Solar irradiance estimation method integrating cloud and aerosol information from geostationary satellites

Abstract

The present invention discloses a solar irradiance estimation method that integrates geostationary satellite cloud and aerosol information. The method comprises the following steps: calculating cloud statistical characteristics, constructing a full-sky observation error model, and constructing a cloud-dependent quality control method based on this model; adding cloud correction factors to an adaptive variational bias correction model, combining the full-sky observation error model and the cloud-dependent quality control method to construct a full-sky assimilation model to obtain an optimal analysis field; inputting real-time geostationary satellite aerosol inversion observations into the model to obtain a solar numerical prediction model with time-varying aerosol optical properties; and using the optimal analysis field as the initial field, performing a deterministic forecast using the solar numerical prediction model with time-varying aerosol optical properties to obtain a solar irradiance estimate. The present invention improves the interaction between the cloud-aerosol-radiation system in the solar numerical prediction model, thereby enhancing the forecast capability of the solar numerical prediction system.

Description

Technical Field

[0001] The present invention belongs to the field of atmospheric science technology, and in particular relates to a solar irradiance estimation method integrating geostationary satellite cloud and aerosol information. Background Art

[0002] As a new clean energy source, solar energy is poised to become a major energy source in the future. Therefore, accurate forecasts of solar power generation are becoming increasingly important for estimating available energy and the share of renewable energy. Solar irradiance is one of the most important variables in most solar energy forecasting systems. There are two main types of forecasting methods: statistical and numerical weather prediction. Numerical weather prediction methods typically offer good performance for irradiance forecasts four to six hours out and are more competitive on shorter timescales.

[0003] With the growing demand for accurate irradiance forecasts in solar energy applications, solar numerical prediction models (NPMs) are numerical weather forecast models specifically designed to meet these needs. These models improve the representation of the cloud-aerosol-radiation system, significantly increasing the accuracy of irradiance forecasts under clear-sky conditions. However, NPMs lack an analysis of the cloud initialization process, which is crucial for short-term irradiance forecasts.

[0004] The accuracy of cloud information in the model's initial field strongly influences model forecast performance. Assimilating high-temporal and spatial resolution infrared radiation observations from geostationary satellites will undoubtedly significantly improve numerical weather forecasting skills. However, to better utilize geostationary infrared radiation observations in cloud and rain areas, full-sky assimilation still needs further development, especially in the full-sky observation error model, quality control of the matching between observed and model cloud distributions, and optimization of bias correction models that consider cloud influencing factors.

[0005] Meanwhile, as another major factor influencing irradiance forecasts, aerosol optical properties not only directly affect irradiance intensity but also indirectly influence irradiance forecast accuracy by influencing cloud formation and development. However, in numerical solar prediction models, aerosol optical properties are typically estimated using the regional climate mean or through parameterized schemes, which cannot fully adapt to the irradiance forecast requirements under various weather conditions. Therefore, effectively utilizing the time-varying aerosol information provided by geostationary satellites is crucial to improving irradiance forecast accuracy. Summary of the Invention

[0006] The present invention aims to solve one of the technical problems existing in the related art at least to a certain extent.

[0007] The purpose of the present invention is to provide a solar irradiance estimation method that integrates geostationary satellite cloud and aerosol information, rationally utilizes multivariate data provided by geostationary satellites and solar numerical forecast models, and improves the estimation accuracy of solar irradiance.

[0008] To achieve the above objectives, the present invention provides a method for estimating solar irradiance by integrating cloud and aerosol information from geostationary satellites, comprising the following steps:

[0009] S1. Based on the differences between geostationary satellite infrared radiation observations and the background field of solar numerical prediction models, cloud statistical characteristics are calculated, and a full-sky observation error model suitable for the effective assimilation of geostationary satellite infrared radiation observations is constructed;

[0010] S2. Using the all-sky observation error model obtained in step S1, a cloud-dependent quality control method is constructed to eliminate samples whose cloud radiances do not match those in the geostationary satellite infrared radiation observation and the solar numerical prediction model background field, and to correctly extract the cloud information contained in the geostationary satellite infrared radiation observation;

[0011] S3. The brightness temperature of the cloud-sensitive infrared window channel is added as a cloud correction factor to the adaptive variational bias correction model to reduce the systematic bias caused by the non-Gaussian and nonlinear nature of the cloud process.

