30 results about "Climate pattern" patented technology

The invention discloses a river sediment scouring and silting simulation method, and belongs to the technical field of hydraulic engineering and hydrological simulation. In order to solve the problem that it is difficult to accurately simulate the river sediment erosion and deposition evolution trend in the prior art, the method comprises the steps that on the basis of actually measured topographic data and channel chart data of a target river reach, a water-sediment coupling mathematical model is constructed by adopting a non-uniform sediment unbalance sediment transportation theory, and the model interacts with a water power module and a wave module in real time through an MCT coupler; actually measuring a flood and water and sediment series data calibration verification model; and obtaining future water and sediment process data predicted by a plurality of climate modes, inputting the future water and sediment process data into the verified model, dividing river reaches according to the characteristics such as riverbed gradient and river channel curvature, and calculating the erosion and deposition amount of each sub-river reach in the future 50 years. The method can simulate water flow and sediment movement of rivers, reservoirs and lakes, can be applied to the fields of riverway regulation planning, flood control and disaster reduction decision making, reasonable development and utilization of water resources and the like, and provides important data support and scientific basis for construction and management of water conservancy projects.

The invention discloses a vegetation vulnerability constraint estimation method and system under dry-heat composite stress. The vegetation vulnerability constraint estimation method comprises the following steps: 1, acquiring observed vegetation total primary productivity data, meteorological data and similar meteorological data of each lattice point in a global climate mode; 2, determining a standardized rainfall evapotranspiration index time scale of each lattice point and determining dominant stress types of different lattice points in growing seasons; 3, respectively constructing two-dimensional Copula models of corresponding dominant stress and total primary productivity, and determining a stress threshold value corresponding to induced vegetation loss; 4, calculating future estimation of dominant stress based on a climate mode, and constructing an emergence constraint model; 5, correcting a future estimated mean value and uncertainty of the dry heat stress index based on the emergence constraint relation; and 6, inputting the corrected mean value and uncertainty range of the future dry heat stress indexes into a Copula model, and calculating a corresponding loss probability. According to the method, the response difference of different vegetation ecosystems to dry heat stress is considered while the prediction accuracy of the drought heat waves and the compound events thereof is improved.

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 a U-Net-based multi-mode wind energy resource monthly scale prediction correction method and system, belongs to the technical field of wind energy resource evaluation and climate numerical value prediction crossing, and is used for correcting monthly scale wind speed output by a climate mode. According to the method, a climate state wind speed field is constructed by utilizing ERA5 reanalysis, and a monthly-scale 10m wind speed anomaly is calculated to serve as a correction reference; monthly-scale historical return data of a plurality of dynamic climate modes are obtained, 10m wind speed and multilayer meteorological elements are extracted, unified interpolation and standardization are carried out, and a sample set is formed; a U-Net correction model with a coding and decoding structure is constructed based on a sample set, feature combination and hyper-parameters are optimized through cross validation, and nonlinear mapping from a multi-mode forecast field to an ERA5 distance flat field is learned. And correcting future monthly scale forecast by using the optimal model, generating a wind speed product of which space structure and amplitude distribution are closer to observation, and providing high-credibility wind energy climate information for wind power planning, power generation planning and power grid dispatching.

The invention relates to a rainfall objective prediction method based on an intelligent algorithm, and the method comprises the steps: decomposing the midsummer rainfall observation data of a time sequence based on an empirical orthogonal function, obtaining a spatial mode and a time coefficient, calculating a circulation field mean value of a target time period based on an atmospheric circulation field monthly value data set predicted by a climate mode, and obtaining the rainfall objective prediction result. The method comprises the following steps: obtaining a circulation abnormal field through a difference value with a same-period climate state circulation field, carrying out correlation analysis on a time coefficient and the circulation abnormal field, identifying and extracting a first correlation region with high correlation, carrying out correlation analysis on the time coefficient and atmosphere reanalysis data, obtaining a second correlation region with high correlation, and identifying and extracting a second correlation region with high correlation. And extracting a circulation anomaly average value of a similar region in the first related region and the second related region as an input feature, training a prediction model according to the input feature and a corresponding time coefficient, obtaining the corresponding time coefficient according to the prediction model and circulation anomaly feature data in the second time period, and performing reduction to obtain rainfall prediction data. And the accuracy of rainfall prediction is improved.

