CN114970222B - A HASM-based correction method and system for daily mean temperature deviation in regional climate models - Google Patents

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

HASM-based correction method and system for daily average temperature deviation in regional climate models

technical field

This application relates to the technical field of electrical digital data processing, in particular to a HASM-based method and system for correcting the daily average temperature deviation of a regional climate model.

Background technique

Against the background of global warming, extreme climate events occur frequently. In order to effectively predict the trend of future climate change on a regional scale and make corresponding policy adjustments to adapt to the challenges brought by climate change, the primary scientific issue is how to accurately simulate historical climate and clarify the law of historical climate change.

As the climate element that human society is most concerned about, temperature is currently obtained through the following methods: meteorological station observation, satellite remote sensing inversion, and climate model simulation. Among them, the regional climate model (Regional Climate Model) in the climate model simulation can simulate the climate of a specific region and obtain the temperature data of the region.

However, due to various constraints, the temperature data simulation results of regional climate models often have certain deviations, making them unable to accurately reflect the process of local climate change.

Therefore, it is necessary to provide an improved technical solution for the above-mentioned deficiencies in the prior art.

Contents of the invention

The purpose of this application is to provide a HASM-based method and system for correcting the daily average temperature deviation of regional climate models, so as to solve or alleviate the problems in the above-mentioned prior art.

In order to achieve the above object, the application provides the following technical solutions:

This application provides a HASM-based method for correcting the daily average temperature deviation of regional climate models, including:

Obtain topographic data, reanalysis data, sea temperature data and land use data of the target area;

Determine the initial field and boundary field of the regional climate model RegCM4 according to the topographic data, the reanalysis data, the sea temperature data and the land use data;

According to the initial field and the boundary field, the temperature in the target area is simulated by the regional climate model RegCM4 to obtain the simulated value of the daily average temperature data;

Correcting the deviation of the simulated value of the daily average temperature data based on HASM to obtain the corrected value of the daily average temperature data.

Preferably, the initial field and boundary field of the regional climate model RegCM4 are determined according to the topographic data, the reanalysis data, the sea temperature data and the land use data, specifically:

performing grid division on the target area to obtain a model grid of the target area;

Based on the model grid of the target area, the terrain data, the reanalysis data, the sea temperature data and the land use data are interpolated to obtain the initial field of the regional climate model RegCM4 and the boundary field.

Preferably, according to the initial field and the boundary field, the air temperature in the target area is simulated by the regional climate model RegCM4 to obtain the simulated value of the daily average air temperature data, specifically:

Based on the preset time window and the regional range of the target area, according to the initial field and the boundary field, the simulated value of the temporal and spatial continuous air temperature data of the target area is obtained through the regional climate model RegCM4;

Post-processing the simulated value of the time-space continuous temperature data of the target area to obtain the simulated value of the daily average temperature data.

Preferably, the HASM is used to correct the simulated value of the daily average temperature data to obtain the corrected value of the daily average temperature data, specifically:

The simulated value of the daily average temperature data is used as the input data of HASM, and the temperature observation data of the meteorological station obtained in advance is used as the optimal control condition, and the deviation correction is carried out to the simulated value of the daily average temperature data to obtain the daily average temperature data the corrected value of .

Preferably, the method also includes:

Based on preset error evaluation indicators, error evaluation is performed on the simulated value of the daily average temperature data and the corrected value of the daily average temperature data;

Wherein, the error evaluation index includes any one or more of root mean square error, mean absolute error, and correlation coefficient.

The embodiment of the present application also provides a HASM-based regional climate model daily average temperature deviation correction system, including:

an acquisition unit configured to acquire terrain data, reanalysis data, sea temperature data and land use data of the target area;

A processing unit configured to determine an initial field and a boundary field of a regional climate model RegCM4 based on the terrain data, the reanalysis data, the sea temperature data, and the land use data;

The simulation unit is configured to simulate the air temperature in the target area through the regional climate model RegCM4 according to the initial field and the boundary field to obtain the simulated value of the daily average air temperature data;

The correction unit is configured to perform deviation correction on the simulated value of the daily average temperature data based on HASM, to obtain a corrected value of the daily average temperature data.

Preferably, the processing unit is further configured to:

performing grid division on the target area to obtain a model grid of the target area;

Based on the model grid of the target area, the terrain data, the reanalysis data, the sea temperature data and the land use data are interpolated to obtain the initial field of the regional climate model RegCM4 and the boundary field.

