A spatial radiation benchmark accuracy prediction method based on a climate element dataset

Abstract

The present application relates to a kind of space radiation benchmark precision prediction method based on climate element dataset, first collect the satellite observation data of specific area and carry out regional average and month average processing, generate the regional monthly average dataset eliminating random noise;Then using difference method eliminates the seasonality in time series data, using least square method eliminates the linear trend in time series data, obtain the dataset that can characterize natural variability size;Again, the standard deviation of this dataset is obtained natural variability size σ var , using autoregressive model obtains natural variability correlation time τ var ;Finally, σ var , τ var And other parameters are substituted into climate precision prediction model to obtain benchmark load detection precision σ cal The present application uses difference method, least square method and autoregressive model method to process climate element time series dataset, so as to obtain space radiation benchmark load precision.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of space radiation reference load climate element analysis method, and particularly relates to a space radiation reference precision prediction method based on a climate element data set. BACKGROUND

[0002] Accurate prediction of space radiation reference precision can be used to guide the development of reference load, realize the balance between the precision of detecting interdecadal climate change and the development cost of reference load, and help to accurately quantify and attribute the changing earth climate elements. At present, meteorological satellite data of interdecadal scale are mainly used to predict climate change trends based on statistical and linear regression methods, and there is no complete method for predicting space radiation reference precision based on interdecadal climate element data sets. Therefore, developing a complete method for predicting space radiation reference precision based on climate element data sets is still a key problem to be solved in the technical field of space radiation reference load climate element analysis method. SUMMARY

[0003] To solve the above technical problems, the application provides a space radiation reference precision prediction method based on a climate element data set, which uses mean method, difference method, least square method and autoregressive model to process the climate element data set to obtain various parameters required for predicting reference load detection precision, and meets the accurate prediction requirement of space radiation reference load detection precision.

[0004] To achieve the above purpose, the application provides the following technical scheme.

[0005] A space radiation reference precision prediction method based on a climate element data set comprises the following steps:

[0006] Step 1: Collecting satellite observation data of interdecadal scale in a specific region and generating a climate element daily mean data set Wherein, i represents the region row number, j represents the region column number, t represents the observation time, and d is the observation value.

[0007] Step 2: Using mean method to calculate the regional monthly mean of the daily mean data generated in step 1 and generating a regional monthly mean data set d k Wherein, k represents the data number.

[0008] Step 3: Using difference method to eliminate the seasonality of the data set generated in step 2 and generating a de-seasonality data set;

[0009] Step 4: Using least square method to eliminate the linear trend of the de-seasonality data set generated in step 3 and generating a de-trend data set;

[0010] Step 5: Calculate the standard deviation of the detrended data set generated in step 4 to obtain the natural variability σ var ;

[0011] Step 6: Calculate the natural variability correlation time τ var of the detrended data set generated in step 4 using an autoregressive model

[0012] Step 7: Calculate the reference load detection accuracy prediction value σ cal based on the results of step 5 and step 6.

[0013] Further, in step 3, the difference function of the difference method is:

[0014] △d k = d k - d k-12

[0015] Further, in step 4, the trend size obtained by the least squares method is:

[0016]

[0017] Where N represents the total number of data in the data set, i and j represent the data numbers in the data set, are the average values of i and j, respectively.

[0018] Further, in step 6, the autoregressive model is:

[0019] d k = c + a × d k-1 + ε k

[0020] Where c is a constant term; ε k is a random error value with an average of 0 and a standard deviation of σ, and σ is assumed to be constant for any t; a is the autoregressive coefficient.

[0021] Further, in step 7, the reference load detection accuracy prediction model is:

[0022]

[0023] Where τ cal is the reference load design life, and U a is the reference load accuracy uncertainty factor.

[0024] The beneficial effects of the present application are:

[0025] The present application uses mean method, difference method, least square method and autoregressive model to process climate element data set, obtains various parameters required for prediction of reference load detection precision, and meets the accurate prediction requirement of spatial radiation reference load detection precision. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a flow chart of a spatial radiation reference precision prediction method based on climate element data set of the present application;

[0027] Figure 2 is a monthly average time series data graph;

[0028] Figure 3 is a seasonal data graph;

[0029] Figure 4 is a trend data graph;

[0030] Figure 5 is a data graph after removing seasonality and trend;

[0031] Figure 6 is a prediction result graph of the present application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application are clearly and completely described below in combination with the drawings and specific embodiments of the present application.

