Rainstorm falling area identification method and system based on geostationary satellite cloud air guide fused wind field

Abstract

The invention discloses a rainstorm falling area identification method and system based on a static satellite cloud air guide fusion wind field, and the method comprises the steps: obtaining the cloud air guide original data of a static meteorological satellite, carrying out the preprocessing, and generating the high-quality cloud air guide data of the static satellite. And acquiring a numerical mode forecast wind field in real time, and fusing the satellite cloud wind guide data and the numerical mode forecast wind field to generate three-dimensional fused wind field data. And based on the three-dimensional fusion wind field data, through a first preset method, calculating and generating a diagnosis physical quantity. And performing rainstorm falling area identification based on the diagnosis physical quantity to generate a first rainstorm falling area. And correcting a forecast rainstorm falling area based on the first rainstorm falling area to generate a second rainstorm falling area. Therefore, according to the rainstorm falling area identification method based on the static satellite cloud wind guide fusion wind field, accurate forecasting of the rainstorm falling area is achieved, and therefore the timeliness and accuracy of weather forecasting are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of meteorological forecasting, and in particular to a method and system for identifying rainstorm areas based on a geostationary satellite cloud-wind-guided fusion wind field. Background Art

[0002] In existing weather services, the forecast of rainstorm areas mainly relies on numerical models, but due to the limitations of initial conditions and the description of real weather physical processes, the forecast accuracy is limited, especially for sudden rainstorm events. Although weather radar and conventional sounding data are used in severe convective weather forecasts, their temporal and spatial resolution is low and it is difficult to meet the needs of continuous monitoring. Although satellite observations can provide cloud map information for the entire region and all times, direct application in the forecast of rainstorm areas still requires further data processing and fusion. Summary of the invention

[0003] The embodiment of the present invention provides a method and system for identifying a rainstorm area based on a geostationary satellite cloud-wind fusion wind field, which realizes accurate prediction of the rainstorm area, thereby improving the timeliness and accuracy of weather forecasts.

[0004] To achieve the above objectives, in a first aspect, the present invention provides a method for identifying rainstorm areas based on geostationary satellite cloud-guided wind fusion wind field, comprising:

[0005] Obtain the original cloud guide wind data of the geostationary meteorological satellite, and perform preprocessing to generate high-quality cloud guide wind data of the geostationary satellite;

[0006] Acquire the wind field predicted by the numerical model in real time, and fuse the satellite cloud wind guide data and the wind field predicted by the numerical model to generate three-dimensional fused wind field data;

[0007] Based on the three-dimensional fused wind field data, a diagnostic physical quantity is calculated and generated by a first preset method;

[0008] Based on the diagnostic physical quantity, a rainstorm area is identified to generate a first rainstorm area;

[0009] The predicted heavy rain area is corrected based on the first heavy rain area to generate a second heavy rain area.

[0010] In one embodiment of the present invention, the obtaining of the original cloud guide wind data of the geostationary meteorological satellite and preprocessing to generate high-quality cloud guide wind data of the geostationary satellite includes:

[0011] Obtain the cloud-wind information of the water vapor channel and infrared channel of the geostationary meteorological satellite;

[0012] Generate the cloud guide wind raw data based on the integration of the water vapor channel cloud guide wind information and the infrared channel cloud guide wind information;

[0013] The cloud guide wind raw data is quality controlled to generate the geostationary satellite high-quality cloud guide wind data.

[0014] In one embodiment of the present invention, the real-time acquisition of the numerical model forecast wind field, and the fusion of the satellite cloud wind guide data and the numerical model forecast wind field to generate three-dimensional fused wind field data includes:

[0015] Acquiring the wind field forecasted by the numerical model in real time;

[0016] The satellite cloud wind guide data and the numerical model forecast wind field are fused through a second preset method to generate the three-dimensional fused wind field data.

[0017] In one embodiment of the present invention, the fusing of the satellite cloud wind guide data and the numerical model forecast wind field by a second preset method to generate the three-dimensional fused wind field data includes:

[0018] Calculate the background error covariance matrix B of the wind field predicted by the numerical model;

[0019] Calculate the observation error covariance matrix R of the satellite cloud wind data;

[0020] Based on the background error covariance matrix B and the observation error covariance matrix R, the satellite cloud wind guide data and the numerical model forecast wind field are fused through an optimal interpolation algorithm to generate the three-dimensional fused wind field data.

[0021] In the second aspect, the present invention provides a rainstorm area identification system based on the geostationary satellite cloud guide wind fusion wind field, including: a first generation module, a second generation module, a third generation module, a fourth generation module and a fifth generation module. The first generation module is used to obtain the original cloud guide wind data of the geostationary meteorological satellite, and perform preprocessing to generate high-quality cloud guide wind data of the geostationary satellite. The second generation module is used to obtain the numerical model forecast wind field in real time, and fuse the satellite cloud guide wind data and the numerical model forecast wind field to generate three-dimensional fused wind field data. The third generation module is used to calculate and generate diagnostic physical quantities based on the three-dimensional fused wind field data through a first preset method. The fourth generation module is used to identify the rainstorm area based on the diagnostic physical quantity and generate a first rainstorm area. And the fifth generation module is used to correct the forecast rainstorm area based on the first rainstorm area to generate a second rainstorm area.

[0022] In one embodiment of the present invention, the first generation module includes: a first acquisition unit, a first generation unit, and a second generation unit. The first acquisition unit is used to acquire the water vapor channel cloud wind information and the infrared channel cloud wind information of the geostationary meteorological satellite. The first generation unit is used to integrate the water vapor channel cloud wind information and the infrared channel cloud wind information to generate the cloud wind raw data. The second generation unit is used to perform quality control on the cloud wind raw data to generate the geostationary satellite high-quality cloud wind data.

[0023] In one embodiment of the present invention, the second generation module includes: a second acquisition unit and a third generation unit. The second acquisition unit is used to acquire the numerical model forecast wind field in real time. And the third generation unit is used to fuse the satellite cloud wind guide data and the numerical model forecast wind field through a second preset method to generate the three-dimensional fused wind field data.

[0024] In one embodiment of the present invention, the third generating unit includes:

[0025] The first calculation subunit is used to calculate the background error covariance matrix B of the wind field predicted by the numerical model;

[0026] A second calculation subunit is used to calculate the observation error covariance matrix R of the satellite cloud wind data;

[0027] A generating subunit is used to fuse the satellite cloud wind guide data and the numerical model forecast wind field based on the background error covariance matrix B and the observation error covariance matrix R through an optimal interpolation algorithm to generate the three-dimensional fused wind field data.

