Downscaling Method and Device for Climate Data

Abstract

The present invention discloses a method and device for downscaling climate data, which relates to the technical field of climate data processing. The method includes: cropping climate data with a unified resolution using the scope of the study area; making the first and second data sets for evaluating climate data by using the cropped climate data; determining the multi-year daily average climate data of each grid cell in the study area during the historical period according to the first data set; constructing a geographically weighted matrix to describe the local spatial heterogeneity among different grid cells in the spatial dimension; constructing a spatio-temporal weighted regression model according to the matrix and the multi-year daily average climate data to obtain multi-year daily average regression coefficients; and performing bias correction on the second data set by using the smoothed coefficients to obtain the downscaled climate data. The present invention can accurately capture the spatio-temporal heterogeneity of climate data by dynamically estimating the multi-year daily average regression coefficients, combining spatial weighting and temporal dynamic changes, and improve the accuracy of climate data simulation.

Description

Technical Field

[0001] The present invention relates to the technical field of climate data processing, and in particular to a downscaling method and device for climate data. Background Art

[0002] This section aims to provide background or context for the technical features stated in the claims. The description herein is not admitted to be prior art merely by including it in this section.

[0003] Downscaling refers to a method of converting large-scale climate model data into higher-resolution regional climate data. It aims to improve the spatial or temporal resolution of climate model data so that it can better reflect local climate characteristics, especially climate variables such as precipitation.

[0004] In recent years, with the continuous in-depth study of climate change, accurate regional climate prediction and disaster warning have become the focus of research. As an important data source in global climate research, climate model data provides various climate scenarios and future climate predictions. However, the spatial resolution of climate model data is relatively low, making it difficult to meet the needs of regional climate research and disaster prediction. Therefore, it is of great research significance to study more accurate downscaling methods.

[0005] Current downscaling methods include physical methods and statistical methods. Traditional statistical downscaling methods, such as regression analysis and interpolation techniques, mainly perform downscaling based on the simple relationship between observed data and climate model data. Common statistical downscaling methods include linear regression, least squares method, distribution mapping method, etc. Although these methods can improve the spatial resolution of climate model data to a certain extent, they usually ignore the complex changes of climate variables in the spatio-temporal dimension, resulting in the inability to comprehensively and accurately reflect the spatio-temporal heterogeneity of climate. Summary of the Invention

[0006] The technical problem existing in the present invention is: the problem of insufficient accuracy in dealing with the spatial heterogeneity and temporal dynamic characteristics of precipitation in traditional downscaling methods, and the low accuracy of climate data simulation.

[0007] To solve the above technical problems, in a first aspect, the present invention provides a downscaling method for climate data, which is used to accurately capture the spatio-temporal heterogeneity of climate variables and improve the simulation accuracy of climate data by dynamically estimating the daily regression coefficients of multiple years and combining spatial weighting and temporal dynamic changes. The method includes:

[0008] Obtain climate model data within the scope of the research area and perform unit conversion on the climate model data;

[0009] Perform interpolation processing on the climate model data after unit conversion to unify the resolution;

[0010] The climate model data after unified resolution is cropped using the research area range to obtain climate model data with the same range size and corresponding spatial positions.

[0011] Using the climate model data with the same range size and corresponding spatial positions, the first dataset and the second dataset for climate data evaluation are produced; the first dataset is the experimental data into which the daily precipitation data in the historical period of the climate model data is divided, and the second dataset is the verification data into which the daily precipitation data in the historical period of the climate model data is divided.

[0012] Based on the first dataset, the multi-year average daily climate data of each grid cell in the research area in the historical period is determined.

[0013] A geographical weighted matrix is constructed to describe the local spatial heterogeneity among different grid cells in the spatial dimension; based on the geographical weighted matrix and the multi-year average daily climate data, a spatio-temporal weighted regression model is constructed to obtain the multi-year average daily regression coefficients.

[0014] The multi-year average daily regression coefficients are smoothed to obtain the smoothed multi-year average daily regression coefficients.

[0015] Using the smoothed multi-year average daily regression coefficients, the second dataset is bias-corrected to obtain the downscaled climate model data.

