49 results about "Tropical cyclone" patented technology

The invention discloses an inland area TRP feature recognition and evaluation method based on data driving and numerical simulation, a medium and a program product, and relates to the technical field of meteorological disaster monitoring and early warning and numerical simulation. And objectively identifying a TRP event which occurs outside the typhoon circulation and is connected with the strong water vapor conveying belt by combining the typhoon peripheral circulation radius, the distance constraint and the whole-layer water vapor conveying flux. Key physical fields such as whole-layer water vapor flux, water vapor income and expenditure, dry invasion and monsoon water vapor surge are diagnosed by utilizing a regional numerical mode and reanalysis data, and a characteristic index set reflecting typhoon, monsoon and dry and cold air coupling influence is formed; on the basis, a data-driven learning algorithm is introduced, a TRP occurrence and rainstorm intensity grading recognition model is obtained through training, a risk assessment product is generated through calibration and risk grading, and therefore the inland typhoon long-distance rainstorm recognition and assessment capacity is improved.

The invention provides a multi-task tropical cyclone strength prediction method under guidance of physical information, a TC-MulPred tropical cyclone strength prediction model is constructed, and the TC-MulPred tropical cyclone strength prediction model sequentially comprises a feature aggregation module, a backbone prediction module, a feature enhancement module and a feedforward output module. The feature aggregation module performs feature aggregation on an input satellite image through encoding space information and tropical cyclone strength information to obtain global feature representation, and the backbone prediction module predicts the global feature representation based on an encoder and decoder framework to obtain a tropical cyclone strength initial prediction sequence. The feature enhancement module performs data enhancement on the initial prediction sequence of the tropical cyclone strength to obtain an enhanced prediction sequence, and the feed-forward output module maps the enhanced prediction sequence into a tropical cyclone strength index prediction value; in the training process, a physical guidance constraint loss function based on the tropical cyclone air pressure relation is introduced, and the tropical cyclone strength prediction precision is improved.

The application discloses a LES super-high-resolution TC atmospheric flow field numerical simulation method and system, and relates to the fields of atmospheric observation, numerical assimilation and high-resolution numerical simulation. The method is characterized in that: three-dimensional dynamic-thermal information is obtained by implementing airship platform radar observation and downward sounding observation in the core area of a tropical cyclone; the radar radial wind and profile observation operators are preprocessed and constructed; a data assimilation strategy is adopted to integrate multi-source observation in a multi-nested grid numerical model to generate a three-dimensional initial / reanalysis field suitable for large eddy simulation; then, a large eddy simulation solver is configured on the innermost grid covering the boundary layer of the tropical cyclone; fine horizontal grids and boundary layer encryption vertical stratification are adopted to explicitly analyze the boundary layer vortex, small-scale convection and turbulent structure; and a super-high-resolution three-dimensional flow field product for fine evaluation of strong wind and disaster risk is output.

The application relates to a tropical cyclone disaster loss assessment method and system, and relates to the technical field of natural disaster risk assessment. The method is executed by a computer device and comprises the following steps: acquiring historical cyclone paths, wind field parameters and social and economic statistical data; performing uniform spatial resolution and coordinate reference alignment processing on population and economic spatial distribution data; calculating a hazard intensity index P; determining a cyclone influence range, extracting population exposure PE and economic exposure G; selecting a population and economic vulnerability index set, constructing a population vulnerability index PV and an economic vulnerability index EV; adopting Poisson regression to construct a death assessment model with P, PE and PV as independent variables; and adopting gamma regression to construct an economic loss assessment model with P, G and EV as independent variables. The application realizes double-dimensional and comparable collaborative attribution analysis of historical tropical cyclone event economic loss and population death, and outputs the relative contribution degrees of hazard, exposure and vulnerability to the two types of losses.

