Method and device for predicting number of typhoons of various grades under preset climate scene

Abstract

The invention relates to the technical field of typhoon disaster prediction, and discloses a method and device for predicting the number of typhoons of all grades under a preset climate scenario, and the method comprises the steps: obtaining change rates corresponding to a first typhoon activity parameter and a second typhoon activity parameter of the preset climate scenario under different research perspectives compared with a current climate scenario, and fitting a corresponding change rate probability distribution function, carrying out sampling matching, determining the change rate of a third typhoon activity parameter according to the change rates and historical data corresponding to different typhoon activity parameters in each sampling matching group, determining an association formula corresponding to each sampling matching group by combining the change rate of the second typhoon activity parameter, and determining the typhoon activity parameter. According to the method and the device, the change rate of the annual average occurrence number corresponding to the typhoon under each sub-grade corresponding to the matching group is determined, the annual average number of the typhoon of each grade under the preset climate scene is determined by combining historical data, and the problem that the annual average occurrence number of the typhoon of different grades under the preset climate scene cannot be predicted can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of typhoon disaster prediction, and in particular to a method and device for predicting the number of typhoons of various grades under a preset climate scenario. Background Art

[0002] The study of typhoon activity under the background of climate change is a hot scientific issue in the current climate community. The northwest Pacific Ocean bordering China is one of the most active sea areas for tropical cyclone activity in the world, with an average of 27 tropical cyclones generated each year, accounting for about 1 / 3 of the global total frequency.

[0003] Due to the population growth and infrastructure increase in coastal areas in recent years, the losses caused by typhoon activities to the countries in the northwest Pacific are becoming increasingly serious. Therefore, governments and relevant financial institutions have successively carried out measures such as typhoon year forecasts, disaster loss assessments, and physical risk stress tests under climate change, in order to clarify the risk changes of typhoon-induced disasters under climate change, which is conducive to subsequent disaster prevention and loss reduction and other related risk diversification measures. Therefore, it is necessary to predict the typhoon activity parameters under future climate scenarios under climate change, that is, to predict the typhoon year frequency under future climate scenarios.

[0004] In existing related studies, different research methods are used to discretely give the annual occurrence frequency of typhoons of all levels and the annual occurrence frequency of typhoons of higher levels under specific climate scenarios. In existing studies, it is impossible to predict the average annual number of typhoons of different levels under preset climate scenarios. Summary of the invention

[0005] In view of this, the present invention provides a method, device, equipment and medium for predicting the number of typhoons of various levels under a preset climate scenario, so as to solve the problem that it is impossible to predict the average annual 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] Obtain a first change rate of a first typhoon activity parameter and a second change rate of a second typhoon activity parameter of a preset climate scenario compared to the current climate scenario under different research perspectives, wherein the first typhoon activity parameter is the average annual number of typhoons under all parent level ranges, and the second typhoon activity parameter is the average annual number of typhoons under the first preset parent level range;

[0008] Probability distribution fitting is performed on the first change rate and the second change rate under different research perspectives respectively to obtain the probability distribution function of the first change rate and the probability distribution function of the second change rate;

[0009] Performing random sampling of the first change rate probability distribution function and the second change rate probability distribution function for the same preset number of times respectively and matching the sampling results to obtain a plurality of sampling matching groups;

[0010] Determine, according to the first change rate and the second change rate corresponding to each sampling matching group and the historical data corresponding to the current climate scenario, a third change rate of the third typhoon activity parameter corresponding to each sampling matching group, wherein the third typhoon activity parameter is the average annual number of typhoons under the second preset parent level range, and the second preset parent level range is the parent level range among all parent level ranges except the first preset parent level range;

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

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

[0013] This method fits, samples and matches the change rate of typhoon activity parameters under different research perspectives, so as to obtain the change rate of the average annual number of typhoons under different preset parent level ranges, and then determines the correlation formula corresponding to each sampling matching group in combination with historical data to obtain the change rate of the average annual number of typhoons of different levels corresponding to each sampling matching group, and then obtains the number of typhoons of each level under the preset climate scenario in combination with historical data. It can relatively finely 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.

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

[0015] Obtain the change ratio parameters corresponding to the first typhoon activity parameter and the second typhoon activity parameter under unit temperature change under different research perspectives;

[0016] Determine the expected temperature for each year in the future period 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 of each year in the future period, the historical temperature of each year in the historical period, and the corresponding change ratio parameters of the first typhoon activity parameter and the second typhoon activity parameter, the first change rate of the first typhoon activity parameter and the second change rate of the second typhoon activity parameter of the preset climate scenarios under different research perspectives compared with the current climate scenario are determined.

[0018] In this implementation, by determining the change ratio parameters corresponding to the first typhoon activity parameter and the second typhoon activity parameter under unit temperature change under different research perspectives, and combining the temperature information corresponding to the preset climate scenario and the current scenario, the change rate of the first typhoon activity parameter and the second typhoon activity parameter of the preset climate scenario compared with the current climate scenario under different research perspectives can be determined, so as to effectively determine the changes in typhoon activity parameters under different climate scenarios, and ensure the diversity of preset climate scenarios when predicting the number of typhoons of each level.

[0019] In an optional implementation, respectively performing probability distribution fitting on the first change rate and the second change rate under different research perspectives to obtain the first change rate probability distribution function and the second change rate probability distribution function includes:

[0020] Sort the first change rates and the second change rates under different research perspectives, and determine the first change rates and the second change rates under multiple preset quantiles;

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

[0022] In this embodiment, by fitting the logarithmic positive attitude distribution of the first change rate and the second change rate under multiple preset quantiles and using maximum likelihood estimation to determine the confidence interval of parameter estimation, the fitting of the final probability distribution function of the first change rate and the second change rate probability distribution function can be effectively guaranteed, and the accuracy of the subsequently determined average annual number of typhoons of various levels can be guaranteed.

[0023] In an optional implementation, the first change rate probability distribution function and the second change rate probability distribution function are randomly sampled for the same preset number of times and the sampling results are matched to obtain multiple sampling matching groups, including:

[0024] Performing random sampling for a preset number of times based on the first change rate probability distribution function and the second change rate probability distribution function respectively, to obtain the same number of corresponding first sampling results and second sampling results;

[0025] Sort the first sampling result and the second sampling result respectively based on the magnitude of the change rate in each sampling result;

[0026] Based on the corresponding ranking 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, and the sampling matching groups include: a first change rate and a second change rate.

[0027] In this embodiment, by performing the same number of random sampling on the first change rate probability distribution function and the second change rate probability distribution function, and sorting and matching them, multiple sampling matching groups are obtained, which can ensure the accuracy of subsequent determination of the third change rate.

