# 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

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**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