Preferably, the simulation unit is further configured as:

Based on the preset time window and the regional range of the target area, according to the initial field and the boundary field, the simulated value of the temporal and spatial continuous air temperature data of the target area is obtained through the regional climate model RegCM4;

Post-processing the simulated value of the time-space continuous temperature data of the target area to obtain the simulated value of the daily average temperature data.

Preferably, the correcting unit is further configured to:

The simulated value of the daily average temperature data is used as the input data of HASM, and the temperature observation data of the meteorological station obtained in advance is used as the optimal control condition, and the deviation correction is carried out to the simulated value of the daily average temperature data to obtain the daily average temperature data the corrected value of .

Preferably, the system further includes an error evaluation unit configured to:

Based on preset error evaluation indicators, error evaluation is performed on the simulated value of the daily average temperature data and the corrected value of the daily average temperature data;

Wherein, the error evaluation index includes any one or more of root mean square error, mean absolute error, and correlation coefficient.

Beneficial effect:

In this application, first obtain the terrain data, reanalysis data, sea temperature data and land use data of the target area; then determine the initial field and boundary of the regional climate model RegCM4 according to the terrain data, reanalysis data, sea temperature data and land use data Then, according to the initial field and boundary field of RegCM4, the air temperature in the target area is simulated by the regional climate model RegCM4, and the simulated value of the daily average temperature data is obtained; finally, the deviation correction is performed on the simulated value of the daily average temperature data based on HASM, and the obtained The correction value of the daily mean air temperature data. In this way, the HASM technology is used to correct the simulated value of the daily average temperature data obtained by the regional climate model RegCM4, which to a certain extent eliminates the simulation deviation of the daily average temperature data of the regional climate model RegCM4, thereby obtaining high-precision, time-space continuous daily average The temperature data makes it more accurate to represent the local climate process, improves the accuracy of climate simulation on the regional scale, and helps to improve the simulation accuracy of future climate change trends.

Description of drawings

The accompanying drawings constituting a part of the present application are used to provide further understanding of the present application, and the schematic embodiments and descriptions of the present application are used to explain the present application, and do not constitute improper limitations to the present application. in:

Fig. 1 is a schematic flow chart of a HASM-based regional climate model daily average temperature deviation correction method provided according to some embodiments of the present application;

2 is a schematic diagram of a HASM-based regional climate model daily average temperature deviation correction method provided according to some embodiments of the present application;

Fig. 3 is a schematic structural diagram of a HASM-based correction system for daily average temperature deviation of a regional climate model provided according to some embodiments of the present application.

Detailed ways

The present application will be described in detail below with reference to the accompanying drawings and embodiments. Each example is provided by way of explanation of the application, not limitation of the application. In fact, those skilled in the art will recognize that modifications and variations can be made in the present application without departing from the scope or spirit of the application. For example, features illustrated or described as part of one embodiment can be used on another embodiment to yield a still further embodiment. Accordingly, it is intended that the present application cover such modifications and variations as come within the scope of the appended claims and their equivalents.

In the following description, the terms "first/second/third" are only used to distinguish similar objects, and do not represent a specific ordering of objects. It is understandable that "first/second/third" allows The specific order or sequence may be interchanged under certain circumstances such that the embodiments of the application described herein can be practiced in sequences other than those 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 terms used herein are only for the purpose of describing the embodiments of the present disclosure, and are not intended to limit the present disclosure.

As mentioned in the background technology, under the background of global warming, extreme climate events occur frequently, especially in recent years, extreme temperature events such as high temperature and heat waves occur frequently, and the intensity continues to increase, which brings great impact on the economic development and physical health of human society. had a great impact. How to take effective measures to deal with climate risks is an important challenge facing all mankind. In order to effectively predict the trend of future climate change on a regional scale and make corresponding policy adjustments to adapt to the challenges brought about by climate change, the primary scientific issue is how to accurately simulate historical climate and clarify the law of historical climate change.

Temperature is the climate element most concerned by human society. At present, the main ways to obtain temperature data are: meteorological station observation, satellite remote sensing inversion, and climate model simulation. in:

Meteorological station observation is the main way to obtain temperature data at present. Its advantages are high observation accuracy and time continuity of the obtained temperature data. However, the observation method of meteorological stations is sparse observation, and it is difficult to obtain spatially continuous temperature data, which cannot meet the demand for spatially continuous data in agriculture, hydrology and other fields.

