A method and apparatus for predicting the number of typhoons of various levels under a preset climate scenario.

Abstract

This invention relates to the field of typhoon disaster prediction technology, and discloses a method and apparatus for predicting the number of typhoons of various levels under a preset climate scenario. The method includes: obtaining the rate of change of the first typhoon activity parameter and the second typhoon activity parameter under different research perspectives compared with the current climate scenario, thereby fitting the corresponding rate of change probability distribution function, and performing sampling matching; determining the rate of change of the third typhoon activity parameter based on the rate of change of the different typhoon activity parameters in each sampling matching group and historical data; determining the correlation formula corresponding to each sampling matching group by combining the rate of change of the second typhoon activity parameter; further determining the rate of change of the annual average number of typhoons under each sub-level of the matching group; and further determining the annual average number of typhoons of each level under the preset climate scenario by combining historical data. This invention can solve the problem of being unable to predict the annual average number of typhoons of different levels under a preset climate scenario.

Description

Technical Field

[0001] This invention relates to the field of typhoon disaster prediction technology, specifically to a method and apparatus for predicting the number of typhoons of various levels under a preset climate scenario. Background Technology

[0002] The study of typhoon activity in the context of climate change is a hot scientific issue in the current climate community.

[0003] It is necessary to predict typhoon activity parameters under future climate scenarios under climate change, that is, to predict the annual frequency of typhoons under future climate scenarios.

[0004] Existing studies, using various research methods, have discretely provided the annual frequency of typhoons of all levels under specific climate scenarios, as well as the annual frequency of higher-level typhoons. However, current research cannot predict the average annual number of typhoons of different levels under a pre-defined climate scenario. Summary of the Invention

[0005] In view of this, the present invention provides a method, apparatus, equipment and medium for predicting the number of typhoons of different levels under a preset climate scenario, so as to solve the problem that it is impossible to predict the annual average number of typhoons of different levels under a preset climate scenario.

[0006] In a first aspect, the present invention provides a method for predicting the number of typhoons of various levels under a preset climate scenario, the method comprising:

[0007] The first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives relative to the current climate scenario are obtained. The first typhoon activity parameter is the annual average number of typhoons under all parent level ranges, and the second typhoon activity parameter is the annual average number of typhoons under the first preset parent level range.

[0008] Probability distributions of the first and second rates of change were fitted to different research perspectives to obtain the probability distribution functions of the first and second rates of change.

[0009] The first rate of change probability distribution function and the second rate of change probability distribution function are randomly sampled the same number of times, and the sampling results are matched to obtain multiple sampling matching groups;

[0010] Based on the first and second rates of change corresponding to each sampling matching group and the historical data corresponding to the current climate scenario, the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group is determined. The third typhoon activity parameter is the annual average number of typhoons under the second preset parent level range. The second preset parent level range is the parent level range other than the first preset parent level range among all parent level ranges.

[0011] Based on the second and third rates of change corresponding to each sampling matching group and historical data, the correlation formula corresponding to each sampling matching group is determined, and based on the correlation formula, the rate of change of the annual average number of typhoons under each sub-level corresponding to the matching group is determined. The correlation formula is used to characterize the relationship between the rate of change of the annual average number of typhoons and the typhoon intensity.

[0012] Based on the rate of change of the annual average number of typhoons in each sub-level under all parent levels corresponding to each matching group and historical data, the annual average number of typhoons in each level under the preset climate scenario is determined.

[0013] This method fits, samples, and matches the rate of change of typhoon activity parameters under different research perspectives to obtain the rate of change of the annual average number of typhoons under different preset parent level ranges. Then, it combines historical data to determine the correlation formula corresponding to each sampling matching group, so as to obtain the rate of change of the annual average number of typhoons of different levels corresponding to each sampling matching group. Thus, combined with historical data, it can obtain the number of typhoons of each level under preset climate scenarios, and can relatively accurately determine the annual average number of typhoons corresponding to each sub-typhoon level under future scenarios, so as to facilitate relevant researchers to conduct relevant studies based on the annual average number of typhoons.

[0014] In one optional implementation, obtaining the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives relative to the current climate scenario under preset climate scenarios includes:

[0015] Obtain the percentage change parameters of the first and second typhoon activity parameters under unit temperature change from different research perspectives.

[0016] Determine the expected temperature for each year in the future under the preset climate scenario and the historical temperature for each year in the historical period under the current climate scenario;

[0017] Based on the expected temperature for each year in the future period, the historical temperature for each year in the historical period, and the change ratio parameters corresponding to the first typhoon activity parameter and the second typhoon activity parameter, the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives relative to the current climate scenario are determined.

[0018] This implementation method determines the percentage change parameters of the first and second typhoon activity parameters under unit temperature change from different research perspectives. By combining the temperature information corresponding to the preset climate scenario and the current scenario, the rate of change of the first and second typhoon activity parameters under different research perspectives relative to the current climate scenario can be determined. This effectively determines the changes in typhoon activity parameters under different climate scenarios and ensures the diversity of preset climate scenarios when predicting the number of typhoons of various levels.

[0019] In one optional implementation, the step of fitting probability distributions of the first rate of change and the second rate of change under different research perspectives to obtain the probability distribution functions of the first rate of change and the second rate of change includes:

[0020] The first and second rates of change under different research perspectives are sorted to determine the first and second rates of change under multiple preset quantiles.

[0021] The confidence interval for parameter estimation is determined by the maximum likelihood estimation method. Based on the confidence interval, the first rate of change and the second rate of change under multiple preset quantiles are fitted with a log-normal distribution to obtain the probability distribution function of the first rate of change and the probability distribution function of the second rate of change.

[0022] In this embodiment, by fitting the first and second rates of change under multiple preset quantiles with a log-positive attitude distribution, and by using maximum likelihood estimation to determine the confidence interval of the parameter estimation, the fitting of the final first and second rate of change probability distribution functions can be effectively guaranteed, thus ensuring the accuracy of the annual average number of typhoons of each level subsequently determined.

[0023] In one optional implementation, the step of randomly sampling the first rate of change probability distribution function and the second rate of change probability distribution function the same preset number of times and matching the sampling results to obtain multiple sample matching groups includes:

[0024] Random sampling is performed a preset number of times based on the first rate of change probability distribution function and the second rate of change probability distribution function respectively, to obtain the same number of first sampling results and second sampling results;

[0025] The first and second sampling results are sorted based on the magnitude of the rate of change in each sampling result;

[0026] Based on the sorting of the first sampling result and the second sampling result, the first sampling result and the second sampling result are matched respectively to obtain the same number of sampling matching groups, wherein the sampling matching group includes: a first rate of change and a second rate of change.

[0027] In this embodiment, by performing the same number of random samplings on the first rate of change probability distribution function and the second rate of change probability distribution function respectively, and sorting and matching them to obtain multiple sample matching groups, the accuracy of the subsequent determination of the third rate of change can be guaranteed.

[0028] In one optional implementation, determining the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group based on the first rate of change and the second rate of change corresponding to each sampling matching group and historical data corresponding to the current climate scenario includes:

[0029] Determine the average annual number of typhoons in all parent-level ranges under the historical period corresponding to the current climate scenario and the average annual number of typhoons in the first preset parent-level range;

[0030] Based on the first and second rates of change in each sampled matching group, determine the annual average number of typhoons under all parent level ranges and the annual average number of typhoons under the first preset parent level range under the preset climate scenario.

[0031] The annual average number of typhoons in the second preset parent level range is determined based on the annual average number of typhoons in all parent level ranges and the annual average number of typhoons in the first preset parent level range. Combined with historical data, the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group is determined.