[0028] at least one processor; and

[0029] a memory communicatively coupled to the at least one processor;

[0030] In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for identifying heavy rain areas based on the geostationary satellite cloud-wind-guided fusion wind field as described above.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium, including a computer program and instructions. When the computer program or the instructions are executed on a computer, the computer executes the method for identifying heavy rain areas based on the geostationary satellite cloud-guided wind field fusion as described above.

[0032] Compared with the prior art, the method and system for identifying rainstorm areas based on geostationary satellite cloud-wind guidance and wind field fusion according to the present invention have the following beneficial effects:

[0033] 1. The adaptive intelligent optimal interpolation method can dynamically adjust the interpolation coefficient according to real-time data, and combine the neural network to accurately estimate the observation error, which significantly improves the accuracy of wind field fusion;

[0034] 2. It is applicable to the data fusion requirements of different atmospheric vertical layers and provides stable and reliable interpolation results under rapidly changing meteorological conditions;

[0035] 3. The fusion process is rapid, which meets the timeliness requirements of weather forecasting and can quickly generate three-dimensional fused wind field data with high temporal and spatial resolution.

[0036] 4. It not only provides the best three-dimensional wind field grid products, but also provides diagnostic physical quantity products such as vorticity, divergence, and vertical wind shear, providing forecasters with more comprehensive decision support;

[0037] 5. By monitoring the strong convective activities in the potential rainstorm areas, the rainstorm areas predicted by the model are effectively corrected, thus improving the accuracy and reliability of the forecast. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of a method for identifying a rainstorm area based on a geostationary satellite cloud-wind guide fusion wind field in Embodiment 1 of the present invention;

[0039] Figure 2 It is a structural schematic diagram of a rainstorm area identification system based on geostationary satellite cloud-wind guide fusion wind field in Embodiment 2 of the present invention;

[0040] Figure 3 It is a schematic diagram of the structure of an electronic device in Embodiment 3 of the present invention;

[0041] Figure 4 It is a logical flow diagram of a method for identifying a rainstorm area based on a geostationary satellite cloud-guided wind fusion wind field in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0042] The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention, rather than to limit the embodiments of the present invention. It is also necessary to explain that, for ease of description, only parts related to the embodiments of the present invention are shown in the accompanying drawings, rather than all structures.

[0043] For ease of understanding, the main implementation concepts of the embodiments of the present invention are first briefly described.

[0044] The current weather service mainly relies on numerical models to forecast the rainstorm area. With the improvement of numerical forecasting technology and the rapid improvement of computing power, the accuracy of weather forecasts by models has been continuously improved. However, the models are limited by initial conditions and the description of real weather physical processes, making the forecast of rainstorms still the focus and difficulty of today's weather forecasts, especially for sudden rainstorms. The forecasts of rainstorm areas by multiple models at home and abroad have large deviations, which seriously threaten the safety of people's lives and property. Therefore, we cannot rely solely on numerical models to forecast the rainstorm area.

[0045] Although weather radar has been widely used in the nowcasting of severe convective weather, precipitation has already occurred when the weather radar captures the precipitation echo. Conventional sounding data is a variety of meteorological elements in the atmosphere from top to bottom detected by sounding balloons. However, the temporal and spatial resolution of conventional sounding data is low (twice a day, stations cannot be deployed in special terrain areas), which cannot meet the needs of continuous monitoring of weather events.

[0046] The inventor discovered the technical defects as described in the previous background technology and fused the information of cloud-guided wind in stationary weather and wind field forecast by numerical model to form three-dimensional fused wind field data with high temporal and spatial resolution. The fused wind field can well reflect the characteristics of weather-scale and mesoscale weather systems, and at the same time conform to the usage habits of forecasters, and can also effectively realize the correction of numerical model wind field. At the same time, the fusion method is rapid and meets the business timeliness requirements of weather forecast. After fusion, it can not only provide the optimal fine three-dimensional wind field grid product, but also provide relevant physical quantity diagnosis products, ultimately improving the accuracy of heavy rain area forecast.

[0047] Embodiment 1, Figure 1 : is a flow chart of a method for identifying a rainstorm area based on a geostationary satellite cloud-wind guide fusion wind field in Embodiment 1 of the present invention, such as Figure 1 As shown, the first embodiment provides a method for identifying a rainstorm area based on a geostationary satellite cloud-wind-guided fusion wind field, including:

[0048] Step S100, obtaining the original cloud guide wind data of the geostationary meteorological satellite, and performing preprocessing to generate high-quality cloud guide wind data of the geostationary satellite;

[0049] Specifically, the original cloud-guide wind data of the water vapor channel and infrared channel are collected from geostationary meteorological satellites. By integrating this information, a comprehensive cloud-guide wind data set is constructed. Subsequently, a strict quality control process is adopted to screen the original data, eliminate low-quality data, and retain high-quality information. This series of preprocessing operations ensures the accuracy and reliability of the data, and ultimately generates high-quality cloud-guide wind data from geostationary satellites for subsequent analysis, laying a solid foundation for subsequent wind field fusion and identification of rainstorm areas.

[0050] Step S200, obtaining the wind field predicted by the numerical model in real time, and fusing the satellite cloud wind guide data and the wind field predicted by the numerical model to generate three-dimensional fused wind field data;

[0051] Specifically, for example, the numerical model forecast wind field data is accurately obtained from the meteorological bureau's data acquisition platform, and these data provide a preliminary prediction of the atmospheric wind field. Next, the advanced adaptive intelligent optimal interpolation method is used to deeply integrate the satellite cloud wind data with the numerical model forecast wind field. This process fully considers the error characteristics of the observation field and the background field, and dynamically adjusts the interpolation coefficient through an intelligent algorithm to ensure that the error of the fused wind field data is minimized. Finally, three-dimensional fused wind field data with high temporal and spatial resolution is generated. This data not only retains the macro-prediction ability of the numerical model, but also incorporates the fine observation information of the satellite cloud wind, providing strong support for the accurate identification of the rainstorm area.

[0052] Step S300, based on the three-dimensional fused wind field data, a diagnostic physical quantity is calculated and generated by a first preset method;

[0053] Specifically, based on the three-dimensional fused wind field data generated in step S200, a pre-set first preset method is used to perform in-depth calculations and analyses. In this process, by carefully processing key parameters such as wind speed and wind direction in the fused wind field, important diagnostic physical quantities such as vorticity, divergence, and vertical wind shear are calculated. These physical quantities not only reveal the inherent laws of atmospheric motion, but also provide a key basis for the subsequent identification of rainstorm areas. Through the calculation and generation of step S300, more comprehensive and accurate wind field diagnostic information is provided to weather forecasters.