The development of space detection technology has greatly increased the means for people to obtain surface data. Meteorological satellites carry out meteorological observations of the earth from space through their carried sensors, obtain satellite remote sensing data, and invert satellite remote sensing data to obtain continuous air temperature in space. data. However, the observation of the earth by meteorological satellites is periodic, and it is impossible to obtain time-continuous satellite remote sensing data. Moreover, meteorological satellite observations are easily affected by weather conditions. For example, the observation results of the target area cannot be effectively obtained in cloudy weather. In addition, the temperature data obtained from the inversion of satellite remote sensing data, the inversion results of the temperature data are greatly affected by the inversion algorithm.

The climate model is based on the operation and changes of the atmosphere and climate system, based on the basic laws of physics (such as Newton's law of motion, the law of conservation of energy and mass), and is developed using computer technology to simulate climate changes.

Climate models include global climate models and regional climate models.

Among them, the Global Climate Model (GCM) can simulate and predict climate on a global scale. However, due to the complexity of the climate system itself, as well as the different conditions in different regions such as latitude, altitude, sea and land positions, and underlying surfaces, the climate system presents different characteristics and intensities of changes in different regions, resulting in GCMs that often vary on a regional scale. The simulation effect is not good, and it is difficult to represent local weather processes, especially extreme weather and climate events.

The Regional Climate Model (RCM) can simulate the climate of a specific region and obtain data of certain climate elements, such as temporal and spatial continuous temperature data. However, due to the lack of human understanding of the climate system and other objective Due to limited conditions, the temperature data simulated by regional climate models often have certain deviations, making them unable to accurately reflect the process of local climate change.

In the related art, the temperature data is corrected for deviation by a delta correction method or a quantile mapping method.

The delta correction method takes the observed value as the real value of the contemporary climate, and then subtracts the value of the temperature observation data from the temperature data simulated by the climate model to calculate the "gap" between the two, assuming that the climate model simulates The deviation of the simulated value of the air temperature data does not change with time, that is, the gap exists all the time, and the size is fixed, then the “gap” is added to the simulation results of the model, and the corrected result is obtained. Since the delta correction method uses a fixed deviation value to correct the temperature data, it can only be applied to cases with relatively small variability such as annual and interdecadal scales, and can only correct the deviation of the average state, but cannot be applied to the monthly scale. , Daily scale data with relatively large variability.

The quantile mapping method (Quantile-Mapping, QM) calculates the cumulative probability distribution function (Cumulative Distribution Function, CDF) of the temperature observation data and the temperature data simulation value respectively within the selected reference period, and then constructs the relationship between the two. The transfer function (Transfer Function, TF), using the transfer function, corrects the CDF of the simulated value in other time periods, and finally achieves the purpose of reducing the model simulation error. Since the quantile mapping method needs to calculate the cumulative probability distribution function during the correction process, it is necessary to sort the data on each grid point (for example, from small to large), which disrupts the time distribution of the data, although it makes the temperature The cumulative probability distribution functions of the observed data and the simulated values of the temperature data are similar, but they cannot be "one-to-one" on the time scale, and the correction effect is poor when the climate is abnormal; and when the simulation time period is short, Due to the insufficient amount of data, the calculated cumulative probability distribution function is not smooth enough, and it is difficult to establish an effective transfer function between the cumulative probability distribution function of the temperature observation data and the simulated value of the temperature data.

With global warming, extreme weather events occur frequently, and climate anomalies are more common, such as sudden low temperatures in summer (different from the average climate for many years), and sudden extreme high temperatures in winter. In this context, the above two methods have obvious defects, and a more effective "point-to-point" correction method in time is needed. Based on this, this application provides a method and system for correcting the daily average temperature deviation of regional climate models based on HASM, combined with the observation data of meteorological stations, through High Accuracy Surface Modeling (HASM) for regional climate models The temperature data simulation results are corrected for deviation, so as to obtain high-precision, time-space continuous temperature data of the target area with different time resolutions in any time period, so that it can accurately reflect the regional local climate change.

exemplary method

The embodiment of the present application provides a method for correcting the daily average temperature deviation of the regional climate model based on HASM, and Fig. 1 is a schematic flow chart of the method for correcting the daily average temperature deviation of the regional climate model based on HASM provided according to some embodiments of the present application; Fig. 2 It is a schematic diagram of a HASM-based method for correcting the daily average temperature deviation of a regional climate model according to some embodiments of the present application. As shown in Figure 1 and Figure 2, the method includes:

Step S101, acquiring terrain data, reanalysis data, sea temperature data and land use data of the target area.