[0032] The determination of the correlation formula for each sampled matching group based on the second and third rates of change and historical data includes:

[0033] A preset formula is determined, which includes unknown parameters and is used to characterize the relationship between the rate of change of the annual average number of typhoons and the intensity of typhoons.

[0034] The values ​​of the unknown parameters are determined based on the second and third rates of change corresponding to each sampled matching group and the intensity of the typhoons corresponding to each sub-level under all parent levels.

[0035] The association formula for the corresponding sampling matching group is determined based on the value of the unknown parameter.

[0036] In this implementation, by combining the first and second rates of change corresponding to each sampling matching group with historical data, the number of typhoons in all parent level ranges under the preset climate scenario and the number of typhoons in the first preset parent level range are determined respectively. This determines the number of typhoons in the second preset parent level range. By combining historical data, the rate of change corresponding to the third typhoon activity parameter is determined. Based on the second and third rates of change corresponding to each matching group and historical data, the association formula corresponding to different matching groups is determined by substituting them into a preset formula. This ensures the accuracy of subsequently determining the average annual number of typhoons under different typhoon levels in the preset climate scenario corresponding to each matching group.

[0037] In an optional implementation, before determining the annual average number of typhoons of each level under the preset climate scenario based on the rate of change and historical data of the annual average number of typhoons corresponding to each sub-level under all parent levels of each matching group, the method further includes:

[0038] Obtain the average rate of change in typhoon intensity under different research perspectives compared to the current climate scenario;

[0039] Based on the rate of change of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group and historical data, the average typhoon intensity change rate corresponding to each matching group is determined.

[0040] Determine whether the probability distribution of the average typhoon intensity change rate corresponding to each matching group is consistent with the probability distribution of the average typhoon intensity change rate under different research perspectives.

[0041] If so, execute the step of determining the annual average number of typhoons of each level under the preset climate scenario based on the rate of change of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group and historical data.

[0042] In this implementation, the probability distribution of the average typhoon intensity change rate corresponding to each matching group is consistent with the probability distribution of the average typhoon intensity change rate under different research perspectives. This is used to verify the change rate of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group, so as to ensure the accuracy of the annual average number of typhoons under each level in the final determined preset climate scenario.

[0043] In an optional implementation, the method further includes:

[0044] If not, adjust the probability distribution parameters of the fitting process, return to the step of fitting the probability distribution of the second rate of change under different research perspectives to obtain the probability distribution function of the second rate of change, and re-determine whether the probability distribution law of the average typhoon intensity change rate corresponding to each matching group is consistent with the probability distribution law of the average typhoon intensity change rate under different research perspectives.

[0045] In this implementation, by adjusting the probability distribution parameters of the fitting process, the second rate of change under different research perspectives is refitted, and a series of steps are re-executed. The probability distribution law of the average typhoon intensity change rate is then re-evaluated to ensure the accuracy of the annual average number of typhoons of each level under the final predetermined climate scenario.

[0046] Secondly, the present invention provides a device for predicting the number of typhoons of various levels under a preset climate scenario, the device comprising:

[0047] The data acquisition module is used to acquire the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives compared with the current climate scenario. The first typhoon activity parameter is the annual average number of typhoons under all parent level ranges, and the second typhoon activity parameter is the annual average number of typhoons under the first preset parent level range.

[0048] The function fitting module is used to fit the probability distribution of the first rate of change and the second rate of change under different research perspectives, and obtain the probability distribution function of the first rate of change and the probability distribution function of the second rate of change.

[0049] The sampling matching module is used to randomly sample the first rate of change probability distribution function and the second rate of change probability distribution function the same preset number of times and match the sampling results to obtain multiple sampling matching groups;

[0050] The rate of change determination module is used to determine the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group based on the first rate of change and the second rate of change corresponding to each sampling matching group and the historical data corresponding to the current climate scenario. The third typhoon activity parameter is the annual average number of typhoons under the second preset parent level range. The second preset parent level range is the parent level range other than the first preset parent level range among all parent level ranges.

[0051] The formula determination module is used to determine the correlation formula corresponding to each sampling matching group based on the second and third rates of change and historical data, and to determine the rate of change of the annual average number of typhoons under each sub-level corresponding to the matching group based on the correlation formula. The correlation formula is used to characterize the relationship between the rate of change of the annual average number of typhoons and the typhoon intensity.

[0052] The number prediction module is used to determine the annual average number of typhoons of each level under the preset climate scenario based on the rate of change of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group and historical data.

[0053] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for predicting the number of typhoons of various levels under a preset climate scenario as described in the first aspect or any corresponding embodiment.

[0054] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the method for predicting the number of typhoons of various levels under a preset climate scenario according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0055] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0056] Figure 1 This is a flowchart illustrating a method for predicting the number of typhoons of various levels under a preset climate scenario according to an embodiment of the present invention.

[0057] Figure 2 This is an example diagram showing the average annual number of typhoons of different levels under a preset climate scenario according to an embodiment of the present invention.

[0058] Figure 3 This is a flowchart illustrating a method for predicting the number of typhoons of various levels under another preset climate scenario according to an embodiment of the present invention.

[0059] Figure 4 This is a flowchart illustrating a probability distribution parameter determination process according to an embodiment of the present invention;

[0060] Figure 5 This is a structural block diagram of a device for predicting the number of typhoons of various levels under a preset climate scenario according to an embodiment of the present invention.

[0061] Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0063] The study of typhoon activity in the context of climate change is a hot scientific issue in the current climate community.

[0064] It is necessary to predict typhoon activity parameters under future climate scenarios under climate change, that is, to predict the annual frequency of typhoons under future climate scenarios.

[0065] Existing studies, using various research methods, have discretely provided the annual frequency of typhoons of all levels under specific climate scenarios, as well as the annual frequency of higher-level typhoons. However, current research cannot predict the average annual number of typhoons of different levels under a pre-defined climate scenario.

[0066] To address this, this invention provides a method for predicting the number of typhoons of various levels under a preset climate scenario. By fitting, sampling, and matching the rate of change of typhoon activity parameters from different research perspectives, the method obtains the rate of change of the average annual number of typhoons under different preset parent level ranges. Then, by combining historical data, the method determines the correlation formula corresponding to each sampling matching group, thereby obtaining the rate of change of the average annual number of typhoons of different levels corresponding to each sampling matching group. By combining historical data, the method can obtain the number of typhoons of various levels under the preset climate scenario, and can relatively accurately determine the average annual number of typhoons corresponding to each subdivided typhoon level under future scenarios, so that relevant researchers can conduct relevant research based on the average annual number of typhoons.

[0067] According to an embodiment of the present invention, a method for predicting the number of typhoons of various levels under a preset climate scenario is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0068] This embodiment provides a method for predicting the number of typhoons of various levels under a preset climate scenario, which can be used for the prediction of the number of typhoons mentioned above. Figure 1 This is a flowchart of a method for predicting the number of typhoons of various levels under a preset climate scenario according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0069] Step S101: Obtain the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives compared to the current climate scenario. The first typhoon activity parameter is the annual average number of typhoons under all parent level ranges, and the second typhoon activity parameter is the annual average number of typhoons under the first preset parent level range.

[0070] In studies on changes in typhoon activity, it is generally believed that the logarithmic values ​​of the basic parameters of typhoon activity are linearly correlated with global surface temperature. These basic parameters can be understood as the relative changes in the annual frequency of typhoons and the relative changes in the average typhoon intensity within a specific range relative to a certain climate scenario.

