28 results about "Nowcasting" patented technology

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 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 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.

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 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 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.

The invention discloses a radar echo extrapolation method integrating physical guidance and a diffusion coupling mechanism, and relates to the technical field of meteorological short-term and imminent forecast. The method comprises the following steps: firstly, coding radar echo data by using a physical guidance network, capturing long-distance space-time dependence through a multi-scale gating axial block, and generating a preliminary prediction sequence with macroscopic physical consistency in combination with a multi-domain space-time evolution module and an explicit physical advection guidance mechanism; and then, adopting a channel decoupling and cascade diffusion strategy, taking the preliminary prediction sequence as a physical prior skeleton, extracting guide features by using a double-flow spatio-temporal context network, and executing reverse iteration denoising and refined texture repair through a diffusion model. According to the method, the physical guidance composite loss function is introduced, the problems of image blurring and physical evolution logic deficiency existing in a traditional method are effectively relieved, and the forecasting precision and image fidelity of severe convective weather are improved.

This application proposes a convective nowcasting system and method based on multimodal data, belonging to the field of convective nowcasting technology. This invention fundamentally solves the problem of fusion distortion caused by physical conflicts in multi-source observation data, achieving end-to-end optimization from "raw multimodal input" to "high-quality forecast output." Compared with existing technologies, this invention significantly improves the ability to capture the initial stage of convection, enhances the predictive stability of strong echo structures in the mature stage, and effectively eliminates false signals in the dissipation stage, thereby greatly improving the accuracy, physical rationality, and timeliness of the 0–2 hour radar reflectivity factor prediction field. This not only enhances the lead time and hit rate of short-term severe convective warnings but also provides more reliable technical support for disaster prevention and mitigation decision-making, possessing outstanding substantive features and significant progress.

The invention discloses a thunderstorm gale short-time nowcasting method based on an ENTwo evolution network and Uform network hybrid architecture, and the method comprises the steps: obtaining historical live data, which comprises a radar echo sample data set, a maximum wind speed data set and a lightning data set; generating a grid thunderstorm gale label data set based on the gale wind speed and the lightning data; an ENTwo evolution network and a Uform hybrid model are constructed, the evolution network performs space-time extrapolation on the input data to generate a forecast field, and the Uform fuses an extrapolation result and historical data to output a thunderstorm and gale forecast field; training the model through a loss function; and inputting real-time data into the training model, and outputting a lattice forecasting field. According to the method, the accuracy and timeliness of 0-2-hour thunderstorm gale forecasting are effectively improved, the spatial resolution reaches 1 kilometer, the strong echo and extreme wind speed forecasting performance is superior to that of a traditional method, and high-precision technical support is provided for disaster prevention and reduction.

The invention relates to the field of meteorological observation, and particularly discloses a high mountain valley region hydropower station dam region short-time approaching rainfall forecasting method based on deep learning multi-model fusion, and the method comprises the steps: obtaining a three-dimensional grid structure of a target region; performing short-time extrapolation prediction on the three-dimensional grid structure through a plurality of time sequence prediction models to obtain a plurality of short-time extrapolation prediction results; based on a deep learning model, performing fusion processing on the plurality of short-time extrapolation prediction results to obtain a fused extrapolation prediction result; and generating a short-time approaching rainfall forecast of the target area based on the fused extrapolation prediction result. According to the method, radar echo extrapolation is performed on the three-dimensional grid structure in combination with a plurality of time sequence prediction models so as to predict the three-dimensional grid structure, the short-time extrapolation prediction results are obtained, the plurality of short-time extrapolation prediction results are fused by using the deep learning model, the short-time approaching rainfall forecast of the target area is generated, the accuracy of the short-time approaching rainfall forecast is improved, and the accuracy of the short-time approaching rainfall forecast is improved. Meteorological disaster prevention and reduction of the dam area of the hydropower station are facilitated, and meteorological guarantee is provided for safe production of the hydropower station.

