CN111401634A - Processing method, system and storage medium for acquiring climate information - Google Patents
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Abstract
The invention belongs to the technical field of information processing, and discloses a method, a system and a storage medium for acquiring climate information processing, which convert monthly mean data of coarse resolution in a global system mode into grid point data of global climate background fields by a method of longitude and latitude conversion, grid conversion, projection conversion and interpolation to adjacent points; carrying out localized configuration through a regional climate mode WRF, and continuously combining and preferably selecting a physical parameterization scheme and a numerical mode resolution; and integrating the WRF mode by using a forced field output by the earth system mode, and outputting a local meteorological element simulation field with the horizontal resolution of 1 km. The invention realizes the refined forecast and prediction of regional weather and climate events under the condition of local complex terrain, and can perform reliable simulation evaluation and prediction of the weather conditions on the complex terrain and the underlying surface by using the 1km horizontal high-resolution data output by a numerical mode.
Description
Technical Field
The invention belongs to the technical field of information processing, and particularly relates to a processing method, a system and a storage medium for acquiring climate information.
Background
At present, the downscaling method mainly comprises global numerical mode dynamic downscaling with high resolution or mixed resolution, wherein the dynamic downscaling method can select a regional climate modelAnalog implementation of equation (iv). The regional mode is widely used in the field of climate research due to its higher resolution, can reasonably characterize the current climate, and is widely used in global warming research. Because the resolution of the regional mode is obviously improved compared with the global mode, the simulation capability of the regional mode on local weather and climate is obviously improved. High quality and accurate weather service is supported without the need for high temporal and spatial resolution weather field data. The traditional weather observation station data provides long-time sequence weather data, but is influenced by the unbalance of regional development, the representativeness of the layout of the weather observation stations and the like, the requirements of weather resource demonstration and evaluation cannot be completely met at present, and the refined demonstration and evaluation even cannot be realized for sparse regions of observation stations. Existing weather reanalysis data can provide grid climate data for the last decades, but spatial resolution is still relatively low, and existing reanalysis data are only historical grid data and do not have predictive data for the future. Thus, to obtain higher horizontal resolution and predicted data for future climates than existing reanalyzed data, the earth system model can be modeled at different COs by regional climate or mesoscale weather patterns2And (3) carrying out power downscaling on the estimated data in the emission situation to generate a high-resolution weather field data set in the future of 50-100 years, so that the accuracy of prediction on the future local temperature or precipitation can be improved, and a decision and reference basis is provided for the possible future occurrence of the disastrous weather or climate change trend.
At present, the popular power downscaling method is to nest a global climate mode and a high-resolution regional climate mode, so as to improve the simulation performance of regional climate, especially complex underlying regional climate, by using higher resolution of the regional mode and a more detailed physical process parameterization scheme. With the improvement of computer technology, the long-time integration of the high-resolution area mode is shorter and shorter, so that the timeliness of the application of the power downscaling method in climate prediction is guaranteed, and the application is wider and wider. However, most of the current grid point data predicted by using the global model comes from historical reanalysis data, and is mainly provided by organizations such as national atmospheric research center (NCAR), middle european weather forecast center (ECMWF), and japan meteorological office (JMA), and the future global climate prediction grid point data is relatively small.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) although the global climate re-analysis data can provide grid data as a forced field driven regional climate mode, all grid data are assimilation data based on the past 30-50 almanac history observation data, and therefore future weather and climate events cannot be forecast.
(2) The horizontal resolution of the global climate pattern reanalysis data is thicker (2.5 × 2.5.5 degrees or 1 × 1 degrees), the horizontal level is about 250km or 100km, the prediction effect is good for an area with a larger horizontal scale, but the accurate prediction cannot be made for the local weather climate with a smaller horizontal scale (below 10 km), so that the high-resolution simulation and prediction for the weather information under the local complex terrain conditions cannot be made by using the result of the global climate pattern.
(3) Although the earth system mode can provide global grid point meteorological data for 50-100 years in the future, the horizontal resolution is also coarse (2.5 × 2.5 degrees), and a set of methods and technologies which are generally applicable to regional mode dynamic downscaling earth system mode data are lacked at present, and particularly, it is difficult to generate a regional climate mode driving field.
The difficulty in solving the above problems and defects is:
(1) there is a need to extract meteorological elements in earth system mode for driving regional climate mode and to quickly process and generate global climate background field lattice data. Longitude and latitude conversion, grid conversion, projection conversion, adjacent point interpolation and high-altitude layer sequencing are required to be carried out on non-uniform grid points of the earth system mode so as to generate boundary conditions and initial values of regional climate modes.
(2) Local configuration is needed to be carried out on regional climate modes WRF, a physical parameterization scheme and a numerical mode resolution are selected through continuous combination, and finally a simulation result with the horizontal resolution reaching 1km is output.
