43results about How to "Improve the forecast effect" patented technology

The invention discloses a wind speed forecasting method based on fine boundary layer figure mode for a wind farm in complex terrain. The method comprises the following steps: acquiring historical wind measuring tower data; converting acquired terrain data of a geological information system and the like into static data which can be directly called by a mesoscale weather forecast mode and the fine boundary layer figure mode; carrying out wind farm localization configuration for the mesoscale weather forecast mode and the fine boundary layer figure mode, so as to achieve optimal mode meteorological environment simulation; and taking the mesoscale weather forecast mode and the fine boundary layer figure mode as main body to build a wind farm wind speed forecasting system, and carrying out dynamically adjustable wind farm wind speed forecasting for areas 500 square kilometers around the wind farm for 3-7 days, wherein the horizontal grid resolution is 100m, and the time interval is 5-15 minutes. According to the invention, as the fine terrain data is introduced, and the fine boundary layer figure mode is adopted for100m-resolution dynamic downscaling forecasting. Therefore, the method is more suitable for wind farm wind speed forecasting under complex terrain conditions.

The invention relates to a decision tree index-based neural network air quality prediction method. The method comprises the following steps of establishing a time sequence data set of related meteorological factors, air quality and atmospheric pollutant discharge; classifying the obtained training samples by using a decision tree DT algorithm to generate an optimal tree structure T alpha orientedby air quality characteristics and a corresponding classification result; according to the classification result, establishing a BP neural network model for each classification, and performing model training; inputting a prediction data set, performing classification indexing based on a decision tree, and selecting the trained DT-BP neural network model or the comprehensive BP neural network to predict the air quality; obtaining a continuous air quality prediction result based on an iterative algorithm; recording the frequency of occurrence of data sets which do not meet the decision tree classification matching rule, and automatically starting model updating when a set value is exceeded. The method is suitable for predicting and forecasting the air quality of conventional weather, abruptchange weather and heavy pollution weather.

An atmospheric heavy pollution forecast method based on a combination of a numerical model and a statistic analysis includes that: national centers for environmental prediction (NCEP) global forecast ambient field data are obtained; a forecast trigger command is compulsively generated by manual power or automatically generated after the operation of a meteorological model finishes; a forecast command is started to obtain a meteorological factor data set of a simulation area and surrounding areas; air quality monitoring data are obtained; a forecast factor set data file is generated; the visibility of a forecast day is obtained through a visibility forecast sub-mode; an air quality level of the forecast day is differentiated in a qualitative mode through a pollution level initial differentiation sub-mode; a weather type of the forecast day is diagnosed and identified through a weather type identification sub-mode; pollutant concentration of the forecast day is calculated through a heavy pollution quantitative forecast sub-mode; a hazard level of the pollution level to a human body is confirmed, and administrators are provided with a decision basis for emergency management. Compared with a high concentration pollution weather forecast effect of existing air quality forecast systems at home and abroad, the atmospheric heavy pollution forecast method based on the combination of the numerical model and the statistic analysis remarkably improves the air pollution forecast effect.

The invention belongs to the weather prediction and forecast technical field and relates to a convective weather approach prediction method and system. The method includes the following steps that: a radar mosaic in a detection area is acquired; a locally-constrained optical flow method is adopted to perform calculation based on the radar mosaic, so that the optical flow field of radar echoes can be obtained; radar echo extrapolation is performed according to the optical flow field of the radar echoes, so that approach prediction of convective weather can be completed. According to the convective weather approach prediction method and system of the invention, the locally-constrained optical flow method is adopted to perform calculation, so that the optical flow field of the radar echoes can be obtained, and therefore, a motion vector field in a cross-correlation method can be replaced, and thus, tracking and motion estimation of the radar echoes can be completed accurately, and the prediction performance of convective weather approach prediction can be improved.

