Seasonal Prediction Method of Total Solar Radiation Based on ENSO and Multiple Teleconnection Patterns

Abstract

The present invention discloses a seasonal prediction method for total solar radiation based on ENSO and multiple teleconnection patterns, belonging to the technical field of solar radiation prediction. It uses a seasonal prediction model for total solar radiation to predict the total solar radiation in the corresponding season. The seasonal prediction model for total solar radiation is constructed through the following steps: calculating the seasonal anomalies of solar radiation year by year and performing EOF spatio-temporal decomposition; calculating the Niño3.4 index, EU index, and EAP index respectively, and reconstructing the solar radiation anomaly field based on the Niño3.4 index, EU index, and EAP index to obtain the reconstruction results of the solar radiation anomaly field in each season. Finally, adding back the total solar radiation climatology can obtain the seasonal prediction model for total solar radiation. Thus, it can be seen that the present invention combines the leading memory of the sea surface temperature in the tropical Pacific and the close influence of the mid-high latitude circulation, and can improve both the prediction lead time and prediction accuracy simultaneously.

Description

Technical Field

[0001] The present 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 the energy structure transformation, the grid-connected proportion of renewable energy such as wind power and photovoltaic power is increasing continuously. However, at the same time, new energy has certain volatility and randomness and is vulnerable to weather and climate, which poses challenges to the stable and safe operation of the power system under large-scale photovoltaic grid connection.

[0003] With global climate change, both energy supply and energy demand will be affected, and the importance of medium- and long-term prediction of solar energy resources has become increasingly prominent. As the strongest signal of large-scale air-sea interaction in the equatorial Pacific, ENSO (i.e., El Niño-Southern Oscillation) has been recognized as the key source of predictability for seasonal to interannual climate prediction. At the same time, multiple teleconnection modes such as the Eurasian teleconnection have a significant impact on climate. Summary of the Invention

[0004] The purpose of the present invention is to provide a seasonal prediction method for total solar radiation based on ENSO and multiple teleconnection patterns to achieve seasonal prediction of total solar radiation.

[0005] To achieve the above technical purpose, 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 uses a seasonal prediction model for total solar radiation 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, calculate the annual seasonal anomaly of solar radiation and perform EOF spatio-temporal decomposition on the annual seasonal anomaly of solar radiation to obtain the corresponding spatial principal mode and time series and perform standardization processing on the time series to obtain a standardized time series ;

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

[0009] Obtain the monthly 500hPa geopotential height data 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 The leading and lagging correlations with the Niño3.4 index / EU index / EAP index are examined, and a significance test is performed. The maximum correlation coefficient passing the significance test and its corresponding month are obtained, and the Niño3.4 index, EU index, and EAP index for the corresponding month are selected as the teleconnection combined index representing this season, denoted as index, index, and index;

[0011] For different seasons, the teleconnection combined index representing this season is used to reconstruct the solar radiation anomaly field, and the reconstruction result of the solar radiation anomaly field is obtained , and finally the solar total radiation climatology is added back to obtain the solar total radiation seasonal prediction model .

[0012] Preferably, when reconstructing the solar radiation anomaly field for different seasons, a multiple regression model is established using the teleconnection combined index representing this season to complete; the reconstruction results of the solar radiation anomaly field for each season are expressed as:

[0013] ;

[0014] In the formula: is the influence coefficient field of the index; is the influence coefficient field of the index; is the influence coefficient field of the index; is the intercept field.

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

[0016] Preferably, the obtained index, index, and The sea surface temperature and 500hPa geopotential height data corresponding to the months of the index are real-time updated observational data or forecast data.

[0017] Preferably, before calculating the Niño3.4 index, EU index and EAP index, it is necessary to consider the responses of their respective related air-sea element fields to the standardized time series to adjust the core regions of ENSO, EU type and EAP type in their traditional definitions respectively, and then calculate the corresponding core indices within the adjusted regions.

[0018] Preferably, the adjustment of the core regions of ENSO, EU type and EAP type in their traditional definitions is specifically achieved in the following way: using the standardized time series to perform regression analysis on different air-sea element fields to obtain the corresponding regression coefficient fields, and performing significance tests on each regression coefficient field to screen out the regional ranges with significance exceeding 95%, and accordingly adjusting the core regions of ENSO, EU type and EAP type in their traditional definitions.