The significance of solving the problems and the defects is as follows: book (I)The invention has high simulation space and time resolution, dense vertical atmosphere layering, and can be used for different CO for 50-100 years in the future2And carrying out downscaling local prediction on the climate data of the emission scene. The system also has the functions of multiple nesting, parallel calculation, graphical display of results and the like, can improve the accuracy of prediction of future local temperature or precipitation, and provides decision and reference basis for government departments or meteorological departments for possible future disastrous weather or climate change trends.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a processing method, a system and a storage medium for acquiring climate information.
The invention is realized in such a way that a processing method for acquiring climate information comprises the following steps:
converting coarse resolution monthly mean data in a global system mode into global climate background field lattice point data by a method of longitude and latitude conversion, grid conversion, projection conversion and interpolation to adjacent points; the rough-resolution monthly-average data comprises rough-resolution climate data output in a global earth system mode for 6 hours, wherein the climate data comprises land utilization types, water vapor, surface air pressure, sea ice, soil temperature and humidity, temperature, sea temperature, surface temperature, latitudinal direction and transverse direction wind of each layer above land; the projection conversion method comprises a numerical transformation method; interpolation methods, including a near point interpolation method, a linear interpolation method, a cubic interpolation method;
secondly, carrying out local configuration through a regional climate mode, and continuously combining and preferably selecting a physical parameterization scheme and a numerical mode resolution; the regional climate mode comprises a mesoscale regional mode and a post-processing module, wherein the mesoscale regional mode comprises a weather research and forecast mode, a regional climate mode and a regional weather mode; the post-processing module comprises a grid analysis and display system, a scientific data processing and visual design language system and a geographic data grid drawing system;
and thirdly, integrating the WRF mode by using a forced field output by the earth system mode, and outputting a local meteorological element simulation field with the horizontal resolution of 1 km.
Further, the climate information acquisition processing method utilizes CDO software to extract climate information generated by ESM and convert the climate information into data once in 6 hours, wherein the data once in 6 hours comprise 00 hours, 06 hours, 12 hours and 18 hours in world time every day, and the climate information comprises land utilization type, water vapor, surface air pressure, sea ice, soil temperature and humidity, temperature, sea temperature, surface temperature and latitudinal direction and warp direction wind of each layer above land.
Further, the method for processing the obtained climate information can be used for processing historical data and different COs in the future2Selecting path data under the emission scene, formulating the year of initial extraction, renaming the extracted data name, performing projection conversion to generate 0.9-degree × 1.25.25-degree land surface coverage and land-sea boundary grid data and surface potential height data, wherein the contents of selection, extraction and renaming comprise selection historical data and different CO in the future2Path data under emission scenarios, the historical data including monthly mean temperature, water vapor, surface pressure, sea ice, soil temperature and humidity, sea temperature, surface temperature, and latitudinal grid point data of each layer above land generated by earth system mode for 1850 years, the future different CO2The emission scenarios include four greenhouse gas concentration scenarios, ranging from low to high different representative path concentrations (RCPs) of RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively, with the latter numbers representing the radiation compelling level 2.6W m up to 2100 years-2To 8.5Wm-2。
Further, the first step of converting the coarse resolution month average data in the earth system mode into global climate background field lattice point data by longitude and latitude conversion, grid conversion, projection conversion and interpolation to adjacent points includes:
(1) reading the extracted data, compiling a script program, and converting a projection mode and longitude and latitude to be matched with the BNU-ESM; interpolating the earth surface potential height and sea-land coverage data, writing the descriptions of the execution source grid and the target grid into a NetCDF file by a difference method by using a near point matching method, generating weights, writing the weights into the NetCDF file, redefining the data by applying the weights and copying metadata as much as possible; the important point of the interpolation method is the source nearest to the target method, and each target point is mapped to the nearest source point;
(2) projecting, converting and interpolating sea surface temperature and sea ice, redefining sea temperature and sea ice variables on POP grids as latticed with latitude/longitude of 0.9 degree × 1.25.25 degrees;
(3) calculating soil moisture data in kg/m2Converting into percentage, and interpolating the soil temperature and humidity to 4 layers below the earth surface again;
(4) calculating the air pressure-potential height of each layer above the earth surface, calculating the relative humidity of each layer, and calculating the 2-m temperature, the 2-m relative humidity, the 2-m water vapor mixing ratio and the 10-m latitudinal and longitudinal wind near the earth surface;
(5) carrying out dynamic downscaling simulation on the WRF high-resolution regional climate mode by adopting the generated boundary driving field data for once every 6 hours; initializing regional high-resolution terrain fields, atmospheric initial fields and static earth surface data information through a preprocessing system WRF, and then interpolating to a pattern grid region.
Further, the static surface data includes underlying vegetation type, soil type, terrain height, vegetation coverage.
