Double-resolution mode typhoon forecast post-processing method and system

Abstract

The invention discloses a double-resolution mode typhoon forecast post-processing method and system, and the method comprises the steps: correcting typhoon path forecast in global scale ensemble forecast, obtaining corrected typhoon path forecast and rainfall forecast, carrying out the probability transformation of mesoscale deterministic forecast, and obtaining the typhoon path forecast and rainfall forecast. And finally, fusing the corrected typhoon rainfall ensemble forecast and mesoscale probability rainfall forecast, and giving full play to the advantages of global scale ensemble forecast and mesoscale determinacy forecast to obtain a rainfall forecast result with relatively high accuracy. According to the post-processing method, firstly, global scale ensemble forecast data is corrected, members with better forecast quality in the global scale ensemble forecast are screened out, and corrected typhoon paths and rainfall ensemble forecast are formed according to the typhoon forecast of the members with better quality, so that the accuracy of the ensemble forecast is improved, and the accuracy of the rainfall ensemble forecast is improved. And the accuracy of the forecast post-processing result is further improved.

Description

Technical Field

[0001] The invention belongs to the technical field of meteorological forecasting, and in particular relates to a dual-resolution mode typhoon forecast post-processing method and system. Background Art

[0002] At present, the research and application of ensemble forecasting to typhoons mainly focuses on two aspects: one is to establish a typhoon ensemble forecasting system; the other is the application in the post-processing and correction of ensemble forecast products (i.e., ensemble forecast interpretation). Overall, the research on the post-processing and correction of typhoon ensemble forecasts is still relatively small, and mainly focuses on the correction of global-scale model ensemble forecasts.

[0003] The heavy precipitation caused by typhoons is not only related to the intensity, scale, structure, etc. of the typhoon itself, but also to the interaction of the multi-scale system of the environmental field and the characteristics of the underlying surface. In the current research on typhoon forecasting, the simulation of large-scale environmental fields by global-scale ensemble forecasts is better than that of other scales / models, and the typhoon path can be simulated more accurately. However, due to the low resolution of global-scale ensemble forecasts, it is impossible to accurately simulate the internal structure of the typhoon vortex and the influence of terrain on precipitation. It will be systematically smaller due to insufficient model resolution or systematically larger due to strong terrain effects. At the same time, the mesoscale model can more accurately simulate the mesoscale characteristics of typhoon precipitation and the influence of terrain on precipitation, but there are still large errors in the forecast of typhoon paths and precipitation areas. Therefore, some scholars have conducted typhoon precipitation forecasts by integrating ensemble forecasts of different resolutions, that is, using low-resolution global-scale ensemble forecasts to estimate the optimal typhoon path, thereby correcting the spatial structure of precipitation distribution, and using high-resolution mesoscale ensemble forecasts to adjust the heavy rain rainfall forecast. However, most of the current fusion methods directly fuse the ensemble forecast, and the ensemble forecast itself has certain inaccuracies. If the ensemble forecast is directly fused, the accuracy of the final fusion forecast will be reduced due to the influence of the forecast results of the members with poor forecast quality in the ensemble forecast. Therefore, some studies have combined and corrected the low-resolution (36km) ensemble forecast and the high-resolution (4km) ensemble forecast to forecast the precipitation caused by Typhoon Morakot in Taiwan Province, my country. The results show that the combined ensemble precipitation forecast can reduce the errors of the high-resolution and low-resolution ensemble forecasts, but because the ensemble forecasts of the two resolutions are directly fused, the accuracy of the combined ensemble precipitation forecast is also increased to a certain extent. At the same time, the high-resolution ensemble forecast processed by this combined correction method has difficulties in obtaining data sources, for example, high-resolution ensemble forecasts are difficult to obtain. For the forecast business at the provincial and municipal levels, due to various conditions, the development accuracy makes it difficult to make local high-resolution ensemble forecasts, so more existing high-resolution deterministic forecasts and related correction products are used. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention provides a dual-resolution model typhoon forecast post-processing method and system.

[0005] The present invention adopts the following technical solution:

[0006] A dual-resolution model typhoon forecast post-processing method, wherein the dual-resolution model typhoon forecast includes a global-scale ensemble forecast and a mesoscale deterministic forecast that are matched in time and space; the post-processing method comprises:

[0007] Combining the historical typhoon data of the target area, the typhoon track forecast in the global scale ensemble forecast is corrected in terms of forecast quality;

[0008] Perform probability conversion on mesoscale deterministic forecasts to form mesoscale probabilistic forecasts;

[0009] The revised typhoon track forecast is integrated with the mesoscale probability forecast.

