ENSO and various remote correlation type-based total solar radiation seasonal prediction method

Abstract

The invention discloses a total solar radiation season prediction method based on ENSO and various remote correlation types, and belongs to the technical field of solar radiation prediction. A total solar radiation seasonal prediction model is adopted to predict total solar radiation in corresponding seasons, and the total solar radiation seasonal prediction model is constructed by the following steps: calculating solar radiation year-by-year seasonal runoff and performing EOF space-time decomposition; and respectively calculating a Nio3.4 index, an EU index and an EAP index, reconstructing the solar radiation abnormal field based on the Nio3.4 index, the EU index and the EAP index to obtain a reconstruction result of the solar radiation abnormal field in each season, and finally adding the reconstruction result back to the solar total radiation climate state to obtain a solar total radiation season prediction model. Therefore, the method combines the advanced memory of the sea surface temperature of the tropical pacific ocean and the close influence of the mid-high latitude circulation, and can improve the prediction time efficiency and the prediction precision at the same time.

Description

Technical Field

[0001] The invention relates to a seasonal prediction method for total solar radiation based on ENSO and multiple teleconnection patterns, belonging to the technical field of solar radiation prediction. Background Art

[0002] Under the background of energy structure transformation, the proportion of renewable energy such as wind power and photovoltaics connected to the grid has continued to rise. However, at the same time, new energy has certain volatility and randomness, and is easily affected by weather and climate, which brings challenges to the stable and safe operation of the power system under large-scale photovoltaic grid connection.

[0003] As global climate change affects both energy supply and energy demand, the importance of mid- to long-term predictions of solar energy resources becomes increasingly prominent. As the strongest signal of large-scale ocean-atmosphere interaction in the equatorial Pacific, ENSO (El Nino-Southern Oscillation) has been recognized as a key source of predictability for seasonal to interannual climate forecasts. At the same time, various teleconnection modes, such as the Eurasian teleconnection, have a significant impact on the climate. Summary of the invention

[0004] The present invention aims to provide a seasonal prediction method for total solar radiation based on ENSO and multiple teleconnection patterns, so as to realize seasonal prediction of total solar radiation.

[0005] In order to achieve the above technical objectives, the present invention will adopt the following technical solutions:

[0006] A seasonal prediction method for total solar radiation based on ENSO and multiple teleconnection patterns is proposed. The seasonal prediction model for total solar radiation is used to predict the total solar radiation in the corresponding season. The seasonal prediction model for total solar radiation is constructed through the following steps:

[0007] Obtain the monthly total solar radiation within a specified historical period and calculate the annual seasonal anomaly of solar radiation , and perform EOF spatiotemporal decomposition of the annual seasonal anomaly of solar radiation to obtain the corresponding spatial main mode and time series , and for time series Perform standardization to obtain a standardized time series ;

[0008] Obtain monthly sea surface temperature data within the same specified historical period and calculate the monthly Niño3.4 index;

[0009] Obtain monthly data of 500hPa geopotential height within the same specified historical period, and calculate the monthly distributed EU index and EAP index respectively;

[0010] For different seasons, calculate the standardized time series separately The lead-lag correlation between the Niño3.4 index / EU index / EAP index is analyzed, and a significance test is performed to obtain the maximum correlation coefficient and its corresponding month that pass the significance test. The Niño3.4 index, EU index, and EAP index of the corresponding month are selected as the teleconnection joint index characterizing this season, which is recorded as index, Index and index;

[0011] For different seasons, the solar radiation anomaly field is reconstructed using the teleconnection joint index that characterizes the season, and the reconstruction results of the solar radiation anomaly field are obtained. , and finally add back the total solar radiation climate state The seasonal prediction model of total solar radiation can be obtained .

[0012] Preferably, when reconstructing the solar radiation anomaly field for different seasons, a multivariate regression model is established using the teleconnection joint index that characterizes this season; the reconstruction results of the solar radiation anomaly field for each season It is expressed as:

[0013] ;

[0014] Where: for Exponential influence coefficient field; for Exponential influence coefficient field; for Exponential influence coefficient field; is the intercept field.

