A regional composite heat wave population exposure risk analysis method based on multi-source data

Abstract

The present application relates to the technical field of climate event analysis, and particularly relates to a regional composite heat wave population exposure risk analysis method based on multi-source data. The method comprises the following steps: obtaining daily temperature observation data, monthly reanalysis data of various meteorological elements and population distribution data in a target region; determining a composite heat wave event by using a percentile threshold method, and counting a composite heat wave frequency; calculating an integrated atmospheric heat source according to the monthly meteorological reanalysis data based on a reverse algorithm; analyzing the correlation between the composite heat wave frequency and the atmospheric heat source in the target region, and establishing a linear regression equation; and calculating the target region composite heat wave population exposure degree caused by abnormal variation of the atmospheric heat source, and quantitatively evaluating the population exposure risk grade. The present application reveals the formation mechanism of the composite heat wave event from a new perspective, helps to improve the prediction ability, and at the same time provides a solid scientific basis for accurately identifying the distribution of relevant high-risk population, which has important scientific significance and application prospect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of climate event analysis, and particularly relates to a regional composite heat wave population exposure risk analysis method based on multi-source data. BACKGROUND

[0002] It has important scientific significance and research value to deeply explore the influence of atmospheric heat source anomaly on the population exposure risk of composite heat wave. At present, the research on identifying the driving factors of the population exposure risk of composite heat wave events is still relatively limited, and the influence mechanism of the atmospheric heat source area on the composite heat wave is still lack of systematic quantitative analysis. The existing methods are mostly focused on single high temperature event or local influence, and it is difficult to fully reveal the regulation and control effect of the atmospheric heat source on the population exposure risk. SUMMARY

[0003] To solve the above technical problems, the present application provides a regional composite heat wave population exposure risk analysis method based on multi-source data, comprising the following steps:

[0004] S1. Obtain the daily 2m temperature observation data, monthly meteorological reanalysis data and population distribution data in the target region, and construct a grid network of the target region;

[0005] S2. According to the daily 2m temperature observation data, the daily maximum temperature threshold and the daily minimum temperature threshold in the grid network are calculated based on the percentile threshold method, and the composite heat wave event is determined based on the daily maximum temperature threshold and the daily minimum temperature threshold, and the composite heat wave frequency is obtained;

[0006] S3. According to the monthly meteorological reanalysis data, the non-adiabatic heating term in the vertical direction in the grid network is calculated based on the inverse algorithm, and the non-adiabatic heating term is vertically integrated along the surface to the convection layer direction to obtain the atmospheric heat source distribution data;

[0007] S4. According to the composite heat wave frequency, the spatial distribution mode analysis of the composite heat wave frequency climate state, standard deviation and linear trend of the grid network of the target region is carried out with the maximum change value of the composite heat wave frequency as the target, the key change grid is obtained, the composite heat wave frequency of the key change grid is de-trended, standardized and averaged, the composite heat wave frequency index is obtained, and the correlation coefficient of the composite heat wave frequency index and the atmospheric heat source distribution data of the key change grid is calculated. The correlation coefficient is subjected to significance test, and the atmospheric heat source of the grid area with significant correlation is de-trended, standardized and averaged to obtain the atmospheric heat source index;

[0008] S5. Based on the atmospheric heat source index and the composite heat wave frequency, a linear regression equation is constructed, and the regression coefficient spatial distribution data in the grid network is calculated, and the regression coefficient represents the change degree of the composite heat wave frequency of the target region caused by 1 standard deviation of the abnormal change of the atmospheric heat source;

[0009] S6. According to the regression coefficient spatial distribution data, the population distribution data is multiplied to obtain the spatial distribution of the population exposure degree of the target region caused by 1 standard deviation of the abnormal change of the atmospheric heat source, the population exposure degree of the target region caused by 1 standard deviation of the abnormal change of the atmospheric heat source is obtained according to the population distribution data multiplied by the climate state of the composite heat wave frequency calculated in S4, and the overall population exposure degree of the region is calculated, and the population exposure relative risk level of each grid point in the target region caused by the abnormal change of the atmospheric heat source is obtained by dividing the population exposure degree caused by the abnormal change of the atmospheric heat source by the overall population exposure degree of the region.

