Regional composite hot wave population exposure risk analysis method based on multi-source data

Abstract

The invention relates to the technical field of climate event analysis, in particular to a regional composite thermal wave population exposure risk analysis method based on multi-source data. The method comprises the following steps: acquiring day-by-day temperature observation data, month-by-month reanalysis data of various meteorological elements and population distribution data in a target area; judging a composite heat wave event by adopting a percentile threshold method, and counting the composite heat wave frequency; on the basis of an inverse algorithm, calculating a whole-layer atmospheric heat source according to monthly meteorological reanalysis data; analyzing the correlation between the composite heat wave frequency in the target area and an atmospheric heat source, and establishing a linear regression equation; and calculating the population exposure degree of the composite heat wave in the target area caused by the abnormal change of the atmospheric heat source, and quantitatively evaluating the population exposure risk level. The method disclosed by the invention reveals the formation mechanism of the composite heat wave event from a new perspective, assists in improving the prediction capability, provides a solid scientific basis for accurately identifying the distribution of related high-risk crowds, and has important scientific significance and application prospects.

Description

Technical Field

[0001] This invention relates to the field of climate event analysis technology, and in particular to a method for analyzing the population exposure risk of regional complex heat waves based on multi-source data. Background Technology

[0002] In-depth research into the impact of atmospheric heat source anomalies on the population exposure risk of complex heat waves is of significant scientific importance and research value. Currently, research on identifying the relevant driving factors of complex heat wave events on the population exposure risk remains relatively limited. Furthermore, the mechanism by which atmospheric heat source regions influence complex heat waves lacks systematic quantitative analysis. Existing methods mostly focus on single high-temperature events or localized effects, making it difficult to comprehensively reveal their regulatory role on population exposure risk. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a method for analyzing the population exposure risk of regional composite heat waves based on multi-source data, comprising the following steps:

[0004] 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;

[0005] 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.

[0006] S3. Based on monthly meteorological reanalysis data, the non-adiabatic heating term in the vertical direction within the grid network is calculated using the inverted algorithm. The non-adiabatic heating term is then vertically integrated along the direction from the Earth's surface to the troposphere to obtain atmospheric heat source distribution data.

[0007] 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.

[0008] 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.

[0009] 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 calculated 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 at each grid point in the target area.

[0010] The beneficial effects of this invention are:

[0011] (1) Based on the daily maximum and minimum temperatures, the target area is defined as a diurnal heat wave, which is different from the traditional heat waves determined only by the daily maximum temperature. It is not only more extreme, but also takes into account the more serious harm to the human body caused by the simultaneous occurrence of high temperatures during the day and night. Analyzing its evolution characteristics and influence mechanism helps to deepen the understanding of extreme high temperature events in the region, which has important scientific significance and extremely high application value.

[0012] (2) This invention constructs key factors that affect the frequency of composite heat waves in the target area, namely, the frequency of composite heat waves in the target area is significantly correlated with atmospheric heat sources. Based on this correlation, a linear regression equation is established to quantify the degree of change in the frequency of composite heat waves in the target area caused by abnormal changes in atmospheric heat sources. This effectively establishes a statistical model of the influence of atmospheric heat sources on composite heat waves, providing a new perspective for a deeper understanding of its formation mechanism and improving the ability to predict extreme high temperature events.

[0013] (3) Based on the population exposure of the composite heat wave, this invention analyzes the changes in population exposure of the composite heat wave in the target area caused by abnormal changes in atmospheric heat sources. At the same time, in order to further quantitatively analyze the impact of abnormal changes in atmospheric heat sources on the composite heat wave in the target area, a population exposure relative risk level evaluation method is introduced. The severity of population exposure to the composite heat wave caused by abnormal changes in atmospheric heat sources is divided, the risk level distribution is quantified in a more refined manner, and the spatial difference characteristics of population exposure risk are revealed, providing a scientific basis for effectively identifying the distribution of high-risk groups. Attached Figure Description

[0014] Figure 1 This is a flowchart of a method for analyzing the population exposure risk of regional composite heat waves based on multi-source data, according to the present invention.

[0015] Figure 2 This is a schematic diagram illustrating the establishment of a linear regression equation according to an embodiment of the present invention.

[0016] Figure 3 This is a schematic diagram illustrating the calculation of the relative risk level of population exposure in an embodiment of the present invention.

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

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

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

[0020] To enable those skilled in the art to better understand the present invention and to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are for illustrative purposes only and are not intended to further limit the present invention.

[0021] Example 1

[0022] like Figure 1 This invention provides a method for analyzing the population exposure risk of regional complex heat waves based on multi-source data, including the following steps:

[0023] 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;

[0024] 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.

[0025] S3. Based on monthly meteorological reanalysis data, the non-adiabatic heating term in the grid network is calculated using the inverted algorithm. The non-adiabatic heating term is then vertically integrated along the direction from the Earth's surface to the troposphere to obtain atmospheric heat source distribution data.

