Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 3.668
IF3.668
IF 5-year value: 3.707
IF 5-year
3.707
CiteScore value: 6.3
CiteScore
6.3
SNIP value: 1.383
SNIP1.383
IPP value: 3.75
IPP3.75
SJR value: 1.525
SJR1.525
Scimago H <br class='widget-line-break'>index value: 77
Scimago H
index
77
h5-index value: 49
h5-index49
Volume 6, issue 3
Atmos. Meas. Tech., 6, 549–563, 2013
https://doi.org/10.5194/amt-6-549-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 6, 549–563, 2013
https://doi.org/10.5194/amt-6-549-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Mar 2013

Research article | 01 Mar 2013

Improved cloud mask algorithm for FY-3A/VIRR data over the northwest region of China

X. Wang, W. Li, Y. Zhu, and B. Zhao X. Wang et al.
  • Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China

Abstract. The existence of various land surfaces always leads to more difficulties in cloud detection based on satellite observations, especially over bright surfaces such as snow and deserts. To improve the cloud mask result over complex terrain, an unbiased, daytime cloud detection algorithm for the Visible and InfRared Radiometer (VIRR) on board the Chinese FengYun-3A polar-orbiting meteorological satellite is applied over the northwest region of China. The algorithm refers to the concept of the clear confidence level from Moderate Resolution Imaging Spectroradiometer (MODIS) and the unbiased structure of the CLoud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA). Six main channels of VIRR centered at the wavelengths of 0.455, 0.63, 0.865, 1.595, 1.36, and 10.8 μm are designed to estimate the degree of a pixel's cloud contamination judged by the clear confidence level. Based on the statistical data set during four months (January, April, July, and October) in 2010, seasonal thresholds are applied to improve the accuracy of the cloud detection results. Flags depicting snow and water are also generated by the specific threshold tests for special surfaces. As shown in image inspections, the cloud detection results over snow and deserts, adopting the proposed scheme, exhibit better correlations with true-color images than the VIRR official cloud mask results do. The performance of the proposed algorithm has been evaluated in detail for four seasons in 2011, using cloud mask products from MODIS and the ground-based observations. The evaluation is based on, overall, 47 scenes collocated with MODIS and 96 individual matchups between VIRR and the ground-based observations from two weather stations located in the research region. The quantitative validations suggest that the estimations of clear-sky regions have been greatly improved by the proposed algorithm, while a poor identification of the cirrus clouds occurs over deserts.

Publications Copernicus
Download
Citation