Intercomparison between CMIP5 model and MODIS satellite‐retrieved data of aerosol optical depth, cloud fraction, and cloud‐aerosol interactions
Sign Up to like & getrecommendations! Published in 2017 at "Earth and Space Science"
DOI: 10.1002/2017ea000288
Abstract: Aerosols are a critical component of the Earth's atmosphere and can affect the climate of the Earth through their interactions with solar radiation and clouds. Cloud fraction (CF) and aerosol optical depth at 550 nm… read more here.
Keywords: aerosol interactions; cloud; model; cloud aerosol ... See more keywords
Long-term cloud fraction biases in CMIP5 GCMs over India during monsoon season
Sign Up to like & getrecommendations! Published in 2019 at "Theoretical and Applied Climatology"
DOI: 10.1007/s00704-018-02760-1
Abstract: Using 24 years of cloud fraction (CF) data from the International Satellite Cloud Climatology Project (ISCCP) observations and their corresponding simulators in general circulation models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5), we… read more here.
Keywords: radiative forcings; climatology; cloud; monsoon season ... See more keywords
Diurnal (24 h) cycle and seasonal variability of cloud fraction retrieved from a Whole Sky Imager over a complex terrain in the Western Ghats and comparison with MODIS
Sign Up to like & getrecommendations! Published in 2021 at "Atmospheric Research"
DOI: 10.1016/j.atmosres.2020.105180
Abstract: Abstract Continuous monitoring of cloud fraction (CF) using a Whole Sky Imager (WSI) equipped with an infrared (IR) camera, play a crucial role in validating satellite data and numerical models. WSI is operated at a… read more here.
Keywords: whole sky; variability; monsoon; day ... See more keywords
Improving Low‐Cloud Fraction Prediction Through Machine Learning
Sign Up to like & getrecommendations! Published in 2024 at "Geophysical Research Letters"
DOI: 10.1029/2024gl109735
Abstract: In this study, we evaluated the performance of machine learning (ML) models (XGBoost) in predicting low‐cloud fraction (LCF), compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each… read more here.
Keywords: low cloud; improving low; cloud fraction; machine learning ... See more keywords
Analysis of the cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning
Sign Up to like & getrecommendations! Published in 2024 at "Atmospheric Chemistry and Physics"
DOI: 10.5194/acp-24-13025-2024
Abstract: Abstract. Aerosol–cloud interactions (ACI) have a pronounced influence on the Earth's radiation budget but continue to pose one of the most substantial uncertainties in the climate system. Marine boundary-layer clouds (MBLCs) are particularly important since… read more here.
Keywords: machine learning; clf; cloud fraction; clf sensitivity ... See more keywords
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Sign Up to like & getrecommendations! Published in 2024 at "Atmospheric Measurement Techniques"
DOI: 10.5194/amt-17-6769-2024
Abstract: Abstract. We introduce a novel method to retrieve the cloud fraction and the optical thickness of liquid clouds over a water surface based on polarimetry. The approach is well suited for satellite observations providing multi-angle… read more here.
Keywords: fraction; polarization; cloud fraction; optical thickness ... See more keywords