Space simulation method and system for relative humidity field in rainy season

Abstract

The invention discloses a spatial simulation method and system for a relative humidity field in a rainy season, and the method comprises the steps: obtaining a monthly relative humidity baseline field and an annual relative humidity baseline field of a dry season reference month, and the relative humidity of a reference month and the relative humidity of a to-be-simulated month of a meteorological observation station; calculating a reference month relative humidity month distance flat baseline field; obtaining a reference month relative humidity monthly average base line value and an annual relative humidity base line value at the meteorological observation station, and calculating monthly relative humidity average and average difference of a reference month of the meteorological observation station; acquiring a monthly relative humidity distance adjustment field of the reference month and a non-monsoon influence quantity field of the relative humidity distance adjustment of the to-be-simulated month; and calculating the monsoon influence quantity of the relative humidity distance of the to-be-simulated month of the meteorological station, obtaining the monsoon influence component field of the relative humidity distance of the to-be-simulated month, and then calculating to obtain the relative humidity field of the to-be-simulated month in the rainy season. The application level of the system in the fields of ecology, forestry, agriculture, natural disaster monitoring and early warning and the like can be improved.

Description

Technical Field

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

[0002] Mountain relative humidity is another dominant factor in various mountain ecological phenomena and processes besides temperature. It is an important meteorological factor for utilizing and developing mountain climate resources and digital ecosystems, and conducting resource disaster monitoring and early warning. With the cross-integration of modern space technology with mountain meteorology, ecology, forestry and other disciplines, the continuous forecast of mountain relative humidity at all-regional geographic space grid points and over all time processes has become an urgent need for industry applications. The influence and mechanism of topography, landforms and airflow on the formation of spatiotemporal fields are the key contents of the refined simulation of spatiotemporal processes of mountain relative humidity fields, and are also the theoretical basis for the regional application of mountain climate. In general, data from conventional meteorological stations and field observation stations are the main data sources for mountain climate research, but there are problems such as difficulty in obtaining data in mountainous areas, poor representativeness and limited coverage. With the development of remote sensing technology, the inversion algorithms for surface temperature fields and soil moisture fields based on remote sensing images have gradually matured. Theoretically, this can make up for the shortcomings of traditional observation methods, but in actual application, the transit time, coverage and cloud cover of multispectral remote sensing images are difficult to meet the application requirements in the mountainous areas of southwest China, which are covered by clouds, rain and snow all year round and have high resolution remote sensing images across multiple scenes. Therefore, a method for spatially measuring and reporting relative humidity fields during the rainy season is urgently needed. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a spatial simulation method and system for the relative humidity field in the rainy season. A spatial simulation method for the relative humidity field in the rainy season comprises:

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

[0005] Based on the monthly relative humidity baseline field of the dry season base month and the annual relative humidity baseline field, the base month relative humidity monthly anomaly baseline field is calculated;

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

[0007] Obtain the relative humidity anomaly difference field of the base month, and calculate the anomaly baseline difference between the base 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 monthly relative humidity anomaly difference field of the reference month, the monthly relative humidity anomaly baseline field of the reference month, and the anomaly baseline difference between the reference month and the month to be simulated, a grid addition operation is performed to obtain the non-monsoon influence field of the monthly relative humidity anomaly to be simulated;

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

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

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

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

[0013] Preferably, the monthly relative humidity baseline field is the monthly average relative humidity field grid data of several years, which is obtained by arithmetic averaging of the grid relative humidity fields of several months;

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

[0015] The monthly average product of atmospheric precipitable water in the base year MODIS and the actual water vapor pressure data observed by ground meteorological stations were used to obtain the monthly average ground water vapor pressure grid data with a spatial scale of 1 km through remote sensing inversion and GIS assimilation technology.

[0016] Based on the monthly average surface temperature product of MODIS and the monthly average temperature data observed by ground meteorological stations, the monthly average temperature raster data with a spatial scale of 1 km was obtained through GIS assimilation technology.

[0017] According to the meteorological calculation formula of relative humidity, the monthly average relative humidity grid data at a spatial scale of 1 km is calculated as the monthly relative humidity baseline field.

