Summer sunstroke prevention climate index evaluation and forecasting method, medium and program product

Abstract

The invention discloses a summer sunstroke prevention climate index evaluation and forecasting method, a medium and a program product, and belongs to the technical field of climate environment evaluation and weather forecast, the method comprises the following steps: constructing a sunstroke prevention climate index evaluation index model, and setting second-level indexes such as air temperature, humidity, rainfall, wind and human body comfort and a plurality of third-level indexes; obtaining summer meteorological data of the target area, and calculating a human body comfort index; calculating and carrying out normalization processing on the three-level index values; calculating a secondary index value; a weighted calculation formula is adopted to obtain a sunstroke prevention climate index; performing quantitative grade evaluation on the target area according to the sunstroke prevention climate index, and generating a spatial distribution map; future meteorological element forecast data are obtained, grid division is carried out on the target area, and spatial continuous forecast is formed. The problem that it is difficult to quantitatively evaluate and forecast summer sunstroke prevention climate conditions is solved, gridding evaluation and forecasting of the sunstroke prevention climate indexes are achieved, the construction cost is low, practicability is high, and the method is suitable for application and popularization.

Description

Technical Field

[0001] The present invention belongs to the field of climate environment assessment and weather forecast technology, and relates to a quantitative assessment and grid forecasting technology for summer climate conditions. Specifically, it is a summer climate index assessment and forecasting method, medium and program product. Through the construction and normalization of multi-dimensional meteorological indicators, combined with meteorological observation data and climate models, accurate assessment and spatial continuous forecasting of summer climate conditions are achieved. Background Art

[0002] Climate is the natural environment on which human beings depend for survival and has an important impact on human life. Its complexity and regional characteristics make climate research a core research field in earth science, environmental science and meteorology. Against the background of global warming, the frequency, number of days and intensity of high temperature heat wave events are generally increasing and strengthening. High temperature heat waves have adverse effects on human health. With the increasingly prominent problem of population aging, the demand for summer heat escape is increasing. The existing research methods used for the assessment of summer climate conditions can be divided into three categories: empirical models, mechanism models and indicator systems. Each method has its own advantages in theoretical basis and practical application, but it has obvious limitations in dealing with complex terrain conditions, regional differences and comprehensive analysis of multiple meteorological factors.

[0003] Empirical models are generally linear equations about temperature, humidity and wind speed, which reflect the impact of climate conditions on the human body through simple calculations. Typical representatives of this type of model include perceived temperature, effective temperature, temperature and humidity index, human comfort index, etc. For example, researchers have evaluated the spatiotemporal distribution characteristics of the number of summer vacation days in a specific area and the suitability of summer vacation destinations through perceived temperature or human comfort index models. However, the core problem of empirical models is that they consider a limited number of meteorological factors, usually only involving basic elements such as temperature, humidity and wind speed. Due to the neglect of precipitation, topography and the diversity of regional climate, empirical models are difficult to fully reflect the summer vacation climate conditions in complex environments.

[0004] The mechanism model mainly uses the Universal Thermal Climate Index (UTCI) model, which is based on the principle of human heat balance and meteorological data to simulate the human perceived temperature in an actual thermal environment based on a standard reference environment. The UTCI model integrates multiple meteorological factors such as temperature, humidity, wind speed and radiation, and calculates the human perceived temperature under standardized conditions. However, the UTCI model requires solar radiation observation data as input, while the number of solar radiation observation sites across the country is limited (only about 130), and they do not completely correspond to the locations of conventional meteorological sites, making it difficult to carry out assessment work in large areas where radiation observation data is lacking. The use of spatial interpolation technology to infer the radiation values ​​of unobserved areas is prone to introduce large errors in the interpolation process, especially under complex terrain conditions. In addition, the standard reference conditions of the UTCI model do not fully consider the unique geographical and climatic characteristics of each region, such as the impact of complex terrain such as mountains and basins on climate factors.

[0005] In recent years, with the deepening of the understanding of summer climate, the evaluation method of summer climate index system has developed rapidly. Such methods usually construct a multi-level evaluation index system, comprehensively considering multiple dimensions such as climate comfort, average climate conditions, high temperature days, ecological environment, etc. In addition, such methods are easy to calculate and can be applied to large-scale regional assessments, but the comprehensive quantitative evaluation is insufficient, making it difficult to form an effective, large-scale, and spatially continuous forecast of summer climate conditions.

[0006] In summary, the existing summer climate condition assessment and forecasting technologies have problems such as strong model dependence, poor data applicability, insufficient regional spatial continuity, and difficulty in meeting the requirements of low cost and wide applicability. Therefore, there is an urgent need for a quantitative summer climate condition assessment and forecasting method with low construction cost, no geographical restrictions, and can be promoted in space. Summary of the invention

[0007] 1. Purpose of the invention In view of the above-mentioned defects and deficiencies of the prior art, in order to solve at least one of the above-mentioned and other technical problems, the present invention aims to provide a summer climate index assessment and forecasting method, medium and program product, by constructing a summer climate index model including multi-dimensional indicators such as temperature, humidity, precipitation, wind speed and human comfort, and combining meteorological observation data with climate system model prediction results, to achieve accurate quantitative assessment and spatial continuous forecast of summer climate conditions. The present invention adopts normalization processing and grid calculation methods, without relying on specific meteorological observation sites, and can be reliably evaluated and promoted in complex terrain and data-missing areas. Through this method, not only can the assessment accuracy and forecasting efficiency of summer climate conditions be significantly improved, but also the construction and operation costs can be greatly reduced. It has the characteristics of strong practicality and wide adaptability, and meets the actual needs of regional summer climate research and tourism planning.