Further, the air pressure-potential heights of the layers above the earth surface are calculated, the relative humidity of each layer is calculated, the 2-m temperature, the 2-m relative humidity, the 2-m water vapor mixing ratio and the latitudinal direction and the transverse direction wind at 10-m near the earth surface are calculated, the air pressure layers are redefined to be 27 layers and the air pressure values of each layer are 1000.0, 975.0, 950.0, 925.0, 900.0, 850.0, 800.0, 750.0, 700.0, 650.0, 600.0, 550.0, 500.0, 450.0, 400.0, 350.0, 300.0, 250.0, 200.0, 150.0, 100.0, 70.0, 50.0, 30.0, 20.0 and 10.0 hectopascal, and the initial conditions, the underlying surface and the boundary conditions for driving the climate mode of the area are generated.
Further, the second step is to carry out localization configuration through a regional climate mode WRF, the localization configuration content comprises a physical parameterization scheme and a numerical mode horizontal space and vertical resolution which are continuously combined and optimized through regional high-resolution numerical mode system configuration of the WRF, the physical parameterization scheme comprises a micro-physics scheme, a radiation transmission scheme, an atmospheric boundary layer scheme, a land process scheme and a cloud parameterization scheme, the horizontal and vertical resolution comprises setting of a simulation region horizontal grid interval and atmospheric vertical layering, a terrain following quality η coordinate is adopted, a vertical layer is encrypted to 50 layers by a nonlinear hyperbolic tangent method, and the atmospheric pressure at the top of the model is 50 hPa.
Further, the η value is 1.000, 0.9947, 0.9895, 0.9843, 0.979, 0.9739, 0.9684, 0.9626, 0.9564, 0.9498, 0.9426, 0.9348, 0.9262, 0.9167, 0.9062, 0.8946, 0.8816, 0.8671, 0.8509, 0.833, 0.813, 0.7909, 0.7667, 0.7402, 0.7116, 0.6809, 0.6483, 0.6141, 0.5785, 0.5419, 0.5047, 0.4672, 0.4299, 0.3931, 0.357, 0.322, 0.2883, 0.256, 0.2253, 0.1963, 0.169, 0.1435, 0.1171, 0.0952, 0.0753, 0.0571, 0.0407, 0.0257, 0.0122, 0.000.
It is another object of the present invention to provide a program storage medium for receiving user input, the stored computer program causing an electronic device to perform the steps comprising:
converting coarse resolution monthly mean data in a global system mode into global climate background field lattice point data by a method of longitude and latitude conversion, grid conversion, projection conversion and interpolation to adjacent points;
secondly, carrying out local configuration through a regional climate mode WRF, and continuously combining and preferably selecting a physical parameterization scheme and a numerical mode resolution;
and thirdly, integrating the WRF mode by using a forced field output by the earth system mode, and outputting a local meteorological element simulation field with the horizontal resolution of 1 km.
Another object of the present invention is to provide an acquired climate information processing system implementing the acquired climate information processing method, the acquired climate information processing system including:
the data conversion module is used for converting the coarse resolution monthly average data in the earth system mode into global climate background field lattice point data;
the optimization module is used for carrying out local configuration through a regional climate mode WRF, and continuously combining and optimizing a physical parameterization scheme and a numerical mode resolution;
and the output module is used for integrating the WRF mode by utilizing the forced field output by the earth system mode and outputting a local meteorological element simulation field with the horizontal resolution of 1 km.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention relates to a method for carrying out dynamic downscaling on an earth system mode output data product by utilizing a mesoscale regional climate mode. The initial conditions, underlying surface and boundary conditions of the driving region climate mode are provided by the global system mode BNU-ESM of university of Beijing teachers, which is updated every 6 hours. Boundary conditions such as sea temperature, sea ice are also provided by BNU-ESM historical climate simulation. The time of test simulation is 1980-2005. The forced field data is integrated with the high-resolution regional climate mode, and a horizontal simulation field with the resolution of 1km and a regional meteorological element simulation result with the time resolution of 1 hour can be output as historical inspection. The method and the device can be used for locally and finely predicting the future weather and climate.
The invention uses regional climate WRF mode system to dynamically downscale global climate data output by earth system mode, thus solving the problem of finely forecasting and predicting regional weather climate events under local complex terrain conditions, and can reliably simulate, evaluate and predict the weather conditions on complex terrain and underlying surface by using 1km level high-resolution data output by numerical mode.
The invention can treat different CO for 50-100 years in the future2Carrying out downscaling local prediction on climate data of the emission scene; the method has the characteristics of optimized localization parameters, high simulation space and time resolution and dense vertical atmosphere layering. The invention also integrates earth system mode data extraction, data projection conversion, longitude and latitude conversion and grid conversion, has the functions of multiple nesting, parallel calculation, graphical display of results and the like, is convenient and feasible for application and demonstration popularization, and can improve the future bureauThe accuracy of the prediction of the earth temperature or the precipitation provides decision-making and reference basis for the government department or the meteorological department to the possible disastrous weather or climate change trend in the future.
