50 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 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 sequence data and corresponding meteorological element data of a target region, and generating a regional basic data set; analyzing 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; based on the cross-regional new energy output collaborative fluctuation mode, constructing an output characteristic migration matrix of the target region; fusing the output characteristic migration matrix and climate mode forecast data to obtain a medium and long term weather-output mapping relation of the target area; and outputting a new energy power prediction result of the target area according to the medium and long term weather-output mapping relation, the method can improve the accuracy of medium and long term new energy power prediction.

The invention discloses a staged design flood estimation method under the influence of climate change and reservoir regulation and storage. The method comprises the following steps: acquiring meteorological and hydrological data, human water consumption data and global climate mode output data of a reservoir; a reduced natural reservoir runoff series is obtained based on a water balance method, a GR4J-9 hydrological model is calibrated to achieve natural reservoir runoff simulation, and a long-short-term memory model is constructed to simulate the influence of human activities such as reservoir regulation and storage on runoff; based on a global climate mode set and a quantile deviation correction method, driving a GR4J-9 hydrological model and a long-short term memory model to obtain a reservoir runoff series under climate change and performing flood season staging; establishing a staging design flood calculation model of a future scene based on a Copula function, and deducing the most probable staging design flood; and constructing an emergence constraint model based on the dew point temperature so as to obtain a corrected stage design flood result. According to the method, the comprehensive influence of climate change and reservoir regulation and storage is considered, and a reference basis is provided for deducing stage design flood.

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 relates to the technical field of hydrology and water resources, in particular to a medium-and-long-term reservoir runoff intelligent forecasting technology coupling a climate mode and a bidirectional LSTM. According to the technical scheme, the method comprises the following steps of (1) constructing a multi-source heterogeneous data set, (2) automatically screening variables most related to runoff changes by adopting an MIC method and eliminating redundant features, (3) constructing an end-to-end prediction model composed of a CNN-GRU module and a bidirectional LSTM (Bi-LSTM) module, decomposing a runoff time sequence through an empirical mode decomposition method, and calculating the runoff time sequence according to the runoff time sequence. The method comprises the steps of (1) carrying out prediction on runoff and optimizing a prediction result by adopting a multi-factor nearest neighbor regression method, (2) training a model by adopting historical hydrometeorological data and evaluating prediction performance, and (3) inputting data to the trained model, obtaining a medium-and-long-term runoff prediction result, and optimizing final prediction output by combining a deviation correction method. According to the method, the precision and stability of medium and long-term runoff prediction are improved by coupling the climate mode and the deep learning method.

The invention discloses a climate mode pre-estimation downscaling correction method based on generative deep learning, and the method comprises the steps: recognizing the global climate mode hydro meteorological variable pre-estimation deviation through an unsupervised generative adversarial network, continuously reducing the adversarial loss through the adversarial training of a generator and a discriminator, and improving the accuracy of the adversarial training. Therefore, the trained generative deep learning network has the capability of processing a complex non-linear relationship so as to achieve the comprehensive optimum of downscaling and deviation correction. The method can be used for correcting the estimated average deviation of a multi-mode variable (rainfall, temperature and the like) set and improving the resolution ratio of the multi-mode variable set. The embodiment of the invention shows that the method can effectively estimate and downscale a multi-model set, and has a better downscaling effect than common quantile mapping and a convolutional neural network.

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 method for predicting reservoir inflow and under-dam water resource change trend in a future change environment. The method comprises the steps of obtaining future monthly rainfall data, future monthly temperature data, future monthly evapotranspiration data and future land utilization data based on a GCM climate mode; the future runoff process of the reservoir control section is obtained through simulation in combination with an RCCC-WBM model; based on a data mining technology and an LSTM deep learning coupling model, mining relation characteristics of future reservoir runoff and the under-dam water resource quantity, and obtaining the under-dam water resource quantity by inputting the reservoir runoff; and a nonlinear trend analysis method and the like are adopted to analyze change trend characteristics of water resources under the dam in the future. The method can fully solve the problem of insufficient consideration of climate change and land utilization in the existing method, and can significantly improve the prediction precision of the water resource under the dam in the future.