[0018] Preferably, the annual relative humidity baseline field is the annual average relative humidity grid data of several years, which is obtained by performing grid arithmetic mean operation 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 monthly average products of MODIS atmospheric precipitable water 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 were obtained through remote sensing inversion and GIS assimilation technology.

[0021] Based on the monthly average products of MODIS surface temperature from January to December and the monthly average temperature data from January to December observed by ground meteorological stations, the monthly average temperature raster data from January to December with a spatial scale of 1km was obtained by GIS assimilation technology.

[0022] According to the meteorological calculation formula of relative humidity, the monthly average relative humidity grid data from January to December at a spatial scale of 1 km was calculated;

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

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

[0025] The monthly anomaly baseline field of the relative humidity in the base month and the annual relative humidity baseline field are sampled according to the geographical coordinates of the meteorological observation site, and the monthly anomaly baseline value of the relative humidity in the base month and the annual relative humidity baseline value at the meteorological observation site are extracted;

[0026] The monthly relative humidity anomaly of the meteorological observation station in the base month is obtained by subtracting the annual relative humidity baseline value from the monthly relative humidity to be simulated at the meteorological observation station; the monthly relative humidity anomaly baseline value of the base month is subtracted from the monthly relative humidity anomaly of the base month to obtain the monthly relative humidity anomaly difference of the meteorological observation station in the base month.

[0027] Preferably, the process of obtaining the relative humidity anomaly difference field of the reference month and calculating the anomaly 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 anomaly baseline field comprises:

[0028] Using the GIS interpolation method, taking the monthly relative humidity anomaly difference of the base month of the meteorological observation station as a point sample, a spatially continuous base month relative humidity anomaly difference field is obtained;

[0029] Substitute the months of the base 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 corresponding anomaly baseline values ​​of the base month and the month to be simulated on the monthly variation curve of the relative humidity monthly anomaly baseline field. Perform difference calculation on the anomaly baseline values ​​to obtain the anomaly baseline difference between the base month and the month to be simulated.

[0030] Preferably, the formula expression of 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 is the month and y is the deviation from the baseline;

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

[0034] Use the random point generation tool commonly used in GIS software to establish enough random vector points in the whole area;

[0035] Taking the geographical location of the random vector point as the standard, sampling the relative humidity monthly anomaly baseline field from January to May and November to December in the dry season to obtain the dry season relative humidity anomaly baseline sample data;

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

[0037] According to the cubic curve relationship, a cubic polynomial model is used to fit the curve of the relative humidity deviation from the baseline with the month, and a monthly variation curve fitting model of the relative humidity deviation from the baseline in the range of January to December is obtained.

[0038] Preferably, the process of calculating the monsoon influence amount of the relative humidity anomaly to be simulated at the meteorological station includes:

[0039] The relative humidity of the month to be simulated observed at the meteorological station 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 obtaining the monsoon impact component field of the relative humidity anomaly of the month to be simulated and then calculating the relative humidity field of the month to be simulated in the rainy season comprises:

[0041] Using the GIS interpolation method, taking the monsoon impact amount of the monthly relative humidity anomaly to be simulated at the meteorological station as a point sample, a spatially continuous monsoon impact component field of the monthly relative humidity anomaly to be simulated is obtained;

[0042] The non-monsoon influence field of the relative humidity anomaly of the month to be simulated and the monsoon influence component field of the relative humidity anomaly of the month to be simulated are subjected to grid addition operation to obtain the relative humidity anomaly field of the rainy season month to be simulated; the relative humidity anomaly field of the rainy season month to be simulated and the annual relative humidity baseline field are subjected to grid addition operation to obtain the relative humidity field of the rainy season month to be simulated.