[0010] Furthermore, the specific method of correcting the global scale ensemble forecast related to the forecast quality in combination with the historical typhoon data of the target area includes:

[0011] Based on the historical typhoon data of the target area, the members in the global-scale ensemble forecast are screened in terms of forecast quality. Based on the screened members, a revised typhoon path forecast and corresponding typhoon precipitation forecast are formed.

[0012] Furthermore, based on the historical typhoon data of the target area, the members in the global scale ensemble forecast are screened for forecast quality, and based on the screened members, a revised typhoon path forecast and a corresponding typhoon precipitation forecast are formed using the following method:

[0013] 1) Obtain the real-time location and subjective forecast of the typhoon impact process from the historical typhoon data of the target area;

[0014] 2) Based on the global-scale ensemble forecast data, the distance errors between the path forecast of each member in the global-scale ensemble forecast and the real-time position and subjective forecast during the typhoon impact process are calculated, and the N members with the smallest errors are selected;

[0015] 3) The arithmetic mean of the typhoon path forecasts of the screened N members is used as the revised typhoon path forecast and the corresponding typhoon precipitation forecast.

[0016] Furthermore, the probability conversion of the mesoscale deterministic forecast is performed by the following method:

[0017] The ROC curve areas of the mesoscale deterministic forecast at different scales are calculated, and the scale corresponding to the ROC curve with the largest area is selected as the neighborhood scale. The mesoscale deterministic forecast is converted into a probability according to the selected neighborhood scale.

[0018] Furthermore, the corrected typhoon path forecast and the mesoscale probability forecast after probability conversion are fused using a frequency matching method.

[0019] Furthermore, the frequency matching fusion method includes using the typhoon precipitation forecast in the revised global-scale ensemble forecast as the typhoon precipitation area forecast, using the probabilistically transformed mesoscale probability forecast as the typhoon precipitation magnitude forecast, and then using the frequency matching method for fusion.

[0020] Furthermore, the frequency matching fusion method includes using the typhoon precipitation forecast in the revised global-scale ensemble forecast and the mesoscale deterministic forecast as the typhoon precipitation area forecast, using the probabilistically converted mesoscale probability forecast as the typhoon precipitation magnitude forecast, and then using the frequency matching method for fusion.

[0021] A dual-resolution model typhoon forecast post-processing system, wherein the dual-resolution model typhoon forecast includes a global-scale ensemble forecast and a mesoscale deterministic forecast with time and space matching; the post-processing system includes a correction module, a probability conversion module and a fusion module;

[0022] The correction module is used to correct the typhoon path forecast in the global scale ensemble forecast in relation to the forecast quality in combination with the historical typhoon data of the target area;

[0023] The probability conversion module is used to perform probability conversion on the mesoscale deterministic forecast to form a mesoscale probabilistic forecast;

[0024] The fusion module is used to fuse the revised typhoon path forecast and the mesoscale probability forecast.

[0025] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0026] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention provides a dual-resolution model typhoon forecast post-processing method and system. The post-processing method corrects the typhoon path forecast in the global-scale ensemble forecast to obtain a corrected typhoon path forecast and precipitation forecast, and performs probability conversion on the mesoscale deterministic forecast. Finally, the corrected typhoon precipitation ensemble forecast and the mesoscale probabilistic precipitation forecast are integrated, so as to give full play to the advantages of the global-scale ensemble forecast and the mesoscale deterministic forecast and obtain a precipitation forecast result with higher accuracy.

[0029] The post-processing method of the present invention first corrects the global-scale ensemble forecast data, screens out members with better forecast quality in the global-scale ensemble forecast, and forms a corrected typhoon path and precipitation ensemble forecast based on the typhoon forecasts of the members with better quality, thereby improving the accuracy of the ensemble forecast and further improving the accuracy of the forecast post-processing results.

[0030] The post-processing method of the present invention does not directly use the mesoscale ensemble forecast data, but performs probability conversion on the mesoscale deterministic forecast to form a mesoscale probabilistic forecast. Therefore, it can reduce the requirements for computer hardware and software, reduce the difficulty of processing for technical personnel, and make the post-processing method of the present invention fast and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Flowchart of the correction method for typhoon track and precipitation forecast.