[0010] The beneficial effects of the present application are:

[0011] (1) Based on the daily maximum and daily minimum temperature, the diurnal composite heat wave of the target region is defined, which is different from the traditional high temperature heat wave determined only according to the daily maximum temperature, which is more extreme, and also considers that the simultaneous occurrence of high temperature during the day and at night will cause more serious harm to the human body, and analyzing its evolution characteristics and influencing mechanism helps to deepen the understanding of the extreme high temperature events in the region, which has important scientific significance and high application value;

[0012] (2) The present application quantifies the change degree of the composite heat wave frequency of the target region caused by the abnormal change of the atmospheric heat source by constructing a key factor affecting the composite heat wave frequency in the target region, i.e. the composite heat wave frequency of the target region and the atmospheric heat source have a significant correlation, establishing a linear regression equation according to the correlation, effectively establishing a statistical model of the influence of atmospheric heat source on composite heat wave, and providing a new perspective for in-depth understanding of the formation mechanism and improving the prediction ability of extreme high temperature events;

[0013] (3) The present application analyzes the change of the composite heat wave population exposure of the target region caused by the abnormal change of the atmospheric heat source, and introduces the population exposure relative risk level evaluation method to further quantitatively analyze the influence degree of the abnormal change of the atmospheric heat source on the composite heat wave of the target region, divides the severity of the composite heat wave population exposure caused by the abnormal change of the atmospheric heat source, and quantifies the risk level distribution more finely, and reveals the spatial difference characteristics of the population exposure risk, which provides a scientific basis for effectively identifying the distribution of high-risk population. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a flow chart of a regional composite heat wave population exposure risk analysis method based on multiple source data of the present application.

[0015] Figure 2 is a schematic diagram of establishing a linear regression equation of an embodiment of the present application.

[0016] Figure 3 is a schematic diagram of calculating the relative risk level of population exposure of an embodiment of the present application.

[0017] Figure 4 is a schematic diagram of a terminal device structure of an embodiment of the present application which proposes a regional composite heat wave population exposure risk analysis method based on multi-source data;

[0018] Figure 5 is a schematic diagram of a computer readable storage medium structure of an embodiment of the present application which proposes a regional composite heat wave population exposure risk analysis method based on multi-source data;

[0019] In the figure, 200 is a terminal device, 210 is a memory, 211 is a RAM, 212 is a cache memory, 213 is a ROM, 214 is a program / utility, 215 is a program module, 220 is a processor, 230 is a bus, 240 is an external device, 250 is an I / O interface, 260 is a network adapter, and 300 is a program product. DETAILED DESCRIPTION

[0020] In order for those skilled in the art to better understand the content of the present application, make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with embodiments and drawings. The illustrative embodiments of the present application and their descriptions are only used to explain the present application and do not further limit the present application.

[0021] Embodiment one

[0022] As Figure 1 , the embodiment of the present application provides a regional composite heat wave population exposure risk analysis method based on multi-source data, which comprises the following steps:

[0023] S1. Obtain daily 2m temperature observation data, monthly meteorological reanalysis data and population quantity distribution data in a target region, and construct a grid network in the target region;

[0024] S2. According to the daily 2m temperature observation data, calculate the daily maximum temperature threshold and the daily minimum temperature threshold in the grid network based on the percentile threshold method, and determine the composite heat wave event based on the daily maximum temperature threshold and the daily minimum temperature threshold, to obtain the composite heat wave frequency;

[0025] S3. According to the monthly meteorological reanalysis data, calculate the non-adiabatic heating term in the grid network based on the inverse algorithm, and vertically integrate the non-adiabatic heating term along the surface to the convective layer direction to obtain the atmospheric heat source distribution data;

[0026] S4. According to the composite heat wave frequency, the spatial distribution mode analysis of the composite heat wave frequency climate state, standard deviation, linear trend of the target area grid network is carried out with the maximum composite heat wave frequency change value as the target, the key change grid is obtained, the composite heat wave frequency of the key change grid is detrended, standardized and averaged, the composite heat wave frequency index is obtained, and the correlation coefficient of the composite heat wave frequency index and the atmospheric heat source distribution data of the key change grid is calculated, the correlation coefficient is subjected to significance test, and the atmospheric heat source of the grid area with significant correlation is subjected to detrending, standardization and grid averaging, and the atmospheric heat source index is obtained;

[0027] S5. A linear regression equation is constructed based on the atmospheric heat source index and the composite heat wave frequency, and the regression coefficient spatial distribution data in the grid network is calculated, which represents the change degree of the composite heat wave frequency of the target area caused by 1 standard deviation of the abnormal change of the atmospheric heat source;

[0028] S6. According to the regression coefficient spatial distribution data, the population distribution data is multiplied to obtain the spatial distribution of the target area composite heat wave population exposure degree caused by 1 standard deviation of the abnormal change of the atmospheric heat source, the overall situation of the target area composite heat wave population exposure degree is obtained by multiplying the composite heat wave frequency climate state calculated in S4 and the population distribution data, and the overall population exposure degree of the regional average is calculated, the composite heat wave population exposure degree caused by the change of the heat source is divided by the overall population exposure degree of the regional average, and the relative risk level of the population exposure caused by the abnormal change of the atmospheric heat source in each grid point in the target area is obtained.

