Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements
Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements
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DOI：
10.3390/rs11212463
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发表时间：
2019-10
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通讯作者：
A. Moraux;S. Dewitte;Bruno Cornelis;A. Munteanu
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作者：
A. Moraux;S. Dewitte;Bruno Cornelis;A. Munteanu
在未来几年，人工智能（AI），其中深度学习（DL）是一个重要组成部分，预计将以一种与引入电力或引入互联网相比的方式改变社会。高期望是基于最近对人工智能任务（例如计算机视觉，文本翻译，图像或文本生成……）的深度学习研究的许多令人印象深刻的结果。此外，在天气和气候观测方面，人工智能应用的潜力也很大。我们展示了最近一篇论文的结果[Moraux等人，2019]，这是将尖端深度学习技术应用于实际天气观测问题的首批演示之一。我们利用8.7、10.8和12.0微米的三个最相关的SEVIRI/MSG光谱图像和现场雨量计测量数据作为输入，开发了一个多尺度编码器-解码器卷积神经网络。该网络经过训练，可以再现比利时、荷兰和德国的雨量计测量到的降水。沉降像素的POD值为0.75，FAR值为0.3。[参考文献][Moraux等，2019]Moraux, a .；Dewitte,美国;Cornelis b;基于卫星和雨量计测量的降水估计的深度学习。遥感，2019,11,2463。
In the coming years, Artificial Intelligence (AI), for which Deep Learning (DL) is an essential component, is expected to transform society in a way that is compared to the introduction of electricity or the introduction of the internet. The high expectations are founded on the many impressive results of recent DL studies for AI tasks (e.g. computer vision, text translation, image or text generation...). Also for weather and climate observations, a large potential for AI application exists. We present the results of the recent paper [Moraux et al, 2019], which is one of the first demonstrations of the application of cutting edge deep learning techniques to a practical weather observation problem. We developed a multiscale encoder-decoder convolutional neural network using the three most relevant SEVIRI/MSG spectral images at 8.7, 10.8 and 12.0 micron and in situ rain gauge measurements as input. The network is trained to reproduce precipitation measured by rain gauges in Belgium, the Netherlands and Germany. Precipitating pixels are detected with a POD of 0.75 and a FAR of 0.3. Instantaneous precipitation rate is estimated with a RMSE of 1.6 mm/h. Reference:[Moraux et al, 2019] Moraux, A.; Dewitte, S.; Cornelis, B.; Munteanu, A. Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements. Remote Sens. 2019, 11, 2463.