A method for improving turbulence parameters in strong and weak weather backgrounds

Abstract

The application provides a turbulent parameter improvement method in strong and weak weather backgrounds, which estimates heat and water vapor vertical turbulent diffusion coefficients based on regional meteorological automatic station data, corrects water vapor turbulent diffusion coefficients according to the proportional relationship obtained by observation, designs multiple groups of judgment conditions based on cloud water content, wind speed and other key meteorological elements to distinguish sea fog and typhoon weather, and then determines the best scheme through typhoon and sea fog simulation prediction and multi-index evaluation. The method solves the compatibility problem of turbulent parameterization scheme in strong and weak weather backgrounds, improves the sea fog prediction accuracy, reduces the missing and false alarm rates, and does not have a negative impact on typhoon prediction, but further optimizes the prediction accuracy of typhoon path, central pressure and maximum wind speed.

Description

Technical Field

[0001] This invention relates to the field of meteorological detection technology, and in particular to a method for improving turbulence parameters under strong and weak weather conditions. Background Technology

[0002] The current inadequacy of numerical weather prediction models in describing turbulent physical processes in the boundary layer is one of the important reasons for the low accuracy of sea fog formation and dissipation forecasts. Currently, the China Meteorological Administration's Regional Typhoon Model (CMA-TYM) needs to provide daily real-time typhoon and sea fog forecasts for coastal areas. However, while turbulence parameterization improvements for sea fog processes have a positive effect on sea fog forecasts, they have a negative impact on typhoon forecasts. Summary of the Invention

[0003] The purpose of this invention is to provide a method for improving turbulence parameters under strong and weak weather conditions. This method makes multiple judgments on three variables: cloud water content, horizontal wind speed, and stability parameters, in order to improve the accuracy of sea fog forecasting by the CMA-TYM model without affecting typhoon forecasting.

[0004] To achieve the above objectives, the present invention provides the following solution: A method for improving turbulence parameters under varying weather conditions includes the following steps: Turbulence parameters were estimated based on data from regional automatic weather stations. Multiple judgment conditions are set based on turbulence parameters and tested. The test results are then compared to obtain candidate solutions. Based on the candidate schemes, typhoon weather simulation forecasts and evaluations are conducted to obtain typhoon forecast results; Based on the candidate scenarios, simulate, forecast, and evaluate sea fog weather to obtain sea fog forecast results; The best course of action will be determined based on typhoon and sea fog forecasts.

[0005] Optionally, the turbulence parameters include: the heat turbulent diffusion coefficient and the water vapor vertical turbulent diffusion coefficient; the formula for calculating the heat turbulent diffusion coefficient is: The formula for calculating the vertical turbulent diffusion coefficient of water vapor is: ;in, This refers to vertical wind speed fluctuations. The potential temperature pulsation is at height z. The potential temperature at height z. This represents the fluctuation in water vapor density at height z. Let z be the water vapor density at height z. For parameters The average time value.

[0006] Optionally, multiple judgment conditions are set based on turbulence parameters and tested, and the test results are compared to obtain candidate solutions, including: The vertical turbulent diffusion coefficient of water vapor is corrected based on the proportional relationship between the heat turbulent diffusion coefficient and the water vapor vertical turbulent diffusion coefficient to obtain the correction parameter; Determining the boundary layer scheme based on modified parameters; The judgment criteria were determined based on the results of the literature review. Based on the judgment conditions, the boundary layer schemes are verified and compared, and the verification results are compared to obtain candidate schemes.