[0012] S4. Inputting the real-time geostationary satellite aerosol inversion observations into the solar energy numerical prediction model to obtain the solar energy numerical prediction model with time-varying aerosol optical properties;

[0013] S5. Combining the full-sky observation error model, cloud-dependent quality control method, and adaptive variational bias correction model obtained in steps S1 to S3, constructing a full-sky assimilation model for geostationary satellite infrared radiation observations; and absorbing cloud information contained in the geostationary satellite infrared radiation observations based on the full-sky assimilation model to obtain an optimal analysis field.

[0014] S6. Using the optimal analysis field obtained in step S5 as the initial field, a deterministic forecast is performed using the solar energy numerical forecast model with time-varying aerosol optical characteristics obtained in step S4 to obtain an estimated value of solar irradiance.

[0015] A further preferred technical solution of the present invention is that, in step S1, based on the difference between the geostationary satellite infrared radiation observation and the background field of the solar numerical prediction model, cloud statistical characteristics are calculated, and an all-sky observation error model suitable for effective assimilation of the geostationary satellite infrared radiation observation is constructed; specifically,

[0016] S11. Obtaining geostationary satellite infrared radiation observations , full-sky background field of solar numerical prediction model , and the clear sky background field of the solar numerical prediction model ;

[0017] S12. Calculate cloud statistical characteristics representing average cloud effects , the calculation formula is:

[0018] ;

[0019] S13. Using cloud statistical features The piecewise function of estimates the standard deviation of observation minus background under different cloud statistical characteristics, and uses it as the observation error to construct an all-sky observation error model suitable for the effective assimilation of geostationary satellite infrared radiation observations, which is expressed as:

[0020] ;

[0021] in, and are the average cloud effect thresholds for clear sky and fully cloud-affected areas, respectively, represents the clear sky area, and its corresponding observation error is , represents the complete cloud affected area, and its corresponding observation error is , , whose observation error is The linear function of , that is: Quantify.

[0022] Preferably, step S2 utilizes the full-sky observation error model obtained in step S1 to construct a cloud-dependent quality control method, thereby eliminating samples in which the cloud radiances in the geostationary satellite infrared radiation observations and the background field of the solar numerical prediction model do not match, and correctly extracting the cloud information contained in the geostationary satellite infrared radiation observations; specifically,

[0023] S21. Eliminate the absolute value of the difference between the observed brightness temperature and the background brightness temperature based on the distribution characteristics of the observed brightness temperature and the background brightness temperature. Samples larger than 7K;

[0024] S22, using the full sky observation error model obtained in step S1, and combining the difference between the observed brightness temperature and the background brightness temperature Follow The distribution characteristics of the changes are used to obtain the threshold parameters , as shown below:

[0025] ;

[0026] in, It is used to explain the observation error of cloud samples in the all-sky observation error model. is the scale factor, and its size depends on Depends on the size;

[0027] S23, according to The size of each channel is divided into three categories, and each channel is retained The near-clear sky samples are removed and and Greater than , that is, cloud area samples where the cloud effect is strong but the solar numerical forecast model fails to correctly simulate the cloud effect.

[0028] Preferably, in step S3, the brightness temperature of the infrared window channel, which is sensitive to clouds, is used as a cloud correction factor and added to the adaptive variational bias correction model to reduce the systematic bias caused by the non-Gaussian and nonlinear nature of the cloud process; specifically:

[0029] The brightness temperature of the infrared window channel that is sensitive to clouds in the infrared radiation observation of the geostationary satellite is converted to As a cloud correction factor added to the adaptive variational bias correction model, it is expressed as: Add As the cloud correction factor, ;in, is the observation operator after deviation correction, is the observation operator, is the background field state vector, is the constant term of the deviation, is the i-th correction factor, is the coefficient of the i-th correction factor, is the number of correction factors.