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

[0029] Determine 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 in the historical period corresponding to the current climate scenario;

[0030] Determine 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 according to the first change rate and the second change rate in each sampling matching group;

[0031] Determine the average annual number of typhoons in the second preset parent level range according to 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, and determine the third change rate of the third typhoon activity parameter corresponding to each sampling matching group in combination with historical data;

[0032] The determining of the association formula corresponding to each sampling matching group based on the second change rate and the third change rate corresponding to each sampling matching group and the historical data includes:

[0033] Determining a preset formula, wherein the preset formula includes unknown parameters and is used to characterize the relationship between the rate of change of the average annual number of typhoons and the typhoon intensity;

[0034] Determine the value of the unknown parameter based on the second change rate and the third change rate corresponding to each sampling matching group and the intensity of the typhoon corresponding to each sub-level under all parent levels;

[0035] The association formula of the corresponding sampling matching group is determined according to the value of the unknown parameter.

[0036] In this embodiment, the first change rate and the second change rate corresponding to each sampling matching group are combined with historical data to respectively determine the number of typhoons in all parent level ranges and the first preset parent level range under the preset climate scenario, thereby determining the number of typhoons corresponding to the second preset parent level range, and then combining with historical data to determine the change rate corresponding to the third typhoon activity parameter, and according to the second change rate and the third change rate corresponding to each matching group and the historical data, substitute them into the preset formula to determine the association formula corresponding to different matching groups, so as to ensure the accuracy of the subsequent determination of the average annual number of typhoons under different typhoon levels under the preset climate scenario corresponding to each matching group.

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

[0038] Obtain the average typhoon intensity change rate of the preset climate scenarios compared with the current climate scenarios under different research perspectives;

[0039] Determine the average typhoon intensity change rate corresponding to each matching group based on the change rate and historical data of the average annual number of typhoons corresponding to each child level under all parent levels corresponding to each matching group;

[0040] 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;

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

[0042] In this implementation mode, by determining 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, the change rate of the average annual number of typhoons corresponding to each child level under all parent levels corresponding to each matching group currently obtained is tested to ensure the accuracy of the average annual number of typhoons of each level under the final preset climate scenario.

[0043] In an optional embodiment, 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 change rate under different research perspectives, obtain the probability distribution function of the second change rate, and re-judge 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] This implementation method adjusts the probability distribution parameters of the fitting process, re-fits the second change rate under different research perspectives, re-executes a series of steps, and re-judges whether the probability distribution law of the average typhoon intensity change rate is consistent, so as to ensure the accuracy of the average annual number of typhoons of each level under the final preset climate scenario.

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

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

[0048] A function fitting module is used to perform probability distribution fitting on the first change rate and the second change rate under different research perspectives to obtain a first change rate probability distribution function and a second change rate probability distribution function;

[0049] a sampling matching module, configured to perform random sampling of the first change rate probability distribution function and the second change rate probability distribution function for the same preset number of times and match the sampling results to obtain a plurality of sampling matching groups;

[0050] A change rate determination module, used to determine a third change rate of a third typhoon activity parameter corresponding to each sampling matching group according to the first change rate and the second change rate corresponding to each sampling matching group and the historical data corresponding to the current climate scenario, wherein the third typhoon activity parameter is the average annual number of typhoons under a second preset parent level range, and the second preset parent level range is a parent level range among all parent level ranges except the first preset parent level range;

[0051] A formula determination module, for determining the association formula corresponding to each sampling matching group based on the second change rate and the third change rate corresponding to each sampling matching group and historical data, and determining the change rate of the average annual number of typhoons corresponding to each sub-level corresponding to the corresponding matching group based on the association formula, wherein the association formula is used to characterize the relationship between the change rate of the average annual number of typhoons and the typhoon intensity;

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

[0053] In a third aspect, 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 execute the method for predicting the number of typhoons of various levels under a preset climate scenario of the first aspect or any corresponding embodiment thereof.

[0054] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for predicting the number of typhoons of various levels under a preset climate scenario of the above-mentioned first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 is a flow chart 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;

[0057] Figure 2 This is an example chart of a list of the average annual occurrence of typhoons of different grades under a preset climate scenario according to an embodiment of the present invention;

[0058] Figure 3 is a flow chart of a method for predicting the number of typhoons of different levels under another preset climate scenario according to an embodiment of the present invention;

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

[0060] Figure 5 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 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0063] The study of typhoon activity under the background of climate change is a hot scientific issue in the current climate community. The northwest Pacific Ocean bordering China is one of the most active sea areas for tropical cyclone activity in the world, with an average of 27 tropical cyclones generated each year, accounting for about 1 / 3 of the global total frequency.

[0064] Due to the population growth and infrastructure increase in coastal areas in recent years, the losses caused by typhoon activities to the countries in the northwest Pacific are becoming increasingly serious. Therefore, governments and relevant financial institutions have successively carried out measures such as typhoon year forecasts, disaster loss assessments, and physical risk stress tests under climate change, in order to clarify the risk changes of typhoon-induced disasters under climate change, which is conducive to subsequent disaster prevention and loss reduction and other related risk diversification measures. Therefore, it is necessary to predict the typhoon activity parameters under future climate scenarios under climate change, that is, to predict the typhoon year frequency under future climate scenarios.

[0065] In existing related studies, different research methods are used to discretely give the annual occurrence frequency of typhoons of all levels and the annual occurrence frequency of typhoons of higher levels under specific climate scenarios. In existing studies, it is impossible to predict the average annual number of typhoons of different levels under preset climate scenarios.

[0066] To this end, an embodiment of the present invention provides a method for predicting the number of typhoons of various levels under a preset climate scenario. By fitting, sampling and matching the change rates of typhoon activity parameters under different research perspectives, the change rate of the average annual number of typhoons under different preset parent level ranges is obtained, and then the association formula corresponding to each sampling matching group is determined in combination with historical data to obtain the change rate of the average annual number of typhoons of different levels corresponding to each sampling matching group, and then the number of typhoons of various levels under the preset climate scenario is obtained in combination with historical data. The average annual number of typhoons corresponding to each subdivided typhoon level under future scenarios can be determined relatively finely, 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, an embodiment of 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 of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0068] In this embodiment, a method for predicting the number of typhoons of different levels under a preset climate scenario is provided, which can be used for predicting the number of typhoons mentioned above. Figure 1 is a flow chart 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. Figure 1 As shown, the process includes the following steps:

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

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

[0071] For example, basic parameters of typhoon activity such as the relative change percentage of annual frequency of SSCAT0-5 typhoons, the relative change percentage of annual frequency of SSCAT4&5 typhoons, the relative change percentage of the proportion of SSCAT4&5 typhoons, and the relative change percentage of the average intensity of SSCAT0-5 typhoons.

