PrecipGAN: Merging Microwave and Infrared Data for Satellite Precipitation Estimation Using Generative Adversarial Network
PrecipGAN: Merging Microwave and Infrared Data for Satellite Precipitation Estimation Using Generative Adversarial Network
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DOI：
10.1029/2020gl092032
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发表时间：
2021-03
影响因子：
5.2
通讯作者：
Cunguang Wang;G. Tang;P. Gentine
中科院分区：
文献类型：
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作者：
Cunguang Wang;G. Tang;P. Gentine
高时空分辨率的全球卫星降水估计对于水文和气象应用至关重要，但仍然是一项具有挑战性的任务。一个主要的挑战是微波数据在空间和时间上是不连续的。我们提出了一种新的方法来合并不完整的被动微波（PMW）降水估计使用的条件信息提供的完整的红外（IR）降水估计的基础上生成对抗网络（GAN），并命名为算法的GAN。将降水系统分解为内容子空间和演化子空间，将PMW估计传播到PMW传感器轨道覆盖范围之外的区域。AGGAN可以巧妙地模拟降水事件的时空变化，并产生降水估计，其总体统计性能优于基线产品美国大陆上空GPM（IMERG）未校准的综合多卫星检索。AGGAN提供了一种准确和计算效率高的算法，可以在全球范围内实施，以产生基于卫星的降水估计。
Global satellite precipitation estimation at high spatiotemporal resolutions is crucial for hydrological and meteorological applications but is still a challenging task. One major challenge is that the microwave data are discontinuous in space and time. We present a novel approach to merge incomplete passive microwave (PMW) precipitation estimates using the conditional information provided by complete infrared (IR) precipitation estimates based on the generative adversarial network (GAN), and name the algorithm PrecipGAN. PrecipGAN decomposes the precipitation system into content and evolution subspaces to propagate PMW estimates to regions outside the orbit coverage of PMW sensors. PrecipGAN can skillfully simulate the spatiotemporal changes of precipitation events, and produce precipitation estimates with overall better statistical performance than the baseline product Integrated MultisatellitE Retrievals for GPM (IMERG) Uncalibrated over the Continental US. PrecipGAN provides an alternative of accurate and computationally efficient algorithm that can be implemented globally to produce satellite‐based precipitation estimates.