Open AccessImproving Short-Term Storm Predictions by Assimilating both Radar Radial-Wind and Reflectivity Observations
Author(s) -
Qingyun Zhao,
J. W. Cook,
Qin Xu,
Paul R. Harasti
Publication year - 2008
Publication title -
weather and forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 106
eISSN - 1520-0434
pISSN - 0882-8156
DOI - 10.1175/2007waf2007038.1
Subject(s) - data assimilation , nowcasting , meteorology , radar , mesoscale meteorology , environmental science , storm , remote sensing , doppler radar , term (time) , computer science , geology , geography , telecommunications , physics , quantum mechanics
A high-resolution data assimilation system is under development at the Naval Research Laboratory (NRL). The objective of this development is to assimilate high-resolution data, especially those from Doppler radars, into the U.S. Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System to improve the model’s capability and accuracy in short-term (0–6 h) prediction of hazardous weather for nowcasting. A variational approach is used in this system to assimilate the radar observations into the model. The system is upgraded in this study with new capabilities to assimilate not only the radar radial-wind data but also reflectivity data. Two storm cases are selected to test the upgraded system and to study the impact of radar data assimilation on model forecasts. Results from the data assimilation experiments show significant improvements in storm prediction especially when both radar radial-wind and reflectivity observations are assimilated and the analysis incremental fields are adequately constrained by the model’s dynamics and properly adjusted to satisfy the model’s thermodynamical balance.