In this 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 center point and the range of the area.

Among them, terrain data is also called digital elevation data (Digital Elevation Model, referred to as DEM), which is used to provide basic terrain for the target area. It mathematically expresses the topography of the earth's surface through the finite sequence of three-dimensional vectors on the target area, and uses the form of function Expressed as:

In the formula, V _i represents the three-dimensional vector of the i -th point, (X _i , Y _i ) represents the plane coordinates of the i -th point, Z _i represents the elevation of the i -th point.

Reanalysis data and SST data can provide initial and boundary fields for regional climate models. In the embodiment of the present application, the reanalysis data and SST data use the ERA-Interim data in the climate reanalysis data set, with an original spatial resolution of 0.75° and a temporal resolution of 6 hours.

Land use data (LUCC) is data used to reflect the status, characteristics, dynamic changes, and distribution characteristics of the land use system and land use elements in the target area. In the embodiment of this application, the land use data adopts the data officially provided by the regional climate model RegCM4.

Step S102, according to the terrain data, the reanalysis data, the sea temperature data and the land use data, determine the initial field and boundary field of the regional climate model RegCM4.

It should be noted that the operation of the regional climate model RegCM4 is a process of solving equations based on the initial field and boundary field on the basis of climate dynamics. Based on this, before using the regional climate model RegCM4 to simulate the temperature data of the target area, The initial field and boundary field required by the regional climate model RegCM4 need to be generated first.

Specifically, in some embodiments, the determination of the initial field and boundary field of the regional climate model RegCM4 according to the terrain data, reanalysis data, sea temperature data and land use data is specifically: grid division of the target area, The model grid of the target area is obtained; based on the model grid of the target area, the terrain data, reanalysis data, sea temperature data and land use data are interpolated to obtain the initial field and boundary field of the regional climate model RegCM4.

In the embodiment of the present application, before determining the initial field and boundary field of the regional climate model RegCM4, the target area is first divided into grids to obtain the model grid of the target area. During specific implementation, the size of the model grid can be determined with reference to the scope of the target area, wherein the model grid can be an irregular grid or a regular grid, for example, the model grid is set to a regular grid of 3km×3km .

After the grid division of the target area, based on the model grid of the target area, the preprocessing module of the regional climate model RegCM4 is used to interpolate the terrain data, reanalysis data, sea temperature data and land use data to obtain the regional climate model Initial field and boundary field for RegCM4.

Specifically, firstly, the terrain data and land use data are interpolated to the model grid of the target area through the terrain module provided by RegCM4, and then the SST data are interpolated to the model grid of the target area through the sst module provided by RegCM4, and finally through RegCM4 The provided icbc module combines the interpolation results from the previous two steps with the reanalysis data to generate the initial and boundary fields required for the regional climate model RegCM4 for the target area.

Step S103 , according to the initial field and the boundary field, simulate the air temperature in the target area through the regional climate model RegCM4 to obtain the simulated value of the daily average air temperature data.

During specific implementation, based on the preset time window and the regional range of the target area, according to the initial field and the boundary field, the simulated value of the time-space continuous air temperature data of the target area is obtained through the regional climate model RegCM4; Post-processing the simulated value of the time-space continuous temperature data of the target area to obtain the simulated value of the daily average temperature data.

In the embodiment of the present application, the time window can be determined according to application requirements, for example, it can be set to conduct a simulation of the target area for a period of one year, or to conduct a simulation of the target area for a period of one month.

The initial field and the boundary field are used as the input of the regional climate model RegCM4, and the simulated value of the time-space continuous temperature data of the target area is obtained through the operation mode of the regcmMPI module of RegCM4. The simulated value of the temperature data can reflect the target area in the set time. The continuous temperature change within the window range. Then, through the post-processing module of the regional climate model RegCM4, the simulated value of the temperature data is converted into the simulated value of the daily average temperature data.