[0071] For example, basic parameters of typhoon activity include the percentage change in the annual frequency of SSCAT 0-5 level typhoons, the percentage change in the annual frequency of SSCAT 4 & 5 level typhoons, the percentage change in the proportion of SSCAT 4 & 5 level typhoons, and the percentage change in the average intensity of SSCAT 0-5 level typhoons.

[0072] SSCAT refers to the Saffir-Simpson Hurricane Scale, where SSCAT0 indicates a maximum sustained wind speed near the center of the typhoon of 17.2 m / s to 32.6 m / s, SSCAT1 indicates a maximum sustained wind speed near the center of 32.7 to 42.5 m / s, SSCAT2 indicates a maximum sustained wind speed near the center of 42.6 to 49.2 m / s, SSCAT3 indicates a maximum sustained wind speed near the center of 49.3 to 58.1 m / s, SSCAT4 indicates a maximum sustained wind speed near the center of 58.2 to 69.2 m / s, and SSCAT1 indicates a maximum sustained wind speed near the center of 69.3 m / s.

[0073] In existing studies, based on the annual average number of typhoons at all levels and the annual average number of typhoons at high levels, some scientists have given the percentage change in the annual frequency of typhoons in the SSCAT0-5 category and the percentage change in the annual frequency of typhoons in the SSCAT4 & 5 category when the global average surface temperature rises by 2°C. The annual frequency can be understood as the average number of typhoons that occur each year, and SSCAT4 & 5 refer to SSCAT4 and SSCAT5 categories.

[0074] Therefore, based on the percentage change in the annual typhoon frequency given by these scientists when the temperature rises by 2 degrees Celsius, and combined with the linear correlation between the logarithm of the basic parameters of typhoon activity and global surface temperature, we can obtain the rate of change of the annual typhoon frequency in the SSCAT0-5 range and the rate of change of the annual typhoon frequency in the SSCAT4&5 range under the pre-set climate scenarios of different scientists' research perspectives compared with the current climate scenario.

[0075] This refers to the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter mentioned in step S101 above. The first typhoon activity parameter is the average annual number of typhoons occurring within the SSCAT 0-5 range, and the second typhoon activity parameter is the average annual number of typhoons occurring within the SSCAT 4 & 5 range.

[0076] The rate of change is usually calculated as (corresponding value under the preset scenario - corresponding value under the current scenario) / corresponding value under the current scenario. This can result in a negative rate of change. Since the calculated annual typhoon frequency is meaningless when the rate of change is negative, the rate of change in this application refers to: corresponding value under the preset scenario / corresponding value under the current scenario, which can be understood as the regular rate of change plus 1.

[0077] For example, the first rate of change of the first typhoon activity parameter under different research perspectives compared to the current climate scenario can be as follows:

[0078] Based on the rate of change in the annual number of typhoons in the SSCAT 0-5 category under a 2-degree temperature change provided by some scientists, and combined with the temperature change under a pre-defined climate scenario compared to the current climate scenario, the first rate of change of the first typhoon activity parameter under the pre-defined climate scenario is calculated as follows, from different research perspectives: 1.1, 1.4, 1.3, 1.5, 0.9, 1.2, and 0.8. Assuming that the annual average number of typhoons in the SSCAT 0-5 category corresponding to the current climate scenario is 10, then the corresponding annual average numbers under these different research perspectives are 11, 14, 13, 15, 9, 12, and 8. Similarly, the second rate of change of the second typhoon activity parameter can be understood in the same way, which will not be elaborated here.

[0079] Step S102: Fit the probability distributions of the first rate of change and the second rate of change under different research perspectives to obtain the probability distribution function of the first rate of change and the probability distribution function of the second rate of change.

[0080] The study obtained the first rate of change, namely the rate of change of the annual average number of typhoons in the SSCAT0-5 range, and the second rate of change, namely the rate of change of the annual average number of typhoons in the SSCAT4 &5 range, from different research perspectives.

[0081] The first rate of change corresponding to different perspectives constitutes a set of data. By fitting a probability distribution to this set of data, we obtain a corresponding fitting function, which is a probability distribution function that represents the probability distribution corresponding to different first rates of change; that is, the probability distribution function of the first rate of change. Similarly, we can obtain the fitting function corresponding to the second rate of change, i.e., the probability distribution function of the second rate of change.

[0082] Step S103: Randomly sample the first rate of change probability distribution function and the second rate of change probability distribution function the same number of times, and match the sampling results to obtain multiple sampling matching groups.

[0083] After obtaining the first rate of change probability distribution function and the second rate of change probability distribution function, random sampling can be performed on the first rate of change probability distribution function and the second rate of change probability distribution function a predetermined number of times. For example, 1000 random samples can be performed on each of the two probability distribution functions.

[0084] For each random sampling of the first rate of change probability distribution function, a corresponding first rate of change is obtained, which is the rate of change of the average annual number of typhoons in the SSCAT0-5 range under the preset climate scenario. Similarly, for each random sampling of the second rate of change probability distribution function, a corresponding second rate of change is obtained, which is the rate of change of the average annual number of typhoons in the SSCAT4&5 range under the preset climate scenario.

[0085] The sampling results are matched according to the magnitude of the rate of change, resulting in multiple sampling matching groups: the first group with a large rate of change is matched with the second group with a large rate of change, and the first group with a small rate of change is matched with the second group with a small rate of change.

[0086] Step S104: Based on the first and second rates of change corresponding to each sampling matching group and the historical data corresponding to the current climate scenario, determine the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group. The third typhoon activity parameter is the average number of typhoons occurring annually under the second preset parent level range. The second preset parent level range is the parent level range excluding the first preset parent level range among all parent level ranges.

[0087] For each sampled matching group, there is a first rate of change and a second rate of change, where the first rate of change and the second rate of change refer to the rate of change of the annual average number of typhoons in the SSCAT0-5 range and the SSCAT4&5 range under the preset climate scenario, respectively.

[0088] Therefore, by combining the historical data corresponding to the current climate scenario with the average annual number of typhoons in the SSCAT0-5 range and the SSCAT4 & 5 range, the average annual number of typhoons in the SSCAT0-3 range under the preset climate scenario is determined.

[0089] By comparing the annual average number of typhoons within the SSCAT0-3 level range under the preset climate scenario with the historical average number of typhoons within the SSCAT0-3 level range, the rate of change of the annual average number of typhoons within the SSCAT0-3 level range corresponding to this sampling matching group is obtained, which is the third rate of change of the third typhoon activity parameter. The third typhoon activity parameter is the annual average number of typhoons within the SSCAT0-3 level range.

[0090] Step S105: Based on the second and third rates of change and historical data corresponding to each sampling matching group, determine the correlation formula corresponding to each sampling matching group, and determine the rate of change of the annual average number of typhoons under each sub-level corresponding to the matching group based on the correlation formula. The correlation formula is used to characterize the relationship between the rate of change of the annual average number of typhoons and the typhoon intensity.

[0091] Studies on typhoon activity suggest that the rate of change of the annual average number of typhoons of different levels is positively linearly correlated with the logarithm of the maximum intensity of the typhoon's life cycle, while the annual average number of typhoons remains unchanged within the SSCAT0-3 and SSCAT4&5 levels.

[0092] For each sampling matching group, after determining its corresponding second and third rates of change, that is, after determining the rate of change of the annual average number of typhoons in the SSCAT0-3 and SSCAT4&5 levels under the preset climate scenario, the correlation formula corresponding to the matching group can be determined by combining the linear relationship mentioned above and historical data. This correlation formula can represent the relationship between the rate of change of the annual average number of typhoons corresponding to different wind force levels and the wind intensity. Since the wind intensity is known, the rate of change of the annual average number of typhoons in each sub-level corresponding to the matching group can be obtained based on the wind intensity corresponding to each sub-level.