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

[0020] Preferably, the adjustment of the core region of ENSO in its traditional definition is specifically achieved in the following way: using the standardized time series to perform regression analysis on the sea surface temperature anomaly SSTA to obtain the corresponding regression coefficient field, and performing significance tests on this regression coefficient field to screen out the regional ranges with significance exceeding 95%, and accordingly adjusting the core region of ENSO in its traditional definition;

[0021] The adjustments of the core regions of EU type and EAP type in their respective traditional definitions are specifically achieved in the following way: using the standardized time series to perform regression analysis on the 500hPa geopotential height anomaly to obtain the corresponding regression coefficient field, and performing significance tests on this regression coefficient field to screen out the regional ranges with significance exceeding 95%, and accordingly adjusting the core regions of EU type and EAP type in their traditional definitions respectively.

[0022] Preferably, based on the obtained monthly total solar radiation, first calculate the monthly anomaly of solar radiation , 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, including a memory, a processor, and a computer program stored on the memory and running on the processor, and the computer program runs to execute the above-mentioned total solar radiation seasonal prediction method based on ENSO and multiple teleconnection patterns.

[0024] Based on the above technical objects, compared with the prior art, the present invention has the following advantages:

[0025] 1. When constructing the total solar radiation seasonal prediction model, the present invention decomposes the annual seasonal anomaly of solar radiation in each season into a fixed spatial mode (i.e., the spatial principal mode ), that is, the spatial principal mode ), and a changing time component (i.e., the time series ), through the EOF spatio-temporal decomposition method. The spatial principal mode explains the basic spatial characteristics of the annual seasonal anomaly of solar radiation ), and the time series shows the interannual variability characteristics of the annual seasonal anomaly of solar radiation ). By grasping the change characteristics of the time series ), the change characteristics of the annual seasonal anomaly of solar radiation can be grasped. The EOF method can reduce the data dimension and is beneficial to improving the model prediction accuracy.

[0026] Furthermore, the present invention comprehensively considers the influence of the Niño3.4 index (the core index of ENSO), the EU index (the core index of the EU type), and the EAP index (the core index of the EAP type) on the annual seasonal anomaly of solar radiation ), and based on the lead-lag correlation between the Niño3.4 index, the EU index, and the EAP index distributed monthly in different seasons and the time series ), selects the Niño3.4 index, the EU index, and the EAP index corresponding to a certain month to characterize the teleconnection combined index of this season, and then reconstructs the annual seasonal anomaly of solar radiation to obtain the reconstruction result of the solar radiation anomaly field in each season . By calculating the corresponding a, b, and c coefficients for different seasons respectively, the influence weights of each index on different seasons can be modulated; in addition, compared with only considering the contemporaneous correlation, the lead-lag correlation considered by the present invention not only provides the possibility for longer-term preliminary prediction with the model forecast data as the model input source, but also provides the possibility for optimized prediction with the observed data as the model input source in practical applications.

[0027] 2. As a product of large-scale air-sea interaction, ENSO can affect the climate in China and even the world more than 6 months in advance. Although various teleconnection patterns, as atmospheric variability modes, have a relatively short memory, they play an important role in the spatio-temporal evolution of the total solar irradiance (TSI) in some regions. The model combines the leading memory of the sea surface temperature in the tropical Pacific and the close influence of the mid-high latitude circulation, which can improve both the prediction lead time and prediction accuracy simultaneously.

[0028] 3. The input data of the seasonal prediction model of total solar irradiance constructed in the present invention are mainly the Nino3.4 index calculated from the sea surface temperature and the teleconnection index calculated from the geopotential height. The data are highly accessible and the model has strong applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flowchart of the seasonal prediction method of total solar irradiance based on ENSO and various teleconnection patterns according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] Hereinafter, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a 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 in no way serves as a limitation to the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. Unless otherwise specifically stated, the relative arrangements, expressions and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present invention. Technologies, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods and devices should be regarded as part of the specification. In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0031] As Figure 1 shown, for the seasonal prediction method of total solar irradiance based on ENSO and various teleconnection patterns according to the present invention, a seasonal prediction model of total solar irradiance is used to predict the total solar irradiance in the corresponding season. Its main work includes two parts. One is to construct a seasonal prediction model of total solar irradiance, and the other is to predict the total solar irradiance in the corresponding season based on the constructed seasonal prediction model of total solar irradiance.

