CN114970222B - HASM-based regional climate mode daily average air temperature deviation correction method and system - Google Patents
HASM-based regional climate mode daily average air temperature deviation correction method and system Download PDFInfo
- Publication number
- CN114970222B CN114970222B CN202210919143.7A CN202210919143A CN114970222B CN 114970222 B CN114970222 B CN 114970222B CN 202210919143 A CN202210919143 A CN 202210919143A CN 114970222 B CN114970222 B CN 114970222B
- Authority
- CN
- China
- Prior art keywords
- data
- air temperature
- daily average
- temperature data
- average air
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Air Conditioning Control Device (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The application relates to the technical field of electric digital data processing, and provides a method and a system for correcting daily average temperature deviation of a regional climate mode based on HASM. The method comprises the following steps: acquiring topographic data, re-analysis data, sea temperature data and land utilization data of a target area; then determining an initial field and a boundary field according to the topographic data, the reanalysis data, the sea temperature data and the land utilization data; then according to the initial field and the boundary field, simulating the air temperature of the target area through RegCM4 to obtain a simulation value of daily average air temperature data; and finally, deviation correction is carried out on the analog value of the daily average air temperature data based on the HASM to obtain a correction value of the daily average air temperature data. Therefore, the simulation deviation of daily average temperature data of the regional climate mode RegCM4 is eliminated to a certain extent, and the accuracy of climate simulation on a regional scale is improved.
Description
Technical Field
The application relates to the technical field of electric digital data processing, in particular to a method and a system for correcting daily average temperature deviation of a regional climate mode based on HASM.
Background
In the context of global warming, extreme climatic events are frequent. In order to effectively estimate the trend of future climate change on the regional scale and make corresponding policy adjustment to adapt to the challenge brought by climate change, the first scientific problem is how to accurately simulate historical climate and clarify the change rule of the historical climate.
Air temperature is the most concerned climate factor of human society, and the acquisition mode thereof is mainly as follows: meteorological site observation, satellite remote sensing inversion and climate mode simulation. The Regional Climate mode (Regional Climate mode) in the Climate mode simulation can simulate the Climate of a certain specific region, and temperature data of the region is obtained.
However, due to various conditions, the result of the simulation of the air temperature data in the regional climate mode often has a certain deviation, so that the process of the local climate change cannot be accurately reflected.
Therefore, there is a need to provide an improved solution to the above-mentioned deficiencies of the prior art.
Disclosure of Invention
The present application aims to provide a HASM-based regional climate mode daily average air temperature deviation correction method and system, so as to solve or alleviate the above problems in the prior art.
In order to achieve the above purpose, the present application provides the following technical solutions:
the application provides a HASM-based regional climate mode daily average air temperature deviation correction method, which comprises the following steps:
acquiring topographic data, re-analysis data, sea temperature data and land utilization data of a target area;
determining an initial field and a boundary field of a regional climate mode RegCM4 according to the terrain data, the reanalysis data, the sea temperature data and the land utilization data;
according to the initial field and the boundary field, simulating the air temperature of the target area through a regional climate mode RegCM4 to obtain a simulation value of daily average air temperature data;
and performing deviation correction on the simulated value of the daily average air temperature data based on the HASM to obtain a corrected value of the daily average air temperature data.
Preferably, the determining an initial field and a boundary field of the regional climate pattern RegCM4 according to the terrain data, the re-analysis data, the sea temperature data, and the land use data includes:
carrying out grid division on the target region to obtain a mode grid of the target region;
and performing interpolation processing on the terrain data, the reanalysis data, the sea temperature data and the land utilization data based on the mode grid of the target area to obtain the initial field and the boundary field of the regional climate mode RegCM 4.
Preferably, the simulating the air temperature of the target area according to the initial field and the boundary field by using the area climate mode RegCM4 to obtain a simulated value of daily average air temperature data, specifically:
based on a preset time window and the area range of the target area, obtaining a simulation value of space-time continuous air temperature data of the target area through a regional climate mode RegCM4 according to the initial field and the boundary field;
and post-processing the simulated value of the space-time continuous air temperature data of the target area to obtain the simulated value of the daily average air temperature data.
Preferably, the correcting the analog value of the daily average air temperature data based on the HASM to obtain a corrected value of the daily average air temperature data includes:
and taking the analog value of the daily average air temperature data as input data of the HASM, taking the air temperature observation data of the meteorological site acquired in advance as an optimization control condition, and correcting the deviation of the analog value of the daily average air temperature data to obtain a corrected value of the daily average air temperature data.
Preferably, the method further comprises:
respectively carrying out error evaluation on the analog value of the daily average air temperature data and the correction value of the daily average air temperature data based on a preset error evaluation index;
wherein the error evaluation index comprises any one or more of root mean square error, average absolute error and correlation coefficient.
