67 results about "Meteorological models" patented technology

The invention discloses a three-dimensional wind field spatial-temporal feature reconstruction and efficient prediction method, and relates to the field of spatial-temporal feature reconstruction and efficient prediction. Constructing a three-dimensional terrain computational domain based on the digital elevation model of the target mountain region, performing multi-scene numerical simulation by adopting a fluid mechanics method or a mesoscale meteorological model, generating a wind field training data set, and constructing and training a wind field spatial feature mapping model; training a wind speed and wind direction short-time prediction model based on the actually measured data set; inputting the monitoring data obtained in real time into the wind speed and wind direction short-time prediction model to obtain a future wind speed and wind direction prediction value of each monitoring station; and inputting the wind speed and direction predicted values into the wind field spatial feature mapping model to obtain the mountain overall wind field distribution of the target mountain region at the future moment. By constructing an'actual measurement-simulation-modeling-prediction-reconstruction 'integrated technical framework, high-temporal-spatial-resolution short-time prediction from observation of local wind speed and wind direction to the overall three-dimensional wind field of the mountainous region is realized.

The invention discloses a storm surge water increase prediction method and device, electronic equipment and a storage medium, and relates to the technical field of seawater monitoring, and the method comprises the steps: obtaining multi-source observation data of a target sea area, the multi-source observation data comprising satellite remote sensing data, near-shore monitoring data, meteorological mode data and drainage basin data; performing space-time alignment and exception processing on the multi-source observation data to generate standardized space-time grid data; performing hybrid prediction modeling on the standardized space-time grid data through a physical data dual-drive modeling layer to obtain a target probability water increasing field; and dynamically correcting the target probability water increasing field by using real-time observation data to obtain a storm surge water increasing predicted value. A physical mechanism model and a probability generation model are combined, limitation of a single model is overcome, and the nonlinear evolution process of the storm surge is effectively captured; the time-space dual-drive architecture is adapted to complex coast terrains and changeable meteorological conditions, and the risk of missing report or false report is reduced.

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 a distribution line icing disposal environment modeling and application method, and relates to the technical field of disaster prevention and reduction and intelligent decision cross of a power system, and the method comprises the steps: obtaining the original data of a multi-source sensor, generating a standardized feature vector, constructing an environment model of a reinforcement learning agent, and designing an action space and a reward function. Integrating a physical model, and constructing a multi-dimensional feature, a line physical model, a meteorological model, a line and equipment state model and a historical and auxiliary information model so as to obtain a lead icing environment model; designing an icing increment formula fusing collision coefficients, and designing a comprehensive network model; carrying out reinforcement learning training on the integrated network model in a simulation environment; integrating the trained comprehensive network model to a power system monitoring platform, and carrying out real-time monitoring and automatic disposal; the method can effectively improve the icing handling capacity of the distribution line, reduces the load current fluctuation, and guarantees the safety and stability of the line.

The invention belongs to the field of electric power, and discloses a downscaling method, device and equipment for a rainfall forecast field, and a storage medium, and the method comprises the steps: obtaining meteorological data, inputting the meteorological data into a fortune meteorological model, and enabling the fortune meteorological model to generate an initial forecast field based on the meteorological data; cutting the initial forecasting field to obtain an initial rainfall forecasting field of the target area, and inputting the initial rainfall forecasting field into a pre-trained downscaling model which comprises a shallow feature extraction module, a deep feature extraction module and a reconstruction module, a shallow feature extraction module extracts initial features from the initial rainfall forecast field based on a convolutional layer; the deep feature extraction module captures the spatial dependency relationship of the precipitation field from the shallow feature map through the residual block; the reconstruction module converts the deep feature map into a high-resolution downscaling precipitation field based on a pixel rearrangement operation. The spatial resolution and accuracy of rainfall forecast are improved through a downscaling model based on SwinIR.

