Composite heat wave ozone event refined identification method

Abstract

The invention relates to the technical field of composite heat wave ozone event evaluation, in particular to a composite heat wave ozone event refined identification method, which comprises the following steps: acquiring two types of preprocessed observation daily scale data sequences; performing percentile conversion on the preprocessed observation daily scale data sequence to obtain a percentile sequence; calculating the rising rate of the observation data in the development stage according to the percentile sequence; determining a pollution event of the to-be-identified area and a plurality of characteristic values of the pollution event through the rising rate; and if the pollution event is a non-independent composite heat wave ozone event, combining the non-independent composite heat wave ozone event by adopting an IC method to obtain a new combined composite heat wave ozone event. According to the invention, the accuracy of composite heat wave ozone event identification is improved by determining the rising rate and combining the non-independent composite heat wave ozone event.

Description

Technical Field

[0001] The present invention relates to the technical field of compound heatwave ozone event assessment, and particularly relates to a method for precisely identifying compound heatwave ozone events. Background Art

[0002] For compound heatwave ozone events, heatwaves provide favorable conditions for ozone generation. As an important greenhouse gas, ozone further exacerbates heatwaves through the greenhouse effect. The strong positive feedback promotes the tendency of the two to occur synchronously in space and time. Compound heatwave ozone events may suddenly break out in a short period of time, and they have the characteristic of rapid intensification. This not only increases the difficulty of event prediction and response but also may cause more serious impacts on the ecosystem and human society. For example, the sudden superposition of rapid temperature rise and high-concentration ozone may cause serious damage to crops in a short period of time, and even result in a significant reduction in production before they have time to activate their defense mechanisms. Therefore, there is an urgent need to propose a method for identifying compound heatwave ozone events.

[0003] Existing methods for identifying compound heatwave ozone events, such as those based on threshold methods, have the following deficiencies: First, they do not fully consider the sudden characteristics of events, thus unable to achieve a fine classification of compound heatwave ozone events; Second, taking some "small events" with weak intensity or short duration as independent samples for statistical analysis will make it difficult to ensure the independence assumption of the samples, thereby affecting the accuracy of characteristics such as event frequency, duration, and intensity. Summary of the Invention

[0004] In order to solve the technical problem of the low accuracy of the existing identification results of compound heatwave ozone events, the purpose of the present invention is to provide a method for precisely identifying compound heatwave ozone events, and the specific technical solution adopted is as follows:

[0005] An embodiment of the present invention provides a method for precisely identifying compound heatwave ozone events, and the method includes the following steps:

[0006] Obtain two types of preprocessed observed daily-scale data sequences corresponding to the area to be identified during the research period. The observed daily-scale data sequences include observed data for several days, and the observed data are daily average temperature and daily average near-surface ozone concentration;

[0007] Perform percentile conversion on the preprocessed observed daily-scale data sequences to obtain percentile sequences;

[0008] Calculate the rising rate of the observed data in the development stage according to the percentile sequences;

[0009] Determine the pollution events and several characteristic values of the pollution events in the area to be identified through the rising rate;

[0010] If the pollution event is a non - independent compound heatwave - ozone event, the IC method is used to merge the non - independent compound heatwave - ozone event, and a new merged compound heatwave - ozone event is obtained.

[0011] Further, the obtaining of the pre - processed observed daily - scale data sequence corresponding to the area to be identified during the research period includes:

[0012] Obtain the observed data of meteorology and air pollution for each day in the area to be identified during the research period to form an observed daily - scale data sequence;

[0013] Establish a sliding average window on a weekly scale and smooth the observed daily - scale data sequence to obtain a smoothed observed daily - scale data sequence;

[0014] Perform detrending processing on the smoothed observed daily - scale data sequence day by day to obtain a detrended observed daily - scale data sequence.

