194 results about "Downscaling" patented technology

The invention provides a meteorological downscaling method based on space-time fusion and physical constraint, and belongs to the technical field of meteorological downscaling, and the method comprises the steps: carrying out the preprocessing of multi-source meteorological related data and a high-resolution meteorological truth value, and constructing a training data set; an improved U-Net model is constructed, spatiotemporal features and multi-source auxiliary features are obtained through a multi-branch feature extraction unit, high-resolution information is recovered through fusion and decoding, and an attention enhancement module is embedded to highlight a key area; a model is trained through a training data set, parameters are optimized by adopting a loss function fusing topographic features and physical rules, and prediction error differentiation constraint on a complex area and violating the physical rules is achieved; and preprocessing target low-resolution data, inputting the preprocessed target low-resolution data into the model, and outputting high-resolution meteorological data and a physical attribution result. The problems that in the prior art, multi-source meteorological data fusion is insufficient, downscaling precision of a complex terrain area is insufficient, and prediction errors violating physical laws are lack of effective constraints are solved.

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

The invention relates to the technical field of typhoon path prediction, and discloses a typhoon path prediction method, system, terminal and storage medium based on multi-modal data of sounding equipment and a sky big model, and the method comprises the steps: obtaining multi-source heterogeneous data collected by detection equipment related to typhoon prediction, carrying out fusion and adaptive enhancement on the multi-source heterogeneous data on the basis of a big Pantou meteorological model, and generating a high-resolution three-dimensional field; constructing a cascaded AI downscaling network, introducing a physical constraint layer, carrying out local refined modeling on a typhoon eye region according to the high-resolution three-dimensional field, and outputting a high-resolution three-dimensional meteorological field; and designing a typhoon-environment field interaction model based on a graph neural network, performing typhoon prediction according to the high-resolution three-dimensional meteorological field, and outputting a typhoon path ensemble forecast containing a confidence interval and a typhoon thermodynamic structure analysis report. The typhoon path prediction speed and precision are improved, the prediction time is shortened, the prediction error is reduced, and calculation resources are saved.

The invention discloses an urban blue-green space microclimate prediction system and method based on AI fusion. The method comprises the following steps of: 1, acquiring field microclimate measured data through linkage of a portable weather station and a GPS (Global Positioning System); 2, utilizing a machine learning downscaling algorithm and a deep learning semantic segmentation technology to extract high-resolution land surface temperature and urban land surface coverage classification data from the remote sensing image; 3, constructing a time-space aligned multi-modal GIS data set, and constructing a high-precision microclimate prediction model by using an ensemble learning algorithm; and 4, introducing an SHAP interpretability framework, carrying out deep analysis on the model, and quantifying the contribution degree of various environmental elements to a microclimate prediction result and the complex nonlinear influence of the contribution degree. According to the method, unprecedented high-precision and explainable scientific decision support can be provided for formulating urban planning, landscape design and thermal environment mitigation strategies, and development of healthy, sustainable and climate-flexible cities is powerfully promoted.

The invention discloses a summer rainfall forecast correction downscaling method and system based on deep learning, and belongs to the technical field of meteorological prediction and climate simulation. In order to solve the technical problems of systematic deviation and spatial detail missing in forecasting, a deep learning downscaling model is constructed for summer multi-mode climate forecasting data published six months ahead of time in combination with high-resolution observation data and topographic data, deviation correction and spatial super-resolution reconstruction are performed on a summer rainfall forecasting result, and a deep learning downscaling model is constructed. And outputting the kilometer-level summer precipitation field of the target area. The model improves the spatial feature capture capability of multi-scale climate elements through an attention mechanism, and realizes the adaptive fusion of meteorological elements and topographic factors through a topographic perception module, thereby enhancing the medium and long term prediction precision of summer rainfall in a complex topographic region. According to the method, global climate information and local topographic features can be effectively integrated, and high-precision technical support is provided for medium-and-long-term prediction of regional summer rainfall, disaster prevention and reduction in flood seasons and water resource scheduling.

