No 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.

This study builds upon previous efforts to document the performance of the U.S. Army Test and Evaluation Command's Four-Dimensional Weather Modeling System using conventional metrics. Winds, temperature, and specific humidity were verified for almost 15 000 forecasts at five U.S. Army test ranges using near-surface mesonet data. The primary objective was to use conventional metrics to characterize the degree to which forecast accuracy varies from range to range, within the diurnal cycle, with elapsed forecast time, and among the seasons. It was found that there are large interrange differences in forecast error, with larger errors typically associated with the ranges located near complex orography. Similarly, significant variations in accuracy were noted for different times in the diurnal cycle, but the diurnal dependency varied greatly among the ranges. Factor of 2 differences in accuracy were also found across the seasons.

Since 2007, meteorologists of the U.S. Army Test and Evaluation Command (ATEC) at Dugway Proving Ground (DPG), Utah, have relied on a mesoscale ensemble prediction system (EPS) known as the Ensemble Four-Dimensional Weather System (E-4DWX). This article describes E-4DWX and the innovative way in which it is calibrated, how it performs, why it was developed, and how meteorologists at DPG use it. E-4DWX has 30 operational members, each configured to produce forecasts of 48 h every 6 h on a 272-processor high performance computer (HPC) at DPG. The ensemble’s members differ from one another in initial-, lateral-, and lower-boundary conditions; in methods of data assimilation; and in physical parameterizations. The predictive core of all members is the Advanced Research core of the Weather Research and Forecasting (WRF) Model. Numerical predictions of the most useful near-surface variables are dynamically calibrated through algorithms that combine logistic regression and quantile regression, generating statistically realistic probabilistic depictions of the atmosphere’s future state at DPG’s observing sites. Army meteorologists view E-4DWX’s output via customized figures posted to a restricted website. Some of these figures summarize collective results—for example, through means, standard deviations, or fractions of the ensemble exceeding thresholds. Other figures show each forecast, individually or grouped—for example, through spaghetti diagrams and time series. This article presents examples of each type of figure.

No abstract