The invention discloses a predicting method for diffusion of air pollution for complex terrain emergency response. The method comprises the steps of firstly collecting discharge data of air pollutants of parts of a zone, determining a needed discharge source list of air pollution source for calculation of a numerical model, and collecting fine terrain topographic data of the parts of the zone as the input data of a numerical model system; constructing a medium-sized weather forecast mode of the numerical model of a fine coupling boundary layer, regulating the medium-sized weather forecast mode and the numerical model of the fine coupling boundary layer so as to get an optimized simulated result of meteorological conditions, utilizing the medium-sized weather forecast mode and the numerical model of the fine coupling boundary layer as the main body to construct a predicting calculation model of air pollution diffusion, and establishing the predicting method for diffusion of air pollution for complex terrain emergency response. The predicting method for diffusion of air pollution for complex terrain emergency response can complete the predicting calculation of the diffusion range and influence degree of the air pollution for a period of 1-3 h and in an area of 10 square kilometers around an air pollution accident site within 20 minutes after the accident, is dynamically adjustable, and has a horizontal grid resolution of 100 meters and a time interval of 10 minutes.

The invention brings forward a constructing method of a combined air quality forecasting model, wherein the method is based on a BP neural network and multi-element stepwise regression. The method comprises the following steps: (1), establishing a BP neural network forecasting module based on a training sample set; (2), carrying out severe-pollution scene determination based on the BP neural network forecasting result; to be specific, (21), defining a severe-pollution scene; (22), establishing a determination equation; (23), carrying out determination by using a neural network forecasting value; and (24), carrying out determination on the determination equation based on the neural network forecasting value determination result; (3), establishing a severe-pollution multi-element stepwise regression forecasting model according to the severe-pollution scene determination result; and (4), with combination of the forecasting determination process, outputting a forecasting result. According to the invention, the forecasting precision of the urban air quality, especially the early-warning forecasting of the severe-pollution scene, is improved comprehensively; and thus stable air quality precision forecasting under different pollution degrees can be realized.

The invention relates to the field of remote sensing information fishery application, in particular to an ensemble learning fishery forecasting method utilizing ocean remote sensing multi-environmental elements. The method aims at the problem that an existing fishery forecasting model is prone to be caught in overfitting on sample data, and consequently the generalization ability of the forecasting model is reduced, an ensemble learning method is adopted, a plurality of decision-making trees of simple structures are adopted as meta learning machines, learning machine integration is carried out based on a boosting algorithm, and the ensemble learning fishery forecasting method utilizing the ocean remote sensing multi-environmental elements is constructed. Each simple meta learning machine only learns a subset of characteristic space, the weight of samples, forecast to be wrong, in trained sub-learning machines as samples of the subsequent meta learning machines can be improved in the model training process to guarantee the different degree of the meta learning machines, and the learning machines learn information of different characteristic space subsets. According to the method, the generalization error can be reduced while prediction precision is improved, and the position of a fishery is effectively, fast and accurately located.

The invention discloses a quasi-ensemble-variation based mixed data assimilation method which comprises the following steps of: selecting 12-hour and 24-hour forecast data stored per 6 hours in historical forecast data of a past month, adjacent to forecast moment, and taking the data as a quasi-ensemble forecast sample; calculating the difference between 24-hour forecast and 12-hour forecast at the same moment, and obtaining quasi-ensemble forecast errors; calculating a mean value of the quasi-ensemble forecast errors, and substituting the mean value and the quasi-ensemble forecast errors into an unbiased estimation formula to obtain unbiased estimation; and substituting the unbiased estimation into a quasi-ensemble-variation assimilation algorithm, and carrying out mixed assimilation. The method is used for calculating historical forecast errors to obtain quasi-ensemble background errors, and is applied to quasi-ensemble-variation mixed data assimilation. The quasi-ensemble background errors are generated through adjacent historical forecast results without real ensemble forecast, so that the calculated amount for the ensemble forecast is effectively reduced and the efficiency for business data assimilation and forecast is improved.

