A spatial simulation method and system for relative humidity field during the rainy season

Abstract

This invention discloses a spatial simulation method and system for the relative humidity field during the rainy season, comprising: acquiring the monthly relative humidity baseline field of the dry season reference month, the annual relative humidity baseline field, and the reference monthly relative humidity and the relative humidity of the month to be simulated at meteorological observation stations; calculating the monthly anomaly baseline field of the reference monthly relative humidity; acquiring the monthly anomaly baseline value and the annual relative humidity baseline value of the reference monthly relative humidity at the meteorological observation station, and calculating the monthly relative humidity anomaly and anomaly difference of the meteorological observation station in the reference month; acquiring the monthly relative humidity anomaly difference field of the reference month and the non-monsoon influence field of the relative humidity anomaly of the month to be simulated; calculating the monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station, and after acquiring the monsoon influence component field of the relative humidity anomaly of the month to be simulated, calculating the relative humidity field of the rainy season month to be simulated. This invention can improve its application level in the fields of ecology, forestry, agriculture, and natural disaster monitoring and early warning.

Description

Technical Field

[0001] This invention belongs to the field of mountain climate, and in particular relates to a spatial simulation method and system for the relative humidity field during the rainy season. Background Technology

[0002] Relative humidity in mountainous areas, besides air temperature, is another dominant factor contributing to various mountain ecological phenomena and processes. It is also a crucial meteorological factor for utilizing and developing mountain climate resources and digital ecosystems, as well as for monitoring and early warning of resource disasters. With the interdisciplinary integration of modern space technology with mountain meteorology, ecology, and forestry, continuous forecasting of relative humidity in mountainous areas across all geographic grid points and time periods has become an urgent need for industry applications. The influence and mechanisms of topography and airflow on the formation of spatiotemporal fields are key aspects of refined simulation of spatiotemporal processes in mountainous relative humidity fields and form the theoretical basis for regional applications of mountain climate. Generally, data from conventional meteorological stations and field observation stations are the main data sources for mountain climate research, but these data suffer from difficulties in obtaining data in mountainous areas, poor representativeness, and limited coverage. With the development of remote sensing technology, algorithms for inverting surface temperature and soil moisture fields based on remote sensing imagery have gradually matured. Theoretically, this can compensate for the shortcomings of traditional observation methods. However, in practical applications, issues such as the transit time, coverage area, and cloud cover of multispectral remote sensing images make it difficult to meet application requirements in the mountainous areas of southwestern China, which are covered by clouds, rain, and snow year-round and span multiple medium-to-high resolution remote sensing images. Therefore, there is an urgent need for a method that can spatially measure and report the relative humidity field during the rainy season. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a spatial simulation method and system for the relative humidity field during the rainy season. Specifically, the spatial simulation method for the relative humidity field during the rainy season includes:

[0004] Obtain the monthly relative humidity baseline field of the dry season reference month, the annual relative humidity baseline field, and the reference monthly relative humidity and the relative humidity of the month to be simulated at the meteorological observation station;

[0005] Based on the monthly relative humidity baseline field of the dry season reference month, the annual relative humidity baseline field is used to calculate the monthly relative humidity anomaly baseline field of the reference month.

[0006] Obtain the baseline value of monthly relative humidity and the baseline value of annual relative humidity at the meteorological observation station, and calculate the monthly relative humidity anomaly and anomaly difference of the meteorological observation station in the baseline month.

[0007] Obtain the relative humidity anomaly field of the reference month, and calculate the anomaly difference between the reference month and the month to be simulated based on the monthly variation curve fitting model of the relative humidity monthly anomaly baseline field.

[0008] Based on the relative humidity anomaly field of the reference month, the relative humidity baseline field of the reference month, and the baseline difference between the reference month and the month to be simulated, a raster addition operation is performed to obtain the non-monsoon influence field of the relative humidity anomaly of the month to be simulated.

[0009] The monsoon influence of the relative humidity anomaly of the meteorological station in the month to be simulated is calculated. After obtaining the monsoon influence component field of the relative humidity anomaly in the month to be simulated, the relative humidity field of the month to be simulated during the rainy season is calculated.

[0010] Preferably, the dry season reference month is any one of January to May or November to December;

[0011] The simulated rainy season month is any month from June to October;

[0012] The base month for the meteorological observation station is the same year as the month to be simulated.

[0013] Preferably, the monthly relative humidity baseline field is a raster data of monthly average relative humidity fields over several years, obtained by the raster arithmetic mean of the relative humidity fields over several months;

[0014] The process of obtaining the monthly relative humidity baseline field includes:

[0015] The monthly average surface water vapor pressure raster data at a spatial scale of 1 km were obtained by remote sensing inversion and GIS assimilation techniques using the base year MODIS monthly average atmospheric precipitable water product and actual water vapor pressure data observed by ground meteorological stations.

[0016] Monthly average temperature data with a spatial scale of 1km was obtained from MODIS land surface temperature monthly average product and monthly average temperature data observed by ground meteorological stations through GIS assimilation technology.

[0017] Based on the meteorological formula for calculating relative humidity, the monthly average relative humidity grid data at a spatial scale of 1 km is calculated and is thus the monthly relative humidity baseline field.

[0018] Preferably, the annual relative humidity baseline field is the annual average relative humidity raster data for several years, obtained by performing a raster arithmetic mean calculation on the monthly relative humidity baseline fields from January to December;

[0019] The process of obtaining the annual relative humidity baseline field includes:

[0020] Based on the MODIS monthly average atmospheric precipitable water product from January to December and the actual water vapor pressure data from January to December observed by ground meteorological stations, the monthly average surface water vapor pressure raster data from January to December with a spatial scale of 1 km was obtained through remote sensing inversion and GIS assimilation technology.

[0021] Based on the MODIS land surface temperature monthly average product from January to December and the monthly average air temperature data from January to December observed by ground meteorological stations, GIS assimilation technology was used to obtain 1km spatial scale monthly average air temperature raster data from January to December.

