175 results about "Numerical weather prediction" patented technology

A double-path ultra-short-term wind power prediction method based on numerical weather forecast and multi-order time sequence dynamic gating fusion comprises the following steps: acquiring wind power generation historical data and numerical weather forecast data of a wind power plant, and screening weather factors highly related to wind power by using an MIC; the CEEMDAN is adopted to decompose the power sequence into a plurality of intrinsic mode functions (IMF); a dual-path prediction architecture is constructed, one path adopts xLSTM to predict an intrinsic mode function (IMF), all subsequences are superposed, and a prediction result is obtained; in the other path, the XGBoost is combined with key meteorological characteristics of an intrinsic mode function (IMF) and a numerical weather forecast (NWP) for prediction, and all the subsequences are superposed to obtain a prediction result; the method comprises the following steps: designing an MT-DGFusion module through an enhanced attention and dynamic gating network; and fusing the dual-path prediction results through an MT-DGFusion module to obtain a final prediction result. According to the method, double breakthrough of prediction precision and stability is realized, and a new technical path is provided for a complex time sequence prediction task.

The invention discloses a regional numerical forecasting method and system based on multi-scale background field error covariance. The method comprises the following steps: firstly, constructing a multi-scale background field error covariance model containing different spatial scale error structures, then obtaining an initial atmospheric state and a boundary condition of a forecast area, and generating a background field set sample by using an ensemble forecast algorithm; thirdly, performing multi-scale decomposition on a background field set sample based on the model to obtain error components of different spatial scales, and determining a corresponding error covariance matrix; and combining the matrixes to form a complete multi-scale background field error covariance. Then, meteorological observation data are obtained, a variational assimilation algorithm is applied, optimal combination of the observation data and a background field set sample is achieved through multi-scale background field error covariance, and an optimal initial field is obtained; and finally, performing regional numerical forecasting based on the optimal initial field to obtain a future weather condition forecasting result of the forecast region. According to the method, the forecasting accuracy and reliability are improved.

The invention discloses a photovoltaic power generation power prediction method and system based on a large language model. The method comprises the following steps: converting historical power data and numerical weather forecast data into time sequence embedded representation; through cross-modal semantic alignment, semantic embedding representation is generated; constructing a natural language prompt containing task context information, encoding the natural language prompt into prompt embedding, combining prompt embedding with semantic embedding representation to form a fusion input sequence, inputting the fusion input sequence into a pre-trained large language model, and outputting implicit features; synchronously generating an initial power prediction result and a weather prediction result obtained by correcting the numerical weather prediction data through a parallel collaborative prediction mechanism; and taking the meteorological prediction result as a correction signal, performing joint optimization on the preliminary power prediction result, and outputting a power generation power prediction value. According to the method, the problem of deep fusion of heterogeneous data is effectively solved, and the prediction accuracy is improved.

The invention relates to a photovoltaic power grid energy storage optimization regulation and control method based on multi-scale prediction. The method comprises the following steps: A1, obtaining historical power generation data, real-time meteorological data and numerical weather forecast of a photovoltaic power station; a2, generating a multi-time-scale photovoltaic output prediction sequence; a3, establishing an energy storage dynamic model of charge and discharge efficiency, capacity attenuation and operation constraint; a4, generating an energy storage charging and discharging demand curve under different time scales; a5, constructing a multi-time scale coupled optimization model by taking power grid operation cost minimization and renewable energy consumption maximization as targets; a6, updating an energy storage scheduling instruction in a rolling manner based on latest prediction data by adopting a model prediction control framework; a7, monitoring the deviation between the actual photovoltaic output and the power grid load, and dynamically adjusting the energy storage charging and discharging power; and A8, correcting a prediction error through Kalman filtering and a closed-loop feedback mechanism. According to the invention, high-efficiency operation can be realized, and power grid cost minimization and renewable energy consumption maximization can be realized.