The embodiment of the present application further provides a system for correcting daily average air temperature deviation of a regional climate mode based on HASM, including:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is configured to acquire topographic data, re-analysis data, sea temperature data and land utilization data of a target area;
a processing unit configured to determine an initial field and a boundary field of a regional climate pattern RegCM4 from the terrain data, the re-analysis data, the sea temperature data and the land use data;
the simulation unit is configured to simulate the air temperature of the target area through an area climate mode RegCM4 according to the initial field and the boundary field to obtain a simulation value of daily average air temperature data;
and a correction unit configured to perform deviation correction on the analog value of the daily average air temperature data based on the HASM to obtain a corrected value of the daily average air temperature data.
Preferably, the processing unit is further configured to:
performing grid division on the target region to obtain a mode grid of the target region;
and performing interpolation processing on the terrain data, the reanalysis data, the sea temperature data and the land utilization data based on the mode grid of the target area to obtain the initial field and the boundary field of the regional climate mode RegCM 4.
Preferably, the simulation unit is further configured to:
based on a preset time window and the area range of the target area, obtaining a simulation value of space-time continuous air temperature data of the target area through a regional climate mode RegCM4 according to the initial field and the boundary field;
and post-processing the simulated value of the space-time continuous air temperature data of the target area to obtain the simulated value of the daily average air temperature data.
Preferably, the correcting unit is further configured to:
and taking the analog value of the daily average air temperature data as input data of the HASM, taking the air temperature observation data of the meteorological site acquired in advance as an optimization control condition, and correcting the deviation of the analog value of the daily average air temperature data to obtain a corrected value of the daily average air temperature data.
Preferably, the system further comprises an error evaluation unit configured to:
respectively carrying out error evaluation on the analog value of the daily average air temperature data and the correction value of the daily average air temperature data based on a preset error evaluation index;
wherein the error evaluation index comprises any one or more of root mean square error, average absolute error and correlation coefficient.
Has the advantages that:
in the method, topographic data, re-analysis data, sea temperature data and land utilization data of a target area are obtained; then determining an initial field and a boundary field of a regional climate mode RegCM4 according to the topographic data, the reanalysis data, the sea temperature data and the land utilization data; then according to the initial field and the boundary field of the RegCM4, simulating the air temperature of the target area through the regional climate mode RegCM4 to obtain a simulation value of daily average air temperature data; and finally, deviation correction is carried out on the analog value of the daily average air temperature data based on the HASM to obtain a correction value of the daily average air temperature data. Therefore, the analog value of the daily average air temperature data obtained by the regional climate mode RegCM4 is corrected through the HASM technology, and the analog deviation of the daily average air temperature data of the regional climate mode RegCM4 is eliminated to a certain extent, so that the daily average air temperature data with high precision and continuous space-time is obtained, the local climate process is more accurately represented, the precision of climate simulation on a regional scale is improved, and the improvement of the simulation precision of the future climate change trend is facilitated.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. Wherein:
fig. 1 is a schematic flow diagram of a HASM-based regional climate pattern daily average air temperature deviation correction method according to some embodiments of the present application;
fig. 2 is a logic diagram of a HASM-based regional climate pattern daily average air temperature deviation correction method according to some embodiments of the present application;
fig. 3 is a schematic structural diagram of a HASM-based regional climate pattern daily average air temperature deviation correction system according to some embodiments of the present application.
Detailed Description
The present application will be described in detail below with reference to the embodiments with reference to the attached drawings. The various examples are provided by way of explanation of the application and are not limiting of the application. In fact, it will be apparent to those skilled in the art that modifications and variations can be made in the present application without departing from the scope or spirit thereof. For instance, features illustrated or described as part of one embodiment, can be used with another embodiment to yield a still further embodiment. It is therefore intended that the present application cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.
In the following description, references to the terms "first/second/third" are only to distinguish similar objects and do not denote a particular order, but rather "first/second/third" may, where permissible, be interchanged with a particular order or sequence so that the embodiments of the present application described herein may be practiced other than as specifically illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing embodiments of the disclosure only and is not intended to be limiting of the disclosure.
As described in the background art, in the context of global warming, extreme climatic events, especially extreme temperature events such as high temperature and hot wave, are frequent in recent years, and the intensity is increasing, which has a great influence on the economic development and physical health of the human society. How to take effective measures to deal with the climate risk is an important challenge for all people. In order to effectively estimate the trend of future climate change on the regional scale and make corresponding policy adjustment to adapt to the challenge brought by climate change, the first scientific problem is how to accurately simulate historical climate and clarify the change rule of historical climate.