[0022] Based on the meteorological formula for relative humidity, the monthly average relative humidity raster data for January to December at a spatial scale of 1 km were calculated.

[0023] The arithmetic mean raster operation is performed on the monthly average relative humidity raster data from January to December to obtain the annual average relative humidity raster data, which is the annual relative humidity baseline field.

[0024] Preferably, the process of obtaining the baseline value of the monthly relative humidity anomaly and the baseline value of the annual relative humidity at the meteorological observation station, and calculating the monthly relative humidity anomaly and anomaly difference of the meteorological observation station in the baseline month includes:

[0025] Based on the geographical coordinates of the meteorological observation station, the baseline field of the reference monthly relative humidity and the baseline field of the annual relative humidity are sampled, and the baseline values ​​of the reference monthly relative humidity and the annual relative humidity at the meteorological observation station are extracted.

[0026] Subtract the annual relative humidity baseline value from the relative humidity of the month to be simulated at the meteorological observation station to obtain the monthly relative humidity anomaly of the reference month at the meteorological observation station; subtract the monthly relative humidity anomaly baseline value of the reference month from the monthly relative humidity anomaly of the reference month to obtain the monthly relative humidity anomaly deviation of the reference month at the meteorological observation station.

[0027] Preferably, the process of obtaining the relative humidity anomaly field of the reference month and calculating the baseline anomaly difference between the reference month and the month to be simulated based on the fitting model of the monthly variation curve of the relative humidity baseline field includes:

[0028] Using GIS interpolation, the spatially continuous monthly relative humidity anomaly field of the reference month is obtained by taking the monthly relative humidity anomaly of the reference month of the meteorological observation station as point samples.

[0029] Substitute the months of the reference month and the month to be simulated into the fitting model of the monthly variation curve of the relative humidity monthly anomaly baseline field, and obtain the anomaly baseline values ​​of the reference month and the month to be simulated on the monthly variation curve of the relative humidity monthly anomaly baseline field. Calculate the difference between the anomaly baseline values ​​to obtain the anomaly baseline difference between the reference month and the month to be simulated.

[0030] Preferably, the formula expression for the monthly variation curve fitting model of the relative humidity monthly anomaly baseline field is:

[0031] y(x)=-0.12x3+2.17x2-9.17x-3.6

[0032] Where x represents the month and y represents the baseline anomaly value;

[0033] The process of obtaining the fitting model for the monthly variation curve of the relative humidity monthly anomaly baseline field includes:

[0034] Using the random point generation tools commonly used in GIS software, a sufficient number of random vector points are established across the entire area;

[0035] Using the geographical location of the random vector points as the standard, the monthly relative humidity anomaly baseline field for the dry season (January-May and November-December) is sampled to obtain the relative humidity anomaly baseline sample data for the dry season.

[0036] Using the month as the independent variable and the baseline sample of relative humidity anomaly in the dry season as the dependent variable, a scatter plot was drawn to obtain the cubic curve relationship between the two.

[0037] Based on the aforementioned cubic curve relationship, a cubic polynomial model is used to fit the curve of relative humidity anomaly baseline variation with the month, resulting in a monthly variation curve fitting model of relative humidity anomaly baseline in the range of 1-12 months.

[0038] Preferably, the process of calculating the monsoon impact of the monthly relative humidity anomaly at the meteorological station includes:

[0039] The relative humidity of the month to be simulated is subtracted from the annual relative humidity baseline value at the meteorological station to obtain the relative humidity anomaly of the month to be simulated at the meteorological station; then, the non-monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station is subtracted from the relative humidity anomaly of the month to be simulated at the meteorological station to obtain the monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station.

[0040] Preferably, the process of calculating the relative humidity field of the month to be simulated during the rainy season after obtaining the monsoon influence component field of the relative humidity anomaly of the month to be simulated includes:

[0041] Using GIS interpolation, the monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station is used as point samples to obtain the spatially continuous monsoon influence component field of the relative humidity anomaly of the month to be simulated.

[0042] The non-monsoon influence field of the relative humidity anomaly to be simulated for the month to be simulated is added to the monsoon influence component field of the relative humidity anomaly to be simulated for the month to be simulated by a raster addition operation to obtain the relative humidity anomaly field of the month to be simulated during the rainy season; the relative humidity anomaly field of the month to be simulated during the rainy season is added to the annual relative humidity baseline field by a raster addition operation to obtain the relative humidity field of the month to be simulated during the rainy season.

[0043] The present invention also provides a spatial simulation system for the relative humidity field during the rainy season, comprising:

[0044] The data acquisition module is used to acquire the monthly relative humidity baseline field of the dry season reference month, the annual relative humidity baseline field, and the reference monthly relative humidity and the relative humidity of the month to be simulated at the meteorological observation station;

[0045] The baseline calculation module for relative humidity of the reference month is used to calculate the monthly relative humidity of the reference month based on the monthly relative humidity baseline field of the dry season reference month and the annual relative humidity baseline field.

[0046] The module for calculating monthly relative humidity anomaly and anomaly difference is used to obtain the baseline value of monthly relative humidity and the baseline value of annual relative humidity at the meteorological observation station, and to calculate the monthly relative humidity anomaly and anomaly difference of the meteorological observation station in the baseline month.

[0047] The baseline difference calculation module is used to obtain the relative humidity anomaly field of the reference month and calculate the baseline difference between the reference month and the month to be simulated based on the monthly variation curve fitting model of the relative humidity monthly baseline field.

[0048] The non-monsoon influence field acquisition module is used to perform grid addition operations based on the relative humidity anomaly field of the reference month, the relative humidity anomaly baseline field of the reference month, and the anomaly baseline difference between the reference month and the month to be simulated, to obtain the non-monsoon influence field of the relative humidity anomaly of the month to be simulated.