The invention firstly carries out the power downscaling experiment on the Chongqing area in the southwest of China, and establishes a simple and practical method operation flow. The invention provides a better idea and method for climate estimation and evaluation of the ecological vulnerable area affected by climate change in China in the future.
Drawings
Fig. 1 is a flowchart of a processing method for acquiring climate information according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a climate information acquisition processing system according to an embodiment of the present invention;
in the figure: 1. a data conversion module; 2. a preference module; 3. and an output module.
Fig. 3 is a schematic diagram of a 6-hour output result of the earth system mode BNU-ESM after the projection conversion and the longitude and latitude conversion according to the embodiment of the present invention.
In the figure: (a) global earth surface pressure field (unit: hPa); (b) a 2m temperature field (unit: K); (c) surface temperature (unit: K); (d) altitude (unit: m); (e) land soil water content (unit:%); (f) land soil temperature (unit: K).
Fig. 4 is a schematic elevation view of Chongqing areas in China and southwest provided by an embodiment of the invention.
Fig. 5 is a schematic view of land utilization/vegetation types of an underlying surface in a Chongqing area according to an embodiment of the invention.
FIG. 6 is a schematic diagram of a WRF mode power downscaling simulation Chongqing regional ground meteorological element simulation result provided by an embodiment of the invention;
in the figure: (a) surface pressure (unit: hPa); (b)2m air temperature is superposed with a 10m horizontal wind field (unit: DEG C); (c) specific humidity at 2m (unit: g/kg); (d) cumulative precipitation (unit: mm); (e) the temperature of the soil at 10cm (unit:. degree. C.); (f) water content of soil at 10cm (unit: cm)3/cm3)。
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a processing method, a system, a storage medium, and a weather observation station for acquiring weather information, and the present invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the processing method for acquiring climate information provided by the present invention includes the following steps:
s101: converting the coarse resolution monthly mean data in the earth system mode into global climate background field lattice point data by a method of longitude and latitude conversion, grid conversion, projection conversion and interpolation to adjacent points;
s102: carrying out local configuration through a regional climate mode WRF, and continuously combining and preferably selecting a physical parameterization scheme and a numerical mode resolution;
s103: and integrating the WRF mode by using a forced field output by the earth system mode, and outputting a local meteorological element simulation field with the horizontal resolution of 1 km.
As shown in fig. 2, the system for processing climate information according to the present invention comprises:
and the data conversion module 1 is used for converting the coarse resolution monthly average data in the earth system mode into global climate background field lattice point data.
And the optimization module 2 is used for performing localized configuration through a regional climate mode WRF, and continuously combining and optimizing a physical parameterization scheme and a numerical mode resolution.
And the output module 3 is used for integrating the WRF mode by utilizing the forced field output by the earth system mode and outputting a local meteorological element simulation field with the horizontal resolution of 1 km.
The processing method for acquiring the climate information provided by the invention specifically comprises the following steps:
firstly, land use types, water vapor, surface air pressure, sea ice, soil temperature and humidity, temperature, sea temperature, surface temperature, latitudinal direction and transverse direction of each layer above the land are extracted by utilizing Climate Data Operators (CDO) software and converted into Data once in 6 hours.
Second, a script program is written to execute the selection of historical data and future different COs2And (3) setting the route data under the discharge scene, setting the initial extraction year, naming the extracted data name, and performing projection conversion to generate land surface coverage and land-sea boundary grid data and surface potential height data of 0.9 degrees × 1.25.25 degrees.
Reading the data extracted in the two steps, compiling a script program, and converting a projection mode and longitude and latitude to be matched with the BNU-ESM; the method comprises the steps of interpolating the earth surface potential height and sea-land coverage data, writing descriptions of an execution source grid and a target grid into a NesterdF file by a difference method through a near point matching method (nearest), generating weights, writing the weights into the NesterdF file, redefining the data and copying metadata (attribute and coordinate arrays) as much as possible through the application of the weights. The important point of the interpolation method is the source (nearest) closest to the target method. In this approach, each target point maps to the nearest source point to ensure that sea-land distribution and land-table coverage do not occur as decimal values.
And fourthly, recalculating sea ice coverage, namely, converting the percentile representation into decimal representation, performing projection conversion and interpolation on sea surface temperature and sea ice, and redefining sea temperature and sea ice variables on a POP grid into a latitude/longitude grid (0.9-degree × 1.25.25-degree grid).
Fifthly, calculating soil humidity data with unit of kg/m2Converted to a percentage and re-interpolated to 4 layers below the surface for soil temperature and humidity.