Temperature is the most concerned climate factor of human society, and currently, the mode of acquiring temperature data mainly comprises: meteorological site observation, satellite remote sensing inversion and climate mode simulation. Wherein:
the meteorological station observation is the main mode for acquiring the air temperature data at present, and has the advantages of high observation precision and time continuity of the acquired air temperature data. However, the meteorological station observation method belongs to sparse observation, and it is difficult to obtain spatially continuous temperature data, and the requirements of the fields of agriculture, hydrology and the like on the spatially continuous data cannot be met.
The development of the space detection technology greatly increases the means for people to obtain earth surface data, and meteorological satellites carry out meteorological observation on the earth from the outer space through sensors carried by the meteorological satellites to obtain satellite remote sensing data, and the satellite remote sensing data is inverted to obtain space continuous temperature data. However, the meteorological satellite has periodicity in earth observation, and cannot acquire satellite remote sensing data with continuous time, and meteorological satellite observation is easily affected by weather conditions, for example, observation results of a target area cannot be effectively acquired in cloudy weather. In addition, the temperature data is obtained by inversion from the satellite remote sensing data, and the inversion result of the temperature data is greatly influenced by the inversion algorithm.
The climate mode is a model developed by computer technology based on the operation and change of atmosphere and climate system and based on basic physical laws (such as Newton's law of motion, energy and mass conservation law) for simulating the change of climate.
The climate modes include a global climate mode and a regional climate mode.
The Global Climate Model (GCM) can simulate and estimate the Climate on a Global scale. However, due to the complexity of the climate system itself and the different conditions of dimensions, altitude, land and sea position and underlying surface of different areas, the climate system exhibits different variation characteristics and intensities in different areas, so that the GCM is often not effective in simulating regional dimensions, and is difficult to represent local weather processes, especially extreme weather climate events.
A Regional Climate Model (RCM) can simulate the Climate of a specific region to obtain certain Climate factor data, for example, space-time continuous air temperature data, however, due to the limitation of objective conditions such as insufficient human knowledge of the Climate system, the air temperature data obtained by the Regional Climate Model simulation often has a certain deviation, which makes it impossible to accurately reflect the process of local Climate change.
In the related art, deviation correction is performed on air temperature data by a Delta correction method or a quantile mapping method.
The Delta correction method takes the observed value as the true value of the current climate, so that the value of the temperature observed data and the temperature data simulation value obtained by the climate mode simulation can be subtracted to calculate the 'difference' between the value of the temperature observed data and the temperature data simulation value obtained by the climate mode simulation, and the 'difference' is added to the simulation result of the mode to obtain the corrected result on the assumption that the deviation of the temperature data simulation value obtained by the climate mode simulation does not change along with the time, namely the difference exists all the time and is fixed in size. The Delta correction method corrects the air temperature data by adopting the deviation value with fixed size, can only be applied to the cases with small variability of annual scale and chronologic scale, and can only correct the deviation of the average state, but can not be applied to the data with large variability of monthly scale and daily scale.
The Quantile-Mapping (QM) respectively calculates a Cumulative probability Distribution Function (CDF) of the temperature observed data and the temperature data analog value in a selected reference time period, then constructs a Transfer Function (TF) between the CDF and the CDF, and corrects the CDF of the analog value in other time periods by using the Transfer Function, thereby finally achieving the purpose of reducing the mode simulation error. Because the quantile mapping method needs to calculate the cumulative probability distribution function in the correction process, the data on each grid point needs to be sequenced (for example, from small to large), so that the time distribution of the data is disturbed, although the cumulative probability distribution functions of the temperature observation data and the temperature data analog value are similar, the time scale cannot be in one-to-one correspondence, and the correction effect is poor when the climate abnormal time is met; and when the simulation time period is short, because the data volume is not enough, the calculated accumulative probability distribution function is not smooth enough, and an effective transfer function between the accumulative probability distribution functions of the starting temperature observation data and the temperature data simulation value is difficult to establish.
With global warming, extreme climatic events occur frequently, and abnormal phenomena of climate are more common, such as sudden appearance of low temperature in summer (different from the average climate state for many years) and sudden appearance of extremely high temperature in winter. In this context, the drawbacks of the two methods are evident, and a more efficient "point-to-point" correction method in time is needed. Based on the above, the present application provides a method and a system for correcting daily average air temperature deviation of a regional climate mode based on HASM, which combine with observation data of a meteorological site, and perform deviation correction on an air temperature data simulation result of the regional climate mode through High Accuracy Surface Modeling (HASM for short), so as to obtain High Accuracy, space-time continuous air temperature data of a target region with different time resolutions in any time period, so that the method and the system can accurately reflect regional local climate change conditions.