[0049] The relative humidity field calculation module is used to calculate the monsoon influence of the relative humidity anomaly of the meteorological station in the month to be simulated. After obtaining the monsoon influence component field of the relative humidity anomaly of the month to be simulated, the relative humidity field of the month to be simulated during the rainy season is calculated.

[0050] Compared with the prior art, the present invention has the following advantages and technical effects:

[0051] This invention uses the monthly average relative humidity anomaly field during the dry season to predict the monthly average relative humidity anomaly field during the rainy season, and then predicts the monthly average relative humidity field during the rainy season. This solves the problem that multispectral remote sensing images cannot obtain large-scale spatial data on relative humidity due to cloudy and foggy weather under the influence of monsoons during the rainy season, providing a key technical method for obtaining annual relative humidity data. Especially in mountainous areas, regional-scale relative humidity data can effectively support refined spatial monitoring and early warning of mountain ecosystem disasters. This invention provides a method and system for the research and industry application of remote sensing technology in forest fire monitoring and early warning, and mountain debris flow monitoring and early warning. It is a much-needed technology to overcome the technological bottlenecks in the field of mountain disaster monitoring and early warning in the forestry and grassland industry. Attached Figure Description

[0052] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0053] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of the relative humidity anomaly in April 2017, according to an embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram of the relative humidity anomaly field in June 2017 under non-monsoon influence, according to an embodiment of the present invention.

[0056] Figure 4 This is a schematic diagram of the relative humidity anomaly component field under the influence of the area monsoon in June 2017, according to an embodiment of the present invention.

[0057] Figure 5 This is a schematic diagram of the relative humidity anomaly field in June 2017, according to an embodiment of the present invention.

[0058] Figure 6 This is a schematic diagram of the relative humidity field in June 2017, according to an embodiment of the present invention. Detailed Implementation

[0059] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0060] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0061] The southern Hengduan Mountains region experiences abundant cloud and fog due to the warm and humid southwest monsoon airflow from May to October. This cloud cover results in generally low quality of multispectral remote sensing images during the rainy season (May-October). Remote sensing technology cannot acquire the relative humidity field of this region at a regional scale during the rainy season. In the era of big data, the lack of relative humidity data during the rainy season, a fundamental meteorological element, hinders the development and application of spatial information and intelligent technologies in natural disaster monitoring and early warning systems for forestry, environmental protection, and land resources sectors. This invention addresses these issues by proposing a spatial simulation method and system for the monthly average relative humidity field during the rainy season, based on relative humidity field data retrieved from remote sensing.

[0062] like Figure 1As shown, this embodiment provides a spatial simulation method for the relative humidity field during the rainy season, including the following steps:

[0063] Obtain the monthly relative humidity baseline field of the dry season reference month, the annual relative humidity baseline field, as well as the geographical coordinates of the meteorological observation station, the relative humidity of the reference month, and the relative humidity of the month to be simulated;

[0064] The monthly relative humidity baseline field of the reference month is obtained by calculating the annual relative humidity baseline field based on the monthly relative humidity baseline field of the reference month.

[0065] Based on the geographical coordinates of the meteorological observation station, the baseline field of monthly relative humidity and the baseline field of annual relative humidity are sampled to extract the baseline value of monthly relative humidity and the baseline value of annual relative humidity at the meteorological observation station.

[0066] Subtract the baseline value of the relative humidity of the previous year from the relative humidity of the month to be simulated at the meteorological observation station to obtain the monthly relative humidity anomaly of the meteorological observation station in the reference month; subtract the baseline value of the monthly relative humidity anomaly of the reference month from the monthly relative humidity anomaly of the reference month to obtain the monthly relative humidity anomaly difference of the meteorological observation station in the reference month.

[0067] Using GIS interpolation, the spatially continuous monthly relative humidity anomaly field of the reference month is obtained by taking the monthly relative humidity anomaly of the meteorological observation station as point samples.

[0068] Substitute the months of the reference month and the month to be simulated into the fitting model of the monthly variation curve of the relative humidity monthly anomaly baseline field, and obtain the anomaly baseline values ​​of the reference month and the month to be simulated on the monthly variation curve of the relative humidity monthly anomaly baseline field. Calculate the difference between the anomaly baseline values ​​to obtain the anomaly baseline difference between the reference month and the month to be simulated.

[0069] The non-monsoon influence field of relative humidity anomaly in the month to be simulated is obtained by performing a raster addition operation on the relative humidity anomaly field of the reference month, the relative humidity anomaly baseline field of the reference month, and the anomaly baseline difference between the reference month and the month to be simulated.

[0070] The relative humidity of the month to be simulated is subtracted from the annual relative humidity baseline value at the meteorological station to obtain the relative humidity anomaly of the month to be simulated at the meteorological station; then the non-monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station is subtracted from the relative humidity anomaly of the month to be simulated at the meteorological station to obtain the monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station.

[0071] Using GIS interpolation, the monsoon impact of the relative humidity anomaly of the month to be simulated at the meteorological station is used as point samples to obtain the spatially continuous monsoon impact component field of the relative humidity anomaly of the month to be simulated.

[0072] The non-monsoon influence field of the relative humidity anomaly to be simulated for the month to be simulated is added to the monsoon influence component field of the relative humidity anomaly to be simulated for the month to be simulated, and the relative humidity anomaly field of the rainy season to be simulated for the month is obtained by adding the relative humidity anomaly field of the rainy season to be simulated for the month to be simulated to the annual relative humidity baseline field.

[0073] Furthermore, this embodiment also specifically includes:

[0074] S1: Load the monitoring input data. Load the monthly relative humidity baseline field for any month in the dry season, denoted as RH_i_base, where i is any selected month in the dry season, with the i-th month as the base month; load the annual relative humidity baseline field, denoted as RH_Y_base; read the geographic coordinates of a few meteorological observation stations, and import the monthly relative humidity of the month to be simulated and the base month of the same year at the few meteorological observation stations, denoted as rh_j and rh_i respectively, where j is the month to be simulated in the rainy season.