[0015] Further, the percentile conversion of the pre - processed observed daily - scale data sequence to obtain a percentile sequence includes:

[0016] Select the sub - sequence of observed data corresponding to the same day in the observed daily - scale data sequence, sort each sub - sequence of observed data in ascending order, and obtain the arrangement serial number of each observed data in its corresponding sub - sequence of observed data;

[0017] Determine the percentile of each observed data through the ratio of the arrangement serial number of each observed data in its corresponding sub - sequence of observed data to the total number of all observed data in the corresponding sub - sequence of observed data, and form a percentile sequence.

[0018] Further, determining the percentile of each observed data includes:

[0019] The arrangement serial number of the j - th observed data is n j , the total number of all observed data is N, and the formula for calculating the percentile m j of the j - th observed data is: <s

[0020] Further, the calculating of the rising rate of the observed data in the development stage according to the percentile sequence includes:

[0021] Determine the highest percentile and time length of the observed data in the development stage based on the percentile sequence;

[0022] Combine the highest percentile and time length to determine the rising rate of the observed data in the development stage.

[0023] Further, determining the rising rate of the observed data in the development stage by combining the highest percentile and the time length includes:

[0024] In the formula, v′ represents the rising rate of the observed data in the development stage, and x max represents the highest percentile of the observed data in the development stage, t represents the time length of the development stage, and th represents the abbreviation suffix of the percentile.

[0025] Further, determining the pollution event in the area to be identified by the rising rate includes:

[0026] First identifying single sudden heatwave pollution events and ozone pollution events based on the rising rate;

[0027] When the single sudden heatwave pollution event and the ozone pollution event overlap within the time window, and the overlapping part includes the development stage and the recovery stage of the single event, the overlapping part is defined as a composite heatwave ozone event;

[0028] Judging the nature of the composite heatwave ozone event according to the rising rate in the development stage, and the nature of the event is a slow-onset event and a sudden event.

[0029] Further, several characteristic values of the pollution event include event frequency, duration, and intensity;

[0030] The event frequency is used to represent the number of pollution events occurring in the area to be identified during the research period; the duration is used to represent the ratio of the total number of days of the pollution event in the area to be identified during the research period to the event frequency; the intensity is used to represent the ratio of the total intensity of all pollution events in the area to be identified during the research period to the event frequency, and the intensity of a single pollution event is equal to the area enclosed by the percentile curve of the observed data and the horizontal axis.

[0031] Further, the duration and intensity of the new combined composite heatwave ozone event are calculated by the following formulas respectively:

[0032] d pool =d i +d i+1 +t i ; In the formula, d pool represents the duration of the new combined composite heatwave ozone event, d i represents the duration of the i-th non-independent composite heatwave ozone event, d i+1 represents the duration of the (i + 1)-th non-independent composite heatwave ozone event, t i represents the time interval corresponding to the i-th and (i + 1)-th non-independent composite heatwave ozone events;

[0033] V pool =Vi +V i+1 +z i ; where, V pool represents the intensity of the newly combined composite heatwave ozone event, V i represents the intensity of the i-th non-independent composite heatwave ozone event, V i+1 represents the intensity of the (i + 1)-th non-independent composite heatwave ozone event, and z i represents the intensity of the intermediate non-abnormal period event corresponding to the i-th and (i + 1)-th non-independent composite heatwave ozone events.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] Based on the comprehensive statistical model between heatwave events and ozone pollution events, the present invention uses the percentile threshold method to construct a comprehensive identification index of composite heatwave ozone events that can reflect the information of near-surface air temperature and ozone concentration, overcoming the shortcomings of previous identification methods that do not consider the event development rate and the statistical errors brought by non-independent event samples; the method used in the present invention involves multiple fields such as meteorology, ecology, and mathematics, involving the cross-integration of multiple disciplines, with simple calculations, easy to operate, and having an ecological meteorological physical mechanism, facilitating popularization and application in practice; the present invention considers elements such as near-surface air temperature and ozone concentration, and the input data is relatively easy to obtain, and can be adopted by relevant departments such as national meteorology and agriculture at the same time, and can provide more scientific and reasonable technology and decision-making support for multiple departments at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0037] Figure 1 is a flowchart of a method for accurately identifying composite heatwave ozone events provided by an embodiment of the present invention;