The invention belongs to the technical field of remote sensing, and relates to a multispectral remote sensing atmospheric correction method based on fusion of a physical model and a neural network, which comprises the following steps: S1, classifying aerosol parameter data obtained based on foundation observation, remote sensing inversion or meteorological model and satellite data fusion inversion by adopting an unsupervised clustering algorithm, extracting a typical aerosol mode with physical representativeness; s2, in combination with the typical aerosol mode, simulating the apparent reflectivity of an observation channel under a plurality of atmospheric states and observation geometric conditions by using a radiation transfer model, generating a lookup table covering a wide parameter space, and constructing a training data set; and S3, constructing a nonlinear regression model, taking the training data set as a training sample, learning a mapping relation among an observation angle, an aerosol condition and surface reflectance, performing atmospheric interference correction on an actual multispectral remote sensing image under a pollution condition, and outputting a surface reflectance result.

The invention discloses an observation station data assimilation method based on deep learning, and belongs to the technical field of meteorological data assimilation, and the method comprises the steps: obtaining forecast background field data of a Fuhu meteorological big model, obtaining observation station data, the observation data comprises the actual measurement data of a ground meteorological observation station, a radar, a sounding station and a wind profile observation station, and the actual measurement data of the ground meteorological observation station, the radar, the sounding station and the wind profile observation station; performing spatial interpolation, time alignment and standardization on the background field and the observation data, and introducing position codes and time codes; constructing a deep learning assimilation model based on a sliding window Transform, inputting a background field and observation data, and outputting an assimilation analysis field result; and the effectiveness of the method is verified through comparison and evaluation with fifth-generation global atmosphere reanalysis data of the European mid-term weather forecast center. According to the method, the fineness and the physical consistency of local meteorological elements can be remarkably improved while the integrity of large-scale forecasting is kept, and high-precision assimilation of meteorological forecasting data is realized.

The invention relates to the technical field of aerospace meteorology, and particularly discloses a domestic numerical weather forecast mode-based evaporation waveguide height determination method, which comprises the following steps of: obtaining meteorological coupling data output by a domestic numerical weather forecast mode CMA-MESO; determining parameters according to the meteorological coupling data and a turbulent flux algorithm COARE; constructing a temperature vertical profile and a specific humidity vertical profile according to the parameters, the Monin-Obchhoff similarity theory and an NPS model; constructing a modified refractive index profile according to the temperature vertical profile and the specific humidity vertical profile; and determining the height of the evaporation waveguide according to the height corresponding to the lowest point of the corrected refractive index profile. The method is suitable for determining the evaporation waveguide height in a domestic business meteorological mode.

The invention relates to the technical field of fire spreading prediction, and discloses a fire spreading prediction method and system based on local real-time high-precision weather prediction, a terminal and a storage medium, and the method comprises the steps: obtaining fire observation data collected by a plurality of pieces of information collection equipment for a target region, preprocessing the fire observation data through an edge calculation layer to obtain target fire observation data; a lightweight 3D-Transform architecture is designed based on a big archaeological meteorological model, and the target fire observation data and historical fire scene case data are fused to generate a meteorological prediction result; and constructing a fire spread prediction network model, jointly inputting the meteorological prediction result, the terrain elevation and the fuel humidity, and outputting a fire spread probability graph and a spread velocity field in a future preset time. According to the method, the fire spreading prediction precision and speed are improved, and a high-precision and high-response full-chain solution is provided for forest fire emergency management.

The invention discloses a regional meteorological model error control method based on a neural network, and particularly relates to the field of regional meteorological model error control, and the method comprises the steps: obtaining reanalysis data of a first data source and station observation data of a second data source, and forming an input matrix containing space coordinates, time indexes and meteorological element values; preprocessing operation is executed on the input matrix, and preprocessing comprises the steps that interpolation correction is conducted on missing values, normalization processing is conducted on data of different dimensions, sequence alignment is executed based on a unified time index, and a first input matrix and a second input matrix which are consistent in structure are formed. By constructing a double-residual channel and executing residual reciprocal correction in a training process, dynamic balance fusion of reanalysis data and station observation data is realized on a spatial-temporal scale, so that the problem of cross-source data fusion imbalance in the prior art is solved.