The embodiment of the invention discloses a method and device for evaluating the influence of tropical cyclones on the marine environment. The method comprises the following steps: continuously acquiring marine environment parameters of a tropical cyclone influenced sea area, and constructing a multi-dimensional parameter data set based on the marine environment parameters; derivation features are calculated based on the multi-dimensional parameter data set; taking the cyclone strength in the vortex category and the tropical cyclone data as classification features, taking data except the vortex category and the cyclone strength in the multi-dimensional parameter data set and the derivative features as continuous features, and constructing input data according to the classification features and the continuous features; and outputting an environment evaluation result by the trained multi-parameter coupling model based on the input data. In the embodiment, the classification features and the continuous features can be input into the multi-parameter coupling model at the same time, then the dominant factors are determined by quantifying importance ranking, the environment evaluation result is generated according to the dominant factors to predict the environment evolution in a period of time in the future, and the influence of the tropical cyclone on the marine environment is more accurately and quantitatively predicted.

This invention provides a method and apparatus for reconstructing the three-dimensional temperature and humidity field of a tropical cyclone based on deep learning and sparse observations. The method includes: acquiring radiosonde observation data and satellite remote sensing data for a target tropical cyclone region; mapping the radiosonde observation data onto a two-dimensional grid on multiple preset pressure layers to generate an initial temperature and humidity field containing missing data regions, and generating a mask to identify the missing data locations; interpolating and aligning the satellite remote sensing data onto the two-dimensional grid to form an auxiliary remote sensing field; combining the initial temperature and humidity field, the mask, and the auxiliary remote sensing field at each pressure layer to form a multi-channel input tensor, which is then input into a pre-trained deep neural network model. The model infers and completes the missing data regions, outputting the complete two-dimensional temperature and humidity field for the current layer; and vertically synthesizing the complete two-dimensional temperature and humidity fields corresponding to all pressure layers to obtain the three-dimensional temperature and humidity field of the target tropical cyclone.

The application discloses a kind of tropical cyclone temperature body structure calculation method and system, wherein the method includes: based on satellite microwave detection data and tropical cyclone position information, the three-dimensional temperature data of tropical cyclone area is smoothed, and temperature grid with tropical cyclone center as origin is formed.Three-dimensional temperature anomaly value is calculated according to the smoothed temperature grid.Based on three-dimensional temperature anomaly value, three-dimensional region growth method is used to extract tropical cyclone warm core main area.Based on the extracted tropical cyclone warm core main area, the isosurface is calculated using Marching Cubes algorithm.According to the isosurface, the light model is rendered to draw the three-dimensional visualization structure of tropical cyclone warm core.Based on the three-dimensional temperature anomaly value of tropical cyclone warm core main area, the tropical cyclone warm core intensity index is calculated.Thus, the application effectively solves the problem of insufficient overall understanding of tropical cyclone temperature structure in the prior art, and provides the ability of systematic quantitative analysis.

The invention discloses a cyclone strength prediction result correction method and device based on a diffusion model, equipment and a medium, and relates to the technical field of meteorological prediction, and the method comprises the steps: obtaining target meteorological data in a target time range; inputting the target meteorological data into a preset meteorological prediction server, so that a meteorological prediction model in the preset meteorological prediction server analyzes the target meteorological data by taking a preset ERA5 reanalysis data set as a boundary condition to obtain a tropical cyclone strength prediction result; and obtaining a tropical cyclone strength prediction result from a preset weather server, and correcting the tropical cyclone strength prediction result by using the target cyclone strength correction model. The model constructed based on the neural network is adopted to transform the initial prediction result, rapid and accurate correction of the tropical cyclone strength is realized, and the problems of insufficient prediction precision and low calculation efficiency are solved.

