利用ATMS被动微波谱仪反演NPOESS降水

C. Surussavadee, D. Staelin
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引用次数: 10

摘要

本文评价了美国国家极轨环境传感器系统(NPOESS)先进技术微波测深仪(ATMS)反演地表降水速率(mm/h)的能力;雨、雪和霰的水径估计(mm);峰值垂直风(对流强度,m/s)。将ATMS的模拟检索精度与其前身AMSU进行了比较。这些检索算法使用神经网络进行训练,这些神经网络使用NCEP/MM5/TBSCAT/F(lambda)全球真实模型预测的大气参数及其相应的亮度温度,用于106次全球风暴。除了雪和云冰之外,ATMS在所有检索参数上的表现都优于AMSU。图像锐化放大了噪声,因此它的好处主要局限于相对罕见的孤立风暴。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NPOESS Precipitation Retrievals using the ATMS Passive Microwave Spectrometer
This paper evaluates the ability of the United States National Polar Orbiting Environmental Sensor System (NPOESS) Advanced Technology Microwave Sounder (ATMS) to retrieve surface precipitation rates (mm/h); water path estimates for rain, snow, and graupel (mm); and peak vertical wind (convective strength, m/s). Simulated retrieval accuracies for ATMS were compared to those for its predecessor, AMSU. These retrieval algorithms employ neural networks trained using atmospheric parameters and their corresponding brightness temperatures predicted by a global ground-truth model, NCEP/MM5/TBSCAT/F(lambda), for 106 global storms. ATMS performs better than AMSU for all retrieved parameters except for snow and cloud ice, where they perform comparably. Image sharpening amplifies noise and so its benefits are restricted primarily to relatively rare isolated storms.
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