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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Spatial scales of tropical precipitation inferred from TRMM microwave imager data
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Spatial scales of tropical precipitation inferred from TRMM microwave imager data

机译：根据TRMM微波成像仪数据推断的热带降水的空间尺度

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The local spatial scales of tropical precipitating systems were studied using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rate imagery from the TRMM satellite. Rain rates were determined from TMI data using the Goddard Profiling (GPROF) Version 5 algorithm. Following the analysis of Ricciardulli and Sardeshmukh (RS), who studied local spatial scales of tropical deep convection using global cloud imagery (GCI) data, active precipitating months were defined alternatively as those having greater than either 0.1 mm/h or 1 mm/h of rain for more than 5% of the time. Spatial autocorrelation values of rain rate were subsequently computed on a 55/spl times/55 km grid for convectively active months from 1998 to 2002. The results were fitted to an exponential correlation model using a nonlinear least squares routine to estimate a spatial correlation length at each grid cell. The mean spatial scale over land was 90.5 km and over oceans was 122.3 km for a threshold of 0.1 mm/h of rain with slightly higher values for a threshold of 1 mm/h of rain. An error analysis was performed which showed that the error in these determinations was of order 2% to 10%. The results of this study should be useful in the design of convective schemes for general circulation models and for precipitation error covariance models for use in numerical weather prediction and associated data assimilation schemes. The results of the TMI study also largely concur with those of RS, although the more direct relationship between the TMI data and rain rate relative to the GCI imagery provide more accurate correlation length estimates. The results also confirm the strong impact of land in producing short spatial scale convective rain.
机译：使用TRMM卫星的热带降雨测量任务（TRMM）微波成像仪（TMI）降雨率图像研究了热带降水系统的局部空间尺度。使用Goddard分析（GPROF）版本5算法从TMI数据确定降雨率。在对Ricciardulli和Sardeshmukh（RS）进行分析之后，他们使用全球云影像（GCI）数据研究了热带深对流的局部空间尺度，然后将活跃降水月份定义为大于0.1 mm / h或1 mm / h的那些月份。下雨时间超过5％。随后在1998年至2002年的对流活动月份中，在55 / spl次/ 55 km网格上计算降雨率的空间自相关值。使用非线性最小二乘例程将结果拟合到指数相关模型，以估计在每个网格单元。陆地的平均空间尺度为90.5 km，海洋的平均空间尺度为122.3 km，降雨阈值为0.1 mm / h，而降雨阈值为1 mm / h则略高。进行了误差分析，结果表明，这些测定中的误差约为2％至10％。这项研究的结果对于设计用于一般循环模型的对流方案以及用于数值天气预报和相关数据同化方案的降水误差协方差模型应该是有用的。尽管相对于GCI图像，TMI数据和降雨率之间更直接的关系提供了更准确的相关长度估计，但TMI研究的结果在很大程度上也与RS一致。结果还证实了土地对产生短空间尺度对流雨的强烈影响。

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