Thunderstorm gale objective forecasting and predicting method based on Doppler radar and mesoscale mode

Abstract

The invention relates to the technical field of weather forecast, and discloses a thunderstorm and gale objective forecast and prediction method based on a Doppler radar and a mesoscale mode, and the method comprises the following steps: 1, building a data collection module which is used for collecting various meteorological data, including ground observation data, radar echo data and satellite remote sensing data; 2, performing model simulation and construction of a virtual environment data collection module, and constructing the virtual environment data collection module on the basis of the model; 3, establishing a data calculation module, and performing fusion calculation on the collected data and the data generated by the virtual environment data collection module in the data calculation module; 4, establishing a verification module, and performing formula verification by substituting historical storage data in the data collection module to verify the validity of the model; 5, establishing an evaluation module, and evaluating the accuracy of the forecast according to a calculation result; and step 6, establishing a management module, and adjusting model parameters according to information of the evaluation module.

Description

Technical Field

[0001] The invention relates to the technical field of meteorological forecasting, and in particular to an objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model. Background Art

[0002] The existing objective forecasting method for thunderstorms and gale winds combines advanced remote sensing technology and numerical simulation technology. Although it has achieved remarkable results in improving forecast accuracy, it still has some defects, mainly in terms of forecast timeliness and spatial resolution.

[0003] First, existing methods have certain limitations in forecasting timeliness. Due to the suddenness and local nature of thunderstorms, their generation and development process is complex and changeable, making it difficult to accurately predict. Therefore, existing forecasting methods often fail to provide a sufficiently long lead time for relevant departments to take countermeasures. This is mainly because current forecasting models still have a certain lag when dealing with rapidly changing meteorological conditions and are unable to capture the generation and evolution of thunderstorms in real time.

[0004] Secondly, the existing methods also need to be improved in spatial resolution. Although the spatial resolution of the mesoscale model is already high, it still cannot capture all the key small and medium-scale features, which will affect the details and accuracy of the forecast. For example, for some areas with complex terrain, the mesoscale model may not be able to accurately simulate the impact of terrain on thunderstorm winds, resulting in deviations in forecast results. In addition, although Doppler radar can provide observation data with high temporal and spatial resolution, its coverage is limited and is easily affected by factors such as weather conditions and terrain obstruction, resulting in missing or erroneous observation data.

[0005] In summary, in order to further improve the accuracy and timeliness of objective forecasts of thunderstorms and gale winds, it is necessary to continuously improve and perfect existing forecasting methods and technical means. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] In view of the shortcomings of the prior art, the present invention provides an objective forecasting method for thunderstorms and gale winds based on Doppler radar and mesoscale models, which has the advantages of no limitations in forecast timeliness and high spatial resolution, and solves the problems of certain limitations in forecast timeliness and low spatial resolution in the prior art.

[0008] (II) Technical solution

[0009] To achieve the above object, the present invention provides the following technical solution: a method for objectively forecasting thunderstorm gale based on Doppler radar and mesoscale model, comprising the following steps:

[0010] Step 1: Establish a data collection module to collect various meteorological data, including ground observation data, radar echo data and satellite remote sensing data;

[0011] Step 2: Conduct model simulation and construct a virtual environment data collection module, and construct a virtual environment data collection module based on the model;

[0012] Step 3: Establish a data calculation module, in which the collected data is integrated with the data generated by the virtual environment data collection module;

[0013] Step 4: Establish a verification module to verify the formula by substituting historical storage data in the data collection module to verify the validity of the model;

[0014] Step 5: Establish an evaluation module to evaluate the accuracy of the forecast based on the calculation results;

[0015] Step 6: Establish a management module and adjust the model parameters and forecasting strategies based on the information from the evaluation module.