The invention discloses a multi-drive process multi-factor agricultural non-point source pollution prediction method based on coupling meteorological numerical forecasting. The method comprises the following steps: firstly, introducing numerical weather forecast data, and constructing a high-precision weather driving field with kilometer-level space and hour-level time resolution by combining WRF dynamic downscaling, DEM terrain correction and conservation resampling; secondly, establishing a multi-drive process coupling model system which comprises a meteorological drive layer, a hydrological response layer, a pollutant migration layer and a crop feedback layer and is used for simulating runoff production, sediment erosion, nitrogen and phosphorus migration and transformation and crop transpiration and root nutrient absorption processes; thirdly, performing precision verification on a simulation result by using observation data, and identifying key meteorological and hydrological factors through an error transfer matrix and a sensitivity analysis method; and finally, realizing parameter adaptive correction by adopting a long short-term memory network, finishing parameter optimization in combination with a multi-target genetic algorithm, packaging the model chain through a containerization technology, and realizing cross-platform deployment and visual output of a pollution load result. The method can be used for agricultural non-point source pollution prediction and management.

The invention provides an interpretable random forest surface temperature downscaling method coupled with meteorological factors, and relates to the technical field of space downscaling. The method comprises the following steps: acquiring low-resolution surface temperature remote sensing data and multi-source feature data of a target area, and performing radiometric calibration, unified projection and normalization to form a preprocessed data set; dividing earth surface type subareas according to the earth surface classification map, solving a radiation correction coefficient by combining historical meteorological statistics, and performing subarea-level correction to obtain a subarea correction data set; training a random forest regression model by using the partition correction data set, and establishing a nonlinear mapping relationship between the surface temperature and the multi-source features; inputting high-resolution feature data to deduce a preliminary high-resolution surface temperature, and performing residual interpolation correction to obtain a final result; and the SHAP algorithm is adopted to explain feature contributions, so that the result precision, stability and interpretability are improved.

The invention relates to the technical field of satellite hydrological data processing, and particularly discloses a GRACE data super-resolution network space downscaling method fusing geographic information and environmental variables, which comprises the following steps: S1, acquiring original resolution GRACE data and original GLDAS data of a research area, and preprocessing the data; s2, dividing the data obtained by preprocessing in the step S1 into a training set and a test set, and training the GRACE data space downscaling model by using the training set to obtain a trained discriminator and a trained generator; and S3, inputting the GRACE low-resolution data in the test set and the high-resolution environment variable at the moment corresponding to the data into the generator trained in the step S2, and finally obtaining a downscaled high-resolution GRACE image. The method not only can be used for dynamic monitoring of regional scale underground water reserves and flood risk assessment, but also can be expanded and applied to scenes such as agricultural drought monitoring and ecological hydrological process simulation.

The invention relates to the technical field of remote sensing image intelligent processing, in particular to a multi-scale progressive surface temperature fusion downscaling method and device, and the method comprises the steps: preprocessing a surface temperature image in a multi-scale image library; constructing a multi-scale training data set containing the surface temperature and at least one auxiliary parameter; constructing a surface temperature downscaling network based on multi-parameter fusion, and constructing a domain transformation network based on heterogeneous high-frequency information guidance; training a surface temperature downscaling network and a domain transformation network based on a progressive dual-network joint training strategy; finely adjusting the downscaling model meeting a preset low-medium resolution condition; and performing low-medium-high progressive downscaling on the low-resolution surface temperature image to be processed to generate a multi-scale progressive surface temperature meeting a target high-resolution condition. The method fully considers the problem of descending scale difference of different resolutions, achieves the maintenance of the physical characteristics of the surface thermal field, and improves the processing efficiency of large-area remote sensing data.