[0093] For the five typhoon categories SSCAT0 to SSCAT5, each category can be further subdivided into multiple subcategories. SSCAT0 corresponds to a wind speed range of 17.2-32.6 m / s, which can be further subdivided into levels 8-11, with level 8 corresponding to wind speeds of 17.2-20.7 m / s. Similarly, the wind intensity corresponding to each subcategory of SSCAT0 to SSCAT5 can be determined according to relevant standards. Specifically, SSCAT0 to SSCAT5 can be divided into the following categories: 8, 9, 10, 11 (SSCAT0); 12, 13, 14-1 (SSCAT1); 14-2, 15-1 (SSCAT2); 15-2, 16, 17-1 (SSCAT3); 17-2, 18 (SSCAT4); 19, 20, 21 (SSCAT5).

[0094] Step S106: Based on the rate of change of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group and historical data, determine the annual average number of typhoons of each level under the preset climate scenario.

[0095] After obtaining the rate of change of the annual average number of typhoons in each sub-level corresponding to each sampling matching group, and combining it with the annual average number of typhoons in each sub-level in historical data, the annual average number of typhoons in each sub-level corresponding to the sampling matching group under the preset climate scenario can be obtained.

[0096] For example, for sampling matching group 1, the annual rate of change of the number of typhoons with wind force of level 8 is 1.1. In historical data, the annual average number of typhoons with wind force of level 8 is 10. Therefore, the annual average number of typhoons with wind force of level 8 corresponding to this sampling matching group is 11.

[0097] The above method can be used to obtain the average annual number of typhoons corresponding to different wind force levels for multiple sampling matching groups. Assuming 1000 samples are taken, 1000 sampling matching groups can be obtained, along with the average annual number of typhoons corresponding to different wind force levels for each sampling matching group.

[0098] In the sampling matching process, matching is based on the magnitude of the first and second rates of change. Specifically, the sampling results can be sorted and matched according to the magnitude of the first and second rates of change. Therefore, the final 1000 sampled matching groups can also correspond to the above sorting of the first and second rates of change, outputting the average annual number of typhoons corresponding to different wind force levels at multiple preset quantiles, as well as the average of the average annual number of typhoons corresponding to different wind force levels across the 1000 sampled matching groups. For example, as shown... Figure 2The figure shown is an example of a list of the average number of typhoons of different levels occurring annually under a preset climate scenario according to an embodiment of the present invention.

[0099] The method for predicting the number of typhoons of various levels under a preset climate scenario provided in this embodiment fits, samples, and matches the rate of change of typhoon activity parameters from different research perspectives to obtain the rate of change of the annual average number of typhoons under different preset parent level ranges. Then, it combines historical data to determine the correlation formula corresponding to each sampling matching group, so as to obtain the rate of change of the annual average number of typhoons of different levels corresponding to each sampling matching group. Thus, the number of typhoons of various levels under the preset climate scenario can be obtained by combining historical data. This method can relatively accurately determine the annual average number of typhoons corresponding to each sub-typhoon level under future scenarios, so that relevant researchers can conduct relevant studies based on the annual average number of typhoons.

[0100] According to an embodiment of the present invention, another method for predicting the number of typhoons of various levels under a preset climate scenario is provided, which can be used for the above-mentioned prediction of the number of typhoons. Figure 3 This is a flowchart of a method for predicting the number of typhoons of various levels under another preset climate scenario according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0101] Step S201: Obtain the first rate of change of the first typhoon activity parameter, the second rate of change of the second typhoon activity parameter, and the average typhoon intensity change rate under different research perspectives compared to the current climate scenario under the preset climate scenario.

[0102] For a detailed explanation of the first rate of change of the activity parameters of the first typhoon and the second rate of change of the activity parameters of the second typhoon, please refer to [link / reference]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0103] The average typhoon intensity refers to the rate of change of the average typhoon intensity over future historical periods under different research perspectives, relative to the average typhoon intensity over historical periods under the current scenario. Specifically, it is obtained by inferring the rate of change of average typhoon intensity under two-degree temperature variations provided by multiple scientists, the temperature change under the pre-set climate scenario compared to the current climate scenario, and the positive correlation between the logarithm of the rate of change of average typhoon intensity and temperature change. The specific determination method can be found in the detailed determination process of the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter described below, which will not be elaborated here. The rate of change of average typhoon intensity can be used to subsequently verify the annual average number of typhoon variations under each wind force level, to obtain a more accurate annual average number of typhoon variations under different wind force levels under the pre-set scenario.

[0104] Specifically, in step S201, the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives relative to the current climate scenario are obtained. This includes:

[0105] Step S201-1: Obtain the change ratio parameters corresponding to the activity parameters of the first and second typhoons under unit temperature change from different research perspectives.

[0106] In the above Figure 1 In step S101 of the illustrated embodiment, it is mentioned that in relevant studies, the percentage change in the annual frequency of typhoons within the SSCAT0-5 range and the percentage change in the annual frequency of typhoons within the SSCAT4 & 5 range were obtained by different scientists under a 2-degree temperature change.

[0107] Taking the relative change in the annual frequency of typhoons within the SSCAT0-5 category under a temperature change of 2 degrees as an example, let's assume it is K.

[0108] Since the annual number of typhoons is positively correlated with temperature, assuming the rate of change of the annual typhoon frequency within the SSCAT0-5 range in year i... The logarithm of this is a linear function of the global surface temperature Ti:

[0109]

[0110]

[0111] In the formula, represents the percentage change of typhoon activity parameters when the global sea surface temperature increases by 1°C relative to a certain standard climate scenario, which is the percentage change parameter corresponding to the first typhoon activity parameter mentioned in step S201-1 above.

[0112] If the global surface temperature increases from m years in the future... The change takes n years The relative change rate of typhoon activity parameters in nm years is:

[0113]

[0114] Assumption If it is 2, then =K, since K is known, we can obtain the corresponding results from different scientists. That is, the change ratio parameters corresponding to the activity parameters of the first typhoon under different research perspectives.

[0115] Similarly, based on the same principle, we can obtain the corresponding change ratio parameters of the second typhoon activity parameters from different research perspectives, which will not be elaborated here.

[0116] Step S201-2: Determine the expected temperature for each year in the future under the preset climate scenario and the historical temperature for each year in the historical period under the current climate scenario.

[0117] Preset climate scenarios typically refer to the climate scenarios corresponding to a certain period of time in the future under a certain gas emission state. Based on relevant technical means, the expected temperature of each year in the future period under a certain gas emission scenario can be determined, thereby obtaining the expected temperature of each year in the future period under the preset climate scenario.

[0118] The historical temperature for each year in a historical period can be determined based on historical data. The temperature mentioned in this step usually refers to the average temperature of the year or the average temperature during a period when typhoons are frequent.

[0119] Step S201-3: Based on the expected temperature for each year in the future period and the historical temperature for each year in the historical period, as well as the change ratio parameters corresponding to the first and second typhoon activity parameters, determine the first rate of change of the first typhoon activity parameters and the second rate of change of the second typhoon activity parameters under different research perspectives compared to the current climate scenario under the preset climate scenario.

[0120] Taking the activity parameters of the first typhoon as an example, the corresponding percentage change parameter is determined from a certain research perspective. Then, by combining the temperature of each year in the historical period and the temperature of each year in the future period, the first rate of change of the first typhoon activity parameters under the presupposed climate scenario compared to the current climate scenario can be determined. The specific determination method can be as follows:

[0121]

[0122] in, This refers to a historical period, such as 1980-2023. This refers to a future time period, such as 2040-2060. and They represent time periods respectively. and The rate of change in the annual average number of typhoons compared to the standard climate scenario. This refers to the rate of change in the annual average number of typhoons under the preset climate scenario compared to the current climate scenario, i.e., the rate of change in the first typhoon activity parameter.

