CN108647740A - The method for carrying out multi-source precipitation fusion using high-resolution landform and meteorological factor - Google Patents
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Abstract
Description
技术领域technical field
本发明属于水文和气象技术领域,具体涉及一种利用高分辨率的地形和气象因子进行多源降水融合方法。The invention belongs to the technical field of hydrology and meteorology, and in particular relates to a multi-source precipitation fusion method using high-resolution terrain and meteorological factors.
背景技术Background technique
中国是一个洪涝灾害频发的国家,水文模型是解决洪涝灾害的重要手段。降水作为水文模型最关键的输入源,降水的精度和时效性直接影响模拟结果的精度和可靠性。China is a country with frequent flood disasters, and hydrological model is an important means to solve flood disasters. Precipitation is the most critical input source of hydrological models, and the accuracy and timeliness of precipitation directly affect the accuracy and reliability of simulation results.
目前降水数据的获取方式主要有地面观测、卫星和雷达定量降水估计、模式定量降水预报。长期以来,降水的常规观测主要依赖于布设于地表的观测站点,采用有限的观测结果代表周边几十甚至几百平方公里范围内的真实降水。实际降水的大小、类型等具有显著的时空变异性,地面站点存在以点代面的问题,特别是站点稀少的区域观测降水不能有效反映空间降水的空间变异性,降水观测的空间局限性成为水文研究中的难点。雷达定量降水估计具有空间分辨率高、实时性强的优点,但是因容易受覆盖物的影响,其覆盖范围有限。伴随着国内外卫星遥感技术的发展,基于天气雷达与卫星的遥感降水观测得以不断完善,弥补了地面站点空间分布的不足,也为降水的监测提供了新的手段。目前,卫星遥感在获取时空变化的全球降水方面具有独特的优势,提供了前所未有的卫星降水产品如TRMM、GPM、COMRPH、PERSIANN、FY-3B、FY-3C等。卫星定量降水估计具有覆盖面广、观测时间较连续的优势,但是由于遥感探测仪器、反演算法等限制,卫星降水产品的空间分辨率低、精度相对较低,并且对固态降水的反演能力非常有限。At present, the methods of obtaining precipitation data mainly include ground observation, satellite and radar quantitative precipitation estimation, and model quantitative precipitation forecasting. For a long time, the routine observation of precipitation has mainly relied on the observation stations deployed on the surface, and the limited observation results represent the real precipitation within tens or even hundreds of square kilometers around. The size and type of actual precipitation have significant temporal and spatial variability, and there is a problem of substituting points for surface at ground stations, especially in areas where there are few stations, the observed precipitation cannot effectively reflect the spatial variability of spatial precipitation. Difficulties in the research. Radar quantitative precipitation estimation has the advantages of high spatial resolution and strong real-time performance, but its coverage is limited because it is easily affected by cover. With the development of satellite remote sensing technology at home and abroad, remote sensing precipitation observations based on weather radar and satellites have been continuously improved, making up for the lack of spatial distribution of ground stations and providing new means for monitoring precipitation. At present, satellite remote sensing has unique advantages in obtaining global precipitation that changes in time and space, providing unprecedented satellite precipitation products such as TRMM, GPM, COMRPH, PERSIANN, FY-3B, FY-3C, etc. Satellite quantitative precipitation estimation has the advantages of wide coverage and relatively continuous observation time. However, due to the limitations of remote sensing detection instruments and inversion algorithms, satellite precipitation products have low spatial resolution and relatively low accuracy, and the inversion ability for solid precipitation is very poor. limited.
融合站点数据与卫星雷达降水成为提高降水产品的有效途径,目前降水融合的方法很多,比如:最优化插值、卡尔曼滤波、粒子滤波、贝叶斯估计、概率密度等方法,以上方法均未涉及到卫星降水的降尺度。Fusion of station data and satellite radar precipitation has become an effective way to improve precipitation products. At present, there are many methods of precipitation fusion, such as: optimal interpolation, Kalman filter, particle filter, Bayesian estimation, probability density and other methods, none of which are involved Downscaling to satellite precipitation.