[0038] Figure 2 is a calculation flowchart of the method for identifying composite heatwave ozone events in the embodiments of the present invention;

[0039] Figure 3 is a schematic diagram for identifying sudden events;

[0040] Figure 4 is a schematic diagram for identifying slow-onset events;

[0041] Figure 5Schematic diagram of merging non - independent composite heat - wave ozone events based on the IC method in the embodiments of the present invention. Detailed implementation manners

[0042] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and their effects of the technical solutions proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0044] The application scenarios targeted by the present invention can be:

[0045] Traditional pollution event recognition methods mainly rely on statistical analysis and threshold determination. For example, percentiles or absolute thresholds of air temperature and ozone concentration are used to define events, but the recognition efficiency and accuracy of this method are low. With the development of machine learning and numerical simulation technologies, attempts have been made to apply artificial intelligence algorithms to event recognition, such as by integrating multi - source observation data and machine - learning - assisted causal inference techniques. However, current recognition methods often ignore an important feature, namely the event development rate. The development rate is a key indicator for measuring the dynamic process of compound events, and it directly affects the response mode and adaptation ability of disaster - bearing bodies to stress. For example, a rapidly developing composite heat - wave ozone event may cause crops to be unable to activate physiological defense mechanisms in time, thereby exacerbating damage such as photosynthesis inhibition and membrane lipid peroxidation, and ultimately having a greater impact on yield.

[0046] In addition, the time series of composite heat - wave ozone events identified based on the threshold method often contains some "small events" with weak intensity or short duration. "Small events" that are temporally continuous may belong to different stages of the same composite heat - wave ozone event, and some of the "small events" with weak intensity may only be temporary rebounds during the main event process. If all "small events" are taken as independent samples for statistical analysis, it will be difficult to ensure the independence assumption of the samples, thus affecting the accuracy of characteristics such as event frequency, duration, and intensity. Therefore, it is crucial to reasonably merge "small events" to ensure sample independence.

[0047] In view of the deficiencies of the prior art, an embodiment of the present invention provides a refined recognition method for composite heat - wave ozone events, as Figure 1 shown, and the calculation flow chart of the composite heat - wave ozone event recognition method is as Figure 2 shown.

[0048] S1, obtain two types of pre-processed observation daily scale data sequences corresponding to the area to be identified during the study period.

[0049] Here, the observation day scale data sequence includes observation data of several days, which are the daily average temperature and the daily average near-ground ozone concentration, that is, the daily meteorological and air pollution observation data. The size of several days is equal to the number of days in the study period.

[0050] The study period can be a certain period of the calendar year, such as June to August every year in the past 10 years. Specifically, if a complex pollution event in June to August in a certain area is to be identified, it is necessary to obtain the daily average temperature series and daily average near-ground ozone concentration series corresponding to June to August in the area for no less than 10 years, that is, two types of observational daily-scale data series.

[0051] In order to avoid the influence of drastic fluctuations in daily data and the possible time lag between temperature and ozone concentration, the initial observational daily data series were preprocessed, including the following steps:

[0052] S11, establishing a sliding average window of one week scale, smoothing the observation day scale data sequence, and obtaining a smoothed observation day scale data sequence.

[0053] S12, performing detrending processing on the smoothed observation day scale data sequence on a daily basis to obtain a detrended observation day scale data sequence.

[0054] In this embodiment, the detrended daily-scale data sequence is used as the preprocessed daily-scale data sequence. Trend processing can, to a certain extent, mitigate the effects of seasonal cycles. Preprocessing operations include, but are not limited to, smoothing and detrending. Data preprocessing operations are conventional techniques and fall outside the scope of this invention, so they will not be elaborated on in detail here.

