A typhoon forecasting method based on domestic satellite data

Abstract

The present application discloses a typhoon forecasting method based on domestic satellite data, which includes: a data fusion process: used to fuse GNSS-R sea surface wind data and IFS forecast data from the European center to generate a two-dimensional gridded sea surface vector wind field; a meteorological forecasting process combined with artificial intelligence, including a large meteorological model forecasting step and a numerical model forecasting step; a multi-typhoon path discrimination and typhoon intensity forecasting process, which is used to extract key meteorological element variables from regional forecast data files at different times, and judge the typhoon center based on the key meteorological element variables. The present application combines domestic satellite data and artificial intelligence technology, and through the deep combination of data fusion and numerical forecasting technology, improves the accuracy and refinement of typhoon path and intensity forecasts. In addition, through the application of large meteorological models, nonlinear and massive data can be processed more effectively, improving the forecast accuracy.

Description

Technical Field

[0001] The present invention relates to typhoon prediction technology in the field of meteorological science and technology, and in particular to a typhoon forecasting method based on domestic satellite data. Background Art

[0002] A typhoon is a weather system with rapid changes and complex structures, which is often accompanied by extreme weather such as strong winds, heavy rains, and huge waves, which seriously threaten people's lives and property. In the past two decades, with the development of meteorological satellites, numerical forecasting, data assimilation and other technologies, typhoon forecasting has made certain progress. However, in the face of the demand for timely, accurate and refined forecasts, typhoon forecasting is still a complex and challenging problem, mainly due to the inadequacy of typhoon monitoring and numerical forecasting models.

[0003] Typhoon monitoring is the basis for ensuring the accuracy and timeliness of typhoon forecasts. On the ocean, typhoon monitoring mainly relies on satellite detection. The development of GNSS-R remote sensing detection technology has provided a large amount of data for typhoon monitoring on the ocean. This detection method is not affected by weather such as clouds, rain, and fog. It has the advantages of all-day, all-weather observation and relatively low cost. my country's FY-3C / D / E / F / G satellites, Tianmu-1 constellation, and Yunyao constellation all use this detection technology. However, the sea breeze product inverted by GNSS-R remote sensing detection technology only has wind speed, not wind direction, which limits the application of GNSS-R sea breeze in typhoon forecasting.

[0004] Typhoon forecasting currently relies mainly on traditional numerical forecasting models, which are based on atmospheric dynamic equations and use numerical calculations of elements such as the atmosphere and ocean to predict weather changes in the future. Since the atmosphere is a highly nonlinear and complex system, initial errors will gradually accumulate over time, so the accuracy of numerical forecasts decreases significantly over time. In addition, numerical forecasting models require huge computing resources and hours of running time, which undoubtedly restricts the further development of weather forecasting technology, especially for typhoon forecasts with high timeliness requirements.

[0005] In recent years, large meteorological models with artificial intelligence technology as the core have become a new weather forecasting method. They are good at predicting continuous changes in time series and still have good results for medium- and long-term weather forecasts. However, large meteorological models lack descriptions of physical processes, and their interpretability and credibility are still lacking.

[0006] To this end, an invention patent with a publication number of CN114139760A discloses a method, system, storage medium and device for predicting typhoon paths, the method comprising: collecting multiple existing typhoon records as samples, extracting the characteristic information of each sample respectively, and building a typhoon sample library based on the characteristic information of the sample; matching the corresponding meteorological cloud map group for the sample in the typhoon sample library, and identifying the corresponding typhoon center data set in the meteorological cloud map group; building a prediction model, and training the prediction model based on the characteristic information of each sample in the typhoon sample library, the meteorological cloud map group of each sample and the typhoon center data set corresponding to each sample; using the trained prediction model to predict the typhoon path. The invention improves the accuracy of typhoon prediction and provides a new solution and effective technical support for the prediction of typhoon paths.

[0007] Another invention patent with publication number CN117172372A discloses a typhoon path prediction method and system. First, the intensity and eye of the typhoon are predicted based on current data. Then, a historical typhoon path closest to the current typhoon is matched using the similar path method, and a typhoon path cluster is determined based on the path. Finally, an improved particle swarm optimization neural network is used to select a second typhoon path from the typhoon path cluster. The neural network can simultaneously input typhoon climate persistence factors and atmospheric environment field factor data with different expressions, learn the relationship between the two and the typhoon path, and obtain more accurate forecast results.

[0008] Although the above-mentioned existing technologies have solved some problems in typhoon forecasting, there are still problems such as large long-term forecast errors in numerical models, lack of physical description of large meteorological models, and the inability to effectively apply GNSS-R sea surface wind observation information in typhoon forecasting. Summary of the invention

[0009] A brief summary of embodiments of the present invention is provided below to provide a basic understanding of certain aspects of the present invention. It should be understood that the following summary is not an exhaustive summary of the present invention. It is not intended to identify key or important parts of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is merely to provide certain concepts in a simplified form as a prelude to a more detailed description discussed later.

[0010] In response to the above problems, this technical solution proposes a typhoon forecasting method, which solves the application of GNSS-R sea breeze data products of domestic satellites in typhoon path forecasting, and deeply combines the advantages of two different scientific paradigms, artificial intelligence and numerical forecasting models, to solve the problems of large long-term forecast errors in numerical forecasting models and lack of physical description of large meteorological models. In addition, this technical solution can identify multiple typhoon paths and typhoon intensities in the same time period based on the forecast results of multiple meteorological elements.