发明内容Contents of the invention
为了获取高时空分辨率的降水产品,本发明提供了利用高分辨率的地形和气象因子进行多源降水融合方法。利用高时空分辨率的地形数据和气象数据,融合站点实测降水和卫星降水产品并对卫星降水进行降尺度,得到高时空分辨率的降水产品,能够实现降水数据由点到面的转换,为精细化水文模型的输入提供数据支撑。In order to obtain precipitation products with high spatial and temporal resolution, the present invention provides a multi-source precipitation fusion method using high-resolution terrain and meteorological factors. Utilizing topographic data and meteorological data with high temporal and spatial resolution, combining the actual precipitation at the site and satellite precipitation products, and downscaling the satellite precipitation to obtain precipitation products with high spatial and temporal resolution, which can realize the conversion of precipitation data from point to area, and provide fine The input of the hydrological model provides data support.
为了获取高分辨率的降水融合产品,本发明具体采用以下技术方案:In order to obtain high-resolution precipitation fusion products, the present invention specifically adopts the following technical solutions:
一种利用高分辨率的地形和气象因子进行多源降水融合方法,其特征在于,包括以下步骤:A method for multi-source precipitation fusion using high-resolution terrain and meteorological factors, characterized in that it comprises the following steps:
步骤1,提取目标流域的DEM栅格数据,获取流域的mask文件;Step 1, extract the DEM raster data of the target watershed, and obtain the mask file of the watershed;
步骤2,由目标流域DEM栅格数据计算出目标流域的坡度、坡向、地表粗糙度、到海岸线的距离;Step 2, calculate the slope, aspect, surface roughness, and distance to the coastline of the target watershed from the DEM raster data of the target watershed;
步骤3,根据mask文件提取流域的风速数据;Step 3, extract the wind speed data of the watershed according to the mask file;
步骤4,利用mask文件提取流域的卫星降水数据;Step 4, using the mask file to extract the satellite precipitation data of the watershed;
步骤5,对原始卫星降水进行降尺度,得到Rkm的卫星降水:Step 5, downscale the original satellite precipitation to obtain the satellite precipitation of Rkm:
步骤6,计算降尺度之后的卫星降水与实测站点降水的偏差;Step 6, calculating the deviation between the satellite precipitation after downscaling and the precipitation at the measured station;
步骤7,利用地理加权回归模型,计算Rkm网格的降水偏差;其中,地理加权回归模型为:Step 7, using the geographically weighted regression model to calculate the precipitation deviation of the Rkm grid; where the geographically weighted regression model is:
式中:yi为降水偏差;a0为常数项;xik是i*k的矩阵,i为网格数,k代表变量的种类,即DEM、坡度、坡向、地表粗糙度、到海岸线的距离以及风速;aik为相应的系数项;In the formula: y i is the precipitation deviation; a 0 is a constant item; x ik is the matrix of i*k, i is the number of grids, and k represents the type of variable, namely DEM, slope, aspect, surface roughness, and coastline distance and wind speed; a ik is the corresponding coefficient term;
aik=(xik Tw(i)xik)-1xik Tw(i)yi (5)a ik =(x ik T w(i)x ik ) -1 x ik T w(i)y i (5)
式中:xik T为矩阵转置;w(i)为权重;In the formula: x ik T is matrix transposition; w(i) is weight;
步骤8,Rkm网格的降水偏差加上Rkm的卫星降水,得到Rkm融合降水产品;Step 8, the precipitation deviation of the Rkm grid is added to the satellite precipitation of Rkm to obtain the Rkm fusion precipitation product;
式中:为融合降水产品,为原始Rkm的卫星降水,为Rkm网格的偏差。In the formula: To incorporate precipitation products, is the satellite precipitation of the original Rkm, is the deviation of the Rkm grid.
地理加权回归模型的系数项aik为:The coefficient term a ik of the geographically weighted regression model is:
aik=(xik Tw(i)xik)-1xik Tw(i)yi (5)a ik =(x ik T w(i)x ik ) -1 x ik T w(i)y i (5)
式中:xik为DEM、坡度、坡向、地表粗糙度、到海岸线距离、风速的矩阵;XT为矩阵转置;w(i)为权重。In the formula: x ik is the matrix of DEM, slope, aspect, surface roughness, distance to the coastline, and wind speed; X T is the matrix transpose; w(i) is the weight.