[0008] (II) Technical solution In order to achieve the purpose of the invention and solve the technical problems, the present invention adopts the following technical solutions: The first invention object of the present invention is to provide a summer climate index assessment and forecasting method for quantitatively assessing and spatially continuously forecasting the summer climate conditions in a target area. The method at least comprises the following steps when implemented: SS1. Construct a summer climate index evaluation index model, including: The summer vacation climate index is taken as the first-level index, and according to the impact mechanism of summer vacation climate conditions on human comfort, at least five second-level indexes are set, namely, temperature, humidity, precipitation, wind and human comfort. Among them, the third-level index under the second-level index of temperature at least includes the number of days with suitable human temperature, the number of days with daily minimum temperature less than 22℃, the number of days with daily maximum temperature less than 28℃, the number of days with daily temperature difference greater than or equal to 8℃, the average temperature, the average minimum temperature, the average maximum temperature and the average daily temperature difference. The third-level index under the second-level index of humidity at least includes the average relative humidity and the number of days with suitable human humidity. The third-level index under the second-level index of precipitation at least includes the number of days with suitable precipitation. The third-level index under the second-level index of wind at least includes the number of days with suitable wind. The third-level index under the second-level index of human comfort at least includes the number of days with comfortable human body. SS2. Obtain summer meteorological data for the target area, including: Obtain daily meteorological data for the target area during the summer period, including at least daily average temperature, maximum temperature, minimum temperature, relative humidity, precipitation, and average wind speed data; calculate daily range data based on daily maximum and minimum temperatures; and calculate human comfort index data based on daily average temperature, relative humidity, and wind speed. I The calculation formula is:

[0009] in, Tis the daily average temperature, H is the daily average relative humidity, V is the average wind speed, and when the human comfort index I Satisfy 51≤ I When the temperature is less than 76, the day is considered a comfortable day for human body; SS3. Calculate the values ​​of the third-level indicators, including: Calculate the values ​​of the three-level indicators based on the summer meteorological data of the target area obtained in step SS2, including calculating the cumulative value of the number of days with suitable temperature for human body, the number of days with daily minimum temperature less than 22°C, the number of days with daily maximum temperature less than 28°C, the number of days with daily temperature difference greater than or equal to 8°C, the number of days with suitable humidity for human body, the number of days with suitable precipitation, the number of days with suitable wind and the number of days with human comfort, and calculate the average value of the summer average temperature, the average minimum temperature, the average maximum temperature and the average daily temperature difference; SS4. Normalize the three-level indicator values, including: First, according to the indicator attributes, each third-level indicator is classified into one of the three indicator types: positive indicator, negative indicator and interval indicator. Then, each third-level indicator is normalized according to the indicator type. For the positive indicator, the formula Normalize and use the formula for the reverse indicator Normalize and use the formula for interval indicators based on the relationship between the indicator value and the optimal interval Normalize, in the above formulas, x is the original value of the indicator, x ' is the normalized index value, x min and x max are the minimum and maximum values ​​of the normalized parameters, q 1 and q 2 are the minimum and maximum values ​​of the optimal interval of the indicator respectively; SS5. Calculate the secondary indicator values, including: According to the categories of the secondary indicators of temperature, humidity, precipitation, wind and human comfort, the arithmetic mean of the normalized values ​​of the three-level indicators are calculated to obtain the corresponding secondary indicator values. The secondary indicator value of temperature is the arithmetic mean of the normalized values ​​of the three-level indicators of temperature, namely, the number of days with suitable human temperature, the number of days with daily minimum temperature less than 22°C, the number of days with daily maximum temperature less than 28°C, the number of days with daily temperature difference greater than or equal to 8°C, the average temperature, the average minimum temperature, the average maximum temperature and the average daily temperature difference. The secondary indicator values ​​of humidity, precipitation, wind and human comfort are calculated as the arithmetic mean of the normalized values ​​of the corresponding categories. SS6. Calculate the summer climate index, including: Using weighted calculation formula , combined with the values ​​of each secondary index to calculate the summer climate index of the target area I c ,in I t , I h , I p ,I w and I s are the normalized secondary index values ​​of temperature, humidity, precipitation, wind and human comfort, w t , w h , w p , w w and w s They are the weight coefficients of the secondary indicators of temperature, humidity, precipitation, wind and human comfort; SS7. Evaluate the summer climate level, including: According to the calculated summer climate index I c , compared with the preset summer resort grade threshold range, the summer resort climate conditions in the target area are quantitatively evaluated, and the spatial distribution is visualized based on the evaluation results. The GIS (geographic information system) technology is used to generate the summer resort climate grade spatial distribution map in the target area, where the summer resort grade threshold range divides the summer resort climate conditions into seven levels: very poor, poor, relatively poor, average, relatively good, good and very good; SS8. Conduct summer weather forecasts, including: Obtain the forecast data of future summer meteorological elements in the target area, grid the target area based on its geographical scope and the resolution of meteorological data, repeat steps SS3 to SS7, obtain the forecast results of summer climate index and summer grade of each grid unit, and form a gridded spatial continuous forecast of the target area.