[0072] Among them, SSCAT refers to the Saffir-Simpson Hurricane Scale, in which SSCAT0 refers to the maximum wind speed near the center of the typhoon of 17.2m / s~32.6m / s, SSCAT1 refers to the maximum wind speed near the center of the typhoon of 32.7~42.5m / s, SSCAT2 refers to the maximum wind speed near the center of the typhoon of 42.6~49.2m / s, SSCAT3 refers to the maximum wind speed near the center of the typhoon of 49.3~58.1m / s, SSCAT4 refers to the maximum wind speed near the center of the typhoon of 58.2~69.2m / s, and SSCAT1 refers to the maximum wind speed near the center of the typhoon greater than 69.3m / s.

[0073] In existing research, based on the study of the average annual number of typhoons at all levels and the average annual number of typhoons at high levels, some scientists have given the relative change percentage of annual frequency of typhoons in the SSCAT0-5 range and the relative change percentage of annual frequency of typhoons in the SSCAT4&5 range when the global average surface temperature rises by 2°C. The annual frequency can be understood as the average annual number of occurrences, and SSCAT4&5 refers to SSCAT4 and SSCAT5.

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

[0075] That is, the first change rate of the first typhoon activity parameter and the second change rate of the second typhoon activity parameter mentioned in the above step S101. The first typhoon activity parameter is the average annual number of typhoons within the SSCAT0-5 level range, and the second typhoon activity parameter is the average annual number of typhoons within the SSCAT4&5 level range.

[0076] For the rate of change, usually, it refers to (corresponding value under the preset scenario - corresponding value under the current scenario) / corresponding value under the current scenario. Such a situation may cause the rate of change to be negative. Since when the rate of change is a negative value, the calculated annual typhoon frequency is a negative number and has no meaning. Therefore, 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 conventional rate of change plus 1.

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

[0078] Assuming that the annual average number of typhoons in the SSCAT0-5 range under a 2-degree temperature change given by some scientists is changing, combined with the temperature change of the preset climate scenario compared to the current climate scenario, the first change rate of the first typhoon activity parameter under the preset climate scenario is calculated to be 1.1, 1.4, 1.3, 1.5, 0.9, 1.2, and 0.8 under different research perspectives. Assuming that the annual average number of typhoons in the SSCAT0-5 range 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. By analogy, the second change rate of the second typhoon activity parameter can also be understood in this way, which will not be repeated here.

[0079] Step S102, performing probability distribution fitting on the first change rate and the second change rate under different research perspectives respectively, to obtain a first change rate probability distribution function and a second change rate probability distribution function.

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

[0081] The first change rates corresponding to different perspectives constitute a set of data. Probability distribution fitting is performed on this set of data to obtain the corresponding fitting function, which is a probability distribution function, which can represent the probability distribution corresponding to different first change rates, that is, the first change rate probability distribution function. And so on. The fitting function corresponding to the second change rate can also be obtained, that is, the second change rate probability distribution function.

[0082] Step S103 , performing random sampling on the first change rate probability distribution function and the second change rate probability distribution function for the same preset number of times respectively and matching the sampling results to obtain a plurality of sampling matching groups.

[0083] After obtaining the first change rate probability distribution function and the second change rate probability distribution function, random sampling may be performed for a preset number of times on the first change rate probability distribution function and the second change rate probability distribution function, for example, 1000 random samplings may be performed on the two probability distribution functions, respectively.

[0084] For each random sampling of the first change rate probability distribution function, a corresponding first change rate will be obtained, that is, the change rate 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 change rate probability distribution function, a corresponding second change rate will be obtained, that is, the change rate 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 size of the change rate to obtain multiple sampling matching groups, that is, the first change rate is large, and the second change rate is large, and the first change rate is small, and the second change rate is small.

[0086] Step S104, determining the third change rate of the third typhoon activity parameter corresponding to each sampling matching group according to the first change rate and the second change rate corresponding to each sampling matching group and the historical data corresponding to the current climate scenario, the third typhoon activity parameter being the average annual number of typhoons within the second preset parent level range, and the second preset parent level range being the parent level range among all parent level ranges except the first preset parent level range.

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

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

[0089] The average annual number of typhoons in the SSCAT0-3 range under the preset climate scenario is compared with the average annual number of typhoons in the SSCAT0-3 range in the historical data to obtain the change rate of the average annual number of typhoons in the SSCAT0-3 range corresponding to the sampling matching group, that is, the third change rate of the third typhoon activity parameter. Among them, the third typhoon activity parameter is the average annual number of typhoons in the SSCAT0-3 range.

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

[0091] In relevant research on typhoon activity, it is believed that the change rate of the average annual number of typhoons of different levels is linearly positively correlated with the logarithm of the maximum intensity of the typhoon life cycle. At the same time, the average annual number of typhoons within the SSCAT0-3 and SSCAT4&5 level ranges remains unchanged.

[0092] For each sampling matching group, after determining its corresponding second change rate and third change rate, that is, after determining the change rate of the average annual number of typhoons within the SSCAT0-3 and SSCAT4&5 level range under the preset climate scenario, the linear relationship mentioned above and the historical data can be combined to determine the association formula corresponding to the matching group. The association formula can express the relationship between the change rate of the average annual number of typhoons corresponding to different wind force levels and the wind intensity, where the wind intensity is known. Therefore, the change rate of the average annual number of typhoons of each sub-level corresponding to the matching group can be obtained based on the wind intensity corresponding to each sub-level.

[0093] For SSCAT0 to SSCAT5, these five typhoon levels, each of which can be subdivided into multiple sub-levels, for SSCAT0, the corresponding wind force range is 17.2-32.6m / s, which can be further divided into levels 8-11, where level 8 corresponds to a wind force of 17.2-20.7m / s. By analogy, the wind strength corresponding to the sub-level of each wind force level from 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).

[0094] Step S106, determining the average annual number of typhoons of each level under the preset climate scenario according to the change rate and historical data of the average annual number of typhoons corresponding to each child level under all parent levels corresponding to each matching group.

[0095] After obtaining the change rate of the average annual number of typhoons at each sub-level corresponding to each sampling matching group, combined with the average annual number of typhoons at each sub-level in the historical data, the average annual number of typhoons at 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 corresponding annual average number of typhoons with wind force 8 is changing at a rate of 1.1. The annual average number of typhoons with wind force 8 in historical data is 10. Therefore, the annual average number of typhoons with wind force 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 corresponding to multiple sampling matching groups. Assuming 1,000 samples are taken, 1,000 sampling matching groups and the average annual number of typhoons corresponding to different wind force levels corresponding to each sampling matching group can be obtained.