And sixthly, calculating the air pressure-potential height of each layer above the earth surface, and calculating the relative humidity of each layer. The 2-m temperature, 2-m relative humidity, 2-m water-vapor mixture ratio and 10-m latitudinal and longitudinal winds near the earth's surface were calculated. The air pressure layer is redefined to be 27 layers and the air pressure values of the layers (1000.0, 975.0, 950.0, 925.0, 900.0, 850.0, 800.0, 750.0, 700.0, 650.0, 600.0, 550.0, 500.0, 450.0, 400.0, 350.0, 300.0, 250.0, 200.0, 150.0, 100.0, 70.0, 50.0, 30.0, 20.0, 10.0 hectopascal). And finally generating initial conditions, underlying surfaces and boundary conditions for driving the regional climate modes.
And seventhly, performing power downscaling simulation on the WRF high-resolution regional climate mode by adopting the boundary driving field data generated by the method once in 6 hours. The regional high-resolution terrain field, the atmospheric initial field, static land surface data (underlying vegetation type, soil type, terrain height and vegetation coverage) and other information are initialized and interpolated to the pattern grid region through a Preprocessing System (WRF Preprocessing System).
And eighthly, selecting a proper physical parameterization scheme, horizontal and vertical spatial resolutions by configuring a WRF regional high-resolution numerical mode system, wherein the system adopts a terrain following mass η coordinate, and a vertical layer adopts a nonlinear hyperbolic tangent method (hyperbaric tangent) to be encrypted to 50 layers (η values are set to be 1.000, 0.9947, 0.9895, 0.9843, 0.979, 0.9739, 0.9684, 0.9626, 0.9564, 0.9498, 0.9426, 0.9348, 0.9262, 0.9167, 0.9062, 0.8946, 0.8816, 0.8671, 0.8509, 0.833, 0.813, 0.7909, 0.7667, 0.7402, 0.7116, 0.6809, 0.6483, 0.6141, 0.5785, 0.5419, 0.5047, 0.5047, 0.4299, 0.5047, 0.357, 0.322, 0.5047, 0.256, 0.5047, 36169, 0.169, 0.5, 0.5047, 0.5047, 0.5047, 0.5047, 3650 a and hPa modes are adopted as a modes.
The technical solution of the present invention is further described below with reference to the accompanying drawings.
The BNU-ESM earth system mode adopts a NCAR-CP L6.5.5 coupler, is developed on the basis of the American atmospheric science research center, modifies coupling flux and a data transmission interface module between the components according to the requirements of each coupled component mode, realizes the exchange of energy and substances between ocean and atmosphere, atmosphere and land interaction interfaces, and the circulation of elements such as carbon and the like among the component modes, wherein the atmospheric component module is based on a NCAR general atmospheric mode CAM3.5, comprises troposphere ozone and a related measured gas chemical transmission Module (MOZART), improves a cloud convection parameterization scheme, perfects a volcano aerosol module 3.5, perfects a bipolar parallel operation mode of the CAM3.5, improves the overall operation performance of the U-ESM, adopts a BNM 4p1 module of an American geohydrodynamic laboratory (GFD L), improves a marine biochemical scheme, adopts a BNU-ESM 4.1.2 and a BNU-ESM mode of an Almorus national laboratory, and adopts a BNU-ESM mode of an equatorial ice mode of about 50.1.8.1.8.8.8.8.26 and a sea ice mode of an ocean vertical ice mode of an equatorial layer.
FIG. 3 shows data of global earth surface pressure field (unit: hPa), 2m temperature field (unit: K), earth surface temperature (unit: K), altitude (unit: m), land soil water content (unit:%) and land soil temperature (unit: K) extracted by BNU-ESM after script program conversion, longitude and latitude conversion and projection conversion and once output for 6 hours. It can be seen that the ground surface air pressure field is a low pressure center in the Qinghai-Tibet plateau area of China, which is about 600hPa, and the sea level air pressure is relatively uniform, which is about 1000 hPa. The distribution of the 2m temperature field is higher in the northern hemisphere in 7 th of the month, and the temperature on land is higher than that on the sea except the Qinghai-Tibet plateau in China; the distribution of the surface temperature is similar to the 2-m temperature, but the surface temperature value is about 20-30 ℃ higher than the 2-m temperature. The global distribution condition of the terrain elevation field is similar to that of the ground pressure field, the altitude of the Tibet plateau is about 6000m, but the detail condition of the surrounding terrain relief area is difficult to identify in the global distribution map. The distribution of soil moisture on the global land is uniform, about 10-20% but local distribution details are difficult to see; the soil temperature and surface temperature profiles are similar. In conclusion, because the resolution of the output result of the BNU-ESM is low (about 2.8 °), it is difficult to embody the horizontal distribution details and distribution rules of the local meteorological elements, which requires a higher resolution and a more detailed physical process parameterization scheme in a regional mode, and thus, the simulation performance of regional climate, especially the climate of a complex underlying region, is improved.