[0015] Preferably, when using the constructed seasonal prediction model for total solar radiation to predict the total solar radiation of the corresponding season, first obtain the sea surface temperature and 500hPa potential height data corresponding to the month of the teleconnection joint index representing this season in the seasonal solar radiation prediction model, so as to calculate the Niño3.4 index, EU index and EAP index of the corresponding month respectively, and substitute the calculated Niño3.4 index, EU index and EAP index of the corresponding month as the actual teleconnection joint index representing this season into the constructed seasonal prediction model for total solar radiation , the optimized total solar radiation forecast data for the corresponding season can be calculated.

[0016] Preferably, the obtained solar radiation prediction model for each season index, Index and The sea surface temperature and 500hPa potential height data corresponding to the index month are observation data updated in real time or forecast data.

[0017] Preferably, before calculating the Niño3.4 index, EU index and EAP index, it is necessary to consider the normalized time series of their related sea-air elements. The core areas of ENSO, EU type and EAP type in the traditional definition are adjusted according to their responses, and then the corresponding core index is calculated in the adjusted areas.

[0018] Preferably, the adjustment of the core areas of ENSO, EU type and EAP type in the traditional definition is achieved by the following formula: Regression analysis is performed on different sea-atmosphere element fields to obtain the corresponding regression coefficient fields, and significance tests are performed on each regression coefficient field to screen out the regional range with a significance rate of more than 95%, and accordingly adjust the core areas of ENSO, EU type and EAP type in the traditional definition.

[0019] Preferably, the sea-air element field includes sea surface temperature, 500hPa geopotential height, 850hPa wind field, vertical velocity, whole-layer water vapor field and total cloud cover.

[0020] Preferably, the adjustment of the core area of ​​ENSO in the traditional definition is achieved by: using the standardized time series Regression analysis is performed on the sea surface temperature anomaly SSTA to obtain the corresponding regression coefficient field, and a significance test is performed on the regression coefficient field to screen out the area with a significance exceeding 95%, and accordingly adjust the core area of ​​ENSO in the traditional definition;

[0021] The adjustment of the core areas of the EU type and the EAP type in their respective traditional definitions is achieved by the following formula: 500hPa geopotential height anomaly Regression analysis is performed to obtain the corresponding regression coefficient field, and a significance test is performed on the regression coefficient field to screen out the area range with a significance rate of more than 95%, and accordingly adjust the core areas of the EU type and EAP type in the traditional definition respectively.

[0022] Preferably, based on the acquired monthly total solar radiation, the monthly solar radiation anomaly is first calculated: , and then calculate the seasonal anomaly of solar radiation year by year according to different seasons .

[0023] Another technical object of the present invention is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the computer program runs and executes the above-mentioned seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns.

[0024] Based on the above technical objectives, the present invention has the following advantages over the prior art:

[0025] 1. When constructing the seasonal prediction model of total solar radiation, the present invention uses the EOF spatiotemporal decomposition method to calculate the seasonal anomaly of solar radiation in each season. Decompose into fixed spatial modes (i.e. spatial main modes ) and the temporal component of the variation (i.e., the time series ), spatial main mode Explaining the seasonal anomalies of solar radiation from year to year Basic spatial characteristics of anomalies, time series The seasonal anomaly of solar radiation is shown in Figure 2. Abnormal interannual variability characteristics, grasp the time series The seasonal anomaly of solar radiation can be captured by the variation characteristics The EOF method can reduce the data dimension, which is beneficial to improve the prediction accuracy of the model.

[0026] Furthermore, the present invention comprehensively considers the seasonal anomaly of solar radiation from year to year using the Niño3.4 index (the core index of ENSO), the EU index (the core index of EU type), and the EAP index (the core index of EAP type). The Niño3.4 index, EU index and EAP index distributed monthly in different seasons are compared with the time series. The lead-lag correlation of the solar radiation is analyzed by selecting the Niño3.4 index, EU index and EAP index corresponding to a certain month to represent the teleconnection joint index of this season, and then the seasonal anomaly of solar radiation is analyzed. Reconstruct and obtain the reconstruction results of solar radiation anomaly field in each season By calculating the corresponding a, b, and c coefficients for different seasons respectively, the different impact weights of each index on different seasons can be modulated; in addition, compared with only considering the contemporaneous correlation, the lead-lag correlation considered in the present invention not only uses the model forecast data as the model input source for a longer-term preliminary prediction, but also provides the possibility for optimizing prediction using the observed data as the model input source in practical applications.