[0055] So far, this embodiment has obtained two types of observation day-scale data series for realizing pollution event identification.

[0056] S2, perform percentile transformation on the preprocessed observation day-scale data series to obtain a percentile series.

[0057] This embodiment uses percentiles of observation data (daily average temperature and daily average near-surface ozone concentration) to identify sudden and slow-onset compound heat wave ozone events, and the conversion of percentiles must be compared with historical data for the same period.

[0058] The above step S2 can be implemented through steps S21 to S22 (not shown):

[0059] S21. Select the sub-sequence of observation data corresponding to the same day in the daily-scale data sequence, sort each sub-sequence of observation data in ascending order, and obtain the ranking number of each observation data in its corresponding sub-sequence of observation data.

[0060] S22. Determine the percentile of each observation data based on the ratio of the ranking number of each observation data in its corresponding sub-sequence of observation data to the total number of all observation data in the sub-sequence, and form a percentile sequence.

[0061] For example: For the daily-scale average temperature data on June 1st, to convert it into a percentile, it is necessary to select the daily-scale average temperature data on June 1st in the past 10 years. There are a total of 10 daily-scale average temperature data. After arranging them in ascending order, if a certain daily-scale average temperature data ranks 4th, then the percentile of this daily-scale average temperature data is equal to

[0062] As an example, the calculation formula for the percentile of the j-th observation data can be:

[0063] In the formula, m j represents the percentile of the j-th observation data, n j represents the ranking number of the j-th observation data, N represents the number of observation data in the sub-sequence of observation data to which the j-th observation data belongs, and th represents the abbreviation suffix of the percentile.

[0064] So far, in this embodiment, the percentile of each observation data has been obtained, and a percentile sequence has been formed.

[0065] S3. Calculate the rising rate of the observation data in the development stage based on the percentile sequence.

[0066] Here, the rising rate can be used to characterize the event development rate.

[0067] The above step S3 can be implemented through steps S31 to S32 (not shown in the figure):

[0068] S31. Determine the highest percentile and time length of the observation data in the development stage based on the percentile sequence.

[0069] For a single sudden pollution event, the definition of the development stage in this embodiment can be: when the percentile of the daily average temperature or daily average ozone concentration exceeds 60 th %, the event enters the development stage; secondly, the average rising rate of the percentile of the daily average temperature or daily average ozone concentration should not be lower than 5 th / day; the percentile of the daily average temperature or daily average ozone concentration should exceed 80 th, thereafter, if the daily average temperature or the percentile of the daily average ozone concentration starts to decline or the average rising rate is lower than 5 th / day, the event development stage ends and enters the recovery stage. Thus, the development stage of a single sudden pollution event is obtained. After obtaining the development stage, the highest percentile and the time length of the observed data are obtained based on the development stage.

[0070] S32. Combine the highest percentile and the time length to determine the rising rate of the observed data in the development stage.

[0071] In this embodiment, sudden pollution events and slow-onset pollution events are identified according to the rising rates of temperature and ozone concentration in the development stage.

[0072] As an example, the calculation formula for the rising rate of the observed data in the development stage can be:

[0073] In the formula, v′ represents the rising rate of the observed data in the development stage (th / day), x max represents the highest percentile of the observed data in the development stage, t represents the time length of the development stage, and th represents the abbreviation suffix of the percentile.

[0074] So far, the rising rate of the observed data in the development stage has been obtained in this embodiment.

[0075] S4. Determine the pollution events and several characteristic values of the pollution events in the area to be identified through the rising rate.

[0076] Here, the rising rate can characterize the development rate, which is a key indicator for measuring the dynamic process of the event and can directly affect the response mode and adaptation ability of the disaster-bearing body to stress.

[0077] The above determination of the pollution events in the area to be identified can be achieved through steps S41 to S43 (not shown in the figure):

[0078] S41. First, identify single sudden heatwave pollution events and ozone pollution events based on the rising rate.