According to the invention, a regional climate high-resolution regional climate mode (WRF mode) is adopted for carrying out power downscaling on the BNU-ESM output result, and initial field and boundary field data used in the WRF mode are global water vapor, air pressure of each layer, sea ice, soil temperature and humidity, temperature, sea temperature, earth surface temperature and latitudinal and transverse wind information of each layer above the land at 00, 06, 12 and 18 times of the world after being converted by a script program. And the simulation time period is 2015, 13 months, 00 days to 7 months, 15 days and 18 days, taking the Chongqing mountain area in the southwest region of China as an example, performing a power downscaling historical evaluation experiment on the BNU-ESM output result by using a WRF mode, and verifying the result. Table 1 shows the prediction region nested grid parameters.
TABLE 1 forecast region nested grid parameters
According to the method, through a regional high-resolution numerical model WRF, global meteorological field data output by BNU-ESM are subjected to a power downscaling experiment, a parameterization scheme of the WRF mode is configured locally, a physical parameterization scheme and a numerical mode resolution suitable for Chongqing mountainous and hilly regions are selected, and a meteorological element simulation field with the horizontal resolution of 1km in the Chongqing regions is simulated through WRF mode integration. The accuracy of the mesoscale regional mode in predicting the local temperature or precipitation under the complex terrain condition can be improved, and decision and reference basis is provided for government departments or meteorological departments for possible disastrous weather or climate change trends in the future.
Taking the Chongqing area as an example, after a dynamic downscaling experiment, the terrain and the mountain range trend of the Chongqing area can be reflected more truly in the simulated underlying surface terrain and vegetation type graphs (fig. 4 and 5). Fig. 5 shows the land utilization type and vegetation type distribution of the underlying surface in the Chongqing area, and it can be seen that pink color scale in the area is urban and construction land, most of farmland, broad-leaved forest and the like are distributed around the Chongqing main urban area, and the light blue color scale part is water and is a Yangtze river. The north and western regions in this area are mostly mountainous regions, and the western and northern regions are distributed with landed broad-leaved forests and farmlands, which also conform to the actual underlying surface type.
Fig. 6(a) shows the regional distribution of the ground surface air pressure at 14 days (noon) in 7/13/2015, and it can be seen that the ground surface air pressure distribution in the whole simulation area is very uneven, because the Chongqing area has many mountainous regions and large altitude difference, the ground surface air pressure can reach about 980hPa in the valley region, and in the mountainous regions in the west and the southeast, the terrain is complex, the mountains are many, the distribution of the air pressure is uneven, the air pressure can reach about 900hPa at the lowest, which is equivalent to the air pressure at high altitude of 1000m, which is consistent with the distribution of the Chongqing area altitude in fig. 3. Because the resolution ratio of the global model is coarse, the horizontal non-uniform distribution condition of the local atmospheric pressure field in the Chongqing area cannot be reflected in the result of the BNU-ESM, and after the dynamic downscaling experiment, refined terrain height data is added, so that the distribution of the horizontal atmospheric pressure field meets the actual condition.
FIG. 6(b) is a superimposed view of 2-m air temperature and 10-m air field, and it can be seen that the air temperature at 2m on the ground of the area is higher at 14 noon, and exceeds 25 ℃ in both the valley and valley areas, but is lower at the mountain areas, and lower than 20 ℃, and is around 20 ℃ in the southeast mountain areas. The background wind field of the whole area is mainly west wind and north wind, the wind speed is about 2-3 m/s, the large value can reach 5m/s, the wind direction of the north area is mainly north wind, and the wind direction of the south area is mainly west wind. Fig. 6(c) shows the distribution of the specific humidity in the Chongqing area, which shows that the specific humidity on the ground near the Chongqing area is also very uneven, the lowest value is about 15g/kg, and the specific humidity is distributed in a mountain area with higher altitude, while the specific humidity in the valley area is obviously increased to about 18g/kg, because the Chongqing area is in the intersection area of the Yangtze river and the Jialing river, the main stream of the Yangtze river traverses the whole situation from west to east, and the flow length is up to 665 km. Under the dual functions of terrain and water vapor, the water vapor content in the valley areas is high, and the average relative humidity is more than 70-80%. And in mountainous areas, the water vapor content in the air is lower due to higher altitude. The climate features influenced by the topography cannot be reflected in the simulation result of the coarse resolution of the earth system mode, and after the dynamic downscaling, the terrain fineness and the horizontal resolution are improved by the regional mode, so that the climate features can be consistent with the actual condition. It can also be seen from the soil moisture and soil temperature level profiles (fig. 6e, 6f) in the Chongqing area that: the soil temperature is characterized by high valley zone and low mountain zone, and is similar to the distribution of 2-m air temperature. Soil moisture is characterized by high water content of soil along the Yangtze river basin. FIG. 6d is a graph of the horizontal distribution of the cumulative 48 hour precipitation over the simulation period, showing that: the precipitation of the mountainous area in the southeast is high and can reach about 200 mm. And inquiring the evaluation of the influences of the Chongqing climate in 7 months in 2015 (http:// www.weather.com.cn/chongqing/qxfwcp/yqhpj/08/2368516.shtml), wherein storm disaster events occur in Chongqing areas in 14 months, rainstorm disasters occur in 14 months in 7 months, and 19 days 08 in 7 months and 15 days in 14 months in Chongqing cities, 19 stations of rainstorm are monitored by 19 national weather stations in 19 districts of Chongqing cities, Beibei, Ba nan, Hechuan, copper beam, Dazu, Bingshan, Rongchang, Yongchuan, Jiangjiang, Qijiang, Wansheng, Nanchuan, Yuyou Yang, Peng shu, Zhongxian, 3 stations of heavy rain, 123.1mm (Ba nan) of the maximum daily rainfall value of the national station, and 134.6mm (Ba nan, Peng station) of the regional station. And (4) comprehensively judging according to the rainstorm assessment indexes (only counting the national weather stations), wherein the comprehensive index of the rainstorm process is 0.36, the comprehensive intensity is severe, and the rainstorm process is the area rainstorm process with the second highest intensity in 2015. The WRF mode can accurately simulate and evaluate the rainstorm event of the local area in the Chongqing area, can improve the accuracy of local temperature or precipitation prediction, and provides decision and reference basis for the possibly occurring disastrous weather or climate change trend in the future.