[0075] The dry season is any one of the months between January and May or November and December.

[0076] The simulated rainy season month is one month between June and October.

[0077] The base month for meteorological observation stations should be the same as the month to be simulated. Using any month of the dry season as the base month, the year in which relative humidity is recorded at the meteorological observation station should be consistent with the month to be simulated, in order to minimize the impact of interannual variations on spatial simulation.

[0078] The monthly relative humidity baseline field is a multi-year monthly average relative humidity field raster data, which is the result of the raster arithmetic mean calculation of the multi-year monthly relative humidity field.

[0079] Optionally, the steps for obtaining the monthly relative humidity baseline field are as follows:

[0080] Based on the multi-year MODIS monthly average atmospheric precipitable water product and actual water vapor pressure data observed by ground meteorological stations, multi-year monthly average surface water vapor pressure raster data with a spatial scale of 1km was obtained through remote sensing inversion and GIS assimilation technology.

[0081] Based on multi-year MODIS monthly average surface temperature products and monthly average air temperature data observed by ground meteorological stations, multi-year monthly average air temperature raster data with a spatial scale of 1km was obtained through GIS assimilation technology.

[0082] Based on the meteorological formula for relative humidity, the multi-year monthly average relative humidity grid data at a spatial scale of 1 km is calculated, which is the monthly relative humidity baseline field.

[0083] The annual relative humidity baseline field is the result of a raster arithmetic mean of the annual average relative humidity raster data over many years, calculated from the monthly relative humidity baseline fields from January to December.

[0084] Optionally, the steps for obtaining the annual relative humidity baseline field are as follows:

[0085] Based on the multi-year MODIS monthly average atmospheric precipitable water product from January to December and the actual water vapor pressure data from January to December observed by ground meteorological stations, multi-year monthly average surface water vapor pressure raster data with a spatial scale of 1km was obtained through remote sensing inversion and GIS assimilation technology.

[0086] Based on the multi-year MODIS land surface temperature monthly average product from January to December and the monthly average air temperature data from January to December observed by ground meteorological stations, GIS assimilation technology was used to obtain multi-year monthly average air temperature raster data from January to December at a spatial scale of 1km.

[0087] Based on the meteorological formula for relative humidity, multi-year monthly average relative humidity raster data from January to December at a spatial scale of 1 km were calculated.

[0088] The arithmetic mean raster operation is performed on the monthly average relative humidity raster data from January to December over many years to obtain the annual average relative humidity raster data, which is the annual relative humidity baseline field.

[0089] S2: Calculate the baseline field of relative humidity for the base month. Subtract the baseline field of relative humidity for the previous year from the baseline field of relative humidity for the i-th month to obtain the baseline field of relative humidity for the i-th month, denoted as RH_i_jp_base.

[0090] S3: Extract the baseline values ​​of monthly and annual relative humidity relative humidity for the meteorological observation station. Read the geographic coordinates of the meteorological observation station, denoted as cor(x,y); use the geographic coordinates cor(x,y) to sample the baseline fields of monthly and annual relative humidity relative humidity for the i-th month, obtaining the baseline values ​​of monthly and annual relative humidity relative humidity for the i-th month, denoted as rh_i_jp_base and rh_y_base.

[0091] Optionally, the monthly relative humidity baseline field, the annual average relative humidity baseline field, and the geographic coordinates of meteorological stations can be unified into planar coordinates using projection tools.

[0092] S4: Calculate the monthly relative humidity anomaly and anomaly difference for the meteorological observation station in the baseline month. Read the monthly average relative humidity *rh_i* of the meteorological observation station in the baseline month, subtract the annual relative humidity baseline value *rh_y_base* of the meteorological observation station, and obtain the monthly relative humidity anomaly of the meteorological observation station in the *i*th month of the same year as the month to be simulated, denoted as *rh_i_jp*. Subtract the baseline value of the monthly relative humidity anomaly of the meteorological observation station in the *i*th month from the monthly relative humidity anomaly of the meteorological observation station in the *i*th month to obtain the monthly relative humidity anomaly difference for the meteorological observation station in the baseline month, denoted as Δ*rh_i_jp*. Then we have...

[0093] Δrh_i_jp=rh_i_jp-rh_i_jp_base

[0094] S5: Obtain the monthly relative humidity anomaly field for the baseline month. Using GIS interpolation, with the monthly relative humidity anomaly Δrh_i_jp of the meteorological observation station for the baseline month as point samples, obtain the spatially continuous monthly relative humidity anomaly field for the baseline month, denoted as K_Δrh_i_jp.

[0095] Optional GIS interpolation methods include Kriging interpolation and inverse distance interpolation.

[0096] Optionally, the spatial scale of the target raster data in the point-to-surface process can be set to 1 km.

[0097] S6: Calculate the non-monsoon influence field of the relative humidity anomaly of the month to be simulated. Let the month to be simulated be j. Substitute the month i of the base month and the month j of the month to be simulated into the fitting model y(x)=-0.12x3+2.17x2-9.17x-3.6 for the monthly variation curve of the relative humidity monthly anomaly baseline field, to obtain the anomaly baseline values ​​y(j) and y(i) of the j-th month and the i-th month on the fitting curve. Calculate the difference between the anomaly baseline values ​​y(j) and y(i) to obtain the anomaly baseline difference between the j-th month and the i-th month, denoted as Δrh_j_i_jp. Perform a raster addition operation on the monthly relative humidity anomaly difference field K_Δrh_i_jp of the i-th month obtained in S5 above, the monthly relative humidity anomaly baseline field RH_i_jp_base of the i-th month in S2 above, and the anomaly baseline difference Δrh_j_i_jp of the j-th month and the i-th month to obtain the non-monsoon influence field of the relative humidity anomaly of the month to be simulated, denoted as RH_j_jp_a.