[0079] In this embodiment, before identifying sudden compound heatwave ozone events, single sudden heatwave / ozone pollution events are first identified.

[0080] Taking the identification of sudden heatwave pollution events as an example, the pollution event generally includes two stages and needs to meet the following criteria: First, when the percentile of the daily average temperature exceeds 60 th the event enters the development stage; second, the average rising rate of the percentile of the daily average temperature should not be lower than 5 th / day; after experiencing a rapid rise, the percentile of the daily average temperature should exceed 80 th, thereafter, if the daily average temperature percentile starts to decline or the average rising rate is lower than 5 th / day, the event development stage ends and enters the recovery stage. When the daily average temperature percentile is lower than 80 again th at this time, this event ends.

[0081] The identification process of sudden ozone pollution events is the same as that of sudden heatwave pollution events.

[0082] S42. When a single sudden heatwave pollution event and an ozone pollution event overlap within the time window, and the overlapping part includes the development stage and the recovery stage of the single event, the overlapping part is defined as a compound heatwave ozone event.

[0083] S43. Judge the nature of the compound heatwave ozone event according to the rising speed in the development stage.

[0084] Among them, the natures of the compound heatwave ozone event are slow-onset events and sudden events. The schematic diagram for identifying sudden events is as Figure 3 shown, and the schematic diagram for identifying slow-onset events is as Figure 4 shown. Figure 3 and Figure 4 The abscissa of is the date (day), and the ordinate is the percentile (th).

[0085] In this embodiment, the difference in identifying slow-onset compound heatwave ozone events and sudden compound heatwave ozone events lies only in: whether the rising rate of the daily average temperature / ozone concentration in the event development stage is lower than 5 th / day. Except for this, the identification processes are the same. When the rising rate of the daily average temperature / ozone concentration in the event development stage is lower than 5 th / day, it is a slow-onset compound heatwave ozone event; otherwise, it is a sudden compound heatwave ozone event.

[0086] In addition, this embodiment defines that a single compound heatwave ozone event should last at least 7 days. In actual application, the event duration threshold can be set by itself according to the application scenario and research purpose.

[0087] Among them, several characteristic values of the compound heatwave ozone event characteristics include event frequency F, duration D, and intensity Q.

[0088] In this embodiment, the event frequency F is used to represent the number of compound heatwave ozone events occurring in the area to be identified during the research period; the duration D is used to represent the ratio of the total number of days of the compound heatwave ozone event in the area to be identified during the research period to the event frequency; the intensity Q is used to represent the ratio of the total intensity Q of all compound heatwave ozone events in the area to be identified during the research period to the event frequency F. The intensity Q of each compound heatwave ozone event is equal to the sum of the areas enclosed by the percentile curve of the daily average temperature / ozone concentration and the horizontal axis (x-axis).

[0089] Regarding the eigenvalue intensity Q, the intensity is a dimensionless value because the horizontal axis of the percentile curve is the number of days and the vertical axis is the percentile, which are dimensionless data. The area summation obtained by integrating the two is only a degree value. In addition, the intensity is an average concept. If there are three events, the intensity of the first event is 10, the intensity of the second event is 20, and the intensity of the third event is 30, then the average intensity of the event is 20.

[0090] So far, this embodiment has determined the pollution events in the area to be identified and several eigenvalues of the pollution events.

[0091] S5. If the pollution event is a non-independent composite heatwave ozone event, the IC method is used to merge the non-independent composite heatwave ozone event to obtain a new merged composite heatwave ozone event.

[0092] Here, whether the events are independent is strictly determined according to the IC method. The non-independent composite heatwave ozone event is a "small event" with a weak intensity or a short duration. These temporally continuous "small events" may belong to different stages of the same composite heatwave ozone event. Some of the "small events" with a weak intensity may only be temporary rebounds during the main event. If all "small events" are statistically analyzed as independent samples, it will be difficult to ensure the independence assumption of the samples, thus affecting the accuracy of characteristics such as event frequency, duration, and intensity. Therefore, it is crucial to reasonably merge the "small events" to ensure sample independence.