[0010] The second invention object of the present invention is to provide a computer program product, including computer instructions, for executing the above-mentioned summer climate index evaluation and forecasting method.

[0011] The third invention object of the present invention is to provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned summer climate index evaluation and forecasting method is implemented.

[0012] (III) Technical Effect Compared with the prior art, the summer climate index evaluation and forecasting method, medium and program product of the present invention have the following beneficial and significant technical effects: (1) The present invention constructs a multi-level summer climate index evaluation index model, covering core elements such as temperature, humidity, precipitation, wind and human comfort. Through multi-dimensional index calculation and weighted integration, a scientific quantitative evaluation of the summer climate conditions in the target area is achieved. Through a grid forecasting method based on existing data and forecast data, a spatially continuous summer climate condition evaluation can be achieved under complex terrain conditions, and effective, large-scale and accurate forecasts can be made for the areas within the grid range, providing a more efficient solution for summer climate research.

[0013] (2) The present invention uses dynamic grid division and data fusion technology, without the need to establish specific meteorological observation sites in the target area. It only needs to obtain meteorological forecast data from the local meteorological bureau to construct a meteorological grid and forecast summer climate conditions for all areas within the grid. This method greatly reduces construction and maintenance costs, and enables reliable summer climate assessment and forecasting even in mountainous areas, hills and other complex terrain conditions, significantly enhancing the practicality and scope of application of the method.

[0014] (3) Through normalization processing and weighted integration of indicators, the present invention can accurately reflect the dynamic changes of summer climate conditions from multiple perspectives, including meteorological factors, geographical characteristics, and human comfort. The forecast model adopts a grid calculation strategy to ensure the uniform distribution of meteorological data in space, and at the same time, it is corrected in combination with local climate characteristics, so that the forecast results have higher accuracy and regional adaptability. In addition, by dynamically updating the evaluation criteria, the method can effectively adapt to climate change trends and long-term needs.

[0015] (4) The summer resort climate index distribution map and grade evaluation results generated by the present invention provide a scientific basis for regional summer resort resource development, tourism route design and urban planning. In particular, in the resource management and market promotion of hot summer resort destinations, the low-cost, high-precision forecasting function of the present invention can effectively promote local tourism development, enhance regional competitiveness, and promote the sustainable development of related economic activities.

[0016] (5) The present invention is based on universal meteorological data input and standardized calculation process. It is applicable to a unified grid-based assessment and forecasting method regardless of whether it is in plains or complex terrain areas, and is easy to promote and use nationwide. By combining real-time meteorological forecast data, this method achieves high-precision forecasts of summer climate conditions without relying on expensive observation equipment. It has low construction cost and strong practicality. It is not only suitable for regional summer climate research and development, but also can provide long-term and stable technical support for the public and managers, and has significant social benefits and economic value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Shown is a flow chart of a summer climate index evaluation and forecasting method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. The described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain the present invention, and should not be understood as limiting the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] As a specific example, Figure 1 As shown, the summer climate index evaluation and forecasting method provided by the embodiment of the present invention mainly includes the following steps when implemented: SS1. Construct a summer climate index evaluation index model, including: The summer vacation climate index is taken as the first-level indicator, and according to the impact mechanism of summer vacation climate conditions on human comfort, at least five second-level indicators of temperature, humidity, precipitation, wind and human comfort are set. Among them, the third-level indicators under the second-level temperature indicator include at least the number of days with suitable human temperature, the number of days with daily minimum temperature less than 22℃, the number of days with daily maximum temperature less than 28℃, the number of days with daily temperature difference greater than or equal to 8℃, the average temperature, the average minimum temperature, the average maximum temperature and the average daily temperature difference. The third-level indicators under the second-level humidity indicator include at least the average relative humidity and the number of days with suitable humidity for human body. The third-level indicators under the second-level precipitation indicator include at least the number of days with suitable precipitation. The third-level indicators under the second-level wind indicator include at least the number of days with suitable wind. The third-level indicators under the second-level human comfort indicator include at least the number of days with human comfort.

[0020] Specifically, the summer vacation climate index constructed by the present invention is composed of 5 secondary indicators and 13 tertiary indicators, as shown in Table 1 below. From the perspective of summer vacation climate comfort, the influence of temperature, humidity, precipitation, wind and human comfort on the comfort of summer vacation tourism is mainly considered, among which the suitable temperature in summer is the core condition of summer vacation climate. At the same time, since the average value may mask the influence of the extreme values ​​of local meteorological elements, when constructing the evaluation index system, not only the average climate conditions over many years are considered, but also the suitable days of different meteorological elements are particularly considered. Considering that there are differences in climate characteristics in different regions, the temperature factors with summer vacation advantages may be different. Therefore, the evaluation of temperature conditions is composed of 8 related indicators such as average temperature, maximum temperature, minimum temperature and daily range. The humidity condition evaluation is composed of two indicators: average relative humidity and suitable humidity days for human body. The precipitation evaluation condition is composed of suitable precipitation days. The wind evaluation condition is composed of suitable wind days. The human comfort evaluation condition is composed of human comfort days.