The invention relates to the field of photovoltaic power prediction, in particular to a photovoltaic power prediction method, and the method comprises the steps: obtaining the spectral response function data of different photovoltaic modules in a photovoltaic power station, and measuring the spectral irradiance distribution of the surface of each photovoltaic module in real time; according to the spectral response function data and the spectral irradiance distribution, calculating the effective spectral irradiance of each component type in a grouping manner according to the component types; counting the temperature coefficient of the batch to which each photovoltaic module belongs according to historical data, and establishing a module-level temperature-power correction factor; according to the string topological relation, string-level temperature correction power is obtained, and the string-level temperature correction power, the effective spectral irradiance and the global irradiance data of the numerical weather forecast are output into a corrected power station-level photovoltaic power predicted value; the problem of low photovoltaic power prediction precision caused by inaccurate modeling due to parameter difference and temperature influence of photovoltaic modules in photovoltaic power station power prediction is solved.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention relates to the technical field of photovoltaic prediction, in particular to a distributed photovoltaic power prediction method and system based on high-dimensional gridding numerical weather forecast, and the method comprises the steps: carrying out the standardization of the numerical weather forecast data and photovoltaic power historical data of a target region, and achieving the time-space alignment based on a preset grid, generating a gridding data set; utilizing convolution processing to extract local space features, and converting and fusing the local space features into a feature sequence containing space and historical time sequence information at the same time; modeling is carried out through an encoder-decoder architecture, an encoder excavates historical power dependence, and a decoder dynamically couples future meteorological characteristics with historical power through an attention mechanism and outputs a grid-level predicted value; aggregating to obtain a system total power prediction result; by establishing a unified space-time grid, refined alignment of data is realized, cross-space-time dynamic fusion is performed in combination with convolution and an attention mechanism, and prediction precision and stability can be kept in complex weather.

The invention belongs to the technical field of wind power prediction, and discloses an ultra-short-term wind power prediction method based on Bayesian optimization XGboost-LSTM, and the method comprises the following specific steps: 1, inputting wind power data collected by a wind power plant and numerical weather prediction data of a corresponding time sequence; 2, data preprocessing, wherein missing value interpolation and data deduplication are carried out on input data; according to the method, an XGBoost feature optimization method is used for selecting key features influencing the wind power, the influence of irrelevant feature noise on the model prediction precision and accuracy is eliminated, then a Bayesian optimization algorithm is used for carrying out hyper-parameter tuning on an LSTM model, and finally an XGBoost-LSTM-BO model is constructed. XGboost feature optimization and Bayesian hyper-parameter optimization can obviously improve the prediction effect of the LSTM on future data and improve the model prediction precision, and compared with a traditional prediction model, the wind power data generalization ability of the model can be improved while the high prediction precision is kept, and higher prediction performance is achieved.

The invention discloses a wind power ultra-short-term prediction method based on time fusion Transform, and the method comprises the following steps: 1, collecting historical wind power data, numerical weather prediction (NWP) data and corresponding static data of a wind power plant, carrying out the coding of the data, and constructing an input vector; 2, constructing a data processing module, and performing feature extraction on historical wind power data, NWP data and static data to obtain different data representations; 3, constructing a time fusion coding module, carrying out time fusion coding, and enhancing time features represented by data; 4, constructing an output module, and completing the construction of a prediction model; and 5, training the prediction model by using related data to obtain optimal main parameters of the model. The method can achieve the ultra-short-term prediction of the wind power, and improves the prediction accuracy.

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

The invention provides a wind field inversion method and device based on multi-source prior data, equipment and a medium, and the method comprises the steps: building a target function of a three-dimensional fusion wind field based on the multi-source prior data and a fluid mechanics model simulation wind field, carrying out the iterative optimization of the target function, and determining the three-dimensional fusion wind field; constructing an initial deep learning neural network model, taking the three-dimensional fusion wind field as a truth value label, inputting the urban underlying surface features, the terrain elevation and the numerical weather forecast wind field into the deep learning neural network model, and training to obtain a target deep learning neural network model; and inputting the new numerical weather forecast wind field, the urban underlying surface features and the terrain elevation into the target deep learning neural network model, and outputting a refined wind field. According to the technical scheme provided by the embodiment of the invention, through the trained deep learning neural network model, high-precision rapid inversion of the low-altitude wind field is realized without depending on laser radar and ground observation data and depending on prior information such as numerical prediction and terrain.