The invention discloses an electric power climate risk assessment method for the influence of high temperature and / or drought events on both sides of supply and demand, a medium and a program product, and belongs to the technical field of meteorological disaster risk assessment and energy system safety crossing. Then identifying a high-temperature or drought event based on a temperature percentile threshold value and an SPEI drought index, and constructing a bivariate distribution model by adopting a joint probability density function to identify a high-temperature drought composite event; respectively constructing a power supply side response model and a load side response model, and quantitatively evaluating output changes of different power supplies and load response characteristics of various users in a high-temperature and / or drought scene; key indexes such as a power gap and load risk exposure intensity are calculated through supply-demand coupling offset analysis; and finally, based on the climate mode prediction data, driving the risk model, and outputting a risk evolution trend in a future scene. The method can provide support for climate toughness improvement and scheduling decision making of the power system.

The invention discloses a conditional multi-mode gathering method and device for improving ENSO cycle simulation, and relates to the technical field of climate prediction and numerical simulation. The method comprises the following steps: acquiring ENSO simulation results of a plurality of climate modes; calculating a correlation coefficient between the ENSO simulation result and the ENSO observation result of each climate mode; determining a threshold value based on the performance index of each climate mode; dividing climate modes into a positive correlation class, a negative correlation class and a weak correlation class according to the relation between the correlation coefficient and a threshold value; multiplying the simulation result of the negative correlation climate mode by-1 to obtain a correction result of the negative correlation climate mode; and carrying out set average on the correction result of the negative correlation climate mode and the simulation result of the positive correlation climate mode to obtain a final improved ENSO simulation result. According to the method, the consistency of each feature of the ENSO is simulated, the simulation of climate change influencing the ENSO is improved, and the accuracy of ENSO cycle simulation and the reliability of future estimation are improved.

The invention discloses a control method for a refrigeration house refrigeration system, and relates to the technical field of refrigeration system automatic control, climate domain division and domain self-adaptive prediction models are introduced, and the control robustness and adaptability of the refrigeration house refrigeration system in a multi-climate-region environment are remarkably improved; according to climate domain division, refrigeration houses are grouped based on external climate characteristics, so that the model can focus on a specific climate mode, and deviation caused by data distribution differences is reduced; domain adaptive training enables the prediction model to learn cross-domain shared characterization through a feature alignment mechanism, so that accurate energy consumption and temperature prediction can be maintained in cold or hot regions, and the problem of failure of a traditional single model in local climate is avoided; the optimization control part is combined with a rolling time domain strategy and a climate perception algorithm, actuator parameters are dynamically adjusted, it is ensured that under the sudden load or extreme weather, energy consumption and temperature stability can still be balanced, the defrosting frequency and equipment stress are reduced, and the service life of the system is prolonged.

The application belongs to the technical field of data analysis and processing, and specifically discloses a typhoon track trend change confirmation method, system, device and medium. The method obtains the sea surface pressure disturbance field, the sea surface wind field and the diagnostic field in the sea basin scale. Based on the multi-field joint determination, the typhoon track and the influence area in the climate model data are determined. Further iteration is performed to determine the typhoon track and the influence area data set. Based on the modal decomposition and by using the influence area data set and the TDF method, the typhoon trend change is determined. The method can comprehensively utilize the forecast model data wind pressure field data to jointly research and judge, effectively determine the typhoon center position and the typhoon track, and objectively and reliably determine the typhoon influence range by the sea surface pressure disturbance field, so that the influence of the artificial factor introduced by the artificial given area weight influence factor in the TDF method can be reduced. The typhoon change trend is determined by using the principal component analysis method on the objective typhoon influence range, and the operability is high.

The application discloses a kind of based on multi-stage mixed physics-data driven wind field revision method, it is related to wind field data processing and climate model application technical field. Utilize regional climate simulation tool to execute dynamic downscaling simulation, obtain multidimensional candidate variable field;According to ERA5 wind speed data, target candidate climate variable is screened, and multidimensional key variable field is formed;Hierarchical multi-scale spatio-temporal fusion network for revising wind field data is obtained by constructing the to-be-trained model including multi-scale spatial feature extraction module, time dynamic feature extraction module, multi-scale spatio-temporal feature fusion, output module and training using multidimensional key variable field;With wind field revision field as boundary condition, high-resolution wind field revision field is generated by using complex terrain microscale flow field refining solver.The application not only improves the accuracy of wind speed prediction, but also ensures the physical authenticity and consistency of high-resolution wind field under the influence of complex terrain through physical constraint.