[0032] Figure 2(a) is a schematic diagram of the path of Typhoon Lekima every three hours from 14:00 on August 4 to 08:00 on August 13;

[0033] Figure 2(b) shows the cumulative precipitation map of Typhoon Lekima in East China from 08:00 on August 9 to 08:00 on August 12;

[0034] Figure 3 (a) shows the spatial distribution of the accumulated precipitation of the global-scale ensemble forecast (EC-eps mean) from 08:00 on August 9 to 08:00 on August 12;

[0035] Figure 3(b) shows the spatial distribution of the mesoscale deterministic forecast (WARMS) accumulated precipitation from 08:00 on August 9 to 08:00 on August 12;

[0036] Figure 4 This is a comparison chart of the time-effectiveness path error of the ensemble forecast formed by the subjective and objective member selection method for Typhoon Lekima and the original ensemble forecast at 6-hour intervals;

[0037] Figure 5(a) shows the ensemble forecast for Typhoon Lekima formed by the subjective and objective member selection method and the original ensemble forecast at 6-hour intervals, corresponding to the precipitation RPS score map of 0.1 mm / 6h precipitation;

[0038] Figure 5(b) shows the ensemble forecast formed by the subjective and objective member selection method for Typhoon Lekima and the 6-hour interval forecast time of the original ensemble forecast, corresponding to the precipitation RPS score map of 5mm / 6h precipitation;

[0039] Figure 5(c) shows the ensemble forecast formed by the subjective and objective member selection method for Typhoon Lekima and the 6-hour interval forecast time of the original ensemble forecast, corresponding to the precipitation RPS score map of 10 mm / 6 h precipitation;

[0040] Figure 5(d) shows the ensemble forecast formed by the subjective and objective member selection method for Typhoon Lekima and the 6-hour interval forecast time of the original ensemble forecast, corresponding to the precipitation RPS score map of 25 mm / 6 h precipitation;

[0041] Figure 6 This is the spatial distribution map of the difference in precipitation RPS scores between the original ensemble forecast and the revised ensemble forecast for Typhoon Lekima.

[0042] Figure 7(a) is a comparison of the S1, S2, EC-eps average and WARMS ETS scores corresponding to 0.1 mm precipitation during the typhoon Lekima;

[0043] Figure 7(b) is a comparison of the S1, S2, EC-eps average and WARMS ETS scores corresponding to 5 mm precipitation during the typhoon Lekima;

[0044] Figure 7(c) is a comparison of the S1, S2, EC-eps average and WARMS ETS scores corresponding to 10 mm precipitation during the typhoon Lekima;

[0045] Figure 7(d) is a comparison of the S1, S2, EC-eps average and WARMS ETS scores corresponding to 25 mm precipitation during the precipitation process of Typhoon Lekima. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with specific embodiments and corresponding drawings.

[0047] Embodiment 1:

[0048] The present invention provides a dual-resolution model typhoon forecast post-processing method, wherein the dual-resolution model typhoon forecast includes a global-scale ensemble forecast and a mesoscale deterministic forecast that are matched in time and space. The global-scale ensemble forecast uses an ECMWF ensemble forecast including 51 members; Figure 1 As shown, the post-processing method includes:

[0049] Combined with the historical typhoon data of the target area, the typhoon track forecast in the global-scale ensemble forecast is corrected in terms of forecast quality. That is, the members with better quality and closer to the actual situation in the global-scale ensemble forecast are screened out through the typhoon track correction method, and then the corrected typhoon track forecast and corresponding typhoon precipitation forecast are formed based on these members (corresponding forecasts) to improve the accuracy of the ensemble forecast.

[0050] Perform probability conversion on mesoscale deterministic forecasts to form mesoscale probabilistic forecasts;

[0051] The revised typhoon track forecast is integrated with the mesoscale probability forecast.

[0052] Embodiment 2:

[0053] This example is further designed on the basis of the first embodiment in that the specific method for correcting the forecast quality of the global scale ensemble forecast in combination with the historical typhoon data of the target area in this example includes:

[0054] Based on the historical typhoon data of the target area, the members in the global-scale ensemble forecast are screened in terms of forecast quality. Based on the corresponding forecasts of the screened members, a revised typhoon path forecast and corresponding typhoon precipitation forecast are formed.

