On the Propagation of Satellite Precipitation Estimation Errors: From Passive Microwave to Infrared Estimates
On the Propagation of Satellite Precipitation Estimation Errors: From Passive Microwave to Infrared Estimates
复制标题
论卫星降水估算误差的传播：从被动微波到红外估算
DOI：
10.1175/jhm-d-19-0293.1
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发表时间：
2020
影响因子：
3.8
通讯作者：
Kuligowski, Robert J.
中科院分区：
文献类型：
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作者：
Upadhyaya, Shruti A.;Kirstetter, Pierre-Emmanuel;Gourley, Jonathan J.;Kuligowski, Robert J.
NOAA最新一代地球静止卫星的发射被称为地球静止业务环境卫星（GOES）-R系列，为量化降水率提供了新的机会。最近的努力是力求利用这些数据改进天基降水量反演。本工作的总体目标是进行详细的误差预算分析的改进的自校准多变量降水反演（SCaMPR）算法的GOES-R和被动微波（MW）结合（MWCOMB）降水数据集用于校准它的目的，提供有关这些产品的优势和劣势的见解。这项研究系统地分析了不同气候区域的误差，也作为一个功能，不同的降水类型在美国接壤。参考降水数据集是地面验证多雷达多传感器（GV-MRMS）。总体而言，MWCOMB显示出比SCaMPR更小的误差。然而，分析表明，SCaMPR中的大部分误差来自MWCOMB校准数据。主要的挑战始于MWCOMB的不良检测，它在SCaMPR中传播。特别是，MWCOMB错过了90%的冷层状降水，总体检测分数约为40%。与对流和热带/对流混合类别相比，算法量化暖层状、冷层状和热带/层状混合类别降水量的能力较差，在复杂地形区域存在额外挑战。进一步分析表明，两种产品的系统和随机误差模型具有很强的相似性。这表明，由于校准器MWCOMB的误差，高分辨率GOES-R观测的潜力在SCaMPR中仍未得到充分利用。
The launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.