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遥感技术与应用  2021, Vol. 36 Issue (3): 571-580    DOI: 10.11873/j.issn.1004-0323.2021.3.0571
青藏高原遥感监测     
青藏高原GPM降水数据空间降尺度研究
盛夏(),石玉立(),丁海勇
南京信息工程大学 遥感与测绘工程学院,江苏 南京 210044
Spatial Downscaling of GPM Precipitation over the Tibetan Plateau
Xia Sheng(),Yuli Shi(),Haiyong Ding
School of Remote Sensing & Geomatics Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China
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摘要:

高分辨率的降水数据对于复杂地形区的精确水文预报和气候模拟至关重要。利用青藏高原的植被、地形和地理位置特征,建立了与降水的回归模型,将全球降水测量(GPM)IMERG的年降水量从0.1° 降尺度至1 km,通过分解年降水获得月降水量数据,并用气象站点的实测数据进行校准。得出以下结论:①GPM IMERG月降水量略大于地面观测值,与2015~2017年的站点数据相关性较高(R2=0.79);②通过建立降尺度模型,提高了研究区GPM IMERG的空间分辨率;③利用站点数据校准后的月降水量,可以反映降水的细节特征,尤其是在雨季和湿润地区。该模型可用于获得地形复杂地区的高空间分辨率降水资料,对水文学和气象学研究具有重要意义。

关键词: GPM降尺度降水青藏高原随机森林    
Abstract:

Precipitation dataset with high resolution are essential for accurate hydrology predictions and meteorology simulations over complex terrains. A regression model was built to downscale the Global Precipitation Measurement (GPM) IMERG precipitation data from 0.1° to 1 km on an annual scale, using vegetation, topography and geographical location features over the Tibetan Plateau. Then monthly precipitation data were obtained by disaggregating the annual downscaled estimates, which were calibrated with observations of local rain gauge stations. The major conclusions are summarized as follows: (1) Monthly GPM IMERG precipitation demonstrated good agreement with the rain gauge data during the period 2015 to 2017 (R2=0.79), though GPM was slightly larger than ground observations; (2) Annual downscaled precipitation improved the spatial resolution of the GPM IMERG in the study area; (3) Monthly donscaled precipitation calibrated with rain gauge data reflected detailed characteristics with better predictive performance especially in summer or in wet regions.We concluded that the model can be used to obtain precipitation data with high spatial resolution from heavy rain to light one over the areas with complex tography, which is meaning for applications in hydrology and metorology studies.

Key words: Global Precipitation Measurement (GPM)    Downscale    Precipitation    Tibetan Plateau    Random Forest.
收稿日期: 2020-04-17 出版日期: 2021-07-22
ZTFLH:  TP75  
基金资助: 国家自然科学基金项目“异速增长和资源限制模型结合多源遥感数据估算森林地上生物量研究”(41471312);国家自然科学基金项目 “城市边缘区地表组分温度反演模型的构建”(41571350)
通讯作者: 石玉立     E-mail: 20171206339@nuist.edu.cn;ylshi@nusit.edu.cn
作者简介: 盛夏(1995-),女,江苏扬州人,硕士研究生,主要从事遥感降水降尺度研究。E?mail:20171206339@nuist.edu.cn
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引用本文:

盛夏,石玉立,丁海勇. 青藏高原GPM降水数据空间降尺度研究[J]. 遥感技术与应用, 2021, 36(3): 571-580.

Xia Sheng,Yuli Shi,Haiyong Ding. Spatial Downscaling of GPM Precipitation over the Tibetan Plateau. Remote Sensing Technology and Application, 2021, 36(3): 571-580.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.3.0571        http://www.rsta.ac.cn/CN/Y2021/V36/I3/571

图1  青藏高原高程及气象站点分布图
图2  GPM数据降尺度流程图
图3  地面站点与GPM IMERG、TRMM3B43月降水数据的散点图
站点名TRMMGPM平均年降水量/mm站点名TRMMGPM平均年降水量/mm
托勒0.800.81372.77同仁0.810.76357.97
海西州0.840.88357.60曲麻莱0.870.84351.03
刚察0.920.98144.00玛多0.920.87342.10
门源0.790.83257.27治多0.860.92346.53
乌兰0.860.6964.67日喀则0.950.95363.30
塔什库尔干0.050.57113.87海东0.840.84268.97
茶卡0.690.73287.07尼木0.880.92311.93
兴海0.720.75297.97泽当0.930.93398.73
安多0.710.79110.03隆子0.850.90296.57
西宁0.850.93375.53拉孜0.910.91335.50
同德0.690.79341.83江孜0.800.88274.77
恰卜恰0.650.83246.97得荣0.840.82341.13
托托河0.880.96296.17定日0.910.85243.80
伍道梁0.830.90335.20八宿0.610.76210.07
都兰0.730.7259.47帕里0.830.83399.60
那曲0.930.96226.03小灶火0.740.7731.77
表1  GPM、TRMM月降水数据与地面站点降水量决定系数(年降水量小于400 mm的32个站点)
图4  GPM、TRMM月降水数据的决定系数(R2)、偏差(Bias)序列图
图5  2015~2017年0.1°分辨率原始GPM降水数据和1 km分辨率降尺度降水数据
图6  2015~2017年GPM降水数据与模型预测降尺度结果的散点图
图7  2017年月降尺度数据
图8  验证站点的精度指标
检验指标原始GPM校正前校正后
R20.590.590.60
RMSE/mm20.5320.4717.38
MAE/mm14.8814.8612.37
Bias0.240.240.08
表2  2015~2017年校正前后降尺度结果精度
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