[0098] The process of obtaining the above-mentioned monthly variation curve fitting model for the monthly relative humidity anomaly baseline field is as follows:

[0099] Using the random point generation tools commonly used in GIS software, a sufficient number of random vector points are established across the entire area;

[0100] Using the geographical location of random vector points as the standard, the monthly relative humidity anomaly baseline field was sampled for the dry season from January to May and from November to December to obtain the dry season relative humidity anomaly baseline sample data;

[0101] Using the month as the independent variable and the dry season relative humidity anomaly baseline sample as the dependent variable, a scatter plot was drawn, and it was found that the two showed a good cubic curve relationship.

[0102] A cubic polynomial model was used to fit the curve of the relative humidity anomaly baseline as a function of months, resulting in a monthly variation curve model of the relative humidity anomaly baseline in the range of 1-12 months.

[0103] The aforementioned monthly variation curve model indicates the annual relative humidity anomaly variation pattern without monsoon influence. Based on this curve model, the baseline field components of the monthly relative humidity anomaly under the combined effects of numerous other factors during the rainy season from June to October, without monsoon influence, can be derived. These other factors include topographical elements such as altitude, slope aspect, high mountains, deep valleys, and vegetation cover.

[0104] This invention assumes that the relative humidity anomaly field during the rainy season is composed of the non-monsoon influence and the monsoon influence of the relative humidity anomaly. Therefore, the non-monsoon influence field of the relative humidity anomaly for the month to be simulated, i.e., the relative humidity anomaly field of the month to be simulated under non-monsoon influence, is one component field of the relative humidity anomaly field for the month to be simulated; while the monsoon influence field of the relative humidity anomaly for the month to be simulated, i.e., the relative humidity anomaly field of the month to be simulated under monsoon influence, is the other component field of the relative humidity anomaly field for the month to be simulated.

[0105] S7: Calculate the monsoon influence of the relative humidity anomaly of the meteorological observation station in the month to be simulated. Calculate the monthly relative humidity anomaly of the meteorological observation station in the j-th month and extract the non-monsoon influence of the monthly relative humidity anomaly of the meteorological observation station in the j-th month. Subtract the two to obtain the monsoon influence of the monthly relative humidity anomaly of the meteorological observation station in the month to be simulated, denoted as rh_j_jp_b. The monthly relative humidity anomaly of the meteorological observation station in the j-th month is obtained by subtracting the annual relative humidity baseline value rh_y_base of the meteorological observation station from the relative humidity rh_j of the meteorological observation station in the month to be simulated, as input in S1 above, denoted as rh_j_jp; the non-monsoon influence of the monthly relative humidity anomaly of the meteorological observation station in the j-th month is obtained by sampling the non-monsoon influence field RH_j_jp_a of the monthly relative humidity anomaly of the meteorological observation station using the geographical coordinates cor(x,y) of the meteorological observation station, denoted as rh_j_jp_a.

[0106] S8: Obtain the monsoon influence field of the relative humidity anomaly of the month to be simulated. Using the GIS interpolation method, with the monsoon influence of the relative humidity anomaly of the month to be simulated calculated by the meteorological observation station in S7 as the sample points, the monsoon influence field of the relative humidity anomaly of the j-th month is obtained by extrapolating from the points to the surface. This is the monsoon influence field of the relative humidity anomaly of the month to be simulated, denoted as K_rh_j_jp_b.

[0107] Optional GIS interpolation methods include Kriging interpolation and inverse distance interpolation.

[0108] Optionally, the spatial scale of the target raster data in the point-to-surface process is 1km.

[0109] S9: Calculate the relative humidity field for the month to be simulated during the rainy season. Perform a raster addition operation on the non-monsoon influence field RH_j_jp_a of the relative humidity anomaly for the month to be simulated calculated in S6 and the monsoon influence field K_rh_j_jp_b of the relative humidity anomaly for the month to be simulated obtained in S8 to obtain the relative humidity anomaly field for the j-th month, denoted as RH_j_jp; perform a raster addition operation on the relative humidity anomaly field RH_j_jp for the j-th month and the annual relative humidity baseline field RH_Y_base to obtain the relative humidity field for the j-th month, which is the relative humidity field for the month to be simulated during the rainy season.

[0110] To further optimize the scheme, this invention applies a spatial simulation method and system for the relative humidity field during the rainy season to the spatial simulation and forecasting of the monthly relative humidity field in June 2017 in Yunnan Province, located in the Hengduan Mountains. April 2017 was used as the dry season baseline month. The specific process is as follows:

[0111] (1) Calculate the monthly anomaly field of relative humidity in April.

[0112] Acquiring data from sparse meteorological observation stations

[0113] The relative humidity data recorded by 85 ground observation stations in Yunnan Province in April and June 2017 were used as simulated sample data, as shown in Table 1. The latitude and longitude information of the ground monitoring stations was imported into ArcMap software to generate vector files of the sample points.

[0114] Table 1

[0115]

[0116]

[0117]

[0118] Load the monthly average relative humidity field data RH_4_base for April of the past 20 years, retrieved from MODIS atmospheric precipitable water products, as the baseline field data for April's monthly relative humidity field, and the annual average relative humidity field RH_Y_base for the whole year; and calculate the monthly anomaly baseline field for April's relative humidity according to the following formula:

[0119] RH_4_jp_base=RH_4_base-RH_Y_base.

[0120] The monthly relative humidity anomaly baseline and annual relative humidity baseline values ​​for April were extracted from the above 85 meteorological observation stations.

[0121] Using the sample point vector file generated in ArcMap software in step (1), the sample tool is used to sample the above-mentioned April relative humidity monthly anomaly baseline field RH_4_jp_base and annual relative humidity baseline field RH_Y_base to obtain the April relative humidity monthly anomaly baseline value rh_4_jp_base and annual relative humidity baseline value rh_y_base data table file of meteorological observation stations.

