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Remote Sens. 2016, 8(6), 511; doi:10.3390/rs8060511

Using the NASA EOS A-Train to Probe the Performance of the NOAA PATMOS-x Cloud Fraction CDR

NOAA NESDIS Center for Satellite Applications and Research, Madison, WI 53706, USA
Space Science and Engineering Center (SSEC), University of Wisconsin, Madison, WI 53706, USA
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editors: Ken Knapp, Richard Müller and Prasad S. Thenkabail
Received: 29 January 2016 / Revised: 1 May 2016 / Accepted: 31 May 2016 / Published: 18 June 2016
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
View Full-Text   |   Download PDF [1670 KB, uploaded 18 June 2016]   |  


An important component of the AVHRR PATMOS-x climate date record (CDR)—or any satellite cloud climatology—is the performance of its cloud detection scheme and the subsequent quality of its cloud fraction CDR. PATMOS-x employs the NOAA Enterprise Cloud Mask for this, which is based on a naïve Bayesian approach. The goal of this paper is to generate analysis of the PATMOS-x cloud fraction CDR to facilitate its use in climate studies. Performance of PATMOS-x cloud detection is compared to that of the well-established MYD35 and CALIPSO products from the EOS A-Train. Results show the AVHRR PATMOS-x CDR compares well against CALIPSO with most regions showing proportional correct values of 0.90 without any spatial filtering and 0.95 when a spatial filter is applied. Values are similar for the NASA MODIS MYD35 mask. A direct comparison of PATMOS-x and MYD35 from 2003 to 2014 also shows agreement over most regions in terms of mean cloud amount, inter-annual variability, and linear trends. Regional and seasonal differences are discussed. The analysis demonstrates that PATMOS-x cloud amount uncertainty could effectively screen regions where PATMOS-x differs from MYD35. View Full-Text
Keywords: clouds; cloud detection; satellite remote sensing; satellite climate data records clouds; cloud detection; satellite remote sensing; satellite climate data records

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Heidinger, A.; Foster, M.; Botambekov, D.; Hiley, M.; Walther, A.; Li, Y. Using the NASA EOS A-Train to Probe the Performance of the NOAA PATMOS-x Cloud Fraction CDR. Remote Sens. 2016, 8, 511.

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