The invention discloses a sub-season meteorological disaster risk early warning effect improvement method and system, and the method comprises the steps: building a disaster process library based on climate observation data and a climate mode forecasting product; based on the disaster process library, calculating a monthly-scale disaster risk space level by using a disaster risk assessment model; based on the monthly-scale disaster risk space level, establishing a five-dimensional inspection index of the monthly disaster risk early warning capability of the multi-climate mode, and calculating an average score of an early warning product of each climate mode; and carrying out weighted fusion on the average score of each climate mode early warning product to obtain a final risk early warning result, and completing improvement on a traditional early warning method. According to the invention, by comprehensively evaluating the performance of the climate mode and the disaster model, a scientific basis is provided for optimizing the forecast mode and the report starting time in the business, and the precision and foresight of the disaster early warning service are powerfully supported.

This invention discloses a method, medium, and program product for assessing the impact of high-temperature and / or drought events on both the supply and demand sides of the power system. It belongs to the interdisciplinary field of meteorological disaster risk assessment and energy system security. First, multi-source data from the target area is collected and preprocessed. Then, high-temperature or drought events are identified based on temperature percentile thresholds and the SPEI drought index. A bivariate distribution model is constructed using a joint probability density function to identify combined high-temperature and drought events. Next, response models for the power supply side and load side are constructed separately to quantitatively assess the output changes of different power sources and the load response characteristics of various users under high-temperature and / or drought scenarios. Furthermore, key indicators such as power shortages and load risk exposure intensity are calculated through supply-demand coupling offset analysis. Finally, the risk model is driven by climate model prediction data to output the risk evolution trend under future scenarios. This invention can provide support for improving the climate resilience of power systems and for dispatching decisions.

The present application relates to an interpretable and machine learning-based climate model preferably combined with a collection estimation method, belonging to the technical field of climate models, the method comprising: obtaining area-weighted average ozone column total of reanalysis data and CMIP6 model data, and constructing a data set; based on the data set, respectively adopting a traditional method and a decision tree SHAP method to screen the CMIP6 model; based on the CMIP6 model data screened by the traditional method and the decision tree SHAP method, a machine learning model is constructed; based on the constructed machine learning model, the optimal screening method and the corresponding optimal machine learning model are determined; based on the optimal screening method and the corresponding optimal machine learning model, the Antarctic ozone under different greenhouse gas emission scenarios is estimated. Through the present application, more accurate Antarctic ozone recovery estimation results can be obtained.

The invention provides a monthly-scale meteorological drought risk quantitative estimation method based on climate mode forecasting, and the method comprises the steps: determining an evaluation time period according to the occurrence condition of a drought process in an early-stage rainfall time period of an evaluation region, and carrying out the evaluation of the early-stage rainfall time period through the historical day-by-day rainfall observation data of a meteorological station in the evaluation time period for many years. Counting the number of duration days without effective precipitation and the average percentage of the accumulated precipitation in the evaluation period of each year, calculating a meteorological drought risk index of each year, and determining a grading threshold value of a meteorological drought risk grade by adopting a percentile method; and calculating a meteorological drought risk index of an estimated month and determining a risk level through the estimated data of the evaluation time period. According to the method, the non-effective rainfall duration day index considering the characteristic of less rainfall duration and the accumulated rainfall average percentage index reflecting the characteristic of less rainfall total amount are selected as estimation indexes, the complexity of an existing monthly-scale meteorological drought estimation method is overcome, and the estimation operability and reliability are improved.

The invention relates to the technical field of data processing, and discloses a horizontal grid registration and parallel computing method and system in climate mode coupling. The method comprises the following steps: performing registration processing on an atmospheric mode component through a component identity verification algorithm to obtain a mode component identifier; analyzing the horizontal grid geometric parameters according to the mode component identifier to obtain a grid basic information set; constructing masked and maskless horizontal grid configurations based on the grid basic information set; performing parallel domain decomposition processing on the dual grid configuration to obtain a parallel decomposition mapping table; establishing a global and local grid index bidirectional mapping relationship; and performing time step synchronization registration based on the bidirectional mapping relation to obtain a coupling validity verification result. The technical problem that a self-adaptive grid registration and parallel decomposition processing mechanism for air-sea coupling special requirements is lacked in an existing climate mode coupling technology is solved.

