This paper proposes a multimodal and multi-task deep-learning model for instantaneous precipitation rate estimation. Using both thermal infrared satellite radiometer and automatic rain gauge measurements as input, our encoder-decoder convolutional neural network performs a multiscale analysis of these two modalities to estimate simultaneously the rainfall probability and the precipitation rate value. Precipitating pixels are detected with a Probability Of Detection (POD) of 0.75 and a False Alarm Ratio (FAR) of 0.3. Instantaneous precipitation rate is estimated with a Root Mean Squared Error (RMSE) of 1.6 mm/h.
To improve precipitation estimation accuracy, new methods, which are able to merge different precipitation measurement modalities, are necessary. In this study, we propose a deep learning method to merge rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network, composed of an encoder–decoder architecture, performs a multiscale analysis of the three input modalities to estimate simultaneously the rainfall probability and the precipitation rate value with a spatial resolution of 2 km. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Our results for instantaneous precipitation detection, instantaneous precipitation rate estimation, and for daily rainfall accumulation estimation show that the best accuracy is obtained for the model combining all three modalities. The ablation study, done to compare every possible combination of the three modalities, shows that the combination of rain gauges measurements with radar data allows for a considerable increase in the accuracy of the precipitation estimation, and the addition of satellite imagery provides precipitation estimates where rain gauge and radar coverage are lacking. We also show that our multi-modal model significantly improves performance compared to the European radar composite product provided by OPERA and the quasi gauge-adjusted radar product RADOLAN provided by the DWD for precipitation rate estimation.
<p><span>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 </span><span>AI</span><span> application exists. </span></p><p><span>We present the results of the recent paper [Moraux et al, 2019], which is one of the first demonstrations of the application </span><span>of </span><span>cutting edge deep learning technique</span><span>s</span><span> 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.</span></p><p>&#160;</p><p><span>Reference:</span></p><p><span>[Moraux et al, 2019] Moraux, A.; Dewitte, S.; Cornelis, B.; Munteanu, A. Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements. </span><em><span>Remote Sens.</span></em> <span><strong>2019</strong></span><span>, </span><em><span>11</span></em><span>, 2463. </span></p>
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