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A processing method for acquiring climate information is characterized by comprising the following steps: the processing method for acquiring the climate information comprises the following steps:
converting coarse resolution monthly mean data in a global system mode into global climate background field lattice point data by a method of longitude and latitude conversion, grid conversion, projection conversion and interpolation to adjacent points; the rough-resolution monthly-average data comprises rough-resolution climate data output in a global earth system mode for 6 hours, wherein the climate data comprises land utilization types, water vapor, surface air pressure, sea ice, soil temperature and humidity, temperature, sea temperature, surface temperature, latitudinal direction and transverse direction wind of each layer above land; the projection conversion method comprises a numerical transformation method; interpolation methods, including a near point interpolation method, a linear interpolation method, a cubic interpolation method;
secondly, carrying out local configuration through a regional climate mode, and continuously combining and preferably selecting a physical parameterization scheme and a numerical mode resolution; the regional climate mode comprises a mesoscale regional mode and a post-processing module, wherein the mesoscale regional mode comprises a weather research and forecast mode, a regional climate mode and a regional weather mode; the post-processing module comprises a grid analysis and display system, a scientific data processing and visual design language system and a geographic data grid drawing system;
and thirdly, integrating the WRF mode by using a forced field output by the earth system mode, and outputting a local meteorological element simulation field with the horizontal resolution of 1 km.
2. The method as claimed in claim 1, wherein the method for processing climate information acquisition utilizes CDO software to extract climate data generated by ESM and convert the data into data of once in 6 hours, wherein the once in 6 hours includes 00 hours, 06 hours, 12 hours and 18 hours of world time each day, and the climate data includes land use type, water vapor, surface pressure, sea ice, soil temperature and humidity, temperature, sea temperature, surface temperature, and latitudinal direction and transverse direction wind of each layer above land.
3. The method of claim 1, wherein the method of processing climate information acquisition is performed on historical data and different COs in the future2Selecting path data under the emission scene, formulating the year of initial extraction, renaming the extracted data name, performing projection conversion to generate 0.9-degree × 1.25.25-degree land surface coverage and land-sea boundary grid data and surface potential height data, wherein the contents of selection, extraction and renaming comprise selection historical data and different CO in the future2Path data under emission scenarios, the historical data including monthly mean temperature, water vapor, surface pressure, sea ice, soil temperature and humidity, sea temperature, surface temperature, and latitudinal grid point data of each layer above land generated by earth system mode for 1850 years, the future different CO2The emission scenario includes four greenhouse gas concentration scenarios, with low to high different representative path concentrations RCP ranked as RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively, with the latter numbers representing radiation compulsive levels up to 2100 years of 2.6W m-2To 8.5W m-2。
4. The processing method for acquiring climate information according to claim 1, wherein the first step of converting the coarse resolution month average data in the earth system mode into the global climate background field lattice point data by the methods of longitude and latitude conversion, grid conversion, projection conversion and interpolation to the neighboring points comprises:
(1) reading the extracted data, compiling a script program, and converting a projection mode and longitude and latitude to be matched with the BNU-ESM; interpolating the earth surface potential height and sea-land coverage data, writing the descriptions of the execution source grid and the target grid into a NetCDF file by a difference method by using a near point matching method, generating weights, writing the weights into the NetCDF file, redefining the data by applying the weights and copying metadata as much as possible; the important point of the interpolation method is the source nearest to the target method, and each target point is mapped to the nearest source point;
(2) projecting, converting and interpolating sea surface temperature and sea ice, redefining sea temperature and sea ice variables on POP grids as latticed with latitude/longitude of 0.9 degree × 1.25.25 degrees;
(3) calculating soil moisture data in kg/m2Converting into percentage, and interpolating the soil temperature and humidity to 4 layers below the earth surface again;
(4) calculating the air pressure-potential height of each layer above the earth surface, calculating the relative humidity of each layer, and calculating the 2-m temperature, the 2-m relative humidity, the 2-m water vapor mixing ratio and the 10-m latitudinal and longitudinal wind near the earth surface;
(5) carrying out dynamic downscaling simulation on the WRF high-resolution regional climate mode by adopting the generated boundary driving field data for once every 6 hours; initializing regional high-resolution terrain fields, atmospheric initial fields and static earth surface data information through a preprocessing system WRF, and then interpolating to a pattern grid region.