The invention relates to the technical field of power systems and automation thereof, in particular to a generative adversarial network-based source-load joint scene generation method, which comprises the following steps of: constructing a multi-source time sequence database and extracting weather, time, space and historical state driving factors; establishing a joint probability distribution model based on a vine connection function; taking a numerical weather forecast path and a date type as conditional input, constructing a generative adversarial network embedded with a physical constraint microloss function of the power system, and forming a physical information generator; performing dependent structure fidelity verification on the generated scene by using the joint probability distribution model; generator parameters are fixed, potential space vectors are optimized through a gradient ascending method to maximize power grid risk indexes, and a high-risk source-load joint scene set is generated. According to the technical scheme, accurate generation of the source-load joint scene which is physically feasible and reasonable in statistics and focuses on the high-risk working condition is realized, and the safe operation toughness and the risk early warning capability of the novel power system are remarkably improved.

The invention discloses a numerical weather forecast deviation correction method and system based on physical conservation optimization. The method comprises the following steps: acquiring a specified prognostic variable in a numerical weather forecast system; inputting the prognostic variable into a prediction deviation correction model pre-trained by a loss function based on physical conservation optimization so as to obtain a deviation correction prediction result of numerical weather prediction; in the loss function of the physical conservation optimization, the physical conservation optimization refers to adding part or all of Coriolis force and barometric gradient force loss, static equation constraint derivation loss and vertical integral loss to the loss function of the value weather forecast deviation correction model. The invention aims to solve the problem of systematic deviation correction in mid-term numerical forecasting, better capture the complex dependency relationship in meteorological data, quantify the uncertainty of numerical weather forecasting and improve the forecasting precision.

A computing system identifies a geographical region of interest. The computing system generates a background numerical weather prediction using a mesoscale numerical weather prediction model. The computing system identifies information associated with wind farms for inclusion in the geographical region of interest. For each wind farm, the computing system determines a wake effect of neighboring farms to the first wind farm by projecting wind deficits for the neighboring farms to generate a plurality of wake effects for the geographical region. The computing system generates an estimated power output for the wind farms in the geographical region of interest based on the plurality of wake effects.

The invention provides an occultation constellation observation system simulation and forecast capability evaluation method, which comprises the following steps of: simulating and calculating an atmospheric refractive index by using a GNSS (Global Navigation Satellite System) radio occultation refractive index observation operator; simulating and calculating a one-dimensional bending angle by using a GNSS radio occultation one-dimensional bending angle observation operator; simulating and calculating a two-dimensional bending angle by using a GNSS radio occultation two-dimensional bending angle observation operator; correcting the bending angle of the L2 frequency on the basis of the difference between the bending angles of the L1 and L2 frequencies; and completing quality control of occultation observation data based on the atmospheric refractive index, the one-dimensional bending angle, the two-dimensional bending angle, the corrected bending angle of the L2 frequency and the actually collected occultation observation data, wherein the atmospheric refractive index, the one-dimensional bending angle and the two-dimensional bending angle are calculated through simulation. By establishing the error evaluation model for the autonomous occultation constellation observation data, the assimilation application effect of the autonomous occultation constellation observation data in the numerical weather forecasting system can be optimized, and direct technology and experience can be provided for business operation of future observation data in the numerical weather forecasting system.