[0098] Among them, when sampling and matching, matching is performed according to the size of the first change rate and the second change rate. Specifically, the sampling results can be sorted and matched according to the size of the first change rate and the second change rate. Therefore, the final 1000 sampling matching groups can also correspond to the sorting of the first change rate and the second change rate, and output the average annual number of typhoons corresponding to different wind force levels corresponding to multiple preset quantiles, as well as the average of the average annual number of typhoons corresponding to different wind force levels corresponding to the 1000 sampling matching groups. For example, Figure 2As shown, it is an example chart of a list of the average annual occurrence number of typhoons of different grades 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 change rates of typhoon activity parameters under different research perspectives, thereby obtaining the change rates of the average annual number of typhoons under different preset parent level ranges, and then determining the correlation formulas corresponding to each sampling and matching group in combination with historical data to obtain the change rates of the average annual number of typhoons of different levels corresponding to each sampling and matching group, thereby obtaining the number of typhoons of various levels under the preset climate scenario in combination with historical data, and can relatively finely 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.

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

[0101] Step S201, obtaining a first change rate of a first typhoon activity parameter, a second change rate of a second typhoon activity parameter, and an average typhoon intensity change rate of preset climate scenarios under different research perspectives compared with the current climate scenario.

[0102] The specific explanation of the first change rate of the first typhoon activity parameter and the second change rate of the second typhoon activity parameter can be seen in Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0103] The average typhoon intensity refers to the rate of change of the average typhoon intensity in the future historical period under the preset climate scenario relative to the average typhoon intensity in the historical period under the current scenario under different research perspectives. The specific acquisition method is also based on the rate of change of the average typhoon intensity under a two-degree temperature change given by multiple scientists, the temperature change of the preset climate scenario compared to the current climate scenario, and the logarithm of the rate of change of the average typhoon intensity is positively correlated with the temperature change. The specific determination method can refer to the specific 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 repeated here. The rate of change of the average typhoon intensity can be used for the subsequent test of the final average annual change number of typhoons under each wind force level, so as to obtain a more accurate average annual change number of typhoons under different wind force levels under the preset scenario.

[0104] Specifically, in step S201, a first change rate of a first typhoon activity parameter and a second change rate of a second typhoon activity parameter of preset climate scenarios under different research perspectives compared with the current climate scenario are obtained.

[0105] include:

[0106] Step S201 - 1 , obtaining the change ratio parameters corresponding to the first typhoon activity parameter and the second typhoon activity parameter under unit temperature change under different research perspectives.

[0107] In the above Figure 1 It is mentioned in step S101 of the illustrated embodiment that in relevant studies, the relative change percentage of annual typhoon frequency within the SSCAT0-5 range and the relative change percentage of annual typhoon frequency within the SSCAT4&5 range obtained by different scientists under a 2-degree temperature change are given.

[0108] Taking the relative change of annual typhoon frequency within the SSCAT0-5 range under a 2-degree temperature change as an example, assume it is K.

[0109] Since the annual number of typhoons is positively correlated with temperature, it is assumed that the change rate of the annual frequency of typhoons within the SSCAT 0-5 level in the i-th year is F i The logarithm of is a linear function of the global surface temperature Ti:

[0110] f i =α+βT i

[0111]

[0112] The formula represents the change ratio of the typhoon activity parameter when the global sea surface temperature rises by 1°C relative to a certain standard climate scenario, that is, the change ratio parameter corresponding to the first typhoon activity parameter mentioned in the above step S201-1.

[0113] If the future global surface temperature changes from T in year m m Changes in T for n years n , then the relative change rate of typhoon activity parameters in nm years is:

[0114]

[0115] Assume T n -T m is 2, then e 2β -1=K. Since K is known, we can obtain the β corresponding to different scientists, that is, the change proportion parameter corresponding to the first typhoon activity parameter under different research perspectives.

[0116] By analogy, based on the same principle, we can obtain the corresponding change proportion parameters of the second typhoon activity parameters under different research perspectives, which will not be elaborated here.

[0117] Step S201 - 2 , determining the expected temperature of each year in the future period under the preset climate scenario and the historical temperature of each year in the historical period under the current climate scenario.

[0118] A preset climate scenario usually refers to a climate scenario 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 a certain period of time in the future 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.

[0119] The historical temperature of each year in the historical period can be determined based on historical data. The temperature mentioned in this step usually refers to the average temperature in a year, or the average temperature in a typhoon-prone period.

[0120] Step S201-3, based on the expected temperature of each year in the future period, the historical temperature of each year in the historical period, and the corresponding change ratio parameters of the first typhoon activity parameter and the second typhoon activity parameter, determine the first change rate of the first typhoon activity parameter and the second change rate of the second typhoon activity parameter of the preset climate scenarios under different research perspectives compared with the current climate scenario.

[0121] Taking the first typhoon activity parameter as an example, after determining the corresponding change ratio parameter β under a certain research perspective, the temperature of each year in the historical period and the temperature of each year in the future period can be combined to determine the first change rate of the first typhoon activity parameter under the research perspective, the preset climate scenario compared with the current climate scenario. The specific determination method can be as follows:

[0122]

[0123] Among them, P1 refers to the historical period, such as 1980-2023, and P2 refers to the future period, such as 2040-2060. M1 and M2 represent the change rate of the annual average number of typhoons in the time periods P1 and P2 respectively compared with the standard climate scenario. It refers to the rate of change of the average annual number of typhoons in the preset climate scenario compared with the current climate scenario, that is, the rate of change of the first typhoon activity parameter.

[0124] By analogy, the change rate of the first typhoon activity parameter under the corresponding research perspective can be obtained according to β under different research perspectives. Based on the same calculation logic, the second change rate of the second typhoon activity parameter under different research perspectives can be obtained, which will not be repeated here.

[0125] Step S202, performing probability distribution fitting on the first change rate and the second change rate under different research perspectives respectively, to obtain a first change rate probability distribution function and a second change rate probability distribution function.

[0126] Specifically, in step S202, it includes:

[0127] Step S202-1, sorting the first change rate and the second change rate under different research perspectives, and determining the first change rate and the second change rate under multiple preset quantiles.

[0128] After obtaining the first change rate and the second change rate corresponding to multiple research perspectives, they are sorted to obtain the first change rate and the second change rate corresponding to different quantiles. Specifically, they can be the change rates corresponding to the 5%, 25%, 50%, 75%, and 95% quantiles.

[0129] Step S202-2, determine the confidence interval of the parameter estimate by the maximum likelihood estimation method, and based on the confidence interval, perform log-normal distribution fitting on the first change rate and the second change rate under multiple preset quantiles to obtain the first change rate probability distribution function and the second change rate probability distribution function.