[0016] Preferably, the data collection module includes a ground meteorological station unit, a meteorological satellite unit and a Doppler radar unit. The ground meteorological station unit collects ground meteorological data through sensors, data collectors and operation monitoring equipment. The meteorological satellite unit collects meteorological satellite forecast data through satellite payloads and ground station receiving stations. The Doppler radar unit collects Doppler radar measurement data through antennas, transmitters and signal processors.

[0017] Preferably, the virtual environment data collection module collects virtual environment prediction data through a model simulation environment.

[0018] Preferably, the data calculation module includes a forecast timeliness evaluation unit, a spatial resolution test unit and a precipitation forecast scoring unit. The forecast timeliness evaluation unit calculates the model forecast timeliness accuracy Az based on the virtual environment prediction data, the spatial resolution test unit calculates the spatial resolution overlap rate Sx based on the Doppler radar measurement data and the virtual environment prediction data, and the precipitation forecast scoring unit calculates the simulation model prediction accuracy Dh based on the ground meteorological data and the virtual environment prediction data.

[0019] Preferably, the ground meteorological station unit numbers the actual accumulated precipitation within a period of time according to the characteristics of the ground meteorological data, and the actual accumulated precipitation within a period of time is numbered as q1, q2, q3, ... q n The meteorological satellite unit numbers the predicted cumulative precipitation within a period of time according to the characteristics of the meteorological satellite prediction data, and the predicted cumulative precipitation within a period of time is numbered as y1, y2, y3, ...y nThe Doppler radar unit performs data numbering on the number of antenna elements, the wavelength, and the effective aperture of the antenna array according to the characteristics of the Doppler radar measurement data, and the number of antenna elements, the wavelength, and the effective aperture of the antenna array are numbered T, α, and D respectively.

[0020] Preferably, the virtual environment data collection module numbers the number of times the model actually occurs and is predicted to occur, the number of times it actually does not occur but is predicted to occur, and the number of times it actually occurs but is predicted not to occur according to the virtual environment prediction data characteristics, and the number of times it actually occurs and is predicted to occur, the number of times it actually does not occur but is predicted to occur, and the number of times it actually occurs but is predicted not to occur are numbered n1, n2, and n3, respectively.

[0021] Preferably, the forecast timeliness evaluation unit calculates the forecast timeliness accuracy Az of the model based on the virtual environment prediction data, and the calculation formula is:

[0022]

[0023] In the formula, Az represents the accuracy of the model forecast timeliness, n1, n2, and n3 represent the number of times that the event actually occurred and was predicted to occur, the number of times that the event actually did not occur but was predicted to occur, and the number of times that the event actually occurred but was not predicted to occur. It represents the proportion of actual thunderstorms and gales that are also predicted by the forecast, that is, the hit rate P. It indicates the proportion of thunderstorms and gales that did not actually occur but were predicted to occur, that is, the false alarm rate X. It represents the ratio of correctly predicted thunderstorms to the total number of predicted thunderstorms, that is, the accuracy V.

[0024] Preferably, the spatial resolution test unit calculates the spatial resolution overlap rate Sx according to the Doppler radar measurement data and the virtual environment prediction data, and the calculation formula is:

[0025]

[0026] In the formula, Sx represents the spatial resolution overlap rate, T represents the number of antenna elements, α represents the wavelength, and D represents the effective aperture of the antenna array. represents the spatial resolution of radar measurement data, K represents the spatial resolution of virtual environment prediction data, which is a fixed parameter set by the model, and C represents the maximum spatial resolution, which is the maximum spatial resolution between the spatial resolution of radar measurement data and the spatial resolution of virtual environment prediction data. The spatial resolution of radar measurement data and the spatial resolution of virtual environment prediction data are compared, and the larger one is selected as the maximum spatial resolution.

[0027] Preferably, the precipitation forecast scoring unit calculates the cumulative precipitation forecast score Dh based on ground meteorological data and meteorological satellite forecast data, and the calculation formula is:

[0028]

[0029] In the formula, Dh represents the cumulative precipitation forecast score, m represents the number of observation points, q1, q2, q3, ...q n Indicates the actual cumulative precipitation over a period of time, y1, y2, y3, ...y n represents the predicted cumulative precipitation over a period of time, q i and i They represent the actual cumulative precipitation and predicted cumulative precipitation at the i-th observation point over a period of time respectively.

