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Program","award":["2021AB40108"],"award-info":[{"award-number":["2021AB40108"]}]},{"name":"High-level Talent Program","award":["2021AB40137"],"award-info":[{"award-number":["2021AB40137"]}]},{"name":"High-level Talent Program","award":["311021001"],"award-info":[{"award-number":["311021001"]}]},{"name":"High-level Talent Program","award":["E2290702"],"award-info":[{"award-number":["E2290702"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Currently, most deep learning (DL)-based models for precipitation forecasting face two conspicuous issues: the smoothing effect in the precipitation field and the degenerate effect of forecasting precipitation intensity. Therefore, this study proposes \u201ctime series residual convolution (TSRC)\u201d, a DL-based convolutional neural network for precipitation nowcasting over China with a lead time of 3 h. The core idea of TSRC is it compensates the current local cues with previous local cues during convolution processes, so more contextual information and less uncertain features would remain in deep networks. We use four years\u2019 radar echo reflectivity data from 2017 to 2020 for model training and one year\u2019s data from 2021 for model testing and compare it with two commonly used nowcasting models: optical flow model (OF) and UNet. Results show that TSRC obtains better forecasting performances than OF and UNet with a relatively high probability of detection (POD), low false alarm rate (FAR), small mean absolute error (MAE) and high structural similarity index (SSIM), especially at longer lead times. Meanwhile, the results of two case studies suggest that TSRC still introduces smoothing effects and slightly outperforms UNet at longer lead times. The most considerable result is that our model can forecast high-intensity radar echoes even for typhoon rainfall systems, suggesting that the degenerate effect of forecasting precipitation intensity can be improved by our model. Future works will focus on the combination of multi-source data and the design of the model\u2019s architecture to gain further improvements in precipitation short-term forecasting.<\/jats:p>","DOI":"10.3390\/rs15010142","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T07:31:56Z","timestamp":1672126316000},"page":"142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["TSRC: A Deep Learning Model for Precipitation Short-Term Forecasting over China Using Radar Echo Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Qiqiao","family":"Huang","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences, Northwest Institute of Eco-Environment and Resources, Lanzhou 730000, China"}]},{"given":"Sheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Northwest Institute of Eco-Environment and Resources, Lanzhou 730000, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8003-7214","authenticated-orcid":false,"given":"Jinkai","family":"Tan","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China"},{"name":"School of Atmospheric Sciences, and Key Laboratory of Tropical Atmosphere-Ocean System (Ministry of Education), Sun Yat-sen University, Zhuhai 519082, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"ref_1","unstructured":"Ehsani, M.R., Zarei, A., Gupta, H.V., Barnard, K., and Behrangi, A. 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