The invention discloses a judgment method of a bonding behavior between a continuous casting slab and a crystallizer copper plate, and belongs to the technical field of metallurgical continuous casting processes. The judgment method comprises the following steps: (1) the temperature data acquisition, preprocessing and storage of thermocouples in the crystallizer copper plate are performed; (2) the temperature rise abnormity inspection of single thermocouple is performed through the temperature change speed of the thermocouples; (3) the longitudinal bonding propagation model inspection between rows of the thermocouples with temperature rise abnormity and adjacent rows is performed to calculate the total number of the thermocoupls with temperature rise abnormity and satisfying the model inspection as X; and (4) the judgment of bonding alarming or bonding warning is performed according to the total number of the thermocoupls with temperature rise abnormity and satisfying the model inspection. The judgment method realizes judging timeliness and accuracy of casting slab bonding, shortens the alarming response time as far as possible, and reduces the wrong report frequency when guaranteeing 100% of bonding report rate.

The invention discloses an artificial beach multi-source monitoring data integrated analysis method. The method comprises the following steps: constructing an observation system which comprises a shore-based fixed digital image monitoring system, a beach section measurement system and an unmanned aerial vehicle surveying and mapping system; acquiring multi-source monitoring data; acquiring land topographic data and underwater topographic data of the artificial sand beach by utilizing the beach section measurement system; utilizing the unmanned aerial vehicle surveying and mapping system to obtain land area environment condition data of the artificial sand beach; processing the acquired data; and performing integrated application, complementation and verification of multi-source data on theprocessed data. Real-time, accurate and comprehensive analysis and processing of hydrodynamic force, terrain, engineering, human activities and environmental condition data of the artificial sand beach are realized; the method is used for capturing and explaining the fine and comprehensive change process of the artificial sand beach, contributes to overcoming the limitation of a single measurement means in the traditional sand beach observation on an observation object or space-time resolution, and improves the sand beach management level and the numerical forecasting capability.

The invention provides an ensemble forecasting method and system based on a machine learning algorithm and a medium, and relates to the technical field of air quality forecasting, and the method comprises the steps: 1, building training data of a model according to related data including pollutant concentration and weather forecast; 2, establishing a coupling optimization model by utilizing multiple machine learning methods; and 3, taking the obtained training data as the input of the multiple machine learning method coupling optimization model, and obtaining the air quality forecast in the future time period. The influence of meteorological conditions such as temperature, humidity, wind speed, wind direction, rainfall and air pressure on the pollutant concentration can be introduced, meanwhile, multiple machine learning algorithms are coupled, and the forecasting accuracy of the air quality mode is improved.

The invention discloses a conceptual hydrological model combined forecasting method suitable for a karst region, and belongs to the field of hydrological forecasting. A karst hydrological model is constructed by using a karst water tank technology; the karst hydrological model is fused with a traditional conceptual hydrological model of a full productive flow mechanism; The method comprises the steps of obtaining a conceptual hydrological combination forecasting model suitable for a karst region, calibrating model parameters by adopting a multi-objective optimization algorithm, driving the model by using basin rainfall evaporation data to obtain runoff forecasting of a basin, and comparing a forecasting result with an actually measured runoff process and a forecasting result of a traditional model. According to the method, a combined forecasting concept and a multi-target parameter calibration method are introduced, the defect of forecasting of a pure concept hydrological model and a pure karst water tank model in a karst region is overcome, and the confluence generation and generation characteristics of the karst region are better reflected. The method provided by the invention provides a new method and idea for hydrological forecasting of the karst region.