步骤5对原始卫星降水进行降尺度到Rkm的卫星降水为:In step 5, the satellite precipitation downscaled to Rkm from the original satellite precipitation is:
式中,f(x,y)为坐标点(x,y)处的卫星降水;Q11=(x1,y1)、Q12=(x1,y2)、Q21=(x2,y1)、Q22=(x2,y2)为原始卫星的获取的四个坐标点。In the formula, f(x, y) is the satellite precipitation at the coordinate point (x, y); Q 11 = (x 1 , y 1 ), Q 12 = (x 1 , y 2 ), Q 21 = (x 2 , y 1 ), Q 22 =(x 2 , y 2 ) are the four acquired coordinate points of the original satellite.
所述步骤6中结合站点实测数据与降尺度之后的卫星降水数据,计算降水偏差,包括:In the step 6, the precipitation deviation is calculated by combining the site measured data and the downscaled satellite precipitation data, including:
步骤61,根据实测站点的经纬度,确定实测站点在网格中的位置,即站点在流域的行和列;Step 61, according to the latitude and longitude of the actual measurement site, determine the position of the actual measurement site in the grid, that is, the row and column of the site in the watershed;
式中:row为站点数据所在的行,col为站点数据所在的列,delta为流域数据的空间分辨率,latu为流域的最大纬度,lonl为流域的最小经度,lats为站点的纬度;lons为站点的经度;In the formula: row is the row of the station data, col is the column of the station data, delta is the spatial resolution of the watershed data, lat u is the maximum latitude of the watershed, lon l is the minimum longitude of the watershed, lat s is the latitude of the station ; lon s is the longitude of the site;
步骤62,根据站点的行列数,读取站点所对应的网格降水数据;Step 62, according to the number of rows and columns of the station, read the grid precipitation data corresponding to the station;
步骤63,站点降水所对应的网格的卫星降水减去站点实测降水,得到站点对应网格的降水偏差。In step 63, the satellite precipitation of the grid corresponding to the station precipitation is subtracted from the actual measured precipitation of the station to obtain the precipitation deviation of the grid corresponding to the station.
所述步骤8中,Rkm网格的降水偏差加上Rkm的卫星降水,得到Rkm融合降水产品,包括:In the step 8, the precipitation deviation of the Rkm grid is added to the satellite precipitation of Rkm to obtain the Rkm fusion precipitation product, including:
式中:为融合降水产品,为原始Rkm的卫星降水,为Rkm网格的偏差。In the formula: To incorporate precipitation products, is the satellite precipitation of the original Rkm, is the deviation of the Rkm grid.
所述步骤1中提取目标流域的DEM栅格数据,具体包括以下步骤:Extracting the DEM raster data of the target watershed in the step 1 specifically includes the following steps:
步骤11,填洼;Step 11, filling the pit;
步骤12,计算流向;Step 12, calculate the flow direction;
步骤13,计算汇流流量;Step 13, calculating the confluence flow;
步骤14,确定流域出口站点;Step 14, determine the outlet site of the watershed;
步骤15,提取目标流域。Step 15, extracting the target watershed.
所述步骤2中利用流域DEM栅格数据计算出流域坡度、坡向、地表粗糙度、到海岸线的距离,包括:In the step 2, the watershed slope, aspect, surface roughness, and distance to the coastline are calculated using the watershed DEM raster data, including:
步骤21,由目标流域DEM数据计算得到Rkm的流域坡度;Step 21, calculate the watershed slope of Rkm by the DEM data of the target watershed;
步骤22,由目标流域DEM栅格数据计算得到Rkm的流域坡向;Step 22, calculating the watershed aspect of Rkm from the DEM grid data of the target watershed;
步骤23,由目标流域DEM栅格数据计算得到Rkm的流域地表粗糙度;Step 23, calculating the watershed surface roughness of Rkm from the DEM grid data of the target watershed;
步骤24,由目标流域DEM栅格数据与中国海岸线,计算得到Rkm的到海岸线的距离。In step 24, the distance to the coastline in Rkm is calculated from the DEM grid data of the target watershed and the coastline of China.
