TY - JOUR
T1 - Observational error covariance matrices for radar data assimilation
AU - Keeler, R. J.
AU - Ellis, S. M.
PY - 2000
Y1 - 2000
N2 - Optimal assimilation of meteorological radar data into numerical models requires knowledge of the observation error covariance matrix, i.e., the error variance magnitude at each data point and its correlation to adjacent data points. We use knowledge of basic reflectivity, radial velocity and spectrum width measurements obtainable from most weather radars to determine the instrumentation error component of the complete observation error covariance matrix. Specifically, the technique will be used to ingest radar data into mesoscale numerical weather prediction models. We perform an experimental validation of the predicted errors from the Memphis, Tennessee NEXRAD (WSR-88D) data. (C) 2000 Elsevier Science Ltd. All rights reserved.
AB - Optimal assimilation of meteorological radar data into numerical models requires knowledge of the observation error covariance matrix, i.e., the error variance magnitude at each data point and its correlation to adjacent data points. We use knowledge of basic reflectivity, radial velocity and spectrum width measurements obtainable from most weather radars to determine the instrumentation error component of the complete observation error covariance matrix. Specifically, the technique will be used to ingest radar data into mesoscale numerical weather prediction models. We perform an experimental validation of the predicted errors from the Memphis, Tennessee NEXRAD (WSR-88D) data. (C) 2000 Elsevier Science Ltd. All rights reserved.
UR - https://www.scopus.com/pages/publications/0033780345
U2 - 10.1016/S1464-1909(00)00193-3
DO - 10.1016/S1464-1909(00)00193-3
M3 - Article
AN - SCOPUS:0033780345
SN - 1464-1909
VL - 25
SP - 1277
EP - 1280
JO - Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere
JF - Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere
IS - 10-12
ER -