Exemplary method
The embodiment of the application provides a HASM-based correction method for daily average air temperature deviation of a regional climate mode, and fig. 1 is a schematic flow diagram of the HASM-based correction method for daily average air temperature deviation of the regional climate mode according to some embodiments of the application; fig. 2 is a logic diagram of a HASM-based regional climate pattern daily average air temperature deviation correction method according to some embodiments of the present application. As shown in fig. 1 and 2, the method includes:
s101, obtaining topographic data, re-analysis data, sea temperature data and land utilization data of a target area.
In the embodiment of the present application, the target area may be any local area in the global range. In practical applications, the specific range of the target area can be obtained by defining the position of the central point and the area range.
The topographic data is also called Digital Elevation Model (DEM for short) and is used for providing basic topography for a target area, and the topographic topography of the earth surface is expressed mathematically through a finite sequence of three-dimensional vectors on the target area, and is expressed in a functional form as follows:
in the formula (I), the compound is shown in the specification,V i is shown asiA three-dimensional vector of the points is,（X i ，Y i ）is shown asiThe coordinates of the points in the plane of the plane,Z i is shown asiElevation of the points.
Reanalyzing the data and the sea temperature data can provide initial and boundary fields for the regional climate pattern. In the embodiment of the application, the reanalysis data and the sea temperature data adopt ERA-Interim data in a climate reanalysis data set, the original spatial resolution is 0.75 degrees, and the time resolution is 6 hours.
The land use data (LUCC) is data for reflecting the state, characteristics, dynamic changes, and distribution characteristics of the land use system and the land use elements in the target area. In the embodiment of the application, the land use data adopts data provided by the RegCM4 official part in a regional climate mode.
And S102, determining an initial field and a boundary field of a regional climate mode RegCM4 according to the terrain data, the reanalysis data, the sea temperature data and the land utilization data.
It should be noted that the operation of the regional climate mode RegCM4 is a process of performing equation solution based on climate dynamics according to the initial field and the boundary field, and based on this, the initial field and the boundary field required by the regional climate mode RegCM4 need to be generated before the regional climate mode RegCM4 is used to simulate the air temperature data of the target region.
Specifically, in some embodiments, the determining the initial field and the boundary field of the regional climate pattern RegCM4 based on the terrain data, the re-analysis data, the sea temperature data, and the land use data includes: carrying out grid division on a target region to obtain a mode grid of the target region; and performing interpolation processing on the terrain data, the reanalysis data, the sea temperature data and the land utilization data based on the mode grid of the target area to obtain an initial field and a boundary field of the regional climate mode RegCM 4.
In the embodiment of the application, before the initial field and the boundary field of the regional climate mode RegCM4 are determined, grid division is performed on a target region to obtain a mode grid of the target region. In specific implementation, the size of the mode mesh may be determined with reference to the range of the target region, where the mode mesh may be an irregular mesh or a regular mesh, for example, a regular mesh with a mode mesh of 3km × 3km is set.
After grid division is carried out on a target area, interpolation processing is carried out on terrain data, reanalysis data, sea temperature data and land utilization data by using a pretreatment module of a regional climate mode RegCM4 based on a mode grid of the target area, and an initial field and a boundary field of the regional climate mode RegCM4 are obtained.
Specifically, firstly, the terrains module provided by RegCM4 interpolates the terrain data and the land utilization data onto the pattern grid of the target area, then the sst module provided by RegCM4 interpolates the sea temperature data onto the pattern grid of the target area, and finally the icbc module provided by RegCM4 combines the interpolation results of the first two steps with the re-analysis data to generate the initial field and the boundary field required by the RegCM4 of the regional climate pattern of the target area.
And S103, simulating the air temperature of the target area through an area climate mode RegCM4 according to the initial field and the boundary field to obtain a simulated value of daily average air temperature data.
In specific implementation, based on a preset time window and the area range of the target area, obtaining a simulated value of space-time continuous air temperature data of the target area through a regional climate mode RegCM4 according to the initial field and the boundary field; and post-processing the simulated value of the time-space continuous air temperature data of the target area to obtain the simulated value of the daily average air temperature data.
In the embodiment of the present application, the time window may be determined according to application requirements, for example, a simulation of the target area for 1 year may be set, or a simulation of the target area for 1 month may be set.
And taking the initial field and the boundary field as the input of a regional climate mode RegCM4, and obtaining a simulated value of space-time continuous air temperature data of the target region through a regCMPI module operation mode of the RegCM4, wherein the simulated value of the air temperature data can reflect the continuous air temperature change condition of the target region in a set time window range. The simulated value of the temperature data is then converted into a simulated value of daily average temperature data by the post-processing module of the regional climate mode RegCM 4.