[0030] Preferably, step S4 inputs the real-time changing geostationary satellite aerosol inversion observations into the solar energy numerical prediction model to obtain a solar energy numerical prediction model with time-varying aerosol optical properties; specifically,

[0031] S41. Combine the AOD550nm and Ångström index data from the hourly land-based and marine aerosol products provided by geostationary satellites, and linearly interpolate them onto a uniform grid. In areas where data are missing, replace the data with the climate mean for that area.

[0032] S42. Project the merged AOD550nm and Ångström index data onto the WPS grid to generate an intermediate file. After horizontal interpolation through WPS, it is transferred to the solar numerical prediction model simulation area to generate a netCDF format auxiliary file that complies with the WRF I / O API standard. This auxiliary file is used as one of the initial conditions of the solar numerical prediction model and input into the solar numerical prediction model to obtain a solar numerical prediction model with time-varying aerosol optical properties.

[0033] Preferably, step S5 combines the all-sky observation error model, cloud-dependent quality control method, and adaptive variational bias correction model obtained in steps S1 to S3 to construct an all-sky assimilation model for geostationary satellite infrared radiation observations; and based on the all-sky assimilation model, absorbs the cloud information contained in the geostationary satellite infrared radiation observations to obtain the optimal analysis field:

[0034] The full-sky observation error model obtained from steps S1 to S3, the cloud-dependent quality control method and the adaptive variational bias correction model are combined to construct a full-sky assimilation model for geostationary satellite infrared radiation observations. Based on this full-sky assimilation model, the global reanalysis data are used as the background field. , assimilate the infrared radiation observations of geostationary satellites, effectively absorb the cloud information contained therein, and obtain the optimal analysis field .

[0035] Preferably, in step S6, the optimal analysis field obtained in step S5 is used as the initial field, and a deterministic forecast is performed using the solar energy numerical forecast model with time-varying aerosol optical properties obtained in step S4 to obtain an estimated solar irradiance value; specifically, the following steps are performed:

[0036] Based on the solar numerical prediction model with time-varying aerosol optical properties obtained in step S4, the optimal analysis field obtained in step S5 is used. A deterministic forecast for n hours is made as the initial field, and the forecast result is output every t hours as the estimated value of solar irradiance.

[0037] Another aspect of the present invention provides a non-transitory computer-readable storage medium having computer instructions stored thereon, which enable a computer to execute the above-mentioned solar irradiance estimation method integrating geostationary satellite cloud and aerosol information.

[0038] Another aspect of the present invention provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus, and the processor calls the logic instructions in the memory to execute the above-mentioned solar irradiance estimation method that integrates geostationary satellite cloud and aerosol information.

[0039] On the other hand, the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer executes the above-mentioned solar irradiance estimation method that integrates geostationary satellite cloud and aerosol information.

[0040] Beneficial effects: The solar irradiance estimation method of the present invention integrates the cloud and aerosol information of geostationary satellites. By constructing a full-sky observation error model, a cloud-dependent quality control method and adding brightness temperature as a cloud correction factor, a full-sky assimilation model suitable for the effective assimilation of geostationary satellite infrared radiation observations is obtained. This model rationally absorbs the cloud information contained in the geostationary satellite infrared radiation observations, and at the same time combines the time-varying aerosol optical properties provided by the geostationary satellite to improve the interaction between the cloud-aerosol-radiation system in the solar numerical prediction model, thereby enhancing the forecast level of the solar numerical prediction system.