[0016] In a second aspect, the present invention also provides a downscaling device for climate data, which is used to accurately capture the spatio-temporal heterogeneity of climate variables and improve the simulation accuracy of climate data by dynamically estimating the multi-year average daily regression coefficients and combining spatial weighting and temporal dynamic changes. The device includes:

[0017] A conversion unit for obtaining climate model data within the research area range and performing unit conversion on the climate model data.

[0018] A spatial resolution processing unit for performing interpolation processing on the climate model data after unit conversion to unify the resolution.

[0019] A cropping processing unit for cropping the climate model data after unified resolution using the research area range to obtain climate model data with the same range size and corresponding spatial positions.

[0020] A dataset production unit for using the climate model data with the same range size and corresponding spatial positions to produce the first dataset and the second dataset for climate data evaluation; the first dataset is the experimental data into which the daily precipitation data in the historical period of the climate model data is divided, and the second dataset is the verification data into which the daily precipitation data in the historical period of the climate model data is divided.

[0021] A multi-year average daily climate data determination unit for determining the multi-year average daily climate data of each grid cell in the study area during the historical period according to the first data set;

[0022] A multi-year average daily regression coefficient determination unit for constructing a geographically weighted matrix to describe the local spatial heterogeneity among different grid cells in the spatial dimension; and constructing a spatio-temporal weighted regression model based on the geographically weighted matrix and the multi-year average daily climate data to obtain the multi-year average daily regression coefficient;

[0023] A smoothing processing unit for smoothing the multi-year average daily regression coefficient to obtain the smoothed multi-year average daily regression coefficient;

[0024] A correction unit for correcting the bias of the second data set by using the smoothed multi-year average daily regression coefficient to obtain the downscaled climate model data.

[0025] In a third aspect, the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned downscaling method for climate data is implemented.

[0026] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned downscaling method for climate data is implemented.

[0027] In a fifth aspect, the present invention further provides a computer program product including a computer program, and when the computer program is executed by a processor, the above-mentioned downscaling method for climate data is implemented.

[0028] Compared with the prior art solution for downscaling based on the simple relationship between the observed data and the climate model data, the beneficial technical effect of the downscaling solution for climate data provided by the present invention is that it can accurately capture the spatio-temporal heterogeneity and non-linear characteristics of climate variables by dynamically estimating the multi-year average daily regression coefficient, combining spatial weighting and temporal dynamic changes, and improving the accuracy of climate data simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0030] Figure 1Schematic flowchart of the downscaling method for climate data in an embodiment of the present invention;

[0031] Figure 2 Precipitation data graphs before and after a set of unit conversions in an embodiment of the present invention, Figure 2 where (a) is the original data before unit conversion, Figure 2 and (b) is the data after unit conversion;

[0032] Figure 3 An interpolated precipitation data graph in an embodiment of the present invention;

[0033] Figure 4 Time series variation graph of precipitation before and after calibration of observed data and model data within a preset time period (e.g., 14 years) in the study area in an embodiment of the present invention;

[0034] Figure 5 Line graph of correlation coefficients of downscaling results in an embodiment of the present invention;

[0035] Figure 6 Error analysis graph of climate model data and observed data in an embodiment of the present invention;

[0036] Figure 7 Schematic structural diagram of the downscaling device for climate data in an embodiment of the present invention. Detailed implementation manners

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer and more understandable, the following further describes the embodiments of the present invention in detail with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but do not limit the present invention.

[0038] In the technical solutions of the present application, the acquisition, storage, use, processing, etc. of data all comply with the relevant regulations of laws and regulations.

[0039] Figure 1 Schematic flowchart of the downscaling method for climate data in an embodiment of the present invention, as Figure 1 shown, the method includes the following steps:

[0040] Step 101: Obtain climate model data within the scope of the study area and perform unit conversion on the climate model data;

[0041] Step 102: Perform interpolation processing on the climate model data after unit conversion to unify the resolution;

[0042] So that the climate model data and the observed data have the same spatial resolution;

[0043] Step 103: Crop the climate model data after unified resolution using the research area range to obtain climate model data with the same range size and corresponding spatial positions;