The invention discloses a multi-source information interaction enhanced soil humidity downscaling method and system, and belongs to the field of remote sensing image processing and computer vision. Comprising the following steps: constructing a single-multi-element interactive attention sub-module, a multi-source information interactive attention module, a residual space channel attention sub-module, a multi-scale feature fusion module, an adaptive double-domain joint loss function and a joint supervision type and degeneration type training mechanism, and obtaining a training convergence soil humidity downscaling network. And carrying out downscaling processing on the to-be-processed high-resolution auxiliary geoscience parameter data through training a convergence network to obtain a high-resolution soil humidity downscaling product. According to the method, the internal relevance between earth surface parameters is deeply mined, the multi-source information interaction attention module is constructed, the mapping relation between geoscience parameters is mined through information interaction between geoscience parameter characteristics, efficient fusion of multi-source data is achieved, and effective extraction and accurate calibration of multi-scale characteristics are achieved.

The RIME-RF spatial downscaling-based photovoltaic output prediction method comprises the steps of collecting photovoltaic power data and local meteorological observation LMD data of a photovoltaic power station in a target area, extracting common data of numerical weather forecast NWP data and the local meteorological observation LMD data, and constructing an input feature set; the method comprises the following steps: optimizing hyper-parameters of a random forest (RF) algorithm based on a frost ice optimization (RIME) algorithm, constructing an RIME-RF model, and performing spatial downscaling on numerical weather forecast NWP data; a VMD-CNN-GRU-SE attention mechanism photovoltaic power prediction model optimized based on BKA is adopted, original numerical weather forecast NWP data is combined with photovoltaic power data to train the prediction model, and numerical weather forecast NWP data after spatial downscaling is combined with the photovoltaic power data to train the prediction model. According to the prediction method, changes of fine meteorological factors influencing the photovoltaic power can be more accurately captured, downscaling errors are remarkably reduced, and short-term power prediction precision is improved.

The invention relates to the technical field of supplementing missing measurement data of an anemometer tower, in particular to a method for supplementing missing measurement data of an anemometer tower. In the data preparation stage, historical observation data of a target anemometer tower needs to be collected, and missing time periods and missing features are determined, so that subsequent method selection is linked; meanwhile, data of surrounding meteorological stations in the same period are obtained, the spatial correlation between the data and the anemometer tower is verified, and a basis is provided for probability distribution mapping and quantile matching in subsequent CDF-t and QDM methods. The nonlinear correction method based on cumulative distribution function transformation and quantile increment mapping shows unique advantages in the field of climate downscaling, the CDF-t can effectively eliminate system deviation by establishing a probability distribution mapping relation between observation data and mode output, and the system performance is improved. The QDM maintains statistical characteristics of historical sequences while retaining climatic change signals through quantile matching, and new possibility is provided for long-period data reconstruction through combination of the two methods.

The invention relates to the technical field of meteorological data processing, in particular to a progressive wind speed field depth downscaling method and device fusing multi-source data and space-time perception, electronic equipment and a storage medium. Multi-scale topographic features are introduced and self-adaptive fusion is performed, so that the wind field reconstruction precision in a complex topographic region is improved; a progressive multi-stage downscaling network architecture is adopted, large-multiple stable downscaling from the kilometer level to the hectometer level is effectively achieved, and error accumulation is controlled; reconstruction is carried out by utilizing a network trained through physical constraint loss, it is ensured that the divergence and the vorticity of an output wind field conform to the aerodynamics law, the physical rationality and the scientific credibility of a result are enhanced, and therefore an efficient solution is provided for obtaining a near-ground wind field with high resolution and high physical credibility.

