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  • Author or Editor: Geoffrey S. Dimego x
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Eric Rogers, Dennis G. Deaven, and Geoffrey S. Dimego

Abstract

The analysis component of the National Centers for Environmental Prediction (NCEP) operational “early” 80-km eta model, as implemented in July 1993, is described. This optimum interpolation (OI) analysis is fully multivariate for wind and geopotential height (univariate for specific humidity) and is performed directly on the eta model's vertical coordinate. Although the eta OI analysis and model performance has been generally favorable when compared to the Limited-Area Fine Mesh Model (LFM) and the Nested Grid Model (NGM), deficiencies in the eta OI analysis fields have been observed, especially near the surface.

A series of improvements to the eta OI analysis is described. A refinement to the eta model orography, which created a more realistic depiction of the model terrain, is also discussed along with the impact of these changes on analysis and model performance. These changes were implemented in the early eta system in September 1994.

The operational configuration of the new mesoscale (29 km) eta model system is introduced, consisting of a mesoscale eta-based data assimilation system (EDAS) and the mesoscalee forecast. An example of an analysis produced by the mesoscale EDAS is presented for comparison with the operational 80-km eta OI analysis. A brief description of more recent changes to the early eta system are also described.

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Manuel S. F. V. De Pondeca, Geoffrey S. Manikin, Geoff DiMego, Stanley G. Benjamin, David F. Parrish, R. James Purser, Wan-Shu Wu, John D. Horel, David T. Myrick, Ying Lin, Robert M. Aune, Dennis Keyser, Brad Colman, Greg Mann, and Jamie Vavra

Abstract

In 2006, the National Centers for Environmental Prediction (NCEP) implemented the Real-Time Mesoscale Analysis (RTMA) in collaboration with the Earth System Research Laboratory and the National Environmental, Satellite, and Data Information Service (NESDIS). In this work, a description of the RTMA applied to the 5-km resolution conterminous U.S. grid of the National Digital Forecast Database is given. Its two-dimensional variational data assimilation (2DVAR) component used to analyze near-surface observations is described in detail, and a brief discussion of the remapping of the NCEP stage II quantitative precipitation amount and NESDIS Geostationary Operational Environmental Satellite (GOES) sounder effective cloud amount to the 5-km grid is offered. Terrain-following background error covariances are used with the 2DVAR approach, which produces gridded fields of 2-m temperature, 2-m specific humidity, 2-m dewpoint, 10-m U and V wind components, and surface pressure. The estimate of the analysis uncertainty via the Lanczos method is briefly described. The strength of the 2DVAR is illustrated by (i) its ability to analyze a June 2007 cold temperature pool over the Washington, D.C., area; (ii) its fairly good analysis of a December 2008 mid-Atlantic region high-wind event that started from a very weak first guess; and (iii) its successful recovery of the finescale moisture features in a January 2010 case study over southern California. According to a cross-validation analysis for a 15-day period during November 2009, root-mean-square error improvements over the first guess range from 16% for wind speed to 45% for specific humidity.

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Jordan G. Powers, Joseph B. Klemp, William C. Skamarock, Christopher A. Davis, Jimy Dudhia, David O. Gill, Janice L. Coen, David J. Gochis, Ravan Ahmadov, Steven E. Peckham, Georg A. Grell, John Michalakes, Samuel Trahan, Stanley G. Benjamin, Curtis R. Alexander, Geoffrey J. Dimego, Wei Wang, Craig S. Schwartz, Glen S. Romine, Zhiquan Liu, Chris Snyder, Fei Chen, Michael J. Barlage, Wei Yu, and Michael G. Duda

Abstract

Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.

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