Chao Liu

A Physics‐Guided Feature Selection Machine Learning Approach for Cloud Probability Retrieval and Detection From TEMPO Imagery

Accurate cloud detection is critical for reliable retrievals of aerosols and trace gases from remote‐sensing measurements. TEMPO provides unprecedented hourly observations over North America, but radiometric calibration biases, thousands of spectral channels, and limited spectral coverage make cloud detection challengi...

Fast All‐Sky Hyperspectral Infrared Radiative Transfer With Physically Recursive Cloud Treatment

Clouds strongly modify thermal infrared radiances through absorption, emission, and multiple scattering, yet rigorous line‐by‐line (LBL) gas absorption coupled with multi‐stream scattering remains too expensive for large ensembles of all‐sky hyperspectral simulations. We present a fast monochromatic radiative transfer...

Resolving the Physical Ambiguity of Passive Infrared Cloud Optical Thickness Retrievals With Microwave Observations

Satellite thermal infrared (TIR) observations, combined with machine‐learning methods, enable nighttime retrievals of cloud optical thickness (COT), but their cloud‐top‐dominated radiances provide ambiguous information for optically thick clouds. Passive microwave (MW) observations provide complementary sensitivity to...

Thirteen Winters (2012–2025) of Penetration-Aware Arctic Sea-Ice Emissivity and Emission Temperature from AMSR2