The invention discloses a plum rain season photovoltaic output prediction method and system based on small sample learning. The method comprises the following steps: constructing a multi-source feature system; clustering and dividing meta-learning tasks based on a meteorological mode; the method comprises the following steps: constructing a TCN-Transform hybrid neural network model, and embedding a learnable meteorological adapter; training a hybrid neural network model by adopting an MAML meta-learning framework, and realizing meta-learning task training of different meteorological models in combination with a physical constraint regularization loss function; model parameters are optimized in three stages through a course learning strategy, and a training set is enhanced by utilizing Mixup time sequence data; and deploying the trained model to carry out output prediction, and outputting a prediction result with a confidence interval. According to the method, high-precision photovoltaic output prediction in a small-sample and high-uncertainty plum rain season scene is realized, and reliable support is provided for power grid dispatching.

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 water conservancy digital twin data fusion method, system and device based on multi-source heterogeneous data dynamic alignment and a medium, and belongs to the technical field of data fusion. Performing cleaning processing, format standardization processing and timestamp alignment processing based on a time window on the data to obtain preprocessed data; performing sliding window division processing on the preprocessed data, performing regional grid numbering processing according to a space division rule, and enabling each data point to correspond to a divided time window and a space grid to obtain time-space mapping data; and performing fusion processing on the data subjected to space-time mapping according to the data type. According to the method, the fusion strategy is designed by distinguishing the data types in the fusion stage, and it is guaranteed that the fusion result can still keep high reliability and stability when facing data of different qualities and different sources.

The invention discloses a space-time matching comparison and uniformity evaluation method for a three-dimensional atmosphere live field fusion product. The method comprises the steps of constructing a multi-source data verification framework, implementing refined space-time matching, executing multi-level quality control, comprehensively inspecting and analyzing and outputting a product specification. By constructing a multi-source collaborative verification framework, integrating sounding, microwave radiometer, satellite and foundation observation data, and establishing a refined space-time matching model and a multi-level quality control system, the error condition of a live product can be quantitatively given. The method is suitable for space-time matching comparison and homogeneity inspection of air temperature, humidity and horizontal wind speed / wind direction elements, and supports meteorological model optimization and low-altitude economic application.

The invention relates to the cross technical field of weather forecast and energy prediction, and discloses a physical-data fused weather-wind and light power combined prediction method and system, and the method comprises the steps: constructing an initial combined prediction model, and enabling a weather forecast model and a wind and light power prediction model to be connected into a whole through the model; performing end-to-end training on the initial joint prediction model by adopting a historical meteorological data sample and a historical wind-solar power generation power data sample; and inputting the meteorological data of the target area into the trained joint prediction model, and outputting a wind-solar power generation power prediction value in a future time period. By adopting the end-to-end joint training method based on the differentiable calculation path, global collaborative optimization of the weather forecast model and the wind and light power prediction model is realized, so that the core problem of power prediction misalignment caused by unidirectional transmission and accumulative amplification of the weather model error in the traditional series architecture is solved; and the precision of final power prediction is ensured from the system level.

The invention relates to the technical field of meteorological model optimization and polar region ocean data processing, in particular to a method and system for optimizing a COARE model based on sea ice density, and the method comprises the steps: obtaining the historical actual measurement data of the polar region ocean, calculating the real roughness and the model roughness outputted by the COARE model, and determining the roughness difference value; dividing a wind speed interval, and fitting and establishing a correction function of a difference value between the sea ice concentration and the roughness; and introducing the sea ice concentration into the COARE model, judging the interval according to the wind speed, calling a corresponding function to correct the roughness of the model, and outputting the ocean-atmosphere flux conforming to the polar region sea ice condition to complete the model optimization. According to the method, the sea ice concentration is introduced, and the wind speed partition correction relation is established, so that the roughness of the polar region under the sea ice coverage condition is dynamically corrected by the COARE model, the limitation of the original model in the aspects of wind speed applicability and polar region adaptability is broken through, and the accuracy and practicability of polar region ocean-atmosphere flux calculation are remarkably improved.