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

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

[0045] A base month relative humidity monthly anomaly baseline field calculation module is used to calculate the base month relative humidity monthly anomaly baseline field based on the monthly relative humidity baseline field of the dry season base month and the annual relative humidity baseline field;

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

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

[0048] A non-monsoon influence field acquisition module is used to perform grid addition operation based on the relative humidity anomaly difference field of the base month, the relative humidity monthly anomaly baseline field of the base month, and the anomaly baseline difference between the base month and the month to be simulated, so as 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 on the relative humidity anomaly of the month to be simulated at the meteorological station, and obtain the monsoon influence component field of the relative humidity anomaly of the month to be simulated, and then calculate the relative humidity field of the month to be simulated in the rainy season.

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

[0051] The present invention uses the dry season monthly average relative humidity anomaly field to predict the rainy season monthly average relative humidity anomaly field, and then predicts the rainy season monthly average relative humidity field, which solves the problem that multispectral remote sensing images caused by cloudy and foggy weather under the influence of the monsoon in the rainy season cannot obtain relative humidity spatial data on a large regional scale, and provides a key technical method for obtaining relative humidity data throughout the year. Especially in mountainous areas, regional-scale relative humidity data can effectively support refined spatial monitoring and early warning of mountain ecosystem disasters. The present invention can provide a method and system for producing basic data for the research and industry application of forest fire monitoring and early warning, mountain debris flow monitoring and early warning using remote sensing technology, and is a technology that is urgently needed to break through the technical bottleneck of scientific and technological development in the field of mountain disaster monitoring and early warning in the forestry and grassland industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0053] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention;

[0054] Figure 2 Schematic diagram of relative humidity anomaly difference in April 2017 according to an embodiment of the present invention;

[0055] Figure 3 Schematic diagram of the relative humidity anomaly field under the influence of non-monsoon in June 2017 according to an embodiment of the present invention;

[0056] Figure 4 This is a schematic diagram of the relative humidity anomaly component field of the surface monsoon impact in June 2017 according to an embodiment of the present invention;

[0057] Figure 5 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 DESCRIPTION

[0059] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

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

[0061] The southern part of the Hengduan Mountains area is covered by a large amount of clouds and fog due to the warm and humid air currents of the southwest monsoon from May to October. The quality of multi-spectral remote sensing images in the rainy season from May to October is generally low due to the cover of clouds and fog. The relative humidity field in the rainy season in this area cannot be obtained at a regional scale using remote sensing technology. In the context of the big data era, as a basic meteorological element, the lack of relative humidity data in the rainy season has made it impossible to carry out spatial informationization and intelligent technology research and development and application in the fields of natural disaster monitoring and early warning in the forestry, environmental protection, land and other industries. In response to the above problems, the present invention proposes a spatial simulation method and system for the monthly average relative humidity field in the rainy season based on the relative humidity field data inverted by remote sensing as the basic data.

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

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

[0064] Based on the monthly relative humidity baseline field of the dry season base month, the annual relative humidity baseline field is calculated to obtain the base month relative humidity monthly anomaly baseline field;

[0065] According to the geographical coordinates of the meteorological observation site, the monthly anomaly baseline field of the base month relative humidity and the annual relative humidity baseline field are sampled, and the monthly anomaly baseline value of the base month relative humidity and the annual relative humidity baseline value at the meteorological observation site are extracted;

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

[0067] Using GIS interpolation method, taking the monthly relative humidity anomaly difference of the benchmark month of the meteorological observation station as point sample, the spatially continuous benchmark monthly relative humidity anomaly difference field is obtained;

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

[0069] Perform grid addition operations on the relative humidity anomaly difference field of the base month, the relative humidity monthly anomaly baseline field of the base month, and the anomaly baseline difference between the base month and the month to be simulated to obtain the non-monsoon impact field of the relative humidity anomaly of the month to be simulated;

[0070] The relative humidity of the month to be simulated observed by the meteorological station 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 relative humidity anomaly of the month to be simulated at the meteorological station is subtracted from the non-monsoon influence of 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 method, taking the monsoon impact of the monthly relative humidity anomaly to be simulated at the meteorological station as point sample, the spatially continuous monsoon impact component field of the monthly relative humidity anomaly to be simulated is obtained.

