Improved identification of clouds and ice/snow covered surfaces in SCIAMACHY observations

Abstract

Abstract. In the ultra-violet, visible and near infra-red wavelength range the presence of clouds can strongly affect the satellite-based passive remote sensing observation of constituents in the troposphere, because clouds effectively shield the lower part of the atmosphere. Therefore, cloud detection algorithms are of crucial importance in satellite remote sensing. However, the detection of clouds over snow/ice surfaces is particularly difficult in the visible wavelengths as both clouds an snow/ice are both white and highly reflective. The SCIAMACHY Polarisation Measurement Devices (PMD) Identification of Clouds and Ice/snow method (SPICI) uses the SCIAMACHY measurements in the wavelength range between 450 nm and 1.6 µm to make a distinction between clouds and ice/snow covered surfaces, specifically developed to identify cloud-free SCIAMACHY observations. For this purpose the on-board SCIAMACHY PMDs are used because they provide higher spatial resolution compared to the main spectrometer measurements. In this paper we expand on the original SPICI algorithm (Krijger et al., 2005a) to also adequately detect clouds over snow-covered forests which is inherently difficult because of the similar spectral characteristics. Furthermore the SCIAMACHY measurements suffer from degradation with time. This must be corrected for adequate performance of SPICI over the full SCIAMACHY time range. Such a correction is described here. Finally the performance of the new SPICI algorithm is compared with various other datasets, such as from FRESCO, MICROS and AATSR, focusing on the algorithm improvements.

Airborne observations and simulations of three-dimensional radiative interactions between Arctic boundary layer clouds and ice floes

“…In fact, retrievals of cloud microphysical and optical properties using only visible wavelengths are strongly biased by a bright surface (Platnick et al, 2001(Platnick et al, , 2004Platnick and King, 2003;Krijger et al, 2011). To overcome this limitation, near-infrared channels are introduced in the retrieval algorithms instead of the visible channel used over dark surfaces.…”

Optical thickness and effective radius of Arctic boundary-layer clouds retrieved from airborne nadir and imaging spectrometry

“…This is how airborne observations can assist space-borne retrievals, as the aircraft can go back and observe what is inside and underneath the cloud that was remotely sensed. Additionally, satellite observations are often obstructed by the low contrast between clouds and the snow-or ice-covered surface (Krijger et al, 2011). In general, airborne or ground-based cloud observations are scarce, especially over the Arctic Ocean.…”

Observations and simulations of three-dimensional radiative interactions between Arctic boundary layer clouds and ice floes

“…Tsay and Jayaweera (1984) showed that Arctic stratus has a considerable horizontal homogeneity of cloud morphology, droplet diameter, concentration, and liquid water content, except for the cloud top layer. Here, mixing results in small-scale inhomogeneities identified by Lawson et al (2001) and Klingebiel et al (2015): bi-modal cloud particle size distributions at cloud top, while mono-modal distributions dominate the lower cloud layers representative for the adiabatic and homogeneous character of the clouds. In contrast, sea ice has irregular top and bottom surfaces and is broken into distinct pieces, called floes (Rothrock and Thorndike, 1984).…”