34 results about "Satellite precipitation" patented technology

The invention discloses a space statistical downscaling rainfall estimation method based on a geographical difference analysis method. The method comprises the following steps that: A, a statistical model is built according to the relationship between annual rainfall data of satellite precipitation data and a normalized differential vegetation index, and the downscaling annual rainfall is obtained; B, the geographical difference analysis method is adopted for calibrating the downscaling annual rainfall obtained in the step A; C, the calibrated downscaling annual rainfall obtained in the step B is subjected to weighted decomposition to obtain monthly rainfall data; and D, the geographical difference analysis method is adopted for calibrating the monthly rainfall data obtained in the step C, and the calibrated monthly rainfall data is obtained. Compared with a conventional method, the space statistical downscaling rainfall estimation method has the advantages of higher relevance and smaller error.

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 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.

The invention discloses a method of using high-resolution terrain and meteorology factors for multi-source precipitation fusion. The method comprises the steps of: extracting DEM grid data of a targetwatershed, and obtaining a mask file of the watershed; calculating a slope, an aspect, surface roughness and a to-shoreline distance of the target watershed by the DEM grid data of the target watershed; extracting wind speed data of the watershed according to the mask file; using the mask file to extract satellite precipitation data of the watershed; carrying out downscaling on original satelliteprecipitation to obtain satellite precipitation of Rkm; calculating deviation of satellite precipitation after downscaling and actually measured site precipitation; using a geographically weighted regression model to calculate precipitation deviation of Rkm grids; and obtaining an Rkm fusion precipitation product. According to the method, site data and the satellite precipitation product are fused, downscaling is carried out on the satellite precipitation, the high-time/space-resolution precipitation product is obtained, conversion of precipitation data from points to a surface can be realized, and data support is provided for an input of a refined hydrological model.

The invention discloses a satellite-precipitation-data-based precipitation station network layout method. The method comprises: satellite precipitation data and geographic data of a target area are obtained; according to the satellite precipitation data and the geographic data, a precipitation space distribution characteristic of the target area is obtained; and on the basis of the precipitation space distribution characteristic, an optimized precipitation station network layout algorithm is established to carry out precipitation station network layout. With the method, the station sites can be centralized at a rainstorm center or a position with the strong precipitation space changing, so that the precipitation observation precision can be improved on the condition of the identical or a few station sites. In addition, the invention also discloses a satellite-precipitationl-data-based precipitation station network layout apparatus.

The invention discloses a downscaling correction method of precipitation data of a mountain satellite. The method comprises the following steps: reading of TRMM 3B42.V7 satellite precipitation data and carrying out monthly precipitation statistics; carrying out correcting variable fusion and spatial scale unification; establishing a regression downscaling model; and carrying out cross validation and downscaling correction execution. According to the invention, the observation precipitation data of the mountain meteorological station are fused into the downscaling correction process of the satellite precipitation data and the method optimization is carried out by using the cross-validation technology, so that advantages of the limited observation data in the mountain area are fully utilized, the precision of the precipitation after the down-scaling correction and consistency with the measured data series are greatly improved. In addition, the invention also puts forward a multi-method comparison reviewing type downscaling correction technique, so that a system deviation problem of the single downscaling correction method is solved, the satellite precipitation data downscaling correction method system is enriched, and the credibility of the results is enhanced. According to the method, the adaptability to the downscaling correction of typical mountain satellite precipitation products is enhanced; and related results can provide strong support for the system to grasp the temporal and spatial distribution characteristics of precipitation in mountainous areas.

The invention discloses a satellite precipitation space downscaling optimal regression window screening method and device, and the method comprises the steps: obtaining digital elevation data and normalized vegetation index data of a first spatial resolution as independent variables, carrying out the resampling of a second spatial resolution, and obtaining satellite precipitation data of the second spatial resolution as dependent variables; constructing three regression models of a global window, a local window and a pixel-by-pixel change local window according to the independent variable andthe dependent variable of the second spatial resolution, and screening an optimal local window and an optimal pixel-by-pixel change local window; and according to the regression model and the independent variable data of the first spatial resolution, obtaining rainfall prediction data of the first spatial resolution under the three typical regression windows, and determining an optimal regressionwindow through verification of actually measured rainfall station rainfall data. The method can effectively screen the optimal regression window of satellite rainfall downscaling, and is simple and easy to operate and high in prediction precision.