The invention discloses a monitoring method of large particle metal abrasive dusts in a machine lubrication system. The method is realized by using a machine lubrication oil filter element abrasive dust detection device and an energy chromatic dispersion X-ray fluorescence spectrometer, and the method comprises the following steps: an abrasive dust membrane with large particle metal abrasive dusts is manufactured by using the machine lubrication oil filter element abrasive dust detection device; the chemical components and mass distribution of the large particle metal abrasive dusts on the abrasive dust membrane are analyzed by use of the energy chromatic dispersion X-ray fluorescence spectrometer, so as to obtain X-ray fluorescence spectrum data; and a monitoring threshold value of the large particle metal abrasive dusts is set according to the X-ray fluorescence spectrum data, and the monitoring threshold value is revised along increase of the X-ray fluorescence spectrum data dynamically. According to the invention, chemical components, mass distribution and change trend of the large particle metal abrasive dusts can be monitored, and accordingly the monitoring threshold value can be set so as to monitor the large particle metal abrasive dusts in the machine lubrication system and improve the ability in forecasting abnormal machine abrasion faults.

A three-dimensional variational assimilation method for an aerosol extinction coefficient based on an IMPROVE equation comprises the following steps: according to a three-dimensional variational theory and the IMPROVE equation, a computer program for solving a three-dimensional variational target functional is written and called an assimilation system; H is called as an observation operator and isan M*N-dimensional matrix, wherein M is equal to the length of y, N is equal to the length of x, and a result Hx obtained after x is multiplied by H leftwards is a vector with the same length as y; the function of H further comprises interpolating the values of grid points to observation positions of non-grid points; R is an observation error covariance and is an M*M-dimensional matrix; and T represents the transposition of the vector. Hx calculation is realized according to the IMPROVE equation, and assuming that the observation position of y is just at a regular grid point of a mode, calculation of Hx does not involve an interpolation process.

The invention discloses a wind speed forecasting method based on fine boundary layer figure mode for a wind farm in complex terrain. The method comprises the following steps: acquiring historical wind measuring tower data; converting acquired terrain data of a geological information system and the like into static data which can be directly called by a mesoscale weather forecast mode and the fine boundary layer figure mode; carrying out wind farm localization configuration for the mesoscale weather forecast mode and the fine boundary layer figure mode, so as to achieve optimal mode meteorological environment simulation; and taking the mesoscale weather forecast mode and the fine boundary layer figure mode as main body to build a wind farm wind speed forecasting system, and carrying out dynamically adjustable wind farm wind speed forecasting for areas 500 square kilometers around the wind farm for 3-7 days, wherein the horizontal grid resolution is 100m, and the time interval is 5-15 minutes. According to the invention, as the fine terrain data is introduced, and the fine boundary layer figure mode is adopted for100m-resolution dynamic downscaling forecasting. Therefore, the method is more suitable for wind farm wind speed forecasting under complex terrain conditions.

The invention relates to the technical field of rainfall forecasting, and in particular, relates to a fusion rainfall forecasting method based on multi-model integration, wherein the method comprises the steps: firstly, identifying a falling area of mesoscale mode rainfall forecasting and an error of rainfall intensity, and correcting a position error of a numerical weather mode forecasting rainfall zone by utilizing a phase correction technology; and meanwhile, adjusting the rainfall intensity of the mode according to the rainfall observed in real condition. In a fusion algorithm, weight factors of hourly rainfall forecasting of nowcasting and corrected mode forecasting are determined by a hyperbola function, and the weight of a numerical mode forecasting result is gradually increased along with the prolonging of forecasting time efficiency. The method has the beneficial effects that compared with an existing traditional forecasting mode or a single neural network forecasting mode, the two methods are fused and complement each other, the falling area accuracy of numerical forecasting rainfall is improved, and the accuracy of nowcasting rainfall intensity is also improved. Great economic benefits and social benefits are expected to be realized on meteorological accurate forecast and meteorological disaster prevention.

The invention discloses an industrial melt index soft measurement instrument and method based on BP particle swarm optimization. For the industrial melt index soft measurement method, a BP neural network is taken as a local equation of a fuzzy equation system, and then the particle swarm optimization is introduced for further optimizing a whole soft measurement model. According to the invention, an on-site intelligent instrument for measuring easy-to-measure variables, a control station and a DCS (distributed control system) database are connected with one another, a soft measurement value display instrument comprises the industrial melt index soft measurement model based on the BP particle swarm optimization, the DCS database is connected with an input end of the soft measurement model, and an output end of the industrial melt index soft measurement model based on the BP particle swarm optimization is connected with the melt index soft measurement value display instrument. The industrial melt index soft measurement instrument and method have the characteristics of on-line optimization, high calculation speed, automatic updating of the model and high noise immunity.