[0055] Embodiment three:

[0056] This example is further designed on the basis of the first embodiment in that, based on the historical typhoon data of the target area, the members in the global scale ensemble forecast are screened for forecast quality, and based on the forecasts corresponding to the screened members, a revised typhoon path forecast and a corresponding typhoon precipitation forecast are formed using the following method:

[0057] 1) Obtain the real-time location and subjective forecast of the typhoon impact process from the historical typhoon data of the target area;

[0058] 2) Based on the global-scale ensemble forecast data, the distance errors between the path forecast of each member in the global-scale ensemble forecast and the real-time position and subjective forecast during the typhoon impact process are calculated, and the N members with the smallest errors are selected;

[0059] 3) The arithmetic mean of the typhoon path forecasts of the screened N members is used as the revised typhoon path forecast and the corresponding typhoon precipitation forecast.

[0060] The following is an example to illustrate the screening and correction of the above-mentioned forecast quality. This example collects individual typhoons that have landed on the southeast coast of China in the past 10 years as historical typhoon data, and uses the forecast of the Central Meteorological Observatory as the subjective forecast;

[0061] Based on historical typhoon data, the global scale ensemble forecast ECMWF-eps is systematically calculated using the following subjective and objective fusion member selection method to calculate the path errors of different numbers of members, and the N members with the smallest errors are selected. The subjective and objective fusion member selection method includes:

[0062] The corrected path F is obtained by taking the arithmetic average AVE of the N paths with the smallest error among the typhoon real-time position (subjective forecast, the real-time position of the Central Meteorological Observatory) and the subjective forecast of the Central Meteorological Observatory for the next 72 hours, the typhoon path L0, the latest position of the ECMWF ensemble forecast, and the typhoon path LE forecast for the next 72 hours. track , as shown below:

[0063]

[0064] Embodiment 4:

[0065] This example is further designed on the basis of the first embodiment in that the probability conversion of the mesoscale deterministic forecast is performed by the following method:

[0066] The ROC curve areas of the mesoscale deterministic forecast at different scales are calculated, and the scale corresponding to the ROC curve with the largest area is selected as the neighborhood scale. The mesoscale deterministic forecast is converted into a probability according to the selected neighborhood scale.

[0067] The following example illustrates the specific steps of the neighborhood method. The calculation scope of this example is the East China region, and the calculation time period is the first 6 hours of the precipitation forecast time. Select different scale ranges to calculate the ROC area of ​​the mesoscale deterministic forecast model WARMS in this period.

[0068] The function of hit rate (HR) versus false alarm rate (FAR) is obtained as the ROC curve. The ROC curve close to the upper left corner is usually accompanied by a larger ROC area. The neighborhood scale corresponding to the largest ROC curve is selected.

[0069]

[0070]

[0071] Where a and b represent the number of hits and misses, FA represents the number of false alarms, and CN represents the number of correct negatives.

[0072] The mesoscale deterministic forecast WARMS is converted into the probabilistic forecast WARMS-eps according to the selected neighborhood scale.

[0073] The specific processing method is as follows: for a given position grid point (x0, y0), the value of the grid point is determined by the value in the surrounding "neighborhood", and this definition method is extended to the entire space (x, y). The "neighborhood" constructed by each grid point is determined by the influence radius (or square area). The score value of each grid point is the number of grid points in the "neighborhood" that exceed the defined threshold (for example, 0.1, 5, 10, 25mm / 6h) divided by the total number of grid points in the "neighborhood". The score value can be interpreted as the probability that the precipitation in the range is equal to or exceeds the threshold.

[0074] Embodiment five:

[0075] This example is further designed on the basis of the first embodiment in that the corrected typhoon path forecast and the mesoscale probability forecast after probability conversion are fused by using a frequency matching method.

[0076] Embodiment six:

[0077] This example is further designed on the basis of Example 5 in that the frequency matching fusion method in this example includes using the typhoon precipitation forecast in the revised global-scale ensemble forecast as the typhoon precipitation area forecast, and using the probabilistically converted mesoscale probability forecast as the typhoon precipitation magnitude forecast, and then using the frequency matching method for fusion, and finally obtaining a precipitation forecast with better quality (the predicted precipitation area and magnitude are closer to the actual situation).