[0093] In this embodiment, the IC method (Inter-event Criteria) is used to merge the non-independent composite heatwave ozone event to ensure sample independence. When the interval time t between two temporally consecutive "small events" i is less than a certain set critical time t c , and the ratio of the total surplus of air temperature and ozone concentration in the middle non-abnormal period to the absolute value of air temperature and ozone concentration of the previous "small event" is less than a specific percentage α, the IC method merges these two "small events" into one event, and the duration and intensity of the event are also merged accordingly to obtain a new merged composite heatwave ozone event. The schematic diagram of the merger of non-independent composite heatwave ozone events based on the IC method is as Figure 5 shown. In Figure 5 , the horizontal axis is time, with the unit of day, and the vertical axis is the absolute value of the percentile of air temperature / ozone concentration (th).

[0094] The duration and intensity of the new merged composite heatwave ozone event are calculated by the following formulas respectively:

[0095] d pool = d i + d i+1 + ti ; where d pool represents the duration of the newly merged compound heatwave ozone event, d i represents the duration of the i-th non-independent compound heatwave ozone event, d i+1 represents the duration of the (i + 1)-th non-independent compound heatwave ozone event, t i represents the time interval corresponding to the i-th and (i + 1)-th non-independent compound heatwave ozone events;

[0096] V pool = V i + V i+1 + z i ; where V pool represents the intensity of the newly merged compound heatwave ozone event, V i represents the intensity of the i-th non-independent compound heatwave ozone event, V i+1 represents the intensity of the (i + 1)-th non-independent compound heatwave ozone event, z i represents the intensity of the intermediate non-abnormal period event corresponding to the i-th and (i + 1)-th non-independent compound heatwave ozone events.

[0097] It should be noted that the merging parameters t c and α in the IC method are crucial for the results. In this embodiment, the optimal merging parameters t c and α are selected by means of automatic parameter screening. Specifically, when α gradually changes from 0.1 to 0.9 in steps of 0.05, t c gradually changes from 1 week to 15 weeks. The compound heatwave ozone event frequencies under each combination of t c and α are respectively counted to obtain the curve of the event frequency changing with the parameter values. The optimal values of t c and α are obtained when the event frequency curve tends to be stable.

[0098] Thus far, this embodiment has completed the merging process of non-independent compound heatwave ozone events.

[0099] The present invention provides a refined identification method for compound heatwave ozone events, which can comprehensively and accurately identify the occurrence moments, durations, and intensities of sudden and slow-onset compound heatwave ozone events, and is of great significance for scientifically and effectively identifying the evolution process of compound heatwave ozone events, accurately assessing the risks of compound heatwave ozone events, and weakening the impacts of compound heatwave ozone events on agriculture and social and economic development.

[0100] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A refined identification method for compound heatwave ozone events, characterized in that, Including the following steps: Obtain two types of preprocessed observed daily-scale data sequences corresponding to the area to be identified during the research period. The observed daily-scale data sequences include observed data for several days, and the observed data are daily average temperature and daily average near-surface ozone concentration; Perform percentile transformation on the preprocessed observed daily-scale data sequences to obtain percentile sequences; Calculate the rising rate of the observed data in the development stage according to the percentile sequences; Determine the pollution events and several characteristic values of the pollution events in the area to be identified through the rising rate; If the pollution event is a non-independent compound heatwave ozone event, use the IC method to merge the non-independent compound heatwave ozone event to obtain a new merged compound heatwave ozone event.