The time resolution of the converted analog value of the daily average air temperature data was a daily scale, the spatial resolution was 3km, and the file format was NetCDF. That is, a simulated value of daily average air temperature data is associated with each 3km × 3km grid of the target area, and the simulated value is acquired once a day, i.e., a daily scale.
And step S104, deviation correction is carried out on the simulated value of the daily average air temperature data based on the HASM, and a correction value of the daily average air temperature data is obtained.
The analog values of the daily average air temperature data obtained in the regional climate mode RegCM4 are subject to the restriction of the complexity of the climate system, and therefore, have a certain variance, and therefore, it is necessary to correct the variance.
In the embodiment of the application, the HASM technology is adopted to correct the deviation of the analog value of the daily average air temperature data obtained by the regional climate mode RegCM 4. The HASM is a space simulation method which abstracts the gridding expression of the simulation value of daily average temperature data of a target area into a mathematical curved surface; the deviation of the mathematical surface is then corrected based on the HASM.
In some embodiments, the correcting the analog value of the daily average air temperature data based on the HASM to obtain a corrected value of the daily average air temperature data includes: and taking the analog value of the daily average air temperature data as input data of the HASM, taking the air temperature observation data of the meteorological site acquired in advance as an optimization control condition, and correcting the deviation of the analog value of the daily average air temperature data to obtain a corrected value of the daily average air temperature data.
Since daily average air temperature data obtained by simulation in the regional climate mode RegCM4 has space-time continuity, each pattern mesh corresponds to a simulation value of daily average air temperature data. It is understood that the mathematical surface formed by the simulated values of daily average temperature data, each cell of which is a pattern mesh of the simulated values of daily average temperature data, may be represented by the coordinates of the pattern mesh and the simulated values of daily average temperature data of the pattern mesh.
When deviation of an analog value of daily average air temperature data obtained by a regional climate mode RegCM4 is determined by adopting a HASM technology based on air temperature observation data of a meteorological site, firstly, an observation value of the air temperature observation data of each meteorological site is assigned to a mode grid with the nearest distance to the meteorological site; then, according to daily average air temperature data of all the mode grids, a first basic quantity E, F, G and a second basic quantity L, M, N of each mode grid are calculated; the first type of basic quantity is used for representing the arc length of a curve on a data curved surface, the included angle of two directions on the data curved surface and the area of a data curved surface domain, and the second type of basic quantity is used for representing the flexibility in a data curved surface space; then, a Gaussian equation is used as a basic equation, and a meteorological site is used as a constraint equation to establish a data surface equation set; and finally, calculating a data curved surface equation set by using a Gauss-Seidel iterative algorithm through iterative calculation so as to obtain a corrected value of the daily average air temperature data with high precision and continuous time and space.
In some embodiments, the method further comprises: respectively carrying out error evaluation on the analog value of the daily average air temperature data and the correction value of the daily average air temperature data based on a preset error evaluation index; wherein the error evaluation index comprises any one or more of root mean square error, average absolute error and correlation coefficient.
Specifically, the error of the simulated value of the daily average air temperature data and the error of the corrected value of the daily average air temperature data with respect to the daily average value of the air temperature observation data at the weather station are calculated based on the daily average value of the air temperature observation data at the weather station.
The calculation formula of the root mean square error is as follows:
in the formula (I), the compound is shown in the specification,RMSEa root mean square error between the simulation value representing the daily average air temperature data or the corrected value of the daily average air temperature data and the daily average air temperature observation data at the weather station,Nthe number of the weather stations is shown,p i denotes the firstiA simulation value of daily average air temperature data at each weather station or a correction value of daily average air temperature data,o i is shown asiDaily average value of air temperature observation data of each weather station.
The average absolute error is calculated as follows:
in the formula (I), the compound is shown in the specification,MAEthe average absolute error between the simulation value representing the daily average air temperature data or the corrected value of the daily average air temperature data and the daily average value of the air temperature observation data at the weather station.
The correlation coefficient is calculated as follows:
in the formula (I), the compound is shown in the specification,Ra correlation coefficient between a simulation value representing daily average temperature data or a correction value of the daily average temperature data and a daily average value of temperature observation data at a weather station,
an average value representing a simulation value of daily average air temperature data or a correction value of daily average air temperature data,
the daily average value of the air temperature observation data at the meteorological site is shown.