[0072] The non-monsoon impact field of the relative humidity anomaly of the month to be simulated and the monsoon impact component field of the relative humidity anomaly of the month to be simulated are added by grid operation to obtain the relative humidity anomaly field of the rainy season month to be simulated; the relative humidity anomaly field of the rainy season month to be simulated and the annual relative humidity baseline field are added by grid operation to obtain the relative humidity field of the rainy season month to be simulated.

[0073] Furthermore, this embodiment also specifically includes:

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

[0075] The dry season is any month from January to May and from November to December.

[0076] The month to be simulated in the rainy season is one month between June and October.

[0077] The base month of the meteorological observation station is the same as the month to be simulated. Taking any month in the dry season as the base month, the relative humidity observation year recorded by the meteorological observation station should be consistent with the month to be simulated, so as to minimize the impact of interannual changes on spatial simulation.

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

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

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

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

[0082] According to the meteorological calculation formula of 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 multi-year average relative humidity grid data, which is the result of the grid arithmetic mean operation of the monthly relative humidity baseline field from January to December.

[0084] Optionally, the steps to obtain the annual relative humidity baseline field are:

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

[0086] Based on the multi-year MODIS surface temperature monthly average products from January to December and the monthly average temperature data from January to December observed by ground meteorological stations, the multi-year monthly average temperature grid data from January to December with a spatial scale of 1km was obtained by GIS assimilation technology.

[0087] According to the meteorological calculation formula of relative humidity, the multi-year average monthly relative humidity grid data from January to December at a spatial scale of 1 km was calculated;

[0088] The arithmetic mean grid operation is performed on the multi-year January-December monthly average relative humidity grid data to obtain the multi-year annual average relative humidity grid data, which is the annual relative humidity baseline field.

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

[0090] S3: Extract the monthly relative humidity anomaly baseline value and annual relative humidity baseline value of the base month of the meteorological observation site. Read the geographic coordinates of the meteorological observation site, recorded as cor(x,y); use the geographic coordinates cor(x,y) to sample the above-mentioned monthly relative humidity anomaly baseline field and annual relative humidity baseline field of the i-th month, and obtain the i-th relative humidity anomaly baseline value and annual relative humidity baseline value of the meteorological observation site, recorded 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 the meteorological stations can be unified into plane coordinates using a projection tool.

[0092] S4: Calculate the monthly relative humidity anomaly and anomaly difference of the meteorological observation station in the base month. Read the monthly average relative humidity rh_i of the meteorological observation station in the base 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 base month of the same year as the simulated month, recorded as rh_i_jp. Use the monthly relative humidity anomaly of the meteorological observation station in the i-th month to subtract the monthly relative humidity anomaly baseline value of the meteorological observation station in the i-th month to obtain the monthly relative humidity anomaly difference of the meteorological observation station in the base month, recorded as Δrh_i_jp, then

[0093] Δrh_i_jp=rh_i_jp-rh_i_jp_base

[0094] S5: Obtain the monthly relative humidity anomaly difference field of the base month. Using the GIS interpolation method, take the monthly relative humidity anomaly difference Δrh_i_jp of the base month at the meteorological observation station as the point sample to obtain the spatially continuous monthly relative humidity anomaly difference field of the base month, recorded as K_Δrh_i_jp.

[0095] Optionally, the GIS interpolation method can be selected from Kriging interpolation method, inverse distance interpolation method, etc.;

[0096] Optionally, the spatial scale of the target grid data in the process of pushing the surface from point to surface is 1 km.

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

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

[0099] Use the random point generation tool commonly used in GIS software to establish enough random vector points in the whole area;

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

[0101] Taking month as the independent variable and 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] The cubic polynomial model was used to fit the curve of relative humidity anomaly baseline changing with the month, and the monthly variation curve model of relative humidity anomaly baseline in the range of January to December was obtained.

[0103] The above monthly variation curve model indicates the annual relative humidity anomaly variation law without the influence of monsoon. According to the curve model, the relative humidity monthly anomaly baseline field component under the comprehensive effect of many other factors in the rainy season from June to October without the influence of monsoon can be derived. Many other factors include topographic and geomorphic factors such as altitude, slope aspect, tall mountains, deep valleys, and vegetation cover.