[0041] Since the accuracy of cloud information in the model's initial field strongly affects the forecast performance of the solar energy numerical prediction model, the present invention absorbs the high-temporal and spatial resolution cloud information contained in the geostationary satellite infrared radiation observations through an all-sky assimilation model, and at the same time inputs the real-time changing aerosol optical properties provided by the geostationary satellite into the solar energy numerical prediction model. The more accurate cloud and aerosol information in the model is conducive to improving the interaction between the cloud-aerosol-radiation system, and further improving the estimation and prediction accuracy of solar irradiance. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The present invention provides a flow chart of a solar irradiance estimation method that integrates geostationary satellite cloud and aerosol information. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0044] The following combination Figure 1 The present invention describes a solar irradiance estimation method that integrates geostationary satellite cloud and aerosol information, a non-transitory computer-readable storage medium, an electronic device, and a computer program product.

[0045] Prior to this, the technical terms that may be used in the present invention are first explained.

[0046] Background Field: The background field is an estimate of the system state based on numerical model predictions, without considering the latest observational data. It is the prior information in the assimilated system, representing the best guess at the system state before the observational data are added. The background field is typically derived from a previous analysis field or a forecast result integrated over a longer time period.

[0047] Observation Field: An observation field is a field composed of actual observational data, representing the actual value of the system state observed at a specific time and location. Observation field data may include meteorological elements such as temperature, pressure, humidity, and wind speed, or oceanographic data such as currents, salinity, and temperature. Observational data are often used to verify and improve estimates of the background field.

[0048] Analysis Field: The analysis field is derived by combining the background and observation fields through a data assimilation process. It is an optimal estimate that incorporates both model predictions and observational data. The analysis field aims to provide the most accurate description of the system state and is generally more reliable than either the background or observation fields alone.

[0049] Forecast Field: A forecast field is a prediction of future system states based on an analysis field, using numerical models. The accuracy of the forecast field depends on the quality of the analysis field and the forecasting capabilities of the model.

[0050] Example 1: This example provides a solar irradiance estimation method that integrates geostationary satellite cloud and aerosol information.

[0051] In traditional numerical forecast models, the accuracy of solar radiation forecasts is affected by the accuracy of cloud information in the model's initial field and the aerosol information in the model. The solar irradiance estimation method, which integrates geostationary satellite cloud and aerosol information, provided in this embodiment, effectively utilizes the rich cloud information contained in geostationary satellite infrared radiation observations. Furthermore, the aerosol data provided by geostationary satellites provides the model with aerosol optical information with time-varying characteristics, thereby improving the accuracy of solar radiation forecasts through feedback from the cloud-aerosol-radiation system.

[0052] The solar irradiance estimation method of this embodiment is based on the integration of geostationary satellite cloud and aerosol information. Figure 1 As shown, the following steps are included:

[0053] S1. Based on the differences between geostationary satellite infrared radiation observations and the background field of the solar numerical prediction model, calculate the cloud statistical characteristics and construct a full-sky observation error model suitable for the effective assimilation of geostationary satellite infrared radiation observations. Specifically:

[0054] S11. Obtaining geostationary satellite infrared radiation observations , full-sky background field of solar numerical prediction model , and the clear sky background field of the solar numerical prediction model ;

[0055] S12. Calculate cloud statistical characteristics representing average cloud effects , the calculation formula is:

[0056] ;

[0057] S13. Using cloud statistical features The piecewise function of estimates the standard deviation of observation minus background under different cloud statistical characteristics, and uses it as the observation error to construct an all-sky observation error model suitable for the effective assimilation of geostationary satellite infrared radiation observations, which is expressed as:

[0058] ;

[0059] in, and are the average cloud effect thresholds for clear sky and fully cloud-affected areas, respectively, represents the clear sky area, and its corresponding observation error is , represents the complete cloud affected area, and its corresponding observation error is , , whose observation error is The linear function of , that is: Quantify.