The invention discloses a method for extracting and incorporating multi-source live spatio-temporal forecasting factor into mode interpretation application. A multi-source live spatio-temporal forecasting factor calculation method, the technical process and the software system serve as an independent live forecasting factor pre-processing module in the mode interpretation application and post-processing process, and live forecasting factors with weather forecast indication significance are extracted and prepared; a multi-source live space forecasting factor of hour-level and even minute-level rolling updating is added into the forecasting factor, the defect that high-frequency rolling objective meteorological forecasting cannot be achieved due to the fact that the forecasting frequency of a global numerical forecasting mode is low is overcome, and an effective technical means depending on real-condition updating supporting rolling forecasting is provided. According to the balance between the actual forecasting demand and the operation efficiency, an objective weather forecasting system of hour-level and even minute-level rolling updating based on high-frequency updating of the live space forecasting factor can be developed, and the forecasting accuracy of high-frequency rolling fine weather elements is further remarkably improved.

The invention provides a multi-scale numerical weather forecasting mode fused weather forecasting method and device, and the method comprises the steps: obtaining a multi-scale numerical weather forecasting mode fused target forecasting factor according to atmospheric circulation forecasting data and convective weather forecasting data; and inputting the target forecasting factor into a pre-trained deep learning forecasting model to obtain a weather forecasting result. According to the invention, the forecasting factors of the numerical weather forecasting modes with different scales are fused and input into the pre-trained deep learning forecasting model to obtain the predicted weather forecast, so that the defect that the forecasting effects of weather systems with different scales cannot be considered at the same time in the prior art is overcome; according to the invention, fusion of numerical weather forecasting modes of multiple scales is realized, and the forecasting effect of weather systems of different scales is effectively improved.

The invention belongs to the technical field of engineering geology, and particularly relates to a modeling method of plane morphology of different evolution modes of a meandering river point dam. The method comprises the following steps: A, determining the plane position coordinates of key lateral lamination interfaces inside the meandering river point dam on the basis of an input UTM / self-defined coordinate system; B, smoothing and standardizing control points of the key lateral lamination interfaces inside the meandering river point dam; C, simulating erosion and transformation processes of the lateral lamination interfaces which are of different time periods and inside the meandering river point dam, and forming plane positions of secondary lateral lamination interfaces between key lateral lamination; D, simulating erosion and transformation processes of the lateral lamination interfaces of different time periods in a future riverway evolution process, and forming plane positions of future riverway evolution trajectories; and E, determining plane positions and time of growth stopping of the simulated meandering river point dam, and forming simulation results of the plane morphology of the different evolution modes of the meandering river point dam. The method can scientifically predict the plane evolution morphology, the evolution trajectories and a growth trend of the meandering river point dam.

The invention relates to the technical field of meteorological prediction, in particular to a light rain air elimination method and system suitable for grid prediction, electronic equipment and a storage medium. The invention discloses a light rain air elimination method and system suitable for grid forecasting, electronic equipment and a storage medium. The emptying method comprises the followingsteps: emptying; fitting meteorological data related to rainfall into a logistic function to obtain a regression coefficient, converting a numerical result into a probability between 0 and 1 so as topredict an event occurrence probability, and judging a rainfall event with a small probability as no rainfall so as to solve a light rain air report problem; when the probability of occurrence of therainfall event is greater than or equal to 0.5, it is considered that rainfall occurs, and the original rainfall forecast value is kept unchanged; and when the probability of occurrence of the rainfall event is less than 0.5, it is considered that no rainfall occurs, and the rainfall forecast value is corrected to be 0, so that troubles caused by a light rain air report problem in professional meteorological services are solved, and the rainfall product forecast performance is improved.

