Abstract
The cloud drop effective radius, Re, of the drop size distribution derived from passive satellite sensors is a key variable used in climate research. Validation of these satellite products often took place in stratiform cloud conditions that favored the assumption of cloud horizontal homogeneity used by the retrieval techniques. However, many studies point to concerns of significant biases in retrieved Re arising from cloud heterogeneity, for example, in cumulus cloud fields. Here, we examine data collected during the 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex), which, in part, targeted the objective of providing the first detailed evaluation of Re retrieved across multiple platforms and techniques in a cumulus and congestus cloud region. Our evaluation consists of cross comparisons of Re between the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, the Research Scanning Polarimeter (RSP) onboard the NASA P-3 aircraft, and in situ measurements from both the P-3 and Learjet aircrafts that are all taken in close space-time proximity of the same cloud fields. A particular advantage of our approach lies in RSP’s capability to retrieve Re using a bi-spectral MODIS approach and a polarimetric approach, which allows for evaluating bi-spectral and polarimetric Re retrievals from an airborne perspective using the same samples. Averaged over all P-3 flight segments examined here for warm clouds, the RSP-polarimetric, in situ, and the bias-adjusted MODIS method of Fu et al. (2019) show comparable median (mean and standard deviations) of Re samples of 9.6 (10.2 ± 4.0) μm, 11.0 (13.6 ± 11.3) μm, and 10.4 (10.8 ± 3.8) μm, respectively. These values are far lower than 15.1 (16.2 ± 5.5) μm and 17.2 (17.7 ± 5.7) μm from the bi-spectral retrievals of RSP and MODIS, respectively. Similar results are observed when Re is segregated by cloud top height and in detailed case studies. The clouds sampled during CAMP2Ex consist of mostly small (mean transect length ~1.4 km) and low clouds (mean cloud top height ~ 1 km), which are much smaller than the trade wind cumuli sampled in past field campaigns such as Rain in Shallow Cumulus over the Ocean (RICO) and the Indian Ocean Experiment (INDOEX). RSP bi-spectral Re shows larger relative values compared to RSP polarimetric Re for smaller and optically thinner clouds. Drizzle, cloud top bumpiness and solar-zenith angle, however, are not closely correlated with the overestimate of bi-spectral Re. We show that for shallow, non-drizzling clouds that dominate the liquid cloud cover for the CAMP2Ex region and period, 3D radiative pathways appear to be the leading cause for the large positive biases in bi-spectral retrievals. Because this bias varies with the underlying structure of the cloud field, caution continues to be warranted in studies that use bi-spectral Re retrievals in cumulus cloud fields.
Highlights
Our evaluation consists of cross comparisons of Re between the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, the Research Scanning Polarimeter (RSP) onboard the NASA P-3 aircraft, and in situ measurements 30 from both the P-3 and Learjet aircrafts that are all taken in close space-time proximity of the same cloud fields
The RSP and HSRL-2 cloud top heights (CTH) distributions are in excellent agreement, both showing that ~60% of the cloud elements sampled have mean cloud tops < 1 km
875 The evaluation consists of comparison between airborne RSP bi-spectral and polarimetric retrievals of Re, and crosscomparison between airborne remote sensing, in situ and satellite retrieved Re collected during the CAMP2Ex field campaign
Summary
Satellite retrieved cloud properties have been critical in advancing the understanding of the role of clouds in the 50 Earth’s climate system. Efforts to improve the accuracy of our satellite record of cloud properties continue to be called for (Ohring et al 2005; NASEM 2018). Satellite retrieved Re, owing to its wide spatial coverage and continuous monitoring record, has been applied for a wide range of studies such as estimating aerosol-cloud interactions (e.g., Menon et al 55 2008; Ross et al 2018; IPCC 2013) and evaluating model parameterizations (e.g., Ban-Weiss et al 2014, Suzuki et al 2013). Given its legacy and likely continued use in the future, it is essential to assess the error characteristics of the bi-spectral approach to advance the understanding of climate science, as it applies 65 to cloud feedbacks (e.g., Tan et al 2019) and aerosol-cloud interactions (e.g., Menon et al 2008; Gryspeerdt et al 2019)
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