[0021] Preferably, in the above step SS1, the number of days with suitable temperature for the human body refers to the number of days with an average daily temperature in the range of 15-24°C, the number of days with a daily difference greater than or equal to 8°C refers to the number of days with a difference between the maximum daily temperature and the minimum daily temperature in summer greater than or equal to 8°C, the number of days with suitable humidity for the human body refers to the number of days with an average daily relative humidity in the range of 40%-80%, the number of days with suitable precipitation refers to the number of days with a daily precipitation in the range of 0.1-10mm, the number of days with suitable wind refers to the number of days with an average daily wind speed in the range of 1.5-7.9m / s, and the number of days with human comfort refers to the number of days with a human comfort index in the range of 51-76.

[0022] Table 1 Composition indicators, indicator types and normalized parameters of summer tourism climate index SS2. Obtain summer meteorological data for the target area, including: Obtain daily meteorological data for the target area during the summer period, including at least daily average temperature, maximum temperature, minimum temperature, relative humidity, precipitation, and average wind speed data; calculate daily range data based on daily maximum and minimum temperatures; and calculate human comfort index data based on daily average temperature, relative humidity, and wind speed. I The calculation formula is:

[0023] in, I is the human comfort index; T is the daily average temperature in degrees Celsius (℃); H is the daily average relative humidity, expressed as a percentage (%); V is the average wind speed in meters per second (m / s).I When the temperature is less than 76, the day is considered to be a comfortable day for the human body.

[0024] Preferably, in the above step SS2, after obtaining the daily meteorological data of the target area during the summer period, quality inspection and outlier processing are required, including at least physical restriction inspection, time consistency inspection and spatial consistency inspection, and outliers are eliminated or corrected to ensure the reliability and continuity of the data. The outliers are determined by the 3 times standard deviation method, and the missing values ​​are filled by the spatial interpolation method of nearby sites.

[0025] SS3. Calculate the values ​​of the third-level indicators, including: Based on the meteorological data of the target area in summer obtained in step SS2, the values ​​of each third-level index are calculated, including the calculation of the cumulative values ​​of the number of days with suitable temperature for human body, the number of days with daily minimum temperature <22℃, the number of days with daily maximum temperature <28℃, the number of days with daily temperature difference ≥8℃, the number of days with suitable humidity for human body, the number of days with suitable precipitation, the number of days with suitable wind and the number of days with human comfort, and the calculation of the average values ​​of the average temperature, the average minimum temperature, the average maximum temperature and the average daily temperature difference in summer. Among them, the number of days with suitable temperature for human body (15≤T≤24℃), the number of days with daily minimum temperature <22℃, the number of days with daily maximum temperature <28, the number of days with daily temperature difference ≥8, the number of days with suitable humidity for human body (40%≤H≤80%), the number of days with suitable precipitation (0.1mm≤R<10.0mm), the number of days with suitable wind (1.5m / s<V≤7.9m / s) and the number of days with human comfort are all the cumulative values ​​in summer. The average temperature, the average minimum temperature, the average maximum temperature and the average daily temperature difference are the average values ​​in summer.

[0026] SS4. Normalize the three-level indicator values, including: First, according to the indicator attributes, each third-level indicator is classified into one of the three indicator types: positive indicator, negative indicator and interval indicator. Then, each third-level indicator is normalized according to the indicator type. For the positive indicator, the formula Normalize and use the formula for the reverse indicator Normalize and use the formula for interval indicators based on the relationship between the indicator value and the optimal interval Normalize, in the above formulas, x is the original value of the indicator, x ' is the normalized index value, x min and x max are the minimum and maximum values ​​of the normalized parameters, q 1 and q 2 are the minimum and maximum values ​​of the optimal interval of the indicator.

[0027] Preferably, in the above step SS4, as shown in Table 1, the positive indicators include the number of days with suitable temperature for human body, the number of days with daily minimum temperature less than 22℃, the number of days with daily maximum temperature less than 28℃, the number of days with daily temperature difference greater than or equal to 8℃, the average daily temperature difference, the number of days with suitable humidity for human body, the number of days with suitable precipitation, the number of days with suitable wind and the number of days with human body comfort; the negative indicators include the average temperature, the average minimum temperature and the average maximum temperature; the interval indicators include the average relative humidity. Furthermore, in the normalized parameter settings of each positive indicator, the number of days with suitable temperature for human body, the number of days with daily minimum temperature less than 22℃, the number of days with daily maximum temperature less than 28℃, the number of days with daily temperature difference greater than or equal to 8℃ are all x min =0 days, x max = 92 days, the average daily range is x min =4℃, x max =17℃, the number of days with suitable humidity for human body is x min =2 days, x max =90 days, the number of days with suitable precipitation is x min =5 days, x max =72 days, the number of days with suitable wind is x min =1 day, x max =92 days, the number of comfortable days for human body is x min =0 days, x max = 92 days; in the normalization parameter setting of each reverse index, the average temperature is x min =5℃, x max =33℃, the average minimum temperature is x min =0.5℃, x max =28℃, the average maximum temperature is x min =10℃, x max =40℃, the optimal range of average relative humidity is [50%, 70%].