[0054] Step S400, identifying a rainstorm area based on the diagnostic physical quantity to generate a first rainstorm area;

[0055] Specifically, by comprehensively analyzing the spatial distribution and change trends of these physical quantities, combined with meteorological principles and empirical models, the areas where heavy rain may occur are accurately identified. This process fully considers the dynamic evolution characteristics of the weather system and the indicative role of different physical quantities on the formation of heavy rain, and finally generates the first heavy rain area. This forecast result not only provides an important reference for disaster prevention and mitigation, but also lays the foundation for subsequent model forecast corrections. Among them, vorticity is a physical quantity that describes the strength of fluid rotation. It is often used in meteorology to characterize the intensity of weather systems. Heavy rain is often associated with strong vorticity centers. Analyze the vorticity distribution in the fusion wind field, especially looking for positive vorticity centers (corresponding to trough areas) and negative vorticity centers (corresponding to ridge areas). Heavy rain events are usually associated with the configuration of high-level negative vorticity and low-level positive vorticity, which often indicates significant vertical upward movement. Divergence is a physical quantity that measures the expansion or contraction of fluid volume, which is particularly important for meteorological forecasting; the positive and negative centers in the divergence field and their distribution are closely related to the distribution of severe convective weather. Analyze the divergence distribution in the fused wind field, especially look for the negative center (convergence zone) in the divergence field. These areas are often high-probability areas for heavy rain. In addition, mesoscale convergence zones often appear before precipitation, so monitoring the changes in the divergence field is of great significance for forecasting short-term heavy precipitation. Vertical wind shear refers to the rate of change of wind speed or wind direction with height, which is an important factor in promoting convective tilt and forming strong convective systems (such as supercell storms). Analyze the distribution of vertical wind shear in the fused wind field, especially look for strong wind shear areas. These areas are often associated with heavy rainfall or even heavy rain events. In addition, monitoring the changing trend of vertical wind shear within 24 hours can more accurately predict the time and location of heavy rain. Combined with the analysis results of diagnostic physical quantities such as vorticity, divergence and vertical wind shear, the areas where heavy rain may occur are comprehensively judged. By comparing the spatial distribution and changing trends of different physical quantities, the high-probability areas of heavy rain are determined.

[0056] Step S500, correcting the predicted rainstorm area based on the first rainstorm area to generate a second rainstorm area;

[0057] Specifically, step S500 is the refinement adjustment stage of the torrential rain area forecast. It is based on the first torrential rain area result obtained in step S400, and further combined with the original torrential rain area information predicted by the numerical model, and a detailed comparative analysis is performed. By evaluating the difference between the two, the advantages of the satellite cloud wind guide fusion wind field are used to scientifically and reasonably correct the torrential rain area predicted by the model. This process not only takes into account the complexity and uncertainty of atmospheric movement, but also makes full use of the accuracy of real-time observation data, and finally generates a more accurate second torrential rain area. This correction result significantly improves the accuracy and reliability of the torrential rain area forecast, and provides more powerful support for disaster prevention and mitigation. Among them, the first torrential rain area (derived based on the diagnosis of physical quantities) is compared with the original torrential rain area predicted by the model to compare the spatial distribution, range size and intensity of the two. The differences between the first torrential rain area and the torrential rain area predicted by the model are evaluated. These differences may be due to the uncertainty of the initial conditions of the model, the limitations of the physical process characterization, and the errors of the observation data. According to the difference evaluation results, a correction strategy is formulated. If the first rainstorm area is significantly different from the model forecast, and the analysis based on the diagnostic physical quantity is more reliable, the first rainstorm area is more likely to be used as the basis for correction. In the correction process, the information of multiple diagnostic physical quantities, as well as their interactions and influences, can be comprehensively considered to improve the accuracy and reliability of the correction. According to the correction strategy, the rainstorm area predicted by the model is adjusted and optimized to generate the second rainstorm area. This process involves, for example, adjusting the range, shape and intensity of the rainstorm area to more accurately reflect the actual weather conditions. The revised second rainstorm area is verified to compare its degree of consistency with the actual observation data. Through continuous verification and feedback, the correction strategy and method are optimized to improve the accuracy and reliability of the rainstorm area forecast.

[0058] In one embodiment of the present invention, the step S100 includes:

[0059] Step S101, obtaining water vapor channel cloud guide wind information and infrared channel cloud guide wind information of a geostationary meteorological satellite;

[0060] Specifically, step S101 is the initial step of data collection, focusing on capturing key wind field information from geostationary meteorological satellites. Specifically, this step aims to obtain the satellite's water vapor channel and infrared channel cloud wind data. The water vapor channel is sensitive to the distribution of water vapor in the atmosphere and helps to identify humidity changes in the cloud system; while the infrared channel reveals the temperature structure of the cloud top by measuring the infrared radiation emitted by the surface and clouds, and then infers wind speed and direction. For example, FY-4 (Fengyun-4) is China's new generation of geostationary meteorological satellite, which can simultaneously provide water vapor and infrared cloud images with high temporal and spatial resolution, providing a rich and accurate data source for step S101.

[0061] Step S102, integrating the water vapor channel cloud wind guide information and the infrared channel cloud wind guide information to generate the cloud wind guide original data;

[0062] Specifically, the cloud guide wind information of the water vapor channel obtained in step S101 is integrated with the cloud guide wind information of the infrared channel. For example, the water vapor channel has 100 observation points and the infrared channel also has 100 observation points. When integrating, the two sets of data are merged to form a data set containing 200 observation points. This process takes into account the complementarity of the two channel data, and combines their respective advantages in revealing the dynamic characteristics of the cloud system to generate a more comprehensive and multi-dimensional cloud guide wind original data set. Taking the FY-4 (Fengyun-4) satellite as an example, the multi-channel cloud image data it provides is widely used in cloud guide wind inversion. By integrating the information of its water vapor and infrared channels, it can more accurately capture the changes in wind speed and direction inside the cloud system, laying a solid foundation for subsequent wind field analysis and heavy rain prediction.

[0063] Step S103, performing quality control on the cloud guide wind raw data to generate the geostationary satellite high-quality cloud guide wind data;

[0064] Specifically, step S103 is an important link to ensure data accuracy, and it implements strict quality control on the original cloud guide wind data generated in step S102. Through a series of automated means, abnormal or low-quality data points caused by instrument errors, cloud obstruction or atmospheric interference are eliminated, and high-quality and reliable cloud guide wind information is retained. This process effectively improves the accuracy and credibility of the data, and provides a solid foundation for subsequent meteorological analysis and forecasting. Taking the FY-4 (Fengyun-4) satellite as an example, the quality index (QI: Quality Indictor) of its cloud guide wind data is used to represent the data quality of the inverted cloud guide wind. The larger the value, the higher the accuracy of the cloud guide wind data. We believe that data with a QI value greater than 80 is high-quality cloud guide wind data. After quality control in step S103, it can more accurately reflect the actual situation of the atmospheric wind field and provide strong support for meteorological services such as heavy rain area forecasting.

