Implementation of the WRF Four-Dimensional Data Assimilation Method of Observation Nudging for Use as an ARL Weather Running Estimate-Nowcast
Abstract
The U.S. Army Research Laboratory has been performing research involving the Advanced Research Weather Research and Forecasting numerical weather prediction model and its four-dimensional (4D) data assimilation component called observation nudging. The focus has been on testing of the assimilation technique in nested and limited-area model configurations to resolve scales of 0.5 3-km grid spacing as an Army Weather Running Estimate-Nowcast tool. The tool would provide a rapid-update cycling application for generating short-range forecast/nowcast updates at storm-scale resolutions. The observation nudging method is considerably less computationally expensive than the suite of more advanced 4D-variational and ensemble Kalman filter data assimilation systems used at large operational centers. It is based upon the same general principles of the Kalman gain theory as are the variational and ensemble methods and has been shown to be an effective method of assimilating asynoptic meteorological observations into high-resolution models. Focus here is upon model configuration and the steps required to generate the hourly cycled model runs. Special weather observations collected over Yuma Proving Ground, AZ, during November 30 December 01, 2007, were ingested into the system for a case study application example. However, this report s intent is to focus upon the methodology rather than verification of the meteorology.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2013
- Accession Number
- ADA586776
Entities
People
- Robert Dumais
- Robert Flanigan
- Steve Kirby
Organizations
- United States Army Research Laboratory