[0028] SS5. Calculate the secondary indicator values, including: According to the categories of the secondary indicators of temperature, humidity, precipitation, wind and human comfort, the arithmetic mean of the normalized values ​​of each type of third-level indicator are calculated respectively to obtain the corresponding secondary indicator values. The secondary indicator value of temperature is the arithmetic mean of the normalized values ​​of the third-level indicators of temperature, namely, the number of days with suitable human temperature, the number of days with daily minimum temperature less than 22℃, the number of days with daily maximum temperature less than 28℃, the number of days with daily temperature difference greater than or equal to 8℃, the average temperature, the average minimum temperature, the average maximum temperature and the average daily temperature difference. The secondary indicator values ​​of humidity, precipitation, wind and human comfort are calculated as the arithmetic mean of the normalized values ​​of the corresponding categories.

[0029] SS6. Calculate the summer climate index, including: The summer climate index of the target area is calculated by using a weighted calculation formula and combining the values ​​of each secondary index. I c ,in I t , I h , I p ,I w and I s are the normalized secondary index values ​​of temperature, humidity, precipitation, wind and human comfort, w t , w h , w p , w w and w s They are the weight coefficients of the secondary indicators of temperature, humidity, precipitation, wind and human comfort.

[0030] As a preferred embodiment, in the above step SS6, the weight coefficient of each secondary index is dynamically determined by combining historical meteorological data analysis, regional user preference survey and human comfort model calculation. w t , w h , w p , w w and w s , making the contribution of temperature, humidity, precipitation, wind speed and human comfort in the summer climate index more scientific and reasonable, which can not only adapt to the climate characteristics of different regions, but also meet the diverse needs of users for summer conditions. Specifically, it includes the following sub-steps: SS61. Preliminary weight allocation based on historical meteorological data: Collect meteorological data of the target area in the past 10 to 30 years, analyze the contribution ratio of temperature, humidity, precipitation, wind speed and human comfort to summer climate conditions, and then use regression analysis to calculate the correlation strength of each meteorological factor to the summer climate index. It is preliminarily determined that the weight of temperature is the largest (e.g. 0.6 to 0.8), followed by humidity (e.g. 0.1 to 0.2), and precipitation, wind speed and human comfort are relatively small (e.g. 0.05 to 0.15); SS62. Optimize weights based on user preferences: Conduct a questionnaire survey of users in the target area to collect statistics on the factors that different user groups (such as the elderly, tourists, and local residents) care about in terms of summer climate conditions. Then adjust the weights of each secondary indicator based on the survey results (for example, in areas with high humidity sensitivity, appropriately increase the weight coefficient of humidity). w h ); SS63. Verify the rationality of weights through human comfort model: introduce human thermal balance model (such as UTCI model) to calculate the impact of different weight distribution on summer climate index, ensure that the weight setting can reflect the actual perception of human body to climate conditions, and further adjust the weight according to the simulation results to ensure that the index weight distribution is highly consistent with the actual comfort; SS64. Dynamic weight adjustment mechanism: The weight allocation is updated annually or multi-yearly according to the climate change trend in the target area (for example, in the context of increasing high temperature events, the weight coefficient of the secondary temperature indicator can be gradually increased).

[0031] Further preferably, the weight coefficient of each secondary index is w t , w h , w p , w w , w s The values ​​of are 0.7, 0.1, 0.05, 0.05, 0.1, and satisfy w t + w h + w p + w w + w s =1, as shown in Table 2 below.

[0032] Table 2 Secondary index weight coefficient table SS7. Evaluate the summer climate level, including: According to the data of daily average temperature, maximum temperature, minimum temperature, average relative humidity and daily average wind speed in the target area in summer; according to the daily maximum temperature and minimum temperature, the daily temperature difference data is obtained; according to the daily average temperature, relative humidity and wind speed, the human comfort index data is obtained; according to the summer climate index model, the summer climate index value is calculated; according to the summer climate index level threshold, the summer climate index level is quantitatively evaluated. And preferably, the spatial distribution visualization is performed based on the evaluation results, and the spatial distribution map of the summer climate level in the target area is generated using GIS (geographic information system) technology.