The application discloses a climate mode multivariate statistical downscaling method, a storage medium and equipment, relates to the technical field of climate mode statistical downscaling, and comprises the following steps: acquiring mode circulation information, measured precipitation and temperature information output by a global climate mode, and dividing the information into a training set and a test set; constructing a climate mode multivariate statistical downscaling initial model, including a circulation information compression module, a precipitation processing module, a temperature processing module, a temperature and precipitation output module; training the circulation information compression module, a new circulation information compression module retaining an encoder after training, and replacing the circulation information compression module of the initial model; using the training set and the test set to train and test the climate mode multivariate statistical downscaling model, inputting mode circulation information to be processed into the model after training and testing, and obtaining down-scaled precipitation and temperature information. The application avoids multiple loading of input data and waste of running time and resources.

The application relates to the technical field of data reasoning, and discloses a regional medium and long-term new energy power prediction method, which comprises the following steps: acquiring historical new energy output time series data and corresponding meteorological element data of a target region to generate a regional basic data set; analyzing the space-time coupling characteristics in the regional basic data set and extracting a cross-regional new energy output collaborative fluctuation mode in the space-time coupling characteristics; constructing an output characteristic transfer matrix of the target region based on the cross-regional new energy output collaborative fluctuation mode; fusing the output characteristic transfer matrix and climate mode prediction data to obtain a medium and long-term meteorological-output mapping relationship of the target region; and outputting a new energy power prediction result of the target region according to the medium and long-term meteorological-output mapping relationship; and the application can improve the accuracy of medium and long-term new energy power prediction.

The invention provides a combined temperature forecast correction method and system, and relates to the technical field of weather forecast. According to the method, three kinds of errors are systematically corrected, and the climate mode temperature forecast deviation is corrected. The method comprises the following steps: firstly, considering an error source of temperature forecast, and respectively disassembling observation temperature data and original forecast temperature data in a training period into three independent components, namely a mean term, a trend term and a residual term; and using conditional Gaussian correction to optimize the residual term error, and keeping the rank correlation structure of the original ensemble forecast. And according to requirements, performing mean value correction, trend correction and residual error correction on the original forecast temperature data, combining a mean value correction result, a trend correction result and a residual error correction result of the original forecast temperature data, and outputting a combined temperature forecast correction result. The method is used for temperature forecast correction and has the advantages of being good in correction effect, flexible to use, efficient in calculation and the like.

The invention discloses an all-sky imager aerosol optical parameter inversion algorithm constructed based on machine learning, and belongs to the field of meteorological observation. According to the method, an iterative threshold segmentation method and threshold self-optimization are used, a clear sky extraction quantitative standard is established for the first time, and the problems of manual dependence and poor consistency are solved; according to the method, an equal zenith angle scanning mode is simulated, multi-dimensional feature extraction is carried out from a sky area picture, aerosol scattering information reflecting multi-angle and multi-position information related to an aerosol scattering phase function and multi-angle information of total brightness is obtained, and key support is provided for SSA inversion; according to the method, an integrated regression model is constructed by taking the multi-dimensional features as input and AOD and SSA as output, a spatial difference and time difference matching sample is set, synchronous output is realized, a sample standard is defined, and efficiency and precision are greatly improved. The method is applied to the fields of atmosphere remote sensing, climate mode research, air quality evaluation and the like.

The invention relates to the technical field of weather forecast, discloses a convection forecast optimization method and device in a climate mode, a medium and a product, and can scientifically judge whether an isolated deep convection trigger event is remarkably associated with the altitude and the fluctuation degree or not through a t inspection verification method. Furthermore, on the premise that qualitative association is established, the specific quantitative relation between the occurrence probability of the isolated deep convection trigger event and the altitude and the fluctuation degree is analyzed through linear regression, and the influence degree of topographic parameter changes on the occurrence probability of the isolated deep convection trigger event can be determined. And finally, optimizing the preset cumulus convection parameterization scheme of the target research area by using the first quantitative relationship and the second quantitative relationship, so that the optimized target cumulus convection parameterization scheme can accurately reflect the influence rule of the terrain on the isolated deep convection trigger event. Therefore, the simulation and forecast precision of the climate mode on the convection current in the complex terrain area is improved, and forecast errors of extreme weather such as thunderstorm and heavy rainfall are reduced.