Specifically, taking the a region as an example, first, the daily average air temperature data of a certain year in the a region is simulated based on the regional climate pattern RegCM4, and the simulated value of the daily average air temperature data before the correction of the deviation in 1 to 12 months in the year, which is calculated with a resolution of 3km (i.e., the size of the pattern mesh), is expressed by K. Then, the deviation correction is performed on the simulated value of the daily average air temperature data based on the method provided by the present application, and a corrected value of the daily average air temperature data in region a after the deviation correction is obtained. Finally, the value of the error evaluation index between the simulation value of the daily average air temperature data before the deviation correction and the correction value of the daily average air temperature data before the deviation correction is calculated according to the error evaluation index calculation formula, and the result is shown in table 1, where table 1 is as follows:
the average air temperature data before deviation correction and the daily average air temperature data of the area A after deviation correction are respectively compared with the air temperature observation data of the meteorological site in the same time period in the area, and as can be seen from the table 1, the error between the daily average air temperature data of the area A after deviation correction and the air temperature observation data of the meteorological site is obviously reduced, so that the method provided by the invention is adopted to carry out deviation correction on the analog value of the daily average air temperature data of the regional climate mode RegCM4, and the precision of the daily average air temperature data is improved.
In summary, in the present application, the topographic data, the reanalysis data, the sea temperature data and the land utilization data of the target area are obtained first; then determining an initial field and a boundary field of a regional climate mode RegCM4 according to the topographic data, the reanalysis data, the sea temperature data and the land utilization data; then according to the initial field and the boundary field of the RegCM4, simulating the air temperature of the target area through the regional climate mode RegCM4 to obtain a simulation value of daily average air temperature data; and finally, deviation correction is carried out on the analog value of the daily average air temperature data based on the HASM to obtain a correction value of the daily average air temperature data. Therefore, the analog value of the daily average air temperature data obtained by the regional climate mode RegCM4 is corrected through the HASM technology, and the data deviation of the regional climate mode RegCM4 is eliminated to a certain extent, so that the daily average air temperature data with high precision and continuous space-time is obtained, the local climate process is more accurately represented, the precision of climate simulation on a regional scale is improved, and the improvement of the simulation precision of the future climate change trend is facilitated.
Exemplary System
An embodiment of the present application provides a system for correcting average air temperature deviation per day in a regional climate mode based on HASM, and fig. 3 is a schematic structural diagram of a system for correcting average air temperature deviation per day in a regional climate mode based on HASM according to some embodiments of the present application, and as shown in fig. 3, the system includes: acquisition unit 301, processing unit 302, simulation unit 303, and correction unit 304. Wherein:
an acquisition unit 301 configured to acquire topographic data, re-analysis data, sea temperature data, and land use data of the target area.
A processing unit 302 configured to determine an initial field and a boundary field of a regional climate pattern RegCM4 based on the terrain data, the re-analysis data, the sea temperature data and the land use data.
A simulation unit 303, configured to simulate the air temperature of the target area through a regional climate mode RegCM4 according to the initial field and the boundary field, so as to obtain a simulated value of daily average air temperature data.
A correction unit 304 configured to perform deviation correction on the analog value of the daily average air temperature data based on the HASM to obtain a corrected value of the daily average air temperature data.
In some embodiments, the processing unit 302 is further configured to: carrying out grid division on the target region to obtain a mode grid of the target region; and performing interpolation processing on the terrain data, the reanalysis data, the sea temperature data and the land utilization data based on the mode grid of the target area to obtain the initial field and the boundary field of the regional climate mode RegCM 4.
In some embodiments, the simulation unit 303 is further configured to: based on a preset time window and the area range of the target area, obtaining a simulation value of space-time continuous air temperature data of the target area through a regional climate mode RegCM4 according to the initial field and the boundary field; and post-processing the simulated value of the space-time continuous air temperature data of the target area to obtain the simulated value of the daily average air temperature data.
In some embodiments, the correcting unit 304 is further configured to: and taking the analog value of the daily average air temperature data as input data of the HASM, taking the air temperature observation data of the meteorological site acquired in advance as an optimization control condition, and correcting the deviation of the analog value of the daily average air temperature data to obtain a corrected value of the daily average air temperature data.
In some embodiments, the system further comprises an error evaluation unit (not shown in the figures) configured to: respectively carrying out error evaluation on the analog value of the daily average air temperature data and the correction value of the daily average air temperature data based on a preset error evaluation index; wherein the error evaluation index comprises any one or more of root mean square error, average absolute error and correlation coefficient.