[0027] 2. ENSO, as a product of large-scale ocean-air interaction, can affect my country's and even the global climate more than 6 months in advance; and various teleconnection patterns, as atmospheric variability modes, although with a short memory, play an important role in the spatiotemporal evolution of total solar radiation TSI in some regions. The model combines the advanced memory of tropical Pacific sea surface temperature and the close influence of mid- and high-latitude circulation, which can improve both the prediction timeliness and prediction accuracy.

[0028] 3. The input data of the seasonal prediction model of total solar radiation constructed by the present invention are mainly the Nino3.4 index calculated by sea surface temperature and the teleconnection index calculated by potential height. The data is highly accessible and the model is highly applicable. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of the seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns described in the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means any limitation to the present invention and its application or use. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise specified, the relative arrangement, expressions and numerical values ​​of the components and steps described in these embodiments do not limit the scope of the present invention. The techniques, methods and equipment known to ordinary technicians in the relevant fields may not be discussed in detail, but in appropriate cases, the techniques, methods and equipment should be regarded as part of the specification. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, rather than as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0031] like Figure 1 As shown, the seasonal prediction method of solar total radiation based on ENSO and multiple teleconnection patterns of the present invention adopts the seasonal prediction model of solar total radiation to predict the solar total radiation of the corresponding season. Its main work includes two parts, one is to construct the seasonal prediction model of solar total radiation, and the other is to predict the solar total radiation of the corresponding season based on the constructed seasonal prediction model of solar total radiation.

[0032] The technical solutions involved in the present invention will be described in detail below with reference to the accompanying drawings and several embodiments.

[0033] Example 1

[0034] This embodiment describes in detail the construction of the solar total radiation seasonal prediction model, which specifically includes the following steps:

[0035] Step 1.1: Calculate the seasonal anomaly of solar radiation year by year and perform EOF temporal and spatial decomposition:

[0036] Step 1.1.1. Obtain multi-year monthly data on total solar irradiance (TSI), sea surface temperature (SST), atmospheric circulation, etc., and perform bandpass filtering for 6 to 120 months on the multi-year monthly data on total solar irradiance, sea surface temperature, atmospheric circulation (including 500hPa geopotential height, 850hPa wind field, vertical velocity, whole-layer water vapor field, and total cloud cover) to remove components other than interannual variability.

[0037] Step 1.1.2: Based on the bandpass filtered global solar radiation data for many years and every month, subtract the global solar radiation climate state data. , we can get the monthly anomaly of solar radiation over many years , to remove the annual mean and seasonal cycles, that is:

[0038] ;

[0039] Where: Represents the monthly total solar radiation in a specified historical period; It represents the climate state of global solar radiation calculated based on the monthly global solar radiation in a specified historical period.

[0040] Step 1.1.3: Based on the monthly anomaly of solar radiation over many years , for different seasons, calculate the annual seasonal anomaly of solar radiation ; Among them, spring corresponds to March to May, summer corresponds to June to August, autumn corresponds to September to November, and winter corresponds to December to February of the following year. Therefore, the calculation of the seasonal anomaly of solar radiation in different seasons is The monthly anomaly of solar radiation in spring, which is from March to May, is The average of the solar radiation from June to August in summer is the monthly anomaly over the years. The average of solar radiation in autumn is from September to November. The average of solar radiation from December to February in winter is the monthly anomaly of the year. Average.

[0041] Step 1.1.4: For different seasons, the EOF temporal and spatial decomposition method is used to calculate the corresponding solar radiation seasonal anomaly year by year. Perform EOF time-space decomposition to obtain the corresponding main spatial mode and time series , and the obtained time series Standardize to obtain a standardized time series Specifically, first calculate the time series The mean μ and standard deviation σ of the time series Each element x in is normalized to obtain the corresponding normalized element ,satisfy: .

[0042] EOF temporal and spatial decomposition of seasonal anomalies of solar radiation from year to year After that, the main spatial modes are obtained. Used to explain the annual seasonal anomalies of solar radiation Basic spatial characteristics of anomalies, time series The seasonal anomaly of solar radiation is shown in Figure 2. Abnormal interannual variability characteristics.