[0078] Embodiment seven:

[0079] This example is further designed on the basis of Example 5 in that the frequency matching fusion method in this example includes using the typhoon precipitation forecast in the revised global-scale ensemble forecast and the mesoscale deterministic forecast as the typhoon precipitation area forecast, and using the mesoscale probability forecast after probabilistic conversion as the typhoon precipitation magnitude forecast, and then using the frequency matching method for fusion, and finally obtaining a precipitation forecast with better quality (the predicted precipitation area and magnitude are closer to the actual situation).

[0080] Embodiment eight:

[0081] A dual-resolution model typhoon forecast post-processing system of the present invention, the dual-resolution model typhoon forecast includes a global scale ensemble forecast and a mesoscale deterministic forecast with time and space matching, wherein the global scale ensemble forecast includes a number of members, such as 51; the post-processing system includes a correction module, a probability conversion module and a fusion module;

[0082] The correction module is used to correct the typhoon path forecast in the global-scale ensemble forecast in relation to the forecast quality by combining the historical typhoon data of the target area, that is, to select members of the global-scale ensemble forecast with better quality and closer to the actual situation through the typhoon path correction method, and then form a corrected typhoon path forecast and corresponding typhoon precipitation forecast based on these members (corresponding forecasts) to improve the accuracy of the ensemble forecast;

[0083] The probability conversion module is used to convert the mesoscale deterministic forecast into a mesoscale probability forecast;

[0084] The fusion module is used to fuse the revised typhoon path forecast with the mesoscale probability forecast.

[0085] Embodiment nine:

[0086] An electronic device of the present invention includes a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method described in any embodiment.

[0087] A computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0088] Application examples:

[0089] This example simulates the implementation of the post-processing method of the present invention on Typhoon Lekima No. 1909, which was generated on the eastern sea of ​​the Philippines on August 4, 2019 (Beijing time, the same below), and the Central Meteorological Observatory upgraded it from a tropical depression to a tropical storm at 17:00. Then, "Lekima" moved northwest under the guidance of the outer airflow of the subtropical high pressure in the western Pacific Ocean. At 05:00 on August 7, its intensity reached the typhoon level, and it was strengthened to a super typhoon at 23:00 on the same day. Since then, "Lekima" has maintained this intensity until it landed in the coastal area of ​​Chengnan Town, Wenling City, Zhejiang Province in the early morning of August 10. At the time of landing, the maximum wind force near the center of the typhoon reached level 16 (52 meters / second), and the minimum pressure in the center was 930hPa. After landing, the trajectory of "Lekima" gradually turned from northwest to north. On the evening of the 10th, the typhoon weakened to a tropical storm and entered Jiangsu Province. After entering the Yellow Sea on the 11th, Lekima continued to sail northward and landed again in Qingdao, Shandong Province. During this period, Typhoon Lekima maintained the strength of a tropical storm. In the early morning of the 12th, Lekima crossed the Shandong Peninsula and entered Laizhou Bay and stayed there, then gradually weakened and disappeared.

[0090] Figure 2(a) shows the path of Typhoon Lekima from August 4 to 13, 2019. From August 9 to 12, Typhoon Lekima brought considerable rainfall in the eastern region, with the heaviest rainfall concentrated in Zhejiang, Jiangsu and Shandong provinces. As shown in Figure 2(b), during the three days with the most concentrated rainfall, the cumulative rainfall at more than 60% of the rain gauges in East China exceeded 100 mm. Lekima is the third strongest typhoon to land in Zhejiang since 1949. After landing, its intensity remained at or above tropical storm for 44 hours, ranking sixth since 1949, and the comprehensive wind and rain intensity index was the largest since 1961.