[0060] S2. Use the full-sky observation error model obtained in step S1 to construct a cloud-dependent quality control method to eliminate samples where the cloud radiances in the geostationary satellite infrared radiation observations and the solar numerical prediction model background field do not match, and correctly extract the cloud information contained in the geostationary satellite infrared radiation observations. Specifically:

[0061] S21. Eliminate the absolute value of the difference between the observed brightness temperature and the background brightness temperature based on the distribution characteristics of the observed brightness temperature and the background brightness temperature. Samples larger than 7K;

[0062] S22, using the full sky observation error model obtained in step S1, and combining the difference between the observed brightness temperature and the background brightness temperature Follow The distribution characteristics of the changes are used to obtain the threshold parameters , as shown below:

[0063] ;

[0064] in, It is used to explain the observation error of cloud samples in the all-sky observation error model. is the scale factor, and its size depends on Depends on the size;

[0065] S23, according to The size of each channel is divided into three categories, and each channel is retained The near-clear sky samples are removed and and Greater than , that is, cloud area samples where the cloud effect is strong but the solar numerical forecast model fails to correctly simulate the cloud effect.

[0066] S3. The brightness temperature of the infrared window channel, which is sensitive to clouds, is used as a cloud correction factor and added to the adaptive variational bias correction model to reduce the systematic bias caused by the non-Gaussian and nonlinear nature of the cloud process. Specifically:

[0067] The brightness temperature of the infrared window channel that is sensitive to clouds in the infrared radiation observation of the geostationary satellite is converted to As a cloud correction factor added to the adaptive variational bias correction model, it is expressed as: Add As the cloud correction factor, ;in, is the observation operator after deviation correction, is the observation operator, is the background field state vector, is the constant term of the deviation, is the i-th correction factor, is the coefficient of the i-th correction factor, is the number of correction factors.

[0068] S4. Input the real-time geostationary satellite aerosol inversion observations into the solar energy numerical prediction model to obtain a solar energy numerical prediction model with time-varying aerosol optical properties. Specifically:

[0069] S41. Combine the AOD550nm and Ångström index data from the hourly land-based and marine aerosol products provided by geostationary satellites, and linearly interpolate them onto a uniform grid. In areas where data are missing, replace the data with the climate mean for that area.

[0070] S42. Project the merged AOD550nm and Ångström index data onto the WPS grid to generate an intermediate file. After horizontal interpolation through WPS, it is transferred to the solar numerical prediction model simulation area to generate a netCDF format auxiliary file that complies with the WRF I / O API standard. This auxiliary file is used as one of the initial conditions of the solar numerical prediction model and input into the solar numerical prediction model to obtain a solar numerical prediction model with time-varying aerosol optical properties.

[0071] S5. Combining the full-sky observation error model obtained in steps S1 to S3, the cloud-dependent quality control method, and the adaptive variational bias correction model, a full-sky assimilation model for geostationary satellite infrared radiation observations is constructed; and based on the full-sky assimilation model, the cloud information contained in the geostationary satellite infrared radiation observations is absorbed to obtain the optimal analysis field. Specifically:

[0072] The full-sky observation error model obtained from steps S1 to S3, the cloud-dependent quality control method and the adaptive variational bias correction model are combined to construct a full-sky assimilation model for geostationary satellite infrared radiation observations. Based on this full-sky assimilation model, the global reanalysis data are used as the background field. , assimilate the infrared radiation observations of geostationary satellites, effectively absorb the cloud information contained therein, and obtain the optimal analysis field .

[0073] S6. Using the optimal analysis field obtained in step S5 as the initial field, a deterministic forecast is performed using the solar energy numerical forecast model with time-varying aerosol optical characteristics obtained in step S4 to obtain an estimated solar irradiance value. Specifically:

[0074] Based on the solar numerical prediction model with time-varying aerosol optical properties obtained in step S4, the optimal analysis field obtained in step S5 is used. A deterministic forecast for n hours is made as the initial field, and the forecast result is output every t hours as the estimated value of solar irradiance.

