16 results about "Atmospheric circulation" patented technology

The invention relates to a rainfall objective prediction method based on an intelligent algorithm, and the method comprises the steps: decomposing the midsummer rainfall observation data of a time sequence based on an empirical orthogonal function, obtaining a spatial mode and a time coefficient, calculating a circulation field mean value of a target time period based on an atmospheric circulation field monthly value data set predicted by a climate mode, and obtaining the rainfall objective prediction result. The method comprises the following steps: obtaining a circulation abnormal field through a difference value with a same-period climate state circulation field, carrying out correlation analysis on a time coefficient and the circulation abnormal field, identifying and extracting a first correlation region with high correlation, carrying out correlation analysis on the time coefficient and atmosphere reanalysis data, obtaining a second correlation region with high correlation, and identifying and extracting a second correlation region with high correlation. And extracting a circulation anomaly average value of a similar region in the first related region and the second related region as an input feature, training a prediction model according to the input feature and a corresponding time coefficient, obtaining the corresponding time coefficient according to the prediction model and circulation anomaly feature data in the second time period, and performing reduction to obtain rainfall prediction data. And the accuracy of rainfall prediction is improved.

The invention relates to the field of climate change influence assessment, and discloses a method for quantizing contribution of natural internal variability to drought intensity based on a circulation similarity method, and the method comprises the steps: data collection; evaluating the drought strength based on the difference between the rainfall distance and the potential evapotranspiration distance, and quantifying the relative contribution of rainfall deficit and evapotranspiration demand to the drought strength; based on a circulation similarity method, a regional large-scale atmospheric circulation background is obtained, and contribution of a natural internal variation rate to drought intensity, rainfall deficit and evapotranspiration requirements is evaluated; estimating long-term evolution trends of drought intensity, rainfall deficit and evapotranspiration demands under different carbon emission scenes in the future; the method has the advantages that the natural internal influence mechanism of drought intensity change is defined, the relative contribution of historical and future rainfall deficit and evapotranspiration requirements to the drought intensity is quantified, the reliability of future drought trend estimation is enhanced, theoretical support is provided for making disaster prevention and reduction policies, and the accuracy of drought risk prediction is improved.

The invention relates to the technical field of new energy prediction, and particularly provides a photovoltaic abnormal output prediction method and device based on an atmospheric circulation field, and the method comprises the steps: calculating the difference between potential height grid prediction data of a preset height layer of a preset date in numerical weather prediction and climate state data of the preset height layer corresponding to the preset date, obtaining a forecast distance flat field of a preset height layer on a preset date; and predicting the abnormality of the photovoltaic output of the preset date based on the similarity between the forecast distance flat field of the preset height layer of the preset date and each element in a pre-constructed circulation feature set. According to the technical scheme provided by the invention, the application level of atmospheric circulation characteristics is enhanced in photovoltaic output prediction, so that the prediction capability of photovoltaic output, especially abnormal output, is improved.

The application provides a concrete carbonation depth transient prediction method and system based on a GCM, and the method comprises the following steps: obtaining working condition data of concrete under different climate conditions and carbonation depth of concrete tests, and establishing an empirical model of concrete carbonation depth; based on historical environmental factors of a region to be detected and environmental fluctuation rules, a GCM is used to mark a region where the concrete carbonation working condition data is obtained and to perform grid division, corresponding environmental factors in the target region are obtained, and a trained atmospheric circulation model environmental factor fitting formula is used to calculate and predict the atmospheric circulation environmental factor variables according to the environmental factors, the predicted atmospheric circulation environmental factor variables are substituted into the improved concrete carbonation depth empirical model, a transient prediction model of the concrete carbonation depth under different environmental conditions is established, and the obtained transient prediction model considers the fluctuation of the environmental factors, is more instantaneous in the real environment of the concrete carbonation test site, and has higher prediction accuracy.

The present application provides a kind of drought forecasting method and system based on artificial intelligence and atmospheric circulation mechanism, comprising: S1, obtaining alternative precipitation runoff influence factor;S2, the importance of alternative precipitation runoff influence factor is sorted and the final precipitation runoff influence factor and precipitation runoff state number k value are determined;S3, with the Bayesian-NHMM model of minimum precipitation runoff prediction error as the best precipitation runoff prediction model, obtain precipitation and runoff prediction result;S4, realize the classification of land water storage forecast factor;S5, train machine learning model, and use the trained machine learning model as land water storage forecast model;S6, realize drought forecasting and early warning based on land water storage forecast model.The present application makes full use of the advantages of Bayesian-NHMM model and current machine learning model, makes up for the problem of low accuracy of traditional drought forecasting, and can deduce future drought risk and carry out drought risk early warning.