[0011] Specifically, the present invention provides a typhoon forecasting method based on domestic satellite data, which comprises:

[0012] Data fusion process: used to fuse GNSS-R sea surface wind data with the IFS forecast data from the European Center to generate a two-dimensional gridded sea surface vector wind field;

[0013] Combining artificial intelligence with weather forecasting includes:

[0014] Meteorological big model forecasting step: using the fused two-dimensional gridded sea surface vector wind field as input data, inputting it into a meteorological big model (for example, a single AI-based Pangu meteorological big model can be used, or multiple models can be fused to achieve this), conducting global weather forecasts for a predetermined number of days, and obtaining ground forecast data and multi-level upper-altitude forecast data as global forecast results;

[0015] Numerical model forecasting steps: extract key meteorological variables from the global forecast results of the large meteorological model and process them into the intermediate file format of the WRF model input; based on the intermediate file, use the WRF model to carry out high-resolution refined regional forecasts and generate refined regional forecast data files;

[0016] The multi-typhoon path identification and typhoon intensity forecasting process is used to extract key meteorological element variables from regional forecast data files at different times, and determine the typhoon center based on the key meteorological element variables.

[0017] In the prior art, the use of large meteorological models based on artificial intelligence alone, because of its strong nonlinear mapping ability, well reflects the nonlinear characteristics of typhoon paths, but in actual use, the data-driven large meteorological models are insufficient to capture rare abnormal typhoon paths, especially in extreme weather (especially when high wind speed data exceeding 40m / s appears), the results output by the large meteorological models may have a large deviation from the actual situation. To this end, this application combines the forecast of the large meteorological model with the forecast of the numerical model, that is, it takes into account the atmospheric dynamics theory of the numerical model and the computational efficiency of the large meteorological model, which can effectively improve the accuracy and reliability of the forecast.

[0018] In addition, in order to further solve the problem of the insufficient ability of the meteorological model to capture the abnormal path of typhoons under extreme weather conditions, the meteorological model adopts a multi-model coupling method, uses multiple different meteorological models to conduct data training at the same time, and obtains a meteorological model composed of multiple optimized and trained meteorological models. In the forecasting step of the meteorological model, a global weather forecast for a predetermined number of days is carried out for each meteorological model, and the ground forecast data obtained by each meteorological model and the multi-level high-altitude forecast data are integrated as the global forecast result. The meteorological model composed of multiple separately optimized and trained meteorological models can improve its generalization ability and adaptability. In addition, generative adversarial networks (GANs) and reinforcement learning techniques can also be introduced to enhance the model's ability to handle complex climate dynamics.

[0019] In order to reduce the forecast error of the large meteorological model, as an implementation scheme, data training is performed on multiple different meteorological models at the same time, specifically including: dividing the data set into a training data set and a test data set, classifying the training data set into a normal data set and an abnormal data set (abnormal here refers to data that deviates from the theoretical path, such as data obtained under extreme conditions or a few typhoon path abnormal data), training the meteorological model with the normal data set, the abnormal data set and the entire training data set respectively, adjusting the model parameters during the training process, and testing and verifying with the test data set. After verification, each meteorological model is divided into two categories, one suitable for forecasting normal data and the other suitable for forecasting abnormal data. In the forecasting step of the large meteorological model, global weather forecasts for a predetermined number of days are carried out for the two types of meteorological models, and two types of forecast data are obtained respectively, and the two types of forecast data are fused to improve the prediction accuracy.

[0020] The multi-model ensemble can reduce the error in extreme climate forecasts, especially when dealing with complex climate systems, and enhance the ability to predict extreme weather events. In addition, the multi-model integration method is not only applicable to short-term weather forecasts, but also to long-term forecasts.

[0021] The process of multi-typhoon path identification and typhoon intensity forecasting includes:

[0022] Meteorological element variable extraction and calculation steps: extract key meteorological element variables from regional forecast data files at different times. Key meteorological element variables include sea level pressure data, 850hpa relative vorticity, 850hpa and 200hpa pressure layer thickness, and 10m wind speed;

[0023] Steps to determine the center of a typhoon: Determine the center of a typhoon by combining four meteorological variables: sea level pressure data, 850hPa relative vorticity, 850hPa and 200hPa pressure layer thickness, and 10m wind speed.

[0024] As a feasible solution, the data fusion process specifically includes:

[0025] Historical data collection and threshold calculation steps: Collect historical GNSS-R sea surface wind data and the 10m vector wind field (U 10 ,V 10 ), through data parsing and spatiotemporal matching, the average standard deviation between the two sets of data is calculated and set as the threshold;

[0026] Real-time data download and quality control: Download real-time GNSS-R sea surface wind and 10m vector wind field data from IFS forecast data (U 10 ,V 10 ), perform data analysis and time-space matching; calculate the standard deviation of real-time GNSS-R sea surface wind speed data, and if the standard deviation is greater than a threshold, remove the GNSS-R sea surface wind data of that point; achieve quality control by removing data that does not meet the threshold;

[0027] Data fusion based on data assimilation method: The quality-controlled GNSS-R sea surface wind and the 10m vector wind field (U 10 ,X 10 ) for fusion; the objective function is defined as:

[0028]

[0029]

[0030] Among them, x is the fused two-dimensional gridded sea surface wind speed data, x b is the 10m wind speed data in the IFS forecast data, (U 10 ,V 10 ) is the 10m vector wind field of IFS forecast data, y o is the GNSS-R sea surface wind data, B is the background error covariance matrix, and R is the observation error covariance matrix. The data assimilation method is to achieve the best fit of the 10m vector wind field in the GNSS-R sea surface wind and IFS forecast data by minimizing the objective function.

