196 results about "Weather forecasting" patented technology

The invention belongs to the technical field of wind power weather forecasting, and provides an all-region three-dimensional wind speed correction method and system, and the method comprises the steps: constructing a weather numerical forecasting model, and obtaining wind field forecasting data; fusing the preprocessed multi-source data by adopting an optimal interpolation method to obtain three-dimensional space-time continuous wind field analysis data; based on the wind field forecast data and the wind field analysis data, features are extracted and fused, then a historical forecast error sample set is constructed, a wind speed correction model is constructed, and the historical forecast error sample set is utilized to train the wind speed correction model; introducing an initial value, a physical parameter and boundary condition disturbance, calculating a mean value and a standard deviation of each set result, extracting a probability distribution feature of a wind speed, constructing a confidence interval, estimating a probability density function, and quantifying an occurrence probability of an extreme wind speed event; and the prediction result of the wind speed correction model and the multi-source wind field observation data are fused to generate final three-dimensional wind field data, so that the actual requirements of wind power prediction and power grid dispatching can be met.

The invention discloses a super-set deterministic weather forecast method and device based on machine learning, and the method comprises the steps: obtaining multi-source meteorological data of a target region, carrying out the meshing of the multi-source meteorological data, carrying out the historical static feature analysis and dynamic feature analysis of the meshing features, obtaining the feature weight of each mode, and carrying out the recognition of the multi-source meteorological data. The method comprises the following steps: constructing a prediction sub-model according to an extreme event, obtaining enhanced numerical prediction data, obtaining posterior probability distribution of grid points through a conditional generative adversarial network and a Bayesian neural network based on the numerical prediction data, a gridding feature and a feature weight, taking a maximum probability value as a deterministic weather forecast, and calculating a confidence interval. And obtaining a joint probability product including wind speed and rainfall joint distribution and the characteristic contribution degree. According to the method, numerical forecasting set products of different mode centers are utilized to fuse probability forecasting information, deterministic weather forecasting is obtained, smoothing of extreme events is reduced, deterministic maximum value output is provided, and meanwhile good interpretability is achieved.

The invention discloses a crop irrigation strategy real-time optimization method considering weather forecast. The method comprises the following steps: S1, acquiring historical meteorological data; s2, a localized AquaCrop crop growth model is constructed; s3, coupling the AquaCrop crop growth model and the NSGA-III multi-objective optimization algorithm model to obtain a coupling model, and solving to obtain a fixed irrigation strategy; s4, formulating optimization time periods, and dynamically integrating new all-season meteorological data in each optimization time period by adopting a DTW (Dynamic Time Warning) algorithm; and S5, correspondingly taking the fixed irrigation strategy obtained in the S3 as an irrigation strategy of the first optimization time period of the growing season according to time, taking various data of the optimization time period as input of the coupling model again to obtain a new irrigation strategy, applying the new irrigation strategy to the next optimization time period, and continuously optimizing. According to the method, the irrigation scheme can be adjusted in time in the growing season, and the method has important significance on guaranteeing grain safety and promoting rainfall flood resource utilization.

The invention discloses a complex terrain power transmission line icing thickness probability forecasting method based on a multi-mode set, and relates to the technical field of disaster prevention and reduction and weather forecasting of a power system. Constructing a plurality of icing thickness prediction models based on a physical mechanism and artificial intelligence based on the forecast data; and integrating the icing thickness prediction models into one icing thickness probability prediction method, and quantifying the uncertainty of icing prediction. Through the method, the accuracy and robustness of icing thickness prediction under complex terrain conditions can be effectively improved, a scientific basis with certainty and probabilistic is provided for power grid ice prevention and disaster reduction decisions, and thus the risk early warning and prevention and control capability of a power grid to deal with ice disasters is enhanced.

