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Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms

Hamann, U. and Walther, A. and Baum, B. and Bennartz, R. and Bugliaro, Luca and Derrien, M. and Francis, P. N. and Heidinger, A. and Joro, S. and Kniffka, A. and Le Gléau, H. and Lockhoff, M. and Lutz, H.-J. and Meirink, J. F. and Minnis, P. and Palikonda, R. and Roebeling, R. and Thoss, A. and Platnick, S. and Watts, P. and Wind, G. (2014) Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms. Atmospheric Measurement Techniques, 7 (9), pp. 2839-2867. Copernicus Publications. ISSN 1867-1381

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Official URL: http://www.atmos-meas-tech.net/7/2839/2014/

Abstract

The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) – a crucial parameter to estimate the thermal cloud radiative forcing – can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud–Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 km lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The average CTHs derived by the SEVIRI algorithms are closer to the CPR measurements than to CALIOP measurements. The biases between SEVIRI and CPR retrievals range from -0.8 km to 0.6 km. The correlation coefficients of CPR and SEVIRI observations vary between 0.82 and 0.89. To discuss the origin of the CTH deviation, we investigate three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference data sets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SEVIRI algorithms yield CTHs that are lower than CALIOP and similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to the uncertainties and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed.

Item URL in elib:https://elib.dlr.de/93044/
Document Type:Article
Title:Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Hamann, U.Meteo Swiss, Locarno, SwitzerlandUNSPECIFIED
Walther, A.Univ. of Wisconsin, Madison, WI, USAUNSPECIFIED
Baum, B.Univ. of Wisconsin, Madison, WI, USAUNSPECIFIED
Bennartz, R.Univ. of Wisconsin, Madison, WI, USAUNSPECIFIED
Bugliaro, LucaDLR, IPAUNSPECIFIED
Derrien, M.Meteo-France, Lannion, FranceUNSPECIFIED
Francis, P. N.Met Office, Exeter, UKUNSPECIFIED
Heidinger, A.NESDIS, NOAA, Madison, WI, USAUNSPECIFIED
Joro, S.EUMETSA, Darmstadt, DUNSPECIFIED
Kniffka, A.DWD, Offenbach, DUNSPECIFIED
Le Gléau, H.Meteo-France, Lannion, FranceUNSPECIFIED
Lockhoff, M.DWD, Offenbach, DUNSPECIFIED
Lutz, H.-J.EUMETSA, Darmstadt, DUNSPECIFIED
Meirink, J. F.KNMI, De Bilt, NLUNSPECIFIED
Minnis, P.NASA, Langley Research Center, Hampton, VA, USAUNSPECIFIED
Palikonda, R.Sci. Syst. and Applic., Inc., Hampton, VA, USAUNSPECIFIED
Roebeling, R.EUMETSA, Darmstadt, DUNSPECIFIED
Thoss, A.SMHI, Norrköping, SUNSPECIFIED
Platnick, S.NASA Goddard Space Flight Center, Greenbelt, USAUNSPECIFIED
Watts, P.EUMETSA, Darmstadt, DUNSPECIFIED
Wind, G.NASA Goddard Space Flight Center, Greenbelt, USAUNSPECIFIED
Date:2014
Journal or Publication Title:Atmospheric Measurement Techniques
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:7
Page Range:pp. 2839-2867
Publisher:Copernicus Publications
ISSN:1867-1381
Status:Published
Keywords:Passive Fernerkundung, SEVIRI, Wolkenoberkantenhöhe, Vergleich von Algortihmen
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Atmosphären- und Klimaforschung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Atmospheric Remote Sensing
Deposited By: Bugliaro Goggia, Dr.rer.nat. Luca
Deposited On:04 Dec 2014 08:06
Last Modified:02 May 2019 14:13

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