[0122] (3) Calculate the monthly relative humidity anomaly and anomaly difference observed by the meteorological station in April 2017.

[0123] Using the meteorological station ID number stored in the sample point vector file generated in step (1) as the link field, the attribute table join tool is used to associate the original sample point vector file with the above-mentioned sampled rh_4_jp_base and rh_y_base data table files. Then, the anomaly value rh_4_jp of the meteorological station observation data in April is calculated according to the following formula:

[0124] rh_4_jp = rh_4 - rh_y_base

[0125] In the above formula, rh_4 is the relative humidity recorded by the meteorological station in April 2017; rh_y_base is the annual relative humidity baseline obtained by the meteorological station sampling in step (2).

[0126] Subtracting the baseline value of the monthly relative humidity anomaly at the meteorological station in April 2017, obtained from sampling in step (2), from the relative humidity anomaly rh_4_jp calculated above for April 2017, the monthly relative humidity anomaly Δrh_4_jp of the meteorological station in April 2017 is obtained as follows:

[0127] Δrh_4_jp=rh_4_jp-rh_4_jp_base

[0128] Obtain the area monthly relative humidity anomaly raster data for April 2017. Using GIS kriging interpolation, with the relative humidity anomaly Δrh_4_jp for April as the sample point, and extrapolating from the points to the area, obtain the area anomaly raster data for April 2017 with a spatial resolution of 1km, denoted as K_Δrh_4_jp. Figure 2 As shown.

[0129] (5) Calculate the relative humidity anomaly raster data for the simulated month of June under non-monsoon influence.

[0130] The anomaly values ​​y(4) and y(6) of April and June on the anomaly fitting curve are calculated in the sample point vector attribute table of step (1) according to the following formulas:

[0131] y(4)=-0.12*43+2.17*42-9.17*4-3.6;

[0132] y(6)=-0.12*63+2.17*62-9.17*6-3.6;

[0133] The relative humidity anomaly field RH_6_jp_a under the non-monsoon influence in June to be simulated is calculated using the following formula:

[0134] RH_6_jp_a=RH_4_jp_base+K_Δrh_4_jp+y(6)-y(4);

[0135] Wherein, RH_4_jp_base is the baseline field of monthly relative humidity anomaly in April over many years, K_Δrh_4_jp is the raster data of monthly relative humidity anomaly in April of 2017 (the base month), and y(6)-y(4) are the differences between the simulated month of June and the base month of April on the anomaly fitting curve. The relative humidity anomaly field in June without monsoon influence is as follows: Figure 3 As shown.

[0136] (6) Calculate the magnitude field of the influence of the monsoon on the relative humidity anomaly at the meteorological station in June 2017.

[0137] Calculate the relative humidity anomaly value observed by the meteorological station in June 2017. In the sample point vector file attribute table of step (1), subtract the annual relative humidity baseline value rh_y_base of the meteorological station sampled in step (2) from the relative humidity rh_6 observed by the meteorological station in June 2017, to obtain the monthly relative humidity anomaly value rh_6_jp observed by the meteorological station in June.

[0138] Using the geographic coordinates of the 85 meteorological stations in step (1), the relative humidity anomaly field RH_6_jp_a under the non-monsoon influence in June to be simulated in step (5) is sampled to obtain the relative humidity anomaly value of the meteorological stations in June when there is no monsoon influence, denoted as rh_6_jp_a.

[0139] Calculate the impact of the June 2017 monsoon on relative humidity anomalies at meteorological stations. The impact of the June 2017 monsoon on relative humidity anomalies, rh_6_jp_b, is calculated in the sample point vector file attribute table of step (1) according to the following formula:

[0140] rh_6_jp_b=rh_6_jp-rh_6_jp_a

[0141] (7) Obtain the surface monsoon influence relative humidity anomaly component field in June 2017

[0142] Perform GIS ordinary kriging interpolation on the impact of the June 2017 monsoon on relative humidity anomaly obtained in step (6) to obtain the areal data K_rh_6_jp_b, which is the relative humidity anomaly field to be simulated by the June monsoon, such as... Figure 4 As shown.

[0143] (8) Calculate the relative humidity anomaly field to be simulated in June.

[0144] Load the relative humidity anomaly field RH_6_jp_a under non-monsoon influence in June to be simulated in step (5), load the relative humidity anomaly field K_rh_6_jp_b under monsoon influence in June to be simulated in step (7), and calculate the relative humidity anomaly field RH_6_jp to be simulated in June according to the following formula:

[0145] RH_6_jp=RH_6_jp_a+K_rh_6_jp_b

[0146] The simulation results of the relative humidity anomaly field in June are as follows: Figure 5 As shown.

[0147] (9) Calculate the relative humidity field for the month to be simulated, June.

[0148] Load the relative humidity anomaly field RH_6_jp to be simulated in June from step (8), load the annual relative humidity baseline field RH_Y_base from step (2), and calculate the relative humidity field RH_6 to be simulated in June according to the following formula:

[0149] RH_6 = RH_6_jp + RH_Y_base

[0150] The simulation results of the relative humidity field in June are as follows: Figure 6 As shown.

[0151] (10) Accuracy test of the relative humidity simulation field in June

[0152] Using the coordinate sampling extraction steps (9) of 20 other ground observation stations in Yunnan Province, the simulated values ​​of relative humidity in June at the meteorological stations were obtained. The meteorological station ID was used as the associated field to link the sampled attribute table with the sample point vector file stored in step (1). In the linked attribute table, the observed relative humidity data for June at the meteorological stations was used as the true value, and the simulated relative humidity value for June was subtracted to perform error analysis. After sampling error verification, the simulation results of the relative humidity field in June were obtained, as shown in Table 2. Calculated using conventional mathematical statistical methods, the mean absolute error of this invention is 1.32, and the standard error is 7.24. This indicates that the error is small, and the simulation results of the embodiment are reliable.