The invention discloses a planar turbulence prediction method based on a hybrid prediction model, and relates to the technical field of cross of computational fluid mechanics and artificial intelligence, and the method comprises a sensor point selection method based on a multi-index assignment strategy, and is used for maximizing information acquisition from sparse deployment. A convolutional long short-term memory network, a multi-layer perceptron and a mixed deep learning model of a Fourier neural operator are fused, Conv-LSTM and MLP are used for capturing local nonlinear spatial-temporal features, and FNO is used for modeling global dependence and correcting boundary errors; a physically constrained training framework ensures the physical rationality of a prediction result by introducing undivergence,-5 / 3 energy spectrum slope and other constraint terms into a loss function; under the condition that only a small number of measuring points are used for input, the plane turbulent wind field can be reconstructed in real time in a high-precision and high-fidelity mode, prediction errors are remarkably reduced, the boundary effect problem is solved, the physical credibility of prediction results is guaranteed, and the method has important application value in the fields of wind engineering, weather forecast and the like.

Examples are directed to systems and methods that cause a user interface to be displayed that allows an end-user to combine, via assigning weights and bias corrections, sources that provide weather-related datapoints and emphasize the final combination of weather-related datapoints. The user interface has inputs at each datapoint of the weather-related datapoints and can receive user inputs at the inputs. The user inputs correspond to weights and bias corrections relating to the sources, and the manual or automatic combination of weighted, bias-corrected weather-related datapoints generates a customized weather forecast for a location or region

The invention discloses a multi-scale time sequence prediction method based on an adaptive sparse expert selection strategy and a closed continuous time neural network. The method comprises the following steps: carrying out normalization and low-dimensional feature mapping based on RevIN; performing trend-seasonal structure enhancement processing on the feature sequence after linear mapping; constructing a multi-scale expert model based on the feature sequence after trend-season enhancement; self-adaptive sparse expert selection and load balancing loss calculation are carried out; carrying out weighted aggregation and residual fusion on multi-scale expert output; global modeling of a closed continuous time neural network based on channel weighting is carried out; and finally performing prediction generation and reverse normalization. The multi-scale time series prediction method has the multi-time-scale adaptive modeling capability, the sparse expert efficient selection mechanism and the global continuous time modeling capability, and can be applied to various multivariable time series prediction scenes such as power load prediction, weather prediction, industrial production monitoring, traffic flow prediction and financial price prediction.

The invention discloses a feature fusion and region correction-based weather forecast method and a medium. The method comprises the following steps of: acquiring an initial weather forecast result generated by a weather forecast basic large model; inputting the initial weather forecast result and the multi-source environment auxiliary data of the target area into an area fine correction module; and performing deviation correction and spatial downscaling processing on the initial weather forecast result based on the multi-source environment auxiliary data through a region fine correction module to generate a high-resolution weather element forecast result of the target region. According to the invention, the initial forecast is generated by the basic large model, and the multi-source data is fused by the area correction module to carry out deviation correction and downscaling, so that the high-resolution weather forecast of the target area is generated. Through two-stage cooperative processing, the precision and resolution of weather forecast in a specific area are remarkably improved, end-to-end conversion from a forecast result to an industry decision index is realized, and the practicability is high.

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 belongs to the technical field of numerical weather forecast post-processing, and discloses a physical consistency intelligent optimization method of numerical weather forecast data and a related device. The physical consistency intelligent optimization method comprises the steps that numerical weather forecast data to be optimized and a dynamic graph adjacency matrix of the numerical weather forecast data to be optimized serve as input, a trained physical constraint graph neural network is used for conducting correction prediction, and correction is obtained; and superposing the numerical weather forecast data to be optimized with the correction to generate optimized numerical weather forecast data. The physical consistency intelligent optimization scheme disclosed by the invention can be used for carrying out increment correction and physical consistency constraint on the gridding meteorological field generated by the existing NWP, and can realize correction of local errors and coordination of physical relationships among meteorological elements while keeping the overall trend of numerical weather forecast, so that the prediction accuracy of the numerical weather forecast is improved. And the reliability and the stability of short-time strong weather and medium-term trend forecasting are effectively improved.

