Satellite

Cloud fraction

Operating system

Computer science

Cloud computing

Geology

Physics

Astronomy

Cloud height

Geography

Cloud cover

Environmental science

Remote sensing

Daytime

Lidar

Meteorology

Atmospheric sciences

Cloud top

Remote Sensing of Environment

Clouds play a significant role in the climate system, which affects the radiation balance and modulates the global hydrological cycle. However, the existing cloud property products have poor spatiotemporal continuity with only daytime cloud property retrieval results, which makes it challenging for us to carry out researches related to clouds at night. In this study, the effect of parallax error between the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Himawari-8 data is corrected based on parallax correction algorithm by referring to cloud parameters extracted from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) cloud property product. Due to the different sensitivity of different channels to clouds, this study treats the CALIOP data with cloud optical depth greater than 0.2 as clouds, otherwise as clear sky. Cloud property retrieval model based on the XGBoost machine learning (ML) algorithm is developed for the advanced Himawari imager (AHI) onboard the Himawari-8 satellite. The ML model can achieve unified cloud mask, cloud top temperature (CTT), and cloud top height (CTH) retrieval for both daytime and nighttime with high spatial (0.02°) and temporal (10 min) resolutions using the AHI thermal data. The retrieval results are extensively evaluated over study regions (80° E ~ 135° E, 18° N ~ 55° N) by comparisons with cloud property products of JAXA (Japan Aerospace Exploration Agency) AHI, MODIS, and CALIOP data. The ML algorithm has higher cloud detection accuracy with cloudy sky (clear sky) hit rate (CHR) and false alarm rate (FAR) of 90.79% (88.35%) and 5.74% (4.67%) compared with the JAXA AHI (CHR = 92.81% (60.64%) and FAR = 19.66% (3.61%)) and MODIS (CHR = 88.50% (72.32%) and FAR = 13.10% (6.06%)) cloud products, and the ML algorithm also performs well for thin clouds over bright surfaces. The CTH and CTT retrieved by the ML algorithm (RMSE = 1.83 km and 12.29 K) are in good agreement with the CALIOP cloud property product, and the root mean squared error (RMSE) of CTH and CTT is reduced by 33.70% (18.67%) and 35.28% (16.45%) on average, respectively, compared with the JAXA AHI (MODIS) cloud product with RMSE of 2.76 km (2.25 km) and 18.99 K (14.71 K). In addition, the ML algorithm can obtain higher cloud property retrieval accuracy at night compared with the results during the day. • Machine learning (ML) based retrieval model is developed for AHI cloud retrieval. • The ML model can achieve cloud mask, CTT, and CTH for both day and night time. • The ML cloud detection accuracy is larger than that of JAXA AHI (MODIS) product. • The CTH RMSE decreased by 33.70% (18.67%) compared to the JAXA AHI (MODIS) product. • The CTT RMSE decreased by 35.28% (16.45%) compared to the JAXA AHI (MODIS) product.

Machine learning-based retrieval of day and night cloud macrophysical parameters over East Asia using Himawari-8 data