2. The refined identification method for compound heatwave ozone events according to claim 1, wherein The obtaining of the preprocessed observed daily-scale data sequences corresponding to the area to be identified during the research period includes: Obtain the observed data of meteorology and air pollution every day in the area to be identified during the research period to form an observed daily-scale data sequence; Establish a sliding average window on a one-week scale and perform smoothing processing on the observed daily-scale data sequence to obtain a smoothed observed daily-scale data sequence; Perform detrending processing on the smoothed observed daily-scale data sequence day by day to obtain a detrended observed daily-scale data sequence.

3. The refined identification method for compound heatwave ozone events according to claim 1, wherein, The performing of percentile transformation on the preprocessed observed daily-scale data sequences to obtain percentile sequences includes: Select the observed data subsequences corresponding to the same day in the observed daily-scale data sequences, sort each observed data subsequence in ascending order, and obtain the arrangement serial number of each observed data in its corresponding observed data subsequence; Determine the percentile of each observed data through the ratio of the arrangement serial number of each observed data in its corresponding observed data subsequence to the total number of all observed data in the corresponding observed data subsequence, and form a percentile sequence.

4. The refined identification method for compound heatwave ozone events according to claim 3, characterized in that, Determining the percentile of each observed data includes: The permutation serial number of the j-th observed data is n j , the total number of all observed data is N, and the percentile m j of the j-th observed data is calculated by the following formula:

5. The refined identification method for compound heatwave ozone events according to claim 1, wherein The calculating of the rising rate of the observed data in the development stage according to the percentile sequences includes: Determine the highest percentile and time length of the observed data in the development stage based on the percentile sequence; Combine the highest percentile and time length to determine the rising rate of the observed data in the development stage.

6. The refined identification method for compound heatwave ozone events according to claim 5, wherein The combining of the highest percentile and time length to determine the rising rate of the observed data in the development stage includes: In the formula, v′ represents the rising rate of the observed data in the development stage, and x max represents the highest percentile of the observed data in the development stage, t represents the time length of the development stage, and th represents the abbreviation suffix of the percentile.

7. The refined identification method for compound heatwave ozone events according to claim 1, characterized in that The determining of the pollution events in the area to be identified through the rising rate includes: First identify single sudden heatwave pollution events and ozone pollution events based on the rising rate; When the single sudden heatwave pollution event and the ozone pollution event overlap within the time window, and the overlapping part includes the development stage and the recovery stage of the single event, define the overlapping part as a compound heatwave ozone event; Judge the event nature of the compound heatwave ozone event according to the rising speed in the development stage, and the event nature is a slow-onset event and a sudden event.

8. The refined identification method for compound heatwave ozone events according to claim 7, characterized in that The several characteristic values of the pollution events include event frequency, duration, and intensity; The event frequency is used to represent the number of pollution events that occur in the area to be identified during the research period; the duration is used to represent the ratio of the total number of days of pollution events in the area to be identified during the research period to the event frequency. The intensity is used to represent the ratio of the total intensity of all pollution events in the area to be identified during the research period to the event frequency, and the intensity of a single pollution event is equal to the area enclosed by the percentile curve of the observed data and the horizontal axis.

9. The refined identification method for compound heatwave ozone events according to claim 8, characterized in that, The duration and intensity of the newly combined composite heatwave ozone event are calculated by the following formulas respectively: d pool = d i + d i+1 + t i ; where, d pool represents the duration of the new combined compound heatwave ozone event, d i represents the duration of the i-th non-independent compound heatwave ozone event, d i+1 represents the duration of the (i + 1)-th non-independent compound heatwave ozone event, and t i represents the time interval corresponding to the i-th and (i + 1)-th non-independent compound heatwave ozone events; V pool = V i + V i+1 + z i ; where V pool represents the intensity of the new combined composite heatwave ozone event, V i represents the intensity of the i-th non-independent composite heatwave ozone event, V i+1 represents the intensity of the (i + 1)-th non-independent composite heatwave ozone event, and z i represents the intensity of the intermediate non-anomalous period event corresponding to the i-th and (i + 1)-th non-independent composite heatwave ozone events.

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