[0075] Embodiment 2: This embodiment provides a non-transitory computer-readable storage medium having computer instructions stored thereon. The computer instructions cause a computer to execute a solar irradiance estimation method that integrates geostationary satellite cloud and aerosol information. The method comprises the following steps:

[0076] S1. Based on the differences between geostationary satellite infrared radiation observations and the background field of solar numerical prediction models, cloud statistical characteristics are calculated, and a full-sky observation error model suitable for the effective assimilation of geostationary satellite infrared radiation observations is constructed;

[0077] S2. Using the all-sky observation error model obtained in step S1, a cloud-dependent quality control method is constructed to eliminate samples whose cloud radiances do not match those in the geostationary satellite infrared radiation observation and the solar numerical prediction model background field, and to correctly extract the cloud information contained in the geostationary satellite infrared radiation observation;

[0078] S3. The brightness temperature of the cloud-sensitive infrared window channel is added as a cloud correction factor to the adaptive variational bias correction model to reduce the systematic bias caused by the non-Gaussian and nonlinear nature of the cloud process.

[0079] S4. Inputting the real-time geostationary satellite aerosol inversion observations into the solar energy numerical prediction model to obtain the solar energy numerical prediction model with time-varying aerosol optical properties;

[0080] S5. Combining the full-sky observation error model, cloud-dependent quality control method, and adaptive variational bias correction model obtained in steps S1 to S3, constructing a full-sky assimilation model for geostationary satellite infrared radiation observations; and absorbing cloud information contained in the geostationary satellite infrared radiation observations based on the full-sky assimilation model to obtain an optimal analysis field.

[0081] S6. Using the optimal analysis field obtained in step S5 as the initial field, a deterministic forecast is performed using the solar energy numerical forecast model with time-varying aerosol optical characteristics obtained in step S4 to obtain an estimated value of solar irradiance.

[0082] Example 3: This embodiment provides an electronic device, which may include: a processor, a communications interface, a memory, and a communications bus, wherein the processor, the communications interface, and the memory communicate with each other via the communications bus. The processor may invoke logic instructions in the memory to execute a solar irradiance estimation method that integrates geostationary satellite cloud and aerosol information, the method comprising the following steps:

[0083] S1. Based on the differences between geostationary satellite infrared radiation observations and the background field of solar numerical prediction models, cloud statistical characteristics are calculated, and a full-sky observation error model suitable for the effective assimilation of geostationary satellite infrared radiation observations is constructed;

[0084] S2. Using the all-sky observation error model obtained in step S1, a cloud-dependent quality control method is constructed to eliminate samples whose cloud radiances do not match those in the geostationary satellite infrared radiation observation and the solar numerical prediction model background field, and to correctly extract the cloud information contained in the geostationary satellite infrared radiation observation;

[0085] S3. The brightness temperature of the cloud-sensitive infrared window channel is added as a cloud correction factor to the adaptive variational bias correction model to reduce the systematic bias caused by the non-Gaussian and nonlinear nature of the cloud process.

[0086] S4. Inputting the real-time geostationary satellite aerosol inversion observations into the solar energy numerical prediction model to obtain the solar energy numerical prediction model with time-varying aerosol optical properties;

[0087] S5. Combining the full-sky observation error model, cloud-dependent quality control method, and adaptive variational bias correction model obtained in steps S1 to S3, constructing a full-sky assimilation model for geostationary satellite infrared radiation observations; and absorbing cloud information contained in the geostationary satellite infrared radiation observations based on the full-sky assimilation model to obtain an optimal analysis field.

[0088] S6. Using the optimal analysis field obtained in step S5 as the initial field, a deterministic forecast is performed using the solar energy numerical forecast model with time-varying aerosol optical characteristics obtained in step S4 to obtain an estimated value of solar irradiance.

[0089] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0090] Embodiment 4: This embodiment provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform a solar irradiance estimation method that integrates geostationary satellite cloud and aerosol information. The method includes the following steps:

[0091] S1. Based on the differences between geostationary satellite infrared radiation observations and the background field of solar numerical prediction models, cloud statistical characteristics are calculated, and a full-sky observation error model suitable for the effective assimilation of geostationary satellite infrared radiation observations is constructed;

[0092] S2. Using the all-sky observation error model obtained in step S1, a cloud-dependent quality control method is constructed to eliminate samples whose cloud radiances do not match those in the geostationary satellite infrared radiation observation and the solar numerical prediction model background field, and to correctly extract the cloud information contained in the geostationary satellite infrared radiation observation;

[0093] S3. The brightness temperature of the cloud-sensitive infrared window channel is added as a cloud correction factor to the adaptive variational bias correction model to reduce the systematic bias caused by the non-Gaussian and nonlinear nature of the cloud process.