[0065] In one embodiment of the present invention, step S200 includes:

[0066] Step S201, obtaining the wind field forecasted by the numerical model in real time;

[0067] Specifically, step S201 involves obtaining the latest numerical model forecast wind field data from the Meteorological Bureau data acquisition platform. The numerical model forecast wind field is based on complex mathematical models and a large amount of observation data, and is calculated by high-performance computer simulation to predict the atmospheric wind field in the future. For example, the Meteorological Bureau regularly publishes global wind field forecast data generated by its integrated forecast system, which are widely used in various meteorological services and scientific research activities. In step S201, the system automatically and in real time downloads these numerical model forecast wind field data from the Meteorological Bureau Information Center for subsequent fusion processing with satellite cloud guide wind data.

[0068] Step S202, fusing the satellite cloud wind guide data and the numerical model forecast wind field by a second preset method to generate the three-dimensional fused wind field data;

[0069] Specifically, the second preset method is used to deeply integrate the obtained numerical model forecast wind field with the satellite cloud wind data. This method usually involves optimal interpolation processing technology, which aims to comprehensively consider the macro prediction ability of the numerical model and the fine structure information of satellite observations, dynamically adjust the fusion weight through intelligent algorithms, and generate three-dimensional fused wind field data with high spatiotemporal resolution and small errors.

[0070] In one embodiment of the present invention, step S202 includes:

[0071] Step S2021, calculating the background error covariance matrix B of the wind field forecasted by the numerical model;

[0072] Specifically, the background error covariance matrix B is used to describe the statistical correlation between the prediction errors at different locations in the numerical model forecast wind field. This matrix contains the variance of the prediction error at each grid point (elements on the matrix diagonal) and the covariance of the prediction errors between different grid points (elements on the non-diagonal of the matrix). Calculating the B matrix usually involves statistical analysis of the historical data of the model forecast to determine the distribution of prediction errors at each grid point and their spatial correlation. This process is crucial for the subsequent fusion of satellite cloud wind data and numerical model forecast wind fields, because it can help determine how to weight data from different sources according to the prediction uncertainty of the numerical model during the fusion process, thereby generating more accurate three-dimensional fused wind field data.

[0073] Step S2022, calculating the observation error covariance matrix R of the satellite cloud wind guide data;

[0074] Specifically, the observation error covariance matrix R is used to describe the statistical correlation of observation errors between different observation points in satellite cloud wind data. The R matrix contains the variance of the observation error at each observation point (elements on the matrix diagonal) and the covariance of the observation error between different observation points (elements on the matrix off-diagonal). In order to calculate the R matrix, it is usually necessary to use historical satellite cloud wind data and corresponding sounding observation data for time and space matching, extract the observation error of each observation point (i.e. the difference between the satellite cloud wind value and the sounding observation value), and calculate the variance and covariance of these errors by statistical methods. In addition, neural network technology can also be used to more accurately estimate the complex structure and spatial correlation of observation errors. By training the neural network model, it can be enabled to learn the patterns of different observation errors, thereby obtaining more accurate observation error predictions. The final R matrix contains not only diagonal elements, but also non-diagonal elements that reflect the spatial correlation of observation errors, which is crucial for the subsequent fusion of satellite cloud wind data and numerical model forecast wind field through the optimal interpolation algorithm.

[0075] Step S2023, based on the background error covariance matrix B and the observation error covariance matrix R, the satellite cloud wind guide data and the numerical model forecast wind field are fused through an optimal interpolation algorithm to generate the three-dimensional fused wind field data;

[0076] Specifically, the weights of each observation point and each numerical model grid point are calculated according to the background error covariance matrix B and the observation error covariance matrix R. The weight reflects the relative importance of the observation data and the model data in the fusion process. The size of the weight depends on the statistical characteristics of the observation error and the model error, as well as the spatial correlation between the observation point and the grid point. Using the calculated weights, the satellite cloud wind observation data and the numerical model forecast wind field are weighted averaged to generate the fused wind field data. Among them, for each grid point, its fused wind field value will be the result of the weighted average of the observation values ​​of the surrounding observation points and the model forecast values ​​according to the weight. Through the optimal interpolation algorithm, it is ensured that the fused wind field data has the smallest error in a statistical sense. This is achieved by minimizing a cost function that takes into account the statistical characteristics of the observation error and the model error, as well as the spatial correlation between the observation data and the model data. The fused wind field data is interpolated onto a regular three-dimensional grid to generate three-dimensional fused wind field data. This step ensures that the fused wind field data has a uniform spatial resolution and format, which is convenient for subsequent analysis and application.

[0077] Through the above steps, step S2023 can fully utilize the advantages of satellite cloud wind guide data and numerical model wind field forecasting to generate high-precision, high temporal and spatial resolution three-dimensional fused wind field data, providing strong support for accurate forecasting of heavy rain areas.

[0078] In one embodiment of the present invention, step S300 includes:

[0079] Based on the three-dimensional fused wind field data, vorticity, divergence, vertical wind shear and 24-hour shear trend are calculated to generate the diagnostic physical quantity;

[0080] The formula for calculating vorticity is:

[0081]

[0082] The formula for calculating divergence is:

[0083]

[0084] The formula for vertical wind shear is:

[0085]

[0086] Among them, u is the longitudinal wind speed, v is the zonal wind speed, a is the radius of the earth, φ is the latitude, j is the grid point index in the latitude direction, and i is the grid point index in the longitude direction.

[0087] Among them, rv(j,i) is the vorticity value, which is a rotation measure calculated by the u and v wind components; dv(j,i) is the divergence value, which is a divergence measure calculated by the u and v wind components; dy2(j) is the grid spacing in the latitude direction, and dx2(j) is the grid spacing in the longitude direction; s is the vertical wind shear value, which is the wind shear intensity calculated by the wind speed difference at different altitudes; tan is the tangent function, and tan(φ(j)) is the tangent value of the latitude of the j grid point;

[0088] The 24-hour shear trend is the difference of the vertical wind shear in 24 hours.