The invention discloses a mountain land low temperature refined monitoring prediction method and system based on satellite-ground fusion and deep learning, and belongs to the technical field of ecological meteorological monitoring and agricultural disaster prevention and reduction. The invention aims to solve the problems of insufficient mountain low-temperature monitoring space coverage, neglect of local factors, rough prediction resolution and poor terrain adaptability in the prior art. The method comprises the steps of multi-source data acquisition, high-density meteorological station, multi-source satellite and meteorological mode data acquisition, spatial-temporal feature analysis, correlation analysis, key variable screening and weather type association, satellite-ground fusion air temperature reconstruction, generation of refined monitoring data based on a CNN-ANN deep learning framework, and short-term and temporary prediction modeling. And LSTM is combined with a terrain attention mechanism to construct a prediction model, and two-dimensional reliability verification is carried out. According to the method, the mountain low-temperature monitoring precision and the predicted terrain adaptability can be improved, accurate support is provided for low-temperature disaster prevention and reduction of mountain economic crops, and the method has remarkable practical value.

The invention relates to the technical field of low-altitude atmosphere monitoring, and particularly discloses a low-altitude meteorological digital twinborn reconstruction method based on three-dimensional entity data modeling, seamless connection from a regional macroscopic wind field to an urban microscopic flow field is realized through combination of a mesoscale meteorological model and a computational fluid mechanics model, and the low-altitude meteorological digital twinborn reconstruction method based on three-dimensional data modeling of a real city is realized. The physical effect of surface features on airflow is directly considered, errors of a CFD grid model are continuously corrected through real-time observation data, the uncertainty of pure numerical simulation is made up, the capturing capacity of a space-time mutation phenomenon is improved, a hidden rule in the data is mined through an intelligent prediction model, and the real-time prediction model is established. According to the method, the precision of urban boundary layer airflow simulation can be remarkably improved, and a more accurate meteorological guarantee service is provided for the fields of atmospheric environment monitoring, urban planning, aviation safety and the like.

The invention discloses a weather forecast generation agent method and system introducing human verification nodes. The core is to construct a man-machine cooperation workflow. A manual intervention mechanism is embedded in three key decision nodes, namely model selection, site screening and report auditing, so that deep fusion of large model data processing capability and human expert experience is realized. A user can dynamically select a meteorological model, appoint an analysis site, and optimize defensive measure suggestions by means of a custom cue word template. The system improves the accuracy and scene adaptability of meteorological early warning through the collaborative operation of the knowledge enhancement retrieval node and the tool retrieval node, and is especially suitable for the fields of electric power meteorological services and the like needing personalized decision making.

The invention belongs to the technical field of ship tracking and positioning, and discloses a ship dynamic tracking method and system based on Beidou positioning, and the method comprises the steps: employing a ship-borne Beidou satellite receiver to determine the position in real time, judging whether to trigger a ship dangerous case alarm, marking a ship in distress, and transmitting the dangerous case alarm information to a Beidou comprehensive application service platform; positioning a ship in distress on an electronic chart, marking a dangerous case range, querying surrounding ships or a global marine meteorological model to generate a vector marine field of the dangerous case range, predicting a drifting high-probability area of the ship in distress, searching a prepared rescue ship for the ship in distress, generating a minimum-cost path, and calculating predicted arrival time; the method comprises the following steps: acquiring the position and state of a ship in real time, generating a pre-rescue ship list, sending a rescue request to the pre-rescue ship, marking an actual rescue ship according to a reply, and carrying out parallel obstruction detection and obstruction coordination on the actual rescue ship. And full-process tracking rescue from dangerous case alarm to rescue execution is realized.

A method, device, equipment and medium for independent modeling of troposphere anisotropy of dry and wet convection layers, the method comprising: constructing a troposphere anisotropy model, which adopts a tilted plane horizontal gradient model to respectively describe dry delay anisotropy and wet delay anisotropy; deriving oblique dry delay through a numerical meteorological model, calculating a dry horizontal gradient parameter through the oblique dry delay, and correcting the dry delay anisotropy by using the dry horizontal gradient parameter; taking a wet horizontal gradient parameter as an unknown parameter to be estimated into an observation equation and performing dynamic estimation, and correcting the wet delay anisotropy by using the estimated wet horizontal gradient parameter. The present application adopts an asymmetric processing strategy of dry delay anisotropy model correction and wet delay anisotropy parameter estimation, which not only avoids the ill-conditioned problem of observation equation caused by simultaneous estimation of dry and wet gradients, but also utilizes the high accuracy of the numerical meteorological model in describing the dry delay anisotropy, so that the positioning stability and accuracy are improved at the same time.