[0043] Step 1.2: Obtain the solar radiation anomaly field:

[0044] For different seasons, the teleconnection joint index representing the season is used to reconstruct the solar radiation anomaly field, and the reconstruction results of the solar radiation anomaly field can be obtained. , specifically including the following steps:

[0045] Step 1.2.1. Calculate the core indices of ENSO, EU and EAP respectively:

[0046] The monthly sea surface temperature data within the same specified historical period are obtained (the HadISST monthly sea surface temperature data provided by the Hadley Center in the United Kingdom, with a horizontal resolution of 2°×2°), and the Niño3.4 index of ENSO in its core area distributed monthly is calculated. The Niño3.4 index is the core index of ENSO.

[0047] The monthly data of 500hPa geopotential height in the same specified historical period (ERA5 monthly reanalysis data set provided by the European Centre for Medium-Range Weather Forecasts, with a horizontal resolution of 0.25°×0.25°) were obtained, and the EU index (abbreviated as EUI) of the EU type (i.e., the Eurasian teleconnection type) in its own core area and the EAP index (abbreviated as EAPI) of the EAP type (i.e., the East Asia-Pacific teleconnection type) in its own core area were calculated. The EU index is the core index of the EU type, and the EAP index is the core index of the EAP type.

[0048] The Niño3.4 index represents the sea surface temperature anomaly (SST Anomaly, SSTA) in the traditionally defined core area of ​​ENSO (5°N-5°S, 120°-170°W).

[0049] The calculation formulas for the EU Index (abbreviated as EUI) and the EAP Index (abbreviated as EAPI) are as follows:

[0050] ;

[0051] ;

[0052] Where: represents the standardized winter 500hPa geopotential height anomaly, , is the corresponding latitude.

[0053] The core area of ​​the traditional EU type includes three positions, corresponding to position A , B position and C position The traditional definition of EAP type also includes three core areas, corresponding to the first position , second position and the third position .

[0054] Considering the time series The influence between different meteorological elements, and then affect the selection of the core areas of ENSO, EU and EAP. Therefore, before calculating the Niño3.4 index, EU index and EAP index, the present invention needs to consider the influence of the relevant sea-air element fields (including sea surface temperature, 500hPa geopotential height, 850hPa wind field, vertical velocity, whole-layer water vapor field and total cloud cover) on the standardized time series. The core areas of ENSO, EU type and EAP type in traditional definitions are adjusted according to the response, and then the corresponding core index is calculated. The core index of ENSO is the Niño3.4 index; the core index of EU type is the EU index; and the core index of EAP type is the EAP index.

[0055] Specifically, in the present invention, the standardized time series is used Regression analysis is performed on different sea-atmosphere element fields to obtain the corresponding regression coefficient fields, and a significance test (usually a 95% significance test) is performed on each regression coefficient field to screen out the area with the best significance. The significance test results of the regression coefficient field are then used to adjust the core areas of ENSO, EU type and EAP type in the traditional definitions.

[0056] The adjustment of the core area of ​​ENSO in the traditional definition is specifically achieved by the following formula: A regression analysis of the sea surface temperature anomaly SSTA was performed to obtain the corresponding regression coefficient field, and a significance test was performed on the regression coefficient field to screen out the area with a significance of more than 95%. Based on this, the core area of ​​ENSO in the traditional definition was adjusted, and the Niño3.4 index was calculated in the adjusted area to obtain the Niño3.4 modified index. ; The adjustment of the core areas of EU type and EAP type in their respective traditional definitions is achieved by the following formula: Using the standardized time series 500hPa geopotential height anomaly Regression analysis is performed to obtain the corresponding regression coefficient field, and a significance test is performed on the regression coefficient field to screen out the area range with a significance of more than 95%, and accordingly adjust the core areas of the EU type and EAP type in the traditional definition, and then calculate the EU index and EAP index in the adjusted areas to obtain the EU modified index. , EAP modified index .

[0057] Specifically, the standardized time series The sea surface temperature anomaly SSTA and 500hPa geopotential height anomaly Do regression analysis. The regression analysis calculation formula is as follows:

[0058] ;

[0059] ;

[0060] Where: and is the error term, and is the intercept, and This is the regression coefficient field obtained.

[0061] The calculation formula for significance test is as follows:

[0062] ;

[0063] , ;

[0064] Where: Represents incomplete Function, regression coefficient The corresponding t statistic , Regression coefficient The standard error of is the degree of freedom, That is the significance corresponding to the regression coefficient field. The value of .

[0065] On this basis, the Niño3.4 index, EU index and EAP index were revised, and the results are as follows:

[0066] ;

[0067] ;

[0068] .