The present invention relates to a method and system for restoring an operating wind speed of a wind farm in a complex terrain to a free-stream wind speed. The method comprises: selecting operating wind speed, wind direction and generated active power data series from wind turbines of a wind farm, a wind speed, wind direction, air temperature and air pressure data series from a wind power prediction tower, and a wind speed and wind direction data series from a numerical weather prediction; on the basis of operation data of the wind farm, dividing the wind farm into wind areas on the basis of wind directions; on the basis of the divided wind areas, selecting representative turbine locations, which represent the wind direction of the wind farm; on the basis of a representative turbine wind direction, constructing a wind direction structure of the wind farm, establishing a wake loss matrix of the wind direction structure of the wind farm, and performing consistency checking and correction; and on the basis of the wind direction structure of the wind farm and the corresponding wake loss matrix, restoring the operating wind speed of each wind turbine to a free-stream wind speed. The invention achieves high operability, and can accurately calculate a wake loss of an operating wind speed of each turbine location of a wind farm, thereby improving the accuracy of restoring the operating wind speed of each wind turbine to a free-stream wind speed.

The invention belongs to the technical field of power grids. The high-resolution set numerical weather prediction method comprises the following steps: acquiring geographic information and historical weather information of a target area, and performing grid division on the target area according to the geographic information and the historical weather information to obtain a plurality of grid areas; according to the geographic information and historical meteorological information of the target area, constructing a large high-resolution set numerical weather prediction model, and performing training to obtain a large target weather prediction model; according to the geographic information and historical meteorological information of the target area, generating weather prediction large model parameters of a plurality of grid areas through feature-driven dynamic modeling; using the target weather prediction large model to predict the current weather information of the target area to obtain a prediction result; and adjusting a prediction result according to the weather prediction large model parameters of the plurality of grid regions to obtain a region prediction result of each grid region.

The invention discloses a wind power prediction error decoupling evaluation method considering unit faults and real-time capacity changes. The method comprises the following steps: combining an actual wind power plant prediction process, and dividing wind power prediction into three links of numerical weather prediction, wind-electricity model conversion and power correction; for a power correction link, a unit fault prediction model is constructed, capacity reduction caused by faults is pre-judged in advance, a health index and a capacity attenuation coefficient are combined, a fault unit is removed in real time, the available capacity of the unit is dynamically corrected, and the equivalent actual capacity is calculated according to the health index in a weighted mode; finally, the equivalent actual capacity of the remaining unit is introduced into an error decoupling model, and quantitative evaluation is conducted on prediction errors caused by all links. And determining the proportion of the prediction error caused by each link, and determining the wind power error source after considering the unit fault and the capacity attenuation. The method can realize accurate decoupling of the wind power prediction error, and is suitable for wind power plant power prediction scenes with frequent meteorological sudden change and equipment aging.

The invention relates to the technical field of numerical weather forecast, in particular to a meteorological large model prediction method based on a data correction model, which comprises the following steps of: firstly acquiring multi-source atmospheric observation, screening observation by topology-optimal transmission quantum annealing, and constructing a weighted error covariance; applying mass, energy and earth rotation gradient, and generating a conservation assimilation field through diffusion implicit sampling; calculating a mutual information mask and coupling a cloud top optical flow fine tuning phase; cloud motion consistent field pulse codes are sent to the symplectic decomposition pulse neural network for neural form hardware reasoning, a pulse threshold is adjusted in a closed loop to control energy drift, and an uncertainty field is output through parallel disturbance reasoning. The method has the advantages of high resolution, low power consumption and probability prediction capability, and the extreme weather path and intensity prediction precision is obviously improved.

The invention relates to a wind power prediction method based on cross-modal space-time attention fusion. Accurate wind power prediction in different weather scenes is realized. The method comprises the following steps: preprocessing wind power numerical weather forecast data to obtain preprocessed data; performing feature extraction on the preprocessed data by adopting a Transform model to obtain a time feature; performing feature extraction on the preprocessed data by adopting a GAT model to obtain spatial features; performing dual-scale feature fusion on the time feature and the spatial feature by adopting a preset neural network to obtain a fused feature; and carrying out standardization processing on the fusion features to obtain standard wind power data.

