The Development of 1-D Ice Cloud Microphysics Data Assimilation System (IMDAS) for Cloud Parameter Retrievals by Integrating Satellite Data
The Development of 1-D Ice Cloud Microphysics Data Assimilation System (IMDAS) for Cloud Parameter Retrievals by Integrating Satellite Data
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DOI：
10.1109/igarss.2008.4779038
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发表时间：
2008-07
期刊：
影响因子：
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通讯作者：
C. R. Mirza;T. Koike;Kun Yang;T. Graf
中科院分区：
文献类型：
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作者：
C. R. Mirza;T. Koike;Kun Yang;T. Graf
数值天气预报（NWP）模式对降水的可靠预报依赖于云微物理过程的适当表示和大气变量观测的准确初始条件。因此，一维变分（1D-Var）冰云微物理资料同化系统（IMDAS）的开发是为了恢复合理的云分布，以提高数值预报模式的可预报性。IMDAS的总体框架包括作为模式算子的林冰云微物理方案，作为观测算子的大气中的4流快速微波辐射传输模式（RTM），以及称为洗牌复杂演化（SCE）的全球最小化方法。IMDAS同化了AMSR-E卫星微波辐射计数据集，反演了积分水汽含量和积分云液态水含量。该方法成功地将非均匀性引入大气初始状态，模拟的微波亮温与日本Wakasa Bay Experiment 2003的观测结果吻合较好。通过引入外部全球再分析（GANAL）数据的非均匀性，可以改善大气初始条件，从而显著提高云微物理方案的性能。
Reliable prediction of precipitation by Numerical Weather Prediction (NWP) models depends on the appropriate representation of cloud microphysical processes and accurate initial conditions of observations of atmospheric variables. Therefore, 1D Variational (1D-Var) Ice Cloud Microphysics Data Assimilation System (IMDAS) is developed for retrieving reasonable cloud distributions to improve the predictability of NWP models. The general framework of IMDAS includes the Lin ice cloud microphysics scheme as a model operator, a 4-stream fast microwave radiative transfer model (RTM) in the atmosphere as an observation operator, and a global minimization method known as Shuffled Complex Evolution (SCE). The IMDAS assimilates the satellite microwave radiometer data set of Advanced Microwave Scanning Radiometer (AMSR-E) and retrieves integrated water vapor (IWV) and integrated cloud liquid water content (ICLWC). This new method successfully introduces the heterogeneity into the initial state of the atmosphere, and the modeled microwave brightness temperatures agree well with observations of Wakasa Bay Experiment 2003 in Japan. It has improved the performance of cloud microphysics scheme significantly by the intrusion of heterogeneity into the external Global Reanalysis (GANAL) data, which may improve atmospheric initial conditions.