The invention discloses a method for inverting a cloud condensation nucleus number concentration profile based on a laser radar and a neural network. The method comprises the following steps: collecting multi-wavelength laser radar data including a backscattering coefficient vertical profile, an extinction coefficient vertical profile, a laser radar ratio and a depolarization ratio; the method comprises the following steps: acquiring an atmospheric temperature profile and a water vapor mixing ratio profile which are in time-space synchronization with multi-wavelength laser radar data, performing grid alignment on a vertical height, and constructing a multi-channel input feature set for each preset target supersaturation ratio; and constructing a convolutional neural network profile inversion model, taking the multi-channel input feature set as input and the cloud condensation nucleus number concentration profile data as output, training the model, and obtaining the trained convolutional neural network profile inversion model. According to the method, the limitation of continuous, high-precision and high-temporal-spatial-resolution detection of CCN number concentration vertical distribution is overcome, and key data support is provided for the fields of weather forecast, climate models, artificial influence weather and the like.

The invention relates to a power transmission line icing prediction method considering meteorological and current influence. The method comprises the steps of multi-source data acquisition: acquiring meteorological data, current data and icing thickness data; an icing thickness increasing physical model is constructed through the change relation between the area of the cross section circular ring of the icing wire and time; calculating an icing level and a thickness increase degree, forming a training group by using the icing thickness at a historical moment and the icing thickness increase degree, training the LightGBM model, and obtaining an icing thickness LightGBM prediction model; and obtaining weather forecast information at a future moment, calculating an icing grade and a thickness increase degree at the future moment, and inputting the icing grade and the thickness increase degree into the trained model to obtain a predicted icing thickness at the future moment. According to the method, by considering the influence of the line current except the meteorological condition on the icing thickness, the model can accurately predict that the icing thickness is far lower than a predicted value only based on the meteorological condition, and false positive early warning is greatly reduced.

By changing the operating modes of weather observation sensors, including weather radar, according to the weather phenomenon being predicted, weather phenomena can be predicted with higher accuracy than before. [Solution] The weather forecasting device 3 is a variable-mode weather observation sensor that observes precipitation data and wind direction and wind speed data in an observation range, which is a spatial range to be observed. It includes a weather forecasting unit 5 that receives weather observation data, including precipitation data and wind direction and wind speed data, observed by a weather observation sensor including a weather radar 1 that is positioned so that the observation range encompasses a predetermined weather forecast area, and predicts the weather in the weather forecast area for a predetermined future forecast time range and outputs weather forecast data including precipitation; a sensor data storage unit 4 that stores the installation position, observation range and observation accuracy of each weather observation sensor; and a sensor control unit 6 that receives weather forecast data and controls the weather observation sensor, including changing the operating mode of the variable-mode weather observation sensor.

The invention relates to a physical information guidance artificial intelligence weather forecast method and device for a wind power plant. Comprising the steps that first historical data of multiple key meteorological elements in a target wind power plant are acquired, and the influence degrees of the multiple key meteorological elements on weather in the target wind power plant are all larger than a preset threshold value; performing weather prediction on the first historical data of the multiple key meteorological elements by using a weather prediction model of the target wind power plant to obtain predicted weather data of the target wind power plant at a future moment; the weather prediction model is pre-trained through the various meteorological parameters of the coarse resolution mode, and then fine adjustment of the fine resolution mode is realized by using the historical data of the various key meteorological elements having great influence on the weather, so that the micro-scale weather prediction model is obtained to adapt to weather prediction of the corresponding wind power plant; the method improves the resolution and efficiency of weather prediction, reduces the consumption of computing resources, and reduces the cost of weather prediction.

A combined satellite and mobile weather forecasting system may include a satellite payload having a plurality of sensors configured to capture atmospheric and environmental data including atmospheric radiances and vertical soundings and a receiver for receiving data from other satellites. The satellite payload may also include a processing unit configured to condense the captured atmospheric and environmental data and the received data from other satellites into initialization data to generate a weather forecast. The satellite payload may include a transmission device configured to communicate with a mobile cellular device and to transmit the initialization data. The mobile cellular device may be configured to receive the initialization data from the transmission device of the satellite payload to generate the weather forecast using the initialization data.