This invention provides a method and system for assimilating tropical cyclone wind speeds using spaceborne GNSS-R, comprising: partitioning the tropical cyclone (TC) structure based on a matching dataset of massive historical SAR and ERA5 reanalysis data, and statistically decoupling the radial and tangential correlation scales of the wind corona discharge (BEC) within each partition; establishing an empirical mapping model between BEC statistics and key TC parameters; and driving the empirical model to construct a flow-dependent anisotropic BEC matrix based on real-time TC parameters provided by the international best path archive, and introducing it into a variational assimilation system to assimilate multi-source spaceborne GNSS-R observation data, reconstructing a high-resolution TC sea surface wind field analysis field. This invention, starting from a physical mechanism, effectively suppresses the gradient smoothing effect of background errors in the eyewall region, reducing the root mean square error of the background field by 33% under extreme wind speeds, significantly improving the engineering and operational capabilities of spaceborne GNSS-R data in TC intensity and structure monitoring.

The application discloses a cyclone intensity prediction method and device, and the method comprises the following steps: acquiring physical factor data, satellite cloud image data and cyclone intensity data in a historical period; performing pretreatment on the physical factor data, the satellite cloud image data and the cyclone intensity data to obtain a physical factor image, a satellite image image and historical cyclone intensity; inputting the physical factor image, the satellite image image and the historical cyclone intensity into a tropical cyclone intensity prediction model to obtain a 24-hour cyclone intensity prediction result; wherein the tropical cyclone intensity prediction model comprises a hierarchical fusion subnetwork, a deep convolutional neural network, a prior knowledge module and an uncertainty estimation module. The fusion of the uncertainty estimation and the long-term prior knowledge improves the prediction ability of the cyclone intensity change trend and the accuracy of the intensity prediction.

The application discloses a hurricane circulation assimilation prediction method and system based on space-time attention reinforcement learning, and relates to the technical field of tropical cyclone prediction. The steps are as follows: formalizing the assimilation-prediction circulation process into a Markov decision process, constructing a hurricane assimilation-prediction simulation environment based on WRFDA-WRF to provide a physically consistent interactive environment for reinforcement learning; fusing three-dimensional convolution, spatial attention and a gated recurrent unit to construct a deep space-time encoder to extract the space-time features of the assimilation analysis field, and obtaining a high-dimensional state embedding with physical constraints and statistical representation capabilities; inputting the high-dimensional state embedding into an enhanced proximal policy optimization network based on an Actor-Critic framework to adaptively adjust the mixed background error covariance matrix weight in the circulation assimilation-prediction, and realizing the mixed weight regulation. The application realizes the precision of the tropical cyclone path and intensity prediction by dynamically adjusting the WRFDA background error covariance weight.

This tropical cyclone weakening system involves, firstly, injecting cold water drawn from depths of at least 200 meters into the surface of seas and oceans, and secondly, injecting this water beneath a layer of sargassum seaweed to limit the warming of this deep water. This system operates primarily in areas of the North Atlantic where both the major cyclone tracks and sargassum beds are located. The aim is also to capture the sargassum at sea so that it no longer washes ashore. Furthermore, the system allows for continuous electricity production through its ocean thermal energy conversion (OTEC) plants and includes a sargassum collection system to utilize the harvested sargassum. (Simplified diagram: Fig. 5)

The invention discloses a coastal to deep sea wind energy resource refined evaluation method and system, and relates to the technical field of renewable energy sources in the climate resource development and utilization field, and the method comprises the steps: constructing a Fujian province national meteorological observation station, an ocean buoy station, reanalysis data, an offshore anemometer tower and other multi-source gradient anemometer databases; the hourly 1 * 1km high temporal-spatial resolution 0-300m gradient wind energy resource fine evaluation method based on WRF numerical mode simulation of an optimization design scheme is developed; and researching and developing WTGS exceeding probabilities and thresholds of different levels of wind speeds during the influence of the tropical cyclones, determining available wind speed, early warning wind speed and defense wind speed regions for wind resource development, and drawing a risk zoning map for development and utilization of wind farms from coastal to deep sea. Meanwhile, an offshore wind energy resource refined evaluation system is researched and developed, wind energy resource refined evaluation and developable potential estimation at different heights from the coastal sea to the deep sea are achieved, and wind energy resource development and utilization risk level zoning at any grid point in different sea areas is achieved.