The HASM-based regional climate mode daily average air temperature deviation correction system provided by the embodiment of the application can realize the processes and steps of any HASM-based regional climate mode daily average air temperature deviation correction method, and achieves the same technical effects, and is not repeated here.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (6)
1. A HASM-based regional climate mode daily average air temperature deviation correction method is characterized by comprising the following steps:
acquiring topographic data, re-analysis data, sea temperature data and land utilization data of a target area;
determining an initial field and a boundary field of a regional climate pattern RegCM4 according to the terrain data, the reanalysis data, the sea temperature data and the land utilization data;
according to the initial field and the boundary field, simulating the air temperature of the target area through a regional climate mode RegCM4 to obtain a simulation value of daily average air temperature data; the method comprises the following specific steps:
based on a preset time window and the area range of the target area, obtaining a simulation value of space-time continuous air temperature data of the target area through a regional climate mode RegCM4 according to the initial field and the boundary field;
carrying out post-processing on the analog value of the space-time continuous air temperature data of the target area to obtain the analog value of the daily average air temperature data;
deviation correction is carried out on the analog value of the daily average air temperature data based on HASM to obtain a correction value of the daily average air temperature data, and the method specifically comprises the following steps:
and taking the analog value of the daily average air temperature data as input data of the HASM, taking the air temperature observation data of the meteorological site acquired in advance as an optimization control condition, and correcting the deviation of the analog value of the daily average air temperature data to obtain a corrected value of the daily average air temperature data so as to acquire the daily average air temperature data with high precision.
2. The HASM-based regional climate pattern daily average air temperature deviation correction method according to claim 1, wherein said determining an initial field and a boundary field of a regional climate pattern RegCM4 based on said terrain data, said re-analysis data, said sea temperature data and said land use data, in particular:
carrying out grid division on the target region to obtain a mode grid of the target region;
and performing interpolation processing on the terrain data, the reanalysis data, the sea temperature data and the land utilization data based on the mode grid of the target area to obtain the initial field and the boundary field of the regional climate mode RegCM 4.
3. The HASM-based regional climate pattern daily average air temperature deviation correction method according to claim 1, further comprising:
respectively carrying out error evaluation on the analog value of the daily average air temperature data and the correction value of the daily average air temperature data based on a preset error evaluation index;
wherein the error evaluation index comprises any one or more of root mean square error, average absolute error and correlation coefficient.
4. A HASM-based regional climate pattern daily average air temperature deviation correction system is characterized by comprising:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is configured to acquire topographic data, re-analysis data, sea temperature data and land utilization data of a target area;
a processing unit configured to determine an initial field and a boundary field of a regional climate pattern RegCM4 from the terrain data, the re-analysis data, the sea temperature data and the land use data;
the simulation unit is configured to simulate the air temperature of the target area through an area climate mode RegCM4 according to the initial field and the boundary field to obtain a simulated value of daily average air temperature data; the analog unit is further configured to:
based on a preset time window and the area range of the target area, obtaining a simulation value of space-time continuous air temperature data of the target area through an area climate mode RegCM4 according to the initial field and the boundary field;
carrying out post-processing on the analog value of the space-time continuous air temperature data of the target area to obtain the analog value of the daily average air temperature data;
a correction unit configured to perform deviation correction on the analog value of the daily average air temperature data based on the HASM to obtain a correction value of the daily average air temperature data;
the correction unit is specifically configured to:
and taking the analog value of the daily average air temperature data as input data of the HASM, taking the air temperature observation data of the meteorological site acquired in advance as an optimized control condition, and correcting the deviation of the analog value of the daily average air temperature data to obtain a corrected value of the daily average air temperature data so as to acquire the daily average air temperature data with high precision.
5. The HASM based regional climate mode daily average air temperature deviation correction system of claim 4, wherein the processing unit is further configured to:
carrying out grid division on the target region to obtain a mode grid of the target region;
and performing interpolation processing on the terrain data, the reanalysis data, the sea temperature data and the land utilization data based on the mode grid of the target area to obtain the initial field and the boundary field of the regional climate mode RegCM 4.