The invention discloses a near-surface wind speed statistical downscaling correction method based on relative slope length, which comprises the following steps: step 1, downloading WARMS mode data inreal time, defining and calculating the relative slope length and other topographic parameters by combining topographic data used by the mode, and outputting topographic parameter files, step 2, downloading CIMISS actually measured meteorological data in real time, searching WARMS mode forecasting data corresponding to the actual measurement data, and interpolating a mode high-low altitude forecasting product with the resolution of 9km and a topographic parameter file to each station in the actual measurement data; step 3, integrating interpolation files of each forecast time limit in the current month once a month; and 4, correcting the 10m wind speed forecasted by the monthly mode by using the correction model obtained in the step 3. According to the near-surface wind speed statistics downscaling correction method based on the relative slope length, the relative slope length mode is adopted, the terrain elevation and the terrain form are considered, the concept of the relative positions of the grid points in the mountainous region can be reflected, and the terrain characteristics are quantitatively described.

The invention provides a convective monomer evolution trend discrimination method based on a Doppler weather radar, and the method comprises the following steps: determining radar echo indexes describing convective monomers, and screening key indexes as a feature factor set B; based on the characteristic factor set B, calculating the change trend of each factor Bi of the convection monomers, and determining the weight wi of the change trend of each factor in combination with the evolution process of the typical convection monomers; and according to the evolution trend and the weight wi of each characteristic factor, calculating the evolution trend index of the convective monomer, and setting a threshold value corresponding to the evolution trend index K for developing, maintaining and weakening the convective monomer. The method can quickly and objectively calculate the change trend of the convective monomer, is beneficial to enhancing the pre-judgment of the occurrence and development of disastrous weather, and improves the strong convective disaster defense capability of a power grid.

The invention discloses a dynamic statistics combined sub-seasonal prediction method based on low-frequency increment space-time coupling, and the method comprises the steps: selecting tropical and tropical outside atmosphere abnormal signals as a prediction factor variable, taking the low-frequency increment of the variable as a prediction object and a prediction factor, and eliminating the interference of a weather change rate and a seasonal change rate. On one hand, a synchronous physical relationship between a forecast factor and a forecast quantity increment is considered, a singular value decomposition statistical method is utilized to find a high coupling mode of a synchronous forecast factor increment and a forecast quantity increment, and a multiple linear regression method is adopted to establish a sub-season prediction model based on a physical mechanism. On the other hand, by means of the advantage that the dynamic mode has a good forecasting effect on the sub-season tropical and extra-tropical atmosphere abnormal modes, the time coefficient (namely, the forecasting factor) of the tropical and extra-tropical atmosphere abnormal high coupling modes predicted by the dynamic mode is substituted into the forecasting model, and a power-statistics combined sub-season prediction model is further constructed to predict meteorological elements.

An atmospheric heavy pollution forecast method based on a combination of a numerical model and a statistic analysis includes that: national centers for environmental prediction (NCEP) global forecast ambient field data are obtained; a forecast trigger command is compulsively generated by manual power or automatically generated after the operation of a meteorological model finishes; a forecast command is started to obtain a meteorological factor data set of a simulation area and surrounding areas; air quality monitoring data are obtained; a forecast factor set data file is generated; the visibility of a forecast day is obtained through a visibility forecast sub-mode; an air quality level of the forecast day is differentiated in a qualitative mode through a pollution level initial differentiation sub-mode; a weather type of the forecast day is diagnosed and identified through a weather type identification sub-mode; pollutant concentration of the forecast day is calculated through a heavy pollution quantitative forecast sub-mode; a hazard level of the pollution level to a human body is confirmed, and administrators are provided with a decision basis for emergency management. Compared with a high concentration pollution weather forecast effect of existing air quality forecast systems at home and abroad, the atmospheric heavy pollution forecast method based on the combination of the numerical model and the statistic analysis remarkably improves the air pollution forecast effect.