The invention discloses a rapid calculation method and system for an effective wave height field of a tropical cyclone in an open sea area, and the method comprises the following steps: embedding a dynamic wind field structure into to-be-measured tropical cyclone event information, constructing a space-time distribution field, extracting a cyclone key parameter and a relative position coordinate in a preset time period containing the current moment, and calculating the effective wave height field of the tropical cyclone in the space-time distribution field; forming a time sequence matrix; and inputting the time sequence matrix into a space-time fusion neural network model, and quickly calculating the effective wave height space-time distribution of the open sea area at the target moment. According to the method, the neural network is adopted to replace a traditional numerical value wave model for effective wave height prediction, transformation from low-precision and high-time-consumption traditional numerical value simulation to high-precision and low-time-consumption intelligent modeling is achieved, and the tropical cyclone wave field prediction and modeling quality and efficiency are greatly improved.

The invention relates to a KAN-based tropical cyclone strength change mechanism analysis method and device, and belongs to the technical field of TC strength change prediction. The method comprises the steps that a factor recursive pruning algorithm based on a KAN2.0 attribution score is adopted, and a high-impact forecasting factor set for TC intensity change forecasting is screened and obtained; performing factor contribution analysis based on symbolization regression by using KAN2.0 trained by a high-impact forecasting factor set, obtaining a complete linear equation from high-impact forecasting factors to TC intensity change, and calculating the TC intensity change according to the symbols and absolute values of various linear coefficients in the equation; and quantitatively analyzing the contribution direction and contribution degree of each high-influence forecasting factor to the TC intensity change. According to the method, the stability, the accuracy, the generalization ability and the interpretability of TC intensity forecasting can be effectively improved.

The invention provides a global cyclone forecasting method combining a physical mechanism and data driving, and relates to the field of cyclone forecasting. The invention provides a potential diffusion model architecture of physical inspiration, deep fusion of a Transform codec, physical constraint explicit modeling and a multi-modal information fusion technology. The architecture realizes technical innovation in the following key links: integrating historical tropical cyclone data, historical environmental field data and future environmental field forecast data, and providing comprehensive environmental information support for prediction; in a core modeling link, input is mapped to a high-dimensional submerged space for denoising and then decoded back to a data space to generate a prediction sequence, and particularly, a physical inspired gating feature interaction mechanism is embedded in a decoding stage so as to explicitly model the physical constraint relationship among the trajectory, the wind speed and the air pressure. According to the scheme, the stability and physical consistency of tropical cyclone prediction on the long-term time scale are effectively improved, and the method has remarkable technical advantages and application value.

This tropical cyclone weakening system involves, firstly, injecting cold water drawn from depths of at least 200 meters into the surface of seas and oceans, and secondly, injecting this water beneath a layer of sargassum seaweed to limit the warming of this deep water. This system operates primarily in areas of the North Atlantic where both the major cyclone tracks and sargassum beds are located. The aim is also to capture the sargassum at sea so that it no longer washes ashore. Furthermore, the system allows for continuous electricity production through its ocean thermal energy conversion (OTEC) plants and includes a sargassum collection system to utilize the harvested sargassum. (Simplified diagram: Fig. 5)

The invention provides a tropical cyclone precipitation correction factor acquisition method after landing, a tropical cyclone precipitation correction method, a terminal and a medium. The method comprises the following steps: acquiring an initial comprehensive attenuation coefficient of the tropical cyclone, and acquiring a historical comprehensive attenuation range of a working area; constraining the initial comprehensive attenuation coefficient based on the historical comprehensive attenuation range to obtain a final comprehensive attenuation coefficient; based on the final comprehensive attenuation coefficient and the initial comprehensive attenuation coefficient, constructing a tropical cyclone precipitation correction factor after landing; the tropical cyclone precipitation correction factor obtained by the method can effectively improve the accuracy of tropical cyclone precipitation distribution corresponding to each path point after landing, thereby improving the accuracy of tropical cyclone precipitation forecast, disaster prediction and the like.

