An overview of and issues with sky radiometer technology and SKYNET

Abstract

Abstract. This paper is an overview of the progress in sky radiometer technology and the development of the network called SKYNET. It is found that the technology has produced useful on-site calibration methods, retrieval algorithms, and data analyses from sky radiometer observations of aerosol, cloud, water vapor, and ozone. A formula was proposed for estimating the accuracy of the sky radiometer calibration constant F0 using the improved Langley (IL) method, which was found to be a good approximation to observed monthly mean uncertainty in F0, around 0.5 % to 2.4 % at the Tokyo and Rome sites and smaller values of around 0.3 % to 0.5 % at the mountain sites at Mt. Saraswati and Davos. A new cross IL (XIL) method was also developed to correct an underestimation by the IL method in cases with large aerosol retrieval errors. The root-mean-square difference (RMSD) in aerosol optical thickness (AOT) comparisons with other networks took values of less than 0.02 for λ≥500 nm and a larger value of about 0.03 for shorter wavelengths in city areas and smaller values of less than 0.01 in mountain comparisons. Accuracies of single-scattering albedo (SSA) and size distribution retrievals are affected by the propagation of errors in measurement, calibrations for direct solar and diffuse sky radiation, ground albedo, cloud screening, and the version of the analysis software called the Skyrad pack. SSA values from SKYNET were up to 0.07 larger than those from AERONET, and the major error sources were identified as an underestimation of solid viewing angle (SVA) and cloud contamination. Correction of these known error factors reduced the SSA difference to less than 0.03. Retrievals of other atmospheric constituents by the sky radiometer were also reviewed. Retrieval accuracies were found to be about 0.2 cm for precipitable water vapor amount and 13 DU (Dobson Unit) for column ozone amount. Retrieved cloud optical properties still showed large deviations from validation data, suggesting a need to study the causes of the differences. It is important that these recent studies on improvements presented in the present paper are introduced into the existing operational systems and future systems of the International SKYNET Data Center.

Measurement report: Balloon-borne in-situ profiling of Saharan dust over Cyprus with the UCASS optical particle counter

“…During the SAVEX-D/AER-D campaign at Cape Verde, the AERONET retrievals also showed a similar single coarse mode shifted towards a smaller radius compared to in-situ measurements from aircraft . Simultaneous retrievals from SKYRAD (performed on Prede POM radiometers) also determined a slight shift of the coarse mode to a smaller radius, although in this case the coarse mode was broader or even bimodal, depending on the SKYRAD version used (Nakajima et al, 2020). The coarse mode retrieved by GARRLiC shows a consistent shift towards larger sizes when compared to the AERONET output (Benavent-Oltra et al, 2017;Lopatin et al, 2013;Bovchaliuk https://doi.org/10.5194/acp-2020-977 Preprint.…”

“…Currently, SKYNET uses two versions (4.2 and 5) of the inversion algorithm SKYRAD to analyse the radiance measurements of the PREDE POM sky radiometers, although other versions are being developed and currently tested. In order to benefit the international community of users, a re-organisation of the network structure has been initiated (Nakajima et al, 2020).…”

Harmonized and high-quality datasets of aerosol optical depth at a US continental site, 1997–2018

“…Therefore, long-term records of AOD with high quality and temporal continuity at climate-important locations are required to draw firm conclusions about the importance of this variability in forcing the climate change. These records are provided by several established operational AOD networks, including the National Oceanic and Atmospheric Administration surface radiation budget (SURFRAD) network 10 , the Skyradiometer Network (SKYNET) 11 and the National Aeronautics and Space Administration (NASA) Aerosol Robotic Network (AERONET) 12 with two associated sub-networks 13 , 14 . Observational studies typically involve several instruments with different designs and operational techniques to measure AOD 15 – 17 , and thus individual AODs from different instruments will likely have different continuity and accuracy.…”

Synergy processing of diverse ground-based remote sensing and in situ data using the GRASP algorithm: applications to radiometer, lidar and radiosonde observations

“…There are many suggestions for joint processing of coincident photometric and lidar groundbased observations, which provide complementary information. For example, recently proposed Lidar and Radiometer Inversion Code (LiRIC) (Chaikovsky et al, 2016) and Generalized Aerosol Retrieval form Radiometer and Lidar Combination/Generalized Retrieval of Atmosphere and Surface Properties (GARRLiC/GRASP) (Lopatin et al, 2013) algorithms use the joint data from a multi-wavelength lidar and an Aerosol Robotic Network (AERONET) Sun-skyscanning radiometer to derive vertical profiles of fine and coarse aerosol components as well as extra parameters of the column-integrated properties of aerosols. However, it should be noted that in order to maximize the community benefits from synergy, such retrievals should be efficient for processing data collected within the observational networks.…”

“…Correspondingly, one of the main challenges of implementing synergy retrievals based on coincident radiometric and lidar data is achieving a sufficient flexibility of the retrieval in using different lidar observations and assuring their adequate and consistent fusion with the passive measurements. One of rather successful examples of such a retrieval tool is the GARRLiC algorithm developed by Lopatin et al (2013) integrated into the generalized approach by Dubovik et al (2011) that is now named GRASP (Dubovik et al, 2014). GAR-RLiC/GRASP inverts both the photometric and lidar ob-servations and is currently being employed for operational processing of such combined data within the framework of the European ACTRIS infrastructure (https://www.actris.eu/, last access: 29 March 2021).…”