The Operational Mesogamma-Scale Analysis and Forecast System of the U.S. Army Test and Evaluation Command. Part I: Overview of the Modeling System, the Forecast Products, and How the Products Are Used

Abstract

Given the rapid increase in the use of operational mesoscale models to satisfy different specialized needs, it is important for the community to share ideas and solutions for meeting the many associated challenges that encompass science, technology, education, and training. As a contribution toward this objective, this paper begins a series that reports on the characteristics and performance of an operational mesogammascale weather analysis and forecasting system that has been developed for use by the U.S. Army Test and Evaluation Command. During the more than five years that this four-dimensional weather system has been in use at seven U.S. Army test ranges, valuable experience has been gained about the production and effective use of high-resolution model products for satisfying a variety of needs. This paper serves as a foundation for the rest of the papers in the series by describing the operational requirements for the system, the data assimilation and forecasting system characteristics, and the forecaster training that is required for the finescale products to be used effectively.&& The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Evaluation of the Impacts of Assimilating the TAMDAR Data on 12/4 km Grid WRF-Based RTFDDA Simulations over the CONUS

“…The RTFDDA system was originally built upon the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5, [34]) and was described in detail by Cram et al [35] and later by Liu et al [36]. The core of the RTFDDA system is a data assimilation component that continuously assimilates meteorological observations as they become available, thereby producing model-observation integrated 4D datasets that both define the current atmospheric conditions and serve as the initial conditions for subsequent model forecasts.…”

“…The data assimilation component in RTFDDA employs the Newtonian relaxation approach that uses nonphysical nudging terms in the model predictive equations [36]. These nudging terms synchronize the model atmospheric states at each grid point towards the observations in proportion to the differences between the model solutions and the observations [37].…”

“…Liu et al [36], the first of the series, report the design of the modeling system for each test range and the effective use of the forecast products for satisfying a variety of needs. In the second paper [43], evaluations of the RTFDDA system at the five test ranges using wind, temperature, and specific humidity observations are presented.…”

Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications

“…WRF-RTFDDA is an enhanced WRF modeling system, which is capable of assimilating diverse weather observations (Liu et al, 2008a). The FDDA system employs a Newtonian relaxation approach to continuously nudge the model states toward all available observations with weights specified as functions of space and time according to observation location and time.…”

“…In the last ten years, NCAR has developed a Real-Time FourDimensional Data Assimilation (RTFDDA) weather forecasting system (Liu et al, 2006(Liu et al, , 2008a to support regional and local applications such as wind energy forecasting. RTFDDA, built upon WRF, is a rapidly cycling weather forecasting system with the capability of effectively combining all available weather observations with the full-physics WRF model to produce accurate multiscale four dimensional (4D) weather information from synoptic scale to microscale (o 2 km).…”