[0044] Step 104: Use the climate model data with the same range size and corresponding spatial positions to produce the first dataset and the second dataset for climate data evaluation; the first dataset is the experimental data into which the daily precipitation data in the historical period of the climate model data is divided, and the second dataset is the validation data into which the daily precipitation data in the historical period of the climate model data is divided;

[0045] Step 105: Determine the multi-year average daily climate data of each grid cell in the research area during the historical period based on the first dataset;

[0046] Step 106: Construct a geographically weighted matrix to describe the local spatial heterogeneity among different grid cells in the spatial dimension; based on the geographically weighted matrix and the multi-year average daily climate data, construct a spatio-temporal weighted regression model to obtain the multi-year average daily regression coefficients;

[0047] Step 107: Smooth the multi-year average daily regression coefficients to obtain the smoothed multi-year average daily regression coefficients;

[0048] To reduce the impact of fluctuations and abnormal data in the multi-year average daily regression coefficients on the model;

[0049] Step 108: Use the smoothed multi-year average daily regression coefficients to correct the bias of the second dataset to obtain the downscaled climate model data.

[0050] The downscaling method for climate data provided by the embodiments of the present invention, when working: obtaining climate model data within the research area, performing unit conversion on the climate model data so that the precipitation unit is converted from kilograms per square meter per second to millimeters per day; performing interpolation processing on the unit-converted climate model data to unify the resolution so that the climate model data has the same spatial resolution as the observed data; cropping the climate model data with unified resolution using the research area range to obtain climate model data with the same range size and corresponding spatial positions; using the climate model data with the same range size and corresponding spatial positions to produce the first dataset and the second dataset for climate data evaluation; determining the multi-year daily average climate data of each grid cell in the historical period within the research area according to the first dataset; constructing a geographically weighted matrix to describe the local spatial heterogeneity between different grid cells in the spatial dimension; constructing a spatio-temporal weighted regression model according to the geographically weighted matrix and the multi-year daily average climate data to obtain the multi-year daily average regression coefficients; performing smoothing processing on the multi-year daily average regression coefficients to obtain the smoothed multi-year daily average regression coefficients so as to reduce the influence of the fluctuations and abnormal data of the multi-year daily average regression coefficients on the model; using the smoothed multi-year daily average regression coefficients to perform bias correction on the second dataset to obtain the downscaled climate model data.

[0051] Compared with the prior art solution for downscaling based on the simple relationship between observed data and climate model data, the downscaling method for climate data provided by the embodiments of the present invention can accurately capture the spatio-temporal heterogeneity and non-linear characteristics of climate variables by dynamically estimating the multi-year daily average regression coefficients, combining spatial weighting and temporal dynamic changes, and improves the accuracy of climate data simulation. The following will be described in detail in conjunction with Figures 2 to 6 as follows.

[0052] Aiming at the problem that the current CMIP6 climate model data is difficult to meet the requirements of regional climate research, the embodiments of the present invention provide a downscaling method for climate data. This method relates to the downscaling method in climate modeling. This method is a CMIP6 climate data downscaling method combining Bayesian ridge regression and a spatial weight matrix (which can be called Bayesian ridge spatio-temporal weighted regression in the embodiments of the present invention). By dynamically estimating the multi-year daily average regression coefficients and combining spatial weighting and temporal dynamic changes, it can more accurately capture the spatio-temporal heterogeneity of climate variables and improve the simulation accuracy of climate variables such as precipitation. The following will be described in detail.

[0053] For ease of understanding, the following takes climate data as climate model precipitation data as an example to illustrate the downscaling method for climate data provided by the embodiments of the present invention.

[0054] Step 101: Obtain climate model data within the research area and perform unit conversion on the climate model data: Obtain CMIP6 climate model data (such as climate model precipitation data) and perform unit conversion on it. The precipitation unit is converted from kilograms per square meter per second to millimeters per day to eliminate the unit impact. The climate data in the embodiments of the present invention can be CMIP6 (Coupled Model Intercomparison Project Phase 6) climate model data.

[0055] Further, in the above step 101, obtaining CMIP6 climate model precipitation data and performing unit conversion on it specifically includes:

[0056] 1.1. Select climate (such as precipitation) model data with a time scale of days and a long time series range.