[0029] Further, in step S1, daily 2m temperature observation data, monthly meteorological element reanalysis data and population gridded data in the target area of interest are obtained, the daily temperature includes daily maximum temperature and daily minimum temperature, and the monthly reanalysis data includes temperature, horizontal wind field, vertical velocity on 12 commonly used pressure layers (1000 hPa – 100 hPa), and single-layer surface pressure.

[0030] Further, step S2 includes the following sub-steps:

[0031] S201. Based on the percentile threshold method, a sliding window is constructed with 7 days before and after each calendar day, a total of 15 days, the daily maximum / minimum temperature data in the window of all study years is summarized, and the 90th percentile is calculated as the daily maximum / minimum temperature threshold of the day, and the calculation formula is:

[0032] ;

[0033] Wherein, represents the 90th percentile of the daily maximum / minimum temperature of the i-th grid on the j-th day of the k-th year. The 90th percentile of the daily maximum / minimum temperature of the i-th grid on the j-th day of the k-th year. 90th percentile of the day, represents the grid point on the year on the day temperature value, represents the calendar day, represents the total number of study years, represents the 90th percentile calculation.

[0034] S202. According to the calculated daily maximum / minimum temperature threshold, it is judged whether there is a compound heat wave event in each grid of the target area network, which can be represented as:

[0035] ;

[0036] wherein, represents whether the grid on the year on the day appears a compound extreme high temperature, when 3 days or more appear continuously, it is determined that the grid occurs a compound heat wave event, and respectively represent the daily maximum and daily minimum temperature of the grid on the year on the day, and respectively represent the 90th percentile of the daily maximum and daily minimum temperature of the grid on the day;

[0037] S203. According to the determination result of the compound heat wave event, the number of days of the compound heat wave occurring in each summer of the study period is recorded as the compound heat wave frequency, and the compound heat wave frequency is used to represent the compound heat wave event.

[0038] Specifically, the implementation principle flow of each sub-step in the above embodiment is as follows:

[0039] The 90th percentile threshold of daily maximum temperature / daily minimum temperature is calculated according to daily 2m temperature observation data, the composite heat wave is defined as an extreme high temperature event in which both the daily maximum temperature and the daily minimum temperature exceed the respective 90th percentile threshold in the same day and lasts for three days or more, and the number of days of the composite heat wave occurring in each summer of the research period is recorded as the composite heat wave frequency, which represents the composite heat wave event. Specifically, based on the percentile threshold method, a sliding window of 15 days is constructed with each calendar day as the center, the daily maximum / minimum temperature data in the window is summarized for all research years, and the 90th percentile is calculated as the daily maximum / minimum temperature threshold of the day. According to the calculated daily maximum / minimum temperature threshold, it is judged whether there is a composite heat wave event in each grid of the target regional network, and according to the result of judging the composite heat wave event, the number of days of the composite heat wave occurring in each summer of the research period is recorded as the composite heat wave frequency (unit: days), which represents the composite heat wave event.

[0040] Further, the step S3 comprises the following sub-steps:

[0041] S301. Based on the monthly meteorological reanalysis data, the non-adiabatic heating term in the vertical direction in the grid network is calculated from the temperature data, horizontal wind field data and vertical velocity data on 12 pressure layers per month, and the calculation formula is:

[0042] ;

[0043] wherein, represents the non-adiabatic heating term, represents the specific heat at constant pressure data, represents the temperature, represents the horizontal temperature gradient, represents the horizontal wind vector, represents the pressure, represents the standard atmospheric pressure, and , represents the gas constant, represents the vertical velocity, represents the potential temperature, represents the temperature change with time term, represents the temperature advection term, represents the vertical transport term;

[0044] S302. The non-adiabatic heating term is vertically integrated to obtain the atmospheric heat source distribution data, and the calculation formula is:

[0045] ;

[0046] wherein, represents the atmospheric heat source distribution data, represents the gravitational acceleration constant, Indicates air pressure The differential, Indicates surface air pressure. This represents the tropopause pressure, and .