[0089] Figure 4 FIG. 1 is a logic flow diagram of a method for identifying a rainstorm area based on a geostationary satellite cloud-guided wind fusion wind field in a specific embodiment of the present invention. Figure 4 As shown, in a specific embodiment, the method for identifying rainstorm areas based on geostationary satellite cloud-guided wind fusion wind field of the present invention includes:

[0090] Step 1: Preprocessing of multi-channel information of cloud-wind guidance of Fengyun geostationary meteorological satellite;

[0091] Specifically, the Fengyun geostationary meteorological satellite contains cloud guide wind information of the water vapor channel and the infrared channel. In order to obtain more wind field information, the cloud guide wind data of different channels are integrated. For example, the water vapor channel has 100 observation points and the infrared channel also has 100 observation points. During the integration, the two sets of data are merged to form a data set containing 200 observation points, thereby obtaining the cloud guide wind information of all channels. The data is then marked with an identification code representing the quality of the satellite product. In order to ensure the fusion accuracy, high-quality satellite cloud guide wind data is selected; the cloud guide wind data is quality controlled to obtain the quality identification code QI of the cloud guide wind file. Those greater than 80 are considered to have a higher inversion quality and are retained. Considering that both numerical models and conventional sounding data contain standard pressure layer data commonly used by forecasters (50hPa, 100hPa, 150hPa, 200hPa, 300hPa, 500hPa, 700hPa, 850hPa), and the satellite detects from top to bottom and performs inversion calculations based on the movement vector of the cloud, the satellite cloud wind data obtained are similar to scattered point distribution. There are more cloud wind data in the high layers, while there are fewer cloud wind data in the low layers. Therefore, the distribution position of the available cloud wind data is not the standard pressure layer commonly used by forecasters. In order to subsequently merge with the model wind field data, the commonly used standard pressure layer is used as a benchmark to find the cloud wind data within ±50hPa of the standard pressure layer, and the satellite cloud wind data after quality control are layered and processed layer by layer.

[0092] Step 2: Integrate the wind guidance of Fengyun geostationary meteorological satellites and the wind field forecast by numerical models to construct three-dimensional refined grid wind field data;

[0093] Specifically, the present invention adopts an adaptive intelligent optimal interpolation method to fuse satellite cloud wind and numerical model forecast wind field. The optimal interpolation method can interpolate the observation value at any position to a regular grid, using the background field as the first guess value. There are errors in both the observation field and the background field, and because this method takes into account the expected differences to fuse the observation field and the background field. The error of the fused wind field is the smallest, which is smaller than the error of any data in the cloud wind or numerical model product. The optimal interpolation method is a data fusion method that is widely used and relatively mature internationally. It can consider the statistical characteristics of the background field and observation errors, that is, it includes the inherent relationship between observation, forecast and analysis, and determines the best weight in the sense of least squares, so that the analysis error can be minimized in a statistical sense. The basic principle of this method is that the analysis value on the grid point is obtained by adding the background value of the grid point to the revised value, and the revised value is obtained by weighting the difference between the observation value and the background value of the surrounding stations, and the weight coefficient (that is, the optimal interpolation coefficient) should minimize the error of the grid point analysis value. The general form of optimal interpolation is:

[0094] x a =xb +K(y-Hx b )(Formula 1-1)

[0095] K=BH T (HBH T +R) -1 (Formula 1-2)

[0096] where x a represents the fused analysis field; x b represents the background field, which is generally taken as the model forecast value at the last moment; y represents the observation field; H is the linear observation operator, which represents the conversion from the model space to the observation space; B represents the background error covariance matrix; R represents the observation error covariance matrix, and the observation error contains the representative error of the observation data. The "T" and "-1" above represent the transpose and inverse of the matrix respectively.

[0097] The optimal interpolation is closer to three-dimensional variation in form. It is difficult to calculate the analytical value directly through the above formula because it involves the inversion of the B matrix. For high-resolution numerical models, the B matrix is ​​a huge dimensional matrix, and it is not realistic to directly invert it. However, it can be obtained by minimizing the cost function J:

[0098]

[0099] Let x = xx b ,y=y-Hx b , substituting into (Equation 1-3), and using the linear observation operator assumption, we can get:

[0100]

[0101] Among them, X represents the difference between the analysis field and the background field, also known as the analysis increment; Y represents the difference between the observation field and the background field. That is, through mathematical means (such as iteration method), an appropriate analysis field is found to minimize the functional J. This method makes it easy to make all observation data in space available for analysis, and consumes less computer resources than other more complex methods (such as four-dimensional variation and ensemble Kalman filtering).

[0102] It can be seen from (Formula 1-3) that B and R play an important role in the analysis process. Currently, there is no accurate formula to directly calculate B and R, and they can only be estimated using approximate methods.

[0103] For the estimation of the background error covariance B matrix, the following formula is used:

[0104]

[0105] in, It represents the sum of squares of the error (the difference between the grid point value and the true value of the point) of the jth grid point and is the diagonal element of the B matrix. Represents the statistical variance of the jth grid point. The error term in the statistical variance is also called the anomaly term, which is the difference between each sample value (grid point value) and the average value in the statistical time period, so that the estimation of the diagonal elements of the background error covariance matrix depends only on its statistical variance. and parameter c j .

[0106] The traditional optimal interpolation algorithm, c j is a fixed value, which may lead to insufficient interpolation accuracy at different altitudes. By introducing an adaptive algorithm, c is dynamically adjusted. j The range of values ​​of can be adjusted according to different pressure altitude layers. By using the statistical method of regression analysis, the sounding data and model background field data of different pressure altitude layers are calculated, and the error distribution and statistical variance of each layer are calculated, so as to estimate the optimal c of each altitude layer respectively. j Value, so that formula 1-5 becomes the following 1-6

[0107]

[0108] Among them, c j (h) represents the parameter c at different altitudes h j .

[0109] For the estimation of the non-diagonal elements of the B matrix, the traditional Gaussian distribution form is adopted:

[0110]

[0111] Among them, i and j represent the order of the grid points, Δx and Δy represent the horizontal east-west distance and north-south distance between the i-th grid point and the j-th grid point, respectively. It is assumed here that the horizontal correlation of the forecast error (background error) decreases exponentially with the increase of the horizontal distance. Lx and Ly are the horizontal correlation scales of the forecast error in the east-west and north-south directions, respectively, indicating the horizontal distance when the correlation coefficient of the forecast error is reduced to 1 / e, and are usually taken as constants. In order to improve the accuracy of the estimation, the correlation scales Lx and Ly are dynamically adjusted using the regression analysis method according to different pressure altitude layers and background field data.

[0112] The present invention no longer uses fixed constants when calculating the background error covariance matrix B, but adopts an adaptive method to dynamically adjust the constants of different pressure altitude layers, thereby realizing adaptive dynamic B matrix estimation. This method calculates the error between the model background field data and the sounding observation data of different pressure altitude layers through regression analysis and statistical methods, thereby optimizing the estimation of the B matrix.

[0113] The observation error covariance matrix R represents the observation error of satellite cloud wind data. Compared with the background error covariance matrix B of the numerical model, the R matrix is ​​calculated by neural network technology to more accurately capture the complex structure and spatial correlation of the observation error.

[0114] Through the historical satellite cloud wind data and the corresponding sounding observation data, and matching them in time and space, then extracting features (geographic location, time, different pressure altitude layers) and calculated observation errors (i.e., the difference between satellite cloud wind value and sounding value) from these data, a training data set is constructed. A neural network model (such as a convolutional neural network) is selected, and the model is trained using the extracted features and observation errors so that it can learn the patterns of different observation errors. Through the trained neural network model, the real-time satellite cloud wind data is error predicted to obtain the error variance of each observation point. The diagonal elements of the matrix R are the error variances of each observation point (obtained by the convolutional neural network model). Specifically, the cloud wind data of the geostationary meteorological satellite, including the data of the water vapor channel and the infrared channel, are obtained from the historical archive. These data should contain the geographical location (longitude, latitude), time, and wind speed and direction information of different pressure altitude layers. The sounding observation data of the corresponding time period is obtained from the meteorological observation station. These data usually include the temperature, humidity, wind speed and wind direction of different pressure altitude layers. Satellite cloud wind data are matched with sounding observation data according to time and geographic location to ensure that each pair of matched data points have similar time and spatial locations. If there is a mismatch in time or space, the data can be adjusted by interpolation or extrapolation to improve the accuracy of the match. For each pair of matched data points, the difference between the satellite cloud wind value and the sounding observation value is calculated. This difference is the observation error. The observation error can be expressed as wind speed error and wind direction error, or combined into a comprehensive error value. Features are extracted from the matched data set, which will be used to train the neural network model. Features may include, for example:

[0115] Geographic location: longitude, latitude;

[0116] Time: year, month, day, hour, minute;

[0117] Pressure altitude layer: data of different pressure layers (such as 500hPa, 850hPa, etc.);

[0118] Observation error: wind speed error, wind direction error or combined error.

[0119] The extracted features and the corresponding observation errors are combined into training samples. Each training sample should contain input features (geographic location, time, pressure altitude layer) and output labels (observation error), and all training samples are organized into a data set for subsequent training of the neural network model.

[0120] According to the complexity of the problem and the characteristics of the data, a suitable neural network model is selected. Considering that the wind field elements have obvious spatial correlation, the convolutional neural network model is selected. The training data set is divided into a training set and a validation set (or test set). The neural network model is trained using the training set, and the model parameters are adjusted through the back propagation algorithm to minimize the prediction error. During the training process, the validation set is used to monitor the performance of the model to prevent overfitting. The model parameters can be further optimized by cross-validation and other methods. The trained neural network model can be used to predict the error of real-time satellite cloud wind guide data; for each real-time observation point, its geographical location, time, pressure altitude layer and other features are extracted, and these features are input into the neural network model to obtain the predicted observation error (i.e., error variance). The predicted error variance is used to fill the diagonal elements of the matrix R; for the off-diagonal elements (representing the error covariance between different observation points), the neural network model can be used to directly predict these covariance values; and the optimal spatial correlation model parameters or neural network structure can be determined by cross-validation and other methods. Through the above steps, we can specifically implement the method of building a training data set through historical satellite cloud wind guide data and sounding observation data, and use the neural network model for error prediction, so as to calculate the observation error covariance matrix R, which provides an important basis for subsequent wind field fusion.

[0121] The off-diagonal elements represent the error covariance between the i-th point and the j-th point. The specific formula is: Rij = σ i σ j f, f are the correlation coefficients predicted by the convolutional neural network model, which are used to describe the spatial correlation between observation errors. The optimal number of observation points is automatically selected by the cross-validation method, that is, during the training process, the model is trained and verified according to different numbers of observation points, and the number of observation points with the smallest verification error is selected. In this way, the constructed R matrix contains not only diagonal elements, but also non-diagonal elements reflecting spatial correlation, so as to more accurately describe the statistical characteristics and spatial correlation of observation errors. This method can capture the complex structure of observation errors and improve the accuracy of data fusion.

[0122] Step 3: Calculate diagnostic physical quantities such as vorticity, divergence, and vertical wind shear based on the fused wind field;

[0123] Specifically, based on the fused wind field information, the diagnostic physical quantities such as vorticity, divergence, vertical wind shear, and 24-hour shear trend are calculated. u and v represent the meridional wind speed and zonal wind speed, respectively, a is the radius of the earth, and φ is the latitude.

[0124] Vorticity formula:

[0125]

[0126] Divergence formula:

[0127]

[0128] Vertical Wind Shear:

[0129] Among them, rv(j,i): refers to the vorticity value, which is a rotation measure calculated by the u and v wind components. dv(j,i): refers to the divergence value, which is a divergence measure calculated by the u and v wind components. s: refers to the vertical wind shear value, which is the wind shear intensity calculated by the wind speed difference at different altitudes (200hPa and 850hPa). j: refers to the grid point index in the latitude direction. i: refers to the grid point index in the longitude direction. dy2(j): refers to the grid spacing in the latitude direction. dx2(j): refers to the grid spacing in the longitude direction. tan: refers to the tangent function. tan(φ(j)): refers to the tangent value of the latitude of the j grid points; among them, the 24-hour shear trend is the difference in vertical wind shear in 24 hours.

[0130] Step 4: According to the diagnostic physical quantities, identify the heavy rain area and revise the heavy rain area predicted by the model.

[0131] Specifically, the amount of data contained in the upper atmosphere by satellite cloud guidance is very rich, which is related to the detection principle of satellites. Therefore, the high-level wind field of satellite cloud guidance can effectively correct the high-level wind field predicted by the model. The fused high-level wind field and its related diagnostic physical quantities, vorticity refers to the vertical relative vorticity, which is often used to characterize the strength of the system. According to meteorological principles, the ridge area corresponds to a negative vorticity center, and the trough area corresponds to a positive vorticity center; the configuration of high-level negative vorticity and low-level positive vorticity often reflects a significant vertical upward movement. The positive and negative centers of the divergence field and their distribution are closely related to the distribution of severe convective weather. The movement direction of the precipitation area is very consistent with the convergence area, and the mesoscale convergence area often appears 1 to 2 hours before precipitation. Therefore, mastering the changes in the mesoscale divergence field is an important basis for predicting the occurrence of short-term mesoscale precipitation and rainstorms in the future. Vertical wind shear can promote the tilt of convection and help form strong convective systems, such as supercell storms, which are often accompanied by heavy rainfall or even rainstorms. Enhanced wind shear trends are often associated with strong convective activities, which may bring extreme rainfall events. By monitoring the 24-hour shear trend, the time and location of heavy rain can be predicted more accurately, improving the accuracy of the forecast. It can well reflect the strong center position of the convective weather distribution, which is closely related to the location of the heavy rain area. Forecasters can compare with the model wind field and diagnostic quantities, revise the heavy rain area predicted by the model, and effectively improve the forecast accuracy of the heavy rain area. Among them, the diagnostic variables have certain indicative significance in meteorology for the diagnosis of heavy rain areas. For example, the divergence field indicates a strong radiation center, which indicates that there is strong convective activity below, which will be the location of the heavy rain area.

[0132] Embodiment 2: Figure 2 : is a structural schematic diagram of a rainstorm area identification system based on geostationary satellite cloud-wind guide fusion wind field in Embodiment 2 of the present invention, such as Figure 2 As shown, embodiment 2 provides a system for identifying rainstorm areas based on geostationary satellite cloud guide wind fusion wind field, including: a first generation module, a second generation module, a third generation module, a fourth generation module and a fifth generation module. The first generation module is used to obtain the original cloud guide wind data of the geostationary meteorological satellite, and perform preprocessing to generate high-quality cloud guide wind data of the geostationary satellite. The second generation module is used to obtain the numerical model forecast wind field in real time, and fuse the satellite cloud guide wind data and the numerical model forecast wind field to generate three-dimensional fused wind field data. The third generation module is used to calculate and generate diagnostic physical quantities based on the three-dimensional fused wind field data through a first preset method. The fourth generation module is used to identify the rainstorm area based on the diagnostic physical quantity and generate a first rainstorm area. And the fifth generation module is used to correct the forecast rainstorm area based on the first rainstorm area to generate a second rainstorm area.

[0133] In one embodiment of the present invention, the first generation module includes: a first acquisition unit, a first generation unit, and a second generation unit. The first acquisition unit is used to acquire the water vapor channel cloud wind information and the infrared channel cloud wind information of the geostationary meteorological satellite. The first generation unit is used to integrate the water vapor channel cloud wind information and the infrared channel cloud wind information to generate the cloud wind raw data. The second generation unit is used to perform quality control on the cloud wind raw data to generate the geostationary satellite high-quality cloud wind data.

[0134] In one embodiment of the present invention, the second generation module includes: a second acquisition unit and a third generation unit. The second acquisition unit is used to acquire the numerical model forecast wind field from the meteorological bureau data acquisition platform. And the third generation unit is used to fuse the satellite cloud wind guide data and the numerical model forecast wind field through a second preset method to generate the three-dimensional fused wind field data.

[0135] In one embodiment of the present invention, the third generating unit includes:

[0136] The first calculation subunit is used to calculate the background error covariance matrix B of the wind field predicted by the numerical model;

[0137] A second calculation subunit is used to calculate the observation error covariance matrix R of the satellite cloud wind data;

[0138] A generating subunit is used to fuse the satellite cloud wind guide data and the numerical model forecast wind field based on the background error covariance matrix B and the observation error covariance matrix R through an optimal interpolation algorithm to generate the three-dimensional fused wind field data.

[0139] In one embodiment of the present invention, the third generation module includes a fourth generation unit, which is used to calculate the vorticity, divergence, vertical wind shear and 24-hour shear trend based on the three-dimensional fused wind field data to generate the diagnostic physical quantity;

[0140] The formula for calculating vorticity is:

[0141]

[0142] The formula for calculating divergence is:

[0143]

[0144] The formula for vertical wind shear is:

[0145]

[0146] Among them, u is the longitudinal wind speed, v is the zonal wind speed, a is the radius of the earth, φ is the latitude, j is the grid point index in the latitude direction, and i is the grid point index in the longitude direction.

[0147] Among them, rv(j,i) is the vorticity value, which is a rotation measure calculated by the u and v wind components; dv(j,i) is the divergence value, which is a divergence measure calculated by the u and v wind components; dy2(j) is the grid spacing in the latitude direction, and dx2(j) is the grid spacing in the longitude direction; s is the vertical wind shear value, which is the wind shear intensity calculated by the wind speed difference at different altitudes; tan is the tangent function, and tan(φ(j)) is the tangent value of the latitude of the j grid point;

[0148] The 24-hour shear trend is the difference of the vertical wind shear in 24 hours.

[0149] The various variations and specific examples of the method for identifying a rainstorm area based on a geostationary satellite cloud-guide wind fusion wind field provided in Example 1 are also applicable to a system for identifying a rainstorm area based on a geostationary satellite cloud-guide wind fusion wind field provided in the present embodiment. Through the above detailed description of a method for identifying a rainstorm area based on a geostationary satellite cloud-guide wind fusion wind field, those skilled in the art can clearly know the implementation method of a system for identifying a rainstorm area based on a geostationary satellite cloud-guide wind fusion wind field in the present embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0150] Embodiment 3, Figure 3is a schematic diagram of the structure of an electronic device in Embodiment 3 of the present invention, such as Figure 3 As shown, the third embodiment further provides an electronic device 300 , which may include: a processor 301 and a memory 302 .

[0151] The memory 302 is used to store programs; the memory 302 may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviated: RAM), such as static random-access memory (English: static random-access memory, abbreviated: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviated: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory 302 is used to store computer programs (such as applications, functional modules, etc. that implement the above method), computer instructions, etc., and the above computer programs, computer instructions, etc. can be partitioned and stored in one or more memories 302. And the above computer programs, computer instructions, data, etc. can be called by the processor 301.

[0152] The above-mentioned computer programs, computer instructions, etc. may be stored in partitions in one or more memories 302 . And the above-mentioned computer programs, computer instructions, etc. may be called by the processor 301 .

[0153] The processor 301 is used to execute the computer program stored in the memory 302 to implement each step of the method involved in the above embodiment.

[0154] For details, please refer to the relevant description in the previous method embodiment.

[0155] The processor 301 and the memory 302 may be independent structures or integrated structures. When the processor 301 and the memory 302 are independent structures, the memory 302 and the processor 301 may be coupled and connected via a bus 303 .

[0156] The electronic device of this embodiment can execute the technical solution in the above method, and its specific implementation process and technical principle are the same, which will not be repeated here.

[0157] Embodiment 4, Embodiment 4 also provides a computer-readable storage medium, including a computer program and instructions. When the computer program or instructions are executed on a computer, the computer executes the method for identifying heavy rain areas based on the geostationary satellite cloud-guide wind fusion wind field of any embodiment of the present invention.

[0158] Computer-readable storage media include: USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks, and other media that can store program codes.

[0159] This embodiment also provides a computer program product, which includes: a computer program, which is stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0160] It should be understood that the various forms of the above-described processes may be used to reorder, add or delete steps. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in a different order.

[0161] As long as the expected results of the technical solution disclosed in the present invention can be achieved, this document does not impose any limitations here.

[0162] In summary, the method and system for identifying rainstorm areas based on geostationary satellite cloud-guided wind fusion wind field of the present invention have the following beneficial effects:

[0163] 1. The adaptive intelligent optimal interpolation method adopted by the present invention has significant advantages over the traditional optimal interpolation method; the adaptive algorithm can dynamically adjust the interpolation coefficient according to real-time data and environmental changes to improve the accuracy and reliability of the results; the R matrix is ​​trained and optimized by the convolutional neural network, which can capture complex nonlinear relationships and provide more accurate estimates; considering the characteristics of satellite cloud wind guide and model wind field forecast data, the intelligent algorithm can maximize the advantages of various types of data and improve the wind field estimation effect; in addition, the adaptive intelligent optimal interpolation method has stronger adaptability, can cope with the data fusion requirements of different atmospheric vertical levels, and provide stable and reliable interpolation results under rapidly changing meteorological conditions, thereby significantly improving the correction effect of high-level wind fields and the forecast accuracy of rainstorm areas;

[0164] 2. The three-dimensional and refined fusion wind field products are established based on the cloud wind guidance of geostationary meteorological satellites. They can reflect the characteristics of weather scale and mesoscale weather systems, conform to the usage habits of forecasters, make up for the problem that a single data cannot well capture the occurrence and development of weather systems, and realize continuous dynamic monitoring of severe convective weather. The satellite cloud wind guidance can not only effectively correct the wind field of the model, but also make up for the problem of low temporal and spatial resolution of the sounding wind field;

[0165] 3. The diagnostic quantities related to the strong center position of convective weather distribution, such as vorticity, divergence and vertical wind shear calculated based on the fused wind field, can effectively realize the monitoring and forecast of potential rainstorm areas. Forecasters can forecast the rainstorm areas based on this fused wind field and product correction mode, thereby improving the forecast accuracy of the rainstorm areas.

[0166] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for identifying rainstorm areas based on geostationary satellite cloud-guided wind fusion wind field, characterized in that: include: Obtain the original cloud guide wind data of the geostationary meteorological satellite, and perform preprocessing to generate high-quality cloud guide wind data of the geostationary satellite; Acquire the wind field predicted by the numerical model in real time, and fuse the satellite cloud wind guide data and the wind field predicted by the numerical model to generate three-dimensional fused wind field data; Based on the three-dimensional fused wind field data, a diagnostic physical quantity is calculated and generated by a first preset method; Based on the diagnostic physical quantity, a rainstorm area is identified to generate a first rainstorm area; The predicted heavy rain area is corrected based on the first heavy rain area to generate a second heavy rain area.

2. The method for identifying rainstorm areas based on geostationary satellite cloud-wind guidance and wind field fusion according to claim 1, characterized in that: The method of obtaining the original cloud guide wind data of the geostationary meteorological satellite and performing preprocessing to generate high-quality cloud guide wind data of the geostationary satellite includes: Obtain the cloud-wind information of the water vapor channel and infrared channel of the geostationary meteorological satellite; Generate the cloud guide wind raw data based on the integration of the water vapor channel cloud guide wind information and the infrared channel cloud guide wind information; The cloud guide wind raw data is quality controlled to generate the geostationary satellite high-quality cloud guide wind data.

3. The method for identifying rainstorm areas based on geostationary satellite cloud-wind guidance and wind field fusion according to claim 1, characterized in that: The real-time acquisition of the numerical model forecast wind field, and the fusion of the satellite cloud wind guide data and the numerical model forecast wind field to generate three-dimensional fused wind field data includes: Acquiring the wind field forecasted by the numerical model in real time; The satellite cloud wind guide data and the numerical model forecast wind field are fused through a second preset method to generate the three-dimensional fused wind field data.

4. The method for identifying rainstorm areas based on geostationary satellite cloud-wind guidance and wind field fusion as claimed in claim 3 is characterized in that: The fusing the satellite cloud wind guide data and the numerical model forecast wind field by a second preset method to generate the three-dimensional fused wind field data comprises: Calculate the background error covariance matrix B of the wind field predicted by the numerical model; Calculate the observation error covariance matrix R of the satellite cloud wind data; Based on the background error covariance matrix B and the observation error covariance matrix R, the satellite cloud wind guide data and the numerical model forecast wind field are fused through an optimal interpolation algorithm to generate the three-dimensional fused wind field data.

5. A rainstorm area identification system based on geostationary satellite cloud-wind fusion wind field, characterized in that: include: The first generation module is used to obtain the original cloud guide wind data of the geostationary meteorological satellite, and perform preprocessing to generate high-quality cloud guide wind data of the geostationary satellite; The second generation module is used to obtain the wind field predicted by the numerical model in real time, and fuse the satellite cloud wind guide data and the wind field predicted by the numerical model to generate three-dimensional fused wind field data; A third generating module is used to calculate and generate diagnostic physical quantities based on the three-dimensional fused wind field data by a first preset method; A fourth generating module, configured to identify a rainstorm area based on the diagnostic physical quantity and generate a first rainstorm area; as well as The fifth generating module is used to correct the predicted heavy rain area based on the first heavy rain area to generate a second heavy rain area.

6. The rainstorm area identification system based on geostationary satellite cloud-wind guidance and wind field fusion as claimed in claim 5 is characterized in that: The first generation module comprises: The first acquisition unit is used to acquire the water vapor channel cloud guide wind information and the infrared channel cloud guide wind information of the geostationary meteorological satellite; A first generating unit is configured to generate the cloud guide wind raw data by integrating the water vapor channel cloud guide wind information and the infrared channel cloud guide wind information; and The second generating unit is used to perform quality control on the cloud guide wind raw data and generate the geostationary satellite high-quality cloud guide wind data.

7. The rainstorm area identification system based on geostationary satellite cloud-wind guidance and wind field fusion as claimed in claim 5 is characterized in that: The second generation module comprises: A second acquisition unit is used to acquire the wind field predicted by the numerical model in real time; The third generating unit is used to fuse the satellite cloud wind guide data and the numerical model forecast wind field through a second preset method to generate the three-dimensional fused wind field data.

8. The rainstorm area identification system based on geostationary satellite cloud-wind guidance and wind field fusion as claimed in claim 7 is characterized in that: The third generating unit comprises: The first calculation subunit is used to calculate the background error covariance matrix B of the wind field predicted by the numerical model; A second calculation subunit is used to calculate the observation error covariance matrix R of the satellite cloud wind guide data; A generating subunit is used to fuse the satellite cloud wind guide data and the numerical model forecast wind field based on the background error covariance matrix B and the observation error covariance matrix R through an optimal interpolation algorithm to generate the three-dimensional fused wind field data.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for identifying heavy rain areas based on the geostationary satellite cloud-wind guide fusion wind field as described in any one of claims 1-4.

10. A computer-readable storage medium, characterized in that: It includes a computer program and instructions. When the computer program or the instructions are run on a computer, the computer executes the method for identifying rainstorm areas based on the geostationary satellite cloud-guided wind fusion wind field as described in any one of claims 1 to 4.

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