[0130] The confidence intervals of the parameters when fitting the first change rate probability distribution function and the second change rate probability distribution function are determined respectively by the maximum likelihood estimation method, and the specific probability distribution parameters are determined within the confidence interval to perform log-normal distribution fitting to obtain the first change rate probability distribution function and the second change rate probability distribution function.

[0131] Step S203 , performing random sampling on the first change rate probability distribution function and the second change rate probability distribution function for the same preset number of times respectively and matching the sampling results to obtain a plurality of sampling matching groups.

[0132] Specifically, in step S203, it includes:

[0133] Step S203 - 1 , performing a preset number of random samplings based on the first change rate probability distribution function and the second change rate probability distribution function, respectively, to obtain the same number of corresponding first sampling results and second sampling results.

[0134] After obtaining the first change rate probability distribution function and the second change rate probability distribution function, the same number of random samplings are performed on the two probability distribution functions, and each sampling of the first change rate probability distribution function corresponds to a first change rate, and each sampling of the second change rate probability distribution function corresponds to a second change rate. Assuming that the number of random samplings is 1000 times, there are 1000 random sampling results of the first change rate and 1000 random sampling results of the second change rate.

[0135] Step S203 - 2 , sorting the first sampling result and the second sampling result respectively based on the change rate in each sampling result.

[0136] The sampling results are sorted according to the magnitude of the change rate, so as to match them according to the sorting.

[0137] Step S203-3, based on the corresponding ranking 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, and the sampling matching groups include: the first change rate and the second change rate.

[0138] After sorting, the first change rate and the second change rate that have been effectively reached are matched in turn to obtain a sampling matching group. Assuming there are 1000 samples, 1000 sampling matching groups will be generated. Each sampling matching group includes a first change rate and a second change rate.

[0139] Step S204, determining a third change rate of the third typhoon activity parameter corresponding to each sampling matching group according to the first change rate and the second change rate corresponding to each sampling matching group and the historical data corresponding to the current climate scenario.

[0140] Specifically, in step S204, it includes:

[0141] Step S204-1, determining the average annual number of typhoons in all parent level ranges in the historical period corresponding to the current climate scenario and the average annual number of typhoons in the first preset parent level range.

[0142] Taking the historical period of 1980-2020 as an example, determining the average annual number of typhoons in all parent level ranges in the historical period 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 during this time period.

[0143] Step S204-2, according to the first change rate and the second change rate in each sampling matching group, determine 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.

[0144] The first change rate corresponding to each sampling matching group is the change rate of the average annual number of typhoons corresponding to the future period under the preset climate scenario compared to the average annual number of typhoons in the historical period of the current scenario, and as mentioned above, the change rate referred to in this embodiment is: preset climate scenario / current climate scenario. Therefore, the average annual number of typhoons in the SSCAT0-5 level range and the average annual number of typhoons in the SSCAT4&5 level range in the historical period are multiplied by the first change rate and the second change rate respectively, and 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 can be obtained, that is, the average annual number of typhoons in the SSCAT0-5 level range and the average annual number of typhoons in the SSCAT4&5 level range under the preset climate scenario.

[0145] Step S204-3, determine the average annual number of typhoons in the second preset parent level range based on 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, and determine the third change rate of the third typhoon activity parameter corresponding to each sampling matching group in combination with historical data.

[0146] The second preset parent level range is the SSCAT0-3 level range. The average annual number of typhoons in the SSCAT0-3 level range can be obtained by subtracting the average annual number of typhoons in the SSCAT4&5 level range from the average annual number of typhoons in the SSCAT0-5 level range. By comparing the average annual number of typhoons in the SSCAT0-3 level range in historical data, the change rate of the average annual number of typhoons in the second preset parent level range corresponding to the sampling matching group can be obtained.

[0147] Step S205, based on the second change rate and the third change rate corresponding to each sampling matching group and historical data, determine the association formula corresponding to each sampling matching group, and determine the change rate of the average annual number of typhoons corresponding to each sub-level corresponding to the corresponding matching group based on the association formula.

[0148] Specifically, in step S205, based on the second change rate and the third change rate corresponding to each sampling matching group and the historical data, the association formula corresponding to each sampling matching group is determined, including:

[0149] Step A, determining a preset formula, which contains unknown parameters and is used to characterize the relationship between the rate of change of the average annual number of typhoons and the typhoon intensity.

[0150] Based on the above Figure 1The change rate of the average annual number of typhoons of different levels mentioned in the relevant research pointed out in step S105 during implementation is linearly positively correlated with the logarithm of the maximum intensity of the typhoon life cycle. At the same time, the average annual number of typhoons within the SSCAT0-3 and SSCAT4&5 level ranges remains unchanged, and the average annual number of typhoons corresponding to different wind force levels in historical data can be assumed to be the following formula:

[0151] f windlevel，i =a*ln(V windlevel,i )+b

[0152] Where V windlevel，i Refers to the average value in the wind speed range corresponding to the i-th wind force level, f windlevel It refers to the rate of change of the average annual number of occurrences corresponding to the i-th wind force level.

[0153] Furthermore, based on the fact that the average annual number of typhoons within the SSCAT0-3 and SSCAT4&5 levels remains unchanged, we can obtain:

[0154]

[0155]

[0156] Among them, f 0-3 is the third rate of change in the corresponding sampling matching group, f 4-5 is the second change rate corresponding to the above corresponding sampling matching group. 0-3 is the average annual number of typhoons in the SSCAT0-3 range in historical data; N 4-5 It is the average annual number of typhoons in the SSCAT4-5 range in historical data.

[0157] Step B, based on the second change rate and the third change rate corresponding to each sampling matching group and the intensity of the typhoons corresponding to each sub-level under all parent levels, determine the value of the unknown parameter.

[0158] By substituting the second change rate and the third change rate corresponding to each sampling matching group, as well as the intensity of the typhoon corresponding to each sub-level into the above formula, the values ​​of the position parameters a and b corresponding to the sampling matching group can be obtained.

[0159] The typhoons corresponding to each sub-level can be understood as follows: for SSCAT0 to SSCAT5, these five typhoon levels, each of which can be subdivided into multiple sub-levels. For SSCAT0, the corresponding wind force range is 17.2-32.6m / s, which can be further divided into levels 8-11, where the wind force corresponding to level 8 is 17.2-20.7m / s. By analogy, the wind strength corresponding to the sub-levels corresponding to each wind force level from 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 strength corresponding to each of these sub-levels can be determined according to relevant official standards.

[0160] Step C, determining the association formula corresponding to the sampling matching group according to the value of the unknown parameter.