[0033] Preferably, in the above step SS7, the level threshold range of the summer climate index is dynamically determined by combining historical meteorological data, human comfort distribution statistics and regional applicability analysis to make it more scientific and reasonable and adapt to the climate characteristics of different regions, which specifically includes the following sub-steps: SS71. Preliminary division based on historical meteorological data: Collect summer meteorological data of the target area over the past 10 to 30 years and calculate the corresponding summer climate index I c , statistically analyze historical data by year and space, draw a frequency distribution curve of the summer climate index, and preliminarily divide the index range (e.g. define the lower 10% as "very bad", the upper 10% as "very good", and set the level of the middle range according to equal distribution); SS72. Optimize thresholds based on human comfort statistics: Analyze human comfort under meteorological conditions at each level of summer vacation in historical data, calculate the proportion of human comfort days under each level, and then optimize the interval division so that the index range of each level can more accurately reflect the differences in human comfort (for example, improve the resolution of "better" and "good" levels, and appropriately narrow the interval span); SS73. Regional suitability adjustment: Dynamically adjust the threshold range according to the climate characteristics of different geographical regions (such as plains, mountainous areas, and coastal areas) (for example, in hot and humid climate areas, appropriately relax the humidity range of "better" and "good" levels to reflect the adaptability of residents in the area to high humidity, and in hot and windy areas, increase the weight of the impact of wind speed on the level and adjust the division range of wind speed related index); SS74. Annual update based on climate change trends: Considering the year-on-year changes in climate conditions under the background of global warming, the thresholds of the summer climate index are updated annually or multi-yearly. Through the year-on-year trend analysis of historical meteorological data, the upper limit values ​​of high grades such as "very good" and "good" are dynamically adjusted to ensure that the classification can adapt to the gradually changing climate conditions.

[0034] Further preferably, when the summer climate index I c<30, the summer climate conditions are "very bad"; when 30≤ I c When Ic≤37, the summer climate conditions are "poor"; when Ic≤38≤Ic≤45, the summer climate conditions are "relatively poor"; when Ic≤46≤ I c When ≤53, the summer climate conditions are "average"; when 54≤ I c When ≤61, the summer climate conditions are "good"; when 62≤ I c <70, the summer vacation climate conditions are "good"; I c When the temperature is ≥70, the summer climate conditions are “very good”, as shown in Table 3 below.

[0035] Table 3 Summer climate index level threshold table SS8. Conduct summer weather forecasts, including: Obtain forecast data of daily average temperature, maximum temperature, minimum temperature, average relative humidity and daily average wind speed in the target area in summer; obtain daily range data based on daily maximum temperature and minimum temperature; obtain human comfort index data based on daily average temperature, relative humidity and wind speed; calculate the forecast results of summer climate index based on the summer climate index model. Gridding is preferably performed on the target area in combination with its geographical scope and meteorological data resolution, and steps SS3 to SS7 are repeated to obtain the forecast results of summer climate index and summer grade of each grid unit, forming a gridded spatial continuous forecast of the target area.

[0036] Preferably, in the above step SS8, the source of the meteorological element forecast data includes numerical weather forecast models, statistical forecast methods or artificial intelligence forecast methods, wherein the numerical weather forecast models include global forecast models and regional forecast models, the statistical forecast methods include multivariate regression and time series analysis, and the artificial intelligence forecast methods include machine learning and deep learning algorithms.

[0037] Preferably, the above step SS7 also includes a summer climate assessment result verification step: based on historical observation data, a cross-validation method is used to evaluate the applicability of the summer climate index model, and the reliability of the model is verified by calculating the root mean square error and / or correlation coefficient of statistical indicators and comparing and analyzing with existing evaluation methods; the above step SS8 also includes a forecast result verification step: a deterministic forecast verification method and a probabilistic forecast verification method are used, and the forecast accuracy, forecast skill score and / or forecast deviation of the verification indicators are calculated, and the forecast system is continuously optimized and improved based on the test results, wherein the deterministic forecast verification adopts the deviation correlation coefficient method, and the probabilistic forecast verification adopts the ROC curve scoring method.

[0038] Through the above embodiments, the purpose of the present invention is fully and effectively achieved. Those skilled in the art can understand that the present invention includes but is not limited to the contents described in the drawings and the above specific embodiments. Although the present invention has been described with respect to the most practical and preferred embodiments currently considered, it should be understood that the present invention is not limited to the disclosed embodiments, and any modification that does not deviate from the functional and structural principles of the present invention will be included in the scope of the claims.

