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Day-by-day rainfall data comprehensive interpolation method

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

Active Publication Date: 2021-08-20
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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  • Day-by-day rainfall data comprehensive interpolation method

Examples

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 and R_TPS of KED and TPS on all grid points;

[0069] S3.2: According to the reciprocal value of R_KED and R_TPS, calculate the respective weights grid by grid, the calculation formula is as follows:

[0070]

[0071]

[0072] Among them, i represents the number of rows and j represents the number of columns.

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Abstract

The invention discloses a day-by-day rainfall data comprehensive interpolation method, and relates to the technical field of hydro meteorology, and the method comprises the steps of taking day-by-day rainfall data and elevation data of all stations in a research area as a data basis, and obtaining single interpolation grid point data through employing an external drift Kriging KED method and a thin-plate spline TPS method; obtaining an observation point interpolation error based on grouping cross-check, obtaining a whole-region root mean square error RMSE by using an inverse distance weighted IDW method, calculating the weight of KED and TPS single interpolation lattice point data, and carrying out weighted averaging on the weight of the KED and TPS single interpolation lattice point data to obtain a weighted average interpolation result CI; and finally, calculating an error of the CI at an observation station, and interpolating the error to all grid points by adopting an IDW method for performing error correction on a weighted average interpolation result CI to obtain final day-by-day rainfall comprehensive interpolation grid point data CCI. By utilizing the invention, the site observation data can be fully utilized, and the high-spatial-resolution day-by-day rainfall grid point data interpolation calculation is realized.

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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IPC IPC(8): G06F17/18
CPCG06F17/18
Inventor 王文庞冉孙静静
Owner HOHAI UNIV
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