[0047] Specifically, the implementation principle and flow of each sub-step in the above embodiments are as follows:

[0048] Based on monthly meteorological element reanalysis data, the non-adiabatic heating term in the vertical direction is calculated. This calculated non-adiabatic heating term is then vertically integrated from the surface pressure to the tropopause to obtain the heat source of the entire atmosphere. Specifically, the non-adiabatic heating term in the vertical direction is calculated using the temperature, horizontal wind field, and vertical velocity at 12 monthly pressure layers (1000 hPa – 100 hPa). The atmospheric heat source of the entire layer can be obtained by vertical integration. (Unit: W m) −2 ). The vertical integration range is determined by surface air pressure ( ) to the tropopause pressure ( = 100hPa).

[0049] Furthermore, step S4 includes the following sub-steps:

[0050] S401. Perform climatological analysis, standard deviation analysis, and linear trend analysis on the composite heat wave frequency of the target area grid network, respectively. The specific formulas are as follows:

[0051] ;

[0052] ;

[0053] ;

[0054] in, Indicates the first Climatic data of composite heat wave frequencies in 1000 lattice grids. This indicates the total number of years studied. Indicates the number of days. Indicates the first The first grid cell The frequency of compound heat waves in the day Indicates the first Standard deviation data for each raster cell. Indicates the first Linear trend data for each grid cell. Indicates the average number of days;

[0055] S402. According to the calculated composite heat wave frequency climate data, standard deviation data, linear trend data, a composite heat wave frequency change value matrix is constructed, and the maximum composite heat wave frequency change value is taken as the target, and the grid area with the maximum change value is taken as the key change grid of the composite heat wave frequency;

[0056] S403. According to the obtained key change grid of the composite heat wave frequency, the time series of the average composite heat wave frequency in the key change grid after removing the linear trend and standardizing is taken as the composite heat wave frequency index, and the calculation formula is as follows:

[0057] ;

[0058] ;

[0059] ;

[0060] wherein, represents the composite heat wave frequency after removing the linear trend, represents the composite heat wave frequency after removing the linear trend and standardizing, HWFI represents the composite heat wave frequency index, and M represents the number of grids of the key change grid;

[0061] S404. According to the composite heat wave frequency index, the correlation coefficient of the composite heat wave frequency index and the atmospheric heat source distribution data of the key change grid is calculated to obtain the correlation coefficient spatial distribution data, and the calculation formula is as follows:

[0062] ;

[0063] wherein, represents the correlation coefficient of the atmospheric heat source and the composite heat wave frequency index of the i-th grid, represents the atmospheric heat source distribution data on the i-th grid on the j-th day, represents the average value of the atmospheric heat source distribution data on the i-th grid, represents the composite heat wave frequency index on the j-th day, represents the average value of the composite heat wave frequency index; S405. According to the correlation coefficient spatial distribution data, the key change grid is subjected to significance test, the grid area passing the significance test of correlation is obtained, and the atmospheric heat source of the grid area passing the significance test is subjected to detrending, standardization and grid averaging to obtain the atmospheric heat source index.

[0064] S405. According to the correlation coefficient spatial distribution data, the key change grid is subjected to significance test, the grid area passing the significance test of correlation is obtained, and the atmospheric heat source of the grid area passing the significance test is subjected to detrending, standardization and grid averaging to obtain the atmospheric heat source index.

[0065] ​​​​Further, the significance test is performed on the key change grid. Through the t-test algorithm, the correlation coefficient is substituted into the t-distribution formula to calculate the probability. When the probability value is less than 0.05, it indicates that the correlation between the composite heat wave frequency index and the atmospheric heat source distribution data of the key change grid is significant at the 95% confidence level, so that the current grid area is determined as a grid area that passes the significance test.

[0066] Specifically, the implementation principle of each sub-step in the above embodiment is as follows:

[0067] As Figure 2 , according to the composite heat wave frequency of the key target area, the climate state, standard deviation, and linear trend are analyzed to obtain the area with the most significant change in the composite heat wave frequency in the range. The composite heat wave frequency in the area is detrended, standardized, and averaged to obtain the composite heat wave frequency index. The correlation coefficient between the composite heat wave frequency index and the atmospheric heat source of the Qinghai-Tibet Plateau is calculated to obtain the area with significant correlation. The atmospheric heat source of the grid area with significant correlation is detrended, standardized, and averaged to obtain the atmospheric heat source index. Specifically, the climate state, standard deviation, and linear trend of the composite heat wave frequency obtained in step 2 are analyzed. According to the calculated climate state, standard deviation, and linear trend of the composite heat wave frequency, the area with the maximum value is defined as the key area of the composite heat wave frequency. According to the key area of the composite heat wave frequency, the time series of the regionally averaged composite heat wave frequency in the key area after removing the linear trend and standardizing is defined as the composite heat wave frequency index. The correlation coefficient between the processed composite heat wave frequency index and the atmospheric heat source is calculated to obtain the spatial distribution of the correlation coefficient.