The invention provides an engine oil filter element abrasive dust detector, comprising a filter element cleaning barrel, a particle counter, an abrasive dust membrane making device, an oil tank and a control system; wherein the filter element cleaning barrel, the particle counter, the abrasive dust membrane making device and the oil tank are sequentially connected into a circulating system by an oil pipeline; and the control system is respectively connected the other devices, so as to control the other devices. The detector can realize automatic cleaning on filter element, abrasive dust size and quantitative distribution analysis and abrasive dust membrane making, large particle metal abrasive dust in machine lubricating system is monitored, forecasting capacity of machine abrasion fault is improved, and the defects in the prior art are overcome.

The invention provides an ensemble forecasting method and device. The method includes: acquiring an initial forecasting interval; dividing the interval, obtaining level characteristic values; determining an interval; and determining a forecasting result. According to the method, high forecasting members in a forecasting ensemble are firstly determined through the level characteristic values, then the forecasting result of ensemble interval values is obtained, the forecasting ability of ensemble forecasting for high-impact weather is improved, and the forecasting result is also changed from single-value forecasting to interval-value forecasting; and the method has certain reference significance for all of flood control management and application of basin flood control and disaster reduction institutions and related research of rainfall forecasting.

The invention brings forward a constructing method of a combined air quality forecasting model, wherein the method is based on a BP neural network and multi-element stepwise regression. The method comprises the following steps: (1), establishing a BP neural network forecasting module based on a training sample set; (2), carrying out severe-pollution scene determination based on the BP neural network forecasting result; to be specific, (21), defining a severe-pollution scene; (22), establishing a determination equation; (23), carrying out determination by using a neural network forecasting value; and (24), carrying out determination on the determination equation based on the neural network forecasting value determination result; (3), establishing a severe-pollution multi-element stepwise regression forecasting model according to the severe-pollution scene determination result; and (4), with combination of the forecasting determination process, outputting a forecasting result. According to the invention, the forecasting precision of the urban air quality, especially the early-warning forecasting of the severe-pollution scene, is improved comprehensively; and thus stable air quality precision forecasting under different pollution degrees can be realized.

The invention discloses a method, a device and equipment for determining newborn convection information, and a storage medium. The method comprises the following steps: determining weather forecast data according to acquired satellite data; after a variable required for simulating the infrared brightness temperature value is extracted from the weather forecast data, obtaining the infrared brightness temperature value through simulation based on the variable and the model information of the current meteorological satellite; and when a first preset time condition is satisfied, presenting the obtained infrared brightness temperature value in a meteorological cloud picture, and determining newborn convection information according to the displayed meteorological cloud picture. According to the technical scheme, the variable required for simulating the infrared brightness temperature value can be extracted from the weather forecast data based on the variable and the model information of the current meteorological satellite, the infrared brightness temperature value is obtained through simulation according to the variable, and then the primary convection information is determined through the infrared brightness temperature value; since the predictable duration of the weather forecast data is longer than that of the newborn convection, the forecasting capability of the newborn convection information is improved, and the forecasting duration of the newborn convection information is further improved.

The present invention provides a numerical forecast product anomaly integral dynamic statistical correction method and system, comprising: step 1, data preprocessing process; step 2, climate average state extraction process; step 3, physical decomposition process; step 4, correction process: Using ERA‑Interim reanalysis data to correct the circulation field results of ECMWF medium-range numerical forecast products, and using hourly precipitation datasets to correct the precipitation results of ECMWF medium-range numerical forecast products. The present invention provides a numerical forecast product anomaly integral dynamic statistical correction method and system, which is a numerical forecast correction method and system that can effectively reduce the systematic error of ECMWF medium-term numerical forecast products, significantly improve its forecast accuracy and available forecast timeliness The revised system can effectively improve the forecasting ability of the product in the short-term-extended period, heavy precipitation and other severe weather systems.