Distinction between clouds and ice/snow covered surfaces in the identification of cloud-free observations using SCIAMACHY PMDs

Abstract

Abstract. SCIAMACHY on ENVISAT allows measurement of different trace gases including those most abundant in the troposphere (e.g. CO 2 , NO 2 , CH 4 , BrO, SO 2 ). However, clouds in the observed scenes can severely hinder the observation of tropospheric gases. Several cloud detection algorithms have been developed for GOME on ERS-2 which can be applied to SCIAMACHY. The GOME cloud algorithms, however, suffer from the inadequacy of not being able to distinguish between clouds and ice/snow covered surfaces because GOME only covers the UV, VIS and part of the NIR wavelength range (240-790 nm). As a result these areas are always flagged as clouded, and therefore often not used. Here a method is presented which 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. The algorithm is developed using collocated MODIS observations. The algorithm presented here is specifically developed to identify cloud-free SCIAMACHY observations. The SCIAMACHY Polarisation Measurement Devices (PMDs) are used for this purpose because they provide higher spatial resolution compared to the main spectrometer measurements.

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

“…The SCIAMACHY Polarisation Measurement Devices (PMDs) are used because they provide higher spatial resolution compared to the main spectrometer measurements. The improvements (compared to Krijger et al, 2005a) include a snow over vegetation detection using the PMD 2/PMD 5 compared to PMD 4/PMD 3 ratio for the least saturated, T (= cloudy) observations. In addition we provided a correction for SCIAMACHY PMD degradation in time.…”

“…In Krijger et al (2005a) the complete derivation of the SPICI algorithm is described. In brief, the SPICI method allows for fast and simple identification of cloud-free SCIA-MACHY PMD observations.…”

“…Here we present an updated version of the SCIA-MACHY PMD Identification of Clouds and Ice/snow method (SPICI, pronounced with hard c) (Krijger et al, 2005a). It uses, among others, the SCIAMACHY PMD measurements in the wavelength range around 1.6 µm, where the reflectivity of ice/snow covered surfaces is significantly reduced while the reflectivity of clouds is still high.…”

“…This means that the criterium used so far by SPICI to distinguish snow/ice from clouds, based on this ratio is not adequate for these conditions. In this paper we expand on the original SPICI algorithm (Krijger et al, 2005a) to also adequately detect clouds over such snow-covered forests. Furthermore the SCIAMACHY measurements suffer from degradation with time due to degradation of SCIAMACHY optical and detector elements.…”

“…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.…”