The invention discloses an SMAP soil hydrodynamic downscaling method based on a TOPMODEL theory, and the method comprises the steps: enabling original SMAP data to be matched with DEM data in spatial attributes through standardization processing, and obtaining standardized SMAP data; on the basis of DEM data, a confluence area and a gradient are calculated, and then a terrain index is calculated according to a TOPMODEL model; constructing a time sequence through the standardized SMAP data, and executing space-time stabilization processing to obtain a basic soil moisture field; based on the terrain index and the basic soil moisture field, preliminary soil moisture data is obtained through calculation by means of a downscaling model, and target soil water grating data with high resolution are obtained by optimizing the quality of the preliminary soil moisture data; according to the downscaling method, the structure is simple and clear, batch operation is facilitated, and low-resolution SMAP soil water data can be upgraded into high-resolution soil water data with the same resolution as DEM data in complex terrain areas such as mountainous areas, hills and river valleys.

The invention discloses a wind field correction method based on multi-stage hybrid physics-data driving, and relates to the technical field of wind field data processing and climate mode application. Executing dynamic downscaling simulation by using a regional climate simulation tool to obtain a multi-dimensional candidate variable field; screening target candidate climate variables according to ERA5 wind speed data, and forming a multi-dimensional key variable field; constructing a to-be-trained model comprising a multi-scale spatial feature extraction module, a time dynamic feature extraction module, a multi-scale spatio-temporal feature fusion module and an output module, and training by using the multi-dimensional key variable field to obtain a hierarchical multi-scale spatio-temporal fusion network for correcting wind field data; and by taking the wind field correction field as a boundary condition, generating a high-resolution wind field correction field by using a complex terrain micro-scale flow field refinement solver. The method not only improves the accuracy of wind speed prediction, but also ensures the physical authenticity and consistency of a high-resolution wind field under the influence of complex terrains through physical constraints.

The invention discloses an earth surface downlink short-wave radiation space downscaling method based on a super-resolution reconstruction technology, belongs to the technical field of meteorological data analysis, and solves the problems of insufficient solar radiation data space resolution and weak earth surface heterogeneity characterization capability in the prior art. Carrying out super-resolution reconstruction on the radiation data set by adopting a super-resolution combination model, and carrying out weighted fusion on a reconstructed data set output by the super-resolution combination model; according to the method, the output results of the multiple models are subjected to weighted fusion, weight optimization of the super-resolution sub-models is performed in combination with the high-resolution terrain factors, spatial downscaling from 5km to 1km resolution is finally realized, the spatial precision of the solar radiation data is remarkably improved on the basis of keeping the hour-level time resolution of the original data, and the accuracy of the solar radiation data is improved. And more earth surface heterogeneity information can be captured, and meanwhile, the high-time-resolution application requirement is met.

The invention discloses a surface temperature downscaling method and system based on feature interaction optimization and a multi-model screening mechanism, and belongs to the technical field of surface temperature data processing, and the method comprises the following steps: screening spectral bands, remote sensing indexes and topographic features; resampling the topographic feature parameters to a resolution matched with the optical wave band and the remote sensing index; variables significantly related to the surface temperature are screened through correlation analysis, interaction features are constructed, and high-contribution features are dynamically screened based on an SHAP value; constructing a regression model based on double frameworks of a linear model and a machine learning model, and verifying the regression model; sHAP optimization features extracted from Sentinel-2 data are fused with SRTM topographic data, a verified regression model is input, and surface temperature data with the resolution of 10 meters are generated; according to the method, the high-order nonlinear relation is mined through combined feature interaction, the prediction RMSE in the uniform earth surface area is greatly reduced, the data scale effect is reserved, the prediction is more suitable for the physical process, and the space consistency is better.