[0030] Preferably, the evaluation module evaluates the forecast accuracy of the model simulation environment according to the model forecast time accuracy Az, evaluates the forecast accuracy of the Doppler radar according to the spatial resolution overlap rate Sx, and evaluates the forecast accuracy of the meteorological satellite according to the cumulative precipitation forecast score Dh;

[0031] The management module adjusts the physical process and algorithm of the model according to the information of the evaluation module.

[0032] Compared with the prior art, the present invention provides an objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model, which has the following beneficial effects:

[0033] 1. The present invention calculates the model forecast time accuracy Az. The formula comprehensively measures the forecasting ability of the model on different time scales through the number of actual occurrences and forecasts, the number of actual non-occurrences but forecasts, and the number of actual occurrences but forecasts not to occur, so as to evaluate the accuracy of the model in capturing thunderstorm and gale events, thereby reducing the cases of false alarms and missed alarms in forecasts.

[0034] 2. By calculating the spatial resolution overlap rate Sx, the present invention can effectively evaluate the accuracy of Doppler radar in capturing the detailed characteristics of thunderstorm gale, improve the precision of the forecast, and enhance the model's forecasting ability for thunderstorm gale events.

[0035] 3. The present invention can monitor the accuracy of the forecast data of meteorological satellites in real time by calculating the cumulative precipitation forecast score Dh and performing real-time monitoring of satellite forecasts. This method can promptly discover the difference between the forecast and the actual situation. When the cumulative precipitation forecast score Dh is monitored to be lower than 95%, the difference is adjusted through a rapid feedback mechanism, thereby improving the timeliness of satellite forecasts. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1 , an objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model, comprising the following steps:

[0039] Step 1: Establish a data collection module to collect various meteorological data, including ground observation data, radar echo data and satellite remote sensing data;

[0040] Step 2: Conduct model simulation and construct virtual environment data collection modules (use high-resolution mesoscale numerical models to conduct simulation experiments on thunderstorms and gale winds, use three-dimensional meteorological fields as initial fields, simulate the spatiotemporal variation characteristics of the macroscopic dynamics, microphysical processes and electrical structures of thunderstorm clouds, and combine long short-term memory networks (LSTM)). On the basis of the model, construct a virtual environment data collection module to better capture the complex evolution of thunderstorms and gale winds;

[0041] Step 3: Establish a data calculation module, in which the collected data is integrated with the data generated by the virtual environment data collection module;

[0042] Step 4: Establish a verification module to verify the formula by substituting historical storage data in the data collection module to verify the validity of the model;

[0043] Step 5: Establish an evaluation module to evaluate the accuracy of the forecast based on the calculation results;

[0044] Step 6: Establish a management module to adjust the model parameters and forecast strategies based on the information from the evaluation module. At the same time, formulate targeted response measures based on actual weather conditions and terrain characteristics to improve the practicality and effectiveness of the forecast.

[0045] The data collection module includes a ground meteorological station unit, a meteorological satellite unit and a Doppler radar unit. The ground meteorological station unit collects ground meteorological data through sensors, data collectors and operation monitoring equipment. The meteorological satellite unit collects meteorological satellite forecast data through satellite payloads and ground station receiving stations. The Doppler radar unit collects Doppler radar measurement data through antennas, transmitters and signal processors.

[0046] The virtual environment data collection module collects virtual environment prediction data through the model simulation environment.

[0047] The data calculation module includes a forecast timeliness evaluation unit, a spatial resolution test unit and a precipitation forecast scoring unit. The forecast timeliness evaluation unit calculates the model forecast timeliness accuracy Az based on the virtual environment prediction data. The spatial resolution test unit calculates the spatial resolution overlap rate Sx based on the Doppler radar measurement data and the virtual environment prediction data. The precipitation forecast scoring unit calculates the simulation model prediction accuracy Dh based on the ground meteorological data and the virtual environment prediction data.