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

[0033] Embodiment 1

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

[0035] Step 1.1: Calculate the annual seasonal anomaly of solar radiation and perform EOF spatio-temporal decomposition:

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

[0037] Step 1.1.2: According to the monthly data of total solar radiation over the years after band-pass filtering, subtract the climatology of total solar radiation to obtain the monthly anomaly of total solar radiation over the years to remove the annual mean and seasonal cycle, that is:

[0038] ;

[0039] In the formula: represents the monthly total solar radiation within the specified historical period; represents the climatology of total solar radiation calculated based on the monthly total solar radiation within the specified historical period.

[0040] Step 1.1.3: According to the monthly anomaly of total solar radiation over the years , calculate the annual seasonal anomaly of total solar radiation for different seasons respectively; among them, spring corresponds to March - May, summer corresponds to June - August, autumn corresponds to September - November, and winter corresponds to December - February of the following year. Therefore, when calculating the annual seasonal anomaly of total solar radiation for different seasons, the spring is the average of the monthly anomaly of total solar radiation from March to May, the summer is the average of the monthly anomaly of total solar radiation from June to August, the autumn is the average of the monthly anomaly of total solar radiation from September to November, and the winter is the average of the monthly anomaly of total solar radiation from December to February of the following year.

[0041] Step 1.1.4: For different seasons, the EOF spatio-temporal decomposition method is used for the corresponding annual seasonal anomaly of total solar radiation Perform EOF spatio-temporal decomposition to obtain the corresponding spatial principal modes and time series , and perform standardization on the obtained time series to obtain a standardized time series . Specifically, first calculate the mean μ and standard deviation σ of the time series , and then perform standardization on each element x in the time series to obtain the corresponding standardized element , satisfying: .

[0042] EOF spatio-temporal decomposition of annual seasonal anomalies of solar radiation After that, the obtained spatial principal modes are used to explain the basic spatial characteristics of the annual seasonal anomalies of solar radiation , and the time series shows the interannual variability characteristics of the annual seasonal anomalies of solar radiation .

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

[0044] For different seasons, use the teleconnection combined index characterizing this season to reconstruct the solar radiation anomaly field, and the reconstruction result of the solar radiation anomaly field can be obtained , which specifically includes the following steps:

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

[0046] Obtain the monthly sea surface temperature data for the same specified historical period (the HadISST monthly sea surface temperature data provided by the UK Hadley Centre, with a horizontal resolution of 2°×2°), and calculate the Niño3.4 index of ENSO in its own core area for monthly distribution. The Niño3.4 index is the core index of ENSO.

[0047] Obtain the monthly 500hPa geopotential height data for the same specified historical period (the ERA5 monthly reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts, with a horizontal resolution of 0.25°×0.25°), and calculate 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 Asian-Pacific teleconnection type) in its own core area for monthly distribution. 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) within the core region of the ENSO in the traditional definition (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] In the formula: represents the standardized winter 500hPa geopotential height anomaly, , is the corresponding latitude.

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

[0054] Considering the influence between the time series and different meteorological element fields, which in turn affects the selection of the core regions of ENSO, EU type, and EAP type respectively. Therefore, before calculating the Niño3.4 index, EU index, and EAP index in the present invention, it is necessary to consider the responses of their related air-sea element fields (including sea surface temperature, 500hPa geopotential height, 850hPa wind field, vertical velocity, integrated water vapor field, and total cloud cover) to the standardized time series to adjust the core regions of ENSO, EU type, and EAP type in their traditional definitions respectively, and then calculate the corresponding core indices. The core index of ENSO is the aforementioned Niño3.4 index; the core index of the EU type is the aforementioned EU index; the core index of the EAP type is the aforementioned EAP index.

[0055] Specifically, in the present invention, the standardized time series is used to perform a regression analysis on different air-sea 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 region with the best significance, and then the significance test results of the regression coefficient fields are used to adjust the core regions of ENSO, EU type, and EAP type in their traditional definitions respectively.

[0056] In the present invention, the adjustment of the core region of ENSO in the traditional definition is specifically achieved through the following method: using the standardized time series to perform a regression analysis on the sea surface temperature anomaly (SSTA) to obtain the corresponding regression coefficient field, and performing a significance test on this regression coefficient field to screen out the area range with a significance exceeding 95%. Based on this, the core region of ENSO in the traditional definition is adjusted, and then the Niño3.4 index is calculated within the adjusted region to obtain the Niño3.4 corrected index ; the adjustments of the core regions of the EU type and the EAP type in their respective traditional definitions are specifically achieved through the following method: using the standardized time series to perform a regression analysis on the 500 hPa geopotential height anomaly to obtain the corresponding regression coefficient field, and performing a significance test on this regression coefficient field to screen out the area range with a significance exceeding 95%. Based on this, the core regions of the EU type and the EAP type in their respective traditional definitions are adjusted respectively, and then the EU index and the EAP index are calculated within their respective adjusted regions to obtain the EU corrected index and the EAP corrected index .