[0153] Table 2

[0154]

[0155]

[0156] This embodiment also provides a spatial simulation system for the relative humidity field during the rainy season, including:

[0157] The data acquisition module is used to acquire the monthly relative humidity baseline field of the dry season reference month, the annual relative humidity baseline field, and the reference monthly relative humidity and the relative humidity of the month to be simulated at the meteorological observation station;

[0158] The baseline calculation module for relative humidity of the reference month is used to calculate the monthly relative humidity of the reference month based on the monthly relative humidity baseline field of the dry season reference month and the annual relative humidity baseline field.

[0159] The module for calculating monthly relative humidity anomaly and anomaly difference is used to obtain the baseline value of monthly relative humidity and the baseline value of annual relative humidity at the meteorological observation station, and to calculate the monthly relative humidity anomaly and anomaly difference of the meteorological observation station in the baseline month.

[0160] The baseline difference calculation module is used to obtain the relative humidity anomaly field of the reference month and calculate the baseline difference between the reference month and the month to be simulated based on the monthly variation curve fitting model of the relative humidity monthly baseline field.

[0161] The non-monsoon influence field acquisition module is used to perform grid addition operations based on the relative humidity anomaly field of the reference month, the relative humidity anomaly baseline field of the reference month, and the anomaly baseline difference between the reference month and the month to be simulated, to obtain the non-monsoon influence field of the relative humidity anomaly of the month to be simulated.

[0162] The relative humidity field calculation module is used to calculate the monsoon influence of the relative humidity anomaly of the meteorological station in the month to be simulated. After obtaining the monsoon influence component field of the relative humidity anomaly of the month to be simulated, the relative humidity field of the month to be simulated during the rainy season is calculated.

[0163] This invention uses relative humidity data archived from a few sparse observation stations to support rapid spatial simulation of the relative humidity field of the entire region using geographic grid points as units, thus solving the practical problem of missing relative humidity field data during the rainy season in the southern Hengduan Mountains.

[0164] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A spatial simulation method for the relative humidity field during the rainy season, characterized in that, include: Obtain the monthly relative humidity baseline field of the dry season reference month, the annual relative humidity baseline field, and the reference monthly relative humidity and the relative humidity of the month to be simulated at the meteorological observation station; Based on the monthly relative humidity baseline field of the dry season reference month, the annual relative humidity baseline field is used to calculate the monthly relative humidity anomaly baseline field of the reference month. Obtain the baseline value of monthly relative humidity and the baseline value of annual relative humidity at the meteorological observation station, and calculate the monthly relative humidity anomaly and anomaly difference of the meteorological observation station in the baseline month. Obtain the relative humidity anomaly field of the reference month, and calculate the anomaly difference between the reference month and the month to be simulated based on the monthly variation curve fitting model of the relative humidity monthly anomaly baseline field. Based on the relative humidity anomaly field of the reference month, the relative humidity baseline field of the reference month, and the baseline difference between the reference month and the month to be simulated, a raster addition operation is performed to obtain the non-monsoon influence field of the relative humidity anomaly of the month to be simulated. The process of calculating the monsoon impact on the monthly relative humidity anomaly of the meteorological station to be simulated includes: The relative humidity of the month to be simulated is subtracted from the annual relative humidity baseline value at the meteorological station to obtain the relative humidity anomaly of the month to be simulated at the meteorological station; then the non-monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station is subtracted from the relative humidity anomaly of the month to be simulated at the meteorological station to obtain the monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station. The process of calculating the relative humidity field of the month to be simulated during the rainy season after obtaining the monsoon influence component field of the relative humidity anomaly of the month to be simulated includes: Using GIS interpolation, the monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station is used as point samples to obtain the spatially continuous monsoon influence component field of the relative humidity anomaly of the month to be simulated. The non-monsoon influence field of the relative humidity anomaly to be simulated for the month to be simulated is added to the monsoon influence component field of the relative humidity anomaly to be simulated for the month to be simulated by a raster addition operation to obtain the relative humidity anomaly field of the month to be simulated during the rainy season; the relative humidity anomaly field of the month to be simulated during the rainy season is added to the annual relative humidity baseline field by a raster addition operation to obtain the relative humidity field of the month to be simulated during the rainy season.

2. The spatial simulation method for the relative humidity field during the rainy season according to claim 1, characterized in that, The dry season baseline month is any one of January to May or November to December; The simulated rainy season month is any month from June to October; The base month for the meteorological observation station is the same year as the month to be simulated.

3. The spatial simulation method for the relative humidity field during the rainy season according to claim 1, characterized in that, The monthly relative humidity baseline field is a raster data of monthly average relative humidity fields over several years, obtained by the raster arithmetic mean of the relative humidity fields over several months. The process of obtaining the monthly relative humidity baseline field includes: The monthly average surface water vapor pressure raster data at a spatial scale of 1 km were obtained by remote sensing inversion and GIS assimilation techniques using the base year MODIS monthly average atmospheric precipitable water product and actual water vapor pressure data observed by ground meteorological stations. Monthly average temperature data with a spatial scale of 1km was obtained from MODIS land surface temperature monthly average product and monthly average temperature data observed by ground meteorological stations through GIS assimilation technology. Based on the meteorological formula for calculating relative humidity, the monthly average relative humidity grid data at a spatial scale of 1 km is calculated and is thus the monthly relative humidity baseline field.

4. The spatial simulation method for the relative humidity field during the rainy season according to claim 1, characterized in that, The annual relative humidity baseline field is the annual average relative humidity raster data for several years, obtained by performing a raster arithmetic mean calculation on the monthly relative humidity baseline fields from January to December; The process of obtaining the annual relative humidity baseline field includes: Based on the MODIS monthly average atmospheric precipitable water product from January to December and the actual water vapor pressure data from January to December observed by ground meteorological stations, the monthly average surface water vapor pressure raster data from January to December with a spatial scale of 1 km was obtained through remote sensing inversion and GIS assimilation technology. Based on the MODIS land surface temperature monthly average product from January to December and the monthly average air temperature data from January to December observed by ground meteorological stations, GIS assimilation technology was used to obtain 1km spatial scale monthly average air temperature raster data from January to December. Based on the meteorological formula for relative humidity, the monthly average relative humidity raster data for January to December at a spatial scale of 1 km were calculated. The arithmetic mean raster operation is performed on the monthly average relative humidity raster data from January to December to obtain the annual average relative humidity raster data, which is the annual relative humidity baseline field.

