35 results about "Satellite precipitation" patented technology

A spatial autocorrelation machine learning-based downscaling method of satellite precipitation data includes obtaining the TRMM precipitation data and the land surface parameters; preprocessing the land surface parameters to obtain DEM, day land surface temperature, night surface land temperature, day-and-night land surface temperature difference and NDVI with spatial resolutions of 1 km and 25 km; performing a spatial autocorrelation analysis of the TRMM precipitation data to obtain an estimated spatial autocorrelation value of the precipitation data with a spatial resolution of 25 km; downscaling the spatial resolution of the spatial autocorrelation value of the precipitation data from 25 km to 1 km; establishing a nonlinear regression model; obtaining a precipitation downscaling data with a spatial resolution of 1 km based on the nonlinear regression model. A system and a terminal are also provided.

The invention discloses a downscaling compensation technique for satellite precipitation data. Firstly, based on a mixed geographically weighted regression method, a mixed geographically weighted regression between low resolution satellite precipitation data and multiple high resolution variables is constructed. Secondly, based on the mixed geographically weighted regression and multiple high resolution variables, the downscaling precipitation data with high resolution is calculated. Thirdly, regarding the downscaling precipitation data with high resolution as an ambient field, a difference value distribution diagram with high resolution is acquired by calculating. Lastly, according to the difference value distribution diagram, the downscaling precipitation data with high resolution is calculated and the satellite downscaling precipitation compensation data is acquired. The downscaling compensation technique for satellite precipitation data is capable of obtaining high-precision satellite precipitation data with high resolution and being applied to the calculation of meteorological drought index and a driving hydrological model and providing high-precision input data for the hydrological model.

The invention discloses a CMORPH satellite precipitation data downscaling method based on Fuzzy-OLS (Ordinary Least Squares) and a multi-environment factor variable. The method comprises the following steps: firstly, aggregation calculation is carried out on 1km environment factor variables, i.e., eight pieces of data including a vegetation index, a digital elevation model, daytime surface temperature, night surface temperature, a topographic wetness index, a gradient, a slope direction and a slope length gradient to 25km to serve as independent variables, and the CMORPH data of a corresponding 25km resolution is used as a dependent variable. On the basis of a fuzzy aggregation idea, the optimal aggregation number and aggregation center of the environment variable under the scale of 25km can be calculated so as to divide a whole sample set into subsample sets with most prominent difference, so that samples in each subsample set have a maximum similarity, and a least square regression model is independently established for each subsample set so as to predict the downscaling precipitation data of 1km. A downscaling result based on the Fuzzy-OLS model is obviously superior to the downscaling result of a conventional regression model.

The invention discloses a self-adaptive dynamic fusion method of satellite-earth multi-source precipitation. and roughness; time-varying factors include: NDVI, wind speed, surface temperature, scattering index, and microwave polarization index; downscale each satellite to obtain fine-resolution precipitation for each satellite; construct an adaptive dynamic Bayesian framework , through the distribution conversion of precipitation error and the optimization of precipitation probability density function parameters to calculate the optimal weight of each satellite in the grid where the measured site is located; to calculate the optimal weight of each satellite’s fine-resolution grid, each satellite’s fine-resolution The weighted sum of the grid precipitation is used to obtain the fused precipitation. The invention fuses station precipitation and multi-satellite precipitation through an adaptive dynamic fusion method to obtain a fine-resolution fusion precipitation product, and provides data support for the input of a refined hydrological model.

The invention discloses a satellite precipitation data correction method based on multi-source information fusion and downscaling. The method comprises the following steps: S1) acquiring live precipitation data and a satellite inversion precipitation product; S2) carrying out optimal interpolation according to the real-time rainfall data and a satellite inversion rainfall product; S3) obtaining low spatial resolution NDVI data; S4) selecting an environment variable and low spatial resolution NDVI data, and performing independent variable screening; S5) establishing a GWR downscaling model; S6)utilizing the GWR model to obtain OI-GWR corrected rainfall grid point data of the research area; and S7) checking the OI-GWR corrected precipitation grid point data of the research area by adopting10-fold cross validation. According to the method of the invention, optimal interpolation is introduced to improve the quality of original satellite precipitation data, and a better initial field is obtained; on the basis that an initial field is improved, a GWR method is used for downscaling, and the spatial resolution and precision of obtained downscaled data are improved.