽  
Vol 28 (6) ◽  
pp. 1385-1403 ◽  
Author(s):  
Sharanya J. Majumdar ◽  
Michael J. Brennan ◽  
Kate Howard
Keyword(s):  
United States ◽  
Numerical Models ◽  
The United States ◽  
Short Wave ◽  
Time Range ◽  
Wave Trough ◽  
Hurricane Irene ◽  
Environmental Prediction ◽  
The Impact ◽  
Rawinsonde Data

Abstract Because of the threat that Hurricane Irene (2011) posed to the United States, supplemental observations were collected for assimilation into operational numerical models in the hope of improving forecasts of the storm. Synoptic surveillance aircraft equipped with dropwindsondes were deployed twice daily over a 5-day period, and supplemental rawinsondes were launched from all upper-air sites in the continental United States east of the Rocky Mountains at 0600 and 1800 UTC, marking an unprecedented magnitude of coverage of special rawinsondes at the time. The impact of assimilating the supplemental observations on National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model track forecasts of Irene was evaluated over the period that these observations were collected. The GFS track forecasts possessed small errors even in the absence of the supplemental observations, providing little room for improvement on average. The assimilation of the combined dropwindsonde and supplemental rawinsonde data provided small but statistically significant improvements in the 42–60-h range for GFS forecasts initialized at 0600 and 1800 UTC. The primary improvement from the dropwindsonde data was also within this time range, with an average improvement of 20% for 48-h forecasts. The rawinsonde data mostly improved the forecasts beyond 3 days by modest amounts. Both sets of observations provided small, additive improvements to the average cross-track errors. Investigations of individual forecasts identified corrections to the model analyses of the Atlantic subtropical ridge and an upstream midlatitude short-wave trough over the contiguous United States due to the assimilation of the extra data.

scholarly journals Improving Incremental Balance in the GSI 3DVAR Analysis System

2009 ◽  
Vol 137 (3) ◽  
pp. 1046-1060 ◽  
Author(s):  
Daryl T. Kleist ◽  
David F. Parrish ◽  
John C. Derber ◽  
Russ Treadon ◽  
Ronald M. Errico ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Normal Mode ◽  
Three Dimensional ◽  
Substantial Improvement ◽  
Environmental Modeling ◽  
Variational Data Assimilation ◽  
North American Data ◽  
Analysis System ◽  
Environmental Prediction ◽  
Statistical Interpolation

Abstract The gridpoint statistical interpolation (GSI) analysis system is a unified global/regional three-dimensional variational data assimilation (3DVAR) analysis code that has been under development for several years at the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center. It has recently been implemented into operations at NCEP in both the global and North American data assimilation systems (GDAS and NDAS, respectively). An important aspect of this development has been improving the balance of the analysis produced by GSI. The improved balance between variables has been achieved through the inclusion of a tangent-linear normal-mode constraint (TLNMC). The TLNMC method has proven to be very robust and effective. The TLNMC as part of the global GSI system has resulted in substantial improvement in data assimilation at NCEP.

scholarly journals Prediction of Lake-Effect Snow Using Convection-Allowing Ensemble Forecasts and Regional Data Assimilation

2017 ◽  
Vol 32 (5) ◽  
pp. 1727-1744 ◽  
Author(s):  
Seth Saslo ◽  
Steven J. Greybush
Keyword(s):  
Data Assimilation ◽  
Great Lakes ◽  
Forecast Error ◽  
Weather Prediction ◽  
The United States ◽  
Ensemble Prediction ◽  
Great Lakes Region ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Lake Effect

Abstract Lake-effect snow (LES) is a cold-season mesoscale convective phenomenon that can lead to significant snowfall rates and accumulations in the Great Lakes region of the United States. While limited-area numerical weather prediction models have shown skill in prediction of warm-season convective storms, forecasting the sharp nature of LES precipitation timing, intensity, and location is difficult because of model error and initial and boundary condition uncertainties. Ensemble forecasting can incorporate and quantify some sources of forecast error, but ensemble design must be considered. This study examines the relative contributions of forecast uncertainties to LES forecast error using a regional convection-allowing data assimilation and ensemble prediction system. Ensembles are developed using various methods of perturbations to simulate a long-lived and high-precipitation LES event in December 2013, and forecast performance is evaluated using observations including those from the Ontario Winter Lake-Effect Systems (OWLeS) campaign. Model lateral boundary conditions corresponding to weather conditions beyond the Great Lakes region play an influential role in LES precipitation forecasts and their uncertainty, as evidenced by ensemble spread, particularly at lead times beyond one day. A strong forecast dependence on regional initial conditions was shown using data assimilation. This sensitivity impacts the timing and intensity of predicted precipitation, as well as band location and orientation assessed with an object-based verification approach, giving insight into the time scales of practical predictability of LES. Overall, an assimilation-cycling convection-allowing ensemble prediction system could improve future lake-effect snow precipitation forecasts and analyses and can help quantify and understand sources of forecast uncertainty.

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.

Characterization of Free-nets in the United States and Canada: Facing History and Managing the Future

2013 ◽  
Author(s):  
Timothy S. Chase
Keyword(s):  
United States ◽  
Information Service ◽  
Public Library ◽  
The United States ◽  
The Other ◽  
Community Based ◽  
The Public ◽  
The Past ◽  
History Of

The explosion in the number of Free-Nets and their organizing committees over the past two years attests to the popularity of community-based computing. But, the goal of these organizations is farther reaching than merely strength of numbers; they want to change their communities for the better. In order to assure an important and relevant place in the community, Free-Nets must face the history of the other community-based information service provider: the public library. Once this is done, Free-Nets must focus on achieving results, not merely on achieving continued existence.

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