The invention discloses a dynamic precipitation downscaling method considering local variables and global variables, comprising the following steps: 1, obtaining local variables and global variables of a watershed, wherein the local variables include DEM, slope, aspect, surface roughness and distance to a coastline, and the global variables include vegetation cover index and wind speed; 2, obtaining the original satellite precipitation data of the watershed; and 3, constructing a mixed geographically weighted regression model, establishing a relationship equation between the original satelliteprecipitation and the variables, estimating coefficients of Rkm by a weight function, and obtaining spatial precipitation of Rkm according to the estimated coefficients of Rkm. The invention considers both local variables and global variables, realizes the spatial downscaling of satellite precipitation, further obtains the spatial precipitation with high spatial-temporal resolution, and can moreaccurately reflect the spatial distribution of precipitation.

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 (0.621 miles) and 25 km (15.534 miles); 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 (15.534 miles); downscaling the spatial resolution of the spatial autocorrelation value of the precipitation data from 25 km (15.534 miles) to 1 km (0.621 miles); establishing a nonlinear regression model; obtaining a precipitation downscaling data with a spatial resolution of 1 km (0.621 miles) based on the nonlinear regression model. A system and a terminal are also provided.

The invention discloses a satellite-ground multi-source rainfall adaptive dynamic fusion method. The method comprises the following steps: acquiring time-invariant factors and time-variant factors of coarse resolution and fine resolution of a watershed, wherein the time-invariant factors comprise DEM, the gradient, a Hand index and the roughness, and the time-varying factors comprise NDVI, a wind speed, a surface temperature, a scattering index and a microwave polarization index; downscaling each satellite to obtain fine resolution precipitation of each satellite; constructing an adaptive dynamic Bayesian framework, and calculating the optimal weight of each satellite in a grid where an actual measurement site is located through distribution conversion of precipitation errors and precipitation probability density function parameter optimization; and calculating the optimal weight of the fine-resolution grid of each satellite, and carrying out weighted summation on rainfall of the fine-resolution grid of each satellite to obtain fusion rainfall. According to the method, site precipitation and multi-satellite precipitation are fused through an adaptive dynamic fusion method, a fine-resolution fusion precipitation product is obtained, and data support is provided for input of a refined hydrological model.

The invention discloses a space-time accuracy calibration method for multi-satellite remote sensing precipitation inversion in a large-scale complex watershed. The space-time accuracy calibration method comprises the steps of dividing the large-scale complex watershed into a plurality of sub-watersheds with similar topography, and respectively establishing field high-density test sites in single representative grids with different topographic characteristics; constructing a multi-satellite remote sensing precipitation inversion error quantitative calibration system; determining the minimum number of precipitation observation points required by the grids under different topographic characteristics; respectively screening out valid grids in all sub-watersheds according to the minimum precipitation observation station number requirements; and matching a ground precipitation grid data set with data records of satellite precipitation inversion in the valid grids, and further analyzing errorcharacteristics of satellite precipitation inversion under different topographic characteristics by applying an error calibration system. The space-time accuracy calibration method discloses an influence mechanism of complex terrain for the accuracy of a satellite precipitation inversion algorithm, provides suggestions for the development and improvement of the satellite precipitation inversion algorithm, and further improves the accuracy of satellite precipitation inversion in complex terrain.

The present invention relates to a system for restoring high-resolution precipitation data and a method for same and, more particularly, to a system for applying a weight to precipitation information,derived by means of inputting reanalysis data as initial data for a high-resolution quantitative precipitation model (QPM), and radar echo precipitation information and satellite precipitation information, respectively, and calculating a final restored precipitation. The present invention enables reduction of errors in 0.1-1.0 km precipitation data of a desired region by means of using the reanalysis data as initial data for a high-resolution quantitative precipitation model. In addition, the present invention enables an accurate restoration of a past precipitation by means of applying a weight to precipitation information, derived by means of inputting reanalysis data as initial data for a high-resolution quantitative precipitation model, and radar echo precipitation information and satellite precipitation information, respectively, and calculating a final restored precipitation.