[0161] Calculate a and b corresponding to the sample matching group and substitute them into f windlevel，i =a*ln(V windlevel，i )+b, and obtain the correlation formula corresponding to the sampling matching group, so as to obtain the change rate of the average annual number of typhoon times of different sub-wind force levels by combining the wind speeds corresponding to the specific sub-wind force levels.

[0162] Step S206, based on the change rate and historical data of the average annual number of typhoons under each child level under all parent levels corresponding to each matching group, determine the change rate of the average typhoon intensity corresponding to each matching group.

[0163] After obtaining the correlation formula corresponding to each matching group, we can combine the wind intensity corresponding to each sub-level typhoon to obtain the change rate of the average annual number of typhoons under each sub-level compared to the current climate scenario, and then combine the average annual number of typhoons under the corresponding sub-level in the historical period to obtain the average annual number of typhoons of the corresponding level under the preset climate scenario.

[0164] By multiplying the average annual number of typhoons of each sub-level under the preset climate scenario by the corresponding wind intensity, combined with the average typhoon intensity in the historical period, the average typhoon intensity change rate corresponding to the matching group can be obtained.

[0165] For example, the specific formula is as follows:

[0166]

[0167] Among them, for the format of the change rate mentioned in the method embodiment, in actual calculation, the change rate of the average typhoon intensity should be:

[0168]

[0169] That is, the normal rate of change plus 1.

[0170] Step S207, determining 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.

[0171] In step S201, the average typhoon intensity change rate of the preset climate scenario under different research perspectives compared with the current climate scenario is obtained. As a set of data, the average typhoon intensity change rate under these different research perspectives has its corresponding probability distribution law. The probability distribution law of the average typhoon intensity change rate corresponding to each matching group obtained in the above steps is compared to determine whether the two have the same probability distribution law.

[0172] 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 respectively to determine whether the fitting functions are close, so as to evaluate whether the probability distribution law is consistent.

[0173] Step S208, if yes, execute the step of determining the average annual number of typhoons of each level under the preset climate scenario according to the change rate and historical data of the average annual number of typhoons corresponding to each child level under all parent levels corresponding to each matching group.

[0174] If the probability distribution rules are consistent, it means that the average annual number of typhoons under each wind force level corresponding to each matching group currently calculated is qualified, and the average annual number of typhoons of each level under the preset climate scenario can be determined. For the specific process, refer to Figure 1 Step S106 of the illustrated embodiment will not be described in detail here.

[0175] Step S209, if no, adjust the probability distribution parameters of the fitting process, return to execute the probability distribution fitting of the second change rate under different research perspectives, obtain the step of the probability distribution function of the second change rate, and re-judge 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.

[0176] If not, adjust the probability distribution parameters in the fitting process within the confidence interval of the probability distribution parameters, refit to obtain the second change rate probability distribution function, and re-execute the above steps S202-S207 until the probability distribution rules are consistent.

[0177] The method for predicting the number of typhoons of various levels under a preset climate scenario provided by an embodiment of the present invention fits, samples and matches the change rate of typhoon activity parameters under different research perspectives, thereby obtaining the change rate of the average annual number of typhoons under different preset parent level ranges, and then determining the association formula corresponding to each sampling matching group in combination with historical data to obtain the change rate of the average annual number of typhoons of different levels corresponding to each sampling matching group, thereby obtaining the number of typhoons of various levels under the preset climate scenario in combination with historical data, and can relatively finely 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.

[0178] In order to facilitate understanding of the above method embodiment, a probability distribution parameter determination process example diagram is provided, such as Figure 4 shown.

[0179] For the embodiment of the present invention, firstly, three assumptions need to be made based on the current typhoon research to predict the average annual number of typhoons corresponding to different wind force levels, namely: the correlation between frequency change and intensity change is 1, that is, the intensity change can be derived from the frequency change; the correlation between the probability distribution of SSCAT4&5 and SSCAT0-5 is 1; the frequency change increases linearly with the logarithm of the maximum intensity during the life of the typhoon, while the total number of frequency changes remains unchanged within SSCAT03 and SSCAT4&5. The specific correlation corresponding parameters can be determined according to the specific research situation, and are not limited here, and are only an exemplary representation.

[0180] First, when using the relative change percentage to calculate the typhoon frequency under the background of climate change, it is possible that the calculation result will be a negative value, and a negative typhoon frequency has no practical significance. Therefore, it is necessary to first convert the relative change percentage of the annual frequency of SSCAT0-5 typhoons and the relative change percentage of the annual frequency of SSCAT4&5 typhoons RFi into a change ratio Fi, where Fi=1+RFi.

[0181] Next, the lognormal distribution is used to fit the variable change ratio, and parameter estimation can be achieved through the maximum likelihood estimation method.

[0182] Then, random sampling is performed based on the probability distribution fitted in the previous step, and the absolute change number of SSCAT0-5 and SSCAT4&5 is calculated using the historical typhoon best path data set of the China Meteorological Administration. Specifically, random sampling is performed n (n≥≥1000) times in the probability distribution obtained in the previous step, and the relative change ratio f of the SSCAT0-5 level typhoon frequency obtained by each sampling is recorded. 0-5，i , the relative change ratio of SSCAT4-5 typhoon frequency f 4-5，i ; Count the number of typhoons N with SSCAT0-5 levels in the typhoon best path dataset0-5 Calculate the absolute change number of typhoons of SSCAT0-5 level corresponding to each sampling CN 0-5，i , CN 0-5，i =N 0-5 *f 0-5，i ; Count the number of SSCAT4-5 typhoons in the typhoon best path dataset CN 4-5，i , calculate CN 4-5，i =N 4-5 *f 4-5，i .

[0183] Next, the number of SSCAT0-5 and SSCAT4&5 obtained in the previous step and the second hypothesis can be used to calculate the number of changes in SSCAT0-3 CN 0-3，k =CN 0-5,k -CN 4-5，k (k=1,2,3……n), and the maximum likelihood estimation method was used to fit the probability distribution parameters of SSCAT0-3.

[0184] Next, based on the third assumption, calculate the typhoon frequency change value corresponding to each wind force level under SSCAT0-3 and SSCAT4&5. For details, please refer to the above Figure 2 The relevant contents in step S205 corresponding to the embodiment will not be repeated here.

[0185] Finally, the average intensity change value of SSCAT 0-5 typhoons is calculated according to the typhoon frequency change value corresponding to each wind force level, and the consistency is determined with the existing research intensity change law (10%, 25%, 50%, 75%, 90% quantiles of the relative change percentage of the average intensity of SSCAT0-5 typhoons). If it is consistent with the law, the current SSCAT0-3 and SSCAT4&5 probability distribution parameters are determined. If it is not consistent with the law, the SSCAT4&5 frequency change probability distribution parameters are adjusted within the confidence interval of the parameter estimation.

