# Energy Fluxes Data Overview¶

## Global SW Solar Insolation & LW Radiative Flux¶

The surface shortwave (SW) radiation (or solar insolation) and the longwave (LW) radiation (or thermal radiation) available from the POWER data archives are based upon observational data from satellites. The basic observational data is the amount of radiative energy emerging from the Earth’s atmosphere at certain ranges of wavelengths from the solar through the thermal infrared. The amount of radiation measured is affected by atmospheric absorption, emission and scattering processes. Radiative transfer models and radiative transfer-based parameterizations using the observations and information about the atmospheric constituents, such as gaseous concentrations, aerosols, and clouds, are used for estimating the SW and LW radiative fluxes.

The daily mean solar radiation data for the time period July 1, 1984 – December 31, 2000 are obtained from the NASA’s Global Energy and Water Exchanges - Surface Radiation Budget Project Release 4-IP archive (NASA/GEWEX SRB 4-IP; Stackhouse et al., 2020).

Daily mean solar radiation data for the time period from January 1, 2001 to within approximately 3-4 months of current time, or real time, are taken from NASA’s CERES SYN1deg Edition 4.1. Filling in from that point to near real time (about 1 week) is CERES FLASHFlux Version 4A.

The solar data from these primary sources are produced on a global 1° latitude/longitude grid. Validation of the satellite-based values was conducted via comparisons to surface observations using the 1° gridded values.

We note here that both the NASA GEWEX SRB Project and CERES SYN1deg Edition 4.1 focus on providing high-quality estimates of the Earth’s top-of-atmosphere (TOA) and surface solar insolation in support of NASA’s effort to quantify components of the Earth’s radiation budget, while the focus of the CERES FLASHFlux project is to provide solar data within one week of satellite observations with well validated estimates of its accuracy. The POWER data sets concurrently concatenate these data sets. However, it is not recommended to use these fluxes to assess climate trends that encompass changes in the source data without a careful assessment and analysis that accounts for discontinuities and uncertainties in the parameter values.

While it neither the intent nor purpose of this document to provide a detailed description of the methodology for inferring solar data from satellite observations, a brief synopsis for each is provided in the following sections.

## GEWEXSRB 4-IP SW and LW Radiative Transfer Models¶

The Global Energy and Water Exchanges (GEWEX) program's Surface Radiation Budget (SRB) project has been supported by NASA and organized under the World Climate Research (WCRP) GEWEX program. The project endeavors to collaborate with other GEWEX-organized projects to estimate the surface radiative components of the Earth’s radiative energy budget. The latest version processes long-term estimates of global 3-hourly surface and cloud information and integrates this with ancillary information such as gaseous constituents of the atmosphere (water vapor, carbon dioxide and ozone) and aerosols to estimate the solar and thermal IR radiative fluxes at the surface and TOA. POWER data sets provide both the solar (shortwave) and thermal infrared (longwave) fluxes to users. An overview of the latest version of GEWEX SRB Release 4-IP data products is provided by Stackhouse et al. (2020). Here we briefly describe the algorithms and provide an overview of the data quality of these surface estimates.

The GEWEX longwave (LW) algorithm uses LaRC Fu-Liou (Rose et al., 2013) thermal infrared radiative transfer code with cloud and surface parameters requiring cloud, atmospheric profile information, and surface properties. Inputs to the algorithm were obtained from the following sources: The ISCCP HX cloud properties were separated into categories of high, middle, and low where each layer could be composed of ice or water. Cloud fractions and cloud optical depths were determined within these categories. Cloud particle sizes were assumed, and cloud physical thicknesses were also designed based upon information from literature. Random overlap is used between the high, middle, and low layers to better approximate under cast conditions. Like the shortwave, MACv1 aerosol climatologies are also used in the computations of the fluxes.

### GEWEX Direct and Diffuse Irradiances¶

The Direct and Diffuse Irradiances, consistent of all-sky (i.e. including the effect of clouds if present) total global solar radiation from the SRB archive, is the sum of diffuse and direct radiation on the horizontal surface. Estimates of all-sky diffuse, $(H^{All})_{DIFF}$, and direct normal radiation, $(H^{All})_{DNI}$ are often necessary for the design of hardware such as solar panels, solar concentrators, day lighting, as well as agricultural and hydrology applications. From an observational perspective, $(H^{All})_{DIFF}$ on a horizontal surface is that radiation remaining with $(H^{All})_{DNI}$ from the Sun's beam blocked by a shadow band or tracking disk. $(H^{All})_{DIFF}$ is typically measured using a Sun tracking pyrheliometer with a shadow band or disk to block the direct radiation from the Sun. Similarly, from an observational perspective, $(H^{All})_{DNI}$ is the amount of the beam radiation impinging on a surface perpendicular to the beam, and is typically measured using a pyrheliometer tracking the sun throughout the day.