5. The spatial simulation method for the relative humidity field during the rainy season according to claim 1, characterized in that, The process of obtaining the baseline values ​​of monthly relative humidity anomalies and annual relative humidity at a meteorological observation station, and calculating the monthly relative humidity anomalies and anomaly differences for the baseline month at the meteorological observation station, includes: Based on the geographical coordinates of the meteorological observation station, the baseline field of the reference monthly relative humidity and the baseline field of the annual relative humidity are sampled, and the baseline values ​​of the reference monthly relative humidity and the annual relative humidity at the meteorological observation station are extracted. Subtract the annual relative humidity baseline value from the relative humidity of the month to be simulated at the meteorological observation station to obtain the monthly relative humidity anomaly of the reference month at the meteorological observation station; subtract the monthly relative humidity anomaly baseline value of the reference month from the monthly relative humidity anomaly of the reference month to obtain the monthly relative humidity anomaly deviation of the reference month at the meteorological observation station.

6. The spatial simulation method for the relative humidity field during the rainy season according to claim 1, characterized in that, The process of obtaining the relative humidity anomaly field of the reference month and calculating the baseline anomaly difference between the reference month and the month to be simulated based on the fitting model of the monthly variation curve of the relative humidity monthly anomaly field includes: Using GIS interpolation, the spatially continuous monthly relative humidity anomaly field of the reference month is obtained by taking the monthly relative humidity anomaly of the reference month of the meteorological observation station as point samples. Substitute the months of the reference month and the month to be simulated into the fitting model of the monthly variation curve of the relative humidity monthly anomaly baseline field, and obtain the anomaly baseline values ​​of the reference month and the month to be simulated on the monthly variation curve of the relative humidity monthly anomaly baseline field. Calculate the difference between the anomaly baseline values ​​to obtain the anomaly baseline difference between the reference month and the month to be simulated.

7. The spatial simulation method for the relative humidity field during the rainy season according to claim 1, characterized in that, The formula for fitting the monthly variation curve of the relative humidity monthly anomaly baseline field is as follows: y(x) = -0.12x³ + 2.17x² - 9.17x - 3.6 Where x represents the month and y represents the baseline anomaly value; The process of obtaining the fitting model for the monthly variation curve of the relative humidity monthly anomaly baseline field includes: Using the random point generation tools commonly used in GIS software, a sufficient number of random vector points are established across the entire area; Using the geographical location of the random vector points as the standard, the monthly relative humidity anomaly baseline field for the dry season (January-May and November-December) is sampled to obtain the relative humidity anomaly baseline sample data for the dry season. Using the month as the independent variable and the baseline sample of relative humidity anomaly in the dry season as the dependent variable, a scatter plot was drawn to obtain the cubic curve relationship between the two. Based on the aforementioned cubic curve relationship, a cubic polynomial model is used to fit the curve of relative humidity anomaly baseline variation with the month, resulting in a monthly variation curve fitting model of relative humidity anomaly baseline in the range of 1-12 months.

8. A spatial simulation system for relative humidity fields during the rainy season, characterized in that, include: The data acquisition module is used to acquire the monthly relative humidity baseline field of the dry season reference month, the annual relative humidity baseline field, and the reference monthly relative humidity and the relative humidity of the month to be simulated at the meteorological observation station; The baseline calculation module for relative humidity of the reference month is used to calculate the monthly relative humidity of the reference month based on the monthly relative humidity baseline field of the dry season reference month and the annual relative humidity baseline field. The module for calculating monthly relative humidity anomaly and anomaly difference is used to obtain the baseline value of monthly relative humidity and the baseline value of annual relative humidity at the meteorological observation station, and to calculate the monthly relative humidity anomaly and anomaly difference of the meteorological observation station in the baseline month. The baseline difference calculation module is used to obtain the relative humidity anomaly field of the reference month and calculate the baseline difference between the reference month and the month to be simulated based on the monthly variation curve fitting model of the relative humidity monthly baseline field. The non-monsoon influence field acquisition module is used to perform grid addition operations based on the relative humidity anomaly field of the reference month, the relative humidity anomaly baseline field of the reference month, and the anomaly baseline difference between the reference month and the month to be simulated, to obtain the non-monsoon influence field of the relative humidity anomaly of the month to be simulated. The process of calculating the monsoon impact on the monthly relative humidity anomaly of the meteorological station to be simulated includes: The relative humidity of the month to be simulated is subtracted from the annual relative humidity baseline value at the meteorological station to obtain the relative humidity anomaly of the month to be simulated at the meteorological station; then the non-monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station is subtracted from the relative humidity anomaly of the month to be simulated at the meteorological station to obtain the monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station. The process of calculating the relative humidity field of the month to be simulated during the rainy season after obtaining the monsoon influence component field of the relative humidity anomaly of the month to be simulated includes: Using GIS interpolation, the monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station is used as point samples to obtain the spatially continuous monsoon influence component field of the relative humidity anomaly of the month to be simulated. The non-monsoon influence field of the relative humidity anomaly to be simulated for the month to be simulated is added to the monsoon influence component field of the relative humidity anomaly to be simulated for the month to be simulated by a raster addition operation to obtain the relative humidity anomaly field of the month to be simulated during the rainy season; the relative humidity anomaly field of the month to be simulated during the rainy season is added to the annual relative humidity baseline field by a raster addition operation to obtain the relative humidity field of the month to be simulated during the rainy season.

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