[0031] The longitude and latitude of the fused two-dimensional gridded sea surface wind speed data are matched with the longitude and latitude of the 10m vector wind field in the IFS forecast data, and the wind direction of the 10m vector wind field in the IFS forecast data is used as the wind direction of the fused two-dimensional gridded sea surface wind speed data to form a regular two-dimensional gridded sea surface vector wind field.

[0032] As an implementation scheme, the steps of extracting and calculating meteorological element variables specifically include:

[0033] Extract 10m wind speed and sea level pressure data from regional forecast data files at different times, calculate 850hPa relative vorticity, and the thickness of the pressure layer between 850hPa and 200hPa, and store these meteorological element variables in the database; the calculation formula is as follows:

[0034] The relative vorticity ε at 850 hPa is calculated as follows:

[0035]

[0036] Among them, ε is the relative vorticity at 850 hPa, v and u are the wind fields at the 850 hPa pressure layer (U 850 ,V 850 ), x and y are the resolutions of the forecast data in the X and Y directions.

[0037] The thickness of the pressure layer ΔZ between 850 hPa and 200 hPa is calculated as follows:

[0038] ΔZ=HGT 200 -HGT 850 ;

[0039] Where, ΔZ is the thickness between 850 hPa and 200 hPa, HGT 200 and HGT 850 They are the geopotential heights of the 200hPa and 850hPa pressure layers respectively.

[0040] As an implementation scheme, the steps for determining the typhoon center include determining the low pressure center condition, the relative vorticity condition, the thickness condition, and the maximum wind speed condition near the typhoon center, specifically including:

[0041] Step 1: Obtain the low pressure center that meets conditions 1-4 as the typhoon center at the initial moment:

[0042] Condition 1: Judgment of low pressure center conditions: The low pressure center is judged by the sea level pressure data in the area, and the information of all low pressure centers with pressure values ​​less than 1010hpa is retained (the information includes longitude, latitude, and low pressure center value);

[0043] Condition 2: Relative vorticity condition judgment: Draw an area with the low pressure center as the center and a radius of 280 km to determine whether the maximum relative vorticity in the area is greater than 3.5×10 -5 , retain the low-pressure center that meets the conditions;

[0044] Condition 3: Thickness condition judgment: Draw an area with the longitude and latitude of the low pressure center as the center and a radius of 280km to determine whether there is a maximum thickness center in the area, and retain the low pressure center that meets the conditions;

[0045] Condition 4: Judgment of the maximum wind speed condition near the typhoon center: With the longitude and latitude of the low pressure center as the center and a radius of 280km, search for the maximum wind speed value in the area. If the maximum wind speed of the typhoon is greater than 10.8m / s, retain the low pressure center that meets the conditions;

[0046] The low pressure center that meets conditions 1-4 is the typhoon center at the initial moment. All typhoon centers at the initial moment are numbered (X1, X2, ..., Xn), and the time, longitude and latitude of the typhoon center, sea level pressure of the typhoon center, and maximum wind speed near the typhoon center are stored in the database;

[0047] Step 2: Determine the typhoon center at the next moment:

[0048] Step 21, setting the preset value as the typhoon moving range: the typhoon moving speed is generally below 35km / h. If the time difference between two adjacent moments is t hours, the typhoon moving range is 35tkm.

[0049] Step 22, typhoon center judgment: take the typhoon center at the previous moment as the center of the circle and 35tkm as the radius to search for the lowest pressure center; if the air pressure of the lowest pressure center is less than 1010hPa and meets conditions 2-4 in step 1, then the lowest pressure center is the typhoon center at that moment; corresponding to the number of the typhoon center (X1, X2, ..., Xn), the time of that moment, the longitude and latitude of the typhoon center, the sea level pressure of the typhoon center, the maximum wind speed near the typhoon center and other data are saved in the database.

[0050] Step 21 and step 22 are executed repeatedly until any one of condition 2 and condition 4 is not satisfied, and the typhoon center record is stopped.

[0051] As a further solution, the step of determining the typhoon center also includes step 3 of visualizing the typhoon forecast, where the line connecting the typhoon centers forms the typhoon path, the maximum wind speed and the lowest air pressure at the typhoon center represent the typhoon intensity, and the typhoon forecast results are visualized using NCL software.