This invention relates to the field of AI weather forecasting technology and discloses a weather forecasting method based on an AI weather forecasting model and a WRF model. The method includes: inputting ERA5 global atmospheric reanalysis gridded data into the AI ​​weather forecasting model to obtain an assimilated background field; constructing an effective multi-source observation vector based on observation data from ground automatic weather stations, radiosonde observation data, geostationary meteorological satellite radiance data, and Doppler radar VAD wind profile data; solving for the optimal atmospheric analysis field; extracting atmospheric state variables from the optimal atmospheric analysis field and writing them into the WRF model's side boundary driving file, driving the WRF model to complete the integration operation, and obtaining a gridded forecast field of meteorological elements. This method enables the WRF boundary condition update frequency to be higher than the traditional 6-hour static scheme, effectively characterizing the rapid evolution of the atmospheric state in the boundary region at the sub-hourly scale, thereby improving the WRF model's forecasting capability for high-impact weather events such as severe convection.

This invention discloses a short-term intelligent numerical weather prediction method and system based on environmental perception, belonging to the field of meteorological management technology. The system includes a data acquisition and fusion module, an intelligent zoning and feature mining module, a short-term forecasting and risk assessment module, and a visualization and focusing module. The data acquisition and fusion module collects and fuses environmental data from meteorological radar and weather stations, as well as GIS maps. The intelligent zoning and feature mining module divides the data into grids based on the GIS map, analyzes the rainfall synergy between grids based on historical environmental data to construct analysis groups, and selects and constructs an impact set for each analysis group. The short-term forecasting and risk assessment module fits near-term precipitation intensity relationships and short-term precipitation intensity relationships based on the impact sets of each analysis group. The visualization and focusing module calculates and outputs the near-term and short-term predicted precipitation intensities for each analysis group based on the relationships, identifies key areas of concern using topographic and hydrological models, and enhances visualization.

The invention discloses a multi-modal rainfall forecasting method combined with text description, and belongs to the technical field of weather forecasting. The method comprises the following steps: S1, collecting and processing multi-source structured meteorological data, and constructing a meteorological data space-time sequence; s2, generating a corresponding text description according to the meteorological data space-time sequence by using the fine-tuned large language model, and pairing the text description with the meteorological data; s3, extracting a meteorological spatial-temporal feature vector from the structured meteorological data, and extracting a text semantic feature vector from the text description; s4, fusing the text semantic feature vector into the meteorological spatio-temporal feature vector through a cross attention mechanism to generate a fusion feature representation with spatio-temporal dynamic and semantic context; and S5, decoding the fusion feature representation by using a prediction decoder to generate a rainfall forecast map. According to the method, expert knowledge and high-level semantic information contained in texts such as meteorological analysis reports and the like can be effectively utilized, and the precision and interpretability of forecasting are improved.

The invention provides a mixed three-dimensional variational assimilation method fusing a multi-scale ensemble forecast sample, and belongs to the technical field of numerical weather forecast and data assimilation, and the method comprises the steps: obtaining and processing a global ensemble forecast sample and a regional convective scale ensemble forecast sample, obtaining a large-scale ensemble disturbance sample and a small-scale ensemble disturbance sample, and carrying out the processing of the large-scale ensemble disturbance sample and the small-scale ensemble disturbance sample; inputting into a hybrid three-dimensional variational analysis system, taking a forecasting field of a global numerical weather forecasting mode of a meteorological center as a first background field, constructing a large-scale flow dependent background error covariance by using a large-scale set disturbance sample to obtain a first analysis field, taking the first analysis field as a second background field, and constructing a second analysis field by using the second background field; and constructing a small-scale flow dependent background error covariance by using the small-scale set disturbance sample to obtain a final analysis field. According to the method, the problem of inaccurate numerical weather forecast caused by insufficient information fusion of large-scale information and small-scale information, insufficient observation and static background error covariance in the existing three-dimensional variation assimilation technology is solved.

