澜沧江流域卫星产品降尺度与融合方法

Downscaling and fusion of satellite products:A case study of Lantsang River Basin

卫星降水产品具有覆盖范围广、更适用于无资料区域的优势,但分辨率较低、精度不足,为获取高时空分辨率、高精度的降水数据,需对卫星产品进行降尺度,并与地面观测数据融合,以提高数据质量。以澜沧江流域为例,在综合考虑地形、地理和植被等要素的基础上,建立地理加权回归(geographic weighted regression,GWR)模型对热带降雨测量卫星(tropical rainfall measurement mission,TRMM)和基于人工神经网络的遥感降水估计-气候数据记录(precipitation estimation from remotely sensed information using artificial neural networks-climate data record,PERSIANN-CDR)产品进行空间降尺度,再采用集合卡尔曼滤波算法,将地面气象站点经反距离加权插值法(inverse distance weighted,IDW)插值后的数据作为融合算法观测值,对降尺度后的TRMM、PERSIANN-CDR数据进行融合,以进一步提高降水数据精度。结果表明:1)相比TRMM卫星降水产品,PERSIANN-CDR降水产品对澜沧江流域降水的捕捉能力更弱,但降尺度后两者卫星产品数据精度都有较显著的提升;且两类产品在旱季(11月至次年4月)的精度评估效果相较于雨季(5月至10月)更为明显,表明GWR方法能显著提升这两类卫星降水产品对旱季降水的监测效果。2)对比其他学者的研究表明,对降尺度后的产品进一步使用集合卡尔曼滤波算法,最终得到的融合结果极好,并改善了卫星产品高估降水的现象。综上所述,该研究所使用的降尺度与融合方法,能够显著提升数据空间分辨率与精度,最终得到与地面观测降水数据相关性极高的高空间分辨率卫星产品结果。

Satellite precipitation products have been one of the most important technologies with wide coverage more suitable for areas without data.However,their performance cannot fully meet the harsh requirement of the high resolution and precision precipitation data in recent years.It is very necessary to downscale and then integrate the satellite products with the ground observation data for better data quality.In this study,the Lantsang River Basin in the southern Tibetan Plateau was taken as an example,particularly considering topographic,geographic and vegetational elements.The geographically weighted regression(GWR)model was established with the spatial downscaling data of the tropical rainfall measurement mission(TRMM)satellite and the precipitation estimation from remotely sensed information using artificial neural networks-climate data record(PERSIANN-CDR).The GWR downscaling model was supposed to improve the correlation accuracy between satellite products and ground observation precipitation data.After that,the ensemble Kalman filter was used to take the inverse distance weighted(IDW)interpolated data of the ground meteorological station as the observed values of the fusion and then fused the downscaling TRMM and PERSIANN-CDR data to further improve the accuracy of precipitation data.The results show that:1)The mean value of determination coefficient(R^(2))of PERSIANN-CDR monthly precipitation increased from 0.35 to 0.75 after GWR downscaling.At the same time,the root mean square error(RMSE)and mean absolute error(MAE)decreased by 13.98 and 10.13 mm,respectively.There was a significant increase in the correlation degree of the PERSIANN-CDR satellite precipitation product in all months after GWR downscaling.Meanwhile,the R^(2) of TRMM monthly precipitation increased from 0.53 to 0.85,and the RMSE and MAE decreased by 11.49 and 15.50 mm,respectively.A significant improvement was achieved in the months with the low correlation degree for the surface meteorological stations before downscaling,such as May,June,and December,where the R^(2) reached 0.67 or above after downscaling.In addition,the two types of products presented the more significant effects on the accuracy evaluation in the dry season(from November to April),compared with the rainy season(from May to October).It infers that the GWR greatly improved the monitoring performance of these two types of satellite precipitation products on precipitation in the dry season.2)The accuracy was improved better than before after the data integration and downscaling from the ground stations.Furthermore,the ensemble Kalman filter was used for the data fusion of down-scaled products.The overestimation of precipitation was enhanced at ground meteorological stations by satellite products,especially with the less uncertainty of the data after fusion,indicating the high precision fusion.In summary,the downscaling and fusion can be expected to increase the spatial resolution and accuracy of data.The high spatial resolution of satellite products was also achieved in the high correlation with the precipitation data observed on the ground.