53 results about "Nowcasting" patented technology

The invention relates to a rainfall nowcasting method based on U-KAN grading loss weighting and frequency self-adaption, which comprises the following steps: (1) carrying out quality control and screening on a radar puzzle, and establishing a data set; (2) dividing a training set, a verification set and a test set, and standardizing; (3) constructing a U-KAN model, selecting training parameters and inputting data: combining a traditional Unet structure with a KAN network to construct the U-KAN model; then performing model training to obtain a prediction result; the prediction result is restored to the original magnitude through destandardization; (4) introducing a loss function based on a root-mean-square error and grade weighting in a model training stage, and performing post-processing on model output by adopting a frequency deviation correction method; (5) integrating and averaging the forecast products processed by the two complementary strategies, and recording the forecast products as U-KANE; and (6) predicting a rainfall result in the next three hours by using radar echo data in the past one hour, and outputting a rainfall short-term and imminent forecast result by the U-KANE.

The invention relates to the technical field of meteorological prediction, and discloses a minute-level thunderstorm strong wind nowcasting method and system, and the method comprises the steps: obtaining multi-source meteorological observation data, and carrying out the preprocessing of the multi-source meteorological observation data, and obtaining the preprocessed data; performing time continuity judgment and fusing the processed data to obtain a training sample; processing the training samples based on a sliding window, dividing the training samples into a training set, a verification set and a test set, constructing a neural network model, and training the neural network model to obtain a forecasting model; real-time a frames of observation data are processed based on the forecasting model, a frames of wind speed forecasting images with the resolution ratio of 1 kilometer in the future are obtained, and the wind speed forecasting images comprise a wind speed continuous distribution diagram and wind area falling area forecasting results larger than or equal to 8.0 m / s and larger than or equal to 17.2 m / s. According to the invention, the comprehensive perception capability of strong convection characteristics is improved, and minute-level strong wind nowcasting is realized.

The invention relates to a lightning nowcasting method based on multi-source satellite data, and belongs to the technical field of lightning nowcasting. The method comprises the steps of obtaining and preprocessing satellite data of a multi-source channel and corresponding lightning data, constructing a U-Net lightning prediction model, collecting a historical multi-source satellite image sequence, constructing a PredRNN satellite image prediction model and predicting the lightning occurrence probability. According to the method, the PredRNN and the U-Net model are combined, the lightning distribution can be predicted at the future moment only by using the satellite image, the dependence on historical lightning data is broken through, and the method has a wide application prospect in the field of lightning nowcasting.

The invention discloses a rainfall nowcasting method based on multi-scale feature fusion of a lightweight generative adversarial network, and the method comprises the steps: carrying out the down-sampling and up-sampling of a radar echo sequence through a classic U-net structure, and generating a prediction result; a prediction result and a true value are respectively combined with original input and are input into a discriminator for discrimination, in this way, the generator and the discriminator continuously carry out confrontation training, and finally, the generator can generate radar prediction data which are vivid enough. According to the method, the loss of a discriminator is calculated by using a mixed loss function in which a heavy rainfall area mask is introduced, so that the model pays more attention to the generation quality of the heavy rainfall area. In addition, the parameter quantity of the model is reduced by adopting grouping convolution, so that the requirements on calculation power and hardware resources are reduced.

The invention relates to an improved weather radar echo short-time nowcasting method based on a diffusion model, and the method comprises the steps: obtaining a to-be-measured radar echo image, combining the to-be-measured radar echo image with time sequence information, inputting the to-be-measured radar echo image into a radar echo extrapolation model, and obtaining a denoised radar echo image; the radar echo extrapolation model is obtained by using a training set to train a diffusion model; the training set is a combined reflectivity data set; and processing the image blocks through a core denoising module in the radar echo extrapolation model, and predicting noise. According to the invention, the precision and anti-interference capability of short temporary rainfall prediction can be improved.

The invention discloses a thunderstorm and gale dynamic extrapolation forecasting method based on multi-task MSTA-ConvLSTM, and relates to the technical field of atmospheric sciences, and the method comprises the steps: constructing a multi-source fusion spatio-temporal data set and a gale process record file, and obtaining a data set; a thunderstorm gale nowcasting model based on an MSTA-ConvLSTM model is constructed; according to the method, a dynamic gating mechanism of lightning data is introduced, whether a lightning probability prediction task is started or not is intelligently judged according to the lightning activity intensity, thunderstorm, gale and co-evolution characteristics of the lightning process are considered, 0-3-hour high-temporal-spatial-resolution nowcasting is achieved, the limitation of traditional single-task forecasting is broken through, a multi-task learning structure is adopted, and the prediction efficiency is improved. The model is guided to recognize the lightning occurrence probability during main task gale prediction, and the overall perception capability of a severe convection system is improved; extreme sample learning is enhanced through a weighted loss function, and output is optimized in combination with a gust coefficient model, so that the forecasting precision and practicability are effectively improved.

The invention discloses a precipitation nowcasting method based on deep learning, and the method comprises the following steps: collecting historical radar echo data, carrying out the preprocessing of a collected radar echo image sequence, and constructing a time-space sequence data set; constructing a dynamic flow space-time generative adversarial network model which comprises a generator and a discriminator; designing a discriminator based on a Transform architecture, performing authenticity evaluation on the generated rainfall forecast sequence by using a self-attention mechanism, and evaluating the time-space consistency and visual quality of the generated sequence; a two-stage training strategy is adopted to optimize the network, in the first stage, reconstruction loss and decoupling loss are used to pre-train a generator, in the second stage, adversarial loss and feature matching loss are introduced to carry out joint training, and high-quality rainfall nowcasting is achieved; according to the invention, the modeling capability of the model for complex meteorological phenomena is improved.

The invention discloses a short-time approaching rainfall prediction method based on multi-source fusion data and an MIM network, and the method comprises the steps: carrying out the space-time matching of radar observation data, Chinese land surface data assimilation system data and site observation data, carrying out the preprocessing of the matched data, including feature factor extraction, missing value processing and the like, building a deep learning model, and carrying out the prediction of the short-time approaching rainfall. The model is trained, and a multi-source fusion rainfall inversion result is output based on real-time radar data; furthermore, errors of a falling area and rainfall intensity of intelligent grid rainfall forecast are identified, a phase correction technology is utilized to correct a position error of an intelligent grid forecast rainfall zone, meanwhile, precision evaluation is performed on a prediction effect of the deep learning model according to the rainfall amount of short-time nowcasting, and intelligent prediction of short-time nowcasting is realized.

The invention relates to the technical field of rainfall prediction, in particular to a high-frequency second-length time sequence short-imminent forecasting method and system based on generative AI, and the method comprises the following steps: compressing an original radar reflectivity sequence into a compact hidden variable Token sequence through employing a pre-trained variational auto-encoder; in the compressed hidden space, with the hidden variable Token sequence as a condition, a prediction hidden variable Token sequence in a future time period is generated step by step in a probability generation mode; and reconstructing the predicted hidden variable Token sequence into a predicted radar reflectivity sequence in a future time period by using a decoder of the variational auto-encoder. According to the method, high-resolution and long-time-efficiency radar nowcasting is realized, the accuracy of rainfall forecasting in a 2-6-hour short-term and imminent forecasting grey area time window is improved, and a solid support is provided for constructing early warning and accurate decision-making of extreme weather.

The invention relates to the technical field of meteorology and artificial intelligence, in particular to a short-time rainfall nowcasting method based on multi-source meteorological data and a neural network, and the method comprises the steps: collecting the multi-source meteorological data of a target, and building a historical data set; respectively expanding the training set and the verification set based on a sliding window; fusing the radar data and the multi-source meteorological variable data by using a multi-head self-attention mechanism; training a rainfall nowcasting neural network driven by the multi-source meteorological data by using the historical data set; and on the basis of the trained rainfall nowcasting neural network, selecting parameters meeting a preset optimal condition, performing quantitative evaluation on the test set, generating an evaluation result of short-time rainfall nowcasting, and forecasting the short-time rainfall amount of the target period according to the real-time radar rainfall and the multi-meteorological variable data on the basis of the evaluation result. Therefore, the problems that an existing rainfall nowcasting method is single in driving data source, low in rainfall forecasting precision, difficult to forecast a complex rainfall process and the like are solved.

The invention provides a short-time rainfall nowcasting method fusing quantum calculation and a deep learning model, and belongs to the field of short-time rainfall nowcasting, and the method comprises the following steps: firstly, extracting the precipitation amount of a target region and the long-term historical data of meteorological elements, then taking the precipitation amount at a to-be-predicted moment as a prediction target amount, and carrying out the prediction of the prediction target amount; the meteorological elements and the precipitation amount M hours before the to-be-forecasted moment are used as model input characteristic quantities. And then changing the meteorological elements and the precipitation amount M hours before the to-be-forecasted moment into high-dimensional quantum probability characteristics related to the precipitation amount at the to-be-forecasted moment by adopting a quantum calculation method. Finally, the quantum line precipitation probability information serves as input, the precipitation in the next one hour serves as output, and a short-time precipitation nowcasting model is constructed and trained. The quantum line and the quantum superposition state are utilized, classic data are mapped to a high-dimensional quantum Hilbert space, local optimal solution traps can be avoided, the calculation burden is remarkably reduced, and the calculation efficiency is improved. And short-time rainfall forecasting under the condition of massive high-dimensional meteorological feature input is realized.

The invention discloses a high-resolution low-altitude meteorological real-time and short-time nowcasting method, system and device, and relates to the technical field of low-altitude meteorological evaluation.The method comprises the steps that on the basis of the similarity of meteorological physical processes, according to underlying surface features of a target area and a long-term climate background field used for representing the long-term state of climate elements, the long-term climate background field is used for representing the long-term state of the climate elements; dividing the target area to obtain a plurality of sub-areas and corresponding meteorological physical characteristics; for each sub-region, matching and selecting an adaptive micro-scale meteorological model configuration scheme according to the meteorological physical characteristics of the sub-region; and respectively loading the multi-source real-time meteorological observation data of each sub-region and a real-time climate background field used for representing the recent state of climate elements into the called micro-scale meteorological model configuration scheme, and generating a high-resolution meteorological live field of each sub-region through rapid update cycle assimilation and parallel calculation. The method and the device have the effect of improving the forecasting accuracy.

The invention provides a rainfall nowcasting method and a rainfall nowcasting system based on a space-time attention diffusion model, which are used for simulating the space-time evolution process of rainfall so as to realize accurate rainfall nowcasting. The method comprises the following steps: decoupling historical rainfall data into a motion field feature and an intensity residual feature; based on an STA UNet architecture, extrapolating future evolution features through a noise iteration removal process by taking motion field features and intensity residual features as conditions; and reconstructing the future evolution characteristics into future rainfall prediction by utilizing Warp operation guided by a motion field and intensity residual fusion. According to the method, the uncertainty of the rainfall evolution process is considered, the modeling capability of the space and time dependency relationship in the rainfall process is enhanced, and an accurate and stable short-term rainfall nowcasting result is generated.

The invention discloses a low-altitude three-dimensional wind field nowcasting method and device, and the method comprises the steps: firstly collecting and preprocessing wind field data through an API, constructing a spatial-temporal feature project, and dividing a data set according to a time sequence; thirdly, a LightGBM framework is adopted, and a static optimal forecasting model is trained by means of Bayesian optimization; the method is characterized in that the static optimal forecasting model and an ensemble Kalman filtering framework are deeply fused, a dynamic assimilation forecasting system is constructed, forecasting output is continuously corrected and optimized, and error accumulation is effectively restrained. And finally, after performance verification, the system is operated in a business mode, and a final wind field forecasting result containing the NaN identifier is output.

The application provides a short-time precipitation nowcasting method fusing quantum computation and a deep learning model, and belongs to the field of short-time precipitation nowcasting, and steps are as follows: firstly, long-term historical data of precipitation and meteorological elements of a target area are extracted; then, the precipitation at a time of tentative prediction is taken as a prediction target quantity, and meteorological elements and precipitation of M hours before the time of tentative prediction are taken as model input characteristic quantities; then, a quantum computation method is used to change the meteorological elements and precipitation of M hours before the time of tentative prediction into high-dimensional quantum probability characteristics about the precipitation at the time of tentative prediction; finally, quantum circuit precipitation probability information is taken as input, and the precipitation of 1 hour in the future is taken as output, a short-time precipitation nowcasting model is constructed and trained, and the application uses quantum circuits and quantum superposition states to map classical data to a high-dimensional quantum Hilbert space, can avoid a local optimal solution trap, significantly reduces a calculation burden, and realizes short-time precipitation prediction under the condition of massive high-dimensional meteorological element inputs.