[0069] Step 1.2.2: Obtain teleconnection joint indicators in different seasons:

[0070] For different seasons, calculate the standardized time series separately and Niño 3.4 Index / EU Index / EAP Index (If the Niño 3.4 Index, EU Index and EAP Index have been revised, the Niño 3.4 Index / EU Index / EAP Index here shall adopt the Niño 3.4 revised index , EU revised index and EAP Modification Index ) and perform a significance test to obtain the maximum correlation coefficient that passes the significance test and its corresponding month, and select the Niño3.4 index, EU index, and EAP index of the corresponding month as the joint teleconnection indicators characterizing this season, which are recorded as the Niño3.4_max index, EUI_max index, and EAPI_max index respectively.

[0071] Step 1.2.3: Reconstruct the solar radiation anomaly field:

[0072] For different seasons, the solar radiation anomaly field (i.e., the seasonal anomaly of solar radiation from year to year) can be reconstructed using the teleconnection joint index that represents the season. In the present invention, when reconstructing the solar radiation anomaly field in different seasons, a multivariate regression model is established using the teleconnection joint index that characterizes this season; the reconstruction results of the solar radiation anomaly field in each season It can be expressed as:

[0073] ;

[0074] Where: for Exponential influence coefficient field; is the influence coefficient field of EUI_max index; is the influence coefficient field of the EAPI_max index; is the intercept field.

[0075] Step 1.3: Construct a seasonal prediction model for total solar radiation:

[0076] The reconstruction results of the solar radiation anomaly field in each season obtained in step 1.2 are , add back the total solar radiation climate state The seasonal prediction model of total solar radiation can be obtained . It can be specifically expressed as:

[0077] .

[0078] Example 2

[0079] Based on the seasonal prediction model of total solar radiation constructed in Example 1, the prediction of total solar radiation in the corresponding season can be carried out, which specifically includes:

[0080] Step 2.1, obtain the sea surface temperature and 500hPa geopotential height data corresponding to the month of the teleconnection joint index representing this season in the seasonal solar radiation prediction model, so as to calculate the Niño3.4 index, EU index and EAP index of the corresponding month respectively;

[0081] In step 2.1, the sea surface temperature and 500hPa potential height data corresponding to the months of the Niño3.4_max index / EUI_max index / EAPI_max index in the seasonal solar radiation prediction model are real-time updated observation data (which can come from the National Climate Center, the European Center for Medium-Range Weather Forecasts, the Hadley Center of the United Kingdom, etc.) or forecast data (provided by the National Climate Center CIPAS system, the European Center for Medium-Range Weather Forecasts SEAS5 system, etc.).

[0082] Step 2.2: Substitute the Niño3.4 index, EU index and EAP index of the corresponding month calculated in step 2.1 into the constructed seasonal prediction model of total solar radiation as the actual teleconnection joint index representing this season. , the optimized total solar radiation forecast data for the corresponding season can be calculated.

[0083] When the sea surface temperature and 500hPa geopotential height data obtained in step 2.1 are predicted data, the calculated Niño3.4_max index / EUI_max index / EAPI_max index are estimated indexes, and then these estimated data are brought into the seasonal prediction model of total solar radiation to calculate and output the predicted data of total solar radiation in the corresponding season.

[0084] When the sea surface temperature and 500hPa potential height data obtained in step 2.1 are real-time updated observation data, the calculated Niño3.4_max index / EUI_max index / EAPI_max index are actual indices, and then these actual indices are brought into the seasonal prediction model of total solar radiation to calculate and output the corresponding season's total solar radiation prediction data and optimize the estimation of total solar radiation.

[0085] Example 3

[0086] The present invention also provides a storage medium, and when the program stored in the storage medium is run, the program executes the above-mentioned seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns.

[0087] Example 4

[0088] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned seasonal prediction method for total solar radiation based on ENSO and multiple teleconnection patterns through the operation of the computer program.