[0068] Further, the linear regression equation in step S5 uses the atmospheric heat source index as the independent variable and the composite heat wave frequency as the dependent variable to establish a linear regression equation between them, which has the following calculation formula: ; wherein, is the dependent variable composite heat wave frequency, is the independent variable atmospheric heat source index, is the linear regression coefficient, is the intercept. Since the independent variable is the detrended and standardized atmospheric heat source index, and the dependent variable is the composite heat wave frequency without standardization, the regression coefficient represents the absolute change in the target area composite heat wave frequency caused by an abnormal change of 1 standard deviation in the atmospheric heat source.

[0069] Further, the step S6 includes the following sub-steps:

[0070] S601. Calculate the composite heat wave population exposure degree caused by an abnormal change of 1 standard deviation in the atmospheric heat source, which has the following calculation formula:

[0071] ;

[0072] wherein, represents the population exposure to compound heat wave of the i-th grid caused by 1 standard deviation of the atmospheric heat source anomaly, represents the linear regression coefficient of the i-th grid, represents the population number of the i-th grid;

[0073] S602. Estimate the overall population exposure to compound heat wave in the target area, which is calculated as follows:

[0074] ;

[0075] wherein, represents the population exposure to compound heat wave of the i-th grid, represents the compound heat wave frequency climate state of the i-th grid; S603. Evaluate the relative risk level of the population exposure to compound heat wave in each grid point in the target area affected by the atmospheric heat source anomaly, which is calculated as follows:

[0076]

[0077] ;

[0078] wherein, represents the relative risk level of the population exposure to compound heat wave of the i-th grid affected by the atmospheric heat source anomaly, and M represents the number of grids of the key change grid. Specifically, as

[0079] , multiplying the regression coefficient of the target area with the population distribution data can obtain the spatial distribution of the population exposure to compound heat wave caused by 1 standard deviation of the atmospheric heat source anomaly. At the same time, multiplying the compound heat wave frequency climate state with the population distribution data can estimate the overall population exposure to compound heat wave in the target area. Specifically, calculate the regional average of the overall population exposure, and divide the population exposure caused by the heat source change by the average overall exposure in the region, so as to obtain the relative risk level of the population exposure in each grid point in the target area affected by the atmospheric heat source anomaly. Figure 3 Example Two

[0080]

[0081] ​​​​​​On the basis of the first embodiment, there is a regional composite heat wave population exposure risk analysis scenario based on multi-source data. Based on the atmospheric heat source data of the Qinghai-Tibet Plateau, the influence of the abnormal atmospheric heat source of the Qinghai-Tibet Plateau in summer on the composite heat wave population exposure risk is further explored.

[0082] The Qinghai-Tibet Plateau, as the world's highest and largest plateau, has a profound impact on the formation and development of East Asian monsoon through its dynamic and thermal effects. In summer, the Qinghai-Tibet Plateau serves as a large atmospheric heat source area on land. The atmospheric heat source influences the weather and climate changes in the surrounding areas and even the entire globe through the "heat pump" process. In recent years, when the overall atmospheric heat source of the Qinghai-Tibet Plateau is abnormally strong in summer, high-temperature heat wave events are prone to occur in the eastern and northwestern parts of China. When the atmospheric heat source in the eastern part of the plateau is abnormally strong in early spring, the number of composite heat wave events in the northern part of the Indian subcontinent increases. In addition, when the heat source in the southeastern part of the Qinghai-Tibet Plateau is abnormally weak, the precipitation in the downstream areas decreases abnormally.

