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A Comprehensive Interpolation Method for Daily Precipitation Data

An interpolation method and data technology, applied in the field of hydrology, can solve the problems of insufficient precision and low interpolation precision, and achieve the effect of simple acquisition, reduced error, and small error

Active Publication Date: 2022-04-12
HOHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a comprehensive interpolation method for daily precipitation data, which overcomes the lack of accuracy of the existing single interpolation method, and provides a comprehensive interpolation method for daily precipitation grid point data based on a combination of multiple interpolation methods and error correction, which fully utilizes the rich The observation data of ground stations, considering the influence of elevation on the spatial distribution of precipitation, not only ensures the accuracy of precipitation interpolation at grid points without observation stations, but also ensures that the interpolation results at grid points with observation stations are as close as possible to the observed values, and high spatial resolution can be set The grid points to be interpolated and the daily program calculation can be realized, which can solve the problem of low precision of daily precipitation interpolation with high spatial resolution

Method used

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  • A Comprehensive Interpolation Method for Daily Precipitation Data
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  • A Comprehensive Interpolation Method for Daily Precipitation Data

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056] Said step S1 comprises the following steps:

[0057] S1.1: Download the digital elevation data ASTGTM2_DEM with a spatial resolution of 30m×30m on the data service platform, and use the Aggregate tool in ArcGIS to aggregate the spatial resolution of the grid points to be interpolated (for example, 0.05°×0.05°);

[0058] S1.2: According to the longitude and latitude information of the points to be interpolated at 0.05°×0.05° in the research area, and use the Extract Value to Points tool in ArcGIS to extract the points to be interpolated and the corresponding elevations of 440 stations;

[0059] S1.3: Correspond the station number, latitude and longitude, elevation, year, month, day and daily precipitation of N stations one by one, and obtain the precipitation information table of the whole station;

[0060] S1.4: Perform daily interpolation calculations on the daily precipitation observation data P by the external drift kriging interpolation method KED and the thin plate...

Embodiment 2

[0062] Described step S2 comprises the following steps:

[0063] S2.1: Sort the data of 480 stations from small to large according to latitude, according to the cycle label of 1-6, respectively eliminate stations 1 to 6, and obtain 6 sets of precipitation data datasets P1 to P6 including 400 stations with uniform spatial distribution , to ensure that each site is eliminated only once;

[0064] S2.2: Refer to the method of step S1.4 to interpolate P1~P6, divided into 6 times, each time use the daily precipitation data of 5 groups of stations in P1~P6, use KED and TPS methods to interpolate respectively, and get 6 groups of KED Interpolation results KED1-KED6 and 6 groups of TPS value results TPS1~TPS6;

[0065] S2.3: For KED1~KED6, TPS1~TPS6, respectively calculate the root mean square error at the one group of stations that did not participate in the interpolation calculation each time, and obtain KED and TPS at all N stations in all 6 groups after synthesis Interpolation ro...

Embodiment 3

[0067] refer to figure 2 , the step S3 needs to interpolate the root mean square error of the site, and calculate the weight of the weighted average, which specifically includes the following steps:

[0068] S3.1: Use the inverse distance weighted IDW method to perform spatial interpolation on A_KED and A_TPS to obtain the root mean square error R_KED[i,j] and R_TPS[i,j] of KED and TPS on all grid points [i,j] ;

[0069] S3.2: Calculate the weight W_KED[i,j] and W_TPS[i,j] of P_KED and P_TPS grid by grid according to the reciprocal value of R_KED[i,j] and R_TPS[i,j]. The calculation formula is as follows:

[0070]

[0071]

[0072] Among them, i represents the number of rows where the grid point is located, and j represents the number of columns where the grid point is located.

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Abstract

The invention discloses a comprehensive interpolation method for daily precipitation data, which relates to the field of hydrometeorological technology. Based on the daily precipitation data and elevation data of all stations in the research area, the single item is obtained by using the external drift Kriging KED and thin plate spline TPS methods respectively. Interpolation grid point data; based on the group cross-check to obtain the interpolation error of the observation point, and use the inverse distance weighted IDW method to obtain the root mean square error RMSE of the whole area, so as to calculate the weight of KED and TPS single interpolation grid point data, and weight the two average to obtain the weighted average interpolation result CI; finally, calculate the error of CI at the observation site, and use the IDW method to interpolate the error to all grid points, and use it to correct the error of the weighted average interpolation result CI to obtain the final daily precipitation synthesis Interpolate grid data CCI. The invention can make full use of station observation data, and realize interpolation calculation of daily precipitation grid point data with high spatial resolution.

Description

technical field [0001] The invention relates to the technical field of hydrology, in particular to a comprehensive interpolation method for daily precipitation data. Background technique [0002] Precipitation is an important part of the hydrological cycle. Precipitation data is the basic driving data of hydrological models. Its temporal and spatial resolution and data quality have a key impact on the subsequent analysis and utilization of hydrological data. In my country, due to the constraints of terrain, manpower, material resources and other factors, the surface rain gauge observation that can provide the most accurate precipitation data is faced with the distribution of rainfall stations in the east and few in the west, plains and mountains, and insufficient regional representation; precipitation using satellite remote sensing Data inversion is limited by sensor accuracy and inversion algorithms, and the accuracy of its related precipitation data products needs to be imp...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/18
CPCG06F17/18
Inventor 王文庞冉孙静静
Owner HOHAI UNIV