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

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Uncertainties in the Mean

#### 2.2. Absolute Error in the Mean

#### 2.3. Correlation

#### 2.4. Fourier Analysis

## 3. Results

^{−2}. Assuming a global cloud coverage of 60%, one percent of change in cloud fraction is equivalent to about 0.33 Wm

^{−2}in radiative forcing, which is comparable to the global aerosol direct radiative forcing (−0.35 Wm

^{−2}) [12]. Since long term cloud coverage change can either magnify or reduce the CO

_{2}-of great interest. Studies have found that the trend signal is very small. Over the tropical ocean it, is about 1.4% change per decade and over other areas, the signal is even smaller or nonexistant [13]. The uncertainties shown in the subsampling with temporal resolution coarser than 4-h would have significant impact on the cloud coverage trend study. We do note that in this work, uncertainties from other sources such as those in cloud detection (e.g., [14,15]) which can have larger effects are not considered.

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Yang, Y.; Marshak, A.; Mao, J.; Lyapustin, A.; Herman, J. A method of retrieving cloud top height and cloud geometrical thickness with oxygen A and B bands for the Deep Space Climate Observatory (DSCOVR) mission: Radiative transfer simulations. J. Quant. Spectrosc. Radiat. Trans.
**2013**, 122, 141–149. [Google Scholar] [CrossRef] - Gebremichael, M.; Krajewski, W.F. Characterization of the temporal sampling error in space-time-averaged rainfall estimates from satellites. J. Geophys. Res.
**2004**, 109. [Google Scholar] [CrossRef] - Tiao, G.; Reinsel, G.; Xu, D.; Frederick, J.H.; Zhu, X.; Miller, A.J.; DeLuisi, J.J.; Mateer, C.L.; Wuebbles, D.J. Effects of auto-correlation and temporal sampling schemes on estimates of trends and spatial correlation. J. Geophys. Res.
**1990**, 95, 20507–20517. [Google Scholar] [CrossRef] - Holdaway, D.; Yang, Y. Study of the effect of temporal sampling frequency on DSCOVR observations using the GEOS-5 nature run results (Part I): Earth’s radiation budget. Remote Sens.
**2016**, 8, 98. [Google Scholar] [CrossRef] - Gelaro, R.; Putman, W.M.; Pawson, S.; Draper, C.; Molod, A.; Norris, P.M.; Ott, L.; Prive, N.; Reale, O.; Achuthavarier, D.; et al. Evaluation of the 7-km GEOS-5 Nature Run; Technical Report Series on Global Modeling and Data Assimilation 36; NASA Global Modeling and Assimilation Office: Greenbelt, MD, USA, 2014.
- Bloom, S.; da Silva, A.; Dee, D.; Bosilovich, M.; Chern, J.D.; Pawson, S.; Schubert, S.; Sienkiewicz, M.; Stajner, I.; Tan, W.W.; et al. Documentation and Validation of the Goddard Earth Observing System (GEOS) Data Assimilation System—Version 4; Technical Report Series on Global Modeling and Data Assimilation 26; NASA Global Modeling and Assimilation Office: Greenbelt, MD, USA, 2005.
- Molod, A.; Takacs, L.; Suarez, M.; Bacmeister, J.; Song, I.S.; Eichmann, A. The GEOS-5 Atmospheric General Circulation Model: Mean Climate and Development from MERRA to Fortuna; Technical Report Series on Global Modeling and Data Assimilation 28; NASA Global Modeling and Assimilation Office: Greenbelt, MD, USA, 2012.
- Mann, M.E.; Lees, J.M. Robust estimation of background noise and signal detection in climatic time series. Clim. Chang.
**1996**, 33, 409–445. [Google Scholar] [CrossRef] - Thomson, D.J. The seasons, global temperature, and precession. Science
**1995**, 268, 59–68. [Google Scholar] [CrossRef] [PubMed] - Foster, J.; Richards, F.B. The gibbs phenomenon for piecewise-linear approximation. Am. Math. Mon.
**1991**, 98, 47–49. [Google Scholar] [CrossRef] - Boucher, O.; Randall, D.; Artaxo, P.; Bretherton, C.; Feingold, G.; Forster, P.; Kerminen, V.-M.; Kondo, Y.; Liao, H.; Lohmann, U.; et al. Clouds and aerosols. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013. [Google Scholar]
- Myhre, G.; Shindell, D.; Bréon, F.-M.; Collins, W.; Fuglestvedt, J.; Huang, J.; Koch, D.; Lamarque, J.-F.; Lee, D.; Mendoza, B.; et al. Anthropogenic and natural radiative forcing. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013. [Google Scholar]
- Wylie, D.; Jackson, D.L.; Menzel, W.P.; Bates, J.J. Trends in global cloud cover in two decades of HIRS observations. J. Clim.
**2005**, 18, 3021–3031. [Google Scholar] [CrossRef] - Frey, R.A.; Ackerman, S.A.; Liu, Y.; Strabala, K.I.; Zhang, H.; Key, J.R.; Wang, X. Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for collection 5. J. Atmos. Ocean. Technol.
**2008**, 25, 1057–1072. [Google Scholar] [CrossRef] - Yang, Y.; Di Girolamo, L. Impacts of 3-D radiative effects on satellite cloud detection and their consequences on cloud fraction and aerosol optical depth retrievals. J. Geophys. Res. Atmos.
**2008**, 113. [Google Scholar] [CrossRef]