Claims

1. A summer climate index assessment and forecasting method for quantitatively assessing and spatially continuously forecasting summer climate conditions in a target area, characterized in that: The method, when implemented, comprises: SS1. Take the summer vacation climate index as the first-level index, and set at least five second-level indexes, namely, temperature, humidity, precipitation, wind and human comfort, according to the mechanism of the influence of summer vacation climate conditions on human comfort. The third-level indexes under the second-level index of temperature shall at least include the number of days with suitable human temperature, the number of days with daily minimum temperature <22℃, the number of days with daily maximum temperature <28℃, the number of days with daily temperature difference ≥8℃, the average temperature, the average minimum temperature, the average maximum temperature and the average daily temperature difference. The third-level indexes under the second-level index of humidity shall at least include the average relative humidity and the number of days with suitable human humidity. The third-level indexes under the second-level index of precipitation shall at least include the number of days with suitable precipitation. The third-level indexes under the second-level index of wind shall at least include the number of days with suitable wind. The third-level indexes under the second-level index of human comfort shall at least include the number of days with comfortable human body. SS2. Obtain the daily meteorological data of the target area during the summer period, calculate the daily temperature difference data based on the daily maximum and minimum temperatures, and calculate the human comfort index data based on the daily average temperature, relative humidity and wind speed, where the human comfort index I The calculation formula is: in, T is the daily average temperature, H is the daily average relative humidity, V is the average wind speed; SS3. Calculate the values ​​of the three-level indicators based on the summer meteorological data of the target area obtained in step SS2, including calculating the cumulative values ​​of the number of days with suitable temperature for human body, the number of days with daily minimum temperature <22℃, the number of days with daily maximum temperature <28℃, the number of days with daily temperature difference ≥8℃, the number of days with suitable humidity for human body, the number of days with suitable precipitation, the number of days with suitable wind and the number of days with human comfort, and calculating the average values ​​of the summer average temperature, the average minimum temperature, the average maximum temperature and the average daily temperature difference; SS4. First, the three-level indicators are classified into one of the three types of indicators: positive indicators, negative indicators and interval indicators according to the indicator attributes. Then, the three-level indicators are normalized according to the indicator type. The positive indicators are normalized using the formula Normalize and use the formula for the reverse indicator Normalize and use the formula for interval indicators based on the relationship between the indicator value and the optimal interval Normalize, in the above formulas, x is the original value of the indicator, x ' is the normalized index value, x min and x max are the minimum and maximum values ​​of the normalized parameters, q 1 and q 2 are the minimum and maximum values ​​of the optimal interval of the indicator respectively; SS5. According to the category of the secondary index, the arithmetic mean of the normalized values ​​of each type of third-level index is calculated to obtain the corresponding second-level index value. The second-level index value of temperature is the arithmetic mean of the normalized values ​​of the third-level indicators of temperature, namely, the number of days with suitable human temperature, the number of days with daily minimum temperature <22℃, the number of days with daily maximum temperature <28℃, the number of days with daily temperature difference ≥8℃, the average temperature, the average minimum temperature, the average maximum temperature and the average daily temperature difference. The second-level index values ​​of humidity, precipitation, wind and human comfort are calculated by the arithmetic mean of the normalized values ​​of the corresponding categories. SS6. Use a weighted calculation formula to combine the values ​​of each secondary index to calculate the summer climate index of the target area I c ,in I t , I h , I p ,I w and I s are the normalized secondary index values ​​of temperature, humidity, precipitation, wind and human comfort, w t , w h , w p , w w and w s are the weight coefficients corresponding to each secondary indicator; SS7. Calculated summer climate index I c , compared with the preset summer resort level threshold range, the summer resort climate conditions in the target area are quantitatively evaluated, and the spatial distribution is visualized based on the evaluation results, and the spatial distribution map of the summer resort climate level in the target area is generated using GIS technology; SS8. Obtain the forecast data of future summer meteorological elements in the target area, grid the target area based on its geographical scope and meteorological data resolution, repeat steps SS3 to SS7, obtain the forecast results of the summer climate index and summer grade of each grid cell, and form a gridded spatial continuous forecast of the target area.

2. The summer climate index evaluation and forecasting method according to claim 1, characterized in that: In step SS1, the number of days with suitable temperature for the human body refers to the number of days with an average daily temperature in the range of 15-24°C, the number of days with a daily difference ≥8°C refers to the number of days with a difference between the maximum daily temperature and the minimum daily temperature in summer ≥8°C, the number of days with suitable humidity for the human body refers to the number of days with an average daily relative humidity in the range of 40%-80%, the number of days with suitable precipitation refers to the number of days with a daily precipitation in the range of 0.1-10mm, the number of days with suitable wind refers to the number of days with an average daily wind speed in the range of 1.5-7.9m / s, and the number of days with human comfort refers to the number of days with a human comfort index in the range of 51-76.

3. The summer resort climate index evaluation and forecasting method according to claim 1, characterized in that: In step SS2, the daily meteorological data of the target area in summer obtained include at least the daily average temperature, maximum temperature, minimum temperature, relative humidity, precipitation and average wind speed data. After obtaining the daily meteorological data of the target area in summer, quality inspection and outlier processing are required, including at least physical restriction inspection, time consistency inspection and spatial consistency inspection, and outliers are eliminated or corrected to ensure the reliability and continuity of the data. The outliers are determined by the 3 times standard deviation method, and the missing values ​​are filled by the spatial interpolation method of nearby sites.

4. The summer resort climate index evaluation and forecasting method according to claim 1, characterized in that: In step SS4, the positive indicators include the number of days with suitable temperature for human body, the number of days with daily minimum temperature < 22℃, the number of days with daily maximum temperature < 28℃, the number of days with daily temperature difference ≥ 8℃, the average daily temperature difference, the number of days with suitable humidity for human body, the number of days with suitable precipitation, the number of days with suitable wind and the number of days with human comfort; the negative indicators include the average temperature, the average minimum temperature and the average maximum temperature; the interval indicators include the average relative humidity.

5. The summer resort climate index evaluation and forecasting method according to claim 4, characterized in that: In step SS4, in the normalized parameter settings of each positive indicator, the number of days with suitable human body temperature, the number of days with daily minimum temperature < 22°C, the number of days with daily maximum temperature < 28°C, and the number of days with daily temperature difference ≥ 8°C are all x min =0 days, x max = 92 days, the average daily range is x min =4℃, x max =17℃, the number of days with suitable humidity for human body is x min =2 days, x max =90 days, the number of days with suitable precipitation is x min =5 days, x max =72 days, the number of days with suitable wind is x min =1 day, x max =92 days, the number of comfortable days for human body is x min =0 days, x max =92 days; In the normalization parameter setting of each reverse index, the average temperature is x min =5℃, x max =33℃, the average minimum temperature is x min =0.5℃, x max =28℃, the average maximum temperature is x min =10℃, x max =40℃, the optimal range of average relative humidity is [50%, 70%].