[0104] The present invention assumes that the relative humidity anomaly field in the rainy season is composed of the non-monsoon influence amount and the monsoon influence amount of the relative humidity anomaly. Therefore, the non-monsoon influence amount field of the relative humidity anomaly of the month to be simulated, that is, the relative humidity anomaly field of the month to be simulated under the influence of non-monsoon, is a component field of the relative humidity anomaly field of the month to be simulated; and the monsoon influence amount field of the relative humidity anomaly of the month to be simulated, that is, the relative humidity anomaly field of the month to be simulated under the influence of monsoon, is another component field of the relative humidity anomaly field of the month to be simulated.

[0105] S7: Calculate the monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological observation station. Calculate the monthly relative humidity anomaly of the meteorological observation station in the jth month and extract the non-monsoon influence of the monthly relative humidity anomaly of the meteorological observation station in the jth month, subtract the two, and obtain the monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station, recorded as rh_j_jp_b. The monthly relative humidity anomaly of the jth month at the meteorological observation station is obtained by subtracting the relative humidity rh_j of the meteorological observation station in the month to be simulated input in the above S1 from the annual relative humidity baseline value rh_y_base of the meteorological observation station, recorded as rh_j_jp; the non-monsoon influence of the monthly relative humidity anomaly of the meteorological observation station in the jth month is obtained by sampling the non-monsoon influence field RH_j_jp_a of the relative humidity anomaly of the month to be simulated using the geographic coordinates cor(x,y) of the meteorological observation station, recorded 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, the monsoon influence field of the relative humidity anomaly of the month to be simulated rh_j_jp_b calculated by S7 at the meteorological observation station is used as the sample point, and the monsoon influence field of the relative humidity anomaly of the jth month is obtained by inferring the surface from the point, which is the monsoon influence field of the relative humidity anomaly of the month to be simulated, recorded as K_rh_j_jp_b.

[0107] Optionally, the GIS interpolation method can be selected from Kriging interpolation method, inverse distance interpolation method, etc.;

[0108] Optionally, the spatial scale of the target grid data in the process of pushing points to surfaces is 1 km.

[0109] S9: Calculate the relative humidity field of the month to be simulated in the rainy season. Perform grid addition operation on the non-monsoon influence field RH_j_jp_a of the relative humidity anomaly of the month to be simulated calculated in S6 and the monsoon influence field K_rh_j_jp_b of the relative humidity anomaly of the month to be simulated obtained in S8 to obtain the relative humidity anomaly field of the jth month, recorded as RH_j_jp; perform grid addition operation on the relative humidity anomaly field RH_j_jp of the jth month and the annual relative humidity baseline field RH_Y_base to obtain the relative humidity field of the jth month, that is, the relative humidity field of the month to be simulated in the rainy season.

[0110] To further optimize the scheme, the present invention applies a spatial simulation method and system of the relative humidity field in the rainy season to the spatial simulation and reporting of the monthly relative humidity field in June 2017 in Yunnan Province in the southern part of the Hengduan Mountain area, with April 2017 as the dry season base month. The specific process is as follows:

[0111] (1) Calculation of the monthly relative humidity anomaly baseline field in April

[0112] Obtain sparse meteorological observation site data

[0113] The relative humidity recorded by 85 ground observation stations in Yunnan Province in April and June 2017 was used as the simulated sample data, see Table 1. According to the latitude and longitude information of the ground monitoring station, it was imported into ArcMap software to generate a sample point vector file.

[0114] Table 1

[0115]

[0116]

[0117]

[0118] The monthly average relative humidity field data RH_4_base in April in the past 20 years based on the inversion of MODIS atmospheric precipitable water products is loaded as the monthly relative humidity baseline field data for April and the annual average relative humidity field RH_Y_base; and the monthly relative humidity anomaly baseline field for April is calculated according to the following formula:

[0119] RH_4_jp_base=RH_4_base-RH_Y_base.