## CERES SYN1deg SW and LW Radiative Transfer Model¶

The CERES projects are based upon the algorithms developed for analysis and data collected by the Clouds and the Earth's Radiant Energy System (CERES) project. CERES is currently producing world-class climate data products derived from measurements taken aboard NASA's Terra and Aqua satellites. SYN1deg also incorporates geostationary cloud properties into the model calculations to produce global hourly fluxes. Details of SYN1deg inputs and algorithm can be found in Rutan, et al. (2015). While of exceptional fidelity, CERES data products require extensive calibration checks and validation to assure quality and verify accuracy and precision. The result is that CERES data are typically released more than six months after acquisition of the initial measurements. For climate studies, such delays are of little consequence especially considering the improved quality of the released data products.

### Cloud Properties¶

Cloud Amount and Cloud Optical Visible Depth are derived from NASA's CERES SYN1deg and are available to users at the Hourly, Daily, Monthly, Annual, and Climatology temporal levels. As with the solar data, the cloud cover information is available in its original source spatial resolution. Cloud Optical Visible Depth is the vertical optical thickness between the top and bottom of a cloud as computed in the CERES algorithm.

The percentage of cloud cover is computed in the CERES algorithm. The percentage of cloud cover is computed on 1-hourly time increments for a given month and then averaged over the day to yield the daily averaged percentage for each temporal level. Additionally, we provide hour-based cloud cover values for each temporal level for all GMT times.

Averaged Frequencies:

Name Definition
Daytime Daytime hours only
Nighttime Nighttime hours only
Clear Skies Cloud Cloud <= 10%
Broken-cloud Skies Cloud Cover 10 – 70%
Near-overcast Skies Cloud Cover >=70%

POWER provides the ultraviolet (UV) irradiance directly from CERES SYN1Deg in all temporal levels from 2001 to months of Near Real Time (NRT). The UV Irradiance is available in all sky conditions only. POWER provides ultraviolet A (UVA 315nm-400nm), ultraviolet B (UVB 280nm-315nm), and the ultraviolet radiation exposure index. These UV products are computed using the methodology of Su et al (2005).

## CERESFLASHFlux SW and LW Radiative Transfer Model¶

There are, however, many uses for the CERES data products on a near real time basis such as those referred to within the POWER project. To meet those needs, FLASHFlux has greatly sped up processing by using the earliest stream of data coming from CERES instruments and using fast radiation algorithms to produce results within one week of satellite observations. This results in the loss of climate-quality accuracy due to bypassing some calibration checks, and some gaps in the earliest stream of satellite data. As such, the FLASHFlux provides global estimates of daily solar (shortwave) and thermal infrared (longwave) radiation fluxes for the time period covering the end of SYN1deg to within 1 week of real time.

Both FLASHFlux and SYN1deg rely on similar input data sets from the meteorological products and Moderate Resolution Imaging Spectroradiometer (MODIS). It is important to note that the FLASHFlux endeavor intends to incorporate the latest input data sets (such as GEOS-5.12.4 FPIT meteorology instead of CERES G5.4) and improvements into its algorithms. However, there are no plans to reprocess the FLASHFlux data products once these modifications are in place. Thus, in contrast to the CERES SYN1deg data products, the FLASHFlux data products are not to be considered of climate quality. Users seeking climate quality should instead use the CERES SYN1deg and EBAF data products.

For speedy retrieval of surface solar insolation, FLASHFlux uses the SW Model B that is also used in CERES processing (Kratz et al., 2010 and 2014). This model is named the Langley Parameterized SW Algorithm (LPSA) and described in detail in Gupta et al. (2001). It consists of physical parameterizations which account for the attenuation of solar radiation in simple terms separately for clear atmosphere and clouds. Surface insolation, $F_{sd}$, is computed as:

\begin{align} F_{sd} = F_{toa} T_a T_c \end{align}
\begin{align} Where: \\ F_{toa}: & \text{ The corresponding TOA insolation. }\\ T_a: & \text{ The transmittance of the clear atmosphere. }\\ T_c: & \text{ The transmittance of the clouds. }\\ \end{align}

The LW radiative fluxes are produced via the Langley Parameterized Longwave Algorithm (LPLA). Detailed descriptions of this algorithm can be found in Gupta et al. (1992); Gupta (1989) and Wilber et al. (1999).