[0007] Optionally, the judgment conditions include: a first condition, a second condition, a third condition, a fourth condition, and a fifth condition; the first condition is that the cloud water content qc of all layers is ≥ 0.01 g / kg; the second condition is that the cloud water content of all layers satisfies 0.01 g / kg ≤ qc ≤ 0.7 g / kg, and the wind speed ws of all layers is ≤ 3 m / s; the third condition is that the cloud water content of all layers satisfies 0.01 g / kg ≤ qc ≤ 0.7 g / kg, the wind speed ws of all layers is ≤ 3 m / s, and the stability parameter zol1 is greater than 0; the fourth condition is that the cloud water content of the first layer satisfies 0.01 g / kg ≤ qc ≤ 0.7 g / kg, and the 10 m wind speed ws10 ≤ 3 m / s; the fifth condition is that the cloud water content of all layers satisfies 0.01 g / kg ≤ qc ≤ 0.7 g / kg, and the 10 m wind speed ws10 ≤ 3 m / s.

[0008] Optionally, the validation metrics for the boundary layer scheme include: average path error. Central air pressure error and maximum wind speed error The calculation formulas are as follows: ; ; ; Where R is the Earth's radius, ( , () represents the latitude and longitude of the forecast point. , () represents the latitude and longitude of the observation point. To forecast the central pressure value, To observe the central air pressure value, To forecast the maximum wind speed, To observe the maximum wind speed.

[0009] Optionally, the evaluation indicators for simulating and assessing sea fog weather include: Point of Detection (POD), Skill Score (TS), Fair Skill Score (ETS), Missed Detection Rate (MIS), and Free Detection Rate (FAR), calculated using the following formulas: ; ; ; ; ; Where H represents the number of grid points with fog in both simulation and observation, M represents the number of missed grid points, F represents the number of empty grid points, R represents the random hit item, and N represents the total number of grid points in the simulation area.

[0010] According to specific embodiments provided by the present invention, the following technical effects are disclosed: The present invention provides a method for improving turbulence parameters under strong and weak weather backgrounds. This method includes: estimating turbulence parameters based on data from regional automatic meteorological stations; setting and verifying multiple judgment conditions based on the turbulence parameters, and comparing the verification results to obtain candidate schemes; performing typhoon weather simulation forecasts and evaluations based on the candidate schemes to obtain typhoon forecast results; performing sea fog weather simulation forecasts and evaluations based on the candidate schemes to obtain sea fog forecast results; and determining the optimal scheme based on the typhoon forecast results and sea fog forecast results. This method accurately distinguishes between sea fog and typhoon weather by designing multiple sets of judgment conditions based on key meteorological elements such as cloud water content and wind speed, and optimizes the boundary layer parameterization scheme by correcting the vertical turbulent diffusion coefficient of water vapor based on actual observation data. This solves the problem of incompatibility between improved sea fog turbulence parameterization and typhoon forecasting effects, significantly improving the overall accuracy of the CMA-TYM model for sea fog forecasting without negative impacts and even improving typhoon forecast accuracy. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of the turbulence parameter improvement method according to an embodiment of the present invention; Figure 2 This is a graph showing the average typhoon path error under different judgment conditions according to an embodiment of the present invention; Figure 3 This is a diagram showing the typhoon center pressure error under different judgment conditions according to an embodiment of the present invention; Figure 4 This is a diagram showing the maximum wind speed error of a typhoon under different judgment conditions according to an embodiment of the present invention; Figure 5This invention provides a comparison of the average path error before and after improvement for a specific typhoon case, along with an illustration of the improvement effect. Figure 6 This is a comparison of the center pressure error before and after the improvement of a typhoon example according to an embodiment of the present invention, and an image showing the improvement effect. Figure 7 This invention provides a comparison of the maximum wind speed error before and after improvement in a specific typhoon case, along with an illustration of the improvement effect. Figure 8 This is a comparison chart of the average scores before and after improvements in six sea fog cases according to the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] like Figure 1 As shown, this invention provides a method for improving turbulence parameters under varying weather conditions, comprising the following steps: Step 100: Estimate turbulence parameters based on data from regional automatic weather stations; Step 200: Set multiple judgment conditions based on turbulence parameters and test them, then compare the test results to obtain candidate solutions; Step 300: Conduct typhoon weather simulation forecasting and evaluation based on candidate schemes to obtain typhoon forecast results; Step 400: Perform sea fog weather simulation forecasting and evaluation based on candidate schemes to obtain sea fog forecast results; Step 500: Determine the best plan based on the typhoon forecast and sea fog forecast results.