The present invention belongs to the field of multi-energy complementary coordinated dispatching. Disclosed is a meteorological-downscaling-coupled dispatching method for the power generation and consumption of a hydro-wind-solar system. The method comprises: using a support vector machine regression algorithm to identify different hydrometeorological variable data, and establishing a statistical relationship between observation data and a meteorological factor to implement high-resolution spatial downscaling; using a wind-solar power-generation empirical formula to calculate a wind-solar output process; and introducing a series of time-series peak regulation modes to determine a regulated peak and a hydro-wind-solar power consumption linkage equation, thereby avoiding overestimation of energy consumption caused by neglecting climate change impacts and short-term power generation rules. By means of the analysis of engineering examples consisting of Yunnan Lancang River Basin and surrounding wind-solar power stations thereof, the result shows that the present invention can effectively reduce hydrometeorological downscaling errors, and more accurately describe hydro-wind-solar energy power generation rules by means of a hydro-wind-solar power consumption linkage equation, thereby making the dispatching result show better accuracy and reliability.

The invention relates to the field of remote sensing image processing and surface hydrology information acquisition, and provides a soil moisture downscaling method based on a self-attention mechanism, which is used for realizing high-precision space refinement prediction of a low-resolution soil moisture map. According to the method, multi-source high-resolution remote sensing factors are fully fused, and an end-to-end framework with a remote dependent modeling capability is constructed. According to the method, multi-level spatial features of auxiliary factors are extracted through multi-scale convolution, low-resolution soil moisture features serve as query vectors, high-resolution auxiliary factors serve as key / value vectors, and a cross attention fusion module is introduced to enhance the features. And then modeling a space context and remote dependence through a Transform encoder. And finally, generating a high-resolution soil moisture prediction map through a shallow convolutional decoder. Experiments prove that compared with an existing mainstream method, the method shows better precision and robustness in a soil moisture downscaling task, and has good application potential.

The invention discloses a unified rainfall downscaling method based on a multi-stream analysis diffusion model, and the method comprises the steps: collecting the meteorological data of a target region, including low-resolution rainfall data and preset auxiliary variable data, generating a low-resolution rainfall field, and calculating the rainfall deviation between a high-resolution real rainfall field and the low-resolution rainfall field; based on preset auxiliary variable data, combining the low-resolution rainfall data to form multi-modal condition input; constructing a precipitation downscaling model which takes the analysis diffusion model as a framework and is combined with a mixed attention U-shaped network, and predicting precipitation deviation; and training the rainfall downscaling model, performing reasoning by adopting the rainfall downscaling model, inputting meteorological data of the target area, outputting predicted rainfall deviation, and superposing the predicted rainfall deviation to the low-resolution rainfall field to obtain super-resolution rainfall. According to the method, multi-source meteorological and geographic information is effectively fused, high-fidelity and high-resolution rainfall data is generated, the generalization ability is high, and the calculation efficiency is high.

The invention relates to a multi-modal meteorological data downscaling generation method and device and computer equipment. The method comprises the following steps: acquiring topographic data and meteorological data in a to-be-measured area; inputting the topographic data and the meteorological data into a pre-trained deep learning model, carrying out feature coding on the topographic data and the meteorological data through the deep learning model to obtain a topographic feature vector and a meteorological feature vector, carrying out feature interaction on the topographic feature vector and the meteorological feature vector to obtain interaction features, and carrying out feature extraction on the interaction features; and carrying out downscaling decoding on the interaction features to obtain downscaling meteorological data. By adopting the method, the downscaling generation efficiency and the physical rationality of the meteorological data can be considered.

The invention relates to a typhoon wind field downscaling method and system fusing terrain segmentation and a diffusion generation model, and the method comprises the steps: integrating typhoon wind field data, typhoon background information and terrain data, carrying out the preprocessing, carrying out the automatic segmentation of the terrain data through an SAM model, calculating a DEM mean value of a terrain mask region, and carrying out the downscaling of a typhoon wind field. The method comprises the following steps of: firstly, modulating terrain embedding noise on the basis of the terrain embedding noise, then processing data through an encoder and a decoder, integrating a Gate-ConvNeXt module, a spatial self-attention layer and a terrain cross attention layer, fusing multi-scale information through jump connection, and embedding typhoon background information through a feedforward neural network; finally, Gaussian noise is added to the diffusion model through forward diffusion, a residual error between the high-resolution wind field and the low-resolution wind field is generated through reverse denoising, the residual error is combined with a sampling result of the low-resolution wind field, and high-resolution wind field prediction is generated. The problem that an existing wind field downscaling method is insufficient in precision, real-time performance and adaptability under the condition of processing complex terrains and typhoon weather is solved.