The invention discloses a multi-source rainfall data fusion method based on partition adaptive weight, and the method comprises the following steps: S1, carrying out the preprocessing of satellite rainfall data and ground station observation rainfall data; S2, evaluating the precision of the preprocessed satellite rainfall data, and screening out the satellite rainfall data meeting the precision evaluation requirement; S3, based on a partition adaptive weight fusion method, performing fusion correction on the satellite rainfall data and the ground station observation rainfall data meeting the precision evaluation requirement, and obtaining the optimal weight of each level of satellite rainfall data; and S4, making a weight self-look-up table based on the optimal weight of each level of satellite rainfall data. The method has the advantages that the advantages of high spatial resolution of satellite rainfall data and high precision of ground station observation data are integrated, the disadvantages of the satellite rainfall data and the ground station observation data are also properly discarded, and the two kinds of data are made to complement each other to the greatest extent through a reverse solution mode, so that rainfall data with relatively high accuracy and relatively high applicability are obtained.

The invention discloses a monthly-scale satellite-ground precipitation fusion method based on a random forest and a land surface environment variable, and the method comprises the following steps: firstly, carrying out the time matching of ground station precipitation data and satellite precipitation grid data; secondly, determining the position of a ground rainfall site, and matching the ground rainfall site with a grid of satellite rainfall in space; thirdly, establishing a random forest model on the grids with rainfall sites by taking the ground rainfall data as dependent variables and taking the satellite rainfall data and related land surface environment variables as independent variables; and finally, on the grid without rainfall sites, calculating the satellite rainfall data and the related land surface environment variables through the established random forest model to obtain fused rainfall data. According to the method, the land surface environment variables related to rainfall are combined, the ground rainfall and the satellite rainfall are fused, high-precision fused rainfall data are obtained, and a rainfall input source with higher precision can be provided for a hydrological model and the like.

The invention relates to a short temporary rainfall weather prediction method and device, electronic equipment and a computer readable medium. The method comprises the following steps: respectively generating an observation precipitation probability graph, a satellite precipitation probability graph and a radar precipitation probability graph based on meteorological observation data, a meteorological satellite cloud picture and a radar echo graph; performing longitude and latitude correction on the observation rainfall probability graph, the satellite rainfall probability graph and the radar rainfall probability graph; inputting the corrected observation precipitation probability graph, the corrected satellite precipitation probability graph and the corrected radar precipitation probability graph into a precipitation analysis model to generate a short-term actual precipitation graph; and predicting and/or correcting the short-term rainfall weather based on the short-term actual rainfall map. According to the short temporary rainfall weather prediction method provided by the invention, the short temporary rainfall is predicted in combination with the meteorological data of multiple channels, so that the use range of the socialized meteorological observation data can be expanded, the data source of the short temporary rainfall can be enriched, and the prediction accuracy and precision of the short temporary rainfall are improved.

The invention discloses a refined precipitation time variation characteristic space analysis method. The method comprises the following steps: respectively carrying out preprocessing operation on satellite precipitation data and ground station observation precipitation data; carrying out statistical downscaling by adopting a quadratic multiple regression equation; performing error correction on the two downscaling data by using the site data pair; fusing the two kinds of downscaling data; carrying out precipitation secondary gridding; and analyzing trend, volatility and annual distribution change of precipitation grid points. Compared with a traditional method for obtaining precipitation time variation characteristic space distribution, the method can obtain more effective and fine precipitation space distribution conditions.

The invention discloses a window sliding GPM data correction method considering spatial distribution. The method comprises the following steps of S1, carrying out preprocessing of satellite raster data and ground station data; S2, setting an initial window according to the preprocessed satellite raster data and ground station data; S3, correcting the satellite raster data in the initial window, and moving the initial window to correct all the satellite raster data in sequence; and S4, evaluating the correction result. The method is advantaged in that space-time distribution characteristics of satellite rainfall data can be considered, ground station actually measured rainfall data are used as reference, satellite grid data are locally corrected, correction errors are reduced, and correction results have relatively high precision.

The invention discloses a multi-source satellite rainfall fusion method based on Stacking and EMOS-CSG, and the method comprises the steps: data preprocessing: obtaining rainfall station, original satellite rainfall and auxiliary variable data; downscaling each original satellite product by means of a geographically weighted regression model; a plurality of base learners on the first layer of the Stacking integrated framework are adopted to carry out deviation correction on each downscaling satellite product; integrating the deviation correction results of the downscaling satellite products by using a second-layer learning device of the Stacking integration framework; and based on an EMOS-CSG method, Stacking integration results are fused, and a rainfall fusion product with high temporal-spatial resolution is obtained. According to the method, the advantages of different satellite precipitation products are effectively combined through a Stacking and EMOS-CSG fusion method, the deviation of the satellite products is corrected by means of a machine learning model, the precision of the fusion product is improved, and high-precision precipitation input can be provided for a hydrological model.