所述步骤3中根据流域mask文件,获取流域的风速数据,包括:In the step 3, according to the watershed mask file, the wind speed data of the watershed is obtained, including:
步骤31,在欧盟气象中心下载全球的风速数据;Step 31, download global wind speed data at the European Union Meteorological Center;
步骤32,根据步骤1中mask文件作为流域边界,提取目标流域的风速数据。Step 32, according to the mask file in step 1 as the watershed boundary, extract the wind speed data of the target watershed.
所述步骤4中根据mask文件提取流域的卫星降水数据,包括:In the step 4, extract the satellite precipitation data of the basin according to the mask file, including:
步骤41,下载全球CMORPH卫星降水数据,获取CMORPH的覆盖范围、空间分辨率、以及时间分辨率;Step 41, download the global CMORPH satellite precipitation data, and obtain the coverage, spatial resolution, and time resolution of CMORPH;
步骤42,根据步骤1中mask为流域边界,提取目标流域的卫星降水数据。Step 42, according to the mask in step 1 as the watershed boundary, extract the satellite precipitation data of the target watershed.
本发明的有益效果:本发明提供的一种利用高分辨率的地形和气象因子进行多源降水融合方法,根据流域的DEM,提出了流域的坡度、坡向、地表粗糙度、到海岸线的距离;依据流域的mask文件提取风速、卫星降水数据,结合卫星降水和站点降水,对多源降水融合的同时实现了卫星降水的降尺度。本方法以高时空分辨率的地形因子和气象因子为基础数据,数据来源稳定可靠,方法中变量之间的函数关系明确,利用了站点与卫星降水的偏差,对卫星降水进行了校正,保证了结果的客观合理性;弥补了卫星降水和站点降水各自的缺点,实现了点到面的转换,得到了高时空分辨率的降水产品,为精细化水文模型的输入提供数据支撑。Beneficial effects of the present invention: the present invention provides a multi-source precipitation fusion method using high-resolution topographical and meteorological factors. According to the DEM of the watershed, the slope, aspect, surface roughness, and distance to the coastline of the watershed are proposed ; Extract wind speed and satellite precipitation data according to the mask file of the river basin, combine satellite precipitation and station precipitation, realize the downscaling of satellite precipitation while integrating multi-source precipitation. This method uses topographic and meteorological factors with high temporal and spatial resolution as the basic data. The data source is stable and reliable. The functional relationship between variables in the method is clear. The deviation between the station and satellite precipitation is used to correct the satellite precipitation, ensuring The objective and rationality of the results; make up for the respective shortcomings of satellite precipitation and station precipitation, realize point-to-surface conversion, obtain precipitation products with high temporal and spatial resolution, and provide data support for the input of refined hydrological models.
附图说明Description of drawings
图1是本发明的计算流程示意图。Fig. 1 is a schematic diagram of the calculation flow of the present invention.
图2为本发明提取出的流域DEM示意图。Fig. 2 is a schematic diagram of a watershed DEM extracted by the present invention.
图3为本发明提取的流域mask示意图。Fig. 3 is a schematic diagram of the watershed mask extracted by the present invention.
图4为本发明中计算出的流域坡度示意图。Fig. 4 is a schematic diagram of the watershed slope calculated in the present invention.
图5为本发明中计算出的流域坡向示意图。Fig. 5 is a schematic diagram of the slope aspect of the watershed calculated in the present invention.
图6为本发明中提取出的地表粗糙度示意图。Fig. 6 is a schematic diagram of surface roughness extracted in the present invention.
图7为本发明提取出的到海岸线的距离示意图。Fig. 7 is a schematic diagram of the distance to the coastline extracted by the present invention.
图8为本发明提取出的多年平均风速示意图。Fig. 8 is a schematic diagram of the multi-year average wind speed extracted by the present invention.
图9为本发明中提取的卫星原始降水示意图。Fig. 9 is a schematic diagram of satellite original precipitation extracted in the present invention.
图10为本发明中双线性插值之后的卫星降水示意图。Fig. 10 is a schematic diagram of satellite precipitation after bilinear interpolation in the present invention.