The application relates to the technical field of tropical cyclone dynamics and numerical diagnosis, and discloses a typhoon ventilation flow self-adaptive calculation method and system based on maximum probability characteristics and overcoming radius sensitivity. The method is characterized in that: firstly, typhoon center is located and a relative wind field is constructed based on reanalysis data or numerical prediction data; then, a polar coordinate system is established and a first-order harmonic component of the wind field is extracted; on this basis, a radius-wind direction two-dimensional joint probability distribution P(r, alpha) is constructed, and a sample number threshold and a direction consistency constraint are introduced to self-adaptively identify a connected region Omega which is the most probable and meets statistical stability; in the Omega region, a robust statistics and bootstrap resampling are used to obtain a ventilation flow vector and a quality index, and the unified output of direction, intensity and credibility is realized. The system module design of the application has the capabilities of standardized input and output, automatic processing and business integration. The application eliminates the radius sensitivity problem, significantly improves the stability of ventilation flow estimation, and has important application value.

The invention relates to a severe convection strong wind weather identification method based on machine learning, and solves the problems of poor accuracy and real-time performance of severe convection strong wind early warning in the prior art. The adopted technical scheme is as follows: obtaining radar echo combination reflectivity data, gridding maximum wind speed observation data and historical tropical cyclone path data; after quality control and data resampling, data influenced by the tropical cyclones are eliminated, and effective samples with space, regional statistics and time change characteristics of radar echoes are screened; and inputting the effective samples into a machine learning model for training, and outputting the probability and / or spatial distribution of the strong convection wind. The method has the advantages that by eliminating tropical cyclone influence data, the sample purity is improved, and the misjudgment basis is reduced; multi-dimensional features are extracted, strong convection strong wind attributes are completely captured, and the accuracy can be remarkably improved; and training the effective samples through a machine learning model, outputting the probability and / or spatial distribution of strong convection strong wind, and conforming to a real-time accurate early warning service scene.

This invention discloses a method, system, device, and storage medium for detecting effective precipitation in tropical cyclones, belonging to the field of tropical cyclone precipitation detection technology. The method includes: acquiring the center location, time information, and total daily precipitation of a tropical cyclone; dividing a buffer zone around the center location into a preset range, marking the grid within the buffer zone, and converting the buffer zone into a raster mask document; calculating the distance from the center point of the grid to the center location of each tropical cyclone, selecting the time information corresponding to the tropical cyclone with the smallest distance as the center time of that tropical cyclone passing through the grid; accumulating the total daily precipitation for a preset number of days before and after the center time to obtain the total precipitation caused by the tropical cyclone in the grid, and identifying grids with a total precipitation greater than a preset threshold as valid grids; calculating the cumulative area and cumulative precipitation of all valid grids as the total effective precipitation area and total effective precipitation of the tropical cyclone, thus completing the effective precipitation detection.

The invention provides a tropical cyclone three-dimensional temperature and humidity field reconstruction method and device based on deep learning and sparse observation. The method comprises the steps of obtaining sonde observation data and satellite remote sensing data of a target tropical cyclone area; the observation data of the sonde are respectively mapped to a two-dimensional grid on a plurality of preset air pressure layers, an initial temperature and humidity field containing a data missing area is generated, and a mask for identifying a data missing position is generated; satellite remote sensing data are interpolated and aligned to the two-dimensional grid to form an auxiliary remote sensing field; on each air pressure layer, combining the initial temperature and humidity field, the mask and the auxiliary remote sensing field to form a multi-channel input tensor, inputting the multi-channel input tensor into a pre-trained deep neural network model, reasoning and complementing the data missing region by the model, and outputting a complete two-dimensional temperature and humidity field of the current layer; and performing vertical synthesis on the complete two-dimensional temperature and humidity fields corresponding to all the air pressure layers to obtain a three-dimensional temperature and humidity field of the target tropical cyclone.

