Abstract

Abstract. This work presents a study on the sensitivity of two satellite cloud height retrievals to cloud vertical distribution. The difference in sensitivity is exploited by relating the difference in the retrieved cloud heights to cloud vertical extent. The two cloud height retrievals, performed within the Freie Universität Berlin AATSR MERIS Cloud (FAME-C) algorithm, are based on independent measurements and different retrieval techniques. First, cloud-top temperature (CTT) is retrieved from Advanced Along Track Scanning Radiometer (AATSR) measurements in the thermal infrared. Second, cloud-top pressure (CTP) is retrieved from Medium Resolution Imaging Spectrometer (MERIS) measurements in the oxygen-A absorption band and a nearby window channel. Both CTT and CTP are converted to cloud-top height (CTH) using atmospheric profiles from a numerical weather prediction model. First, a sensitivity study using radiative transfer simulations in the near-infrared and thermal infrared was performed to demonstrate, in a quantitative manner, the larger impact of the assumed cloud vertical extinction profile, described in terms of shape and vertical extent, on MERIS than on AATSR top-of-atmosphere measurements. Consequently, cloud vertical extinction profiles will have a larger influence on the MERIS than on the AATSR cloud height retrievals for most cloud types. Second, the difference in retrieved CTH (ΔCTH) from AATSR and MERIS are related to cloud vertical extent (CVE), as observed by ground-based lidar and radar at three ARM sites. To increase the impact of the cloud vertical extinction profile on the MERIS-CTP retrievals, single-layer and geometrically thin clouds are assumed in the forward model. Similarly to previous findings, the MERIS-CTP retrievals appear to be close to pressure levels in the middle of the cloud. Assuming a linear relationship, the ΔCTH multiplied by 2.5 gives an estimate on the CVE for single-layer clouds. The relationship is stronger for single-layer clouds than for multi-layer clouds. Due to large variations of cloud vertical extinction profiles occurring in nature, a quantitative estimate of the cloud vertical extent is accompanied with large uncertainties. Yet, estimates of the CVE provide an additional parameter, next to CTH, that can be obtained from passive imager measurements and can be used to further describe cloud vertical distribution, thus contributing to the characterization of a cloudy scene. To further demonstrate the plausibility of the approach, an estimate of the CVE was applied to a case study. In light of the follow-up mission Sentinel-3 with AATSR and MERIS like instruments, Sea and Land Surface Temperature Radiometer (SLSTR) and (Ocean and Land Colour Instrument) OLCI, respectively, for which the FAME-C algorithm can be easily adapted, a more accurate estimate of the CVE can be expected. OLCI will have three channels in the oxygen-A absorption band, possibly providing enhanced information on cloud vertical distributions.

Highlights

  • The vertical distribution of clouds plays an important role in both meteorological and climatological applications

  • One of the possible reasons for this is that the Advanced Along Track Scanning Radiometer (AATSR) cloud-top temperature (CTT) might be incorrect due to incorrect assumptions in the forward model, which are related to estimates of the cloud emissivity and ignoring multiple scattering

  • This study presents the evaluation of differences between two cloud height retrievals that are based on independent techniques, and relating the differences to cloud vertical extent (CVE) as observed by ground-based active instruments

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Summary

Introduction

The vertical distribution of clouds plays an important role in both meteorological and climatological applications. They provide first radar and lidar measurements on cloud and aerosol vertical profiles on a global scale Since both instruments have given the atmospheric research community many new insights on clouds and aerosols (e.g., Mace et al, 2007; Sassen et al, 2008) and their observations were extensively used in many evaluation studies (e.g., Naud et al, 2010; Weisz et al, 2007). They have a poor spatial coverage due to the nadir-only measurements and, especially for weather related applications, would benefit from supplement observations on cloud vertical distributions. In contrast to various space-born passive imagers, no long-term measurement data sets exist, which are relevant for many climate studies

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