[0123] Similarly, this can be applied based on different research perspectives. The rate of change of the activity parameters of the first typhoon under the corresponding research perspective is obtained. Based on the same calculation logic, the second rate of change of the activity parameters of the second typhoon under different research perspectives can be obtained, which will not be elaborated here.

[0124] Step S202: Fit the probability distributions of the first rate of change and the second rate of change under different research perspectives to obtain the probability distribution function of the first rate of change and the probability distribution function of the second rate of change.

[0125] Specifically, step S202 includes:

[0126] Step S202-1: Sort the first rate of change and the second rate of change under different research perspectives, and determine the first rate of change and the second rate of change under multiple preset quantiles.

[0127] After obtaining the first and second rates of change corresponding to multiple research perspectives, they are sorted to obtain the first and second rates of change corresponding to different quantiles. Specifically, these can be the rates of change corresponding to the 5%, 25%, 50%, 75%, and 95% quantiles.

[0128] Step S202-2: Determine the confidence interval of parameter estimation using the maximum likelihood estimation method. Based on the confidence interval, perform log-normal distribution fitting on the first rate of change and the second rate of change under multiple preset quantiles to obtain the probability distribution function of the first rate of change and the probability distribution function of the second rate of change.

[0129] The confidence intervals of the parameters for fitting the first rate of change probability distribution function and the second rate of change probability distribution function are determined by the maximum likelihood estimation method. Within the confidence interval, the specific probability distribution parameters are determined and a log-normal distribution is fitted to obtain the first rate of change probability distribution function and the second rate of change probability distribution function.

[0130] Step S203: Randomly sample the first rate of change probability distribution function and the second rate of change probability distribution function the same preset number of times, and match the sampling results to obtain multiple sampling matching groups.

[0131] Specifically, step S203 includes:

[0132] Step S203-1: Perform a preset number of random samplings based on the first rate of change probability distribution function and the second rate of change probability distribution function to obtain the same number of first sampling results and second sampling results.

[0133] After obtaining the first rate of change probability distribution function and the second rate of change probability distribution function, the same number of random samples are performed on both probability distribution functions. Each sample for the first rate of change probability distribution function corresponds to a first rate of change, and each sample for the second rate of change probability distribution function corresponds to a second rate of change. Assuming the number of random samples is 1000, there will be 1000 random sample results with the first rate of change and 1000 random sample results with the second rate of change.

[0134] Step S203-2: Sort the first sampling result and the second sampling result according to the magnitude of the rate of change in each sampling result.

[0135] These sampling results are sorted according to the magnitude of the rate of change, so that matching can be performed based on the sorting.

[0136] Step S203-3: Based on the sorting of the first sampling result and the second sampling result, match the first sampling result and the second sampling result respectively to obtain the same number of sampling matching groups. The sampling matching groups include: the first rate of change and the second rate of change.

[0137] After sorting, the first and second rates of change that are effectively reached are matched sequentially to obtain a sample matching group. Assuming there are 1000 samples, 1000 sample matching groups will be generated. Each sample matching group includes a first rate of change and a second rate of change.

[0138] Step S204: Based on the first and second rates of change corresponding to each sampled matching group and the historical data corresponding to the current climate scenario, determine the third rate of change of the third typhoon activity parameter corresponding to each sampled matching group.

[0139] Specifically, step S204 includes:

[0140] Step S204-1: Determine the average annual number of typhoons in all parent-level ranges under the historical period corresponding to the current climate scenario and the average annual number of typhoons in the first preset parent-level range.

[0141] Taking the historical period from 1980 to 2020 as an example, determining the average annual number of typhoons in all parent level ranges and the average annual number of typhoons in the first preset parent level range can be understood as determining the average annual number of typhoons in the SSCAT0-5 range and the average annual number of typhoons in the SSCAT4 & 5 range within this period.

[0142] Step S204-2: Based on the first rate of change and the second rate of change in each sampling matching group, determine the average annual number of typhoons under all parent level ranges and the average annual number of typhoons under the first preset parent level range under the preset climate scenario.

[0143] The first rate of change for each sampled matching group is the rate of change of the average annual number of typhoons in the future period under the preset climate scenario compared to the average annual number of typhoons in the historical period under the current scenario. As mentioned above, the rate of change referred to in this embodiment is: preset climate scenario / current climate scenario. Therefore, by multiplying the average annual number of typhoons in the SSCAT0-5 range and the average annual number of typhoons in the SSCAT4 & 5 range within the historical period by the first rate of change and the second rate of change, respectively, we can obtain the average annual number of typhoons in all parent level ranges under the preset climate scenario and the average annual number of typhoons in the first preset parent level range. That is, the average annual number of typhoons in the SSCAT0-5 range and the average annual number of typhoons in the SSCAT4 & 5 range under the preset climate scenario.

[0144] Step S204-3: Determine the annual average number of typhoons in the second preset parent level range based on the annual average number of typhoons in all parent level ranges and the annual average number of typhoons in the first preset parent level range. Combined with historical data, determine the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group.

[0145] The second preset parent level range is the SSCAT0-3 range. The average annual number of typhoons in the SSCAT0-3 range can be obtained by subtracting the average annual number of typhoons in the SSCAT4 & 5 range from the average annual number of typhoons in the SSCAT0-5 range. Comparing this to the historical average annual number of typhoons in the SSCAT0-3 range yields the rate of change in the average annual number of typhoons in the second preset parent level range corresponding to this sampling matching group.

[0146] Step S205: Based on the second and third rates of change and historical data corresponding to each sampling matching group, determine the correlation formula corresponding to each sampling matching group, and determine the rate of change of the annual average number of typhoons at each sub-level corresponding to the corresponding matching group based on the correlation formula.

[0147] Specifically, in step S205, based on the second and third rates of change corresponding to each sampled matching group and historical data, the correlation formula corresponding to each sampled matching group is determined, including:

[0148] Step A: Determine the preset formula. The preset formula contains unknown parameters and is used to characterize the relationship between the rate of change of the annual average number of typhoons and the intensity of typhoons.

[0149] Based on the above Figure 1The rate of change of the annual average number of typhoons of different levels mentioned in the relevant studies in step S105 of the implementation process is linearly positively correlated with the logarithm of the maximum intensity of the typhoon's life cycle. At the same time, the annual average number of typhoons in the SSCAT0-3 and SSCAT4&5 levels remains unchanged. Based on the annual average number of typhoons corresponding to different wind force levels in historical data, the following assumption can be obtained:

[0150]

[0151] in This refers to the average value within the corresponding wind speed range for the i-th wind force level. This refers to the rate of change in the annual number of occurrences corresponding to the i-th wind force level.

[0152] Furthermore, based on the assumption that the average annual number of typhoons remains constant within the SSCAT0-3 and SSCAT4&5 typhoon categories, we can obtain:

[0153]

[0154]

[0155] in, This is the third rate of change in the corresponding sampled matching group mentioned above. This is the second rate of change corresponding to the above-mentioned sampled matching group. This represents the average number of typhoons occurring annually within the SSCAT0-3 range, based on historical data. This represents the average annual number of typhoons occurring within the SSCAT 4-5 range based on historical data.

[0156] Step B: Based on the second and third rates of change corresponding to each sampled matching group and the intensity of the typhoons corresponding to each sub-level under all parent levels, determine the values ​​of the unknown parameters.