The invention is suitable for the technical field of meteorological engineering, and provides a rainfall forecast time downscaling method based on data assimilation and source backtracking, which comprises the following steps: firstly obtaining a first forecast time sequence, a second forecast time sequence and an observation time sequence, downscaling the second sequence and then combining with the first sequence to generate a third sequence, and then generating a fourth sequence by adopting an interpolation method, the method comprises the following steps of: acquiring observation data, interpolating the observation data to a lattice point to generate an observation field consistent in space, calculating space and time gradients in a sliding time window, resolving an average motion vector, determining an upstream source based on backtracking of the average motion vector, carrying out dynamic assimilation correction, generating an hour-by-hour fifth sequence, and finally adjusting the fifth sequence through a 3-hour cumulant conservation constraint to obtain an observation result. And outputting a final downscaling result. By fusing observation data, backtracking rainfall sources and introducing conservative constraints, hourly rainfall forecasting with finer time scale, more accurate spatial sources and stronger physical constraints is realized.

The invention discloses a coastal wind turbine group generation power multi-scale space-time prediction method, which mainly comprises the following steps: carrying out space interpolation and error correction on a target wind turbine position by adopting a numerical weather forecast statistical downscaling technology, generating a high-resolution wind speed prediction sequence covering a short term and a long term, aligning and splicing the predicted wind speed, the field actually-measured wind speed, the environment and the unit operation variables into node dynamic input characteristics; the method comprises the following steps: encoding longitude and latitude and time sequence monitoring data of N fans of a coastal fan group into graph nodes, determining an edge weight according to geographic distance and wake flow coupling, and forming a fan graph network containing static and dynamic characteristics; and inputting the static and dynamic feature sequences into a graph neural network comprising a space attention layer, a time recursion layer and a physical constraint regular term, completing model training, and outputting the generated power of each fan in a plurality of time steps in the future and the total power predicted value of the fan group. The method can provide powerful support for wind power plant operation scheduling, power grid-connected management and new energy consumption.

The invention discloses a power transaction supply and demand prediction method and system based on a regional meteorological large model, and the method comprises the steps: collecting and fusing multi-source historical meteorological data and geographic data of a target region, and forming a special data set; training the large base meteorological model by using the special data set, and outputting a preliminary meteorological forecast according to the large base meteorological model and the initial meteorological data of the target area; inputting the preliminary meteorological forecast into the super-resolution downscaling model, and outputting super-resolution meteorological element forecast by taking the super-resolution geographic data of the target area as a condition; and inputting the super-resolution meteorological element forecast, the historical power data of the target area and the market supply and demand data into the supply and demand prediction model, and outputting a supply and demand prediction curve according to a prediction task. According to the invention, super-resolution meteorological element forecast is generated through the large base meteorological model and the super-resolution downscaling model, so that an accurate supply and demand prediction curve can be obtained by using the supply and demand prediction model.

The invention discloses a rainfall downscaling method and system based on a deep learning network model fusing rainfall priori knowledge, and the method comprises the steps: firstly collecting the topographic data and low-resolution day-by-day rainfall data of a target region, and taking the data as input data; a short-term high-resolution precipitation field generated in a mesoscale weather forecast WRF mode is used as training truth value data; according to the method, the function of accurately downscaling the rainfall data in combination with the convolutional neural network and the long and short term memory network is realized, the spatial-temporal correlation of rainfall is fully considered in the downscaling process, and meanwhile, a likelihood function combined with coupled censored data, Box-Cox conversion and time variation variance Gaussian distribution is adopted as a rainfall loss function; the method not only can represent zero expansibility, skewness and heterovariance characteristics of rainfall, but also can improve the rainfall downscaling precision and quantify the uncertainty of rainfall downscaling, and is suitable for wide popularization and use.