[0057] 1.2. Multiply the original climate model data (such as precipitation model data) by 86,400. Taking precipitation climate as an example, the precipitation unit changes from kilograms per square meter per second to millimeters per day, and precipitation data with the same unit as the measured data is obtained. Figure 2 This is a precipitation data graph before and after unit conversion in the embodiments of the present invention. Figure 2 In (a), it is the original data before unit conversion. Figure 2 In (b), it is the data after unit conversion.

[0058] Step 102: Perform interpolation processing on the climate model data after unit conversion to unify the resolution: Perform interpolation processing on the climate model data after the above unit conversion to unify the resolution so that it has the same spatial resolution as the observed data (in the embodiments of the present invention, the CN05.1 dataset is used as the observed data); Figure 3 This is a graph of interpolated precipitation data in the embodiments of the present invention.

[0059] The above step 102 for unifying the spatial resolution of the climate model specifically includes:

[0060] The spatial resolution of the climate model data is increased to a regular grid pixel of 0.25°×0.25° through bilinear interpolation. The calculation formula is as follows:

[0061]

[0062]

[0063]

[0064] Among them, P1(x1, y1), P2(x2, y1), P3(x1, y2), and P4(x2, y2) are known coordinate points, and P(x, y) is the point to be interpolated and the intermediate results of R1 and R2 interpolation.

[0065] Step 103: Crop the climate model data after unifying the resolution with the research area range to obtain climate model data with the same range size and corresponding spatial positions: Crop the interpolated high-resolution climate model data with the research area range to obtain precipitation data with the same range size and corresponding spatial positions.

[0066] Specifically, this Step 103 is as follows: Reproject the research area boundary file into an equiangular equal-area projection, then vector-rasterize the file to convert it into a raster image with a resolution of 0.25, and finally use the exported raster file as a mask to crop the climate model data. That is:

[0067] 3.1. Obtain the boundary range of the research area, check the projection type of the boundary file, and reproject the research area boundary file into an equiangular equal-area projection.

[0068] 3.2. Rasterize the equiangular equal-area vector boundary to obtain a research area raster image with a resolution of 0.25 degrees.

[0069] 3.3. Use the exported raster image file as a mask to crop the climate model data, thereby generating a new raster dataset, that is, generating climate model data (such as precipitation data) with the same range size and corresponding spatial positions.

[0070] Step 104: Use the climate model data with the same range size and corresponding spatial positions to produce the first dataset and the second dataset for climate data evaluation, that is, produce the first dataset and the second dataset for precipitation data evaluation.

[0071] Specifically, this Step 104 is as follows: Divide the daily precipitation data of the climate model data from 1970 to 2014 in the historical period into experimental data (the first dataset) and validation data (the second dataset). The experimental data (the first dataset) from 1970 to 2000 is used to calculate the regression coefficient, and the validation data (the second dataset) from 2001 to 2014 is used to verify the established downscaling method.

[0072] Step 105: Determine the multi-year average daily climate data of each grid cell in the research area during the historical period according to the first dataset: Calculate the average daily data of the multi-year precipitation of each grid cell in the above first dataset. Specifically, this Step 105 is as follows: Calculate the multi-year daily average value of the data in the experimental period from 1970 to 2000, and calculate the multi-year average daily data of 30 years for each grid cell respectively.

[0073] Step 106: Construct a geographically weighted matrix to describe the local spatial heterogeneity among different grid cells in the spatial dimension; based on the geographically weighted matrix and multi-year daily climate data, construct a spatio-temporal weighted regression model to obtain multi-year daily regression coefficients: Construct a geographically weighted matrix to describe the local spatial heterogeneity among different grid cells in the spatial dimension; construct a spatio-temporal weighted regression model for the multi-year daily precipitation data calculated above to obtain a multi-year daily regression coefficient model, and through this model, multi-year daily regression coefficients can be obtained.

