Compared with other disciplines, satellite meteorology is still in its formative stage, but major breakthroughs have been made in improving the technology and calculation of measured values. Among the related products currently available, many are tailor-made for solar users. In the following, we introduce in detail some of the satellite resources that the solar energy research and development group focuses on, including clouds, aerosols, and irradiance. We have not listed a detailed list here, but introduced the spectrum of some resources.
1. International Satellite Cloud Climate Plan
The International Satellite Cloud Climate Program (ISCCP) provides one of the most comprehensive satellite cloud weather services for general research and operating groups. As the first project of the World Climate Research Program (WCRP), ISCCP can provide global visible and infrared ground radiation records, including 5 geostationary satellites and a series of polar orbiting satellites. Its electrode orbit satellites can cross-correct geosynchronous information, and its The observation range covers the polar regions of the earth. This record can be used to obtain basic cloud occlusion and attribute inversion. ISCCP data can also be used to verify and improve cloud parameterization in climate models and to improve our understanding of the Earth’s radiation budget (including downward solar irradiance information). For the end user’s solar irradiance parameters, these data are important auxiliary data sets that can be used as input values for the downward irradiance model.
2. NASA’s global surface radiation budget
NASA’s global surface radiation budget (SRB) data is obtained on the basis of the Earth’s Radiation Budget (ERBE) atmospheric overhead reflectance and ISCCP’s pixel-level (DX) radiation data, and can be used every 3h, every day And provide the global average value of shortwave radiation monthly. On the basis of SRB, NASA’s Applied Science Project (ASP) has established a website called Surface Meteorology and Solar Energy (SSE), which aims to allow people to download global solar data for free. At the beginning of the design, SSE clearly defined its service targets as renewable energy users and agricultural communities. On the basis of the projection global grid of 1 latitude and other longitudes and latitudes, the meteorological data in the fourth edition of NASA Goddard Earth Observation System (GEOS4) model was interpolated, and solar parameters (including special parameters) were derived from the Pinker and Laszlo (1992) model. Parameters customized for the purpose, such as resource assessment and solar array operators). The comparison between the global horizontal irradiance and the Baseline Surface Radiation Network (BSRN) shows that the deviation and root mean square error of the two can reach 0.27% and 8.71% at a position of 60° toward the equator. These valuable climate research data can be traced back to July 1983. It can be said that SSE has provided valuable resources for the site selection of solar energy facilities.
3. Heliosat plan
Since the advent of the first generation of European meteorological geostationary satellites in the late 1970s, the European research community has been devoted to the research of solar satellite applications (for example, Schmetz1989). At present, the European Meteorological Satellite Project (EμmETSAT), which is responsible for the European Meteorological Satellite Development Organization (EμmETSAT), has developed the second generation of meteorological satellites, and is expected to develop to the third generation around 2020. The main and backup satellites of the project cover the longitude range from the mid-Atlantic to the Middle East, and the latitude range is 50° north-south latitude. A well-known example of European meteorological satellite applications in solar energy is the Heliosat project (for example, Diabate et al., 1988; Rigolier et al., 2004). Heliosat includes the inversion of clouds, water vapor, aerosols and ozone and the calculation of solar parameters through physical principles. Through the linear relationship between the hourly atmospheric transmittance measured on the surface and the cloud index calculated from satellite data, the correlation between the observed cloud cover and the solar radiation on the surface is obtained. In other words, this is also an empirical one-step method.
4. NOAA project
The US National Oceanic and Atmospheric Administration (NOAA) also derives some real-time cloud cover and solar energy information from its operating resources. The GOES Surface and Insolation Project (GSIP) is developed on the basis of the PATMOS-x processing system, which can obtain cloud attributes, surface temperature and solar radiation. Among them, the data sources for PATMOS-x predictions include: National Center for Environmental Prediction (NCEP), Global Prediction System (GFS), NOAA Optimal Interpolated Ocean Surface Temperature (OISST) Version 2 and other fast algorithms related to pressure layer atmospheric transmittance ( PFAAST) Auxiliary data set coupled with radiation transfer model. The PFAAST radiative transfer model allows the clear sky condition model to detect and quantify cloud cover. GSIP also uses the Satellite Algorithm for Shortwave Radiation Expenditure (SASRAB) to calculate solar insolation. By using the cloud-detection, cloud-phase state and cloud-height information calculated by PATMOS-x, SASRAB can calculate the total radiation, direct radiation and scattered radiation on the surface. In addition, SASRAB also requires that the background reflection field be established by the second darkest value of each pixel in the 28 days before recording (similar to GASP). NOAA processes the GSIP once every hour. In order to avoid the pixel overlap effect, it is required to scan every other pixel in the cross scan direction. The pixel level of GSIP products is 4km, that is, the average resolution of each grid is 12.5km. PATMOS-x can also obtain a set of global cloud layer attributes with a resolution of 1km or 4km by running POES/AVHRR sensors, but SASRAB does not apply these data. PATMOS-x has been implemented on the historical data of GOES and AVHRR, and is used to generate climate data records of cloud cover and irradiance with a spatial resolution of 11km.
PATMOS-x’s pixel-level cloud layer attribute inversion is conducive to solar predictions on all spatial/temporal scales. Figure 1 shows this approach, in which the running (real-time) generation of information is fed into short-term cloud level movement technology and NWP model analysis, and can be predicted several hours to one day in advance. Figure 2 shows the correlation between the specific satellite observation cloud cover and the downward radiation peak observed on the ground, and the ground camera observation matched with the time further confirms the relationship between the two. Since the software of PATMOS-x can be transplanted to international stationary and polar orbiting satellites, the information data of PATMOS-x is suitable for forecasting and global resource assessment of all time scales in the world.
Since aerosol is widely distributed and can weaken the direct sun beam for a long time, it is very important for concentrating solar power generation. Among the existing satellite aerosol remote sensing products and services, NOAA’s GOESGASP can provide aerosol optical thickness inversion with spatial and temporal resolutions of 4km and 30min, respectively. On the premise that only cloud-free pixels are considered, cloud masks are used on the basis of similar spatial and spectral tests adopted by PATMOS-x. The GASP inversion method (for example, Knapp et al., 2005) can obtain the composite visible light reflection background under clear sky conditions (obtained by monitoring the second darkest value in the past 28 days), by comparing the current observations with the radiation transmission model From the simulated observations, the aerosol optical thickness can be estimated from the background combination. Figure 3 shows an example of the application of GASP over the western United States. The enlarged box represents northern California. The smoke plume caused by forest fires proves the product’s ability to capture details.