[0052] The solution of this application focuses on the application of domestic satellite data in typhoon forecasting. By integrating GNSS-R sea surface wind and the 10m vector wind field of the European Center forecast data IFS, regular two-dimensional gridded sea surface wind vector data is formed, and the fused sea surface wind speed data is input into the Panu meteorological model for global forecasting. The global forecast results are input into the mesoscale meteorological model WRF to carry out refined regional forecasts, and the typhoon path and intensity information are identified based on the refined regional forecast data. Compared with the prior art, the present invention has the following innovations:

[0053] 1. Vector and scalar data fusion: To address the problem that GNSS-R sea surface wind data only has wind speed but no wind direction, data assimilation technology is used to fuse GNSS-R sea surface wind and the 10m vector wind field of the European Center forecast data IFS to form regular two-dimensional gridded sea surface wind vector data, solving the problem that GNSS-R sea surface wind data cannot be effectively used as scalar data in the field of weather forecasting.

[0054] 2. Combination of large meteorological models and numerical forecasts: In order to address the problems of large errors in medium- and long-term forecasts in numerical forecast models, the lack of physical interpretability and poor applicability of regional forecasts in large meteorological models with artificial intelligence technology as the core, we will change the previous approach of global numerical forecast models + regional numerical forecast models, and carry out refined regional forecasts through the global forecast + regional numerical forecast models of large meteorological models, give full play to the role of large meteorological models in the stability of medium- and long-term forecasts, and concentrate on the advantages of numerical forecasts and artificial intelligence technologies.

[0055] 3. Identification of multiple typhoon paths and forecast of typhoon intensity: Multiple typhoon paths and intensities are forecasted by using four meteorological element variables, namely sea level pressure, 10m wind speed, 850hPa relative vorticity, and thickness of the pressure layer between 850hPa and 200hPa. This method takes into account the thermal and dynamic structures of the typhoon, and can reduce the errors caused by using only two meteorological element variables, namely sea surface wind pressure and 10m wind speed. At the same time, it can also identify multiple typhoon paths and intensities.

[0056] In summary, this application combines domestic satellite data and artificial intelligence technology, and through the deep combination of data fusion and numerical forecasting technology, improves the accuracy and refinement of typhoon path and intensity forecasts. In addition, through the application of artificial intelligence technology, nonlinear and massive data can be processed more effectively to improve the forecast accuracy. At the same time, using the high temporal and spatial resolution products provided by the Fengyun meteorological satellite, the evolution of typhoon wind and rain distribution can be better monitored and warned. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present invention may be better understood by referring to the following description in conjunction with the accompanying drawings, wherein the same or similar reference numerals are used throughout the drawings to represent the same or similar components. The accompanying drawings, together with the following detailed description, are incorporated in and form a part of this specification and are used to further illustrate the preferred embodiments of the present invention and to explain the principles and advantages of the present invention.

[0058] In the figure:

[0059] Figure 1 The figure is a flow chart of an algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] Embodiments of the present invention will be described below with reference to the accompanying drawings. Elements and features described in one drawing or one embodiment of the present invention may be combined with elements and features shown in one or more other drawings or embodiments. It should be noted that for the purpose of clarity, the representation and description of components and processes that are not related to the present invention and are known to those of ordinary skill in the art are omitted in the drawings and descriptions.

[0061] In view of the shortcomings of the existing technology, this application proposes a typhoon forecasting method, which solves the application of GNSS-R sea breeze data products of domestic satellites in typhoon path forecasting, and deeply combines the advantages of two different scientific paradigms, artificial intelligence and numerical forecasting technology, to solve the problems of large long-term forecast errors in numerical forecasting models and lack of physical description of large meteorological models. In addition, this technical solution can identify multiple typhoon paths and typhoon intensities in the same time period based on the forecast results of multiple meteorological elements.

[0062] The embodiment of the present invention proposes a typhoon forecasting method based on domestic satellite data. In view of the problem that the GNSS-R sea breeze data product of the domestic satellite only has wind speed but no wind direction, which is difficult to be used in typhoon path forecasting, the 10m vector wind field in the GNSS-R sea breeze data product of the domestic satellite and the European Center Forecast Product (IFS) are integrated by using the data assimilation method. In view of the large long-term forecast error in the numerical forecast model and the lack of physical description of the large meteorological model, refined and intelligent weather forecasting is carried out by combining the advantages of artificial intelligence and numerical forecasting technology. In view of the fact that there may be multiple typhoons in the same time period in the region, multiple forecast results combining multiple meteorological elements will be used to identify multiple typhoon paths and typhoon intensities. The specific technical scheme is as follows:

[0063] 1. Fusion of GNSS-R sea surface wind and 10m vector wind field of European Center forecast data IFS.

[0064] (1) Collect historical data for one year and calculate the standard deviation threshold. Collect GNSS-R sea surface wind data for one year and the 10m vector wind field (U 10 ,V 10 ), and perform data analysis and spatiotemporal matching on the two sets of data, calculate the average standard deviation between the two sets of wind speed data, and set the average standard deviation as the threshold.

[0065] (2) Download real-time data and perform quality control. Download real-time GNSS-R sea surface wind and 10m vector wind field in IFS (U 10 ,V 10 ), perform data analysis, time-space matching and calculate the standard deviation of the real-time sea surface wind speed data. If the standard deviation is greater than the threshold, the GNSS-R sea surface wind data of the point will be eliminated.

[0066] (3) Data fusion. The GNSS-R sea surface wind after quality control in (2) and the 10m vector wind field (U 10 ,V 10 ) are fused. The data assimilation method is to achieve the best fit of the GNSS-R sea surface wind and the 10m vector wind field of IFS by minimizing the objective function. The objective function is defined as:

[0067]

[0068]

[0069] Among them, x is the fused two-dimensional gridded sea surface wind speed data, x b is the 10m wind speed data of IFS, (U 10 ,V 10 ) is the 10m vector wind field of IFS, y o is the GNSS-R sea surface wind data, B is the background error covariance matrix, and R is the observation error covariance matrix.

[0070] The longitude and latitude of the fused two-dimensional gridded sea surface wind speed data are matched with the longitude and latitude of the 10m vector wind field in IFS, and the wind direction of the 10m vector wind field in IFS is used as the wind direction of the fused two-dimensional gridded sea surface wind speed data to form a regular two-dimensional gridded sea surface vector wind field.

[0071] 2. Deep integration of artificial intelligence technology and numerical forecasting technology

[0072] (1) Meteorological model forecast. The fused two-dimensional gridded sea surface vector wind field is used as input data into the meteorological model to conduct a global weather forecast for 7 days, obtaining ground forecast data such as 2m temperature, 2m humidity, 10m wind, and sea level pressure, as well as high-altitude forecast data such as temperature, humidity, wind, and gravity potential at multiple levels (1000hpa, 925hpa, 850hpa, 700hpa, 600hpa…).

[0073] (2) Numerical model forecast. Extract meteorological variables such as 2m temperature, 10m wind, and sea level pressure from the global forecast result file of the meteorological large model in step (1) above, and process it into an intermediate file (FILE file format) for WRF model input. Then use the WRF model to carry out high-resolution refined regional forecasts and generate refined regional forecast data files.

[0074] In this embodiment, the meteorological big model adopts a multi-model coupling method, using multiple different meteorological models to perform data training at the same time, and obtains a meteorological big model composed of multiple optimized and trained meteorological models.

[0075] In the meteorological big model forecasting step, a global weather forecast for a predetermined number of days is conducted for each meteorological model, and the ground forecast data obtained by each meteorological model and the multi-level high-altitude forecast data are integrated as the global forecast result. The meteorological big model composed of multiple separately optimized and trained meteorological models can improve its generalization ability and adaptability.

[0076] In addition, data training is performed on multiple different meteorological models at the same time, specifically including: dividing the data set into a training data set and a test data set, and dividing the training data set into a normal data set and an abnormal data set. The abnormal data here refers to data that deviates from the theoretical path, such as data obtained under extreme conditions or a small number of typhoon path abnormal data. The normal data set, the abnormal data set, and the entire training data set are used to train the meteorological model respectively, and the model parameters are adjusted during the training process. The model is tested and verified through the test data set. After verification, each meteorological model is divided into two categories, one suitable for forecasting normal data and the other suitable for forecasting abnormal data.

[0077] In the large meteorological model forecasting step, global weather forecasts for a predetermined number of days are carried out for two types of meteorological models, and two types of forecast data are obtained respectively. The two types of forecast data are fused to improve the prediction accuracy.

[0078] The multi-model ensemble can reduce the error in extreme climate forecasts, especially when dealing with complex climate systems, and enhance the ability to predict extreme weather events. In addition, the multi-model integration method is not only applicable to short-term weather forecasts, but also to long-term forecasts.

[0079] 3. Multiple typhoon path identification and typhoon intensity forecast

[0080] (1) Extraction and calculation of meteorological element variables. Extract 10m wind speed and sea level pressure data from data files at different times, calculate 850hPa relative vorticity and the thickness of the pressure layer between 850hPa and 200hPa, and store these meteorological element variables in the database. The calculation formula is as follows:

[0081] 850hpa relative vorticity:

[0082]

[0083] Among them, ε is the relative vorticity, v and u are the wind field of the 850hPa pressure layer (U 850 ,V 850 ), x and y are the resolutions of the forecast data in the X and Y directions.

[0084] The thickness of the air pressure layer between 850hPa and 200hPa:

[0085] ΔZ=HGT 200 -HGT 850 ;

[0086] Where, ΔZ is the thickness between 850 hPa and 200 hPa, HGT 200 and HGT 850 They are the geopotential heights of the 200hPa and 850hPa pressure layers respectively.

[0087] (2) Determination of the typhoon center at the initial moment. A typhoon is a low-pressure vortex with a warm core structure, accompanied by strong winds and other phenomena. Here, the typhoon center is determined by combining four meteorological variables: sea level pressure, 850hpa relative vorticity, pressure layer thickness, and wind speed.

[0088] ① Determine the low pressure center conditions. Determine the low pressure center through the sea level pressure data in the area, and retain the information of all low pressure centers with pressure values ​​less than 1010hpa (information includes longitude, latitude, and low pressure center value).

[0089] ② Determine the relative vorticity condition. Draw an area with the low pressure center as the center and a radius of 280 km to determine whether the maximum relative vorticity in the area is greater than 3.5×10 -5 , retain the low pressure center that meets the conditions.

[0090] ③Thickness condition judgment: Draw an area with the longitude and latitude of the low pressure center as the center and a radius of 280km to determine whether there is a center with the maximum thickness in the area, and retain the low pressure center that meets the conditions.

[0091] ④ Determine the maximum wind speed conditions near the typhoon center. With the longitude and latitude of the low pressure center as the center and a radius of 280km, search for the maximum wind speed value in the area. If the maximum wind speed of the typhoon is greater than 10.8m / s, retain the low pressure center that meets the conditions.

[0092] The low pressure center that meets conditions ①-④ is the typhoon center at the initial moment. All typhoon centers are numbered (X1, X2,…, Xn), and the time, longitude and latitude of the typhoon center, sea level pressure of the typhoon center, and maximum wind speed near the typhoon center are stored in the database.

[0093] (3) Determination of the typhoon center at the next moment.

[0094] ⑤Set the typhoon movement range to the preset value.

[0095] ⑥ Determination of the typhoon center. With the typhoon center at the previous moment as the center and the preset value as the radius, search for the lowest pressure center. If the air pressure of the lowest pressure center is less than 1010hpa and meets conditions ②-④ in (1), then the lowest pressure center is the typhoon center at that moment. Corresponding to the number of the typhoon center (X1, X2,…, Xn), the time of that moment, the longitude and latitude of the typhoon center, the sea level pressure of the typhoon center, the maximum wind speed near the typhoon center and other data are saved in the database.

[0096] ⑦ Loop steps ⑤-⑥ to perform conditional judgment until any one of conditions ②-④ is not met, and the typhoon center record stops.

[0097] (4) Visualization of typhoon forecast. The typhoon path is the line connecting the typhoon centers. The typhoon intensity is generally expressed by the maximum wind speed and the lowest air pressure at the typhoon center. The typhoon forecast results are visualized using NCL software.

[0098] This application focuses on the application of domestic satellite data in typhoon forecasting. By integrating GNSS-R sea surface wind and the 10m vector wind field of the European Center forecast data IFS, regular two-dimensional gridded sea surface wind vector data is formed. The fused sea surface wind speed data is input into the Panu meteorological model for global forecasting. The global forecast results are input into the mesoscale meteorological model WRF to carry out refined regional forecasts, and the typhoon path and intensity information are identified based on the refined regional forecast data.

[0099] See also Figure 1 , the specific implementation method of the embodiment of the present invention includes:

[0100] S1: Download historical data for one year, including GNSS-R sea surface wind data and 10m vector wind field (U 10 ,V 10 )Two sets of information.

[0101] S2: Data processing and threshold determination: Data analysis and time-space matching are performed on the GNSS-R sea surface wind and IFS 10m wind vector data files respectively, and the time average standard deviation between the two sets of data is calculated, and the time average standard deviation is used as the threshold.

[0102] S3: Download real-time data and perform quality control. Download the real-time GNSS-R sea surface wind and the 10m vector wind field data files in IFS, calculate the standard deviation between the real-time GNSS-R sea surface wind and the 10m vector wind field data in IFS, and if the standard deviation is greater than the threshold, remove the GNSS-R sea surface wind data of the point; if the standard deviation is less than or equal to the threshold, retain the GNSS-R sea surface wind data of the point.

[0103] S4: Sea surface wind speed data fusion.

[0104] Construct the background error covariance matrix. Calculate the time average of the 10m wind speed data of the IFS for one year of historical data, and then use the following formula to calculate the background error covariance matrix.

[0105]

[0106] in, is the long-term average of the 10m wind speed data of IFS, x b It is the real-time data of 10m wind speed of IFS.

[0107] Construct the observation error covariance matrix. The observation error covariance matrix is ​​calculated in the same way.

[0108] Solve the sea breeze fusion data. According to Bayesian theory, only when the objective function J(x) is the minimum, x is the sea surface wind speed fusion data.

[0109]

[0110]

[0111] Mathematically, the solution x that satisfies min J(x) is The sea surface wind speed fusion data can be solved by minimizing the objective function.

[0112] S5: Sea surface wind direction matching. The longitude and latitude of the two-dimensional gridded sea surface wind speed data fused in step S4 are matched with the longitude and latitude of the 10m vector wind field in IFS, and the wind direction of the 10m vector wind field in IFS is used as the wind direction of the fused two-dimensional gridded sea surface wind speed data to form a regular two-dimensional gridded sea surface vector wind field.

[0113] S6: Global meteorological model forecast. The fused two-dimensional gridded sea surface vector wind field is used as input data into the meteorological model to conduct a 7-day global weather forecast, and obtain ground forecast data such as 2m temperature, 2m humidity, 10m wind, and sea level pressure, as well as high-altitude forecast data such as temperature, humidity, wind, and gravity potential at multiple levels (1000hpa, 925hpa, 850hpa, 700hpa, 600hpa...).

[0114] S7: Regional numerical model forecast. Extract meteorological variables such as 2m temperature, 10m wind, and sea level pressure from the global forecast result file of the meteorological large model in step S6 above, and process it into an intermediate file (FILE file format) for WRF model input. Then use the WRF model to carry out high-resolution refined regional forecasts and generate refined regional forecast data files.

[0115] S8: Extraction and calculation of meteorological element variables. Extract 10m wind speed and sea level pressure data from regional numerical model forecast files at different times, calculate 850hPa relative vorticity, and the thickness of the pressure layer between 850hPa and 200hPa, and store these meteorological element variables in the database.

[0116] S9: Determination of the typhoon center at the initial moment. A typhoon is a low-pressure vortex with a warm core structure, accompanied by strong winds and other phenomena. The low-pressure center that meets the above conditions ①-④ is the typhoon center at the initial moment. All typhoon centers are numbered (X1, X2, ..., Xn), and the time, longitude and latitude of the typhoon center, sea level pressure of the typhoon center, and maximum wind speed near the typhoon center are stored in the database.

[0117] S10: Determination of the typhoon center at the next moment. A typhoon is a low-pressure vortex with a warm core structure, accompanied by strong winds and other phenomena. Here, the typhoon center is determined by combining four meteorological variables: sea level pressure, 850hpa relative vorticity, pressure layer thickness, and wind speed.

[0118] Set the moving range. The moving speed of a typhoon is generally below 35 km / h. If the time difference between two adjacent moments is t hours, the moving range of the typhoon is 35t km.

[0119] Determine the typhoon center. With the typhoon center at the previous moment as the center and the preset moving range - 35t km as the radius, search for the lowest pressure center. If the air pressure at the lowest pressure center is less than 1010hPa and meets conditions ②-④ in (1), then the lowest pressure center is the typhoon center at that moment. Corresponding to the typhoon center number (X1, X2, ..., Xn), the time of that moment, the longitude and latitude of the typhoon center, the sea level pressure of the typhoon center, the maximum wind speed near the typhoon center and other data are saved in the database.

[0120] The moving range and typhoon center judgment are set cyclically until any one of conditions ②-④ is not met, and the typhoon center record is stopped.

[0121] S11: Visualize typhoon forecast results using NCL software.

[0122] This application combines domestic satellite data and artificial intelligence technology, and through the deep integration of data fusion and numerical forecasting technology, it improves the accuracy and refinement of typhoon path and intensity forecasts. In addition, through the application of artificial intelligence technology, nonlinear and massive data can be processed more effectively to improve the forecast accuracy. At the same time, the high temporal and spatial resolution products provided by Fengyun meteorological satellites can better monitor and warn the evolution of typhoon wind and rain distribution.

[0123] In the above description of specific embodiments of the present invention, features described and / or shown for one embodiment may be used in the same or similar manner in one or more other embodiments, combined with features in other embodiments, or replace features in other embodiments.

[0124] In addition, the method of the present invention is not limited to being executed in the time sequence described in the specification, and may also be executed in other time sequences, in parallel or independently. Therefore, the execution order of the method described in this specification does not limit the technical scope of the present invention.

[0125] Although the present invention has been disclosed above by describing specific embodiments of the present invention, it should be understood that all the above embodiments and examples are exemplary rather than restrictive. Those skilled in the art may design various modifications, improvements or equivalents of the present invention within the spirit and scope of the appended claims. These modifications, improvements or equivalents should also be considered to be included in the protection scope of the present invention.

Claims

1. A typhoon forecasting method based on domestic satellite data, characterized in that: include: Data fusion process: used to fuse GNSS-R sea surface wind data with the IFS forecast data from the European Center to generate a two-dimensional gridded sea surface vector wind field; Combining artificial intelligence with weather forecasting includes: Meteorological big model forecasting steps: the fused two-dimensional gridded sea surface vector wind field is used as input data into the meteorological big model, global weather forecasts for a predetermined number of days are carried out, and ground forecast data and multi-level high-altitude forecast data are obtained as global forecast results; Numerical model forecasting steps: extract key meteorological variables from the global forecast results of the large meteorological model and process them into intermediate files; conduct high-resolution refined regional forecasts based on the intermediate files and generate refined regional forecast data files; The multi-typhoon path identification and typhoon intensity forecasting process is used to extract key meteorological element variables from regional forecast data files at different times, and determine the typhoon center based on the key meteorological element variables.

2. The typhoon forecasting method according to claim 1, characterized in that: The data fusion process specifically includes: Historical data collection and threshold calculation steps: Collect historical GNSS-R sea surface wind data and the 10m vector wind field (U 10 ,V 10 ), through data parsing and spatiotemporal matching, the average standard deviation between the two sets of data is calculated and set as the threshold; Real-time data download and quality control steps: Download the real-time GNSS-R sea surface wind and 10m vector wind field data from IFS forecast data (U 10 ,V 10 ), perform data analysis and time-space matching; calculate the standard deviation of real-time GNSS-R sea surface wind speed data, and if the standard deviation is greater than a threshold, remove the GNSS-R sea surface wind data of that point; achieve quality control by removing data that does not meet the threshold; Data fusion steps based on data assimilation method: Use data assimilation method to merge the quality-controlled GNSS-R sea surface wind and the 10m vector wind field (U 10 ,V 10 ) for fusion; the data assimilation method is to achieve the best fit of GNSS-R sea surface wind and IFS 10m vector wind field by minimizing the objective function. The objective function is defined as: Among them, x is the fused two-dimensional gridded sea surface wind speed data, x b is the 10m wind speed data in the IFS forecast data, (U 10 ,V 10 ) is the 10m vector wind field of IFS forecast data, y o is the GNSS-R sea surface wind data, B is the background error covariance matrix, and R is the observation error covariance matrix; The longitude and latitude of the fused two-dimensional gridded sea surface wind speed data are matched with the longitude and latitude of the 10m vector wind field in the IFS forecast data, and the wind direction of the 10m vector wind field in the IFS forecast data is used as the wind direction of the fused two-dimensional gridded sea surface wind speed data to form a regular two-dimensional gridded sea surface vector wind field.