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 discloses a multi-stage coupled climate mode optimization and uncertainty quantification method and system, and relates to the technical field of climate prediction, and the method comprises the steps: obtaining a multi-source meteorological data set, and carrying out the preprocessing of the multi-source meteorological data set; on the basis of the preprocessed multi-source meteorological data set, a multi-dimensional evaluation index system is used for screening a GCM combination with the best adaptability to a research area; based on the screened GCM combination, establishing a segmented statistical relationship between the simulation data and the measured data in the historical period, obtaining a correction factor, and performing deviation correction on the original output of the mode by using the correction factor; and on the basis of a deviation correction result, fitting the GCM data in each scene by adopting a common least square method and a smooth fourth-order polynomial, respectively calculating the variance and proportion of each uncertainty component, and obtaining the total uncertainty through linear superposition. According to the invention, the problems of large atmospheric circulation mode selection deviation, low correction precision and insufficient uncertainty management in the prior art are solved.

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 provides an ocean heat wave interpretability machine learning prediction method based on multi-source data, and the method comprises the steps: collecting simulation data and observation data of a plurality of climate mode members of CMIP6 in a preset time period, carrying out the preprocessing of the simulation data and observation data, and enabling the preprocessed simulation data and observation data to serve as a training set and a test set; constructing a machine learning model for predicting ocean heat waves, and training the machine learning model based on the training set to obtain a trained machine learning model; testing the trained machine learning model by using the test set to obtain a prediction result of the ocean heat wave, and identifying a physical factor with the highest influence degree on the prediction of the ocean heat wave through the interpretability of the trained machine learning model; the method can effectively overcome the problem of insufficient observation data samples of a traditional method, and significantly improves the accuracy of heat wave prediction. Meanwhile, the key functions of different meteorological variables in the prediction process can be determined, so that the scientific interpretability and practicability of the model are improved.

The invention relates to the technical field of climate prediction, in particular to a climate prediction correction method, system and device based on machine learning and a medium, and the method comprises the steps: operating a CMA-CPSv3 climate prediction system, and obtaining an original climate mode prediction data sequence; inputting the original climate mode prediction data sequence into a pre-trained intelligent grid climate prediction model, and outputting a corrected rainfall prediction data sequence; obtaining an actually measured rainfall observation data sequence and an original climate mode prediction data sequence of a corresponding time period after rainfall in the target prediction area; and evaluating the prediction performance of the intelligent grid climate prediction model based on the actually measured rainfall observation data sequence and the corresponding original climate mode prediction data sequence, and updating the intelligent grid climate prediction model according to an evaluation result. According to the method, the gridding data are output through the intelligent grid climate prediction model, the parameters and the closed-loop process are dynamically updated based on the actually measured data, and high-precision, self-adaptive and efficient correction of regional rainfall prediction is realized.

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 invention provides an agricultural climate resource evaluation system based on climate mode driving, and the system can generate future climate estimation data through the cooperation of a plurality of modules including data acquisition, preprocessing, statistical downscaling, mode evaluation, multi-mode collection, agricultural climate resource calculation, visual output and the like. According to the system, the prediction precision can be adjusted according to actual observation data, automatic data downloading and space-time matching are achieved through a Python script library, and a multi-level data output module provides provincial and city-county scale evaluation reports and an interactive visual platform. And real-time query and decision support is provided for the user, so that the precision and reliability of climate prediction are remarkably improved.