[0083] Firstly, the daily 2m temperature observation data, the monthly reanalysis data of various meteorological elements and the population distribution data in the target area are obtained, the reanalysis data of various meteorological elements includes the meteorological elements required for calculating atmospheric heat source, and a grid network is constructed according to the target area; then, based on the percentile threshold method, the daily maximum temperature threshold and the daily minimum temperature threshold in the study period are calculated from the daily 2m temperature observation data, the composite extreme high temperature day is calculated according to the daily maximum temperature threshold and the daily minimum temperature threshold, and the composite heat wave event is statistically determined, and the number of composite heat wave occurrence days is defined as the composite heat wave frequency; then, based on the reverse calculation method, the non-adiabatic heating term in the vertical direction of the Qinghai-Tibet Plateau is calculated according to the monthly meteorological reanalysis data, the calculated non-adiabatic heating term is vertically integrated from the surface pressure to the top of the troposphere to obtain the whole-layer atmospheric heat source; subsequently, the spatial distribution mode of the climatological state, the standard deviation and the linear trend of the composite heat wave frequency in the target area is analyzed, the region with the most significant change of the composite heat wave frequency is obtained, the composite heat wave frequency in the region is detrended, standardized and regionally averaged to obtain the composite heat wave frequency index, the correlation coefficient between the composite heat wave frequency index and the atmospheric heat source of the Qinghai-Tibet Plateau is calculated, and the region with significant correlation is obtained, and the atmospheric heat source in the region is also detrended, standardized and regionally averaged to extract the atmospheric heat source index; the linear regression equation between the atmospheric heat source index as the independent variable and the composite heat wave frequency as the dependent variable is established, and the spatial distribution of the regression coefficient in the target area is obtained, which represents the change degree of the target area composite heat wave frequency caused by 1 standard deviation of the abnormal change of the atmospheric heat source; finally, the regression coefficient of the target area is multiplied by the population distribution data to obtain the spatial distribution of the target area composite heat wave population exposure degree caused by 1 standard deviation of the abnormal change of the atmospheric heat source, the overall population exposure degree of the target area composite heat wave is obtained by multiplying the climatological state of the composite heat wave frequency and the population distribution data, and the overall population exposure degree is calculated. The regional average, the composite heat wave population exposure degree caused by the change of the heat source is divided by the overall population exposure degree of the regional average, and the relative risk level of the population exposure caused by the abnormal change of the atmospheric heat source in each grid point in the target area is obtained.

[0084] Embodiment three

[0085] As Figure 4 On the basis of embodiment 1, the terminal device of the regional composite heat wave population exposure risk analysis method based on multi-source data is proposed, and the terminal device 200 includes at least one memory 210, at least one processor 220 and a bus 230 connecting different platform systems.

[0086] The memory 210 can include a readable medium in the form of a volatile memory, such as a RAM 211 and / or a cache memory 212, and can further include a ROM 213.

[0087] The memory 210 also stores a computer program, which can be executed by the processor 220, so that the processor 220 executes any one of the above-mentioned methods for analyzing regional composite heat wave population exposure risk based on multi-source data according to the embodiments of the present application. The specific implementation manners and the achieved technical effects are consistent with those described in the embodiments of the above-mentioned methods, and some contents will not be described herein. The memory 210 can also include programs / utilities 214 having a set of (at least one) program modules 215, which include but are not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof can include implementation of a network environment.

[0088] Correspondingly, the processor 220 can execute the above-mentioned computer program, and can execute the programs / utilities 214.

[0089] The bus 230 can represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures.

[0090] The terminal device 200 can also communicate with one or more external devices 240, such as a keyboard, a pointing device, a Bluetooth device, etc., and can also communicate with one or more devices that enable interaction with the terminal device 200, and / or with any devices (such as routers, modems, etc.) that enable the terminal device 200 to communicate with one or more other computing devices. Such communication can be carried out through the I / O interface 250. In addition, the terminal device 200 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) through the network adapter 260. The network adapter 260 can communicate with other modules of the terminal device 200 through the bus 230. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in conjunction with the terminal device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0091] Embodiment Four

[0092] As Figure 5On the basis of Embodiment 1, this embodiment provides a computer readable storage medium of a regional composite heat wave population exposure risk analysis method based on multi-source data, and the computer readable storage medium stores instructions. The instructions are executed by a processor to implement any one of the above-mentioned regional composite heat wave population exposure risk analysis methods based on multi-source data. The specific implementation manners and the achieved technical effects are consistent with those described in the embodiments of the above-mentioned methods, and some contents will not be described again.

[0093] The program product 300 for implementing the above-mentioned method provided by the embodiment can adopt a portable compact disc read-only memory (CD-ROM) and include a program code, and can run on a terminal device such as a personal computer. However, the program product 300 of the present application is not limited to this. In the embodiment, the readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, device or apparatus. The program product 300 can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0094] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. Program code for performing operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as a standalone software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet using an Internet service provider).