According to an aspect of an embodiment, a method may include obtaining first real-time observational data corresponding to current conditions within an operational area of a vehicle, the first real-time observational data corresponding to the operational area of the vehicle. A first forecast of one or more weather conditions associated with the operational area of the vehicle may be generated using an artificial intelligence (AI) forecasting model and based on the first real-time observational data. The first forecast may be tailored for and according to the operational area of the vehicle. The method may include generating navigation routes for the vehicle in the operational area based on the first forecast, the vehicle navigating the operational area according to the navigation routes.

The application discloses a meteorological data processing method, a meteorological data processing device and an electronic equipment, and relates to the technical field of meteorology. The method can reduce the error of meteorological data and improve the accuracy of meteorological data. The method comprises the following steps: acquiring base data observed by each weather radar; performing gridding on the base data to obtain first gridding data of an observation area of each weather radar; determining a blur range of radial velocity; performing velocity de-blurring processing on the radial velocity in the first gridding data according to the blur range; determining a shielding range of a reflectivity factor; performing shielding removal processing on the reflectivity factor in the first gridding data according to the shielding range; performing gridding on the first gridding data after the velocity de-blurring processing and the shielding removal processing to obtain second gridding data; merging the second gridding data of each weather radar to obtain meteorological gridding data of a whole area; and converting the meteorological gridding data into target data corresponding to a weather forecasting system, so that the weather forecasting system can use the target data.

An example system may include an energy storage unit and various processors configured to communicate with components of the energy storage unit. An example system may determine a consumption model that predicts future consumption of a node based on local power consumption of the node. The system may receive weather forecast data for the node and determine a power production model for the node that predicts future power production using the weather forecast data. The system may calculate a predicted power difference using the consumption model, the production model, and the current environment of the node. The system may perform automated operations using the predicted power difference, such as charging or discharging an energy storage unit or controlling one or more electrical loads.

This invention provides a dehumidification system that can stably control the dew point in a low-dew-point room based on weather conditions. [Solution] The dehumidification system estimates the predicted value of the outside air dew point at a predetermined date and time in the future based on weather forecast data. The dehumidification system also estimates the supply air inlet temperature, which indicates the temperature at the inlet of the supply air area, and the supply air inlet absolute humidity, which indicates the absolute humidity at the inlet of the supply air area, based on the inlet conditions of the supply air area. Then, based on the predicted value of the outside air dew point, the supply air inlet temperature, and the supply air inlet absolute humidity, the dehumidification system calculates the regeneration inlet target temperature, which indicates the target temperature at the inlet of the regeneration area at a predetermined date and time in the future. After calculating the regeneration inlet target temperature, the dehumidification system performs at least one of the following actions to bring the temperature of the regeneration heater closer to the regeneration inlet target temperature: temperature control of the regeneration heater, airflow control of the first fan installed on the inlet side of the supply air area in the first duct, and airflow control of the second fan installed on the outlet side of the regeneration area in the second duct.

This invention discloses a method and system for generating seamless weather forecast products based on the fusion of multiple time-scale elements, relating to the field of weather forecasting technology. The method first acquires multi-source observational data and numerical model data, and generates short-, medium-, and long-term standardized sequences through time interpolation, matching, and sliding window resampling. Then, a multi-branch convolutional neural network is used to extract corresponding feature maps, calculate physical quantity field parameters, and encode them as physical context vectors. A weight map is generated through a dynamic gating network, and the feature maps are adaptively fused. The data is input into the forecasting network and trained using a joint loss function with physical constraints to generate a seamless forecast product. Finally, the weight strategy is dynamically optimized through continuity verification and error evaluation. The system contains corresponding functional modules, and the executable program is stored in the storage medium.

The embodiment of the application provides a radar echo extrapolation prediction method and system based on a test time training strategy. The embodiment of the application first performs time axis calibration and spatial grid alignment processing on a radar echo historical collection sequence collected by an atmospheric detection system, to generate radar echo sequence data with time and space consistency; then constructs an echo evolution correlation representation model based on the data, to generate an echo evolution dependency graph and a multi-step extrapolation initial condition set; then introduces a test time training strategy for adaptive prediction processing, to adjust the edge weight parameter in real time, to obtain a radar echo multi-step extrapolation prediction sequence with time dynamic characteristics; finally extracts echo intensity variation characteristics and spatial movement trajectory rules according to the sequence, and generates a climate evolution trend description set containing an echo intensity trend curve and a path offset vector in combination with historical climate period evolution data, to realize more accurate climate evolution prediction and weather forecasting.

