Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part II): Cloud Coverage

Remote Sens. 2016, 8(5), 431; doi:10.3390/rs8050431

Author

Daniel Holdaway ^1,2,*  and Yuekui Yang ^2,3

¹

Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

²

Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, MD 21046, USA

³

Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

^*

Author to whom correspondence should be addressed. 

Academic Editors: Yudong Tian, Ken Harrison, Alfredo R. Huete and Prasad S. Thenkabail

Received: 12 March 2016 / Revised: 6 May 2016 / Accepted: 17 May 2016 / Published: 20 May 2016

(This article belongs to the Special Issue Uncertainties in Remote Sensing)

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Abstract 

This is the second part of a study on how temporal sampling frequency affects satellite retrievals in support of the Deep Space Climate Observatory (DSCOVR) mission. Continuing from Part 1, which looked at Earth’s radiation budget, this paper presents the effect of sampling frequency on DSCOVR-derived cloud fraction. The output from NASA’s Goddard Earth Observing System version 5 (GEOS-5) Nature Run is used as the “truth”. The effect of temporal resolution on potential DSCOVR observations is assessed by subsampling the full Nature Run data. A set of metrics, including uncertainty and absolute error in the subsampled time series, correlation between the original and the subsamples, and Fourier analysis have been used for this study. Results show that, for a given sampling frequency, the uncertainties in the annual mean cloud fraction of the sunlit half of the Earth are larger over land than over ocean. Analysis of correlation coefficients between the subsamples and the original time series demonstrates that even though sampling at certain longer time intervals may not increase the uncertainty in the mean, the subsampled time series is further and further away from the “truth” as the sampling interval becomes larger and larger. Fourier analysis shows that the simulated DSCOVR cloud fraction has underlying periodical features at certain time intervals, such as 8, 12, and 24 h. If the data is subsampled at these frequencies, the uncertainties in the mean cloud fraction are higher. These results provide helpful insights for the DSCOVR temporal sampling strategy. View Full-Text

Keywords: cloud fraction;  satellite sampling frequency;  DSCOVR;  EPIC;  time series;  GEOS-5;  Nature Run;  Fourier;  spectral analysis

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