A ConvLSTM tropical cyclone path prediction method fusing an adversarial loss is disclosed, comprising: S1: data acquisition and preprocessing; S2: data processing and reconstruction of a data set to obtain single-channel matrix data; S3: model training, comprising: S31: inputting the single-channel matrix data into a ConvLSTM model for model training, and outputting predicted future point longitude and latitude data; S32: constructing real samples and false samples based on the predicted future point longitude and latitude data, and inputting the real samples and the false samples into an LSTM network model, and updating the LSTM network according to a discriminator loss; S33: calculating a generator loss based on an adversarial loss L GAN and a mean square error loss L2, and updating a ConvLSTM network based on the L G ; S34: repeating the steps S31-S33 until training reaches a maximum training number or triggers an early stop mechanism; and S4: predicting a tropical cyclone path.

The application provides an artificial intelligence-based power equipment weather monitoring and early warning system. The artificial intelligence-based power equipment weather monitoring and early warning system comprises a data acquisition module, a data preprocessing module, a space-time feature fusion module, a weather disaster prediction model, the weather disaster prediction model adopts a deep reinforcement learning framework, inputs the multi-dimensional space-time feature matrix, and outputs weather disaster risk levels and key parameter prediction values in a future preset period, including wind speed, precipitation, temperature anomaly, tropical cyclone path probability, a dynamic early warning threshold generation module, an early warning decision module and a model optimization module. The artificial intelligence-based power equipment weather monitoring and early warning system provided by the application has the advantages of being capable of improving the data interpolation accuracy of complex terrain areas, being capable of realizing high-precision prediction of typhoon paths, short-time strong winds and icing risks, and being capable of comprehensively improving the early warning accuracy and defense response efficiency of the power system in response to weather disasters.

The invention belongs to the technical field of meteorological simulation, discloses a systematic tropical cyclone and high-resolution wind field modeling method, system and device based on environment driving and a medium, and aims to solve the problem of wind field simulation distortion in complex terrains. The method comprises the following steps: acquiring and unifying environment field data; calculating a dynamic generation potential index field based on the base month average climate field, defining a space-time generation probability, and randomly sampling to generate an initial event set of a plurality of synthetic tropical cyclone events; performing iterative simulation on each synthetic tropical cyclone event, and outputting full life cycle information; aiming at the full life cycle information, based on a gradient wind balance physical model, calculating each position point in the moving track information and a corresponding central air pressure difference value, and obtaining a near-surface wind field under an open terrain assumption as a first near-surface wind field; and acquiring high-resolution terrain data, inputting the high-resolution terrain data and the first near-surface wind field into the terrain adaptive correction model, and outputting the corrected near-surface wind field as a second near-surface wind field.

The invention belongs to the field of typhoon precipitation forecasting, and particularly relates to a typhoon precipitation forecasting method based on similar paths, which comprises the following steps: collecting a CMA tropical cyclone optimal path data set, environmental field factor data and precipitation factor data; preprocessing the CMA tropical cyclone optimal path data set and the environmental field factor data; based on the preprocessed CMA tropical cyclone optimal path data set and the environmental field factor, calculating the correlation between a similarity measurement factor and a precipitation factor; performing clustering division on the CMA tropical cyclone optimal path data set by using a K-means algorithm, calculating a Fast DTW minimum cumulative distance, and introducing an RMSE index of distance change to determine that the number of similar typhoon standard cardinal numbers is 17; on the basis of a FastDTW algorithm, other similarity measurement factors after weight distribution are introduced, and then 17 typhoon paths similar to the target typhoon are screened out; and carrying out rainfall prediction and similarity measurement factor evaluation based on the 17 typhoon paths similar to the target typhoon. The objective of the invention is to improve the prediction accuracy of typhoon precipitation prediction.