[0074] In specific implementation, on the time dimension, estimate the multi-year daily regression coefficients day by day based on multi-year daily data to reveal the dynamic changes of climate model data and observed data over time; on the spatial dimension, through the geographically weighted matrix w i describe the local spatial heterogeneity among different grid cells. This "local spatial heterogeneity" refers to the degree of change or distribution difference of precipitation between different regions or grid units, emphasizing the non-uniformity in space. For example: in some regions, precipitation may be relatively uniform, while in other regions, precipitation may have large fluctuations. Through the geographically weighted matrix, when calculating the regression coefficient of the current grid cell, the precipitation values of surrounding cells can be considered. Specifically:

[0075] 6.1. On the spatial dimension, use the geographically weighted matrix to describe the local spatial heterogeneity among different grid cells. This weighted matrix is constructed based on the Gaussian kernel function to reflect the influence of spatially adjacent grid cells on the regression coefficient. The weighted matrix w i is a diagonal matrix. In one embodiment, the geographically weighted matrix can be: w i =diag(w i1 , w i2 ,···,w in ) ; the weighted matrix w i element w ij represents the weighting coefficient of the i-th grid cell to the j-th grid cell, and the calculation formula is:

[0076] where: w ij is the weight of grid cell i to grid cell j; h is the bandwidth parameter, controlling the decay rate of the weight with distance; d ij is the distance between the i-th grid cell and the j-th grid cell, .

[0077] In specific implementation, the above "bandwidth" is a key parameter in the Gaussian kernel function, which controls the smoothness and influence range of the Gaussian kernel function. In the embodiments of the present invention, it determines how the similarity between sample points decays as the distance increases. When the bandwidth is small, only grid cells with relatively close distances can have a greater impact on the target point. When the bandwidth is large, the influence range of the Gaussian kernel increases, and sample points with relatively far distances will also have a greater impact on the target point.

[0078] In specific implementation, n is the total number of samples. Through this weighted matrix, the contribution of spatially adjacent points to the estimation of the regression coefficient is made greater, while the influence of distant points is weakened, so as to effectively capture the heterogeneity of the observed data and the simulated data in the spatial dimension.

[0079] 6.2. Estimate the regression coefficient for each day using multi-year daily precipitation data. At each time point t, calculate the regression relationship between the training period climate model data (GCM simulation data, i.e., the first data set) and the observed data. According to the regression coefficient formula, sequentially obtain the regression coefficients of each position of each day's multi-year daily data.

[0080] In one embodiment, constructing a spatio-temporal weighted regression model based on the geographic weighted matrix and multi-year daily climate data to obtain multi-year daily regression coefficients may include: obtaining the multi-year daily regression coefficients according to the following multi-year daily regression coefficient model (regression model):

[0081]

[0082] Where: β i (t) is the regression coefficient of time t and grid cell i, Y(t) is the observed data vector at time t, X(t) is the climate model data matrix at time t, X(t) and Y(t) are multi-year daily climate data; λ is the regularization parameter; I is the identity matrix, w i is the geographic weighted matrix.

[0083] Step 107: Smooth the multi-year daily regression coefficients to obtain the smoothed multi-year daily regression coefficients: Smooth the above calculated multi-year daily regression coefficients through a sliding time window to reduce the fluctuations of the regression coefficients and the impact of abnormal data on the model.

[0084] In specific implementation, when calculating the regression coefficient of a certain day, the data of several days before and after it is considered, and the fluctuations within a short period are smoothed by means of weighted average. The setting of the weights can be adjusted according to the time distance. The days closer to the current time have larger weights, while the days farther from the current time have smaller weights. Specifically:

[0085] 7.1. Select a suitable size of the sliding time window for calculating the smoothed result of the regression coefficient. The range of the sliding window is from t - w to t + w, where t is the current time point, and the window size 2w + 1 will determine the number of days involved in the smoothing calculation.

[0086] 7.2. For each time point t, the regression coefficients within the sliding window are weighted and averaged to obtain the smoothed regression coefficient.

[0087] In one embodiment, smoothing the daily average regression coefficients over multiple years to obtain the smoothed daily average regression coefficients over multiple years may include: obtaining the smoothed daily average regression coefficients over multiple years according to the following formula:

[0088]

[0089] Where: β i (t) is the regression coefficient smoothed by the sliding time window, β i (k) is the original regression coefficient on the k th day, w k is the weight of the sliding time window, w is the half-width of the sliding time window, and the range of the sliding time window is t - w , t + w .