[0186] The method provided in the embodiment of the present invention uses any set of typhoon best path data sets or full-path typhoon random event sets, extracts typhoon events that need to be increased and typhoon events that need to be reduced from the original data set according to the sea area typhoon change parameter table, and re-counts the typhoon frequency and intensity in the area of ​​concern, so as to obtain the change value of typhoon activity parameters in any area. Based on the latest scientific research progress and typhoon path data, the changes in typhoon activity parameters of different warming scenarios in any region of the northwest Pacific under the background of climate change can be simulated, so the region, SSP (shared socioeconomic path) scenario and research period can be customized according to user needs. The 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 of financial institutions.

[0187] In this embodiment, a prediction device for the number of typhoons of various levels under a preset climate scenario is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions thereof will not be repeated. As used below, the term "module" may implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and contemplated.

[0188] This embodiment provides a prediction device for the number of typhoons of various levels under a preset climate scenario, such as Figure 5 As shown, including:

[0189] The data acquisition module 401 is used to obtain a first change rate of a first typhoon activity parameter and a second change rate of a second typhoon activity parameter of a preset climate scenario compared with a current climate scenario under different research perspectives, wherein the first typhoon activity parameter is the average annual number of typhoons in all parent level ranges, and the second typhoon activity parameter is the average annual number of typhoons in the first preset parent level range;

[0190] Function fitting module 402, used to perform probability distribution fitting on the first change rate and the second change rate under different research perspectives, respectively, to obtain a first change rate probability distribution function and a second change rate probability distribution function;

[0191] A sampling matching module 403 is used to perform random sampling on the first change rate probability distribution function and the second change rate probability distribution function for the same preset number of times and match the sampling results to obtain multiple sampling matching groups;

[0192] The change rate determination module 404 is used to determine the third change rate of the third typhoon activity parameter corresponding to each sampling matching group according to the first change rate and the second change rate corresponding to each sampling matching group and the historical data corresponding to the current climate scenario, where the third typhoon activity parameter is the average annual number of typhoons in the second preset parent level range, and the second preset parent level range is the parent level range in all parent level ranges except the first preset parent level range;

[0193] A formula determination module 405 is used to determine the association formula corresponding to each sampling matching group based on the second change rate and the third change rate corresponding to each sampling matching group and the historical data, and determine the change rate of the average annual number of typhoons corresponding to each sub-level corresponding to the corresponding matching group based on the association formula, and the association formula is used to characterize the relationship between the change rate of the average annual number of typhoons and the typhoon intensity;

[0194] The quantity prediction module 406 is used to determine the average annual number of typhoons of each level under the preset climate scenario based on the change rate and historical data of the average annual number of typhoons corresponding to each child level under all parent levels corresponding to each matching group.

[0195] In some optional implementations, the data acquisition module 401, when acquiring a first change rate of a first typhoon activity parameter and a second change rate of a second typhoon activity parameter of a preset climate scenario compared to a current climate scenario under different research perspectives, includes:

[0196] Obtain the change ratio parameters corresponding to the first typhoon activity parameter and the second typhoon activity parameter under unit temperature change under different research perspectives;

[0197] Determine the expected temperature for each year in the future period under the preset climate scenario and the historical temperature for each year in the historical period under the current climate scenario;

[0198] Based on the expected temperature of each year in the future period, the historical temperature of each year in the historical period, and the corresponding change ratio parameters of the first typhoon activity parameter and the second typhoon activity parameter, the first change rate of the first typhoon activity parameter and the second change rate of the second typhoon activity parameter of the preset climate scenarios under different research perspectives compared with the current climate scenario are determined.

[0199] In some optional implementations, the function fitting module 402, when performing probability distribution fitting on the first change rate and the second change rate under different research perspectives to obtain the first change rate probability distribution function and the second change rate probability distribution function, includes:

[0200] Sort the first change rates and the second change rates under different research perspectives, and determine the first change rates and the second change rates under multiple preset quantiles;

[0201] The confidence interval of the parameter estimate is determined by the maximum likelihood estimation method, and based on the confidence interval, the first change rate and the second change rate under multiple preset quantiles are respectively fitted with log-normal distribution to obtain the first change rate probability distribution function and the second change rate probability distribution function.

[0202] In some optional implementations, the sampling matching module 403, when performing random sampling on the first change rate probability distribution function and the second change rate probability distribution function for the same preset number of times and matching the sampling results to obtain multiple sampling matching groups, includes:

[0203] Performing random sampling for a preset number of times based on the first change rate probability distribution function and the second change rate probability distribution function respectively, to obtain the same number of corresponding first sampling results and second sampling results;

[0204] Sort the first sampling result and the second sampling result respectively based on the magnitude of the change rate in each sampling result;

[0205] Based on the corresponding ranking 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, where the sampling matching groups include: a first change rate and a second change rate.

[0206] In some optional implementations, the change rate determination module 404, when determining the third change rate of the third typhoon activity parameter corresponding to each sampling matching group according to the first change rate and the second change rate corresponding to each sampling matching group and the historical data corresponding to the current climate scenario, includes:

[0207] Determine 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 in the historical period corresponding to the current climate scenario;

[0208] Determine 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 according to the first change rate and the second change rate in each sampling matching group;

[0209] Determine the average annual number of typhoons in the second preset parent level range according to 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, and determine the third change rate of the third typhoon activity parameter corresponding to each sampling matching group in combination with historical data;

[0210] The formula determination module 405, when determining the association formula corresponding to each sampling matching group based on the second change rate and the third change rate corresponding to each sampling matching group and the historical data, includes:

[0211] Determine a preset formula, which includes unknown parameters and is used to characterize the relationship between the rate of change of the average annual number of typhoons and the typhoon intensity;

[0212] Determine the value of the unknown parameter based on the second change rate and the third change rate corresponding to each sampling matching group and the intensity of the typhoon corresponding to each sub-level under all parent levels;

[0213] The association formula of the corresponding sampling matching group is determined according to the value of the unknown parameter.

[0214] In some optional implementations, the quantity prediction module 406, before determining the average annual number of typhoons of each level under the preset climate scenario according to the change rate and historical data of the average annual number of typhoons corresponding to each child level under all parent levels corresponding to each matching group, is further used to:

[0215] Obtain the average typhoon intensity change rate of the preset climate scenarios compared with the current climate scenarios under different research perspectives;

[0216] Based on the change rate and historical data of the average annual number of typhoons under each child level under all parent levels corresponding to each matching group, determine the change rate of the average typhoon intensity corresponding to each matching group;

[0217] 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;

[0218] If yes, execute the step of determining the average annual number of typhoons of each level under the preset climate scenario according to the change rate and historical data of the average annual number of typhoons corresponding to each child level under all parent levels corresponding to each matching group.