[0033] As shown in Figure 1 , the present application provides a spatial radiation reference precision prediction method based on climate element data set, and the specific steps are as follows:

[0034] Step 1: Collecting the daily average ground reflectivity data set of MODIS Libya 4 radiation calibration field area;

[0035] Step 2: Using mean method to calculate the monthly average of the daily average data generated in step 1 and generating Figure 2 the monthly average data set of the area as shown in

[0036] Step 3: Using difference method to eliminate the seasonal data in the data set generated in step 2 as shown in Figure 3 and generating the de-seasonal data set;

[0037] Step 4: Using least square method to eliminate the linear trend data in the data set generated in step 3 as shown in 4 and generating the de-trend data set as shown in Figure 5 ;

[0038] Step 5: Calculating the standard deviation of the data set generated in step 4 to obtain the size of natural variability σ var ;

[0039] Step 6: Calculate the natural variability correlation time τ of the generated data set in step 4 using the autoregressive model AR(1) var ;

[0040] Step 7: Calculate the reference load detection accuracy prediction value σ according to the results of step 5 and step 6 cal , and the calculation results are shown in Figure 6 .

[0041] Specifically, the climate element daily average data set generated in step 1 is:

[0042]

[0043] Where i represents the row number of the region, j represents the column number of the region, t represents the observation time, and d is the observation value.

[0044] The regional monthly average data set generated in step 2 is:

[0045] d k

[0046] Where k represents the data number.

[0047] In step 3, the difference function is:

[0048] △d k = d k - d k-12

[0049] In step 4, the least squares method obtains the trend size:

[0050]

[0051] Where N represents the total number of data in the data set, i and j represent the data numbers in the data set, are the average values of i and j, respectively.

[0052] In step 6, the autoregressive model AR(1) is:

[0053] d k = c + a × d k-1 + ε k

[0054] Where c is the constant term; ε k is assumed to be a random error value with an average of 0 and a standard deviation of σ, and σ is assumed to be constant for any t; a is the autoregressive coefficient.

[0055] In step 7, the reference load detection accuracy prediction model is:

[0056]

[0057] wherein τ cal is the reference load design life, U a is the reference load precision uncertainty factor.

[0058] The foregoing detailed description of the application has been presented with reference to the drawings. The foregoing examples are merely intended to illustrate the technical solutions of the application, and are not intended to limit the application. Those skilled in the art can make various modifications to the foregoing technical solutions within the scope of their knowledge without departing from the spirit of the application.

Claims

1. A spatial radiation benchmark accuracy prediction method based on climate element datasets, characterized in that, Includes the following steps: Step 1: Collect satellite observation data on a decadal scale for a specific region and generate a dataset of daily average values ​​of climate elements. ,in, i Indicates the row number of the region. j Indicates the column number of the region. t Indicates the observation time. d These are the observed values; Step 2: Calculate the regional monthly average of the daily average data generated in Step 1 using the mean method and generate a regional monthly average dataset. ,in, k Indicates the data number; Step 3: Use the difference method to eliminate the regional monthly average dataset generated in Step 2. Seasonality and generate deseasonalized datasets; Step 4: Use the least squares method to eliminate the linear trend in the deseasonalized dataset generated in Step 3 and generate a detrended dataset; Step 5: Calculate the standard deviation of the detrended dataset generated in Step 4 to obtain the natural variability. σ var Size; Step 6: Calculate the natural variability correlation time of the detrended dataset generated in Step 4 using an autoregressive model. τ var ; Step 7: Based on the calculation results of Steps 5 and 6, calculate the predicted value of the reference load detection accuracy. σ cal .

2. The spatial radiation benchmark accuracy prediction method based on climate element datasets according to claim 1, characterized in that, In step 3, the difference function of the difference method is: 。 3. The spatial radiation benchmark accuracy prediction method based on climate element datasets according to claim 2, characterized in that, In step 4, the least squares method yields the trend magnitude as follows: ； in, N This represents the total number of data points in the dataset. i , j Indicates the data number in the dataset. , These are the average values ​​of i and j, respectively.

4. The spatial radiation benchmark accuracy prediction method based on climate element datasets according to claim 3, characterized in that, In step 6, the autoregressive model is: ； Where c is a constant term; ε k It is assumed that the mean is equal to 0 and the standard deviation is equal to σ The random error value, σ It is assumed that it remains unchanged for any t. t Indicates the observation time; a is the autoregressive coefficient.

5. The spatial radiation benchmark accuracy prediction method based on climate element datasets according to claim 4, characterized in that, In step 7, the reference load detection accuracy prediction model is: ； in, τ cal Design life for reference load, U a This is the uncertainty factor for the accuracy of the reference load.

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