[0090] 7.3. Determine the weights and window range. The weights are generally allocated according to the distance from the current time. The days closer to the current time will obtain larger weights, and the days farther from the current time have smaller weights. By experimenting with different weight allocations and window sizes and optimizing according to the performance in the first dataset, the weights and window with the best effect are selected. That is, in one embodiment, the above method for downscaling climate data may further include:

[0091] According to the distance from the current time, perform the weight allocation of the sliding time window. The days closer to the current time have larger weights, and the days farther from the current time have smaller weights;

[0092] Optimize by experimenting with the performance of different sliding time window weights and ranges in the first dataset to obtain the sliding time window weight allocation and range with the best effect.

[0093] Step 108: Use the smoothed multi-year daily average regression coefficients to perform bias correction on the second dataset to obtain downscaled climate model data: Apply the smoothed multi-year daily average regression coefficients to perform bias correction on the second dataset to obtain downscaled climate model data, which can be used for subsequent regional climate change assessment, climate change prediction, disaster warning, etc. Multiply the precipitation data for each day in the second dataset by the corresponding multi-year daily average regression coefficient to obtain the bias-corrected result for that date. Calculate all days in sequence to obtain the bias-corrected second dataset. Figure 4 This is the time series change diagram of precipitation before and after correction of the observed data and model data in the preset time period (e.g., 14 years) of the study area in the embodiment of the present invention.

[0094] In one embodiment, the above method for downscaling climate data may further include: evaluating the accuracy and reliability of the downscaled climate model data.

[0095] Specifically, when implementing, evaluate the accuracy and reliability of the downscaled climate model data, and this content can be achieved through existing technologies. Compare the downscaled climate model data during the verification period with the actual climate (e.g., precipitation) data (CN05.1 dataset) of the study area, and use the correlation coefficient, standard deviation, and root mean square error as evaluation indicators to evaluate the accuracy of this downscaling method. Figure 5 This is the line graph of the correlation coefficient of the downscaling result in the embodiment of the present invention; Figure 6 This is the error analysis diagram (i.e., Taylor diagram, drawn based on the correlation coefficient, standard deviation, and root mean square error) of the climate model data and the observed data in the embodiment of the present invention.

[0096] The beneficial effects of the embodiments of the present invention are as follows: The embodiments of the present invention significantly improve the climate simulation accuracy of CMIP6 climate model data in regional climate research through the proposed Bayesian ridge spatio-temporal weighted regression CMIP6 climate data downscaling method. By dynamically estimating the daily regression coefficients and combining the spatio-temporal weighting method, the present invention can accurately capture the spatio-temporal heterogeneity and non-linear characteristics of climate variables such as precipitation, overcoming the problem of insufficient accuracy in traditional downscaling methods when dealing with the spatial heterogeneity and temporal dynamic characteristics of precipitation. This method also smooths the regression coefficients through a sliding time window, reduces the fluctuations of the regression coefficients, and improves the stability and reliability of long-term climate change prediction. This downscaling method improves the spatial and temporal accuracy of CMIP6 climate model data, enabling climate data to more accurately reflect local climate characteristics at a higher resolution. This method provides more accurate data support for subsequent regional climate change assessment, climate change prediction, and disaster warning, has broad application prospects, and can provide a more reliable basis for related research and decision-making.

[0097] In an embodiment of the present invention, a downscaling processing device for climate data is further provided, as described in the following embodiments. Since the principle of the device for solving problems is similar to the downscaling processing method of climate data, the implementation of the device can refer to the implementation of the downscaling processing method of climate data, and the repeated parts will not be elaborated.