[0016] In the specific implementation process, step 100 estimates the heat turbulence diffusion coefficient based on the 15-layer gradient, 5-layer turbulence flux of the meteorological tower and data from regional automatic meteorological stations. ) and water vapor vertical turbulent diffusion coefficient ( The calculation formulas are as follows: ; ; in, This refers to vertical wind speed fluctuations. The potential temperature pulsation is at height z. The potential temperature at height z. This represents the fluctuation in water vapor density at height z. The value represents the water vapor density at height z, and the horizontal line represents the time average of the parameter.

[0017] It should be noted that in this embodiment, based on the criteria of atmospheric horizontal visibility less than 1 km and relative humidity greater than 90%, the turbulence data is divided into foggy and fog-free stages for separate processing and calculation. Statistical results show that in foggy conditions, the observed water vapor turbulent diffusion coefficient ( ) is approximately the thermal turbulent diffusion coefficient ( 8 times that of ).

[0018] In the specific implementation process, step 200, based on the ratio of the water vapor turbulent diffusion coefficient to the heat turbulent diffusion coefficient in the fog obtained from the observation data, will use the K value from the NYSU scheme of the CMA-TYMV4.0 model. q =K h Corrected to K q =8K h However, this improved turbulence parameterization scheme only applies to sea fog processes and is not applicable under typhoon conditions. This may lead to improved sea fog forecasting but poorer typhoon forecasting. Therefore, threshold conditions need to be set for this improvement to address the forecast compatibility issue of the CMA-TYM model under typhoon and sea fog conditions. In this embodiment, the CMA-TYM model settings are shown in Table 1. The simulation period is from 20:00 on July 20, 2024 to 08:00 on July 25, 2024, with forecasts issued every 12 hours and a forecast lead time of 120 hours. According to literature review, fog usually occurs when cloud water content qc meets the conditions of 0.01g / kg≤qc≤0.7g / kg, wind speed ws≤3m / s, and stable stratification. This embodiment designs 5 sets of judgment condition settings, as shown in Table 2, to distinguish between sea fog and typhoons in the model, ensuring that the improved turbulence parameterization scheme only works for sea fog processes and avoids affecting typhoon forecasts. Apart from the different judgment conditions, all other mode settings are the same.

[0019] Table 1 CMA-TYM Mode Parameter Settings

[0020] Table 2. Judgment Criteria Setting Table for the Proposed Implementation of the New Scheme

[0021] Furthermore, under different judgment conditions, typhoon forecasts were tested hourly for every 12 hours within the 0-120 hour forecast period. The testing indicators included the average path error (APE). ), center pressure error ( ) and maximum wind speed error ( The calculation formulas are as follows: ; ; ; Where R is the Earth's radius, which is taken as 6371 km in this embodiment. , () represents the latitude and longitude of the forecast point. , () represents the latitude and longitude of the observation point. To forecast the central pressure value, To observe the central air pressure value, To forecast the maximum wind speed, To observe the maximum wind speed. The test results are as follows: Figure 2-4 As shown, the comparison reveals that the prediction error of Exp_5 is closest to that of the Ctrl experiment, and all errors are significantly reduced compared to the previous version (Exp_1). Therefore, the conclusion scheme Exp_5 is determined as the candidate scheme, i.e., all layers have a wind speed of 0.01g / kg≤qc≤0.7g / kg, and a wind speed of 10m ws10≤3m / s.