[0048] The ground meteorological station unit numbers the actual accumulated precipitation in a period of time according to the characteristics of the ground meteorological data. The actual accumulated precipitation in a period of time is numbered as q1, q2, q3, ...q n The meteorological satellite unit numbers the predicted cumulative precipitation within a period of time according to the characteristics of the meteorological satellite prediction data. The predicted cumulative precipitation within a period of time is numbered as y1, y2, y3, ...y n The Doppler radar unit numbers the number of antenna elements, wavelength, and effective aperture of the antenna array according to the characteristics of the Doppler radar measurement data. The number of antenna elements, wavelength, and effective aperture of the antenna array are numbered as T, α, and D, respectively.

[0049] The virtual environment data collection module numbers the number of times the model actually occurred and was predicted to occur, the number of times it did not actually occur but was predicted to occur, and the number of times it actually occurred but was not predicted to occur according to the characteristics of the virtual environment prediction data. The number of times it actually occurred and was predicted to occur, the number of times it did not actually occur but was predicted to occur, and the number of times it actually occurred but was not predicted to occur are numbered n1, n2, and n3 respectively.

[0050] The forecast timeliness evaluation unit calculates the forecast timeliness accuracy Az of the model based on the virtual environment prediction data. The calculation formula is:

[0051]

[0052] In the formula, Az represents the accuracy of the model forecast timeliness, n1, n2, and n3 represent the number of times that the event actually occurred and was predicted to occur, the number of times that the event actually did not occur but was predicted to occur, and the number of times that the event actually occurred but was not predicted to occur. It represents the proportion of actual thunderstorms and gales that are also predicted by the forecast, that is, the hit rate P. It indicates the proportion of thunderstorms and gales that did not actually occur but were predicted to occur, that is, the false alarm rate X. It represents the ratio of correctly predicted thunderstorms to the total number of predicted thunderstorms, that is, the accuracy V.

[0053] The advantages are: by calculating the model's forecast timeliness accuracy Az, the formula comprehensively measures the model's forecasting capabilities at different time scales through the number of actual occurrences and forecasts, the number of actual non-occurrences but forecasts, and the number of actual occurrences but non-predicted occurrences, to evaluate the model's accuracy in capturing thunderstorm and gale events, thereby reducing forecast false alarms and missed alarms.

[0054] The spatial resolution test unit calculates the spatial resolution overlap rate Sx based on the Doppler radar measurement data and the virtual environment prediction data. The calculation formula is:

[0055]

[0056] In the formula, Sx represents the spatial resolution overlap rate, T represents the number of antenna elements, α represents the wavelength, and D represents the effective aperture of the antenna array. represents the spatial resolution of radar measurement data, K represents the spatial resolution of virtual environment prediction data, which is a fixed parameter set by the model, and C represents the maximum spatial resolution, which is the maximum spatial resolution between the spatial resolution of radar measurement data and the spatial resolution of virtual environment prediction data. The spatial resolution of radar measurement data and the spatial resolution of virtual environment prediction data are compared, and the larger one is selected as the maximum spatial resolution.

[0057] The advantages are: by calculating the spatial resolution overlap rate Sx, the accuracy of Doppler radar in capturing the detailed characteristics of thunderstorms and gale can be effectively evaluated, the precision of the forecast can be improved, and the model's forecasting ability for thunderstorms and gale events can be enhanced.

[0058] The precipitation forecast scoring unit calculates the cumulative precipitation forecast score Dh based on ground meteorological data and meteorological satellite forecast data. The calculation formula is:

[0059]

[0060] In the formula, Dh represents the cumulative precipitation forecast score, m represents the number of observation points, q1, q2, q3, ...q n Indicates the actual cumulative precipitation over a period of time, y1, y2, y3, ...y n represents the predicted cumulative precipitation over a period of time, q i and i They represent the actual cumulative precipitation and predicted cumulative precipitation at the i-th observation point over a period of time respectively.

