A reasonable background error covariance(B)is the key to data assimilation.Three sample error simulation techniques for calculating B,such as,Randomized Control Variables(RandomCV),Breeding of Growing Modes(BGM)and the NMC method,are used to simulate the background error,and different B are gotusing the WRFDA system,then the statistical characteristics of the background error covariance and their impacts on analysis and forecasting are discussed.The results show that there is more significant background error amplitude in NMC and RandomCV than in BGM,so the observation will carries a bigger weight in data assimilation when NMC and RandomCV are chosen,and the length-scale of RandomCV is the biggest,so the influence range of observation is wider when RandomCV is used.The background error covariance of RandomCV is generated from perturbing the initial condition by the CV3 in WRF model,which achieves almost the similar effect as NMC and better than CV3,but a lower computational and manual cost than NMC.
CHEN Yaodeng
,
CHEN Xiaomeng
,
ZENG Lamei
,
WANG Hongli
,
WANG Yuanbing
. Influence of Background Sample Error Simulation on Data Assimilation and Forecast[J]. Plateau Meteorology, 2016
, 35(3)
: 767
-776
.
DOI: 10.7522/j.issn.1000-0534.2014.00156
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