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

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

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

[0123] Using the ID number of the meteorological station stored in the sample point vector file generated in step (1) as the link field, use the attribute table join tool to associate the original sample point vector file with the rh_4_jp_base and rh_y_base data table files sampled above, and then calculate the anomaly value rh_4_jp of the meteorological station observation data in April 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 in April 2017 recorded by the meteorological station; rh_y_base is the annual relative humidity baseline sampled by the meteorological station obtained in step (2).

[0126] The relative humidity anomaly rh_4_jp at the meteorological station in April 2017 calculated by the above formula is subtracted from the relative humidity monthly anomaly baseline value rh_4_jp_base of the meteorological observation station in April obtained by sampling in step (2) to obtain the relative humidity monthly anomaly difference Δrh_4_jp of the base month April 2017, which is as follows:

[0127] Δrh_4_jp=rh_4_jp-rh_4_jp_base

[0128] Obtain the monthly relative humidity anomaly grid data for April 2017. Using the GIS Kriging interpolation method, take the relative humidity anomaly difference Δrh_4_jp of the fourth month as the sample point, and use the point to infer the surface to obtain the surface anomaly grid data of April 2017 with a spatial resolution of 1km, recorded as K_Δrh_4_jp, as follows: Figure 2 shown.

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

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

[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 influence of non-monsoon in June to be simulated is calculated according to the following formula:

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

[0135] Among them, RH_4_jp_base is the relative humidity monthly anomaly baseline field for April in recent years, K_Δrh_4_jp is the relative humidity monthly anomaly difference grid data of the base month April in 2017, and y(6)-y(4) is the difference between the simulated month June and the base month April on the anomaly fitting curve. The relative humidity anomaly field under the influence of non-monsoon in June is as follows: Figure 3 shown.

[0136] (6) Calculate the impact of the monsoon on relative humidity anomalies at the meteorological station in June 2017

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

[0138] The geographic coordinates of the 85 meteorological stations in step (1) are used to sample the relative humidity anomaly field RH_6_jp_a to be simulated in step (5) under the influence of non-monsoon in June, and the relative humidity anomaly value of the meteorological station in June without the influence of monsoon is obtained, which is recorded as rh_6_jp_a.

[0139] Calculate the impact of the monsoon on the relative humidity anomaly in June 2017 at the meteorological station. In the attribute table of the sample point vector file in step (1), calculate the impact of the monsoon on the relative humidity anomaly in June 2017 rh_6_jp_b according to the following formula:

[0140] rh_6_jp_b=rh_6_jp-rh_6_jp_a

[0141] (7) Obtaining the relative humidity anomaly component field of the monsoon impact in June 2017

[0142] Perform GIS ordinary Kriging interpolation on the impact of the monsoon on the relative humidity anomaly in June 2017 obtained in step (6), and obtain the surface data K_rh_6_jp_b, that is, the relative humidity anomaly field of the monsoon impact in June to be simulated, such as Figure 4 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 the non-monsoon influence in June to be simulated in step (5), load the relative humidity anomaly field K_rh_6_jp_b under the monsoon influence in June to be simulated in step (7), and calculate the relative humidity anomaly field RH_6_jp in June to be simulated 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 shown.

[0147] (9) Calculate the relative humidity field of the simulated month June

[0148] Load the relative humidity anomaly field RH_6_jp for June to be simulated in step (8), load the annual relative humidity baseline field RH_Y_base in step (2), and calculate the relative humidity field RH_6 for June to be simulated 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 shown.

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

[0152] The coordinate sampling and extraction step (9) of the other 20 ground observation stations in Yunnan Province obtains the simulated value of the relative humidity in June at the meteorological station, and the meteorological station ID number is used as the associated field. The attribute table after sampling is associated with the sampled sample point vector file stored in step (1). In the associated attribute table, the relative humidity observation data of the meteorological station in June is used as the true value, and the relative humidity simulation value in June is subtracted to perform error analysis. After sampling error inspection, the relative humidity field simulation result inspection table of June is obtained as shown in Table 2. According to the conventional mathematical statistical inspection method, the average absolute error of the present invention is 1.32, and the standard error is 7.24. This shows that the error is small and the simulation result of the embodiment is reliable.