[0089] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0090] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0091] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0092] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0093] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0094] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0095] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form, and any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A seasonal prediction method for total solar radiation based on ENSO and multiple teleconnection patterns, characterized in that: The solar total radiation seasonal prediction model is used to predict the solar total radiation of the corresponding season. The solar total radiation seasonal prediction model is constructed through the following steps: Obtain the monthly total solar radiation within a specified historical period and calculate the annual seasonal anomaly of solar radiation , and perform EOF spatiotemporal decomposition of the annual seasonal anomaly of solar radiation to obtain the corresponding spatial main mode and time series , and for the time series Perform standardization to obtain a standardized time series ; Obtain monthly sea surface temperature data within the same specified historical period and calculate the monthly Niño3.4 index; Obtain monthly data of 500hPa geopotential height within the same specified historical period, and calculate the monthly distributed EU index and EAP index respectively; For different seasons, calculate the standardized time series separately The lead-lag correlation between the Niño3.4 index / EU index / EAP index is analyzed, and a significance test is performed to obtain the maximum correlation coefficient and its corresponding month that pass the significance test. The Niño3.4 index, EU index, and EAP index of the corresponding month are selected as the teleconnection joint index characterizing this season, which is recorded as index, Index and index; For different seasons, the solar radiation anomaly field is reconstructed using the teleconnection joint index that characterizes the season, and the reconstruction results of the solar radiation anomaly field are obtained. , and finally add back the total solar radiation climate state The seasonal prediction model of total solar radiation can be obtained .

2. The seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns according to claim 1, characterized in that: When reconstructing the solar radiation anomaly field in different seasons, a multivariate regression model is established using the teleconnection joint index that characterizes this season; the reconstruction results of the solar radiation anomaly field in each season It is expressed as: ； Where: for Exponential influence coefficient field; for Exponential influence coefficient field; for Exponential influence coefficient field; is the intercept field.

3. The seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns according to claim 1, characterized in that: When using the constructed seasonal prediction model for total solar radiation to predict the total solar radiation of the corresponding season, first obtain the sea surface temperature and 500hPa potential height data corresponding to the month of the teleconnection joint index representing this season in the seasonal solar radiation prediction model, so as to calculate the Niño3.4 index, EU index and EAP index of the corresponding month respectively, and substitute the calculated Niño3.4 index, EU index and EAP index of the corresponding month as the actual teleconnection joint index representing this season into the constructed seasonal prediction model for total solar radiation , the optimized total solar radiation forecast data for the corresponding season can be calculated.

4. The seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns according to claim 3, characterized in that: The obtained solar radiation prediction model for each season index, Index and The sea surface temperature and 500hPa potential height data corresponding to the index month are observation data updated in real time or forecast data.

5. The seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns according to claim 1, characterized in that: Before calculating the Niño3.4 index, EU index and EAP index, it is necessary to consider the normalized time series of their related sea-air elements. The core areas of ENSO, EU type and EAP type in the traditional definition are adjusted according to their responses, and then the corresponding core index is calculated in the adjusted areas.

6. The seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns according to claim 5, characterized in that: The adjustment of the core areas of ENSO, EU and EAP in the traditional definition is achieved by the following formula: Regression analysis is performed on different sea-atmosphere element fields to obtain the corresponding regression coefficient fields, and significance tests are performed on each regression coefficient field to screen out the regional range with a significance rate of more than 95%, and accordingly adjust the core areas of ENSO, EU type and EAP type in the traditional definition.

7. The seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns according to claim 6, characterized in that: The sea-air element field includes sea surface temperature, 500hPa potential height, 850hPa wind field, vertical velocity, whole-layer water vapor field and total cloud cover.

8. The seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns according to claim 7, characterized in that: The adjustment of the core area of ​​ENSO in the traditional definition is achieved by the following formula: Regression analysis is performed on the sea surface temperature anomaly SSTA to obtain the corresponding regression coefficient field, and a significance test is performed on the regression coefficient field to screen out the area with a significance exceeding 95%, and accordingly adjust the core area of ​​ENSO in the traditional definition; The adjustment of the core areas of the EU type and the EAP type in their respective traditional definitions is achieved by the following formula: 500hPa geopotential height anomaly Regression analysis is performed to obtain the corresponding regression coefficient field, and a significance test is performed on the regression coefficient field to screen out the area range with a significance rate of more than 95%, and accordingly adjust the core areas of the EU type and EAP type in the traditional definition respectively.

9. The seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns according to claim 1, characterized in that: Based on the monthly total solar radiation obtained, the monthly solar radiation anomaly is first calculated , and then calculate the seasonal anomaly of solar radiation year by year according to different seasons .

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: The computer program runs to execute the seasonal prediction method of total solar radiation based on ENSO and multiple teleconnection patterns as described in any one of claims 1 to 8.

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