Customized deep learning for precipitation bias correction and downscaling
Customized deep learning for precipitation bias correction and downscaling
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DOI：
10.5194/gmd-16-535-2023
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发表时间：
2023-01
影响因子：
5.1
通讯作者：
Fang Wang;D. Tian;M. Carroll
中科院分区：
文献类型：
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作者：
Fang Wang;D. Tian;M. Carroll
抽象的。系统性偏差和较低的分辨率是当前降水数据集的主要限制。许多基于深度学习(DL)的研究已被用于降水偏差校正和降尺度。然而，目前的方法对于处理每小时降水的复杂特征仍然具有挑战性，导致无法再现诸如极端事件等小尺度特征。这项研究开发了一个定制的DL模型，通过结合定制的损失函数、多任务学习和物理相关的协变量来偏向校正和缩减小时降雨量数据。我们设计了六个场景来系统地评估加权损失函数、多任务学习和大气协变量的附加值，并与常规的DL和统计方法进行了比较。利用现代研究和应用回顾分析第2版(MERRA2)重新分析和墨西哥湾北部沿海地区每小时的第四阶段雷达观测对模型进行了训练和测试。我们发现，所有具有加权损失函数的情景在小时、每日、每月以及极端情况下的表现都明显好于其他具有传统损失函数和基于分位数映射的方法的情景。多任务学习在捕捉极端事件的细微特征和解释大气协变量方面表现出更好的性能，在小时和聚合时间尺度上极大地提高了模型的性能，但改善幅度不如加权损失函数。我们表明，定制的DL模式可以更好地缩小和偏差校正每小时降水数据集，并在精细的空间和时间分辨率下提供更好的降水估计，而常规的DL和统计方法面临挑战。
Abstract. Systematic biases and coarse resolutions are major limitations of current precipitation datasets. Many deep learning (DL)-based studies have been conducted for precipitation bias correction and downscaling. However, it is still challenging for the current approaches to handle complex features of hourly precipitation, resulting in the incapability of reproducing small-scale features, such as extreme events. This study developed a customized DL model by incorporating customized loss functions, multitask learning and physically relevant covariates to bias correct and downscale hourly precipitation data. We designed six scenarios to systematically evaluate the added values of weighted loss functions, multitask learning, and atmospheric covariates compared to the regular DL and statistical approaches. The models were trained and tested using the Modern-era Retrospective Analysis for Research and Applications version 2 (MERRA2) reanalysis and the Stage IV radar observations over the northern coastal region of the Gulf of Mexico on an hourly time scale. We found that all the scenarios with weighted loss functions performed notably better than the other scenarios with conventional loss functions and a quantile mapping-based approach at hourly, daily, and monthly time scales as well as extremes. Multitask learning showed improved performance on capturing fine features of extreme events and accounting for atmospheric covariates highly improved model performance at hourly and aggregated time scales, while the improvement is not as large as from weighted loss functions. We show that the customized DL model can better downscale and bias correct hourly precipitation datasets and provide improved precipitation estimates at fine spatial and temporal resolutions where regular DL and statistical methods experience challenges.