摘要:
及时准确评估降水的空间分布对国民经济发展有着重要的意义,目前遥感降水产品大多依靠多元回归模型和物理模型来提高降水监测的精度,很少涉及深度学习模型来改善降水精度。文章改进长短时记忆网络(long short-term memory neural network,LSTM)深度学习模型,得到优化后的LSTM深度学习模型; 引入植被、坡向、坡度等多个降水主导因子,以闽浙赣为研究区域,基于2015—2019年69个气象站点的逐日降水数据,首先对Integrated Multi-satellite Retrievals for Global Precipitation Measurement(GPM IMERG)逐日降水产品进行降尺度,继而分别从加密站验证和个例年验证2个角度评估模型的可靠性。结果发现: 降尺度结果与气象站降水的时空分布趋于一致,比GPM IMERG降水产品更能体现出闽浙赣地区的降水空间分布,GPM降水产品对降水区域存在低估和高估的降水数据得到了校正; 通过加密站验证,降尺度模型在7月和10月表现较好,相关系数不低于0.9,4月次之,1月最低,相关系数是0.7; 通过个例年验证,发现2020年日降水降尺度结果与实测值的相关性超过了0.8以上,均方根误差为5.23 mm,平均相对误差为9.43%。可见,基于深度学习的降尺度模型无论在日尺度还是月尺度都取得了较高的精度,且在时间和空间具有一定的普适性。
关键词:
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闽浙赣
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鄱阳湖流域
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降水
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降尺度
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深度学习
Abstract:
A timely and accurate assessment of the spatial precipitation distribution holds great significance to the development of the national economy. At present, most remote sensing-based precipitation products improve their accuracy using multiple regression models and physical models rather than deep learning models. This study improved a long short-term memory neural network (LSTM) deep learning model, yielding an optimized LSTM deep learning model. With the Fujian-Zhejiang-Jiangxi area as the study area, this study conducted downscaling for an integrated multi-satellite retrievals for global precipitation measurement (IMERG) product based on the daily precipitation data of 69 meteorological stations from 2015 to 2019 by introducing multiple factors controlling precipitation such as vegetation, slope aspect, slope gradient. Finally, this study assessed the reliability of the optimized model through verifications based on high-density meteorological stations and individual years. The results show that the downscaling results are consistent with the spatio-temporal distribution of precipitation measured at meteorological stations and, thus, can better reflect the spatial distribution of precipitation in the study area than the original IMERG. Furthermore, underestimated and overestimated precipitation data of the study area from the GPM product were corrected. As indicated by the verification based on high-density meteorological stations, the downscaled model yielded correlation coefficients of 0.9 or above for July and October, which were followed by April. The correlation coefficient was the lowest of 0.7 in January. As shown by the verification based on individual year data, the correlation coefficient between the daily precipitation downscaling results and the measurement results in 2020 was above 0.8, with a root mean square error of 5.23 mm and an average relative error of 9.43%. Therefore, the deep learning-based downscaling model enjoys high accuracy on both daily and monthly scales and can be widely applied in the assessment of both spatial and temporal precipitation distributions.
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