[0157] By substituting the second and third rates of change corresponding to each sampled matching group, as well as the intensity of the typhoon corresponding to each sub-level, into the above formula, the values ​​of the location parameters a and b corresponding to that sampled matching group can be obtained.

[0158] The typhoons corresponding to the various sub-levels can be understood as follows: For the five typhoon levels SSCAT0 to SSCAT5, each level can be further subdivided into multiple sub-levels. For SSCAT0, the corresponding wind speed range is 17.2-32.6 m / s, which can be further subdivided into levels 8-11, with level 8 corresponding to wind speeds of 17.2-20.7 m / s. Similarly, the wind intensity corresponding to each sub-level of SSCAT0 to SSCAT5 can be determined according to relevant standards. Specifically, SSCAT0 to SSCAT5 can be divided into: 8, 9, 10, 11 (SSCAT0); 12, 13, 14-1 (SSCAT1); 14-2, 15-1 (SSCAT2); 15-2, 16, 17-1 (SSCAT3); 17-2, 18 (SSCAT4); 19, 20, 21 (SSCAT5). The wind intensity corresponding to each of these sub-levels can be determined according to relevant official standards.

[0159] Step C: Determine the association formula for the corresponding sampling matching group based on the values ​​of the unknown parameters.

[0160] Calculate a and b corresponding to the sampled matching group, and substitute them into... The correlation formula corresponding to the sampling matching group is obtained, and then combined with the wind speed corresponding to each specific sub-wind force level, the annual average number of typhoon occurrences for different sub-wind force levels is obtained.

[0161] Step S206: Based on the rate of change of the annual average number of typhoons in each sub-level under all parent levels corresponding to each matching group and historical data, determine the average typhoon intensity change rate corresponding to each matching group.

[0162] After obtaining the correlation formulas corresponding to each matching group, we can combine the wind intensity corresponding to each sub-level typhoon to obtain the rate of change of the annual average number of typhoons under each sub-level compared to the current climate scenario. Then, by combining the annual average number of typhoons under the corresponding sub-level in historical periods, we can obtain the annual average number of typhoons of the corresponding level under the preset climate scenario.

[0163] By multiplying the annual average number of typhoons in each sub-level under the preset climate scenario by the corresponding wind intensity, and combining this with the average typhoon intensity over the historical period, the average typhoon intensity change rate corresponding to the matching group can be obtained.

[0164] For example, the specific formula is as follows:

[0165]

[0166] Regarding the format of the rate of change mentioned in the method embodiment, in actual calculations, the rate of change of the average typhoon intensity should be:

[0167]

[0168] That is, the normal rate of change plus 1.

[0169] Step S207: Determine whether the probability distribution of the average typhoon intensity change rate corresponding to each matching group is consistent with the probability distribution of the average typhoon intensity change rate under different research perspectives.

[0170] In step S201, the average typhoon intensity change rate under different research perspectives compared to the current climate scenario is obtained. These average typhoon intensity change rates under different research perspectives, as a set of data, have their corresponding probability distribution patterns. The probability distribution patterns of the average typhoon intensity change rates corresponding to each matching group obtained in the above steps are compared to determine whether the two have the same probability distribution pattern.

[0171] Specifically, the average typhoon intensity change rate corresponding to each matching group and the average typhoon intensity change rate under different research perspectives can be fitted separately to determine whether the fitting functions are close, thereby assessing whether the probability distribution patterns are consistent.

[0172] Step S208: If yes, execute the step of determining the annual average number of typhoons of each level under the preset climate scenario based on the rate of change of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group and historical data.

[0173] If the probability distribution patterns are consistent, it indicates that the annual average number of typhoons at each wind force level corresponding to the currently calculated matching groups is acceptable, and the annual average number of typhoons at each level under the preset climate scenario can be determined. For details, please refer to [link / reference]. Figure 1 Step S106 of the illustrated embodiment will not be described again here.

[0174] Step S209: If not, adjust the probability distribution parameters of the fitting process, return to the step of performing probability distribution fitting on the second rate of change under different research perspectives to obtain the probability distribution function of the second rate of change, and re-determine whether the probability distribution law of the average typhoon intensity change rate corresponding to each matching group is consistent with the probability distribution law of the average typhoon intensity change rate under different research perspectives.

[0175] If there is a discrepancy, adjust the probability distribution parameters during the fitting process within the confidence interval of the probability distribution parameters, refit to obtain the second rate of change probability distribution function, and repeat steps S202-S207 above until the probability distribution pattern is consistent.

[0176] The method for predicting the number of typhoons of various levels under a preset climate scenario provided in this invention involves fitting, sampling, and matching the rate of change of typhoon activity parameters from different research perspectives to obtain the rate of change of the average annual number of typhoons under different preset parent level ranges. Then, by combining historical data, the method determines the correlation formula corresponding to each sampling matching group to obtain the rate of change of the average annual number of typhoons of different levels corresponding to each sampling matching group. Thus, by combining historical data, the method can obtain the number of typhoons of various levels under the preset climate scenario, and can relatively accurately determine the average annual number of typhoons corresponding to each subdivided typhoon level under future scenarios, so that relevant researchers can conduct relevant research based on the average annual number of typhoons.

[0177] To facilitate understanding of the above method embodiments, a flowchart illustrating the probability distribution parameter determination process is provided, as follows: Figure 4 As shown.

[0178] For this embodiment of the invention, three assumptions are first made based on current typhoon research to predict the average annual number of typhoons corresponding to different wind force levels: the correlation between frequency change and intensity change is 1, meaning that intensity change can be derived from frequency change; the correlation between the probability distributions of SSCAT4&5 and SSCAT0-5 is 1; frequency change increases linearly with the logarithm of the maximum intensity during the typhoon's lifespan, while the total number of frequency changes remains constant within SSCAT03 and SSCAT4&5. The specific correlation parameters can be determined based on specific research conditions and are not limited here; this is merely an example.

[0179] Firstly, when calculating the frequency of typhoons under the background of climate change using the relative percentage change, the calculation result may be negative. Since a negative typhoon frequency is meaningless, it is necessary to first convert the relative percentage change of the annual frequency of SSCAT0-5 typhoons and the relative percentage change of the annual frequency of SSCAT4 &5 typhoons RFi into the change ratio Fi, where Fi = 1 + RFi.

[0180] Next, the log-normal distribution is used to fit the proportion of variable change, and the parameter estimation can be achieved through the maximum likelihood estimation method.

[0181] Then, based on the probability distribution fitted in the previous step, random sampling is performed, and the absolute changes in SSCAT0-5 and SSCAT4&5 are calculated using the historical typhoon best path dataset from the China Meteorological Administration. Specifically, n (n≥1000) random samples are taken from the probability distribution obtained in the previous step, and the relative change ratio of the SSCAT0-5 level typhoon frequency is recorded for each sampling. The relative change in the frequency of SSCAT4-5 level typhoons ; Count the number of typhoons of SSCAT 0-5 level in the optimal typhoon path dataset. Calculate the absolute change in the number of typhoons classified as SSCAT0-5 for each sampling. , = * ; Count the number of SSCAT4-5 level typhoons in the optimal typhoon path dataset. ,calculate = * .

[0182] Next, using the quantities of SSCAT0-5 and SSCAT4&5 obtained in the previous step and the second assumption, the number of changes in SSCAT0-3 can be calculated. = - (k=1, 2, 3...n), the probability distribution parameters of SSCAT0-3 are fitted using the maximum likelihood estimation method.

[0183] Next, based on the third assumption, calculate the typhoon frequency variation values ​​corresponding to each wind force level under SSCAT0-3 and SSCAT4&5. For details, please refer to the above. Figure 2 The relevant content in step S205 of the embodiment will not be repeated here.