The application discloses a machine learning-based all-sky imager aerosol optical parameter inversion algorithm and belongs to the meteorological observation field.The application uses an iterative threshold segmentation method, optimizes a threshold automatically, first constructs a clear sky extraction quantitative standard, and solves the problems of artificial dependence and poor consistency.The application simulates an equal zenith angle scanning mode, performs multi-dimensional feature extraction from a sky area picture, obtains scattering information of aerosols which reflects multi-angle, multi-position information related to an aerosol scattering phase function and multi-angle information of total brightness, and provides key support for SSA inversion.The application sets up a regression model with multi-dimensional features as input and AOD and SSA as output, sets spatial difference and time difference matching samples, realizes synchronous output, makes the sample standard clear, and greatly improves efficiency and precision.The application is applied to the fields of atmospheric remote sensing, climate model research and air quality assessment.

The invention relates to a climate mode optimization and set estimation method based on interpretability and machine learning, and belongs to the technical field of climate modes, and the method comprises the steps: obtaining the area weighted average ozone column total amount of reanalysis data and CMIP6 mode data, and constructing a data set; based on the data set, a traditional method and a decision tree SHAP method are adopted to screen a CMIP6 mode; constructing a machine learning model based on CMIP6 mode data screened by a traditional method and a decision tree SHAP method; based on the constructed machine learning model, determining an optimal screening method and a corresponding optimal machine learning model; and based on the optimal screening method and the corresponding optimal machine learning model, performing recovery estimation on the Antarctic ozone under different greenhouse gas emission scenes. According to the method, a more accurate ozone recovery estimation result of the Antarctic can be obtained.

The invention discloses a Qinghai-Tibet Plateau global climate mode rainfall downscaling method based on double attention, and relates to the technical field of rainfall downscaling, and the method comprises the following steps: obtaining coarse resolution global climate mode CMIP6 rainfall data, high resolution reference rainfall data and multi-source auxiliary data, and carrying out the standardization preprocessing of all data; a generative adversarial network model is constructed, and a generator of the generative adversarial network model adopts a dual attention mechanism embedded with a channel attention module and a space attention module and is connected in series with a dense block to form a feature extraction main body; training the generative adversarial network model by adopting a two-stage sequential downscaling strategy; and inputting to-be-downscaled CMIP6 rainfall data into the final model, and outputting to obtain a high-resolution downscaled rainfall product. According to the method, the generative adversarial network fusing dense connection and a double attention mechanism is combined with the multi-scale discriminator, so that high-precision downscaling of the CMIP6 rainfall data is realized.

The invention discloses a land-gas coupling method for improving climate mode carbon-water flux simulation capability, a storage medium and computing equipment, and belongs to the technical field of earth system simulation and land-gas interaction.The method comprises the following steps that a climate mode ECHAM and a dynamic vegetation mode iMAPLE which are to be coupled are obtained; real-time simulation is carried out in the ECHAM to obtain a meteorological field, and the meteorological field is transmitted to the iMAPLE; land surface ecological process calculation is carried out in the iMAPLE, and a calculation result is sent back to the ECHAM; and an ECHAM-iMAPLE online land-gas coupling scheme is established, a long-time test is carried out, and the carbon-water flux simulation capability is evaluated. According to the method, the adopted iMAPLE mode considers more detailed vegetation biological physical and chemical processes and parameterization schemes, the bi-directionally coupled ECHAM6-iMAPLE mode remarkably improves the carbon-water flux simulation capability, and bi-directional feedback of an atmosphere-ecosystem is more comprehensively quantified.

The disclosure discloses a CMIP6 climate model comprehensive comparison and selection method and system, relates to the technical field of data processing, calculates first index data of each climate model through meteorological measured data and CMIP6 climate model data, calculates first index scores of simulation capabilities of each climate model on one meteorological element according to the first index data and second index scores of simulation capabilities of each climate model on another meteorological element, averages the sum of the first index scores and the second index scores corresponding to each climate model to obtain a comprehensive index score corresponding to each climate model, and selects a preset number of climate models from the climate models in order from large to small according to the comprehensive index scores, wherein the selection process of the climate models comprehensively considers simulation effects when different climate elements simulate different meteorological elements, so that the selected climate models can achieve good simulation effects when simulating different meteorological elements.