[0094] S4. Inputting the real-time geostationary satellite aerosol inversion observations into the solar energy numerical prediction model to obtain the solar energy numerical prediction model with time-varying aerosol optical properties;

[0095] S5. Combining the full-sky observation error model, cloud-dependent quality control method, and adaptive variational bias correction model obtained in steps S1 to S3, constructing a full-sky assimilation model for geostationary satellite infrared radiation observations; and absorbing cloud information contained in the geostationary satellite infrared radiation observations based on the full-sky assimilation model to obtain an optimal analysis field.

[0096] S6. Using the optimal analysis field obtained in step S5 as the initial field, a deterministic forecast is performed using the solar energy numerical forecast model with time-varying aerosol optical characteristics obtained in step S4 to obtain an estimated value of solar irradiance.

[0097] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0098] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A solar irradiance estimation method integrating geostationary satellite cloud and aerosol information, characterized in that: The steps include: S1. Based on the differences between geostationary satellite infrared radiation observations and the background field of the solar numerical prediction model, cloud statistical characteristics are calculated, and a full-sky observation error model suitable for the effective assimilation of geostationary satellite infrared radiation observations is constructed; specifically: S11. Obtaining geostationary satellite infrared radiation observations , full-sky background field of solar numerical prediction model , and the clear sky background field of the solar numerical prediction model ; S12. Calculate cloud statistical characteristics representing average cloud effects , the calculation formula is: ； S13. Using cloud statistical features The piecewise function of estimates the standard deviation of observation minus background under different cloud statistical characteristics, and uses it as the observation error to construct an all-sky observation error model suitable for the effective assimilation of geostationary satellite infrared radiation observations, which is expressed as: ； in, and are the average cloud effect thresholds for clear sky and fully cloud-affected areas, respectively, represents the clear sky area, and its corresponding observation error is , represents the complete cloud affected area, and its corresponding observation error is , , whose observation error is The linear function of , that is: quantify; S2. Using the all-sky observation error model obtained in step S1, a cloud-dependent quality control method is constructed to eliminate samples whose cloud radiances do not match those in the geostationary satellite infrared radiation observation and the solar numerical prediction model background field, and to correctly extract the cloud information contained in the geostationary satellite infrared radiation observation; S3. The brightness temperature of the cloud-sensitive infrared window channel is added as a cloud correction factor to the adaptive variational bias correction model to reduce the systematic bias caused by the non-Gaussian and nonlinear nature of the cloud process. Specifically: The brightness temperature of the infrared window channel that is sensitive to clouds in the infrared radiation observation of the geostationary satellite is converted to As a cloud correction factor added to the adaptive variational bias correction model, it is expressed as: Add As the cloud correction factor, ;in, is the observation operator after deviation correction, is the observation operator, is the background field state vector, is the constant term of the deviation, is the i-th correction factor, is the coefficient of the i-th correction factor, is the number of correction factors; S4. Inputting the real-time geostationary satellite aerosol inversion observations into the solar energy numerical prediction model to obtain the solar energy numerical prediction model with time-varying aerosol optical properties; S5. Combining the full-sky observation error model, cloud-dependent quality control method, and adaptive variational bias correction model obtained in steps S1 to S3, a full-sky assimilation model for geostationary satellite infrared radiation observations is constructed; and based on the full-sky assimilation model, cloud information contained in the geostationary satellite infrared radiation observations is absorbed to obtain an optimal analysis field. Specifically, The full-sky observation error model obtained from steps S1 to S3, the cloud-dependent quality control method and the adaptive variational bias correction model are combined to construct a full-sky assimilation model for geostationary satellite infrared radiation observations. Based on this full-sky assimilation model, the global reanalysis data are used as the background field. , assimilate the infrared radiation observations of geostationary satellites, effectively absorb the cloud information contained therein, and obtain the optimal analysis field ; S6. Using the optimal analysis field obtained in step S5 as the initial field, a deterministic forecast is performed using the solar energy numerical forecast model with time-varying aerosol optical characteristics obtained in step S4 to obtain an estimated value of solar irradiance.