The invention provides a machine learning-based sub-season day-by-day rainfall prediction result correction method and system, and relates to the technical field of climate prediction. According to the method, in consideration of the deficiency of the prediction capability of the numerical mode on the circulation element of the predicted region at the time scale of the second season, the circulation element of the predicted region is not selected as the prediction feature, but the high-influence key region reflected by the numerical mode is objectively screened as the prediction feature; compared with some similar machine learning prediction methods, the method is more suitable for sub-seasonal climate prediction in feature selection, and mode data mining is more sufficient. Besides, in consideration of climate state differences in a year, data of adjacent report starting days and forecast days are used as samples, so that information inconsistency caused by importing atmospheric circulation information with great climate state differences into a model is avoided, generalization ability of the model is improved, and higher prediction precision and higher prediction speed are achieved.

The present application relates to a kind of water quantity and water quality automatic regulation and control method, device, storage medium and electronic equipment at watershed scale, the method comprises: constructing watershed SWAT model, the model parameter of watershed SWAT model is verified, obtain the watershed SWAT model after verification, obtain the meteorological data of target region in target period predicted by atmospheric circulation model;The target water quantity and water quality of the target region in the target period are obtained, meteorological data is input into the watershed SWAT model after verification, obtain the water quantity and water quality of target region in target period, the water quantity and water quality regulation and control measure parameter of best management measure in the watershed SWAT model after verification is adjusted using non-dominated sorting genetic method with elite strategy, until the deviation between water quantity and water quality of target period and target water quantity and water quality is within the threshold range for stopping, obtain the water quantity and water quality regulation and control measure of the target region, reduce the artificial cost, improve the efficiency and precision of water quantity and water quality regulation and control.

A kind of multi-modal quantitative inversion method of paleoatmospheric circulation based on spatiotemporal deep learning network, using the powerful non-euclidean space feature extraction capability of spatiotemporal graph neural network (ST-GNN), captures the nonlinear spatiotemporal lag effect of vegetation response to climate; At the same time, coupling empirical orthogonal function decomposition (EOF) technology, orthogonal mode is extracted in potential feature space to capture the macroscopic atmospheric teleconnection pattern in geological record. The present application provides a quantitative inversion paradigm that can integrate micro-ecological mechanism and macro-dynamic mode, solves the problem of mixed signals in paleoclimate records of Qinghai-Tibet Plateau and surrounding transition zone, and realizes high-precision and high-resolution restoration of the evolution process of paleoatmospheric circulation intensity. In the multi-source stratigraphic record verification, not only can the mixed signals be effectively separated, but also the superior robustness and generalization ability are shown when dealing with geological age error and data loss.

This invention discloses a regional precipitation prediction method and system based on multi-source heterogeneous deep integration, belonging to the field of precipitation prediction technology. The method includes: classifying multi-source climate factors into atmospheric circulation, sea surface temperature, and comprehensive factor groups according to their physical properties; performing correlation screening, standardization, and PCA dimensionality reduction on each group of factors; constructing base models for each group of factors on a station-by-station basis using various machine learning algorithms, and employing a station-by-station hyperparameter search strategy; concatenating the out-of-place predictions of all base models and using the result as input to the ensemble layer of a deep neural network, learning dynamic ensemble weights station-by-station; and finally outputting the precipitation prediction results. This invention effectively eliminates the systematic bias of a single algorithm through heterogeneous integration and improves the model's adaptability to spatial heterogeneity through station-by-station hyperparameter search, exhibiting advantages such as high prediction accuracy, good interdecadal stability, and strong predictive ability for extreme precipitation events.

The application discloses a kind of regional precipitation isotope virtual monitoring method and system based on atmospheric circulation model, belong to hydrology meteorological monitoring and isotope hydrology technical field, method includes: based on engineering application demand obtains the reference point of target monitoring area, obtains the precipitation isotope simulation data at reference point and forms reference data sequence;Obtain the virtual precipitation isotope simulation data of multiple atmospheric circulation models, form candidate model set;From candidate model set, the virtual precipitation isotope simulation data of the position where reference point is located is extracted, and mode data sequence is obtained;The matching degree between mode data sequence and reference data sequence is calculated, and the optimal model and corresponding virtual precipitation isotope simulation data are selected from candidate model set;Based on the virtual precipitation isotope simulation data corresponding to optimal model, corresponding reference point in target monitoring area is filled with missing monitoring data or corrected original monitoring data, and the continuity of historical monitoring data of observation station is improved.

The invention discloses a spring rainfall prediction method based on atmospheric circulation abnormal signals. The method comprises the following steps: S1, obtaining and preprocessing multi-source data; s2, key circulation system identification and signal extraction; s3, construction of a synergistic effect index: constructing a circulating current synergistic effect index for comprehensively representing the configuration states of the plurality of circulating current systems in the specific region based on the circulating current signals extracted in the S2; s4, establishing a rainfall prediction model; and S5, testing a prediction result and integrating services. According to the method, abnormal signals of a plurality of key circulation systems including arctic oscillation and remote correlation wave train thereof, a south branch groove, subtropical high pressure, a water vapor transfer channel and a cold air activity path are systematically identified and quantified from multi-source data, so that the problems of high dependence on a single factor and incomplete physical mechanism of a traditional method are solved; and a comprehensive and three-dimensional circulation background support is provided for prediction.

