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Multi-source rainfall fusion method based on principal component regression

A technology of principal component regression and fusion method, which is applied in the fields of instrument, calculation, character and pattern recognition, etc. It can solve the problems of multiple linear autocorrelation between independent variables, affecting the accuracy of the results, inconsistency of the regression coefficient symbols and their values, etc. The effect of eliminating variable collinearity and improving accuracy

Active Publication Date: 2022-07-22
NANJING HYDRAULIC RES INST
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Problems solved by technology

The current mainstream fusion methods include probability matching, objective analysis, Bayesian technology, geographically weighted regression, etc. Most of these methods are based on traditional multiple regression models, which will cause multiple linearity among independent variables in the process of precipitation spatial estimation The problem of autocorrelation causes the sign and value of the calculated regression coefficient to be inconsistent with the theory, thus affecting the accuracy of the result

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  • Multi-source rainfall fusion method based on principal component regression
  • Multi-source rainfall fusion method based on principal component regression
  • Multi-source rainfall fusion method based on principal component regression

Examples

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Embodiment

[0029] Example: In this example, as figure 1 Taking the source area of ​​the Yellow River in my country as an example research area, the specific steps include the following:

[0030] (1) Collect and organize multi-source precipitation information, match the spatial scale of precipitation data, and obtain DEM data, spatial information, and terrain factor data required for fusion. The multi-source precipitation information is specifically:

[0031] like figure 2 As shown in Figure 1, the daily precipitation observation data of daily meteorological stations in the Yellow River source area from 2008.01.01 to 2013.12.31 were collected as the precipitation data of ground stations, of which there were 13 stations in the source area of ​​the Yellow River; four kinds of satellite precipitation products (TMPA) covering the source area of ​​the Yellow River were collected. 3B42V7, TMPA 3B42RT, CMORPH_CRT, IMERG_Final), as remote sensing inversion precipitation data; then bilinear inte...

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Abstract

The invention discloses a multi-source rainfall fusion method based on principal component regression. The method comprises the following steps: acquiring and sorting ground station observation data, remote sensing inversion rainfall data and terrain factor data; comparing the observed rainfall at each ground station with remote sensing inversion rainfall, and estimating a rainfall background error at the grid where each station is located; fusing the multi-source rainfall information and the topographic factors by adopting a principal component regression method to obtain a grid background field residual error; selecting an addition model to obtain a multi-source rainfall estimation result; according to the scheme, multi-source precipitation information is fused through the principal component regression method, important information is reserved while the collinearity problem among independent variables is eliminated, and the precision of precipitation space distribution estimation is further improved.

Description

technical field [0001] The invention relates to the technical field of precipitation space estimation, in particular to a multi-source precipitation fusion method based on principal component regression. Background technique [0002] Precipitation is an important driving factor of water and energy cycles, and it is a meteorological variable that is difficult to obtain accurately because of its spatial and temporal discontinuities and differences. Accurate and reliable precipitation data is not only the key to study the temporal and spatial variation of precipitation, but also an important input condition parameter to improve the accuracy of hydrological simulation, which plays a vital role in regional disaster monitoring, flood control and disaster reduction, and water resources management. [0003] For a long time, the spatial distribution of precipitation has been estimated based on the observation data of ground rainfall stations. This method is characterized by high accu...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2135G06F18/25Y02A90/10
Inventor 陈韬林锦王会容柳鹏李士军曾振宇李伟
Owner NANJING HYDRAULIC RES INST
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