[0098] Figure 7 It is a schematic structural diagram of the downscaling processing device for climate data in an embodiment of the present invention, as Figure 7 shown, the device includes:

[0099] A conversion unit 01, configured to obtain climate model data within the scope of the research area and perform unit conversion on the climate model data;

[0100] A spatial resolution processing unit 02, configured to perform interpolation processing on the climate model data after unit conversion to unify the resolution;

[0101] A clipping processing unit 03, configured to clip the climate model data after unifying the resolution with the scope of the research area to obtain climate model data with the same scope size and corresponding spatial positions;

[0102] A data set production unit 04, configured to use the climate model data with the same scope size and corresponding spatial positions to produce a first data set and a second data set for climate data evaluation; the first data set is the experimental data obtained by dividing the daily precipitation data in the historical period of the climate model data, and the second data set is the verification data obtained by dividing the daily precipitation data in the historical period of the climate model data;

[0103] A multi-year daily average climate data determination unit 05, configured to determine the multi-year daily average climate data of each grid cell in the historical period within the research area according to the first data set;

[0104] A multi-year daily average regression coefficient determination unit 06, configured to construct a geographically weighted matrix to describe the local spatial heterogeneity among different grid cells in the spatial dimension; according to the geographically weighted matrix and the multi-year daily average climate data, construct a spatio-temporal weighted regression model to obtain the multi-year daily average regression coefficient;

[0105] A smoothing processing unit 07, configured to perform smoothing processing on the multi-year daily average regression coefficient to obtain the smoothed multi-year daily average regression coefficient;

[0106] A correction unit 08, configured to use the smoothed multi-year daily average regression coefficient to perform bias correction on the second data set to obtain the downscaled climate model data.

[0107] In one embodiment, the multi-year daily average regression coefficient determination unit is specifically configured to: obtain the multi-year daily average regression coefficient according to the following multi-year daily average regression coefficient model:

[0108]

[0109] Wherein: β i (t) is the regression coefficient of time t and grid cell i, Y(t) is the observation data vector of time t, X(t) is the model data matrix of time t, X(t) and Y(t) are the multi-year daily average climate data; λ is the regularization parameter; I is the identity matrix, w i is the geographically weighted matrix.

[0110] In one embodiment, the geographically weighted matrix w i is a diagonal matrix, and its form is: w i =diag(w i1 , w i2 ,···,w in ) ; The geographically weighted matrix w i The element of w ij represents the weighting coefficient of the i-th grid cell to the j-th grid cell, and the calculation formula is:

[0111] Wherein: h is the bandwidth parameter, which is used to control the attenuation speed of the weight with distance; d ij is the distance between the i-th grid cell and the j-th grid cell.

[0112] In one embodiment, the smoothing processing unit is specifically configured to: obtain the smoothed multi-year daily average regression coefficient according to the following formula:

[0113] ;

[0114] Wherein: β i (t) is the regression coefficient after being smoothed by a sliding time window, β i (k) is the k th day's original regression coefficient, w k is the sliding time window weight, wis the half-width of the sliding time window, and the range of the sliding time window is t - w , t + w .

[0115] In one embodiment, the downscaling processing device for the above climate data further includes:

[0116] A weight allocation unit, configured to perform weight allocation for the sliding time window according to the distance from the current time, where the weights of the days closer to the current time are larger, and the weights of the days farther from the current time are smaller;

[0117] An optimization unit, configured to optimize by testing the performance of different sliding time window weights and ranges in the first data set, and obtain the sliding time window weight allocation and range with the best effect.

[0118] In one embodiment, the downscaling processing device for the above climate data further includes: an evaluation unit, configured to evaluate the accuracy and reliability of the downscaled climate model data.

[0119] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above method for downscaling climate data is implemented.

[0120] An embodiment of the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above method for downscaling climate data is implemented.

[0121] An embodiment of the present invention also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the above method for downscaling climate data is implemented.

[0122] Compared with the prior art downscaling processing solution based on the relationship between observation data and climate model data, the downscaling processing solution for climate data provided by the embodiments of the present invention can accurately capture the spatio-temporal heterogeneity and non-linear characteristics of climate variables by dynamically estimating the daily regression coefficients over the years, combining spatial weighting and temporal dynamic changes, and improving the accuracy of climate data simulation.