[0091] Figure 3(a) shows the spatial distribution of the accumulated precipitation from the global scale ensemble forecast (EC-eps) during 08:00 on August 9 to 08:00 on August 12; Figure 3(b) shows the spatial distribution of the accumulated precipitation from the mesoscale deterministic forecast (WARMS) during 08:00 on August 9 to 08:00 on August 12. It can be seen from Figures 3(a) and 3(b) that the precipitation forecast from WARMS provides a precipitation distribution and magnitude that is closer to the actual situation than that from EC-eps. Due to the low horizontal resolution, the boundary of the precipitation center in the EC-eps forecast is too smooth and the magnitude of heavy precipitation is underestimated. However, the high-resolution WARMS forecast has two obvious problems: on the one hand, it predicts too much precipitation in southeastern China; on the other hand, after 20:00 on August 10, when Typhoon Lekima moved north to Shandong Province, the forecast underestimated the intensity and range of the heavy rain in northeastern China. In general, the low-resolution global model underestimates the precipitation magnitude while the location of the precipitation center is relatively accurate. The high-resolution WARMS forecast predicts a large amount of precipitation but the precipitation center deviates greatly from the actual situation.

[0092] This example verifies whether the correction is correct for the typhoon path forecast correction step in the present invention combined with the subjective and objective member selection method. In this example, the global scale ensemble forecast uses the ECMWF global model forecast ECMWF ensemble forecast. To compare the path error before and after the correction, as shown in Figure 4 As shown in the figure, within the forecast periods of 24, 48 and 72, the corrected ensemble forecast average path error is smaller than the error before correction, indicating that the correction step has a positive correction on the global model path forecast of Typhoon Lekima.

[0093] This example further explores whether the newly formed ensemble forecast also has a positive correction on the precipitation forecast. Figure 5(a) to Figure 5(d)The precipitation RPS scores of the ensemble forecast before and after correction within the 72-hour forecast period (6-hour interval) are shown for different precipitation thresholds. As the forecast period increases, the RPS score of the model's predictability of precipitation gradually decreases at each precipitation threshold. Overall, the RPS values ​​of the selected ensemble precipitation forecasts at each threshold are smaller than the original ensemble forecasts, which means that the member selection method also has a positive correction on the precipitation forecast.

[0094] Figure 6 The figure shows the difference between the original ensemble precipitation forecast and the corrected ensemble precipitation forecast RPS values ​​for the 72-hour forecast period (24-hour interval) under different precipitation thresholds. As shown in the figure, overall, the RPS difference in most areas of East China is positive, that is, the RPS value of the original ensemble forecast is greater than the corrected RPS value in most areas, which is a positive correction of the precipitation forecast. Under each precipitation threshold, with the increase of forecast period, the area with negative RPS difference gradually increases, especially under the thresholds of heavy rain (10mm / 6h) and torrential rain (25mm / 6h), the range of negative RPS difference in East China is larger. Compared with light rain and moderate rain, the correction effect of subjective and objective fusion member selection method for heavy rain and torrential rain in long forecast period is poor.

[0095] This example also verifies the processing results of the post-processing method of the present invention. This example uses the precipitation forecast performance of the mesoscale forecast WARMS 6 hours before the start time to determine the scale side length of the neighborhood method based on the principle of maximum ROC area. As shown in Table 1, under different precipitation thresholds, the scale side lengths of different start times can be used to obtain the mesoscale probability forecast WARMS-eps.

[0096] Table 1

[0097]

[0098] Then, the probability matching method is used to obtain the dual-resolution fusion precipitation forecast through the following two methods (Table 2).

[0099] Method 1 (hereinafter referred to as S1) uses the typhoon precipitation forecast (EC-eps average) in the revised global scale ensemble forecast as the typhoon precipitation area forecast, and uses the mesoscale probability forecast after probabilistic transformation as the typhoon precipitation magnitude forecast, and then uses the frequency matching method to fuse them.

[0100] Method 2 (hereinafter referred to as S2) uses the typhoon precipitation forecast (EC-eps average value) in the revised global-scale ensemble forecast and the mesoscale deterministic forecast (WARMS) as the typhoon precipitation area forecast, and uses the mesoscale probability forecast after probabilistic conversion as the typhoon precipitation magnitude forecast, and then uses the frequency matching method to fuse them.