It should be noted that the time resolution of the simulated values of the converted daily average temperature data is the daily scale, the spatial resolution is 3km, and the file format is NetCDF. That is to say, each 3km×3km model grid in the target area corresponds to a simulated value of daily average temperature data, and the simulated value is acquired once a day, that is, the daily scale.

Step S104 , performing deviation correction on the simulated value of the daily average temperature data based on HASM, to obtain a corrected value of the daily average temperature data.

It should be noted that the simulated values of the daily average temperature data obtained through the regional climate model RegCM4 are constrained by the complexity of the climate system, and there are certain deviations. Therefore, it is necessary to correct the deviation.

In the embodiment of this application, HASM technology is used to correct the deviation of the simulated value of the daily average temperature data obtained by the regional climate model RegCM4. Among them, HASM is a spatial simulation method, which abstracts the grid expression of the simulated value of the daily average temperature data in the target area into a mathematical surface; then corrects the deviation of the mathematical surface based on HASM.

In some embodiments, the HASM is used to correct the simulated value of the daily average temperature data to obtain the corrected value of the daily average temperature data, specifically: using the simulated value of the daily average temperature data as the input of HASM For the data, the temperature observation data obtained in advance from the meteorological station is used as the optimal control condition, and the deviation correction is performed on the simulated value of the daily average temperature data to obtain the corrected value of the daily average temperature data.

Since the daily average temperature data simulated by the regional climate model RegCM4 has temporal and spatial continuity, each model grid corresponds to a simulated value of the daily average temperature data. It can be understood that for a mathematical surface composed of simulated values of daily average temperature data, each unit of the data surface is a model grid of simulated values of daily average temperature data, and the coordinates of the model grid and the daily values of the model grid can be used Simulated value representation of mean air temperature data.

Based on the temperature observation data of meteorological stations, when the HASM technology is used to correct the deviation of the simulated value of the daily average temperature data obtained by the regional climate model RegCM4, firstly, the observed value of the temperature observation data of each meteorological station is assigned to the meteorological station The nearest model grid; then, according to the daily average temperature data of all model grids, calculate the first type of basic quantities E, F, G and the second type of basic quantities L, M, N of each model grid; where One type of basic quantity represents the arc length of the curve on the data surface, the angle between two directions on the data surface and the area of the data surface domain, and the second type of basic quantity is used to represent the curvature in the space of the data surface; subsequently, the Gaussian equation As the basic equation, the weather station is used as the constraint equation to establish the data surface equation group; finally, through iterative calculation, the Gauss-Seidel iterative algorithm is used to solve the data surface equation group, so as to obtain high-precision, time-space continuous daily average temperature data. Correction value.

In some embodiments, the method further includes: based on a preset error evaluation index, performing error evaluation on the simulated value of the daily average temperature data and the corrected value of the daily average temperature data; wherein, the The error evaluation index includes any one or more of root mean square error, mean absolute error, and correlation coefficient.

During specific implementation, the errors of the simulated value of the daily average temperature data and the corrected value of the daily average temperature data relative to the daily average value of the temperature observation data of the weather station are calculated respectively according to the daily average value of the temperature observation data at the weather station.

Among them, the calculation formula of root mean square error is as follows:

In the formula, RMSE represents the root mean square error between the simulated value of the daily average temperature data or the corrected value of the daily average temperature data and the observed data of the daily average temperature of the meteorological station, N represents the number of meteorological stations, p _i represents the ith The simulated value of the daily average temperature data at the meteorological station or the corrected value of the daily average temperature data, o _i represents the daily average value of the temperature observation data of the i -th weather station.

The formula for calculating the mean absolute error is as follows:

In the formula, MAE represents the average absolute error between the simulated value of the daily average temperature data or the corrected value of the daily average temperature data and the daily average value of the temperature observation data of the meteorological station.

The formula for calculating the correlation coefficient is as follows:

表示日平均气温数据的模拟值或日平均气温数据的订正值的平均值，

表示气象站点的气温观测数据的日平均值。In the formula, R represents the correlation coefficient between the simulated value of the daily average temperature data or the corrected value of the daily average temperature data and the daily average value of the temperature observation data of the meteorological station,

Indicates the average value of the simulated value of the daily average temperature data or the corrected value of the daily average temperature data,

Represents the daily average of temperature observation data from weather stations.

