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

Comparison of Global and Seasonal Characteristics of Cloud Phase and Horizontal Ice Plates Derived from CALIPSO with MODIS and ECMWF

“…7): (i) the enhancement of ice clouds could be seen in the tropics due to the Hadley cell at about 88; (ii) the low ice cloud coverage was identified in the subtropical high pressure belt at about 2208 and 238; and (iii) the enhancement of both ice and water clouds was indicated at higher latitudes (about 608) in the known storm-track regions of both hemispheres. Where MODIS water coverage was lower than CTYPE-lidar, it was likely due to the passive MODIS sensors having difficulty observing clouds, especially at high latitudes over bright surfaces such as snow (Wang et al 2013), as can be seen in Fig. The MODIS ice cloud coverage was much lower than CTYPE-lidar, and this reflected the difference in the cloud detection sensitivity between CALIOP and MODIS.…”

A cloud detection scheme for the Chinese Carbon Dioxide Observation Satellite (TANSAT)

“…Single band reflectance tests: Single band reflectance tests for discriminating clouds from clear-sky areas have been well studied (Ackerman et al, 1998;Hutchison et al, 2005;Frey et al, 2008;He, 2011;Nakajima et al, 2011;Wang et al, 2012). In the non-absorption visible and near-infrared bands, the reflectance of clouds typically shows a higher value than that of clear-sky surfaces.…”

“…which has 36 channels that cover wavelengths from the visible to thermal infrared regions, allows for a more precise cloud-screening result. Thus, MODIS data are commonly used to evaluate cloud-screening results of other sensors Wang et al, 2012). To investigate the cloud-screening ability of GOSAT/TANSO-CAI, an inter-satellite comparison with Aqua/MODIS, which uses the same algorithm (CLAUDIA), has been performed by Ishida et al (2011).…”

Answers to Referee 1 comments

“…To eliminate residual clouds from the retrieved AOD fields, a cloud post-processing method has been developed to recognise and discard undetected clouds in AOD retrieved from the AATSR radiances with the ATSR dualview (ADV) algorithm for aerosol retrieval over land and the ATSR single-view (ASV) aerosol retrieval algorithm for application over ocean (Kolmonen et al, 2016). The ATSR has been designed to measure sea surface temperature and, therefore, the cloud detection scheme designed for use with this instrument has been optimised for application over open ocean and does not perform well over land (Závody et al, 2000;Birks, 2007a). Therefore, an improved cloud detection scheme has been developed for application to ADV/ASV (Roblez González, 2003;Kolmonen et al, 2016), but the retrieved AOD is still affected by residual cloud contamination.…”