4 results about "Satellite precipitation" patented technology

This invention discloses a method for downscaling satellite precipitation based on measured stations. It extracts watershed DEM data, including satellite precipitation, wind speed, and temperature. Weighted interpolation of precipitation data from measured stations yields grid data. A deep learning model learns the nonlinear, spatiotemporal coupling relationship between multiple factors and precipitation distribution, and predicts low-resolution satellite precipitation using dynamic weights. High-resolution satellite precipitation is predicted by upsampling satellite precipitation, temperature, wind speed, and station precipitation using a deep learning model and dynamic weights. The residual between the low-resolution precipitation and the measured station precipitation is calculated and upsampled. The high-resolution satellite precipitation is then added to the sampled residual to obtain the downscaled precipitation product. This invention downscales satellite precipitation products based on measured station data to obtain high-resolution precipitation products, achieving refined precipitation data and providing data support for inputting refined hydrological models.

This application provides a satellite precipitation error correction and fusion method based on elevation gradient under complex terrain, belonging to the field of meteorological data processing technology. The method includes: acquiring multi-source precipitation observation data of complex terrain areas, obtaining gridded precipitation data and inputting it into an elevation-error correlation model, obtaining an error intensity distribution map and identifying grid areas with error intensity values ​​greater than or equal to a preset error threshold as uncertain areas and performing feature extraction, obtaining regional feature vectors and inputting them into an error correction strategy selection model, determining the correction strategy type and applicability of the uncertain areas, obtaining the correction areas that need to be deeply fused from the uncertain areas and obtaining ground verification point data, calculating the local precipitation true field and error spatial structure parameters; using a correction algorithm to correct the satellite precipitation data of the correction areas, fusing the satellite precipitation data of the correction areas with those of the uncorrected areas, and generating precipitation products and uncertainty assessment reports.

This invention discloses a real-time precipitation retrieval method for meteorological satellites based on a deep learning geographic attention mechanism. The method includes: constructing a training dataset of FY-4B AGRI infrared cloud images and IMERG Final Run labels, preprocessing it, and inputting it into a U-shaped encoder-decoder network; achieving deep fusion of multi-scale features by embedding a GeoAB geographic attention module; using a dual-objective loss function to complete rain area classification and precipitation intensity regression training; and outputting precipitation data with 0.05° spatial resolution and 15-minute temporal resolution through T-transform decomposition of the cloud image, independent inference, T-inverse transform fusion, and average pooling to eliminate grid effects. Compared with existing technologies, this invention solves the technical problems of balancing real-time performance and accuracy in satellite precipitation retrieval, and the blurred distinction between rain areas and clear skies. This invention achieves a CSI of 0.693, a 3.3% improvement over IMERG Final Run, with a total retrieval time of less than 20 seconds. It balances high accuracy and real-time performance, providing data support for disaster early warning, agricultural management, and other applications, and has promising application prospects.

The application discloses a Gaussian transformation and mixed four-dimensional variation assimilation method for instantaneous precipitation rate of a stationary satellite, constructs a FY-4 stationary meteorological satellite precipitation rate observation operator and a mixed four-dimensional variation assimilation scheme, establishes a Gaussian transformation and error optimization statistical method meeting different precipitation rate data requirements, introduces high-temporal and high-spatial resolution stationary satellite precipitation rate data into a regional model mixed four-dimensional variation assimilation system, and improves analysis and prediction effects of the model on a typhoon. Meanwhile, two selectable Gaussian transformation schemes meeting different requirements and a precipitation rate observation error scheme which changes adaptively with rain intensity are established in the assimilation system, and the problems that the assimilation effect of precipitation physics in the model is weakened due to the strong non-Gaussian distribution characteristics of the precipitation physics are effectively improved.