[0026] 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.

[0027] 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.

[0028] 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 calculated 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 at each grid point in the target area.

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

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

[0031] 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:

[0032] ;

[0033] 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.

[0034] 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:

[0035] ;

[0036] 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;

[0037] S203. Based on the results of determining the complex heat wave event, the number of days that a complex heat wave occurs 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.

[0038] Specifically, the implementation principle and flow of each sub-step in the above embodiments are as follows:

[0039] Based on daily 2m temperature observation data, the 90th percentile threshold of daily maximum / minimum temperature is calculated. A composite heat wave is defined as an extreme high-temperature event in which both the daily maximum and minimum temperatures exceed their respective 90th percentile thresholds for three days or more on the same day. The number of days with composite heat waves occurring each summer during the study period is recorded as the composite heat wave frequency. The composite heat wave frequency is used to characterize composite heat wave events. Specifically, 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 study years are summarized, and its 90th percentile is calculated as the daily maximum / minimum temperature threshold for that day. Based on the calculated daily maximum / minimum temperature threshold, it is determined whether a composite heat wave event has occurred in each grid cell of the target area network. Based on the determination of composite heat wave events, the number of days with composite heat waves occurring each summer during the study period is recorded as the composite heat wave frequency (unit: days). The composite heat wave frequency is used to characterize composite heat wave events.

[0040] Furthermore, step S3 includes the following sub-steps:

[0041] 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:

[0042] ;

[0043] 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;

[0044] S302. Perform vertical integration on the non-adiabatic heating term to obtain atmospheric heat source distribution data. The calculation formula is as follows:

[0045] ;

[0046] 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 .

[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: Wm) −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. 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.

[0056] 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:

[0057] ;

[0058] ;

[0059] ;

[0060] 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.

[0061] 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, and obtain the spatial distribution data of the correlation coefficient. The calculation formula is as follows:

[0062] ;

[0063] 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;

[0064] 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.

[0065] Furthermore, the significance test of the key change grid is performed by substituting the correlation coefficient into the t-distribution formula using the t-test algorithm 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 a 95% confidence level, thus identifying the current grid area as a grid area that has passed the significance test.

[0066] Specifically, the implementation principle and flow of each sub-step in the above embodiments are as follows:

[0067] like Figure 2 Based on the frequency of composite heat waves in key target areas, its climatology, standard deviation, and linear trend are analyzed to identify the region with the most significant frequency changes within that range. The composite heat wave frequencies within this region are then detrended, standardized, and averaged regionally to obtain a composite heat wave frequency index. The correlation coefficient between this index and atmospheric heat sources from the Tibetan Plateau is calculated, identifying regions where the correlation passes a significance test. For the atmospheric heat sources in the gridded regions where the correlation passes the significance test, detrending, standardization, and grid averaging are performed to obtain an atmospheric heat source index. Specifically, the composite heat wave frequencies obtained in step 2 are analyzed for climatology, standard deviation, and linear trend. Based on the calculated climatology, standard deviation, and linear trend of the composite heat wave frequencies, the region with the largest value is defined as the key region for composite heat wave frequencies. Based on the key regions for composite heat wave frequencies, the time series of the regional average composite heat wave frequency within the key regions 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 atmospheric heat sources is calculated, yielding the spatial distribution of the correlation coefficient.

[0068] Furthermore, in step S5, the linear regression equation 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 the two. The calculation formula is 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. The intercept is given. Since the independent variable is the detrended and standardized atmospheric heat source index, and the dependent variable is the unstandardized composite heat wave frequency, the regression coefficients are... This represents the absolute change in the frequency of the composite heat wave in the target area caused by one standard deviation of an abnormal change in the atmospheric heat source.

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

[0070] 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:

[0071] ;

[0072] 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;

[0073] S602. Estimate the overall population exposure to the combined heat wave in the target area using the following formula:

[0074] ;

[0075] in, Indicates the first Composite heat wave population exposure per grid, Indicates the first The composite heat wave frequency climate pattern of each grid;

[0076] 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:

[0077] ;

[0078] 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.

[0079] Specifically, such as Figure 3 Multiplying the regression coefficients of the target area by the population distribution data yields the spatial distribution of population exposure to the complex heat wave caused by one standard deviation of anomalous atmospheric heat source changes. Simultaneously, multiplying the climatological data of the complex heat wave frequency by the population distribution data estimates the overall population exposure to the complex heat wave in the target area. Specifically, calculating the regional average of the overall population exposure and dividing the population exposure caused by heat source changes by the regional average overall exposure yields the relative risk level of population exposure at each grid point within the target area affected by anomalous atmospheric heat source changes.

[0080] Example 2

[0081] Based on Example 1, there is a scenario for analyzing the population exposure risk of regional complex heat waves based on multi-source data. Using atmospheric heat source data from the Tibetan Plateau as a basis, this study explores in depth the impact of summer atmospheric heat source anomalies on the population exposure risk of complex heat waves.