The application discloses a radar echo extrapolation method based on a space-time attention mechanism, first obtains radar echo data of a target area, and carries out pretreatment on the obtained radar echo data; the radar echo data after the pretreatment is subjected to sliding grouping and data augmentation, so that a radar echo sequence data set is obtained, and then the radar echo sequence data set is divided into a training set and a test set; a radar echo prediction network model based on a SimVP architecture is constructed and trained and tested, a multi-target loss function is used to supervise model training; and a future radar echo image is predicted in real time. Through the method, complex weather phenomena such as storms can be more accurately captured in dynamic evolution, the precision and interpretability of short-term nowcasting can be significantly improved, more reliable technical support is provided for timely and accurate early warning of meteorological disasters, and the method has important practical application value.

The invention relates to a local surface layer wind field fusion and nowcasting method, and belongs to the technical field of meteorological wind field fusion and forecasting. According to the method, the wind shear index calculated by a 90-meter anemometer tower is utilized to fill up a wind profile at the height of 90 meters above a surrounding automatic station, Barnes step-by-step corrected wind field fusion is carried out in combination with WRF-LES large eddy simulation, a convolutional neural network model is established between a large eddy simulation data set and a fused wind field data set, model parameters are obtained, and the wind field fusion is realized. And the parameters are used in WRF-LES real-time forecast to obtain proximity correction forecast of the near-earth wind field. According to the method, the local anemometer tower and encrypted automatic station data are fully applied, WRF-LES modeling is integrated, the method can be used for forecasting a local wind field, and the corresponding algorithm and process are suitable for wind field forecasting of wind power, low-altitude economy and the like.

The invention relates to a rainfall time estimation method and system based on a cloud base height change rule, and belongs to the field of radio, and the estimation method comprises the steps: inputting THI data, carrying out the judgment according to a reflectivity threshold value, removing interference data, and calculating the cloud base height in each radial direction, judging whether rainfall is possibly formed or not according to the cloud bottom height change and the cloud bottom speed, and if conditions are met, performing data fitting; and estimating a rainfall moment according to a fitting result and outputting a result. According to the method, the problem that the strong relevance formed by the dynamic change of the cloud base height and rainfall is not fully utilized in the prior art is solved, meteorological service guarantee can be provided for major activities, technical support is provided for carrying out work by artificially influencing weather, and prerequisite service is provided for business forecasting personnel to publish nowcasting information.

The present invention provides a method and system for fog approaching prediction based on GraphSAGE-LSTM. The method includes: collecting original meteorological data, detecting and removing abnormal data in the original meteorological data according to preset time intervals and threshold data to obtain applicable meteorological data; selecting no less than 2 pieces of applicable fog occurrence process information, collecting spatio-temporal information, and processing the spatio-temporal information, applicable meteorological data and applicable fog occurrence process information to obtain time series data, and constructing a data set based on the time series data; using meteorological observation stations as graph nodes, constructing an adjacency matrix by using preset logic and distance thresholds between nodes, and obtaining the graph structure of the observation stations; constructing and using an inductive graph neural network model to process and obtain the spatial distribution characteristics of surrounding meteorological elements, and processing the spatial distribution characteristics to obtain the fog approaching prediction result. The present invention solves the technical problem of being difficult to effectively extract the spatial information between station data.

This invention discloses a method for minute-level rolling spatiotemporal correction of precipitation based on multi-band weather radar fusion, relating to the fields of meteorological observation and short-term nowcasting. The method includes: acquiring multi-band weather radar base data and automatic rain gauge observation data, and preprocessing them; generating a minute-level quantitative precipitation estimation field based on the preprocessed multi-band weather radar base data; extrapolating to generate a minute-level quantitative precipitation forecast field; constructing station residuals and generating a spatial error field; using the spatial error field to spatially correct the minute-level quantitative precipitation forecast field to obtain an intermediate forecast field; extracting precipitation dynamic evolution characteristics to determine time-rolling correction coefficients, and performing time correction on the intermediate forecast field to obtain a corrected minute-level quantitative precipitation forecast field. By generating a precipitation estimation field through multi-band radar fusion, and then combining this with station observations to construct a spatial error field and time correction coefficients, it achieves accurate spatial and temporal correction of minute-level extrapolated forecasts.