图11为本发明中得到融合降水产品示意图。Fig. 11 is a schematic diagram of the fusion precipitation product obtained in the present invention.
图12为本发明中原始卫星数据、站点数据和融合结果对比示意图。Fig. 12 is a schematic diagram of comparison between original satellite data, site data and fusion results in the present invention.
具体实施方式Detailed ways
下面结合附图和具体实施例对本发明作进一步描述。The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.
如图1所示,本发明提供的一种利用高分辨率的地形和气象因子进行多源降水融合方法,包括以下步骤:As shown in Fig. 1, a kind of utilization high-resolution terrain and meteorological factor provided by the present invention carries out multi-source precipitation fusion method, comprises the following steps:
步骤1,提取目标流域的DEM栅格数据和mask文件,具体为:Step 1, extract the DEM raster data and mask file of the target watershed, specifically:
步骤11,填洼;Step 11, filling the pit;
步骤12,计算流向;Step 12, calculate the flow direction;
步骤13,计算汇流流量;Step 13, calculating the confluence flow;
步骤14,确定流域出口站点;Step 14, determine the outlet site of the watershed;
步骤15,提取目标流域;Step 15, extracting the target watershed;
步骤2,利用流域DEM栅格数据计算出流域坡度、坡向、地表粗糙度、到海岸线的距离,具体为:Step 2, use the watershed DEM raster data to calculate the watershed slope, aspect, surface roughness, and distance to the coastline, specifically:
步骤21,由目标流域DEM数据计算得到Rkm的流域坡度;Step 21, calculate the watershed slope of Rkm by the DEM data of the target watershed;
步骤22,由目标流域DEM栅格数据计算得到Rkm的流域坡向;Step 22, calculating the watershed aspect of Rkm from the DEM grid data of the target watershed;
步骤23,由目标流域DEM栅格数据计算得到Rkm的流域地表粗糙度;Step 23, calculating the watershed surface roughness of Rkm from the DEM grid data of the target watershed;
步骤24,由目标流域DEM栅格数据与中国海岸线,计算得到Rkm的到海岸线的距离;Step 24, calculate the distance to the coastline of Rkm from the DEM grid data of the target watershed and the coastline of China;
步骤3,根据流域mask文件,获取流域的风速数据,具体为:Step 3, according to the watershed mask file, obtain the wind speed data of the watershed, specifically:
步骤31,在欧盟气象中心(ECWMF)下载全球的风速数据;Step 31, download global wind speed data at European Union Meteorological Center (ECWMF);
步骤32,根据步骤1中mask文件作为流域边界,提取目标流域的风速数据;Step 32, according to the mask file in step 1 as the watershed boundary, extract the wind speed data of the target watershed;
步骤4,根据mask文件,提取流域的卫星降水数据,具体为:Step 4, according to the mask file, extract the satellite precipitation data of the basin, specifically:
步骤41,下载全球CMORPH卫星降水数据,明确CMORPH的覆盖范围、空间分辨率、时间分辨率等;Step 41, downloading global CMORPH satellite precipitation data, specifying CMORPH coverage, spatial resolution, time resolution, etc.;
步骤42,根据1中mask为流域边界,提取目标流域的卫星降水数据;Step 42, according to the mask in 1 as the watershed boundary, extract the satellite precipitation data of the target watershed;
步骤5,获取的原始卫星降水,采用双线性插值的方法,得到Rkm的卫星降水数据,具体为:Step 5, the obtained original satellite precipitation, adopts the method of bilinear interpolation, obtains the satellite precipitation data of Rkm, specifically:
双线性插值是有两个变量的插值函数的线性插值扩展,其核心思想是在两个方向分别进行线性插值。原始的卫星降水空间分辨率为8km,通过双线性插值得到空间分辨率为Rkm的卫星降水数据。Bilinear interpolation is a linear interpolation extension of the interpolation function with two variables, and its core idea is to perform linear interpolation in two directions respectively. The original satellite precipitation spatial resolution is 8km, and the satellite precipitation data with a spatial resolution of Rkm is obtained through bilinear interpolation.