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 climate mode data calibration method based on a spatio-temporal variation transfer function, which comprises the following steps of: taking a single observation station as an independent individual, solving a transfer function between actual measurement data and mode data of the corresponding observation station by utilizing a quantile mapping method, and comparing the transfer functions at different mode data grid points to obtain a climate mode data calibration result; and analyzing the spatial distribution characteristics of the model data and the change trend of the underlying surface, calculating transfer functions in different observation periods, establishing a space-variant unsteady transfer function, and applying the space-variant unsteady transfer function to calibration of the model data. According to the method, variables can be effectively adjusted to be matched with observation data, and the error correction effect under different landforms and altitude conditions is stable.

This invention discloses a generative deep learning-based downscaling correction method for climate model predictions. This method uses an unsupervised generative adversarial network to identify biases in global climate model hydrological and meteorological variable predictions. Using adversarial training of a generator and a discriminator, the adversarial loss is continuously reduced, enabling the trained generative deep learning network to handle complex nonlinear relationships, achieving a comprehensive optimization of downscaling and bias correction. This method can be used to correct the average bias of ensemble predictions of multi-model variables (precipitation, temperature, etc.) and improve their resolution. Inventive examples demonstrate that this method can effectively downscale multi-model ensemble predictions and achieves superior downscaling performance compared to commonly used quantile mapping and convolutional neural networks.

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 present invention discloses a climate-driven analysis method, equipment and medium based on the spatiotemporal evolution of soil moisture. The method comprises: using multiple sets of surface soil moisture data sets, including satellite remote sensing, land surface assimilation, diagnostic models, reanalysis models and climate model simulations, to quantify the spatiotemporal variations of global surface soil moisture under the background of anthropogenic climate change, clarify the consistency of the long-term spatiotemporal evolution characteristics of surface soil moisture between different data sets and its driving factors; clarifying the atmospheric physical mechanism behind the global surface soil moisture changes, and using maximum covariance analysis to reveal the response of global surface soil moisture changes to anthropogenic climate change and internal climate variability; by clarifying the spatiotemporal evolution characteristics of surface soil moisture and its climate driving factors, the present invention provides a scientific basis for improving the quality of soil moisture data sets and enhancing the prediction ability of cascading extreme weather events, thereby providing scientific support for achieving the goal of mitigating global warming.

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 invention discloses a water resource dynamic bearing capacity evaluation method and system, and relates to the technical field of water resource management. An input-output relation among a climate mode, a meteorological factor and a land water resource system, a water resource circulation conversion relation equation, a pollutant circulation conversion relation equation, an economic and social system internal restriction equation, a water resource bearing index restriction equation and an ecological and environment control target restriction equation are used as constraints; establishing a water resource dynamic bearing capacity model; and on the basis of the established water resource dynamic bearing capacity model, predicting a water resource bearing capacity change trend in future time. According to the method, the dynamic bearing capacity change trend of the water resource can be accurately evaluated in real time, and the defects of an existing static model in practical application are overcome. Under the extreme conditions of water resource shortage, drought and the like, a scientific decision basis can be provided for government departments, and sustainable development of social economy is guaranteed.

The invention discloses a multi-stage coupled climate mode optimization and uncertainty quantification method and system, and relates to the technical field of climate prediction, and the method comprises the steps: obtaining a multi-source meteorological data set, and carrying out the preprocessing of the multi-source meteorological data set; on the basis of the preprocessed multi-source meteorological data set, a multi-dimensional evaluation index system is used for screening a GCM combination with the best adaptability to a research area; based on the screened GCM combination, establishing a segmented statistical relationship between the simulation data and the measured data in the historical period, obtaining a correction factor, and performing deviation correction on the original output of the mode by using the correction factor; and on the basis of a deviation correction result, fitting the GCM data in each scene by adopting a common least square method and a smooth fourth-order polynomial, respectively calculating the variance and proportion of each uncertainty component, and obtaining the total uncertainty through linear superposition. According to the invention, the problems of large atmospheric circulation mode selection deviation, low correction precision and insufficient uncertainty management in the prior art are solved.