[0022] In the specific implementation process, step 300 uses the Exp_5 scheme to conduct comparative tests on three typhoon cases, as shown in Table 3. The results are as follows: Figures 5-7 As shown, compared to the control experiment, the Exp_5 scheme significantly reduced the average typhoon path error from 24 to 96 hours, with only a slight increase in the 120-hour error. However, the errors in central pressure and maximum wind speed decreased significantly overall. In summary, adding the judgment condition not only had almost no negative impact on typhoon forecasts but also reduced forecast errors. On average, the average path error decreased by 2.0%, the central pressure error by 2.27%, and the maximum wind speed error by 4.26%, significantly improving typhoon forecast accuracy.

[0023] Table 3. Case Studies of Typhoons Inspected in Batch

[0024] In the specific implementation process, in order to verify the improvement effect of the new scheme on sea fog, step 400 selected 6 sea fog cases as shown in Table 4 and conducted sensitivity tests. Except for the boundary layer scheme, the other settings were the same, and the forecast lead time was 120 hours. The test using the boundary layer scheme before the improvement was the control experiment Ctrl, and the test using the improved scheme was Exp.

[0025] Table 4: Case Studies of Batch Inspection of Sea Fog

[0026] The assessment of horizontal fog areas uses five indicators: Point of Detection (POD), Skill Score (TS), Fair Skill Score (ETS), Missed Detection Rate (MIS), and Free Detection Rate (FAR). The calculation formulas are as follows: ; ; ; ; ; Where H represents the number of grid points where fog is present in both simulation and observation; M represents the number of missed grid points (i.e., the number of grid points where fog is observed but fog is simulated); F represents the number of false alarm grid points (i.e., the number of grid points where fog is simulated but fog is observed); R = F·((H+M) / N) is the random hit term; and N is the total number of grid points in the simulation area. A higher POD indicates a higher hit rate; a higher FAR indicates a higher false alarm rate; a higher MIS indicates a higher missed detection rate; and a higher TS score indicates a better simulation effect. ETS values ​​range from -1 / 3 to 1; a higher ETS indicates a better overall simulation effect, while 0 and negative numbers indicate no forecasting skill.

[0027] In the specific implementation process, step 500 verifies the improvement effect of turbulence parameterization on sea fog forecasting by comparing the fog area scores before and after the improvement. For example... Figure 8 As shown, the new scheme improves all sea fog cases to varying degrees. Overall, POD increases by an average of 0.027, TS by an average of 0.009, ETS by an average of 0.002, MIS decreases by 0.027, and FAR decreases by 0.006, effectively improving missed and false alarms in fog areas. Regarding fog distribution, taking the case reported starting July 23, 2024 as an example, the improvement significantly reduced missed and false alarms, and the forecast results are closer to observations. In conclusion, by setting the judgment conditions: 0.01g / kg ≤ qc ≤ 0.7g / kg for all layers, and 10m wind speed ws10 ≥ 3m / s, the improved optimal scheme can simultaneously guarantee positive effects on typhoon and sea fog forecasts, making this improved scheme more compatible under strong / weak turbulent weather conditions.

[0028] The beneficial effects of this invention are as follows: 1) It solved the compatibility problem between the improved parameterization of sea fog turbulence and the typhoon forecast effect, and improved the adaptability of numerical forecast models under strong / weak turbulent weather backgrounds; 2) Based on actual observation data, the vertical turbulent diffusion coefficient of water vapor was corrected and the boundary layer parameterization scheme was optimized to better fit the turbulent physical processes during sea fog formation. This significantly improved the overall effect of sea fog forecasting, reduced the false alarm rate and false alarm rate of sea fog, and improved the accuracy and reliability of fog area forecasting. 3) While improving sea fog forecasting, it did not have a negative impact on typhoon forecasting. On the contrary, it further reduced the forecast errors of typhoon track, central pressure and maximum wind speed, and improved the overall accuracy of typhoon forecasting. 4) By designing multiple sets of judgment conditions based on key meteorological elements, the system can accurately distinguish between sea fog and typhoon weather, ensuring that the turbulence parameter improvement scheme only takes effect during sea fog.

