Cloud Detection Method Based on Spectral Area Ratios in MODIS Data

2015 ◽  
Vol 41 (6) ◽  
pp. 561-576
Author(s):  
Feng Guo ◽  
Xiaohua Shen ◽  
Lejun Zou ◽  
Yupeng Ren ◽  
Yi Qin ◽  
...  
Keyword(s):  
Detection Method ◽  
Cloud Detection ◽  
Modis Data ◽  
Spectral Area

scholarly journals Seasonal cycle of cloud cover analyzed using Meteosat images

1998 ◽  
Vol 16 (3) ◽  
pp. 331-341 ◽  
Author(s):  
J. Massons ◽  
D. Domingo ◽  
J. Lorente
Keyword(s):  
Spatial Resolution ◽  
Seasonal Cycle ◽  
Cloud Cover ◽  
High Spatial Resolution ◽  
Detection Method ◽  
Cloud Amount ◽  
Cloud Detection ◽  
Total Cloud Amount ◽  
Cloud Parameters ◽  
Cloud Properties

Abstract. A cloud-detection method was used to retrieve cloudy pixels from Meteosat images. High spatial resolution (one pixel), monthly averaged cloud-cover distribution was obtained for a 1-year period. The seasonal cycle of cloud amount was analyzed. Cloud parameters obtained include the total cloud amount and the percentage of occurrence of clouds at three altitudes. Hourly variations of cloud cover are also analyzed. Cloud properties determined are coherent with those obtained in previous studies.Key words. Cloud cover · Meteosat

scholarly journals An Integrated Convective Cloud Detection Method Using FY-2 VISSR Data

Atmosphere ◽  
2017 ◽  
Vol 8 (12) ◽  
pp. 42 ◽  
Author(s):  
Kuai Liang ◽  
Hanqing Shi ◽  
Pinglv Yang ◽  
Xiaoran Zhao
Keyword(s):  
Detection Method ◽  
Convective Cloud ◽  
Cloud Detection

An automated cloud detection method for daily NOAA-14 AVHRR data for Texas, USA

2002 ◽  
Vol 23 (15) ◽  
pp. 2939-2950 ◽  
Author(s):  
P. Y. Chen ◽  
R. Srinivasan ◽  
G. Fedosejevs ◽  
B. Narasimhan
Keyword(s):  
Detection Method ◽  
Cloud Detection ◽  
Avhrr Data

Characterization of Cirrus Clouds in the Mid-Latitude Tropopause Region

2021 ◽  
Author(s):  
Irene Bartolome Garcia ◽  
Reinhold Spang ◽  
Jörn Ungermann ◽  
Sabine Griessbach ◽  
Michael Höpfner ◽  
...  
Keyword(s):  
Detection Method ◽  
Cirrus Clouds ◽  
Radiation Budget ◽  
Climate Projections ◽  
Cloud Detection ◽  
Reanalysis Dataset ◽  
Height Determination ◽  
Tropopause Region ◽  
Research Aircraft

<p>Cirrus clouds contribute to the general radiation budget of the Earth, playing an important role in climate projections. Of special interest are optically thin cirrus clouds close to the tropopause due to the fact that they are difficult to capture and thus their impact is not yet well understood. This study presents a characterization of cirrus clouds observed by the limb sounder GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) aboard the German research aircraft HALO during the WISE (Wave-driven ISentropic Exchange) campaign in September/October 2017. This campaign took place in Shannon, Ireland (52.70°N, 8.86°W).  We developed an optimized cloud detection method and derived macro-physical characteristics of the detected cirrus clouds: cloud top height, cloud bottom height, vertical extent and cloud top position with respect to the tropopause. The fraction of cirrus clouds detected above the tropopause (> 0 km) is in the order of 13% to 27%, depending on the detection method and the definition of the tropopause. In general, good agreement with the clouds predicted by the ERA5 reanalysis dataset is obtained. However, cloud occurrence is ≈50% higher in the observations for the region close to and above the tropopause. Cloud bottom heights are also detected above the tropopause. Considering the uncertainties for the tropopause height, cloud top height and cloud bottom height determination we could not find unambiguous evidence for the formation of cirrus layers above the tropopause. In addition, for a better understanding of the tropopause cirrus properties and life conditions, two cirrus cases observed during two scientific flights were selected from  the observations and compared with cirrus simulations performed with the 3D Lagrangian microphysical model  CLaMS-Ice, which is based on the two-moment bulk  cirrus model by Spichtinger and Gierens (2009) (doi: 10.5194/acp-9-685-2009). The model is fed by backward trajectories computed from high resolution ERA5 data (hourly, spatial grid 30 km). This contribution summarizes and extends on work described by Bartolome Garcia et al. (2020) (doi:10.5194/amt-2020-394).</p>

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