[0184] Finally, the average intensity change value of SSCAT 0-5 typhoons is calculated based on the typhoon frequency change value corresponding to each wind force level, and compared with the intensity change patterns in existing studies (10%, 25%, 50%, 75%, and 90% quantiles of the relative change in average intensity of SSCAT 0-5 typhoons). If they conform to the pattern, the probability distribution parameters of the current SSCAT 0-3 and SSCAT 4&5 are determined. If they do not conform to the pattern, the probability distribution parameters of the frequency change of SSCAT 4&5 are adjusted within the confidence interval of the parameter estimation.

[0185] The method provided in this invention employs any set of optimal typhoon path datasets or a full-path typhoon random event set. It extracts typhoon events to be added and those to be reduced from the original dataset according to the marine typhoon change parameter table, and re-statistically analyzes the typhoon frequency and intensity within the area of ​​interest to obtain the typhoon activity parameter changes for any region. Based on the latest scientific research and typhoon path data, it can simulate the changes in typhoon activity parameters under different warming scenarios in any region of the Northwest Pacific under the background of climate change. Therefore, it can customize the region, SSP (Shared Socioeconomic Path) scenario, and research period according to user needs. This method and results can provide technical support for climate change typhoon disaster risk assessment, typhoon disaster loss assessment, and climate change physical risk stress testing for financial institutions.

[0186] This embodiment also provides a device for predicting the number of typhoons of various levels under a preset climate scenario. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0187] This embodiment provides a device for predicting the number of typhoons of various levels under a preset climate scenario, such as... Figure 5 As shown, it includes:

[0188] The data acquisition module 401 is used to acquire the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives compared with the current climate scenario. The first typhoon activity parameter is the annual average number of typhoons under all parent level ranges, and the second typhoon activity parameter is the annual average number of typhoons under the first preset parent level range.

[0189] The function fitting module 402 is used to fit the probability distribution of the first rate of change and the second rate of change under different research perspectives to obtain the probability distribution function of the first rate of change and the probability distribution function of the second rate of change.

[0190] The sampling matching module 403 is used to randomly sample the first rate of change probability distribution function and the second rate of change probability distribution function the same preset number of times and match the sampling results to obtain multiple sampling matching groups;

[0191] The rate of change determination module 404 is used to determine the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group based on the first rate of change and the second rate of change corresponding to each sampling matching group and the historical data corresponding to the current climate scenario. The third typhoon activity parameter is the average number of typhoons occurring annually under the second preset parent level range. The second preset parent level range is the parent level range excluding the first preset parent level range among all parent level ranges.

[0192] The formula determination module 405 is used to determine the correlation formula corresponding to each sampling matching group based on the second and third rates of change and historical data, and to determine the rate of change of the annual average number of typhoons under each sub-level of the corresponding matching group based on the correlation formula. The correlation formula is used to characterize the relationship between the rate of change of the annual average number of typhoons and the typhoon intensity.

[0193] The number prediction module 406 is used to determine the annual average number of typhoons of each level under the preset climate scenario based on the rate of change of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group and historical data.

[0194] In some optional implementations, the data acquisition module 401, when acquiring the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives relative to the current climate scenario, includes:

[0195] Obtain the percentage change parameters of the first and second typhoon activity parameters under unit temperature change from different research perspectives.

[0196] Determine the expected temperature for each year in the future under the preset climate scenario and the historical temperature for each year in the historical period under the current climate scenario;

[0197] Based on the expected temperature for each year in the future period and the historical temperature for each year in the historical period, as well as the change ratio parameters corresponding to the first and second typhoon activity parameters, the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives compared to the current climate scenario are determined.

[0198] In some optional implementations, the function fitting module 402, when performing probability distribution fitting on the first rate of change and the second rate of change under different research perspectives to obtain the probability distribution function of the first rate of change and the probability distribution function of the second rate of change, includes:

[0199] The first and second rates of change under different research perspectives are sorted to determine the first and second rates of change under multiple preset quantiles.

[0200] The confidence intervals for parameter estimation are determined by the maximum likelihood estimation method. Based on the confidence intervals, the first rate of change and the second rate of change under multiple preset quantiles are fitted with log-normal distributions to obtain the probability distribution functions of the first rate of change and the second rate of change.

[0201] In some optional implementations, the sampling matching module 403, when randomly sampling the first rate of change probability distribution function and the second rate of change probability distribution function the same preset number of times and matching the sampling results to obtain multiple sampling matching groups, includes:

[0202] Random sampling is performed a preset number of times based on the first rate of change probability distribution function and the second rate of change probability distribution function respectively, to obtain the same number of first sampling results and second sampling results;

[0203] The first and second sampling results are sorted based on the magnitude of the rate of change in each sampling result;

[0204] Based on the sorting of the first and second sampling results, the first and second sampling results are matched to obtain the same number of sampling matching groups, which include the first rate of change and the second rate of change.

[0205] In some optional implementations, the rate of change determination module 404, when determining the third rate of change of the third typhoon activity parameter corresponding to each sampled matching group based on the first rate of change and the second rate of change corresponding to each sampled matching group and historical data corresponding to the current climate scenario, includes:

[0206] Determine the average annual number of typhoons in all parent-level ranges under the historical period corresponding to the current climate scenario and the average annual number of typhoons in the first preset parent-level range;

[0207] Based on the first and second rates of change in each sampled matching group, determine the annual average number of typhoons under all parent level ranges and the annual average number of typhoons under the first preset parent level range under the preset climate scenario.

[0208] The annual average number of typhoons in the second preset parent level range is determined based on the annual average number of typhoons in all parent level ranges and the annual average number of typhoons in the first preset parent level range. Combined with historical data, the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group is determined.

[0209] The formula determination module 405, when determining the correlation formula corresponding to each sampled matching group based on the second and third rates of change and historical data, includes:

[0210] A preset formula is determined, which contains unknown parameters and is used to characterize the relationship between the rate of change of the annual average number of typhoons and the intensity of typhoons.

[0211] Based on the second and third rates of change corresponding to each sampled matching group and the intensity of the typhoons corresponding to each sub-level under all parent levels, the values ​​of the unknown parameters are determined.

[0212] The association formula for the corresponding sampling matching group is determined based on the value of the unknown parameter.

[0213] In some optional implementations, before determining the annual average number of typhoons of each level under a preset climate scenario based on the rate of change and historical data of the annual average number of typhoons corresponding to each sub-level under all parent levels of each matching group, the quantity prediction module 406 is further configured to:

[0214] Obtain the average rate of change in typhoon intensity under different research perspectives compared to the current climate scenario;

[0215] Based on the rate of change of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group and historical data, the average rate of change of typhoon intensity corresponding to each matching group is determined.

[0216] Determine whether the probability distribution of the average typhoon intensity change rate corresponding to each matching group is consistent with the probability distribution of the average typhoon intensity change rate under different research perspectives.

[0217] If so, execute the step of determining the annual average number of typhoons of each level under the preset climate scenario based on the rate of change of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group and historical data.

[0218] In some optional implementations, the quantity prediction module 406 is further configured to adjust the probability distribution parameters of the fitting process, return to the step of fitting the probability distribution of the second rate of change under different research perspectives to obtain the probability distribution function of the second rate of change, and re-determine whether the probability distribution of the average typhoon intensity change rate corresponding to each matching group is consistent with the probability distribution of the average typhoon intensity change rate under different research perspectives when the probability distribution of the average typhoon intensity change rate corresponding to each matching group is inconsistent with the probability distribution of the average typhoon intensity change rate under different research perspectives.