假如要得到未知函数f在P=f(x,y)点的值,假设已知函数f在Q11=(x1,y1)、Q12=(x1,y2)、Q21=(x2,y1)、Q22=(x2,y2)四个点的值。插值公示如下:If you want to get the value of the unknown function f at P=f(x, y), suppose the known function f is at Q 11 =(x 1 ,y 1 ), Q 12 =(x 1 ,y 2 ), Q 21 = (x 2 , y 1 ), Q 22 = (x 2 , y 2 ) the values of the four points. The interpolation announcement is as follows:
步骤6,结合站点实测数据与降尺度之后的卫星降水数据,计算降水偏差,具体为:Step 6. Combining the measured data at the station with the downscaled satellite precipitation data, calculate the precipitation deviation, specifically:
步骤61,根据实测站点的经纬度,确定实测站点在网格中的位置,即站点在流域的行和列;Step 61, according to the latitude and longitude of the actual measurement site, determine the position of the actual measurement site in the grid, that is, the row and column of the site in the watershed;
式中:row为站点数据所在的行,delta为流域数据的空间分辨率,latu为流域的最大纬度,lats为站点的纬度。In the formula: row is the row where the station data is located, delta is the spatial resolution of the watershed data, lat u is the maximum latitude of the watershed, and lat s is the latitude of the station.
式中:col为站点数据所在的列,delta为流域数据的空间分辨率,lonl为流域的最小经度,lons为站点的经度。In the formula: col is the column where the station data is located, delta is the spatial resolution of the watershed data, lon l is the minimum longitude of the watershed, and lon s is the longitude of the station.
步骤62,根据站点的行列数,读取站点所对应的网格降水数据;Step 62, according to the number of rows and columns of the station, read the grid precipitation data corresponding to the station;
步骤63,站点降水所对应的网格的卫星降水减去站点实测降水,得到站点对应网格的降水偏差;步骤7,构建地理加权回归模型,计算Rkm每个网格的降水偏差步,具体为:In step 63, the satellite precipitation of the grid corresponding to the station precipitation is subtracted from the actual precipitation at the station to obtain the precipitation deviation of the grid corresponding to the station; in step 7, the geographic weighted regression model is constructed to calculate the precipitation deviation step of each grid in Rkm, specifically :
步骤71,依据偏差、高时空分辨率的地形和气象因子,构建地理加权回归模型;Step 71, constructing a geographically weighted regression model according to deviation, topographic and meteorological factors with high temporal and spatial resolution;
式中:yi为降水偏差,xik为DEM、坡度、坡向、地表粗糙度、到海岸线距离、风速,aik为相应的系数项。In the formula: y i is the precipitation deviation, x ik is the DEM, slope, aspect, surface roughness, distance to the coastline, wind speed, and a ik is the corresponding coefficient item.
步骤72,估算地理加权回归模型的系数;Step 72, estimating the coefficients of the geographically weighted regression model;
aik=(xik Tw(i)xik)-1xik Tw(i)yi (5)a ik =(x ik T w(i)x ik ) -1 x ik T w(i)y i (5)
式中:aik为第i网格的系数,xik为DEM、坡度、坡向、地表粗糙度、到海岸线距离、风速的矩阵,w(i)为权重。In the formula: a ik is the coefficient of the i-th grid, x ik is the matrix of DEM, slope, aspect, surface roughness, distance to the coastline, and wind speed, and w(i) is the weight.
步骤73,将公示5带入公式4,计算每个网格的偏差;Step 73, bring publicity 5 into formula 4, and calculate the deviation of each grid;
步骤8,Rkm网格的降水偏差加上Rkm的卫星降水,得到Rkm融合降水产品,具体为:Step 8, the precipitation deviation of the Rkm grid is added to the Rkm satellite precipitation to obtain the Rkm fusion precipitation product, specifically:
式中:为融合降水产品,为原始Rkm的卫星降水,为Rkm网格的偏差。In the formula: To incorporate precipitation products, is the satellite precipitation of the original Rkm, is the deviation of the Rkm grid.