6. The HASM based regional climate mode daily average air temperature deviation correction system according to claim 4, further comprising an error evaluation unit configured to:
respectively carrying out error evaluation on the analog value of the daily average air temperature data and the correction value of the daily average air temperature data based on a preset error evaluation index;
wherein the error evaluation index comprises any one or more of root mean square error, average absolute error and correlation coefficient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210919143.7A CN114970222B (en) | 2022-08-02 | 2022-08-02 | HASM-based regional climate mode daily average air temperature deviation correction method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210919143.7A CN114970222B (en) | 2022-08-02 | 2022-08-02 | HASM-based regional climate mode daily average air temperature deviation correction method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114970222A CN114970222A (en) | 2022-08-30 |
| CN114970222B true CN114970222B (en) | 2022-11-11 |
Family
ID=82970089
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210919143.7A Active CN114970222B (en) | 2022-08-02 | 2022-08-02 | HASM-based regional climate mode daily average air temperature deviation correction method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114970222B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116108761B (en) * | 2023-04-12 | 2023-06-27 | 中国科学院地理科学与资源研究所 | Regional climate simulation method and system for coupling deep learning and HASM |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110490384A (en) * | 2019-08-22 | 2019-11-22 | 中国水利水电科学研究院 | A kind of A Regional Climate Model power NO emissions reduction element error revising method |
| CN111401634A (en) * | 2020-03-13 | 2020-07-10 | 成都信息工程大学 | Processing method, system and storage medium for acquiring climate information |
| CN114019579A (en) * | 2021-08-24 | 2022-02-08 | 中国农业科学院农业资源与农业区划研究所 | High-space-time resolution near-surface air temperature reconstruction method, system and equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3123267B1 (en) * | 2014-03-28 | 2019-10-16 | Northeastern University | System for multivariate climate change forecasting with uncertainty quantification |
-
2022
- 2022-08-02 CN CN202210919143.7A patent/CN114970222B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110490384A (en) * | 2019-08-22 | 2019-11-22 | 中国水利水电科学研究院 | A kind of A Regional Climate Model power NO emissions reduction element error revising method |
| CN111401634A (en) * | 2020-03-13 | 2020-07-10 | 成都信息工程大学 | Processing method, system and storage medium for acquiring climate information |
| CN114019579A (en) * | 2021-08-24 | 2022-02-08 | 中国农业科学院农业资源与农业区划研究所 | High-space-time resolution near-surface air temperature reconstruction method, system and equipment |
Non-Patent Citations (3)
| Title |
|---|
| RegCM4.0不同对流参数化方案对山东省气温和降水的模拟;商林等;《干旱气象》;20161215(第06期);全文 * |
| 土地利用变化对中国区域气候影响的RegCM数值模拟;隆永平;《中国优秀硕士学位论文全文数据库 基础科学辑》;20141215(第12期);第二章、第三章 * |
| 基于HASM方法对气候模式气温降水的降尺度研究——以黑河流域为例;赵娜等;《中国沙漠》;20170710;第37卷(第06期);第1-4节 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114970222A (en) | 2022-08-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110738252B (en) | Space autocorrelation machine learning satellite precipitation data downscaling method and system | |
| CN106776481B (en) | Downscaling correction method for satellite precipitation data | |
| Chen et al. | A statistical method based on remote sensing for the estimation of air temperature in China. | |
| CN108983320B (en) | Numerical weather forecast-artificial intelligence coupling prediction method for coastal typhoon extreme wind speed | |
| Oettli et al. | Are regional climate models relevant for crop yield prediction in West Africa? | |
| CN111045046B (en) | Short-term ionosphere forecasting method and device based on NARX | |
| Reikard et al. | Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models | |
| Guo et al. | Bias correction and projection of surface air temperature in LMDZ multiple simulation over central and eastern China | |
| CN111650579B (en) | InSAR mining area three-dimensional deformation estimation method and device for rock migration parameter adaptive acquisition and medium | |
| CN103218780B (en) | Based on the nothing control satellite-borne SAR image ortho-rectification method of inverse RD location model | |
| CN114970222B (en) | HASM-based regional climate mode daily average air temperature deviation correction method and system | |
| Albuquerque et al. | Directional correction of modeled sea and swell wave heights using satellite altimeter data | |
| CN113536576A (en) | Method and system for correcting distance integral power statistics of numerical forecast product | |
| Hatanaka et al. | Diffusion models for high-resolution solar forecasts | |
| CN114819264A (en) | Photovoltaic power station irradiance ultra-short term prediction method based on space-time dependence and storage medium | |
| TWI717796B (en) | System, method and storage medium for estimating the amount of sunshine in geographic location by artificial intelligence | |
| CN115855272A (en) | Method for generating near-real-time hourly all-weather surface temperature | |
| Rhoads et al. | Validation of land surface models using satellite‐derived surface temperature | |
| CN111914396B (en) | Sub-grid terrain three-dimensional earth surface solar radiation forced effect rapid parameterization method based on high-resolution DEM data | |
| Nemes | Statistical analysis of wind speed profile: a case study from Iasi Region, Romania | |
| Forootan et al. | Empirical data assimilation for merging total electron content data with empirical and physical models | |
| Bi et al. | A deep learning-based model for tropospheric wet delay prediction based on multi-layer 1D convolution neural network | |
| CN113255148A (en) | Method for estimating all-weather air temperature and space-time distribution thereof based on MODIS product data | |
| CN114936202B (en) | Reconstruction method and device for polar region albedo remote sensing data and computer equipment | |
| CN114061583B (en) | Mobile robot state estimation and autonomous navigation method based on self-adaptive lattice Kalman filtering |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |