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Variational Assimilation of Global Microwave Rainfall Retrievals: Physical and Dynamical Impact on GEOS Analyses

机译:全球微波降雨反演的变化同化:对GEOS分析的物理和动态影响

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Global microwave rainfall retrievals from a five-satellite constellation, including the Tropical Rainfall Measuring Mission Microwave Imager, Special Sensor Microwave Imager from the Defense Meteorological Satellite Program F13, F14, and F15, and the Advanced Microwave Scanning Radiometer from the Earth Observing System Aqua, are assimilated into the NASA Goddard Earth Observing System (GEOS) Data Assimilation System using a 1D variational continuous assimilation (VCA) algorithm. The physical and dynamical impact of rainfall assimilation on GEOS analyses is examined at various temporal and spatial scales. This study demonstrates that the 1D VCA algorithm, which was originally developed and evaluated for rainfall assimilations over tropical oceans, can effectively assimilate satellite microwave rainfall retrievals and improve GEOS analyses over both the Tropics and the extratropics where the atmospheric processes are dominated by different large-scale dynamics and moist physics, and also over land, where rainfall estimates from passive microwave radiometers are believed to be less accurate. Results show that rainfall assimilation renders the GEOS analysis physically and dynamically more consistent with the observed precipitation at the monthly mean and 6-h time scales. Over regions where the model precipitation tends to misbehave in distinctly different rainy regimes, the 1D VCA algorithm, by compensating for errors in the model's moist time tendency in a 6-h analysis window, is able to bring the rainfall analysis closer to the observed. The radiation and cloud fields also tend to be in better agreement with independent satellite observations in the rainfall-assimilation run especially over regions where rainfall analyses indicate large improvements. Assimilation experiments with and without rainfall data for a midlatitude frontal system clearly indicate that the GEOS analysis is improved through changes in the thermodynamic and dynamic fields that respond to the rainfall assimilation. The synoptic structures of temperature, moisture, winds, divergence, and vertical motion, as well as vorticity, are more realistically captured across the front.
机译:从五个卫星星座获取全球微波降雨,包括热带降雨测量任务微波成像仪,国防气象卫星计划F13,F14和F15的特殊传感器微波成像仪以及对地观测系统Aqua的高级微波扫描辐射计,使用一维变分连续同化(VCA)算法将它们同化到NASA戈达德地球观测系统(GEOS)数据同化系统中。在各种时空尺度上研究了降雨同化对GEOS分析的物理和动态影响。这项研究表明,一维VCA算法最初是为热带海洋上的降雨同化而开发和评估的,它可以有效地吸收卫星微波降雨反演,并改善热带和温带地区大气过程主要由不同的大型气象卫星控制的GEOS分析尺度动力学和湿物理,甚至在陆地上,据认为被动微波辐射计的降雨估计精度较低。结果表明,降雨同化使GEOS分析在物理上和动态上与月平均和6小时尺度下的观测降水更加一致。在模型降水倾向于在明显不同的雨季中表现出异常的区域上,一维VCA算法通过在6小时的分析窗口中补偿模型的湿润时间趋势中的误差,能够使降雨分析更接近观察到的情况。在降雨同化过程中,辐射和云场也往往与独立的卫星观测更好地吻合,特别是在降雨分析表明有较大改善的地区。中纬度额叶系统有和没有降雨数据的同化实验清楚地表明,通过响应于降雨同化的热力学和动态场的变化,GEOS分析得到了改善。温度,湿度,风,发散和垂直运动以及涡度的天气结构在前部更为逼真。

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