以陕西省子午河流域为例,研究区DEM原始数据采用美国地质调查局(USUG)与国家基础地理信息中心联合提供的数字高程数据,具体为:Taking the Ziwu River Basin in Shaanxi Province as an example, the original DEM data of the study area are digital elevation data jointly provided by the United States Geological Survey (USUG) and the National Basic Geographic Information Center, specifically:
步骤1,提取目标流域的DEM栅格数据和流域的mask文件,具体为:Step 1, extract the DEM raster data of the target watershed and the mask file of the watershed, specifically:
步骤11,填洼;Step 11, filling the pit;
步骤12,计算流向;Step 12, calculate the flow direction;
步骤13,设定流量阈值,计算汇流流量;Step 13, setting the flow threshold and calculating the confluence flow;
步骤14,确定流域出口站点;Step 14, determine the outlet site of the watershed;
步骤15,提取目标流域DEM,如图2和3所示。Step 15, extract the DEM of the target watershed, as shown in Figures 2 and 3.
步骤2,利用流域DEM栅格数据计算出流域坡度、坡向、地表粗糙度、到海岸线的距离,具体为:Step 2, use the watershed DEM raster data to calculate the watershed slope, aspect, surface roughness, and distance to the coastline, specifically:
步骤21,由目标流域DEM数据计算得到Rkm的流域坡度,如图4所示;Step 21, calculate the watershed slope of Rkm by the DEM data of the target watershed, as shown in Figure 4;
步骤22,由目标流域DEM栅格数据计算得到Rkm的流域坡向,如图5所示;Step 22, calculate the watershed slope aspect of Rkm from the DEM raster data of the target watershed, as shown in Figure 5;
步骤23,由目标流域DEM栅格数据计算得到Rkm的流域地表粗糙度,如图6所示;Step 23, calculate the watershed surface roughness of Rkm from the DEM raster data of the target watershed, as shown in Figure 6;
步骤24,由目标流域DEM栅格数据与中国海岸线,计算得到Rkm的到海岸线的距离,如图7所示;Step 24, calculate the distance to the coastline in Rkm from the DEM grid data of the target watershed and the coastline of China, as shown in Figure 7;
步骤3,根据流域mask文件,获取流域的风速数据,具体为:Step 3, according to the watershed mask file, obtain the wind speed data of the watershed, specifically:
步骤31,在欧盟气象中心(ECWMF)下载全球的风速数据;Step 31, download global wind speed data at European Union Meteorological Center (ECWMF);
步骤32,根据步骤1中mask文件作为流域边界,提取目标流域的风速数据,如图8所示;Step 32, according to the mask file in step 1 as the watershed boundary, extract the wind speed data of the target watershed, as shown in Figure 8;
步骤4,根据mask文件,提取流域的卫星降水数据,具体为:Step 4, according to the mask file, extract the satellite precipitation data of the basin, specifically:
步骤41,下载全球CMORPH卫星降水数据,明确CMORPH的覆盖范围、空间分辨率、时间分辨率等;Step 41, downloading global CMORPH satellite precipitation data, specifying CMORPH coverage, spatial resolution, time resolution, etc.;
步骤42,根据1中mask为流域边界,提取目标流域的卫星降水数据,如图9所示;Step 42, according to the mask in 1 as the watershed boundary, extract the satellite precipitation data of the target watershed, as shown in Figure 9;
步骤5,取的原始卫星降水,采用双线性插值的方法,得到Rkm的卫星降水,具体为:Step 5, the original satellite precipitation is taken, and the bilinear interpolation method is used to obtain the satellite precipitation of Rkm, specifically:
双线性插值是有两个变量的插值函数的线性插值扩展,其核心思想是在两个方向分别进行线性插值。原始的卫星降水空间分辨率为8km,通过双线性插值得到空间分辨率为Rkm的卫星降水数据,如图10所示。Bilinear interpolation is a linear interpolation extension of the interpolation function with two variables, and its core idea is to perform linear interpolation in two directions respectively. The original satellite precipitation spatial resolution is 8 km, and the satellite precipitation data with a spatial resolution of R km is obtained through bilinear interpolation, as shown in Figure 10.
