Cloud radiative effect, cloud fraction and cloud type at two stations in Switzerland using hemispherical sky cameras

Abstract

Abstract. The current study analyses the cloud radiative effect during the daytime depending on cloud fraction and cloud type at two stations in Switzerland over a time period of 3 to 5 years. Information on fractional cloud coverage and cloud type is retrieved from images taken by visible all-sky cameras. Cloud-base height (CBH) data are retrieved from a ceilometer and integrated water vapour (IWV) data from GPS measurements. The longwave cloud radiative effect (LCE) for low-level clouds and a cloud coverage of 8 oktas has a median value between 59 and 72 Wm −2 . For midand high-level clouds the LCE is significantly lower. It is shown that the fractional cloud coverage, the CBH and IWV all have an influence on the magnitude of the LCE. These observed dependences have also been modelled with the radiative transfer model MODTRAN5. The relative values of the shortwave cloud radiative effect (SCE rel ) for low-level clouds and a cloud coverage of 8 oktas are between −90 and −62 %. Also here the higher the cloud is, the less negative the SCE rel values are. In cases in which the measured direct radiation value is below the threshold of 120 Wm −2 (occulted sun) the SCE rel decreases substantially, while cases in which the measured direct radiation value is larger than 120 Wm −2 (visible sun) lead to a SCE rel of around 0 %. In 14 and 10 % of the cases in Davos and Payerne respectively a cloud enhancement has been observed with a maximum in the cloud class cirrocumulus-altocumulus at both stations. The calculated median total cloud radiative effect (TCE) values are negative for almost all cloud classes and cloud coverages.

Final response to RC2

“…Cloud-free (0 okta) and overcast (8 oktas) are the cloud coverages that are most often detected in the aforementioned time period. This behaviour also agrees with the analysis of the occurrence of fractional cloud coverages over a longer time period in Davos discussed in Aebi et al (2017). All four instruments and algorithms show similar relative occurrences of cloud coverage of 2-6 oktas.…”

“…An algorithm determines the cloud fraction of each image automatically (Wacker et al, 2015;Aebi et al, 2017). Before applying the cloud detection algorithm, the images are preprocessed.…”

“…The cloud classes are determined with a slightly adapted algorithm from Heinle et al (2010) which is based on statistical features (Wacker et al, 2015;Aebi et al, 2017). The cloud classes determined are stratocumulus (Sc), cumulus (Cu), stratus-altostratus (St-As), cumulonimbusnimbostratus (Cb-Ns), cirrocumulus-altocumulus (Cc-Ac), cirrus-cirrostratus (Ci-Cs) and cloud-free (Cf).…”

“…Circumpolar satellites (i.e. the MODIS satellites Terra and Aqua, Baum and Platnick, 2006;Ackerman et al, 2008) determine cloud fraction globally, but for a specific region only four times a day. Satellites cover a larger area than ground-based instruments and are also able to deliver cloud information from regions where few ground-based instruments are available (e.g.…”

Assessment of surface solar irradiance derived from real-time modelling techniques and verification with ground-based measurements

“…The magnitude of the GHI uncertainty due to the effect of aerosols and clouds is significant and has motivated numerous related studies (Federico et al, 2017;Kosmopoulos et al, 2015;Lara-Fanego et al, 2012;Tegen et al, 1996;Lindfors et al, 2013). Under high aerosol loads the SSR can be reduced by 20-50 % (Eck et al, 1998;Gleeson et al, 2016;Kosmopoulos et al, 2017), while under cloudy conditions the impact was up to 60-90 % for overcast conditions and cloud coverage of 8 octas (Aebi, et al, 2017;Kosmopoulos et al, 2015;Zygmuntowska et al, 2012), highlighting the significant effect of these atmospheric parameters (clouds and aerosols) on the GHI calculations and in the performance of PV installations and energy production.…”