Specifically, taking region A as an example, firstly, based on the regional climate model RegCM4, the daily average temperature data of a certain year in region A is simulated, and the year is calculated with a resolution of 3km (that is, the size of the model grid). The simulated value of the daily average temperature data before bias correction from January to December, in K. Then, based on the method provided in this application, the deviation correction is performed on the simulated value of the daily average temperature data, and the correction value of the daily average temperature data in the A region after the deviation correction is obtained. Finally, the value of the error evaluation index between the simulated value of the daily average temperature data before deviation correction and the corrected value of the daily average temperature data before deviation correction was calculated according to the calculation formula of the above error evaluation index. The results are shown in Table 1, Table 1 as follows:

Comparing the average temperature data before deviation correction and the daily average temperature data of area A after deviation correction with the temperature observation data of meteorological stations in the same time period in this area, it can be seen from Table 1 that after deviation correction The error between the daily average temperature data and the temperature observation data of the meteorological station is significantly reduced. It can be seen that the method provided by the invention is used to correct the deviation of the simulated value of the daily average temperature data of the regional climate model RegCM4, which improves the accuracy of the daily average temperature data.

To sum up, in this application, first obtain the terrain data, reanalysis data, sea temperature data and land use data of the target area; then determine the regional climate model RegCM4 according to the terrain data, reanalysis data, sea temperature data and land use data The initial field and boundary field of RegCM4; then, according to the initial field and boundary field of RegCM4, the air temperature in the target area is simulated through the regional climate model RegCM4, and the simulated value of the daily average temperature data is obtained; finally, the simulated value of the daily average temperature data is obtained based on HASM Correct the deviation to obtain the corrected value of the daily average temperature data. In this way, the HASM technology is used to correct the simulated value of the daily average temperature data obtained by the regional climate model RegCM4, which eliminates the data deviation of the regional climate model RegCM4 to a certain extent, thereby obtaining high-precision, time-space continuous daily average temperature data, making it A more accurate representation of local climate processes improves the accuracy of climate simulations on a regional scale and helps improve the accuracy of simulations of future climate change trends.

exemplary system

The embodiment of the present application provides a HASM-based regional climate model daily average temperature deviation correction system, and Fig. 3 is a structural schematic diagram of the HASM-based regional climate model daily average temperature deviation correction system provided according to some embodiments of the present application, as shown in Fig. 3, the system includes: an acquisition unit 301, a processing unit 302, a simulation unit 303, and a correction unit 304. in:

The acquiring unit 301 is configured to acquire terrain data, reanalysis data, sea temperature data and land use data of the target area.

The processing unit 302 is configured to determine an initial field and a boundary field of the regional climate model RegCM4 according to the terrain data, the reanalysis data, the sea temperature data and the land use data.

The simulation unit 303 is configured to simulate the air temperature in the target area through the regional climate model RegCM4 according to the initial field and the boundary field to obtain a simulated value of daily average air temperature data.

The correction unit 304 is configured to perform deviation correction on the simulated value of the daily average temperature data based on HASM, to obtain a corrected value of the daily average temperature data.

In some embodiments, the processing unit 302 is further configured to: perform grid division on the target area to obtain a model grid of the target area; , the reanalysis data, the sea temperature data and the land use data are interpolated to obtain the initial field and the boundary field of the regional climate model RegCM4.

In some embodiments, the simulation unit 303 is further configured to: based on the preset time window and the regional scope of the target area, according to the initial field and the boundary field, obtain the target by using the regional climate model RegCM4 The simulated value of the time-space continuous temperature data of the region; post-processing the simulated value of the time-space continuous temperature data of the target region to obtain the simulated value of the daily average temperature data.

In some embodiments, the correcting unit 304 is further configured to: use the simulated value of the daily average temperature data as the input data of HASM, and use the temperature observation data of the weather station acquired in advance as the optimal control condition, and the daily The simulated value of the average temperature data is corrected for deviation, and the corrected value of the daily average temperature data is obtained.

In some embodiments, the system further includes an error evaluation unit (not shown in the figure), and the error evaluation unit is configured to: based on a preset error evaluation index, respectively analyze the simulated value of the daily average temperature data and The correction value of the daily average temperature data is subjected to error evaluation; wherein, the error evaluation index includes any one or more of root mean square error, average absolute error, and correlation coefficient.

The HASM-based regional climate model daily average temperature deviation correction system provided by the embodiment of the present application can realize the flow and steps of any of the above-mentioned HASM-based regional climate model daily average temperature deviation correction methods, and achieve the same technical effect. No more details here.