The application relates to a nowcast effect evaluation method and system, a terminal and a storage medium. The method comprises the following steps: calculating the membership of radar echo data to a set threshold, calculating the probability mean in the neighborhood range of each grid point by using a spatial neighborhood probability method based on the membership, and obtaining the fuzzy probability of each grid point according to the probability mean; calculating the score of the radar echo data based on the score skill of the Gaussian type membership intensity feature (IFSS); assigning weights to the forecast echo data of each time point by using a time neighborhood adaptive weighting algorithm, calculating the probability of each grid point after the weights are assigned by using a spatial neighborhood probability method, and calculating the score skill of the radar echo data based on the time neighborhood adaptive weighting algorithm (TFSS); and calculating the score skill of the radar echo data based on the intensity feature and the space-time feature (TIFSS) according to the score results of the IFSS and the TFSS. The application can realize more objective and reasonable evaluation and test, and solve the problems of early prediction and delayed prediction.

The present application relates to a kind of strong precipitation nowcasting methods of liquid ratio, comprising: collecting radar reflectivity data and next hour precipitation real-time data within one hour;Radar reflectivity data is converted into three-dimensional data under Cartesian rectangular coordinate system, and precipitation real-time data is converted into two-dimensional precipitation grid field;Strong precipitation nowcasting model is built;Model is trained on training set, hyperparameter is optimized on verification set, and model is tested on test set;Obtain precipitation estimation field data.The present application uses the double decoder based on liquid ratio and attention mechanism to build strong precipitation nowcasting model, uses the echo data of liquid water content conversion processing and carries out the feature coding extraction work using encoder module, using two parallel decoder module respectively output prediction result of strong rain area and rainfall, using comprehensive loss function improves the quantitative estimation and qualitative classification of strong precipitation area, after effectiveness test, the present application can significantly improve nowcasting accuracy.

The invention provides a severe convection rainfall nowcasting method and device, and the method comprises the steps: inputting three-dimensional dual-polarization radar training data into a deep learning model, and obtaining a rainfall nowcasting result of the three-dimensional dual-polarization radar training data outputted by the deep learning model; the self-consistent relation between the dual-polarization radar variables is obtained through fitting, and the deviation of the self-consistent relation is determined according to a rainfall nowcasting result; determining a physical constraint loss function according to the deviation of the self-consistent relationship, determining a total loss function according to the physical constraint loss function and the loss function of the rainfall nowcasting result, and training the deep learning model by using the total loss function; and inputting the three-dimensional dual-polarization radar test data into the trained deep learning model to obtain a rainfall nowcasting result of the three-dimensional dual-polarization radar test data output by the deep learning model. According to the method, the forecasting precision and the physical consistency of severe convection precipitation are improved.

This application relates to the field of meteorological data processing technology, specifically to a method, electronic device, and storage medium for identifying severe convective weather using radar echo images. This application constructs a three-dimensional radar echo image of the target space through multi-elevation angle stereo scanning of radar. It then utilizes a three-dimensional convolutional neural network to extract and analyze multiple echo intensities, radial velocities, and velocity spectral widths contained in the three-dimensional radar echo image, outputting three-dimensional features such as echo hangs, bounded weak echo areas, and mesoscale cyclones upon which the confirmation of severe convective weather is based. This application can identify complex weather phenomena such as severe convection with high accuracy, providing technical support for meteorological station forecasters in nowcasting.

The invention discloses a sea fog nowcasting method based on double-flow space-time Transform, and belongs to the technical field of marine weather forecasting and artificial intelligence. The method comprises the following steps: preprocessing original observation data of a FY-4 satellite to obtain a standardized input tensor; constructing a physical characteristic flow based on a bright temperature difference and a multispectral characteristic flow based on a multispectral channel in parallel; performing space-time embedding and division on the double-flow features respectively; a Transform encoder is used for capturing the respective space-time dependency relationship; through a cross attention mechanism, taking physical features as query, and fusing multispectral feature information; and finally, performing up-sampling reconstruction through a layered decoder, and outputting a sea fog distribution prediction map in a future time period. According to the invention, the accuracy and reliability of sea fog nowcasting are effectively improved through deep fusion of a double-flow architecture and physical priori.