[0219] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0220] In this embodiment, the device for predicting the number of typhoons of various levels under the preset climate scenario is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0221] This invention also provides a computer device having the above-described features. Figure 5 The device shown is a prediction device for the number of typhoons of various levels under a preset climate scenario.

[0222] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 6As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0223] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0224] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0225] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0226] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0227] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0228] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0229] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0230] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for predicting the number of typhoons of various levels under a preset climate scenario, characterized in that, The method includes: The first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives relative to the current climate scenario are obtained. The first typhoon activity parameter is the annual average number of typhoons under all parent level ranges, and the second typhoon activity parameter is the annual average number of typhoons under the first preset parent level range. Probability distributions of the first and second rates of change were fitted to different research perspectives to obtain the probability distribution functions of the first and second rates of change. The first rate of change probability distribution function and the second rate of change probability distribution function are randomly sampled the same number of times, and the sampling results are matched to obtain multiple sampling matching groups; Based on the first and second rates of change corresponding to each sampling matching group and the historical data corresponding to the current climate scenario, the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group is determined. The third typhoon activity parameter is the annual average number of typhoons under the second preset parent level range. The second preset parent level range is the parent level range other than the first preset parent level range among all parent level ranges. Based on the second and third rates of change corresponding to each sampling matching group and historical data, the correlation formula corresponding to each sampling matching group is determined, and based on the correlation formula, the rate of change of the annual average number of typhoons under each sub-level corresponding to the matching group is determined. The correlation formula is used to characterize the relationship between the rate of change of the annual average number of typhoons and the typhoon intensity. Based on the rate of change of the annual average number of typhoons in each sub-level under all parent levels corresponding to each matching group and historical data, the annual average number of typhoons in each level under the preset climate scenario is determined.

2. The method according to claim 1, characterized in that, The acquisition of the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives relative to the current climate scenario includes: Obtain the percentage change parameters of the first and second typhoon activity parameters under unit temperature change from different research perspectives. Determine the expected temperature for each year in the future under the preset climate scenario and the historical temperature for each year in the historical period under the current climate scenario; Based on the expected temperature for each year in the future period, the historical temperature for each year in the historical period, and the change ratio parameters corresponding to the first typhoon activity parameter and the second typhoon activity parameter, the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives relative to the current climate scenario are determined.

3. The method according to claim 1, characterized in that, The process of fitting probability distributions of the first and second rates of change under different research perspectives to obtain the probability distribution functions of the first and second rates of change includes: The first and second rates of change under different research perspectives are sorted to determine the first and second rates of change under multiple preset quantiles; The confidence interval for parameter estimation is determined by the maximum likelihood estimation method. Based on the confidence interval, the first rate of change and the second rate of change under multiple preset quantiles are fitted with a log-normal distribution to obtain the probability distribution function of the first rate of change and the probability distribution function of the second rate of change.

4. The method according to claim 1, characterized in that, The first rate of change probability distribution function and the second rate of change probability distribution function are randomly sampled the same number of times, and the sampling results are matched to obtain multiple sample matching groups, including: Random sampling is performed a preset number of times based on the first rate of change probability distribution function and the second rate of change probability distribution function respectively, to obtain the same number of first sampling results and second sampling results; The first and second sampling results are sorted based on the magnitude of the rate of change in each sampling result; Based on the sorting of the first sampling result and the second sampling result, the first sampling result and the second sampling result are matched respectively to obtain the same number of sampling matching groups, wherein the sampling matching group includes: a first rate of change and a second rate of change.

5. The method according to claim 1, characterized in that, The step of determining the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group based on the first rate of change and the second rate of change corresponding to each sampling matching group and the historical data corresponding to the current climate scenario includes: Determine the average annual number of typhoons in all parent-level ranges under the historical period corresponding to the current climate scenario and the average annual number of typhoons in the first preset parent-level range; Based on the first and second rates of change in each sampled matching group, determine the annual average number of typhoons under all parent level ranges and the annual average number of typhoons under the first preset parent level range under the preset climate scenario. The annual average number of typhoons in the second preset parent level range is determined based on the annual average number of typhoons in all parent level ranges and the annual average number of typhoons in the first preset parent level range. Combined with historical data, the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group is determined. The determination of the correlation formula for each sampled matching group based on the second and third rates of change and historical data includes: A preset formula is determined, which includes unknown parameters and is used to characterize the relationship between the rate of change of the annual average number of typhoons and the intensity of typhoons. The values ​​of the unknown parameters are determined based on the second and third rates of change corresponding to each sampled matching group and the intensity of the typhoons corresponding to each sub-level under all parent levels. The association formula for the corresponding sampling matching group is determined based on the value of the unknown parameter.

6. The method according to claim 1, characterized in that, Before determining the annual average number of typhoons at each level under the preset climate scenario based on the rate of change and historical data of the annual average number of typhoons at each sub-level under all parent levels corresponding to each matching group, the method further includes: Obtain the average rate of change in typhoon intensity under different research perspectives compared to the current climate scenario; Based on the rate of change of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group and historical data, the average typhoon intensity change rate corresponding to each matching group is determined. Determine whether the probability distribution of the average typhoon intensity change rate corresponding to each matching group is consistent with the probability distribution of the average typhoon intensity change rate under different research perspectives. If so, execute the step of determining the annual average number of typhoons of each level under the preset climate scenario based on the rate of change of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group and historical data.

7. The method according to claim 6, characterized in that, The method further includes: If not, adjust the probability distribution parameters of the fitting process, return to the step of fitting the probability distribution of the second rate of change under different research perspectives to obtain the probability distribution function of the second rate of change, and re-determine whether the probability distribution law of the average typhoon intensity change rate corresponding to each matching group is consistent with the probability distribution law of the average typhoon intensity change rate under different research perspectives.

8. A device for predicting the number of typhoons of various levels under a preset climate scenario, characterized in that, The device includes: The data acquisition module is used to acquire the first rate of change of the first typhoon activity parameter and the second rate of change of the second typhoon activity parameter under different research perspectives compared with the current climate scenario. The first typhoon activity parameter is the annual average number of typhoons under all parent level ranges, and the second typhoon activity parameter is the annual average number of typhoons under the first preset parent level range. The function fitting module is used to fit the probability distribution of the first rate of change and the second rate of change under different research perspectives, and obtain the probability distribution function of the first rate of change and the probability distribution function of the second rate of change. The sampling matching module is used to randomly sample the first rate of change probability distribution function and the second rate of change probability distribution function the same preset number of times and match the sampling results to obtain multiple sampling matching groups; The rate of change determination module is used to determine the third rate of change of the third typhoon activity parameter corresponding to each sampling matching group based on the first rate of change and the second rate of change corresponding to each sampling matching group and the historical data corresponding to the current climate scenario. The third typhoon activity parameter is the annual average number of typhoons under the second preset parent level range. The second preset parent level range is the parent level range other than the first preset parent level range among all parent level ranges. The formula determination module is used to determine the correlation formula corresponding to each sampling matching group based on the second and third rates of change and historical data, and to determine the rate of change of the annual average number of typhoons under each sub-level corresponding to the matching group based on the correlation formula. The correlation formula is used to characterize the relationship between the rate of change of the annual average number of typhoons and the typhoon intensity. The number prediction module is used to determine the annual average number of typhoons of each level under the preset climate scenario based on the rate of change of the annual average number of typhoons under each sub-level of all parent levels corresponding to each matching group and historical data.

9. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the prediction method for the number of typhoons of various levels under a preset climate scenario as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the prediction method for the number of typhoons of various levels under the preset climate scenario as described in any one of claims 1 to 7.

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