**Figure 1.**Comparison of land and ocean cloud fraction for the Nature Run and operational versions of GEOS-5. The time series shows the month of September 2006.

**Figure 2.**Simulated Earth Polychromatic Imaging Camera (EPIC) view from the cloud field produced by the Nature Run at 1200 UTC on 25 September 2006.

**Figure 3.**The blue curves/points show $\overline{t}$ for the sunlit land (

**a**) daily; (

**b**) monthly; (

**c**) seasonal; and (

**d**) annual intervals. The red curves/points show $\overline{t}\pm {\sigma}_{r,n}$. Within each panel, the three sub-panels show, from top to bottom, 4-, 8-, and 12-h sampling frequency. Vertical scale is fixed within each set of three panels.

**Figure 7.**The mean of the standard deviations $\overline{{\sigma}_{r,n}}$ for (

**a**) land; (

**b**) ocean; and (

**c**) globally.

**Figure 8.**(

**a**) The time series of sunlit ocean cloud fraction for the two week period beginning 1 August 2006 at 0000 UTC—curves show different sampling frequency but with the same starting point; (

**b**) The mean for the year beginning 1 June 2006 at 0000 UTC for different sampling frequencies and all possible starting points.

**Figure 9.**Power spectrum of the Nature Run time series of mean (

**a**) land; and (

**b**) ocean cloud fraction for 1 June 2006 at 0000 UTC to 1 December 2006 at 0000 UTC.

**Figure 10.**The mean of the correlation coefficients $\overline{{R}_{n}}$ (solid curves) and the standard deviation of the correlation coefficients ${\sigma}_{R,n}$ (dashed curves) for (

**a**) land; (

**b**) ocean; and (

**c**) global regions. The axis on the left of the panels shows the mean values, and the right axis shows the standard deviation values.

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**MDPI and ACS Style**

Holdaway, D.; Yang, Y.
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*, 431.
https://doi.org/10.3390/rs8050431

**AMA Style**

Holdaway D, Yang Y.
Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part II): Cloud Coverage. *Remote Sensing*. 2016; 8(5):431.
https://doi.org/10.3390/rs8050431

**Chicago/Turabian Style**

Holdaway, Daniel, and Yuekui Yang.
2016. "Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part II): Cloud Coverage" *Remote Sensing* 8, no. 5: 431.
https://doi.org/10.3390/rs8050431