The invention belongs to the technical field of weather forecasting, and discloses a shore fog forecasting method and system, and the method comprises the steps: obtaining lattice numerical weather forecasting data in a target forecasting region, and carrying out the calculation based on the forecasting data to obtain shore fog key forecasting factors; according to the nonlinear influence function of each key forecasting factor, calculating an influence index corresponding to each key forecasting factor; multiplying all the influence indexes to obtain a shore fog forecast index; matching the historical forecast data with the visibility observation data, and calibrating a forecast index interval corresponding to each visibility influence level; for future target forecasting time, calculating a shore fog forecasting index of each grid point; and comparing the shore fog forecasting index with each calibrated forecasting index interval, determining the shore fog influence level of the grid point at the forecasting time, and generating a grid point shore fog forecasting product. The method has both specialty and universality, and can provide important reference for the refined forecasting of the occurrence and development trend of the local shore fog.

The invention discloses an emergency method for extreme weather prediction of a power system. The method comprises the following steps: comprehensively analyzing data from a meteorological satellite, a ground meteorological station and a radar detection system by using a multi-source data fusion algorithm; developing a precise weather prediction model by integrating numerical weather prediction, machine learning optimization and data visualization technologies; carrying out vulnerability analysis on the key components of the power system to establish a risk assessment model, and assessing the operation risk of the power system under the extreme weather event; based on a risk assessment result, a real-time monitoring system and an automatic early warning mechanism are established, and an early warning signal is sent to an electric power system operator in time; strategy measures are formulated and implemented according to different weather situations, so that reliable power supply of the key area is ensured; and according to geographical and climate conditions of different regions, configuration and function optimization are carried out on the power management software. Through accurate meteorological data processing and model prediction, the weather prediction accuracy is improved, and early warning information is sent out in time.

The invention discloses a numerical forecasting product intelligent station correction method under a small sample condition, and the method comprises the steps: obtaining the original data of a numerical forecasting product, and the original data comprise rainfall, temperature, wind speed and visibility; a graph neural network is constructed, a diffusion graph convolutional network is adopted as a basic module in a GNN layer of the graph neural network, an SE-ResNet layer is constructed, and the SE-ResNet layer is a residual block combined with an SE module; the SE-ResNet layer is fused into a graph neural network, and a correction model SE-ResGNN is obtained; and training the correction model SE-ResGNN, and correcting the original data through the trained correction model SE-ResGNN to obtain a correction result of the original data.

The invention relates to the technical field of flood control dispatching, and discloses a reservoir flood control water level dynamic control and flood recycling method and system, and the method comprises the steps: obtaining a surface rainfall forecast value of a reservoir basin based on real-time rainwater condition data and numerical weather forecast; a hydrodynamics and hydrology coupled drainage basin runoff model is driven, and an in-reservoir flood hydrograph in a predicted period is generated; judging the flood scale and grade based on the hydrograph, and when the flood is medium and small flood, calculating the dynamic flood control storage capacity of a downstream flood control object by adopting an equivalent flood control effect algorithm taking into account the pre-discharge scheduling capability; according to the dynamic flood control storage capacity, a dynamic flood control water level control value is obtained through backstepping of a water level-storage capacity relation curve of the reservoir; and finally, generating and executing a reservoir dispatching instruction based on the control value, and storing the water level of the reservoir to not exceed the dynamic value. The problems that a fixed flood control water level method is low in water resource utilization rate and inflexible in dispatching are solved, and flood resource efficient utilization on the premise of flood control safety is achieved.

The invention relates to an unmanned aerial vehicle track decision-making method based on turbulence prediction, and belongs to the technical field of unmanned aerial vehicle navigation and weather prediction. Aiming at the problems of difficulty in monitoring and predicting low-altitude turbulence and high air route planning risk, the defects of data heterogeneity, insufficient numerical weather forecast resolution and the like exist in the prior art; according to the method, a three-dimensional turbulence intensity field is generated through multi-source meteorological observation data fusion to serve as an observation benchmark, a diagnosis model is constructed in combination with numerical weather forecast data, linear calibration is carried out, or short-term prediction is generated by adopting an observation extrapolation model when forecast data is lacked; further, the turbulence field is mapped into a weighted graph, the height, the climbing rate and the airspace constraint are combined, the optimal track is solved by using a path search algorithm, and the accumulated turbulence cost is minimized; the method is clear in structure, full-process optimization from data fusion to decision making is achieved through multi-model complementation, and the method is suitable for the fields of low-altitude logistics, urban air travel and the like.