[0095] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing the population exposure risk of regional complex heat waves based on multi-source data, characterized in that, Includes the following steps: S1. Acquire daily 2m temperature observation data, monthly meteorological reanalysis data, and population distribution data within the target area, and construct a grid network for the target area; S2. Based on the daily 2m temperature observation data, the daily maximum temperature threshold and daily minimum temperature threshold within the grid network are calculated using the percentile threshold method. Based on the daily maximum temperature threshold and daily minimum temperature threshold, the composite heat wave event is determined, and the composite heat wave frequency is obtained. S3. Based on monthly meteorological reanalysis data, the non-adiabatic heating term in the grid network is calculated using the inverted algorithm, and the non-adiabatic heating term is vertically integrated along the direction from the surface to the troposphere to obtain atmospheric heat source distribution data. S4. Based on the composite heat wave frequency, with the maximum variation value of the composite heat wave frequency as the objective, perform spatial distribution mode analysis of the composite heat wave frequency climatology, standard deviation, and linear trend of the target area grid network to obtain key change grids. Perform detrending, standardization, and grid averaging on the composite heat wave frequency of the key change grids to obtain the composite heat wave frequency index. Calculate the correlation coefficient between the composite heat wave frequency index and the atmospheric heat source distribution data of the key change grids. Perform a significance test on the correlation coefficient. For the grid areas where the correlation passes the significance test, perform detrending, standardization, and grid averaging on the atmospheric heat sources to obtain the atmospheric heat source index. S5. A linear regression equation is constructed based on the atmospheric heat source index and the frequency of the composite heat wave, and the spatial distribution data of the regression coefficients in the grid network are calculated. The regression coefficients represent the degree of change in the frequency of the composite heat wave in the target area caused by one standard deviation of the abnormal change in the atmospheric heat source. S6. Based on the spatial distribution data of the regression coefficients, multiply them with the population distribution data to obtain the spatial distribution of the population exposure to the composite heat wave in the target area caused by one standard deviation of the abnormal change in atmospheric heat source. Based on the composite heat wave frequency climatology in S4, multiply it with the population distribution data to obtain the overall situation of the population exposure to the composite heat wave in the target area, and calculate the regional average total population exposure. Divide the population exposure to the composite heat wave caused by the change in heat source by the regional average total population exposure to obtain the relative risk level of population exposure caused by the abnormal change in atmospheric heat source for each grid point in the target area.

2. The method for analyzing the population exposure risk of regional composite heat waves based on multi-source data according to claim 1, characterized in that, Step S2 includes the following steps: S201. Based on the percentile threshold method, a sliding window is constructed with each calendar day as the center, taking 7 days before and after it, for a total of 15 days. The daily maximum / minimum temperature data within this window for all research years are summarized, and the 90th percentile is calculated as the daily maximum / minimum temperature threshold for that day. The calculation formula is as follows: ； in, Indicates the first The first grid cell The 90th percentile corresponding to the day, Indicates the first The first grid point Year The temperature value of the day, Indicates calendar day, This indicates the total number of years studied. This indicates the calculation of the 90th percentile; S202. Based on the calculated daily maximum / minimum temperature thresholds, determine whether a composite heat wave event occurs within each grid cell of the target area network, which can be expressed as: ； in, Indicates the first The first grid cell Year Whether there will be a combination of extreme high temperatures, when If a heat wave event occurs for three or more consecutive days, the grid is considered to have experienced a composite heat wave event. and They represent the first The first grid cell Year The day's highest and lowest temperatures, and They represent the first The first grid cell The 90th percentile of the day's highest and lowest temperatures; S203. Based on the results of determining the complex heat wave event, the number of days of complex heat waves that occur each summer during the statistical study period is recorded as the complex heat wave frequency, and the complex heat wave frequency is used to characterize the complex heat wave event.

3. The method for analyzing regional composite heat wave population exposure risk based on multi-source data according to claim 1, characterized in that, Step S3 includes the following steps: S301. Based on monthly meteorological reanalysis data, using temperature data, horizontal wind field data, and vertical velocity data from 12 pressure layers each month, calculate the non-adiabatic heating term in the vertical direction within the grid network. The calculation formula is as follows: ； in, Indicates a non-adiabatic heating term. This represents the specific heat at constant pressure. Indicates temperature. Represents the horizontal temperature gradient. Represents the horizontal wind vector. Indicates air pressure. Indicates standard atmospheric pressure, and , Represents the gas constant. Indicates vertical velocity. Indicates the temperature. This represents the term indicating the change in temperature over time. Represents the temperature advection term. Indicates vertical transport items; S302. Perform vertical integration on the non-adiabatic heating term to obtain atmospheric heat source distribution data. The calculation formula is as follows: ； in, This represents data on the distribution of atmospheric heat sources. Represents the gravitational acceleration constant. Indicates air pressure The differential, Indicates surface air pressure. This represents the tropopause pressure, and .

