86 results about "Tropical cyclone" patented technology

The invention provides a power equipment meteorological monitoring and early warning system based on artificial intelligence. The power equipment meteorological monitoring and early warning system based on artificial intelligence comprises a data acquisition module, a data preprocessing module, a spatio-temporal feature fusion module and a meteorological disaster prediction model, the meteorological disaster prediction model adopts a deep reinforcement learning framework, inputs a multi-dimensional spatio-temporal feature matrix, and carries out meteorological disaster prediction on the multi-dimensional spatio-temporal feature matrix. And outputting meteorological disaster risk levels and key parameter predicted values in a future preset time period, including a wind speed, precipitation, temperature anomaly and tropical cyclone path probability, a dynamic early warning threshold generation module, an early warning decision module and a model optimization module. The power equipment meteorological monitoring and early warning system based on artificial intelligence provided by the invention has the advantages that the data interpolation precision of a complex terrain region can be improved, high-precision prediction of a typhoon path, short-time strong wind and an icing risk can be realized, and the early warning accuracy and defense response efficiency of a power system to meteorological disasters can be comprehensively improved.

The invention belongs to the technical field of weather prediction, discloses a tropical cyclone ensemble forecasting method, system and equipment based on a depth generation diffusion model and a medium, and aims to improve the accuracy, reliability and uncertainty quantification capability of forecasting by taking the output of a big fortune weather model as a condition and generating a diversified forecasting set by using the diffusion model. The method comprises the following steps: acquiring global ERA5 reanalysis data at a current moment and a previous moment as initial field data of deterministic forecasting, and inputting the initial field data into a big fortune weather model to generate a deterministic forecasting result at a (t + 1) moment; taking the deterministic forecast result as the conditional input of a conditional probability diffusion model, and generating N ensemble forecast members through the conditional probability diffusion model in combination with random noise data and auxiliary condition information with the same dimension as the deterministic forecast result; and carrying out statistical analysis and calculation on probability distribution of tropical cyclone paths and strength based on the N ensemble forecasting members, and generating an ensemble forecasting product.

The invention discloses a typhoon intensity prediction method and device fused with a space-time deep network, and a medium, and the method comprises the following steps: 1), collecting typhoon historical observation data, and obtaining typhoon data; 2) data preprocessing; 3) extracting data features; 4) feature fusion; 5) establishing a typhoon intensity prediction model; 6) model training; and 7) performing prediction by using the trained model, outputting a final prediction value, and restoring a prediction result into an actual typhoon intensity value through reverse normalization processing. The influence of the remote sensing satellite image and the characteristics of the tropical cyclone on the tropical cyclone strength estimation accuracy is considered, the typhoon strength prediction integrated with the spatio-temporal information is carried out, the spatio-temporal representation of the typhoon focus area is enhanced through the integration of the spatial characteristics and the time sequence characteristics of the typhoon, and the accuracy of the strength prediction result is improved.

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 relates to the technical field of data analysis, in particular to a mangrove forest health assessment system based on tropical cyclones. The structure risk coefficient, the geological risk coefficient and the ecological risk coefficient are combined to obtain the health risk index of the mangrove forest, so that accurate health assessment is realized, meteorological data and cyclonic state data are monitored to predict a cyclonic prediction path, and the prediction accuracy is improved. And then the cyclonic prediction path and the health risk index of the mangrove forest are combined to form risk response management and control on damaged and undamaged mangrove forests, so that accurate judgment and supervision on key areas are formed, and the situation of excessive damage caused by untimely management and control on the mangrove forest is reduced.