The above descriptions are only preferred embodiments of the present application, and are not intended to limit the present application. For those skilled in the art, there may be various modifications and changes in the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application shall be included within the protection scope of this application.

Claims (6)

1. A HASM-based regional climate model daily average temperature deviation correction method is characterized in that, comprising: Obtain topographic data, reanalysis data, sea temperature data and land use data of the target area; Determine the initial field and boundary field of the regional climate model RegCM4 according to the topographic data, the reanalysis data, the sea temperature data and the land use data; According to the initial field and the boundary field, the temperature in the target area is simulated by the regional climate model RegCM4 to obtain the simulated value of the daily average temperature data; specifically: Based on the preset time window and the regional range of the target area, according to the initial field and the boundary field, the simulated value of the temporal and spatial continuous air temperature data of the target area is obtained through the regional climate model RegCM4; Post-processing the simulated value of the time-space continuous temperature data of the target area to obtain the simulated value of the daily average temperature data; Based on HASM, the deviation correction is carried out to the simulated value of the daily average temperature data, and the corrected value of the daily average temperature data is obtained, specifically: The simulated value of the daily average temperature data is used as the input data of HASM, and the temperature observation data of the meteorological station obtained in advance is used as the optimal control condition, and the deviation correction is carried out to the simulated value of the daily average temperature data to obtain the daily average temperature data The corrected value of , in order to obtain high-precision daily average temperature data. 2. The HASM-based regional climate model daily mean air temperature deviation correction method according to claim 1, characterized in that, according to the terrain data, the reanalysis data, the sea temperature data and the land use Data to determine the initial field and boundary field of the regional climate model RegCM4, specifically: performing grid division on the target area to obtain a model grid of the target area; Based on the model grid of the target area, the terrain data, the reanalysis data, the sea temperature data and the land use data are interpolated to obtain the initial field of the regional climate model RegCM4 and the boundary field. 3. the HASM-based regional climate model daily average temperature deviation correction method according to claim 1, is characterized in that, described method also comprises: Based on preset error evaluation indicators, error evaluation is performed on the simulated value of the daily average temperature data and the corrected value of the daily average temperature data; Wherein, the error evaluation index includes any one or more of root mean square error, mean absolute error, and correlation coefficient. 4. A HASM-based regional climate model daily average temperature deviation correction system, characterized in that it includes: an acquisition unit configured to acquire terrain data, reanalysis data, sea temperature data and land use data of the target area; A processing unit configured to determine an initial field and a boundary field of a regional climate model RegCM4 based on the terrain data, the reanalysis data, the sea temperature data, and the land use data; The simulation unit is configured to simulate the air temperature in the target area through the regional climate model RegCM4 according to the initial field and the boundary field to obtain the simulated value of the daily average temperature data; the simulation unit is further configured as: Based on the preset time window and the regional range of the target area, according to the initial field and the boundary field, the simulated value of the temporal-spatial continuous air temperature data of the target area is obtained through the regional climate model RegCM4; Post-processing the simulated value of the time-space continuous temperature data of the target area to obtain the simulated value of the daily average temperature data; The correction unit is configured to correct the deviation of the simulated value of the daily average temperature data based on HASM to obtain a correction value of the daily average temperature data; The specific configuration of the correction unit is: The simulated value of the daily average temperature data is used as the input data of HASM, and the temperature observation data of the meteorological station obtained in advance is used as the optimal control condition, and the deviation correction is carried out to the simulated value of the daily average temperature data to obtain the daily average temperature data The corrected value of , in order to obtain high-precision daily average temperature data. 5. the HASM-based regional climate model daily mean air temperature deviation correction system according to claim 4, is characterized in that, described processing unit is further configured as: performing grid division on the target area to obtain a model grid of the target area; Based on the model grid of the target area, the terrain data, the reanalysis data, the sea temperature data and the land use data are interpolated to obtain the initial field and the boundary field. 6. the HASM-based regional climate model daily mean air temperature deviation correction system according to claim 4, is characterized in that, described system also comprises error assessment unit, and described error assessment unit is configured as: Based on preset error evaluation indicators, error evaluation is performed on the simulated value of the daily average temperature data and the corrected value of the daily average temperature data; Wherein, the error evaluation index includes any one or more of root mean square error, mean absolute error, and correlation coefficient.

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