3. The typhoon forecasting method according to claim 1, characterized in that: The meteorological big model adopts a multi-model coupling method, uses multiple different meteorological models to perform data training at the same time, and obtains a meteorological big model composed of multiple optimized and trained meteorological models; in the meteorological big model forecasting step, a global weather forecast for a predetermined number of days is carried out for each meteorological model, and the ground forecast data obtained by each meteorological model and the multi-level high-altitude forecast data are fused as the global forecast result.

4. The typhoon forecasting method according to claim 3, characterized in that: Data training is performed on multiple different meteorological models simultaneously, specifically including: dividing the data set into a training data set and a test data set, classifying the training data set into a normal data set and an abnormal data set, training the meteorological model with the normal data set, the abnormal data set and the entire training data set respectively, adjusting the model parameters during the training process, and testing and verifying with the test data set. After verification, each meteorological model is divided into two categories, one suitable for forecasting normal data and the other suitable for forecasting abnormal data.

5. The typhoon forecasting method according to claim 1, characterized in that: The multi-typhoon path identification and typhoon intensity forecasting process specifically includes: Meteorological element variable extraction and calculation steps: extract key meteorological element variables from regional forecast data files at different times. Key meteorological element variables include sea level pressure data, 850hpa relative vorticity, 850hpa and 200hpa pressure layer thickness, and 10m wind speed; Steps to determine the center of a typhoon: Determine the center of a typhoon by combining four meteorological variables: sea level pressure data, 850hPa relative vorticity, 850hPa and 200hPa pressure layer thickness, and 10m wind speed.

6. The typhoon forecasting method according to claim 5, characterized in that: The meteorological element variable extraction and calculation steps specifically include: Extract 10m wind speed and sea level pressure data from regional forecast data files at different times, calculate 850hPa relative vorticity, and the thickness of the pressure layer between 850hPa and 200hPa, and store these meteorological element variables in the database; the calculation formula is as follows: The relative vorticity ε at 850 hPa is calculated as follows: Among them, ε is the relative vorticity at 850 hPa, v and u are the wind fields at the 850 hPa pressure layer (U 850 ,V 850 ), x and y are the resolutions of the forecast data in the X and Y directions; The thickness of the pressure layer ΔZ between 850 hPa and 200 hPa is calculated as follows: ΔZ=HGT 200 -HGT 850 ; Where, ΔZ is the thickness between 850 hPa and 200 hPa, HGT 200 and HGT 850 They are the geopotential heights of the 200hPa and 850hPa pressure layers respectively.

7. The typhoon forecasting method according to claim 5, characterized in that: The steps for determining the typhoon center specifically include: Step 1: Obtain the low pressure center that meets conditions 1-4 as the typhoon center at the initial moment: Condition 1: Judgment of low pressure center conditions: The low pressure center is judged by the sea level pressure data in the area, and the information of all low pressure centers with pressure values ​​less than 1010hpa is retained; Condition 2: Relative vorticity condition judgment: Draw an area with the low pressure center as the center and a radius of 280 km to determine whether the maximum relative vorticity in the area is greater than 3.5×10 -5 , retain the low-pressure center that meets the conditions; Condition 3: Thickness condition judgment: Draw an area with the longitude and latitude of the low pressure center as the center and a radius of 280km to determine whether there is a maximum thickness center in the area, and retain the low pressure center that meets the conditions; Condition 4: Judgment of the maximum wind speed condition near the typhoon center: With the longitude and latitude of the low pressure center as the center and a radius of 280km, search for the maximum wind speed value in the area. If the maximum wind speed of the typhoon is greater than 10.8m / s, retain the low pressure center that meets the conditions; The time corresponding to the low pressure center that meets conditions 1-4, the longitude and latitude of the typhoon center, the sea level pressure of the typhoon center, and the maximum wind speed data near the typhoon center are saved in the database; Step 2: Determine the typhoon center at the next moment: Step 21, setting the typhoon movement range to a preset value; Step 22, typhoon center judgment: take the typhoon center at the previous moment as the center of the circle and the preset value as the radius, search for the lowest pressure center; if the pressure of the lowest pressure center is less than 1010hpa and meets conditions 2-4 in step 1, then the lowest pressure center is the typhoon center at that moment; save the corresponding typhoon center number, the time at that moment, the longitude and latitude of the typhoon center, the sea level pressure of the typhoon center, and the maximum wind speed data near the typhoon center in the database; Step 21 and step 22 are executed repeatedly until any one of condition 2 and condition 4 is not satisfied, and the typhoon center record is stopped.

8. The typhoon forecasting method according to claim 7, characterized in that: The typhoon center determination step also includes step 3 of typhoon forecast visualization, where the line connecting the typhoon centers forms the typhoon path, the maximum wind speed and the lowest air pressure at the typhoon center represent the typhoon intensity, and the typhoon forecast result is visualized using NCL software.

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