The invention discloses a high-resolution low-altitude meteorological real-time and short-time nowcasting method, system and device, and relates to the technical field of low-altitude meteorological evaluation.The method comprises the steps that on the basis of the similarity of meteorological physical processes, according to underlying surface features of a target area and a long-term climate background field used for representing the long-term state of climate elements, the long-term climate background field is used for representing the long-term state of the climate elements; dividing the target area to obtain a plurality of sub-areas and corresponding meteorological physical characteristics; for each sub-region, matching and selecting an adaptive micro-scale meteorological model configuration scheme according to the meteorological physical characteristics of the sub-region; and respectively loading the multi-source real-time meteorological observation data of each sub-region and a real-time climate background field used for representing the recent state of climate elements into the called micro-scale meteorological model configuration scheme, and generating a high-resolution meteorological live field of each sub-region through rapid update cycle assimilation and parallel calculation. The method and the device have the effect of improving the forecasting accuracy.

The application discloses a three-dimensional wind field space-time feature reconstruction and efficient prediction method, relates to the field of space-time feature reconstruction and efficient prediction, and comprises the following steps: acquiring a measured data set; constructing a three-dimensional terrain calculation domain based on a target mountain region digital elevation model, performing multi-scenario numerical simulation by adopting a fluid mechanics method or a mesoscale meteorological model, generating a wind field training data set, constructing a wind field space feature mapping model and training; training a wind speed and wind direction short-time prediction model based on the measured data set; inputting real-time acquired monitoring data into the wind speed and wind direction short-time prediction model to obtain future wind speed and wind direction prediction values of each monitoring station; and inputting the wind speed and wind direction prediction values into the wind field space feature mapping model to obtain a mountain whole wind field distribution of the target mountain region at a future time. Through construction of an integrated technical framework of "measurement-simulation-modeling-prediction-reconstruction", high space-time resolution short-time prediction from local wind speed and wind direction observation to mountain whole three-dimensional wind field is realized.

The invention provides a railway ice and snow disaster risk assessment method, device, equipment, medium and program, and relates to the technical field of railway meteorological disaster monitoring and early warning. The method comprises the following steps: acquiring meteorological data, line equipment data and railway operation and maintenance data of a target railway area; inputting the meteorological data into a pre-trained meteorological model, and outputting meteorological factors; inputting the line equipment data into a pre-trained line equipment model, and outputting a line equipment factor; inputting the railway operation and maintenance data into a pre-trained operation and maintenance model, and outputting an operation and maintenance factor; and determining the ice and snow disaster risk level of the target railway area based on the meteorological factors, the line equipment factors and the operation and maintenance factors. By adopting the technical scheme provided by the invention, a'trigger-amplification-control 'dynamic coupling disaster-causing mechanism in a railway ice and snow disaster scene is obtained, and disaster threats in different scenes can be quantified more accurately based on risk assessment of the mechanism.

This invention belongs to the interdisciplinary field of numerical meteorology and computational fluid dynamics, and discloses a mesoscale-microscale atmospheric coupling simulation method and related apparatus. The mesoscale-microscale atmospheric coupling simulation method includes: acquiring initial field, lateral boundary conditions, and terrain data; based on the initial field, lateral boundary conditions, and terrain data, performing time integration of the microscale meteorological model within a preset time period to obtain the microscale simulation results for all main time steps; and performing spatiotemporal matching and fusion of the microscale simulation results of all main time steps with the background field data of the mesoscale meteorological model to obtain the mesoscale-microscale atmospheric coupling simulation results. The technical solution disclosed in this invention solves the problem of computational collapse in existing atmospheric coupling simulations due to steep terrain or strong wind shear, and can improve the robustness and success rate of simulation operation while ensuring the physical realism of the simulation.