[0123] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0124] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0125] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0127] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for downscaling climate data, characterized in that, Including: Obtain climate model data within the research area and perform unit conversion on the climate model data; Perform interpolation processing on the climate model data after unit conversion to unify the resolution; Crop the climate model data after unifying the resolution with the research area range to obtain climate model data with the same range size and corresponding spatial positions; Use the climate model data with the same range size and corresponding spatial positions to produce the first data set and the second data set for climate data evaluation; the first data set is the experimental data into which the daily precipitation data in the historical period of the climate model data is divided, and the second data set is the verification data into which the daily precipitation data in the historical period of the climate model data is divided; Determine the multi-year average daily climate data of each grid cell in the historical period within the research area according to the first data set; Construct a geographically weighted matrix to describe the local spatial heterogeneity between different grid cells in the spatial dimension; according to the geographically weighted matrix and the multi-year average daily climate data, construct a spatio-temporal weighted regression model to obtain the multi-year average daily regression coefficients; Perform smoothing processing on the multi-year average daily regression coefficients to obtain the smoothed multi-year average daily regression coefficients; Use the smoothed multi-year average daily regression coefficients to perform bias correction on the second data set to obtain the downscaled climate model data.

2. The method according to claim 1, characterized in that According to the geographically weighted matrix and the multi-year average daily climate data, construct a spatio-temporal weighted regression model to obtain the multi-year average daily regression coefficients, including: obtaining the multi-year average daily regression coefficients according to the following multi-year average daily regression coefficient model: Wherein: β i (t) is the regression coefficient for time t and grid cell i, Y(t) is the observation data vector for time t, X(t) is the model data matrix for time t, X(t) and Y(t) are the multi-year daily average climate data; λ is the regularization parameter; I is the identity matrix, w i is the geographically weighted matrix.

3. The method according to claim 2, characterized in that, The geographically weighted matrix is: w i =diag(w i1 ,w i2 ,···,w in ) ; Geographically Weighted Matrix w i elements w ij represent the weighting coefficient of the i-th grid cell to the j-th grid cell, and the calculation formula is: Wherein: h is a bandwidth parameter used to control the rate at which the weight decays with distance; d ij is the distance between the i-th grid cell and the j-th grid cell.

4. The method according to claim 1, wherein Perform smoothing processing on the multi-year average daily regression coefficients to obtain the smoothed multi-year average daily regression coefficients, including: obtaining the smoothed multi-year average daily regression coefficients according to the following formula: ； Wherein: β i (t) is the regression coefficient smoothed by a sliding time window, β i (k) is the original regression coefficient on the k -th day, w k is the weight of the sliding time window, w is the half-width of the sliding time window, and the range of the sliding time window is t - w , t + w .

5. The method according to claim 4, characterized in that, Also including: According to the distance from the current time, perform sliding time window weight allocation, with the weights of the days closer to the current time being larger and the weights of the days farther from the current time being smaller; Optimize by testing the performance of different sliding time window weights and ranges in the first data set to obtain the sliding time window weight allocation and range with the best effect.

6. The method according to claim 1, wherein Also including: Evaluate the accuracy and reliability of the downscaled climate model data.

7. A device for downscaling climate data, characterized in that, Including: A conversion unit for obtaining climate model data within the research area and performing unit conversion on the climate model data; A spatial resolution processing unit for performing interpolation processing on the climate model data after unit conversion to unify the resolution; A cropping processing unit for cropping the climate model data after unifying the resolution with the research area range to obtain climate model data with the same range size and corresponding spatial positions; A data set production unit for using the climate model data with the same range size and corresponding spatial positions to produce the first data set and the second data set for climate data evaluation; the first data set is the experimental data into which the daily precipitation data in the historical period of the climate model data is divided, and the second data set is the verification data into which the daily precipitation data in the historical period of the climate model data is divided; A multi-year daily average climate data determination unit for determining the multi-year daily average climate data of each grid cell in the study area during the historical period according to the first data set; A multi-year daily average regression coefficient determination unit for constructing a geographically weighted matrix to describe the local spatial heterogeneity among different grid cells in the spatial dimension; performing spatio-temporal weighted regression model construction according to the geographically weighted matrix and the multi-year daily average climate data to obtain the multi-year daily average regression coefficient; A smoothing processing unit for smoothing the multi-year daily average regression coefficient to obtain the smoothed multi-year daily average regression coefficient; A correction unit for using the smoothed multi-year daily average regression coefficient to correct the deviation of the second data set to obtain the downscaled climate model data.

8. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that, The computer program product includes a computer program, and when the computer program is executed by the processor, the method according to any one of claims 1 to 6 is implemented.

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