[0153] Table 2

[0154]

[0155]

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

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

[0158] A base month relative humidity monthly anomaly baseline field calculation module is used to calculate the base month relative humidity monthly anomaly baseline field based on the monthly relative humidity baseline field of the dry season base month and the annual relative humidity baseline field;

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

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

[0161] A non-monsoon influence field acquisition module is used to perform grid addition operation based on the relative humidity anomaly difference field of the base month, the relative humidity monthly anomaly baseline field of the base month, and the anomaly baseline difference between the base month and the month to be simulated, so as 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 on the relative humidity anomaly of the month to be simulated at the meteorological station, and obtain the monsoon influence component field of the relative humidity anomaly of the month to be simulated, and then calculate the relative humidity field of the month to be simulated in the rainy season.

[0163] The present invention uses the relative humidity data archived from a few sparse observation sites and supports rapid spatial simulation of the relative humidity field in the entire region with geographic grid points as units, thus solving the practical problem of missing relative humidity field data in the rainy season in the southern Hengduan Mountains.

[0164] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A spatial simulation method for relative humidity field in rainy season, characterized in that: include: Obtain the monthly relative humidity baseline field of the dry season base month, the annual relative humidity baseline field, and the base monthly relative humidity and the monthly relative humidity to be simulated at the meteorological observation station; Based on the monthly relative humidity baseline field of the dry season base month and the annual relative humidity baseline field, the base month relative humidity monthly anomaly baseline field is calculated; Obtain the monthly anomaly baseline value and annual relative humidity baseline value of the base month at the meteorological observation station, and calculate the monthly relative humidity anomaly and anomaly difference of the base month at the meteorological observation station; Obtain the relative humidity anomaly difference field of the base month, and calculate the anomaly baseline difference between the base 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 monthly relative humidity anomaly difference field of the reference month, the monthly relative humidity anomaly baseline field of the reference month, and the anomaly baseline difference between the reference month and the month to be simulated, a grid addition operation is performed to obtain the non-monsoon influence field of the monthly relative humidity anomaly to be simulated; The monsoon influence on the relative humidity anomaly of the month to be simulated at the meteorological station is calculated, and the relative humidity field of the month to be simulated in the rainy season is calculated after obtaining the monsoon influence component field of the relative humidity anomaly of the month to be simulated.

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

3. The spatial simulation method of relative humidity field in rainy season according to claim 1, characterized in that: The monthly relative humidity baseline field is the monthly average relative humidity field grid data of several years, which is obtained by the grid arithmetic mean operation of the relative humidity fields of several months; The acquisition process of the monthly relative humidity baseline field includes: The monthly average product of atmospheric precipitable water in the base year MODIS and the actual water vapor pressure data observed by ground meteorological stations were used to obtain the monthly average ground water vapor pressure grid data with a spatial scale of 1 km through remote sensing inversion and GIS assimilation technology. Based on the monthly average surface temperature product of MODIS and the monthly average temperature data observed by ground meteorological stations, the monthly average temperature raster data with a spatial scale of 1 km was obtained through GIS assimilation technology. According to the meteorological calculation formula of relative humidity, the monthly average relative humidity grid data at a spatial scale of 1 km is calculated as the monthly relative humidity baseline field.

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

5. The spatial simulation method of relative humidity field in rainy season according to claim 1, characterized in that: The process of obtaining the monthly anomaly baseline value and annual relative humidity baseline value of the base month at the meteorological observation station and calculating the monthly relative humidity anomaly and anomaly difference of the base month at the meteorological observation station includes: The monthly anomaly baseline field of the relative humidity in the base month and the annual relative humidity baseline field are sampled according to the geographical coordinates of the meteorological observation site, and the monthly anomaly baseline value of the relative humidity in the base month and the annual relative humidity baseline value at the meteorological observation site are extracted; The monthly relative humidity anomaly of the meteorological observation station in the base month is obtained by subtracting the annual relative humidity baseline value from the monthly relative humidity to be simulated at the meteorological observation station; the monthly relative humidity anomaly baseline value of the base month is subtracted from the monthly relative humidity anomaly of the base month to obtain the monthly relative humidity anomaly difference of the meteorological observation station in the base month.