The application provides a precipitation near extrapolation intelligent prediction method and device and electronic equipment, and the method comprises the following steps: obtaining precipitation data to be predicted; performing clipping processing on the precipitation data, and inputting the clipped precipitation data into an intelligent prediction model; the intelligent prediction model adopts a specific model structure, which comprises: adding a hierarchical upsampling fusion module HUFM and a CA attention mechanism on the basis of a U2-net model; through the intelligent prediction model, feature extraction and deep learning precipitation extrapolation are performed on the clipped real-time data, an extrapolation prediction result is obtained, the extrapolation prediction result is adaptively weighted with the prediction data, and a precipitation prediction result in a specified period in the future is output. The application is a new type of deep learning nowcasting method which simultaneously considers spatial accuracy, prediction timeliness and precipitation intensity description capability, and can meet the urgent needs of modern meteorological services for high-resolution and intelligent precipitation prediction.

The invention provides a rainfall nowcasting method based on a multi-scale adaptive fusion model, and provides a multi-scale space-time fusion attention framework (MSTFA), and through explicit modeling and adaptive fusion of local to global features, the ability to maintain the complex form and strength of a storm is well improved; a learnable space-time wavelet enhancement module (LSTWE) is designed, high-frequency detail perception is enhanced in a frequency domain through adaptive wavelet transform, and the problems of prediction result fuzziness and edge distortion are effectively relieved; an adaptive dynamic convolutional network (ADCN) is constructed, dynamically generated parameters are used for replacing a fixed convolution kernel, and the fitting capability of a nonlinear dynamic process in a precipitation system is enhanced; the three modules are organically embedded into a Transform backbone network to form an end-to-end forecasting framework, leading performance is achieved on a plurality of standard data sets, and effectiveness and complementarity of the modules are verified through detailed ablation experiments.

The application discloses a radar near precipitation prediction method based on frequency domain perception and conditional diffusion, and belongs to the technical field of image data processing. VAE The method comprises the following steps: obtaining a radar echo data set, pre-training a variational autoencoder M VAE ; constructing a convolution enhanced frequency domain Transformer; constructing an improved denoising network and M VAE based on the multi-layer convolution enhanced frequency domain Transformer, generating an improved conditional diffusion model and training a near precipitation prediction model; and being used for near precipitation prediction. The application overcomes the core defects of poor fusion of local and global features, prediction ambiguity caused by signal-noise confusion and the like in the prior art through frequency domain-space collaborative modeling and efficient conditional diffusion architecture design, enhances the adaptation capability to complex weather scenes, can more accurately capture high-frequency details and edge features of a convection system, significantly improves the prediction accuracy of a weak echo area, and improves the accuracy of short-term and near-term precipitation prediction.

The invention relates to a heavy rainfall nowcasting method in combination with a liquid state ratio. The method comprises the following steps: collecting radar reflectivity data within one hour and rainfall live data of the next hour; converting radar reflectivity data into three-dimensional data under a Cartesian rectangular coordinate system, and converting rainfall actual data into a two-dimensional rainfall lattice point field; building a heavy rainfall nowcasting model; training the model on the training set, optimizing the hyper-parameters on the verification set, and testing the model on the test set; and obtaining rainfall estimation field data. According to the invention, a heavy rainfall nowcasting model is established by using double decoders based on a liquid state ratio and an attention mechanism, an encoder module is adopted to receive echo data after liquid state water content conversion processing and carry out feature coding extraction work, and two parallel decoder modules are adopted to output a heavy rainfall area prediction result and a rainfall prediction result respectively. Quantitative estimation and qualitative classification of a heavy rainfall area are improved by using a comprehensive loss function, and effectiveness tests show that the method can significantly improve the nowcasting accuracy.

The invention provides a rainfall proximity extrapolation intelligent forecasting method and device and electronic equipment. The method comprises the following steps: acquiring rainfall data to be forecasted; cutting the rainfall data, and inputting the cut rainfall data into the intelligent forecasting model; the intelligent forecasting model adopts a specific model structure, and comprises the step of adding a hierarchical up-sampling fusion module HUFM and a CA attention mechanism on the basis of a U-net model; performing feature extraction and deep learning rainfall extrapolation on the cut live data through an intelligent forecasting model to obtain an extrapolation forecasting result, performing adaptive weighted fusion on the extrapolation forecasting result and the forecasting data, and outputting a rainfall forecasting result in a specified time period in the future. The method is a novel deep learning nowcasting mode which gives consideration to the spatial precision, the forecasting timeliness and the rainfall intensity description capability at the same time, and can meet the urgent demands of modern meteorological services for high-resolution and intelligent rainfall prediction.

