Error features of the hourly GSMaP multi-satellite precipitation estimates over nine major basins of China
Error features of the hourly GSMaP multi-satellite precipitation estimates over nine major basins of China
复制标题
中国九大流域逐时GSMaP多星降水量估算误差特征
DOI：
10.2166/nh.2017.263
复制
发表时间：
2017
影响因子：
2.7
通讯作者：
Liliang Ren
中科院分区：
文献类型：
--
作者：
Xianhui Tan;Bin Yong;Liliang Ren
作为当前主流的卫星降水估算之一，日本开发的全球卫星降水测绘（GSMaP）系统通过集成几乎所有可用的降水相关卫星传感器来生产高精度、高分辨率的全球降水产品。为了量化GSMaP估计的误差特征并了解其短时间尺度的水文潜力，对中国9个主要流域的三种广泛使用的GSMaP产品（GSMaP_NRT、GSMaP_MVK和GSMaP_Gauge）进行了1小时、0.1°×0.1°分辨率的综合研究。评估结果表明，GSMaP_NRT明显低估了降雨量，而同时具有前向和后向传播过程的GSMaP_MVK算法能够有效捕获最多的降雨事件，并且具有较低的误差和偏差。 GMap_Gauge 在中国大部分流域表现出最好的误差稳定性和误差结构，并且还表现出更强的降雨率依赖性。然而，其在东南盆地的意外正偏差主要来自于降雨率较低时的高估，在未来的发展中仍需进一步改善。我们期望这里记录的结果既可以为检索开发人员提供一些有价值的参考，也可以让 GMap 数据的水文用户更好地了解其误差特征和各种水文应用的潜在用途。
As one of the current mainstream satellite precipitation estimates, the Global Satellite Mapping of Precipitation (GSMaP) system of Japan has been developed to produce high-precision and high-resolution global rainfall products by integrating almost all of the available precipitation-related satellite sensors. To quantify the error features of GSMaP estimates and understand their hydrological potentials at short temporal scale, three widely used GSMaP products (GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge) were comprehensively investigated at 1 hourly and 0.1° × 0.1° resolutions over nine major basins of China. Assessment results show that GSMaP_NRT apparently underestimates the rainfall amounts, while GSMaP_MVK with both forward and backward propagation processes algorithm can effectively capture the most rainfall events and has the lower error and bias. GSMaP_Gauge displays the best error stability and error structure over most basins of China and also exhibits stronger rain-rate dependencies. However, its unexpected positive biases in southeastern basins, which mainly come from the overestimation at lower rain rates, still need to improve further in future developments. We expected the results documented here can both provide the retrieval developers with some valuable references and offer hydrologic users of GSMaP data a better understanding of their error features and potential utilizations for various hydrological applications.