A Novel Real-Time Error Adjustment Method with Considering Four Factors for Correcting Hourly Multi-satellite Precipitation Estimates
A Novel Real-Time Error Adjustment Method with Considering Four Factors for Correcting Hourly Multi-satellite Precipitation Estimates
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DOI：
10.1109/tgrs.2021.3131238
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发表时间：
2021
影响因子：
8.2
通讯作者：
Hanqing Chen;B. Yong;J. Gourley;D. Wen;Weiqing Qi;Kun Yang
中科院分区：
文献类型：
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作者：
Hanqing Chen;B. Yong;J. Gourley;D. Wen;Weiqing Qi;Kun Yang
高精度、近实时的卫星降水估计为水文气象学家提供了一个机会，可以改进对洪水、滑坡、热带气旋和其他极端事件等大规模极端事件的预测。然而，目前运行的近实时SPE仍然存在较大的误差和不确定性。在这项研究中，我们发现有一个明确的空间平面函数（SPF）之间的反演误差和四个关键因素，包括地形，季节，气候类型和降雨率。基于这一发现，我们提出了一种新的误差调整方法来纠正近真实的时间每小时全球卫星降水映射（GSMaP-NRT）估计在真实的时间。然后将新的卫星降水数据集，即ILSF-RT，与最新的近实时全球降水测绘卫星产品套件（即，GSMaP-NRT和GSMaP-Gauge-NRT）。验证结果表明，该方法能有效降低不同地形、不同季节、不同气候类型地区降雨量的GSMaP-NRT反演误差。新的ILSF-RT甚至比GSMaP-Gauge-NRT估计值有了普遍的改进。此外，该方法的一个重要优点是，即使在使用较少的历史数据作为训练样本的情况下，也能取得较好的验证效果，例如，在ILSF-RT的生成过程中，仅使用了中国干旱区冬季的45对卫星反演和地面观测数据。但偏差评分结果表明，该方法不适用于降水率较高（>= 1 mm hr-1）的降水事件，需要进一步改进。
High-accuracy near-real-time satellite precipitation estimates (SPEs) provide an opportunity for hydrometeorologists to improve the forecasting of extreme events, such as flood, landslide, tropical cyclone and other extreme events, at the large scale. However, the currently operational near-real-time SPEs still have larger errors and uncertainties. In this study, we found that there exists a clear relationship of spatial plane function (SPF) between retrieval errors of SPEs and four crucial factors including topography, seasonality, climate type and rain rate. Based on this finding, we proposed a novel error adjustment method to correct the near-real-time hourly Global Satellite Mapping of Precipitation (GSMaP-NRT) estimates in real time. The new satellite precipitation dataset, namely ILSF-RT, was then inter-compared with the latest near-real-time GSMaP product suite (i.e., GSMaP-NRT and GSMaP-Gauge-NRT). Verification results show that the proposed method can effectively reduce the retrieval errors of GSMaP-NRT for various terrains and rain rates over different seasons and climate type areas. The new ILSF-RT even exhibits a general improvement over the GSMaP-Gauge-NRT estimates. Furthermore, one important merit of the new method is that it can perform rather well in validation even not much historical data were applied as training samples in calibration, for example, during the generation of ILSF-RT, only 45 data pairs of satellite retrievals and ground observations were used for winter season over Chinese arid areas. However, the results of bias score show that the current method seems unsuitable to adjust the rainfall events with higher rain rates (>= 1mm hr-1), which needs to be further improved.