The invention discloses a regional high-resolution atmosphere reanalysis data construction method based on dynamic downscaling, and aims to improve the depiction capability of medium and small-scale weather processes. According to the method, global reanalysis data is used as a background field, multi-source observation data and high-resolution land utilization information are fused, multi-time and short-period simulation is executed by constructing a multi-layer nested WRF region mode and combining an assimilation strategy combining analysis Nudging and observation Nudging, and a reanalysis data set with hour-level and kilometer-level resolution is constructed step by step. Through comparison and verification of multi-source observation data, the constructed data set is obviously superior to ERA5 in the aspects of ground meteorological elements and vertical profile structure reduction, and has higher regional adaptability and simulation precision. The method is suitable for a plurality of high-resolution meteorological application scenes such as regional artificial intelligence meteorological large models, urban fine meteorological services, high-influence weather disaster early warning, regional climate change evaluation, energy meteorological resource evaluation and the like.

The invention discloses a medium and long term atmospheric environment situation research and judgment method based on multi-source data and generative artificial intelligence. By integrating multi-dimensional data, comprehensive and accurate input is provided for atmospheric environment simulation and situation research and judgment. And based on the mesoscale meteorological mode and the regional three-dimensional air quality model, calculating an ambient air bearing capacity index, and backtracking and evaluating the influence of meteorological and emission changes on the air quality. And medium-and-long-term environmental meteorological element data and atmospheric pollutant concentration data are calculated based on an artificial intelligence technology. And training and developing a generative artificial intelligence tool in combination with a historical environmental meteorology-economic society-air quality situation analysis result to assist in carrying out medium and long term atmospheric environment situation research and judgment analysis. And an intelligent decision support result is judged by an expert in the field of atmospheric environmental science, the result is subjected to experience judgment and correction, and GenAI output is optimized. According to the method, the environment meteorological condition and air quality simulation capability and the atmospheric environment situation research and judgment capability can be improved.

The invention discloses a global-local meteorological prediction correction method and system based on variation data assimilation and physical conservation, and the method comprises the following steps: S1, obtaining the prediction data of a big archaeological meteorological model in a set region and the actual measurement data of the region, and selecting a set surface meteorological variable as a correction object; s2, preprocessing the meteorological data; s3, establishing a relation between a global model and local observation based on variation data assimilation, and constructing a target function in combination with physical conservation constraints; s4, according to the constructed objective function, on the basis of a Seq2Seq structure in a transformer architecture, designing a meteorological prediction data correction model of the set region; s5, dividing the input data into a training set, a verification set and a test set in proportion; the preprocessed meteorological data are input into the model, the model is optimized through the training set and the verification set, the trained model outputs prediction data of a set area of the corrected large Pantochaeus meteorological model, and output results of a plurality of windows are selected on the test set and compared with actual measurement data to verify the model effect.

The application provides a topographic classification method and system based on typhoon wind speed prediction, and the method comprises the following steps: obtaining topographic basic data of a classification area; constructing a topographic multi-classification set based on regional meteorological model wind speed and the topographic basic data; obtaining typhoon wind speed prediction data and performing risk assessment on the topographic multi-classification set to obtain a risk index grading classification result corresponding to the topography; and obtaining a microtopography distribution map of the classification area based on the risk index grading classification result. The topographic classification method based on typhoon wind speed prediction can realize the risk assessment of a future line under a current wind speed environment by adding the typhoon wind speed prediction data into the topographic classification process for dynamic topographic classification, and the line operation and scheduling efficiency is improved.

The invention discloses a regional weather forecast optimization method and system based on a meteorological model, and particularly relates to the field of regional weather forecast optimization, and the method comprises the steps: obtaining a time segment in which extreme weather has occurred in a historical weather database, extracting the ground temperature, humidity, wind speed, precipitation and terrain height data recorded hour by hour in the time segment, and carrying out the calculation of the data; generating a meteorological original sample; the method comprises the following steps: constructing meteorological original samples into a structured tensor set according to a unified time step and spatial resolution, performing normalization processing, and outputting a standardized extreme sample group; by constructing a standardized extreme sample group and synchronously extracting an intermediate response structure of an old version meteorological model and an updated version meteorological model, difference analysis and capability evaluation are executed on a multi-channel response hierarchy, and then a response degradation area of the model when a key situation scene is processed is dynamically identified and an online blocking and training guide mechanism is triggered.