The invention discloses a wind power prediction method based on a long short-term memory network, and the method comprises the following steps: 1, obtaining a multi-dimensional data set of a target wind power plant in real time through a data collection system, and the multi-dimensional data set at least comprises historical power data, numerical weather forecast data, fan operation state parameters and geographic information data; according to the invention, through a multi-dimensional feature fusion mechanism and a dynamic weight optimization strategy, the prediction precision is significantly improved; the method comprises the following steps: firstly, fusing weather forecast, a fan operation state and topographic feature data, constructing feature data with physical significance, and enhancing the characterization capability of a model to a complex power grid engineering environment; secondly, a self-adaptive feature selection module is introduced to dynamically optimize the input weight, and redundant data interference is reduced; and finally, an attention mechanism is adopted to enhance feature extraction of key time nodes, so that the fluctuation ratio of a prediction curve is reduced.

The application discloses a numerical weather prediction and forecasting method and system based on deep reinforcement learning. The method comprises the following steps: setting a custom environment of ensemble variational data assimilation; obtaining policy network parameters and value network parameters; obtaining updated policy network parameters based on a deep reinforcement learning method according to the custom environment of ensemble variational data assimilation, the policy network parameters and the value network parameters; determining a hybrid background-error covariance matrix according to the updated policy network parameters, wherein the hybrid background-error covariance matrix is a weighted average of a static covariance matrix and an ensemble covariance matrix; updating adaptive hybrid parameter strategy model training according to the hybrid background-error covariance matrix to obtain a numerical weather prediction and forecasting model; and performing numerical weather prediction and forecasting according to the numerical weather prediction and forecasting model. The application can improve the prediction accuracy of numerical weather prediction.

The invention discloses a day-ahead solar irradiance data correction method, and the method comprises the steps: screening historical numerical weather forecast data of the same weather type based on the weather type of day-ahead weather forecast; calculating a Spearman correlation coefficient and a variance of daily solar irradiance, dividing historical days into different clustering groups through a Kmeans clustering algorithm, and obtaining a clustering center; determining a cluster group to which the day-ahead numerical forecast belongs, and selecting historical data with matched weather types and groups to construct a training set; training a COSTLSTM model capable of decomposing an input sequence trend and a seasonal component to obtain a correction model; and inputting the numerical weather data of the day-ahead weather forecast into the trained solar irradiance correction model, and outputting to obtain the corrected day-ahead solar irradiance. Compared with the prior art, the method can effectively cope with complex mixed modes and seasonal periodic differences in the solar irradiance, and realizes accurate data correction of the day-ahead solar irradiance in complex weather.

The invention relates to the technical field of atmospheric science and numerical calculation, discloses an atmospheric numerical simulation method based on multiphase state water substance conservation constraint and application, and aims at evolution calculation of all phase state water substances under the conditions of discrete grids and discrete time steps for atmospheric numerical mode cloud microphysical parameterization. The method comprises the following steps: acquiring the density and velocity field of each component in a grid unit, and constructing a mass ratio variable normalized by wet air density; defining a growth rate per unit volume and per unit time, and representing a phase change source sink by using a component continuity equation; and establishing a coupling discrete updating rule according to the mass ratio accurate evolution equation, calculating a total water substance growth rate residual error and a ratio residual error corresponding to the equivalent conservation expression, and obtaining a corrected growth rate meeting conservation constraint through consistency correction so as to write back and update the mass ratio field. The method can inhibit the income and expenditure drift of water substances, reduces the phase distribution error under the condition of mixed phase cloud, and is suitable for numerical weather forecast and regional numerical simulation.