6. The summer resort climate index evaluation and forecasting method according to claim 1, characterized in that: In step SS6, the weight coefficients of each secondary index are dynamically determined by combining historical meteorological data analysis, regional user preference surveys, and human comfort model calculations. w t , w h , w p , w w and w s ,include: SS61. Preliminary weight allocation based on historical meteorological data: Collect meteorological data of the target area in the past 10 to 30 years, analyze the contribution ratio of temperature, humidity, precipitation, wind speed and human comfort to summer climate conditions, and then use regression analysis to calculate the correlation strength of each factor to the summer climate index. It is preliminarily determined that temperature has the largest weight, followed by humidity, and precipitation, wind speed and human comfort are the least important. SS62. Weight optimization based on user preferences: Conduct a survey of users in the target area, count the factors that different user groups care about in terms of summer climate conditions, and then adjust the weights of each indicator based on the survey results; SS63. Verify the rationality of weights through human comfort model: introduce the human thermal balance model to calculate the impact of different weight distributions on the summer climate index, ensure that the weight setting can reflect the actual perception of human body to climate conditions, and further adjust the weights according to the simulation results to ensure that the index weight distribution is highly consistent with the actual comfort; SS64. Dynamic adjustment mechanism of weights: Annual or multi-annual updates of weight allocations based on climate change trends in target areas.

7. The summer climate index evaluation and forecasting method according to claim 6, characterized in that: In step SS6, the weight coefficient of each secondary indicator w t , w h , w p , w w , w s The values ​​of are 0.7, 0.1, 0.05, 0.05, 0.1, and satisfy w t + w h + w p + w w + w s =1.

8. The summer resort climate index evaluation and forecasting method according to claim 1, characterized in that: In step SS7, the level threshold range of the summer climate index is dynamically determined by combining historical meteorological data, human comfort distribution statistics and regional applicability analysis, including: SS71. Preliminary division based on historical meteorological data: Collect summer meteorological data of the target area over the past 10 to 30 years and calculate the corresponding summer climate index I c ,Then the historical data are statistically analyzed by year and spatial distribution, the frequency distribution curve of the summer climate index is drawn, and the index range is preliminarily divided; SS72. Optimize thresholds based on human comfort statistics: Analyze human comfort under meteorological conditions at each level of summer resort in historical data, calculate the proportion of human comfort days under each level, and then optimize the interval division so that the index range of each level can more accurately reflect the differences in human comfort; SS73. Regional applicability adjustment: adjust the threshold range according to the climate characteristics of different geographical regions; SS74. Annual update based on climate change trends: Considering the annual changes in climate conditions under the background of global warming, the thresholds of the summer climate index are updated annually or multi-yearly. Through the annual trend analysis of historical meteorological data, the upper limit values ​​of high grades such as "very good" and "good" are dynamically adjusted to ensure that the classification can adapt to the gradually changing climate conditions.

9. The summer resort climate index evaluation and forecasting method according to claim 8, characterized in that: In step SS7, the summer resort level threshold range is specifically: when the summer resort climate index I c <30, the summer climate conditions are "very bad"; when 30≤ I c When Ic≤37, the summer climate conditions are "poor"; when Ic≤38≤Ic≤45, the summer climate conditions are "relatively poor"; when Ic≤46≤ I c When ≤53, the summer climate conditions are "normal"; when 54≤ I c When ≤61, the summer climate conditions are "good"; when 62≤ I c When the temperature is <70, the summer climate conditions are "good"; when I c When the temperature is ≥70, the summer vacation climate conditions are "very good".

10. The summer resort climate index evaluation and forecasting method according to claim 1, characterized in that: In step SS8, the sources of meteorological element forecast data include numerical weather forecast models, statistical forecast methods or artificial intelligence forecast methods, wherein the numerical weather forecast models include global forecast models and regional forecast models, the statistical forecast methods include multivariate regression and time series analysis, and the artificial intelligence forecast methods include machine learning and deep learning algorithms.

11. The summer resort climate index evaluation and forecasting method according to claim 1, characterized in that: Step SS7 also includes a summer climate assessment result verification step: based on historical observation data, a cross-validation method is used to evaluate the applicability of the summer climate index model, and the reliability of the model is verified by calculating the root mean square error and / or correlation coefficient of statistical indicators and comparing and analyzing with existing evaluation methods; Step SS8 also includes a forecast result verification step: a deterministic forecast verification method and a probabilistic forecast verification method are used, and the forecast accuracy, forecast skill score and / or forecast deviation of the verification indicators are calculated, and the forecast system is continuously optimized and improved based on the test results, wherein the deterministic forecast verification adopts the deviation correlation coefficient method, and the probabilistic forecast verification adopts the ROC curve scoring method.

12. A computer program product comprising computer instructions, characterized in that: Used to execute the summer climate index evaluation and forecasting method described in any one of claims 1 to 11 above.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the summer climate index evaluation and forecasting method described in any one of claims 1 to 11 is implemented.

Images