The invention relates to a regional composite disaster chain risk assessment method under the influence of a tropical cyclone, and the method comprises the steps: obtaining and preprocessing the historical multi-source observation data of the tropical cyclone, and constructing a multi-dimensional disaster element data set; analyzing non-linear change characteristic parameters of each disaster element based on the multi-dimensional disaster element data set, and constructing a multi-dimensional disaster element quantitative model and a comprehensive disaster intensity index based on the non-linear change characteristic parameters; modeling the historical change trajectory of the comprehensive disaster intensity index, extracting an abnormal mode and time-space characteristic parameters, and constructing a composite disaster chain spatio-temporal evolution prediction model; and acquiring real-time multi-source observation data of the tropical cyclones, inputting the real-time multi-source observation data into the composite disaster chain spatio-temporal evolution prediction model to predict a composite disaster chain spatio-temporal evolution trend, performing quantitative analysis on the composite disaster chain spatio-temporal evolution trend, and determining risk levels of different regions. According to the method, quantitative description and evaluation of the tropical cyclone composite disaster chain spatio-temporal evolution process are realized by integrating multi-source heterogeneous data.

The invention relates to a tropical cyclone composite disaster multi-source data intelligent fusion early warning system, and the system comprises a data collection module which is used for collecting a multi-source data set; the preprocessing module is used for preprocessing the multi-source data set to obtain a standardized multi-source data set; the feature mining module is used for performing spatial feature mining and time feature mining on the standardized multi-source data set to obtain a spatial feature vector matrix and a time feature vector; the fusion module is used for fusing the spatial feature vector matrix and the time feature vector by adopting an attention mechanism to obtain a multi-dimensional fusion feature; the composite disaster prediction module is used for performing trajectory calculation and intensity evolution analysis on the multi-dimensional fusion features, and acquiring occurrence probabilities of rainstorm, flood and storm surge according to trajectory calculation data and intensity evolution data; and the early warning module is used for obtaining a comprehensive early warning result according to the occurrence probability of the rainstorm, the flood and the storm surge. According to the invention, accurate early warning of tropical cyclones and composite disasters caused by the tropical cyclones can be realized.

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 present application relates to a kind of tropical cyclone intensity prediction method and device based on meteorological big model, method includes following stage: first stage: with satellite image and reanalysis atmospheric field data as condition, tropical cyclone intensity information is mapped to discrete vector hidden space by encoder, compare the mapping result with all vectors of learnable discrete codebook, take the closest vector and restore back original tropical cyclone intensity information by decoder, and iteration training is carried out to encoder, discrete codebook and decoder;Second stage: historical meteorological data is input into trained encoder and discrete codebook, is converted into discrete hidden variable, and is carried out autoregressive iteration, obtains forecast hidden variable, and with wind-uw forecast field data as physical constraint, tropical cyclone intensity information is restored back by decoder, obtains tropical cyclone intensity prediction result.Compared with prior art, the present application has the advantages of long-term prediction, good performance, reduced computing cost and the like.

The invention discloses a conditional diffusion method for reconstructing multi-source meteorological data for tropical cyclones. The conditional diffusion method comprises the following steps: acquiring an infrared radiation brightness set, a polarization correction temperature set and a sea surface wind speed of different tropical cyclones in a specified historical period; constructing a polarization correction temperature reconstruction model based on the conditional diffusion model; the input of the polarization correction temperature reconstruction model is an infrared radiation brightness temperature set, and the output target is a polarization correction temperature set; carrying out transfer learning on the trained polarization correction temperature reconstruction model to form a sea surface wind speed reconstruction model; the input of the sea surface wind speed reconstruction model is an infrared radiation brightness temperature set, and the output target is the sea surface wind speed. Therefore, the mapping relation from the infrared radiation brightness temperature to the multi-source meteorological data is established through generative deep learning of conditional diffusion, so that the multi-source meteorological data with the temporal-spatial resolution the same as that of geostationary orbit observation is reconstructed, and good performance is shown on various meteorological indexes and computer vision indexes.

