杨毅,弓中强,王金艳,刘鑫华. 2012. 时间扩展取样集合卡尔曼滤波同化模拟探空试验研究[J]. 气象学报, 70(1):101-108, doi:10.11676/qxxb2012.009
时间扩展取样集合卡尔曼滤波同化模拟探空试验研究
Experiment study of the time expanded sampling approach for the ensemble Kalman filter for simulated soundings assimilation.
投稿时间:2010-01-11  修订日期:2011-02-13
DOI:10.11676/qxxb2012.009
中文关键词:  同化, 集合卡尔曼滤波, 时间扩展取样
英文关键词:Assimilation, Ensemble Kalman filter (EnKF), Time expanded sampling
基金项目:国家自然科学基金(40805044、41175092)、甘肃省自然科学基金(1010RJZA118)和干旱气象科学研究基金项目(IAM200611)
作者单位
杨毅 兰州大学大气科学学院半干旱气候变化教育部重点实验室甘肃省干旱气候变化与减灾重点实验室兰州730000 
弓中强 兰州大学大气科学学院半干旱气候变化教育部重点实验室甘肃省干旱气候变化与减灾重点实验室兰州730000 
王金艳 兰州大学大气科学学院半干旱气候变化教育部重点实验室甘肃省干旱气候变化与减灾重点实验室兰州730000 
刘鑫华 中国气象局国家气象中心北京100081 
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中文摘要:
      目前,集合卡尔曼滤波同化预报循环系统主要的计算量和时间都花费在样本成员的预报上,小样本数虽能减少计算量,但样本数过少,特别是当有模式误差时,又会导致滤波发散。为了提高集合卡尔曼滤波同化预报循环系统的效率并减轻滤波发散等问题,开展了基于WRF的时间扩展取样集合卡尔曼滤波同化模拟探空的试验研究,以考察其在中尺度模式中的同化效果。预报时对一组样本数为Nb的样本,不仅在分析时刻取样,同时也在分析时刻前和后每间隔Δt时间进行M次取样,即在没增加预报样本数的情况下,增加了分析样本成员数(Nb+2M×Nb),从而在保证不降低分析精度的前提下,也达到减小集合卡尔曼滤波的计算量的要求。通过一系列试验来检验时间扩展取样的时间间隔Δt及在分析时刻前和后最大取样次数M对同化结果的影响。试验结果表明,当选择合适的Δt和M时,时间扩展集合卡尔曼滤波的同化效果非常接近于样本数为(1+2M)×Nb的传统集合卡尔曼滤波效果,具有一定的可行性。
英文摘要:
      In the Ensemble Kalman Filter (EnKF) data assimilation prediction system, most of computation time is spent in the prediction runs of the members. A limited or smaller ensemble size does reduce the computational cost, but an excessively small ensemble size usually leads to filter divergence, especially when there are model errors. In order to improve the time efficiency of the EnKF data assimilation prediction system and prevent it against filter divergence, a time-xpanded sampling approach for the EnKF based on the WRF (Weather Research and Forecasting) model is used to assimilate simulated sounding data and investigate the assimilation effect of the approach in a mesoscale model. The approach samples a series of perturbed state vectors from the Nb member prediction runs not at the analysis time (as the conventional approach does) but also at equally separated time levels (time interval is Δt) before and after the analysis time with M times. All the above sampled state vectors are used to construct the ensemble and compute the background covariance for the analysis, so the ensemble size is increased from Nb to Nb+2M×Nb=(1+2M)×Nb without increasing the number of prediction runs (is still Nb). This reduces the computational cost. A series of experiments are conducted to investigate the impact of Δt (the time interval of time expanded sampling) and M (the maximum sampling times) on the analysis. The results show that if Δtand M are properly selected, the time-expanded sampling approach achieves the similar effect to that from the conventional approach with an ensemble size of (1+2M)×Nb, but the number of prediction runs is greatly reduced.
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