Open AccessImpact from a Volumetric Radar-Sampling Operator for Radial Velocity Observations within EnKF Supercell Assimilation
Author(s) -
Therese E. Thompson,
Louis J. Wicker,
Xuguang Wang
Publication year - 2012
Publication title -
journal of atmospheric and oceanic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 124
eISSN - 1520-0426
pISSN - 0739-0572
DOI - 10.1175/jtech-d-12-00088.1
Subject(s) - data assimilation , ensemble kalman filter , supercell , radar , weighting , operator (biology) , doppler radar , meteorology , sampling (signal processing) , remote sensing , kalman filter , computer science , environmental science , geology , mathematics , statistics , extended kalman filter , filter (signal processing) , geography , physics , acoustics , computer vision , repressor , chemistry , biochemistry , transcription factor , telecommunications , gene
Maximizing the accuracy of ensemble Kalman filtering (EnKF) radar data assimilation requires that the observation operator sample the model state in the same manner that the radar sampled the atmosphere. It may therefore be desirable to include volume averaging and power weighting in the observation operator. This study examines the impact of including radar-sampling effects in the Doppler velocity observation operator on EnKF analyses and forecasts. Locally substantial differences are found between a simple point operator and a realistic radar-sampling operator when they are applied to the model state at a single time. However, assimilation results indicate that the radar-sampling operator does not substantially improve the EnKF analyses or forecasts, and it greatly increases the computational cost of the data assimilation.