Search for: All records

Abstract. We trained two Random Forest (RF) machine learning models for cloud mask andcloud thermodynamic-phase detection using spectral observations from Visible InfraredImaging Radiometer Suite (VIIRS)on board Suomi National Polar-orbiting Partnership (SNPP). Observations from Cloud-Aerosol Lidarwith Orthogonal Polarization (CALIOP) were carefully selected toprovide reference labels. The two RF models were trained for all-day anddaytime-only conditions using a 4-year collocated VIIRS and CALIOP dataset from2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPPVIIRS training samples cover a broad-viewing zenith angle range, which is agreat benefit to overall model performance. The all-day model uses three VIIRSinfrared (IR) bands (8.6, 11, and 12 µm), and the daytime model uses fiveNear-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64, and 2.25 µm) together with the three IR bands to detect clear, liquid water, and icecloud pixels. Up to seven surface types, i.e., ocean water, forest, cropland,grassland, snow and ice, barren desert, and shrubland, were consideredseparately to enhance performance for both models. Detection of cloudypixels and thermodynamic phase with the two RF models was compared againstcollocated CALIOP products from 2017. It is shown that, when using a conservativescreening process that excludes the most challenging cloudy pixels forpassive remote sensing, the two RF models have high accuracy rates incomparison to the CALIOP reference for both cloud detection andthermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask andphase products are also evaluated, with results showing that the two RFmodels and the MODIS MYD06 optical property phase product are the top threealgorithms with respect to lidar observations during the daytime. During thenighttime, the RF all-day model works best for both cloud detection andphase, particularly for pixels over snow and ice surfaces. The present RFmodels can be extended to other similar passive instruments if trainingsamples can be collected from CALIOP or other lidars. However, the qualityof reference labels and potential sampling issues that may impact modelperformance would need further attention. 

Abstract. Many passive remote-sensing techniques have beendeveloped to retrieve cloud microphysical properties from satellite-basedsensors, with the most common approaches being the bispectral andpolarimetric techniques. These two vastly different retrieval techniqueshave been implemented for a variety of polar-orbiting and geostationarysatellite platforms, providing global climatological data sets. Priorinstrument comparison studies have shown that there are systematicdifferences between the droplet size retrieval products (effective radius)of bispectral (e.g., MODIS, Moderate Resolution Imaging Spectroradiometer)and polarimetric (e.g., POLDER, Polarization and Directionality of Earth'sReflectances) instruments. However, intercomparisons of airborne bispectraland polarimetric instruments have yielded results that do not appear to besystematically biased relative to one another. Diagnosing this discrepancyis complicated, because it is often difficult for instrument intercomparisonstudies to isolate differences between retrieval technique sensitivities andspecific instrumental differences such as calibration and atmosphericcorrection. In addition to these technical differences the polarimetricretrieval is also sensitive to the dispersion of the droplet sizedistribution (effective variance), which could influence the interpretationof the droplet size retrieval. To avoid these instrument-dependentcomplications, this study makes use of a cloud remote-sensing retrievalsimulator. Created by coupling a large-eddy simulation (LES) cloud modelwith a 1-D radiative transfer model, the simulator serves as a test bed forunderstanding differences between bispectral and polarimetric retrievals.With the help of this simulator we can not only compare the two techniquesto one another (retrieval intercomparison) but also validate retrievalsdirectly against the LES cloud properties. Using the satellite retrievalsimulator, we are able to verify that at high spatial resolution (50m) thebispectral and polarimetric retrievals are highly correlated with oneanother within expected observational uncertainties. The relatively smallsystematic biases at high spatial resolution can be attributed to differentsensitivity limitations of the two retrievals. In contrast, a systematicdifference between the two retrievals emerges at coarser resolution. Thisbias largely stems from differences related to sensitivity of the tworetrievals to unresolved inhomogeneities in effective variance and opticalthickness. The influence of coarse angular resolution is found to increaseuncertainty in the polarimetric retrieval but generally maintains aconstant mean value.