Domain-Level Assessment of the Weather Running Estimate-Nowcast (WREN) Model

Abstract

The Weather Research and Forecasting Model (WRF) is a numerical weather-prediction model that has been used for many applications including My Weather Impacts Decision Aid. WRF is maintained by the National Center for Atmospheric Research, which has developed a suite of Model Evaluation Tools (MET) to evaluate the accuracy of WRF forecasts. In this technical report, we discuss our use of the MET to assess the domain-level errors of the Weather Running Estimate Nowcast (WREN) model. In addition, the domain-level errors were calculated for the Global Forecast System (GFS) model, which is used to initialize the WREN. The output used for this study was generated using 3 different configurations of the WREN, with and without observation assimilation, that were run over 2 different triple-nested domains centered near San Diego, California. We selected 5 case-study days in February March 2012 with varied weather conditions. The results of the study suggest the observation assimilation improves the forecast under certain conditions. The results show the WREN generally performs better than the GFS model with some limitations. More comprehensive verification studies are needed to conclusively determine the value added by varying configurations of the WREN.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2016
Accession Number
AD1021292

Entities

People

  • Jeffrey A. Smith
  • John W. Raby

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Assimilation
  • Atmospheric Sciences
  • Boundary Layer
  • California
  • Case Studies
  • Data Analysis
  • Delphi Method
  • Errors
  • Four Dimensional
  • Geographic Information Systems
  • High Resolution
  • Information Science
  • Lead Time
  • Meteorology
  • Statistics
  • Verification
  • Weather Forecasting

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Computer Science.