4. The method for analyzing regional composite heat wave population exposure risk based on multi-source data according to claim 1, characterized in that, Step S4 includes the following steps: S401. Perform climatological analysis, standard deviation analysis, and linear trend analysis on the composite heat wave frequency of the target area grid network, respectively. The specific formulas are as follows: ； ； ； in, Indicates the first Climatic data of composite heat wave frequencies in 1 grid, This indicates the total number of years studied. Indicates the number of days. Indicates the first The first grid cell The frequency of compound heat waves in the day Indicates the first Standard deviation data for each raster cell. Indicates the first Linear trend data for each grid cell. Indicates the average number of days; S402. Based on the calculated climatological data, standard deviation data, and linear trend data of the composite heat wave frequency, construct a composite heat wave frequency change value matrix, and take the maximum composite heat wave frequency change value as the target, and select the grid area with the maximum change value as the key change grid of the composite heat wave frequency. S403. Based on the obtained key change grid of composite heat wave frequency, the time series of the average composite heat wave frequency within the key change grid after removing the linear trend and standardizing is used as the composite heat wave frequency index, and its calculation formula is as follows: ； ； ； in, This represents the frequency of the composite heat wave after removing the linear trend. This represents the composite heatwave frequency after removing linear trends and standardizing; HWFI represents the composite heatwave frequency index; and M represents the number of grid cells in the key change grid. S404. Based on the composite heat wave frequency index, calculate the correlation coefficient between the composite heat wave frequency index and the atmospheric heat source distribution data of the key change grid. The calculation formula is as follows: ； in, Indicates the first The correlation coefficient between atmospheric heat sources in each grid and the composite heat wave frequency index. Indicates the first The first grid cell Atmospheric heat source distribution data for the day, Indicates the first The average value of atmospheric heat source distribution data on each grid cell Indicates the first The frequency index of compound heat waves over the days This represents the average value of the composite heat wave frequency index; S405. Based on the spatial distribution data of correlation coefficients, a significance test is performed on the key change grids to obtain the grid regions whose correlations pass the significance test. Then, the atmospheric heat sources in the grid regions that pass the significance test are detrended, standardized, and averaged to obtain the atmospheric heat source index.

5. The method for analyzing regional composite heat wave population exposure risk based on multi-source data according to claim 4, characterized in that, The significance test for key change grids is performed by using the t-test algorithm. The correlation coefficient is substituted into the t-distribution formula to calculate the probability. When the probability value is less than 0.05, it indicates that the correlation between the composite heat wave frequency index and the atmospheric heat source distribution data of the key change grids is significant at a 95% confidence level. Thus, the current grid area is identified as a grid area that has passed the significance test.

6. The method for analyzing the population exposure risk of regional composite heat waves based on multi-source data according to claim 1, characterized in that, The linear regression equation described in step S5 is expressed as follows: ； in, The dependent variable is the composite heat wave frequency. Atmospheric heat source index is the independent variable. These are the linear regression coefficients. This is the intercept.

7. The method for analyzing regional composite heat wave population exposure risk based on multi-source data according to claim 1, characterized in that, Step S6 includes the following steps: S601. Calculate the population exposure to a composite heat wave caused by one standard deviation of anomaly in atmospheric heat sources. The calculation formula is as follows: ； in, This indicates the first standard deviation caused by an abnormal change in atmospheric heat source. Composite heat wave population exposure per grid, Indicates the first The linear regression coefficients of each grid cell, Indicates the first Population of each grid cell; S602. Estimate the overall population exposure to the combined heat wave in the target area using the following formula: ； in, Indicates the first Composite heat wave population exposure per grid, Indicates the first The composite heat wave frequency climate pattern of each grid; S603. Evaluate the relative risk level of population exposure to complex heat waves at each grid point within the target area affected by abnormal changes in atmospheric heat sources. The calculation formula is as follows: ； in, Indicates the first The relative risk level of population exposure to complex heat waves affected by anomalous changes in atmospheric heat sources for each grid, where M represents the number of grids with critical changes.

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