The present application provides a data assimilation method and system for predicting seawater quality change by using tide station records, and relates to the technical field of marine observation. The method comprises the following steps: selecting a tide station; calculating the global land mass migration change to obtain the sea level fingerprint effect; extracting the dynamic sea level change of the climate model and interpolating the dynamic sea level to the tide station record data; establishing an observation equation according to the selected tide station and using a sparse matrix to associate the state quantity and the observation quantity; introducing a random variable at each tide station to make up for the deficiency of the climate model in simulating the local dynamic sea level change; determining the initial time state of the tide station, optimizing and adjusting the constraint parameters of the state quantity and the correlation coefficient between adjacent tide stations; and based on Kalman filtering and smoothing, estimating the optimal state quantity and predicting the global seawater quality increase. The present application improves the accuracy of seawater quality estimation and realizes the coincidence and compatibility of model prediction and tide station observation data.

The invention discloses a method for reducing climate mode estimation uncertainty based on variable coordination relationship constraint, which comprises the following steps of: identifying a physical coordination relationship among key variables, and constructing a regression model of multi-mode historical constraint variable data and future constrained variable data; and forming a statistical relationship, namely an emergence constraint equation, which can be checked by the reference data, and further accurately calculating the uncertainty of the corrected global climate mode estimation data according to the multi-model data layer, the reference data layer and the constraint relationship layer in combination with the reference data. According to the method, a physical constraint mechanism is established by mining the cooperative relationship among climate variables, and the uncertainty is calculated by adopting layering, so that the reliability of mode estimation is remarkably improved, and the uncertainty is reduced.

The invention discloses a Qinghai-Tibet plateau summer air temperature estimation method based on the synergistic effect of sea temperature and soil humidity. Belongs to the technical field of climate evaluation and estimation, and particularly relates to the technical field of temperature estimation based on multi-mode cooperative influence. The technical problem that although most of current CMIP6 modes can simulate relatively real summer surface temperature spatial distribution characteristics of the Qinghai-Tibet Plateau, many simulation results still have cold and wet deviation on the Qinghai-Tibet Plateau is solved. Firstly, a sea temperature key area and a soil humidity key area influencing the abnormal high temperature of the Qinghai-Tibet Plateau in summer are determined; therefore, mode optimization is performed by taking a coordinated regulation and control mechanism of sea temperature and soil humidity on the plateau summer air temperature as a constraint condition, and the historical simulation capability of the plateau summer air temperature is evaluated and estimated in combination with a plurality of multi-mode set schemes, so that the cold deviation of the climate mode on the historical simulation of the plateau summer air temperature is remarkably reduced, and the prediction accuracy of the plateau summer air temperature is improved. And a more reliable future estimation result can be obtained.

The application discloses a kind of multi-mode wind energy resource month scale prediction revision method and system based on U-Net, belong to wind energy resource evaluation and climate numerical prediction cross technical field, for the month scale wind speed of climate mode output is revised.The method constructs climatic wind speed field using ERA5 reanalysis, calculates month scale 10m wind speed anomaly as revision benchmark;Obtain the month scale historical return data of multiple dynamic climate models, extract 10m wind speed and multiple layers meteorological elements, uniform interpolation and standardization, form sample set.Based on sample set, the U-Net revision model with encoding and decoding structure is constructed, and the feature combination and hyperparameter are optimized through cross-validation, to learn the nonlinear mapping from multi-mode prediction field to ERA5 anomaly field.The optimal model is used to revise the future month scale prediction, to generate wind speed products with more similar spatial structure and amplitude distribution to observations, to provide high credible wind energy climate information for wind power planning, power generation planning and power grid dispatching.

The invention belongs to the technical field of offshore wind power, and discloses an offshore wind power discrete event simulation method and system of a CMIP6 global climate mode, and the method comprises the steps: building an offshore wind power construction project discrete model; establishing a CMIP6 global climate mode simulation database; the meteorological data time precision is enhanced; the meteorological data space precision is enhanced; the invention relates to discrete event simulation based on enhancement data. According to the method, CMIP6 global climate mode data is adopted in the simulation process, a meteorological data source needed by offshore wind power discrete event simulation is optimized, the time precision of meteorological data is enhanced through meteorological characteristic enhanced spline interpolation, and the space precision of the meteorological data is enhanced through meteorological characteristic enhanced Kriging interpolation.