The invention discloses a story line probability coupling attribution method for a heat wave flood space composite event, and relates to the field of climate change. The method comprises the following steps: firstly, quantifying a spatially compounded heat wave flood extreme event by utilizing a compound index; based on a story line attribution framework, similar circulation is constructed by extracting circulation characteristics and according to a multi-dimensional Euclidean distance, so that contribution of dynamic factors of atmospheric circulation and thermodynamic factors of man-made force to events is disassembled and evaluated; then, under a probability attribution framework, based on the Euclidean distance, the occurrence probability and the recurrence period of similar events in historical and future specific periods are evaluated, and a time change sequence of the future occurrence probability is analyzed; according to the method, quantification, driving factor contribution analysis and future risk estimation of the spatial composite extreme events are realized, and a scientific basis is provided for mechanism understanding, forecasting and early warning and disaster prevention and reduction of the events.

The invention discloses a power grid safety high influence weather dynamics conceptual model construction method and system, and relates to the technical field of meteorological prediction, and the method comprises the steps: analyzing geographic data influencing power grid safety, and processing and interpolating irregular terrain data based on a Kriging interpolation method; key factors are extracted based on geographic data, and weather processes influencing power grid safety are classified according to seasons and influence mechanisms; key configuration of various weather processes is obtained, and a power grid safety high influence weather dynamics conceptual model library is constructed through terrain correction. According to the method, key factors including an atmospheric circulation factor, a convection indication factor and a ground meteorological factor are extracted, so that the accuracy of weather process classification is improved; the comprehensive dynamic conceptual model library is constructed, so that the comprehensiveness and the accuracy of dynamic evaluation of the power grid safety are improved, and the applicability and the reliability of the model library are improved.

The invention discloses a wind speed prediction method and device, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a climatic factor index of a first time interval in a predetermined area; inputting the climatic factor index of the first time interval into a wind speed prediction model to obtain a wind speed of a second time interval in the predetermined area; wherein the wind speed prediction model is trained by the following steps: obtaining training data, the training data comprising climate factor indexes and historical wind speeds in a predetermined area, and the climate factor indexes comprising a plurality of atmospheric circulation indexes and a plurality of sea temperature indexes; the climatic factor index of a third time interval in the preset area is used as input feature data, the historical wind speed of a fourth time interval is used as label data, a wind speed prediction model reflecting the relation between the climatic factor index and the wind speed is trained, and the third time interval is earlier than the fourth time interval. Therefore, the problem of poor wind speed prediction effect can be solved.

This invention discloses a numerical forecasting method and apparatus for marine wind energy combining artificial intelligence, belonging to the field of marine wind energy numerical forecasting technology. The method includes acquiring and assimilating initial multi-source heterogeneous data of the target sea area to generate a standardized meteorological and hydrological field. Then, a deep spatiotemporal feature extraction network is used to complete multi-scale feature learning, capturing the spatiotemporal evolution pattern of wind energy parameters. The physical laws of ocean-atmospheric motion are transformed into loss function terms to constrain network learning, constructing a physically enhanced forecasting model. A causal intervention mechanism is used to simulate the intervention effect of atmospheric circulation background on the local wind field, forming a causally interpretable forecasting model. Real-time observation data is input to deduce multi-level wind energy parameter forecast sequences, and uncertainty quantification generates probabilistic forecast products with confidence intervals. This method addresses the problems of missing physical constraints and insufficient causal interpretability in existing forecasting models, optimizing the rationality and reliability of wind energy forecasts.

The invention discloses a forecasting method for improving convective strong wind by assimilating ground encrypted observation data, which comprises the following steps of: acquiring observation data of an encrypted automatic weather station in a preset area, and performing mode setting and test setting on a convective forecasting model; carrying out precipitation feature extraction and convective evolution analysis according to the observation data to obtain evolution data, and obtaining an atmospheric circulation situation according to the evolution data; carrying out ground lattice Nudging assimilation and simulation evaluation on the ground meteorological element field simulation, determining key ground meteorological elements which influence the convection triggering and development of the heavy rainfall in the warm area by using a ground lattice Nudging technology, and obtaining an influence mechanism; the key meteorological elements are water vapor, temperature and wind field; the influence mechanism comprises ground water vapor influence, ground temperature influence and ground wind field influence; and determining the duration of ground lattice Nudging of warm area heavy rainfall numerical forecasting through a test, performing deviation optimization on the convection forecasting model according to the influence mechanism analysis, and outputting a forecasting result.