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

7. The spatial simulation method of relative humidity field in rainy season according to claim 1, characterized in that: The formula expression of the fitting model of the monthly variation curve of the relative humidity monthly anomaly baseline field is: y(x)=-0.12x3+2.17x2-9.17x-3.6 Where x is the month and y is the deviation from the baseline; The process of obtaining the monthly variation curve fitting model of the relative humidity monthly anomaly baseline field includes: Use the random point generation tool commonly used in GIS software to establish enough random vector points in the whole area; Taking the geographical location of the random vector point as the standard, sampling the relative humidity monthly anomaly baseline field from January to May and November to December in the dry season to obtain the dry season relative humidity anomaly baseline sample data; Taking month as the independent variable and the dry season relative humidity anomaly baseline sample as the dependent variable, a scatter plot is drawn to obtain the cubic curve relationship between the two. According to the cubic curve relationship, a cubic polynomial model is used to fit the curve of the relative humidity deviation from the baseline with the month, and a monthly variation curve fitting model of the relative humidity deviation from the baseline in the range of January to December is obtained.

8. The spatial simulation method of relative humidity field in rainy season according to claim 1, characterized in that: The process of calculating the monsoon influence on the relative humidity anomaly of the month to be simulated at the meteorological station includes: The relative humidity of the month to be simulated observed at the meteorological station 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.

9. The spatial simulation method of relative humidity field in rainy season according to claim 1, characterized in that: The process of obtaining the monsoon impact component field of the relative humidity anomaly of the month to be simulated and then calculating the relative humidity field of the month to be simulated in the rainy season includes: Using the GIS interpolation method, taking the monsoon impact amount of the monthly relative humidity anomaly to be simulated at the meteorological station as a point sample, a spatially continuous monsoon impact component field of the monthly relative humidity anomaly to be simulated is obtained; The non-monsoon influence field of the relative humidity anomaly of the month to be simulated and the monsoon influence component field of the relative humidity anomaly of the month to be simulated are subjected to grid addition operation to obtain the relative humidity anomaly field of the rainy season month to be simulated; the relative humidity anomaly field of the rainy season month to be simulated and the annual relative humidity baseline field are subjected to grid addition operation to obtain the relative humidity field of the rainy season month to be simulated.

10. A spatial simulation system for relative humidity field in rainy season, characterized in that: include: A data acquisition module is used to obtain the monthly relative humidity baseline field of the dry season base month, the annual relative humidity baseline field, and the base monthly relative humidity and the monthly relative humidity to be simulated of the meteorological observation station; A base month relative humidity monthly anomaly baseline field calculation module is used to calculate the base month relative humidity monthly anomaly baseline field based on the monthly relative humidity baseline field of the dry season base month and the annual relative humidity baseline field; The module for calculating the monthly relative humidity anomaly and anomaly difference is used to obtain the monthly anomaly baseline value and the annual relative humidity baseline value of the base month at the meteorological observation site, and calculate the monthly relative humidity anomaly and anomaly difference of the base month at the meteorological observation site; The anomaly baseline difference calculation module is used to obtain the relative humidity anomaly difference field of the base month, and calculate the anomaly baseline difference between the base month and the month to be simulated based on the fitting model of the monthly variation curve of the relative humidity monthly anomaly baseline field; A non-monsoon influence field acquisition module is used to perform grid addition operation based on the relative humidity anomaly difference field of the base month, the relative humidity monthly anomaly baseline field of the base month, and the anomaly baseline difference between the base month and the month to be simulated, so as to obtain the non-monsoon influence field of the relative humidity anomaly of the month to be simulated; The relative humidity field calculation module is used to calculate the monsoon influence on the relative humidity anomaly of the month to be simulated at the meteorological station, and obtain the monsoon influence component field of the relative humidity anomaly of the month to be simulated, and then calculate the relative humidity field of the month to be simulated in the rainy season.

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