[0029] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0030] Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. Furthermore, those skilled in the art will recognize that, based on the ideas of this invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for improving turbulence parameters under strong and weak weather backgrounds, characterized in that, The steps include the following: Turbulence parameters were estimated based on data from regional automatic weather stations. Based on the turbulence parameters, various judgment conditions are set and tested, and the test results are compared to obtain candidate solutions; Based on the candidate schemes, typhoon weather simulation forecasts and evaluations are performed to obtain typhoon forecast results; Based on the candidate schemes, sea fog weather simulation forecasts and evaluations are performed to obtain sea fog forecast results; The optimal solution is determined based on the typhoon forecast results and the sea fog forecast results.

2. The method for improving turbulence parameters under strong and weak weather conditions according to claim 1, characterized in that, The turbulence parameters include: the heat turbulence diffusion coefficient and the water vapor vertical turbulence diffusion coefficient; the formula for calculating the heat turbulence diffusion coefficient is: The formula for calculating the vertical turbulent diffusion coefficient of water vapor is: ;in, This refers to vertical wind speed fluctuations. The potential temperature pulsation is at height z. The potential temperature at height z. This represents the fluctuation in water vapor density at height z. Let z be the water vapor density at height z. For parameters The average time value.

3. The method for improving turbulence parameters under strong and weak weather conditions according to claim 1, characterized in that, Based on the turbulence parameters, various judgment conditions are set and tested, and the test results are compared to obtain candidate solutions, including: The vertical turbulent diffusion coefficient of water vapor is corrected based on the proportional relationship between the heat turbulent diffusion coefficient and the water vapor vertical turbulent diffusion coefficient to obtain the correction parameter; Determine the boundary layer scheme based on the aforementioned correction parameters; The judgment criteria were determined based on the results of the literature review. The boundary layer schemes are verified and compared according to the judgment conditions, and the verification results are compared to obtain the candidate schemes.

4. The method for improving turbulence parameters under strong and weak weather conditions according to claim 3, characterized in that, The judgment conditions include: a first condition, a second condition, a third condition, a fourth condition, and a fifth condition; the first condition is that the cloud water content qc of all layers is ≥ 0.01 g / kg; the second condition is that the cloud water content of all layers satisfies 0.01 g / kg ≤ qc ≤ 0.7 g / kg, and the wind speed ws of all layers is ≤ 3 m / s; the third condition is that the cloud water content of all layers satisfies 0.01 g / kg ≤ qc ≤ 0.7 g / kg, the wind speed ws of all layers is ≤ 3 m / s, and the stability parameter zol1 is greater than 0; the fourth condition is that the cloud water content of the first layer satisfies 0.01 g / kg ≤ qc ≤ 0.7 g / kg, and the 10 m wind speed ws10 ≤ 3 m / s; the fifth condition is that the cloud water content of all layers satisfies 0.01 g / kg ≤ qc ≤ 0.7 g / kg, and the 10 m wind speed ws10 ≤ 3 m / s.

5. The method for improving turbulence parameters under strong and weak weather conditions according to claim 3, characterized in that, The verification metrics for the boundary layer scheme include: average path error. Central air pressure error and maximum wind speed error The calculation formulas are as follows: ； ； ； Where R is the Earth's radius, ( , () represents the latitude and longitude of the forecast point. , () represents the latitude and longitude of the observation point. To forecast the central pressure value, To observe the central air pressure value, To forecast the maximum wind speed, To observe the maximum wind speed.

6. The method for improving turbulence parameters under strong and weak weather conditions according to claim 1, characterized in that, The evaluation indicators for simulating and assessing sea fog weather include: Point of Detection (POD), Skill Score (TS), Fair Skill Score (ETS), Missed Detection Rate (MIS), and Free Detection Rate (FAR), calculated using the following formulas: ； ； ； ； ； Where H represents the number of grid points with fog in both simulation and observation, M represents the number of missed grid points, F represents the number of empty grid points, R represents the random hit item, and N represents the total number of grid points in the simulation area.

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