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 invention discloses a tropical cyclone abnormal path forecasting method based on spatio-temporal multi-scale fusion and evidence learning. The tropical cyclone abnormal path forecasting method comprises the steps of preprocessing collected satellite cloud picture data; marking the preprocessed satellite cloud picture on the basis of the IBTrACS optimal path set; constructing a tropical cyclone abnormal path forecasting model based on space-time multi-scale fusion and evidence learning; training the constructed tropical cyclone abnormal path forecasting model; and forecasting the abnormal path of the tropical cyclone at the future moment by using the trained abnormal path forecasting model of the tropical cyclone. The tropical cyclone abnormal path forecasting model provided by the invention not only can provide a high-precision path forecasting result, but also can accurately identify and forecast an abnormal path type, and provides reliable decision support and reference basis for a service forecasting department.

The invention discloses a tropical cyclone path screening method, system and device, a medium and a product, and relates to the field of ocean and weather forecast, and the method comprises the steps: selecting a tropical cyclone sample, and determining a first path similar typhoon list of the tropical cyclone sample based on a tropical cyclone path similarity method; based on a comparative learning tropical cyclone similarity evaluation model, determining a second path similar typhoon list of the tropical cyclone sample; the comparative learning tropical cyclone similarity evaluation model is obtained by training a training data set; the training data set comprises a CMA tropical cyclone optimal path data set and ERA5 reanalysis data information; according to the first path similar typhoon list and the second path similar typhoon list, a tropical cyclone list similar to the tropical cyclone sample path is determined, and an optimal tropical cyclone path is screened from the tropical cyclone list. And the misjudgment condition is avoided.

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 present invention discloses a method, apparatus, device, and storage medium for analyzing the symmetry structure of a tropical cyclone. The method obtains hourly tropical cyclone center positions and intensity values from a tropical cyclone's optimal path data set, creates a polar coordinate system with the tropical cyclone center position as the origin, obtains hourly infrared cloud top brightness temperature data for the tropical cyclone, and interpolates the infrared cloud top brightness temperature data into a polar coordinate grid of the corresponding polar coordinate system using an interpolation method to obtain infrared cloud top brightness temperature polar coordinate grid data. The method then calculates convective symmetry index parameters for a preset inner core region of the tropical cyclone based on the infrared cloud top brightness temperature polar coordinate grid data, and finally analyzes the relationship between the convective symmetry index parameters of all tropical cyclones and the intensity of the tropical cyclone. The present invention defines the convective symmetry index of the inner core of the cyclone based on the infrared cloud top brightness temperature data of the tropical cyclone, and explores its relationship with changes in cyclone intensity, so as to explore the application of the index in tropical cyclone intensity forecasting.

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.

The application relates to the field of tropical cyclone tracking, in particular to a tropical cyclone automatic tracking method based on a relative threshold value. The method comprises the following steps: obtaining meteorological historical data through a numerical weather prediction model, obtaining relative vorticity and warm core intensity of historical tropical cyclones according to the meteorological historical data; collecting historical tropical cyclone data, calculating relative thresholds of the relative vorticity and the warm core intensity according to the historical tropical cyclone data; obtaining meteorological data at different times according to the numerical weather prediction model, obtaining candidate areas at different times according to the relative thresholds and the meteorological data; obtaining multiple sets of longitude and latitude data and intensity data of tropical cyclone centers at different times according to the candidate areas at different times; and obtaining path and intensity information of each tropical cyclone changing over time by using a clustering algorithm according to the longitude and latitude data and the intensity data. The application solves the problems of low tracking efficiency and poor precision of multiple tropical cyclones in the prior art.

The invention discloses a cyclone strength prediction method and device. The method comprises the following steps: acquiring physical factor data, satellite cloud picture data and cyclone strength data in a historical period of time; preprocessing the physical factor data, the satellite cloud picture data and the cyclone intensity data to obtain a physical factor image, a satellite image and historical cyclone intensity; inputting the physical factor image, the satellite 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 strength prediction model comprises a hierarchical fusion sub-network, a deep convolutional neural network, a priori knowledge module and an uncertainty estimation module. And uncertainty estimation and long-term prior are fused, so that the prediction capability of the change trend of the cyclone strength and the accuracy of strength prediction are improved.

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)