CDL: A Cloud Detection Algorithm Over Land for MWHS-2 Based on the Gradient Boosting Decision Tree

Abstract

This article presents a standalone cloud detection algorithm over the land (CDL) for microwave humidity sounder-2 (MWHS-2), which is characterized by the first operational satellite sensor measuring 118.75 GHz. The CDL is based on the advanced machine learning algorithm gradient boosting decision tree, which achieves the state-of-the-art performance on tabular data with high accuracy, fast training speed, great generalization ability, and weight factor ranking of predictors (or features). Given that the new-generation weather radar of China (CINRAD) provides improved cloud information with extensive temporal-spatial coverage, the observations from CINRAD are used to train the algorithm in this study. There are four groups of radiometric information employed to evaluate the CDL: all frequency ranges from MWHS-2 (all-algorithm), the humidity channels near 183.31 GHz (hum-algorithm), the temperature channels near 118.75 GHz (tem-algorithm), and the window channels at 89 and 150 GHz (win-algorithm). It is revealed that the tem-algorithm (around 118.75 GHz) has a superior performance for CDL along with the optimal values of most evaluation metrics. Although the all-algorithm uses all available frequencies, it shows inferior ability for CDL. Followed are the win-algorithm and hum-algorithm, and the win-algorithm performs better. The analysis also indicates that the latitude, zenith angle, and the azimuth are the top-ranking features for all four algorithms. The presented algorithm CDL can be applied in the quality control processes of assimilating microwave radiances or in the retrieval of atmospheric and surface parameters for cloud filtering.

Keywords

• Cloud detection
• ground-based radar
• machine learning (ML)
• microwave humidity sounder-2 (MWHS-2)