[0219] In some optional embodiments, the quantity prediction module 406 is further used to adjust the probability distribution parameters of the fitting process, return to execute the step of performing probability distribution fitting on the second change rate under different research perspectives to obtain the second change rate probability distribution function, and re-judge 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, when the probability distribution law of the average typhoon intensity change rate corresponding to each matching group is inconsistent with the probability distribution law of the average typhoon intensity change rate under different research perspectives.

[0220] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0221] The prediction device for the number of typhoons of each level under the preset climate scenario in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0222] The embodiment of the present invention also provides a computer device having the above Figure 5 The device for predicting the number of typhoons of various levels under the preset climate scenarios shown.

[0223] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0224] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0225] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0226] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0227] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0228] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0229] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0230] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0231] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all 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 comprises: Obtain a first change rate of a first typhoon activity parameter and a second change rate of a second typhoon activity parameter of a preset climate scenario compared to the current climate scenario under different research perspectives, wherein the first typhoon activity parameter is the average annual number of typhoons under all parent level ranges, and the second typhoon activity parameter is the average annual number of typhoons under the first preset parent level range; Probability distribution fitting is performed on the first change rate and the second change rate under different research perspectives respectively to obtain the probability distribution function of the first change rate and the probability distribution function of the second change rate; Performing random sampling of the first change rate probability distribution function and the second change rate probability distribution function for the same preset number of times respectively and matching the sampling results to obtain a plurality of sampling matching groups; Determine, according to the first change rate and the second change rate corresponding to each sampling matching group and the historical data corresponding to the current climate scenario, a third change rate of the third typhoon activity parameter corresponding to each sampling matching group, wherein the third typhoon activity parameter is the average annual number of typhoons under the second preset parent level range, and the second preset parent level range is the parent level range among all parent level ranges except the first preset parent level range; Based on the second change rate and the third change rate corresponding to each sampling matching group and the historical data, determine the association formula corresponding to each sampling matching group, and determine the change rate of the average annual number of typhoons corresponding to each sub-level corresponding to the corresponding matching group based on the association formula, and the association formula is used to characterize the relationship between the change rate of the average annual number of typhoons and the typhoon intensity; According to the change rate and historical data of the average annual number of typhoons under each child level under all parent levels corresponding to each matching group, the average annual number of typhoons of each level under the preset climate scenario is determined.

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

3. The method according to claim 1, characterized in that The probability distribution fitting of the first change rate and the second change rate under different research perspectives is performed to obtain the first change rate probability distribution function and the second change rate probability distribution function, including: Sort the first change rates and the second change rates under different research perspectives, and determine the first change rates and the second change rates under multiple preset quantiles; The confidence interval of the parameter estimation is determined by the maximum likelihood estimation method, and based on the confidence interval, the first change rate and the second change rate under multiple preset quantiles are respectively fitted with log-normal distribution to obtain the first change rate probability distribution function and the second change rate probability distribution function.

4. The method according to claim 1, characterized in that: The randomly sampling the first change rate probability distribution function and the second change rate probability distribution function for the same preset number of times and matching the sampling results to obtain a plurality of sampling matching groups include: Performing random sampling for a preset number of times based on the first change rate probability distribution function and the second change rate probability distribution function respectively, to obtain the same number of corresponding first sampling results and second sampling results; Sort the first sampling result and the second sampling result respectively based on the magnitude of the change rate in each sampling result; Based on the corresponding ranking 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, and the sampling matching groups include: a first change rate and a second change rate.

5. The method according to claim 1, characterized in that: The determining, according to the first change rate and the second change rate corresponding to each sampling matching group and the historical data corresponding to the current climate scenario, the third change rate of the third typhoon activity parameter corresponding to each sampling matching group comprises: Determine 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 in the historical period corresponding to the current climate scenario; Determine 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 according to the first change rate and the second change rate in each sampling matching group; Determine the average annual number of typhoons in the second preset parent level range according to 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, and determine the third change rate of the third typhoon activity parameter corresponding to each sampling matching group in combination with historical data; The determining of the association formula corresponding to each sampling matching group based on the second change rate and the third change rate corresponding to each sampling matching group and the historical data includes: Determining a preset formula, wherein the preset formula includes unknown parameters and is used to characterize the relationship between the rate of change of the average annual number of typhoons and the typhoon intensity; Determine the value of the unknown parameter based on the second change rate and the third change rate corresponding to each sampling matching group and the intensity of the typhoon corresponding to each sub-level under all parent levels; The association formula of the corresponding sampling matching group is determined according to the value of the unknown parameter.

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

7. The method according to claim 6, characterized in that The method further comprises: If not, adjust the probability distribution parameters of the fitting process, return to the step of fitting the probability distribution of the second change rate under different research perspectives, obtain the probability distribution function of the second change rate, and re-judge 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 comprises: A data acquisition module, used to obtain a first change rate of a first typhoon activity parameter and a second change rate of a second typhoon activity parameter of a preset climate scenario compared with a current climate scenario under different research perspectives, wherein the first typhoon activity parameter is the average annual number of typhoons under all parent level ranges, and the second typhoon activity parameter is the average annual number of typhoons under the first preset parent level range; A function fitting module is used to perform probability distribution fitting on the first change rate and the second change rate under different research perspectives to obtain a first change rate probability distribution function and a second change rate probability distribution function; a sampling matching module, configured to perform random sampling of the first change rate probability distribution function and the second change rate probability distribution function for the same preset number of times and match the sampling results to obtain a plurality of sampling matching groups; A change rate determination module, used to determine a third change rate of a third typhoon activity parameter corresponding to each sampling matching group according to the first change rate and the second change rate corresponding to each sampling matching group and the historical data corresponding to the current climate scenario, wherein the third typhoon activity parameter is the average annual number of typhoons under a second preset parent level range, and the second preset parent level range is a parent level range among all parent level ranges except the first preset parent level range; A formula determination module, for determining the association formula corresponding to each sampling matching group based on the second change rate and the third change rate corresponding to each sampling matching group and historical data, and determining the change rate of the average annual number of typhoons corresponding to each sub-level corresponding to the corresponding matching group based on the association formula, wherein the association formula is used to characterize the relationship between the change rate of the average annual number of typhoons and the typhoon intensity; The quantity prediction module is used to determine the average annual number of typhoons of each level under the preset climate scenario based on the change rate and historical data of the average annual number of typhoons corresponding to each child level under all parent levels corresponding to each matching group.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for predicting the number of typhoons of various levels under the preset climate scenario described in any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for predicting the number of typhoons of various levels under a preset climate scenario according to any one of claims 1 to 7.

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