[0082] The Qinghai-Tibet Plateau, as the world's highest and largest plateau, profoundly influences the formation and development of the East Asian monsoon through its dynamic and thermal effects. In summer, the Qinghai-Tibet Plateau acts as a massive atmospheric heat source region, influencing weather and climate change in its surrounding areas and even globally through a "heat pump" process. In recent years, when the overall atmospheric heat source over the Qinghai-Tibet Plateau is abnormally strong in summer, high-temperature heat waves are prone to occur in eastern and northwestern my country; conversely, when the atmospheric heat source is stronger in the eastern part of the plateau in early spring, complex heat waves in the northern Indian subcontinent increase. Furthermore, when the heat source in the southeastern part of the Qinghai-Tibet Plateau is abnormally weak, precipitation in its downstream areas is abnormally reduced.

[0083] First, daily 2-meter temperature observation data, monthly reanalysis data of various meteorological elements, and population distribution data were acquired within the target area. The reanalysis data of various meteorological elements included those required for calculating atmospheric heat sources. A grid network was constructed based on the target area. Then, using the percentile threshold method, the daily maximum and minimum temperature thresholds for the study period were calculated from the daily 2-meter temperature observation data. Based on these thresholds, composite extreme high-temperature days were calculated, and composite heat wave events were statistically determined. The number of days with composite heat waves was defined as the composite heat wave frequency. Next, using a reciprocal algorithm, the non-adiabatic heating term in the vertical direction of the Tibetan Plateau was calculated from the monthly meteorological reanalysis data. The calculated non-adiabatic heating term was vertically integrated from the surface pressure to the tropopause to obtain the entire atmospheric heat source. Subsequently, the spatial distribution modes of the composite heat wave frequency climatology, standard deviation, and linear trend within the target area were analyzed. The region with the most significant changes in composite heat wave frequency was identified. The composite heat wave frequency within this region was detrended, standardized, and averaged regionally 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 Tibetan Plateau was calculated, and the regions where the correlation passed the significance test were identified. Similarly, the atmospheric heat source in these regions was detrended, standardized, and the atmospheric heat source index was extracted using a regional average. A linear regression equation was established between the atmospheric heat source index as the independent variable and the composite heat wave frequency as the dependent variable, yielding the spatial distribution of the regression coefficients within the target region. These regression coefficients represent the degree of change in the composite heat wave frequency in the target region caused by a one-standard-deviation change in the atmospheric heat source. Finally, the regression coefficients of the target region were multiplied by the population distribution data to obtain the spatial distribution of the population exposure to the composite heat wave in the target region caused by a one-standard-deviation change in the atmospheric heat source. The overall situation of the population exposure to the composite heat wave in the target region was obtained by multiplying the composite heat wave frequency climatology with the population distribution data, and the regional average overall population exposure was calculated. The population exposure to the composite heat wave caused by the heat source change was divided by the regional average overall population exposure to obtain the relative risk level of population exposure caused by the abnormal change in the atmospheric heat source at each grid point within the target region.

[0084] Example 3

[0085] like Figure 4 Based on Example 1, this example proposes a terminal device for a regional composite heat wave population exposure risk analysis method based on multi-source data. 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 may include a readable medium in the form of volatile memory, such as RAM 211 and / or cache memory 212, and may further include ROM 213.

[0087] The memory 210 also stores a computer program that can be executed by the processor 220, causing the processor 220 to execute any of the above-described methods for analyzing the regional composite heat wave population exposure risk based on multi-source data in the embodiments of this application. The specific implementation and technical effects achieved are consistent with those described in the embodiments of the above methods, and some details will not be repeated here. The memory 210 may also include a program / utility 214 having a set (at least one) of program modules 215. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0088] Accordingly, processor 220 can execute the aforementioned computer program, as well as executable program / utility 214.

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

[0090] Terminal device 200 can also communicate with one or more external devices 240, such as keyboards, pointing devices, Bluetooth devices, etc., and with one or more devices capable of interacting with it, and / or with any device that enables it to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via I / O interface 250. Furthermore, terminal device 200 can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 260. Network adapter 260 can communicate with other modules of terminal device 200 via bus 230. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with 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.

[0091] Example 4

[0092] like Figure 5Based on Embodiment 1, this embodiment proposes a computer-readable storage medium for a method of analyzing the population exposure risk of regional complex heat waves based on multi-source data. The computer-readable storage medium stores instructions that, when executed by a processor, implement any of the aforementioned methods of analyzing the population exposure risk of regional complex heat waves based on multi-source data. The specific implementation method and the achieved technical effects are consistent with those described in the embodiments of the above methods, and some details will not be repeated.

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

[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. The non-adiabatic heating term is then vertically integrated along the direction from the Earth's 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 that a complex heat wave occurs 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 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; 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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