This invention provides a method for analyzing the maximum wind speed of offshore wind farms, comprising the following steps: S1. Determining the study area; S2. Statistically analyzing historical typhoons within the study area and converting all 2-minute wind speeds to 10-minute wind speeds; S3. Calculating the maximum wind speed for each time period using typhoon wind field models; S4. Setting a gale threshold and selecting the maximum wind speed for each time period belonging to the same tropical cyclone to form a maximum wind speed sequence; S5. Using the chi-square and Kolmogorov methods to perform goodness-of-fit tests on the frequency and probability distribution of tropical cyclones in S4. If the test fails, return to step S4 and reset the gale threshold; S6. Using the Poisson-Gumbel method, calculating the maximum wind speed at the first altitude once every N years based on the maximum wind speed sequence; S7. Calculating the representative value of the maximum wind speed at the first altitude once every N years; S8. Using wind shear to estimate the maximum wind speed at the hub height, solving the technical problem of poor representativeness of reference meteorological stations in existing technologies.

The invention relates to the technical field of atmospheric science, and discloses a rapid enhanced tropical cyclone precipitation structure parting and environmental influence assessment method. According to the method, a self-organizing mapping technology is utilized to perform clustering analysis on precipitation distribution images of the fast enhanced tropical cyclones, and possible precipitation structure types of the fast enhanced tropical cyclones are clarified. Then, the convection and dynamic characteristics of various types of tropical cyclones are quantitatively tested from the aspects of convection intensity, symmetry, vortex scale, gradient and the like. And finally, comprehensively evaluating the influence of a plurality of large-scale environmental factors such as vertical wind shear, sea surface temperature and latent heat flux on the tropical cyclone precipitation structure through synthetic analysis and correlation analysis methods, and clarifying the physical cause of the difference of the tropical cyclone precipitation structure. The method has the beneficial effects that the method is beneficial to quantitatively evaluating the difference and diversity of the rapid enhancement type tropical cyclone precipitation structure and dynamic characteristics, reveals the internal relation between rapid enhancement and a large-scale environment, and provides an important scientific basis for improving the tropical cyclone forecasting accuracy and resisting tropical cyclone disasters.

This application relates to a method and apparatus for analyzing the mechanism of tropical cyclone intensity change based on KAN, belonging to the field of TC intensity change forecasting technology. The method includes: using a factor recursive pruning algorithm based on KAN2.0 attribution scores to screen and obtain a set of high-impact forecasting factors for TC intensity change forecasting; performing factor contribution analysis based on symbolic regression using KAN2.0 trained on the high-impact forecasting factor set to obtain a complete linear equation from the high-impact forecasting factors to TC intensity change; and quantitatively analyzing the direction and degree of contribution of each high-impact forecasting factor to TC intensity change based on the sign and absolute value of the linear coefficients in the equation. This method can effectively improve the stability, accuracy, generalization ability, and interpretability of TC intensity forecasting.

The application relates to a method for positioning the position of a tropical cyclone by using a wind field, comprising the following steps: obtaining a given wind field, determining the basic attributes of the wind field; performing orthogonal decomposition on the wind field to obtain a zonal wind and a meridional wind; calculating the closed rotation center of the wind field based on the zonal wind and the meridional wind; performing pretreatment on the closed rotation center to obtain a processed closed rotation center; calculating the range of the tropical cyclone based on the processed closed rotation center to obtain a rectangular area corresponding to the east-west-south-north four-direction boundary of different tropical cyclones; performing unreasonable area elimination on the rectangular area according to the maximum wind speed and the range size to obtain a final tropical cyclone. The application can quickly and accurately identify the positions of multiple tropical cyclones in the wind field without relying on the pressure field, satellite inversion brightness temperature products and artificial pre-judgment positioning assistance, and can determine the position of the tropical cyclone while obtaining related tropical cyclone information including the range, intensity and wind circle radius of the tropical cyclone.