2. The solar irradiance estimation method based on the fusion of geostationary satellite cloud and aerosol information according to claim 1 is characterized in that: Step S2 uses the full-sky observation error model obtained in step S1 to construct a cloud-dependent quality control method, eliminates samples whose cloud radiances do not match those in the geostationary satellite infrared radiation observation and the solar numerical prediction model background field, and correctly extracts the cloud information contained in the geostationary satellite infrared radiation observation; specifically: S21. Eliminate the absolute value of the difference between the observed brightness temperature and the background brightness temperature based on the distribution characteristics of the observed brightness temperature and the background brightness temperature. Samples larger than 7K; S22, using the full sky observation error model obtained in step S1, and combining the difference between the observed brightness temperature and the background brightness temperature Follow The distribution characteristics of the changes are used to obtain the threshold parameters , as shown below: ； in, It is used to explain the observation error of cloud samples in the all-sky observation error model. is the scale factor, and its size depends on Depends on the size; S23, according to The size of each channel is divided into three categories, and each channel is retained The near-clear sky samples are removed and and Greater than , that is, cloud area samples where the cloud effect is strong but the solar numerical forecast model fails to correctly simulate the cloud effect.

3. The solar irradiance estimation method of integrating geostationary satellite cloud and aerosol information according to claim 1 is characterized in that: Step S4 is to input the real-time changing geostationary satellite aerosol inversion observation into the solar energy numerical prediction model to obtain the solar energy numerical prediction model with time-varying aerosol optical characteristics; specifically, S41. Combine the AOD550nm and Ångström index data from the hourly land-based and marine aerosol products provided by geostationary satellites, and linearly interpolate them onto a uniform grid. In areas where data are missing, replace the data with the climate mean values ​​for that area. S42. Project the merged AOD550nm and Ångström index data onto the WPS grid to generate an intermediate file. After horizontal interpolation through WPS, it is transferred to the solar numerical prediction model simulation area to generate an auxiliary file in netCDF format that complies with the WRF I / O API standard. This auxiliary file is used as one of the initial conditions of the solar numerical prediction model and input into the solar numerical prediction model to obtain a solar numerical prediction model with time-varying aerosol optical properties.

4. The solar irradiance estimation method of integrating geostationary satellite cloud and aerosol information according to claim 1 is characterized in that: In step S6, the optimal analysis field obtained in step S5 is used as the initial field, and the solar energy numerical prediction model with time-varying aerosol optical characteristics obtained in step S4 is used to perform a deterministic forecast to obtain an estimated solar irradiance value; specifically, the following steps are performed: Based on the solar numerical prediction model with time-varying aerosol optical properties obtained in step S4, the optimal analysis field obtained in step S5 is used. A deterministic forecast for n hours is made as the initial field, and the forecast result is output every t hours as the estimated value of solar irradiance.

5. A non-transitory computer-readable storage medium, characterized in that Computer instructions are stored thereon, and the computer instructions enable the computer to execute the solar irradiance estimation method for integrating geostationary satellite cloud and aerosol information as described in any one of claims 1 to 4.

6. An electronic device, characterized in that: include: A processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus, and the processor calls logic instructions in the memory to execute the solar irradiance estimation method for integrating geostationary satellite cloud and aerosol information as described in any one of claims 1 to 4.

7. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer executes the solar irradiance estimation method for integrating geostationary satellite cloud and aerosol information according to any one of claims 1 to 4.

Images