The invention provides a hectometer-level short temporary rainfall forecasting method based on generative adversarial network downscaling and physical constraint, which belongs to the technical field of weather forecast, and is characterized in that S-band radar jigsaw and high-resolution X-band radar observation data are fused through an adaptive rule to generate a fused radar echo combined reflectivity, and the combined reflectivity is used for forecasting the rainfall of the hectometer-level short temporary rainfall. A super-resolution downscaling model is constructed by using a generative adversarial network, a long-sequence high-resolution radar echo training data set is reconstructed, a deep learning forecasting model is constructed, a high-resolution radar echo sequence is used as input, space-time modeling training is performed by using a composite loss function, and a future radar echo forecasting field is output. The Z-R relation is converted into quantitative rainfall forecast, and finally the quantitative rainfall forecast with the spatial resolution reaching the hectometer level is generated. The interpretability of the model is enhanced through physical constraint, the bottleneck of insufficient hectometer-level resolution training data is effectively solved, and the spatial refinement degree of short temporary rainfall forecasting and the forecasting capacity for a severe convection system are remarkably improved.

The invention belongs to the technical field of climate data processing, discloses a Tranformer-based climate time airborne downscaling method, system and equipment and a medium, and aims to solve the problems that the prior art lacks a combined modeling capability for time and space information and the space-time consistency of generated data is weak. The method comprises the following steps: acquiring low-resolution climate data, label data and historical high-resolution data; performing feature extraction and dimension reduction processing through a convolutional layer to generate a preliminary feature; inputting the initial features into a Transform architecture so as to output high-dimensional spatial-temporal features; the high-dimensional spatial-temporal features are recovered to be high-resolution climate data at 00 o'clock, 06 o'clock, 12 o'clock and 18 o'clock; and fusing the interpolated low-resolution climate data with the recovered high-resolution climate data by adopting a global jump connection mechanism, and outputting final high-resolution climate data.

The invention provides a weather radar data downscaling method, apparatus and device, and a medium. The weather radar data downscaling method comprises the steps of obtaining to-be-downscaled data collected by a weather radar; the to-be-downscaled data comprises meteorological parameters corresponding to the continuous elevation angle layer; inputting the meteorological parameters corresponding to each elevation angle layer into the downscaling model to obtain downscaling radar data output by the downscaling model; the downscaling model is trained based on feature elevation data of the phased array radar and label elevation data corresponding to the feature elevation data, the feature elevation data is meteorological parameters corresponding to continuous sample elevation layers, and the label elevation data is meteorological parameters of a target elevation layer obtained after interpolation of the sample elevation layers. According to the invention, the spatial resolution and the detection precision of radar data are improved, so that the ever-increasing refined requirements of meteorological monitoring and forecasting are met.

The invention belongs to the technical field of ocean data reanalysis space forecasting and image super-resolution, and particularly relates to a sea surface temperature space statistical downscaling method based on a U-shaped convolutional neural network, which comprises the following steps of: 1, acquiring statistical data to be analyzed and performing normalization preprocessing; 2, constructing a data set of network training, and dividing a training set and a test set; 3, constructing a U-shaped convolutional neural network; training the U-shaped convolutional neural network through the training set and the test set, and outputting the trained U-shaped convolutional neural network; 4, judging the downscaling effect of the U-shaped convolutional neural network; and step 5, acquiring real-time low-resolution ocean temperature data, preprocessing the real-time low-resolution ocean temperature data, and outputting downscaled high-resolution ocean temperature data by using the trained U-shaped convolutional neural network. According to the method, details and nonlinear features in the spatial information can be well extracted and restored, and the precision of statistical downscaling can be improved.