论文标题
使用CloudSat和GPM数据进行被动微波沉淀检索的深度学习结构
A Deep Learning Architecture for Passive Microwave Precipitation Retrievals using CloudSat and GPM Data
论文作者
论文摘要
本文提出了一种算法，该算法依赖于一系列密集和深度神经网络用于被动微波降水的检索。神经网络从全球降水测量（GPM）微波成像仪（GMI）中从亮度温度的一致性中学习，并从板上GPM上的双频降水雷达（DPR）的积极降水试验以及{CloudSat} Pripling radar（CPR）中的双频降水雷达（DPR）。该算法首先检测到降水的发生和相位，然后估算其速率，同时将结果调节到某些关键的辅助信息，包括与云微物理特性有关的参数。结果表明，我们可以重建DPR降雨和CPR降雪，检测概率超过0.95，而错误警报的概率分别保持在0.08和0.03以下。在降水的情况下，使用DPR（CPR）数据的降雨量估计（降雪）率的无偏根平方误差小于海洋和土地上的0.8（0.1）mm/hr。除了方法论发展之外，将结果与ERA5重新分析和官方GPM产品进行比较还表明，全球卫星降雪检索的不确定性在降雨产品之间达成很好的一致性的同时，仍在很大。此外，结果表明，CPR主动降雪数据可以改善全球降雪的被动微波估计值，而当前的CPR降雨检索仅应用于检测而不是估计速率。
This paper presents an algorithm that relies on a series of dense and deep neural networks for passive microwave retrieval of precipitation. The neural networks learn from coincidences of brightness temperatures from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) with the active precipitating retrievals from the Dual-frequency Precipitation Radar (DPR) onboard GPM as well as those from the {CloudSat} Profiling Radar (CPR). The algorithm first detects the precipitation occurrence and phase and then estimates its rate, while conditioning the results to some key ancillary information including parameters related to cloud microphysical properties. The results indicate that we can reconstruct the DPR rainfall and CPR snowfall with a detection probability of more than 0.95 while the probability of a false alarm remains below 0.08 and 0.03, respectively. Conditioned to the occurrence of precipitation, the unbiased root mean squared error in estimation of rainfall (snowfall) rate using DPR (CPR) data is less than 0.8 (0.1) mm/hr over oceans and land. Beyond methodological developments, comparing the results with ERA5 reanalysis and official GPM products demonstrates that the uncertainty in global satellite snowfall retrievals continues to be large while there is a good agreement among rainfall products. Moreover, the results indicate that CPR active snowfall data can improve passive microwave estimates of global snowfall while the current CPR rainfall retrievals should only be used for detection and not estimation of rates.