[0057] Specifically, the standardized time series is used to perform regression analyses on the sea surface temperature anomaly (SSTA) and the 500 hPa geopotential height anomaly respectively. The calculation formula for the regression analysis is as follows:

[0058] ;

[0059] ;

[0060] In the formula: and are the error terms, and are the intercepts, and are the obtained regression coefficient fields.

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

[0062] ;

[0063] , ;

[0064] In the formula: represents the incomplete function, the t statistic corresponding to the regression coefficient , Indicates the regression coefficient of the standard error, where 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 are corrected, and the results are as follows:

[0066] ;

[0067] ;

[0068] .

[0069] Step 1.2.2, Obtain the teleconnection combined index for different seasons:

[0070] For different seasons, calculate the standardized time series and the lead-lag correlation with the Niño3.4 index / EU index / EAP index (if the Niño3.4 index, EU index, and EAP index have been corrected, the Niño3.4 index / EU index / EAP index here uses the Niño3.4 corrected index , EU corrected index and the EAP corrected index ), and conduct a significance test to obtain the maximum correlation coefficient passing the significance test and its corresponding month, and select the Niño3.4 index, EU index, and EAP index for the corresponding month as the teleconnection combined index representing this season, denoted 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, use the teleconnection combined index representing this season to reconstruct the solar radiation anomaly field (i.e., the seasonal anomaly of solar radiation year by year), and the reconstruction result of the solar radiation anomaly field can be obtained . In the present invention, when reconstructing the solar radiation anomaly field for different seasons, a multiple regression model is established using the teleconnection combined index representing this season to complete; the reconstruction results of the solar radiation anomaly field for each season can be expressed as:

[0073] ;

[0074] In the formula: is the influence coefficient field of the index; is the influence coefficient field of the EUI_max index; is the influence coefficient field of the EAPI_max index; is the intercept field.

[0075] Step 1.3, construct the seasonal prediction model of total solar radiation:

[0076] Reconstruct the results of the solar radiation anomaly fields of each season obtained in Step 1.2 , and add it back to the total solar radiation climatology to obtain the seasonal prediction model of total solar radiation . Specifically, it can be expressed as:

[0077] .

[0078] Example 2

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

[0080] Step 2.1, obtain the sea surface temperature and 500hPa geopotential height data corresponding to the months of the teleconnection combined index characterizing this season in the solar radiation prediction models of each season, so as to calculate the corresponding Niño3.4 index, EU index and EAP index for each month respectively;

[0081] In Step 2.1, the sea surface temperature and 500hPa geopotential height data obtained corresponding to the months of the Niño3.4_max index / EUI_max index / EAPI_max index in the solar radiation prediction models of each season are real-time updated observational data (which can be from the National Climate Center, the European Centre for Medium-Range Weather Forecasts, the UK Hadley Centre, etc.) or prediction data (provided by the National Climate Center CIPAS system, the European Centre for Medium-Range Weather Forecasts SEAS5 system, etc.).

[0082] Step 2.2, substitute the Niño3.4 index, EU index and EAP index calculated in Step 2.1 for the corresponding months as the actual teleconnection combined index characterizing this season into the constructed seasonal prediction model of total solar radiation , and the optimized seasonal prediction data of total solar radiation can be calculated.

[0083] When the sea surface temperature and 500hPa geopotential height data obtained in Step 2.1 are prediction 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 seasonal prediction data of total solar radiation.

[0084] When the sea surface temperature and 500hPa geopotential 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 indexes. Then, these actual indexes are brought into the total solar radiation seasonal prediction model to calculate and output the total solar radiation prediction data for the corresponding season, optimizing the estimation of total solar radiation.

[0085] Embodiment 3

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

[0087] Embodiment 4

[0088] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor runs the computer program to execute the above-mentioned total solar radiation seasonal prediction method based on ENSO and multiple teleconnection patterns.

[0089] The above serial numbers of the 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 descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0091] In several embodiments provided by the present 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 illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. 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, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of units or modules can be in an electrical or other form.

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

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

[0094] If the above-mentioned 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 such an 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 and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0095] The above has shown and described 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. Any technical solution obtained by using 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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