Long-term Precipitation Estimation Combining Time-Series Retrospective Forecasting and Downscaling-Calibration Procedure
Long-term Precipitation Estimation Combining Time-Series Retrospective Forecasting and Downscaling-Calibration Procedure
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DOI：
10.1007/s11269-022-03190-5
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发表时间：
2022-06
影响因子：
4.3
通讯作者：
H. Gong;F. Jiao;Li Cao;Huiyu Liu
中科院分区：
文献类型：
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作者：
H. Gong;F. Jiao;Li Cao;Huiyu Liu
高空间分辨率的长期降水数据集对于水文、气象和生态研究至关重要。然而，由于卫星降水的空间分辨率较低，观测周期相对较短，因此获得高空间分辨率的长期降水数据仍然是一项具有挑战性的任务。本文应用自回归积分滑动平均模型（ARIMA和ARIMAX）对1982-1997年热带使命测雨任务（TRMM）的月和年降水量进行了0.25°像元尺度的回顾性预报。采用地理加权回归（GWR）和10倍交叉验证地理差异分析（10 CV-GDA）的降尺度校正方法，获得了长江流域（YRB）1982-2018年高空间分辨率的长期降水资料。ARIMAX生成的补充TRMM降水的精度明显高于ARIMA生成的降水，补充TRMM降水适合于后续的降尺度校正过程。与不使用或使用地理差异分析（GDA）校准获得的结果相比，10 CV-GDA校准显著提高了缩小结果的精度（决定系数（R2）：0.86，平均绝对误差（MAE）：106.29 mm，均方根误差（RMSE）：147.29 mm）。将年尺度降尺度结果分解为1 km月降水量，降水量估计的总体性能较好（R2：0.88，MAE：18.11mm，RMSE：31.87mm）。这项研究提供了一个新的框架，在高空间分辨率的长期降水估计，并有很大的潜力，提高利用卫星派生降水。
Long-term precipitation datasets with high spatial resolution are crucial for hydrological, meteorological, and ecological research. However, due to the coarse spatial resolution and relatively short observation period of satellite-derived precipitation, obtaining long-term precipitation data with high spatial resolution remains to be a challenging task. In this study, autoregressive integrated moving average models (ARIMA and ARIMAX) are applied to retrospectively forecast month and annual Tropical Rainfall Measuring Mission (TRMM) precipitation during 1982–1997 at a 0.25°-pixel scale. The downscaling-calibration procedure including geographically weighted regression (GWR) and ten-fold cross-validation geographical difference analysis (10CV-GDA) is employed to obtain long-term precipitation (1982–2018) at high spatial resolution over the Yangtze River Basin (YRB). The precision of supplementary TRMM precipitation generated by ARIMAX is significantly higher than those generated by ARIMA, and supplementary TRMM precipitation is considered suitable for the subsequent downscaling-calibration procedure. The 10CV-GDA calibration significantly improved the precision of the downscaled results in comparison to the results obtained without or with geographical difference analysis (GDA) calibration (coefficients of determination (R2): 0.86, mean absolute error (MAE):106.29 mm, and root mean square error (RMSE): 147.29 mm). The annual downscaled results are disaggregated into 1 km monthly precipitation, and the overall performances of precipitation estimation are excellent (R2: 0.88,MAE: 18.11 mm,RMSE: 31.87 mm). This study provides a novel framework for long-term precipitation estimates at high spatial resolution and has great potential for enhancing the utilizability of satellite-derived precipitation.