Data integration and distribution systems. A system includes a graphical user interface (GUI). Weather and market data are collected. A weather symbology including symbol elements linked to segments of the collected weather data and rules for generating weather symbology instructions are stored. The GUI is generated for display on a user device. A weather symbology instruction is determined based on at least one requested symbol element indicated in a weather data request and the rules. A weather forecast dataset is created from among the collected weather data based on the weather symbology instruction. A presentation package including the weather forecast dataset and the collected market data is generated such that the weather forecast dataset is integrated with the collected market data. The presentation package is presented on the GUI and updated concurrent with changes to at least one of the weather data, the market data and user input.

This application provides a preventative dispatch method, terminal, and storage medium for an integrated electric and thermal energy system, belonging to the field of power system dispatching technology. It constructs an uncertainty set based on the potential failure of transmission lines under extreme weather conditions and employs a two-stage robust optimization framework. First, a first-stage constraint set is established based on known extreme weather forecasts. Then, a second-stage constraint set is established based on the actual occurrence of extreme weather. The objective function is to minimize the sum of the electrical load gap and the thermal load gap under extreme weather conditions. By establishing the uncertainty set and introducing extreme events into the constraints, a complete two-stage robust preventative dispatch model is formed, which is then solved to obtain the preventative dispatch results. Therefore, backup and energy storage are arranged in advance before extreme weather, and network topology is adjusted to reduce the load gap caused by sudden outages. Robust optimization ensures that the system meets both heating and power supply needs under all possible outage scenarios, improving the overall reliability and resilience of the system.

The application relates to the field of agricultural irrigation, and discloses an intelligent irrigation strategy making method based on short-term weather forecast error correction and a Q-learning algorithm, which comprises the following steps: S1. A deep learning framework combining a convolutional neural network, a bidirectional long short-term memory network and an attention mechanism is built; and S2. Meteorological data of a planting area is collected, short-term weather forecast data is corrected for errors by using the deep learning framework, and corrected short-term meteorological data is obtained. By constructing the CNN-BiLSTM-AM deep learning framework, the problem that traditional public weather forecast data has systematic deviation in a specific planting area is effectively solved, the local change characteristics of the meteorological data are extracted by using the convolutional neural network, the bidirectional dependence relationship and key time nodes of the time sequence are captured by combining the bidirectional long short-term memory network and the attention mechanism, and the deviation law between historical forecast data and measured data can be accurately learned.

Data integration and distribution systems. A system includes a graphical user interface (GUI). Weather and market data are collected. A weather symbology including symbol elements linked to segments of the collected weather data and rules for generating weather symbology instructions are stored. The GUI is generated for display on a user device. A weather symbology instruction is determined based on at least one requested symbol element indicated in a weather data request and the rules. A weather forecast dataset is created from among the collected weather data based on the weather symbology instruction. A presentation package including the weather forecast dataset and the collected market data is generated such that the weather forecast dataset is integrated with the collected market data. The presentation package is presented on the GUI and updated concurrent with changes to at least one of the weather data, the market data and user input.

The application provides an influenza risk forecast method, device and equipment based on high-impact weather classification, and relates to the technical field of influenza risk forecast. The method comprises the following steps: acquiring weather forecast data of a future period, and determining future high-impact weather process information based on the weather forecast data of the future period; acquiring current high-impact weather process information and previous high-impact weather process information; determining a high-impact weather complexity intensity index according to the previous high-impact weather process information, the current high-impact weather process information and the future high-impact weather process information; and determining an influenza risk forecast level according to the high-impact weather complexity intensity index and a process type of the future high-impact weather process. The application constructs a forecast model based on the evolution law of the high-impact weather process, breaks through the limitation of meteorological element data, accurately associates the internal relationship between the weather change trend and the occurrence and spread of influenza, and forecasts more accurately.