[0061] The advantages are: by calculating the cumulative precipitation forecast score Dh, satellite forecasts can be monitored in real time, and the accuracy of the forecast data of meteorological satellites can be monitored in real time. This method can promptly discover the difference between the forecast and the actual situation. When the cumulative precipitation forecast score Dh is monitored to be lower than 95%, the difference is adjusted through a rapid feedback mechanism, thereby improving the timeliness of satellite forecasts.

[0062] The evaluation module evaluates the forecast accuracy of the model simulation environment according to the model forecast timeliness accuracy Az (the evaluation module evaluates the timeliness of the model by analyzing the forecast results of the model at different time scales. This includes the accuracy of short-term, medium-term and long-term forecasts to ensure that the model can provide effective thunderstorm and gale warning information in a timely manner), evaluates the forecast accuracy of the Doppler radar according to the spatial resolution overlap rate Sx (using the high spatial resolution data of the Doppler radar to calculate the spatial overlap rate between the radar prediction and the actual observation. This indicator helps to evaluate the accuracy of the radar in capturing the detailed characteristics of thunderstorms and gale winds and improve the precision of the forecast), and evaluates the forecast accuracy of the meteorological satellite according to the cumulative precipitation forecast score Dh (accumulated precipitation forecast score (APFS) is calculated based on ground meteorological data and meteorological satellite forecast data. This score is used to quantify the performance of the model in predicting cumulative precipitation and ensure the accuracy and reliability of precipitation forecasts);

[0063] The management module adjusts the physical processes and algorithms of the model based on the information from the evaluation module. At the same time, it formulates targeted response measures based on actual weather conditions and terrain characteristics (these measures include warning issuance, emergency response and public education, aiming to improve the practicality and effectiveness of forecasts and reduce the risks and losses caused by thunderstorms and gale). This improves the practicality and effectiveness of forecasts, while enhancing the model's ability to forecast thunderstorms and gale, ensuring that the forecast results are more accurate.

[0064] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model, characterized in that: The following steps are involved: Step 1: Establish a data collection module to collect various meteorological data, including ground observation data, radar echo data and satellite remote sensing data; Step 2: Conduct model simulation and construct a virtual environment data collection module, and construct a virtual environment data collection module based on the model; Step 3: Establish a data calculation module, in which the collected data is integrated with the data generated by the virtual environment data collection module; Step 4: Establish a verification module to verify the formula by substituting historical storage data in the data collection module to verify the validity of the model; Step 5: Establish an evaluation module to evaluate the accuracy of the forecast based on the calculation results; Step 6: Establish a management module and adjust the model parameters and forecasting strategies based on the information from the evaluation module.

2. The objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model according to claim 1, characterized in that: The data collection module includes a ground meteorological station unit, a meteorological satellite unit and a Doppler radar unit. The ground meteorological station unit collects ground meteorological data through sensors, data collectors and operation monitoring equipment. The meteorological satellite unit collects meteorological satellite prediction data through satellite payloads and ground station receiving stations. The Doppler radar unit collects Doppler radar measurement data through antennas, transmitters and signal processors.

3. The objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model according to claim 1, characterized in that: The virtual environment data collection module collects virtual environment prediction data through a model simulation environment.

4. The objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model according to claim 3 is characterized by: The data calculation module includes a forecast timeliness evaluation unit, a spatial resolution test unit and a precipitation forecast scoring unit. The forecast timeliness evaluation unit calculates the model forecast timeliness accuracy Az based on the virtual environment prediction data. The spatial resolution test unit calculates the spatial resolution overlap rate Sx based on the Doppler radar measurement data and the virtual environment prediction data. The precipitation forecast scoring unit calculates the simulation model prediction accuracy Dh based on the ground meteorological data and the virtual environment prediction data.