[0101] Table 2

[0102] Solution Name Forecast Type Precipitation area forecast Precipitation Level Forecast S1 Deterministic forecast EC-eps average WARMS-eps S2 Deterministic forecast EC-eps average + WARMS WARMS-eps

[0103] like Figure 7(a) to Figure 7(d) As shown in the figure, the ETS scores of S1 and S2 after the two dual-resolution fusion schemes are compared with the ECMWF ensemble forecast (average) and WARMS precipitation forecast before fusion. It can be seen from the figure that at the light rain, moderate rain, heavy rain and rainstorm thresholds, the ETS scores of S1 and S2 are greater than the ECMWF ensemble average and WARMS forecast, that is, the precipitation forecast after dual-resolution fusion correction is better than the low-resolution global model forecast and high-resolution mesoscale forecast before fusion. Figure 7a , b and Figure 7c It can be seen from the above data that under the light rain and moderate rain thresholds, the correction effect of S1 and S2 precipitation forecasts on the ECMWF ensemble mean is more significant than that on WARMS. In comparison with different thresholds and different forecast time periods, the forecast effect of S2 is slightly better than that of S1 overall.

Claims

1. A dual-resolution model typhoon forecast post-processing method, characterized by: The dual-resolution model typhoon forecast includes a global-scale ensemble forecast and a mesoscale deterministic forecast that are matched in time and space; the post-processing method includes: Combining the historical typhoon data of the target area, the typhoon track forecast in the global scale ensemble forecast is corrected in terms of forecast quality; Perform probability conversion on mesoscale deterministic forecasts to form mesoscale probabilistic forecasts; The revised typhoon track forecast is integrated with the mesoscale probability forecast.

2. The dual-resolution model typhoon forecast post-processing method according to claim 1, characterized in that: The specific method of correcting the global scale ensemble forecast related to the forecast quality by combining the historical typhoon data of the target area includes: Based on the historical typhoon data of the target area, the members in the global-scale ensemble forecast are screened in terms of forecast quality. Based on the screened members, a revised typhoon path forecast and corresponding typhoon precipitation forecast are formed.

3. The dual-resolution model typhoon forecast post-processing method according to claim 2, characterized in that: Based on the historical typhoon data of the target area, the members in the global scale ensemble forecast are screened for forecast quality, and based on the screened members, a revised typhoon path forecast and a corresponding typhoon precipitation forecast are formed using the following method: 1) Obtain the real-time location and subjective forecast of the typhoon impact process from the historical typhoon data of the target area; 2) Based on the global-scale ensemble forecast data, the distance errors between the path forecast of each member in the global-scale ensemble forecast and the real-time position and subjective forecast during the typhoon impact process are calculated, and the N members with the smallest errors are selected; 3) The arithmetic mean of the typhoon path forecasts of the screened N members is used as the revised typhoon path forecast and the corresponding typhoon precipitation forecast.

4. The dual-resolution model typhoon forecast post-processing method according to claim 1, characterized in that: The probability conversion of the mesoscale deterministic forecast is performed by the following method: The ROC curve areas of the mesoscale deterministic forecast at different scales are calculated, and the scale corresponding to the ROC curve with the largest area is selected as the neighborhood scale. The mesoscale deterministic forecast is converted into a probability according to the selected neighborhood scale.

5. The dual-resolution model typhoon forecast post-processing method according to claim 1, characterized in that: The revised typhoon path forecast and the mesoscale probability forecast after probability conversion are fused using a frequency matching method.

6. The dual-resolution model typhoon forecast post-processing method according to claim 5, characterized in that: The frequency matching fusion method includes using the typhoon precipitation forecast in the revised global-scale ensemble forecast as the typhoon precipitation area forecast, using the probabilistically transformed mesoscale probability forecast as the typhoon precipitation magnitude forecast, and then using the frequency matching method for fusion.

7. The dual-resolution model typhoon forecast post-processing method according to claim 5, characterized in that: The frequency matching fusion method includes using the typhoon precipitation forecast in the revised global-scale ensemble forecast and the mesoscale deterministic forecast as the typhoon precipitation area forecast, using the mesoscale probability forecast after probabilistic conversion as the typhoon precipitation magnitude forecast, and then using the frequency matching method for fusion.

8. A dual-resolution model typhoon forecast post-processing system, characterized by: The dual-resolution model typhoon forecast includes a global-scale ensemble forecast and a mesoscale deterministic forecast that are matched in time and space; the post-processing system includes a correction module, a probability conversion module, and a fusion module; The correction module is used to correct the typhoon path forecast in the global scale ensemble forecast in relation to the forecast quality in combination with the historical typhoon data of the target area; The probability conversion module is used to perform probability conversion on the mesoscale deterministic forecast to form a mesoscale probabilistic forecast; The fusion module is used to fuse the revised typhoon path forecast and the mesoscale probability forecast.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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