The application discloses a kind of light precipitation nowcasting methods based on frequency domain perception CBAM and hollow spatial pyramid, first acquire data and pre-process, then construct light U-Net backbone network: construct a backbone network of U-Net form, the U-Net is further carried out multi-scale context feature extraction using encoder-decoder symmetric architecture;Then decode and jump connection, by decoder from the fusion feature step-by-step is carried out up sampling operation;The feature map output by the last layer of decoder, through a 1×1 output convolution layer, it is directly mapped into multiple frames continuous forecast field, to realize from input radar chart sequence to output forecast chart sequence end-to-end sequence mapping;Finally, the model is trained and optimized, after training is completed, model can generate high-quality short-time precipitation forecast result according to the input radar chart sequence.The method realizes high-precision, low false alarm, high computational efficiency short-time precipitation forecast.

This invention relates to the field of meteorological radar signal processing and nowcasting technology, and proposes a near-term gale intelligent early warning system, method, and storage medium based on radial velocity characteristics. The system includes: a radar data preprocessing module for reading weather radar base data and constructing a radial velocity matrix; a semantic segmentation module for performing convergence zone semantic segmentation on the radial velocity matrix using an improved U-Net semantic segmentation network to identify retreating velocity zones and approaching velocity zones; an instance segmentation module for performing connected component analysis on the semantic segmentation results, extracting retreating velocity zone instances and approaching velocity zone instances, and calculating geometric parameters and velocity statistical characteristics; a convergence pair matching module for matching retreating velocity zone instances and approaching velocity zone instances based on spatial location and velocity gradient constraints to identify mid-level radial convergence characteristics; and an early warning decision module for calculating early warning indicators based on convergence intensity and vertical continuity, and classifying gale warning levels.

The application provides a precipitation nowcast method based on trend-to-detail synergistic residual condition diffusion, and belongs to the technical field of meteorological prediction and artificial intelligence, and comprises the following steps: S1, acquiring a radar echo sequence and near-surface meteorological element data; S2, performing time alignment processing on the near-surface meteorological element data; S3, generating a multi-channel meteorological element grid and a confidence map; S4, jointly training a deterministic trend prediction network and a residual condition diffusion model; S5, generating a prediction residual, generating a radar trend prediction field, fusing the prediction residual and the radar trend prediction field, and obtaining precipitation nowcast results at multiple future moments. The method synergistically models a trend prediction branch and a detail generation branch, fuses radar echo and near-surface meteorological element information in a unified framework, takes into account the translation trend of a large-scale precipitation system and the detail expression of a small-scale convective structure, improves the precision and stability of short-time precipitation nowcast, and has good engineering application value.

A short-term nowcasting precipitation prediction method based on multi-source fusion data and MIM networks is proposed. This method involves spatiotemporal matching of radar observation data with data from the China Land Surface Data Assimilation System and station observation data. The matched data undergoes preprocessing, including feature factor extraction and missing value handling, to construct a deep learning model. The model is trained and the multi-source fusion precipitation inversion results are output based on real-time radar data. Furthermore, the method identifies errors in the precipitation area and intensity of intelligent grid precipitation forecasts, uses "phase correction" technology to correct the positional errors of the rainbands in the intelligent grid forecasts, and evaluates the accuracy of the deep learning model's prediction performance based on the short-term nowcast precipitation amount. This achieves intelligent prediction of short-term nowcast precipitation.

The application provides a high-precision minute-level precipitation nowcasting method based on deep learning, acquires various to-be-processed meteorological data; performs clipping processing on each kind of to-be-processed meteorological data, and outputs the clipped meteorological data; each kind of clipped meteorological data belonging to the same numbered position is combined, each group of obtained sub-meteorological data is input into a precipitation nowcasting model, and target precipitation prediction data in a second preset time period is output; in the mode, the precipitation nowcasting model can use independent encoders to encode different to-be-processed meteorological data separately, and then fuse the encoding results output by each encoder through a post-fusion module, so that the features of the various to-be-processed meteorological data can be complementary, the precipitation nowcasting precision can be improved, and then the demand of current meteorological services for regional sub-kilometer, minute-level fine-grained grid quantitative precipitation rapid prediction can be met.