假如要得到未知函数f在P=f(x,y)点的值,假设已知函数f在Q11=(x1,y1)、Q12=(x1,y2)、Q21=(x2,y1)、Q22=(x2,y2)四个点的值。插值公示如下:If you want to get the value of the unknown function f at P=f(x, y), suppose the known function f is at Q 11 =(x 1 ,y 1 ), Q 12 =(x 1 ,y 2 ), Q 21 = (x 2 , y 1 ), Q 22 = (x 2 , y 2 ) the values of the four points. The interpolation announcement is as follows:
步骤6,结合站点实测数据与降尺度之后的卫星降水数据,计算降水偏差具体为:Step 6. Combining the measured data of the station with the downscaled satellite precipitation data, the precipitation deviation is calculated as follows:
步骤61,根据实测站点的经纬度,确定实测站点在网格中的位置,即站点在流域的行和列;Step 61, according to the latitude and longitude of the actual measurement site, determine the position of the actual measurement site in the grid, that is, the row and column of the site in the watershed;
式中:row为站点数据所在的行,delta为流域数据的空间分辨率,latu为流域的最大纬度,lats为站点的纬度。In the formula: row is the row where the station data is located, delta is the spatial resolution of the watershed data, lat u is the maximum latitude of the watershed, and lat s is the latitude of the station.
式中:col为站点数据所在的列,delta为流域数据的空间分辨率,lonl为流域的最小经度,lons为站点的经度。In the formula: col is the column where the station data is located, delta is the spatial resolution of the watershed data, lon l is the minimum longitude of the watershed, and lon s is the longitude of the station.
步骤62,根据站点的行列数,读取站点所对应的网格降水数据;Step 62, according to the number of rows and columns of the station, read the grid precipitation data corresponding to the station;
步骤63,站点降水所对应的网格的卫星降水减去站点实测降水,得到站点对应网格的降水偏差;步骤7,利用地理加权回归模型,计算Rkm每个网格的降水偏差,具体为:In step 63, the satellite precipitation of the grid corresponding to the precipitation at the site is subtracted from the actual precipitation at the site to obtain the precipitation deviation of the grid corresponding to the site; in step 7, the precipitation deviation of each grid in Rkm is calculated using the geographically weighted regression model, specifically:
步骤71,依据偏差、高时空分辨率的地形和气象因子,构建地理加权回归模型;Step 71, constructing a geographically weighted regression model according to deviation, topographic and meteorological factors with high temporal and spatial resolution;
式中:yi为降水偏差,xik为DEM、坡度、坡向、地表粗糙度、到海岸线距离、风速,aik为相应的系数项。In the formula: y i is the precipitation deviation, x ik is the DEM, slope, aspect, surface roughness, distance to the coastline, wind speed, and a ik is the corresponding coefficient item.
步骤72,估算地理加权回归模型的系数;Step 72, estimating the coefficients of the geographically weighted regression model;
aik=(xik Tw(i)xik)-1XxikTw(i)yi (5)a ik =(x ik T w(i)x ik ) -1 X xikT w(i)y i (5)
式中:aik为第i网格的系数,xik为DEM、坡度、坡向、地表粗糙度、到海岸线距离、风速的矩阵,w(i)为权重。In the formula: a ik is the coefficient of the i-th grid, x ik is the matrix of DEM, slope, aspect, surface roughness, distance to the coastline, and wind speed, and w(i) is the weight.
步骤73,将公示5带入公式4,计算每个网格的偏差;Step 73, bring publicity 5 into formula 4, and calculate the deviation of each grid;
步骤8,Rkm网格的降水偏差加上Rkm的卫星降水,得到Rkm融合降水产品,具体为:Step 8, the precipitation deviation of the Rkm grid is added to the Rkm satellite precipitation to obtain the Rkm fusion precipitation product, specifically:
式中:为融合降水产品,为原始Rkm的卫星降水,为Rkm网格的偏差。In the formula: To incorporate precipitation products, is the satellite precipitation of the original Rkm, is the deviation of the Rkm grid.
融合降水的产品如图11所示。The product of fused precipitation is shown in Figure 11.
为展示方法的结果,选取子午河流域的夏季来进行验证,如图12所示,分别为卫星原始降水、站点降水、融合降水。In order to show the results of the method, the summer in the Meridian River Basin is selected for verification, as shown in Figure 12, which are satellite original precipitation, station precipitation, and fusion precipitation.
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