The application discloses a radar echo space-time extrapolation method based on asymmetric space modeling, belongs to the cross field of meteorological radar data processing and artificial intelligence technology, and first constructs an asymmetric input space sample containing a target region and a surrounding extended region by preprocessing original meteorological radar observation data, builds an asymmetric space modeling prediction network based on a 3D U-Net architecture, adopts a regional mask mean square error loss function to complete model training, and finally realizes accurate space-time extrapolation of the target region radar echo. The application solves the technical problems of information fragmentation and significant long-time prediction error accumulation of the traditional symmetric modeling method, improves the precision and stability of radar echo extrapolation under complex weather systems, and is suitable for 0-1 hour nowcasting and meteorological disaster accurate early warning scenes.

The invention provides a physical constraint enhanced type encoder-decoder network model. The model adopts an enhanced encoder-decoder architecture, and the architecture combines a normalized space-time convolutional memory network and a space channel dual attention module. The normalized spatio-temporal convolutional memory network enhances the multi-scale temporal precipitation and cloud top temperature features extracted from the main encoder. The space channel double attention module further guides the network to pay attention to important features in space and channel dimensions. In addition, in order to better capture the space-time evolution trend of rainfall and the learning process of the constraint model, the model designs a new physical constraint loss function. For coastal areas of China and Southeast Asia, the model carries out rainfall forecasting hour by hour by using global rainfall measurement (GPM) rainfall data and Himawari satellite observation data.

The invention relates to the technical field of radar signal processing, in particular to a weather radar echo nowcasting method fusing a mesoscale numerical mode. Comprising the following steps: S1, acquiring radar live echo signal intensity data and radar monitoring terrain data; carrying out binarization processing on the radar real-time echo signal intensity to obtain the radar real-time echo signal intensity after binarization processing; based on the radar monitoring terrain data, matching the radar live echo signal intensity after binarization processing with the radar monitoring terrain to obtain a matching result; and S2, on the basis of a result after matching, radar live echo data are obtained, and CMA-meso original data are collected. According to the method, the Fi-radar and Fusion models are provided, multi-source data are fused to eliminate the space-time difference, the extrapolation definition is optimized, and the disaster weather early warning precision is improved through adaptive adjustment of CSI, FAR, POD and MSE indexes.

The application discloses a precipitation nowcasting method based on a multi-scale feature fusion of a lightweight generative adversarial network, a generator generates a prediction result by performing down-sampling and up-sampling on a radar echo sequence through a classical U-net structure, and the prediction result is combined with a true value and an original input respectively and input into a discriminator for discrimination, in this way, the generator and the discriminator continuously perform adversarial training, and finally, the generator can generate radar prediction data which is realistic enough. The method uses a hybrid loss function with a strong precipitation area mask introduced, calculates the loss of the discriminator, and makes the model pay more attention to the generation quality of the strong precipitation area. In addition, grouping convolution is adopted to reduce the parameter quantity of the model, so as to reduce the requirement for computing power and hardware resources.

The invention discloses a convection inception intelligent forecasting system and method based on multi-source data. The system comprises a data fusion module, a feature extraction module, a trend discrimination module, a fusion deduction module and a forecasting output module which are connected in sequence. The data fusion module is used for constructing a unified spatial-temporal feature data set; the feature extraction module adopts a deep neural network, combines time sequence modeling and an attention mechanism, and extracts weak signal features of a convection newborn stage; the trend discrimination module identifies the vertical development trend of the cloud body and the spatial evolution characteristics of the convection embryo through a characteristic fusion model; the fusion deduction module outputs a convection inception probability field based on the features; the forecast output module generates a short-time nowcasting result and realizes visual display and threshold alarm of the service terminal; by introducing a multi-source meteorological observation and intelligent feature fusion model, potential convection units can be found earlier, the development trend of the potential convection units can be dynamically tracked, and the advance and precision of short-time severe convection forecasting are greatly improved.