5. The process of obtaining climate information of claim 4 wherein the static terrain data includes underlayment vegetation type, soil type, terrain elevation, vegetation coverage.
6. The method of claim 4, wherein the air pressure-potential height of each layer above the earth's surface is calculated, and the relative humidity of each layer is calculated, and the 2-m temperature, 2-m relative humidity, 2-m steam mixing ratio and 10-m latitudinal and transverse wind directions near the earth's surface are calculated, and the air pressure layer is redefined to be 27 layers and the air pressure value of each layer is 1000.0, 975.0, 950.0, 925.0, 900.0, 850.0, 800.0, 750.0, 700.0, 650.0, 600.0, 550.0, 500.0, 450.0, 400.0, 350.0, 300.0, 250.0, 200.0, 150.0, 100.0, 70.0, 50.0, 30.0, 20.0, 10.0 hectopascal, and the initial conditions, bedding and boundary conditions for driving the climate pattern of the area are generated.
7. The processing method for acquiring the climate information according to claim 1, wherein the second step is a localized configuration through a regional climate mode WRF, the content of the localized configuration includes a physical parameterization scheme and a numerical mode horizontal spatial and vertical resolution which are continuously combined and optimized through regional high resolution numerical mode system configuration of the WRF, the physical parameterization scheme includes a micro-physics scheme, a radiation transmission scheme, an atmospheric boundary layer scheme, a land process scheme and a cloud parameterization scheme, the horizontal and vertical resolutions include setting of a simulation region horizontal grid interval and atmospheric vertical layering, a terrain following quality η coordinate is adopted, the vertical layer is encrypted to 50 layers by a nonlinear hyperbolic tangent method, and the atmospheric pressure at the top of the model is 50 hPa.
8. The method of claim 7, wherein the η value is 1.000, 0.9947, 0.9895, 0.9843, 0.979, 0.9739, 0.9684, 0.9626, 0.9564, 0.9498, 0.9426, 0.9348, 0.9262, 0.9167, 0.9062, 0.8946, 0.8816, 0.8671, 0.8509, 0.833, 0.813, 0.7909, 0.7667, 0.7402, 0.7116, 0.6809, 0.6483, 0.6141, 0.5785, 0.5419, 0.5047, 0.4672, 0.4299, 0.3931, 0.357, 0.322, 0.2883, 0.256, 0.2253, 0.1963, 0.169, 0.1435, 0.1171, 0.0952, 0.0753, 0.0571, 0.0407, 0.0257, 0.0122, 0.000.
9. A program storage medium for receiving user input, the stored computer program causing an electronic device to perform the steps comprising:
converting coarse resolution monthly mean data in a global system mode into global climate background field lattice point data by a method of longitude and latitude conversion, grid conversion, projection conversion and interpolation to adjacent points;
secondly, carrying out local configuration through a regional climate mode WRF, and continuously combining and preferably selecting a physical parameterization scheme and a numerical mode resolution;
and thirdly, integrating the WRF mode by using a forced field output by the earth system mode, and outputting a local meteorological element simulation field with the horizontal resolution of 1 km.
10. An acquired climate information processing system for implementing the acquired climate information processing method according to any one of claims 1 to 7, wherein the acquired climate information processing system comprises:
the data conversion module is used for converting the coarse resolution monthly average data in the earth system mode into global climate background field lattice point data;
the optimization module is used for carrying out local configuration through a regional climate mode WRF, and continuously combining and optimizing a physical parameterization scheme and a numerical mode resolution;
and the output module is used for integrating the WRF mode by utilizing the forced field output by the earth system mode and outputting a local meteorological element simulation field with the horizontal resolution of 1 km.
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Application publication date: 20200710 Assignee: Chengdu genbo Radar Technology Co.,Ltd. Assignor: CHENGDU University OF INFORMATION TECHNOLOGY Contract record no.: X2023980052204 Denomination of invention: A method, system, and storage medium for obtaining climate information processing Granted publication date: 20220902 License type: Common License Record date: 20231215 |