A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations
Abstract. We trained two Random Forest (RF) machine learning models for cloud mask and
cloud thermodynamic-phase detection using spectral observations from Visible Infrared
Imaging Radiometer Suite (VIIRS)
on board Suomi National Polar-orbiting Partnership (SNPP). Observations from Cloud-Aerosol Lidar
with Orthogonal Polarization (CALIOP) were carefully selected to
provide reference labels. The two RF models were trained for all-day and
daytime-only conditions using a 4-year collocated VIIRS and CALIOP dataset from
2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP
VIIRS training samples cover a broad-viewing zenith angle range, which is a
great benefit to overall model performance. The all-day model uses three VIIRS
infrared (IR) bands (8.6, 11, and 12  µ m), and the daytime model uses five
Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64, and 2.25  µ m) together with the three IR bands to detect clear, liquid water, and ice
cloud pixels. Up to seven surface types, i.e., ocean water, forest, cropland,
grassland, snow and ice, barren desert, and shrubland, were considered
separately to enhance performance for both models. Detection of cloudy
pixels and thermodynamic phase with the two RF models was compared against
collocated CALIOP products from 2017. It is shown that, when using a conservative
screening process that excludes the most challenging cloudy pixels for
passive remote sensing, the two RF models have high accuracy rates in
comparison to the CALIOP reference for both cloud detection and
thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and
phase products are also evaluated, with results showing that the two RF
models and the MODIS MYD06 optical property phase product are the top three
algorithms with respect to lidar observations during the daytime. During the
nighttime, the RF all-day model works best for both cloud detection and
phase, particularly for pixels over snow and ice surfaces. The present RF
models can be extended to other similar passive instruments if training
samples can be collected from CALIOP or other lidars. However, the quality
of reference labels and potential sampling issues that may impact model
performance would need further attention.