5. The objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model according to claim 2, characterized in that: The ground meteorological station unit numbers the actual accumulated precipitation in a period of time according to the characteristics of the ground meteorological data, and the actual accumulated precipitation in a period of time is numbered as q1, q2, q3, ...q n The meteorological satellite unit numbers the predicted cumulative precipitation within a period of time according to the characteristics of the meteorological satellite prediction data, and the predicted cumulative precipitation within a period of time is numbered as y1, y2, y3, ...y n The Doppler radar unit performs data numbering on the number of antenna elements, the wavelength, and the effective aperture of the antenna array according to the characteristics of the Doppler radar measurement data, and the number of antenna elements, the wavelength, and the effective aperture of the antenna array are numbered T, α, and D respectively.

6. The objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model according to claim 3 is characterized by: The virtual environment data collection module numbers the number of times the model actually occurred and was predicted to occur, the number of times it did not actually occur but was predicted to occur, and the number of times it actually occurred but was predicted not to occur according to the virtual environment prediction data characteristics. The number of times it actually occurred and was predicted to occur, the number of times it did not actually occur but was predicted to occur, and the number of times it actually occurred but was predicted not to occur are numbered n1, n2, and n3, respectively.

7. The objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model according to claim 6, characterized in that: The forecast timeliness evaluation unit calculates the forecast timeliness accuracy Az of the model according to the virtual environment prediction data, and the calculation formula is: In the formula, Az represents the accuracy of the model forecast timeliness, n1, n2, and n3 represent the number of times that the event actually occurred and was predicted to occur, the number of times that the event actually did not occur but was predicted to occur, and the number of times that the event actually occurred but was not predicted to occur. It represents the proportion of actual thunderstorms and gales that are also predicted by the forecast, that is, the hit rate P. It indicates the proportion of thunderstorms and gales that did not actually occur but were predicted to occur, that is, the false alarm rate X. It represents the ratio of correctly predicted thunderstorms to the total number of predicted thunderstorms, that is, the accuracy V.

8. The objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model according to claim 5, characterized in that: The spatial resolution test unit calculates the spatial resolution overlap rate Sx according to the Doppler radar measurement data and the virtual environment prediction data, and the calculation formula is: In the formula, Sx represents the spatial resolution overlap rate, T represents the number of antenna elements, α represents the wavelength, and D represents the effective aperture of the antenna array. represents the spatial resolution of radar measurement data, K represents the spatial resolution of virtual environment prediction data, which is a fixed parameter set by the model, and C represents the maximum spatial resolution, which is the maximum spatial resolution between the spatial resolution of radar measurement data and the spatial resolution of virtual environment prediction data. The spatial resolution of radar measurement data and the spatial resolution of virtual environment prediction data are compared, and the larger one is selected as the maximum spatial resolution.

9. The objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model according to claim 5, characterized in that: The precipitation forecast scoring unit calculates the cumulative precipitation forecast score Dh based on ground meteorological data and meteorological satellite prediction data, and its calculation formula is: In the formula, Dh represents the cumulative precipitation forecast score, m represents the number of observation points, q1, q2, q3, ...q n Indicates the actual cumulative precipitation over a period of time, y1, y2, y3, ...y n represents the predicted cumulative precipitation over a period of time, q i and i They represent the actual cumulative precipitation and predicted cumulative precipitation at the i-th observation point over a period of time respectively.

10. The objective forecasting method for thunderstorm gale based on Doppler radar and mesoscale model according to claim 9, characterized in that: The evaluation module evaluates the forecast accuracy of the model simulation environment according to the model forecast time accuracy Az, evaluates the forecast accuracy of the Doppler radar according to the spatial resolution overlap rate Sx, and evaluates the forecast accuracy of the meteorological satellite according to the cumulative precipitation forecast score Dh; The management module adjusts the physical process and algorithm of the model according to the information of the evaluation module.

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