Preprints
https://doi.org/10.5194/acp-2022-73
https://doi.org/10.5194/acp-2022-73
 
09 Feb 2022
09 Feb 2022
Status: a revised version of this preprint is currently under review for the journal ACP.

An evaluation of liquid cloud droplet effective radius derived from MODIS, airborne remote sensing and in situ measurements from CAMP2Ex

Dongwei Fu1, Larry Di Girolamo1, Robert M. Rauber1, Greg M. McFarquhar2,3, Stephen W. Nesbitt1, Jesse Loveridge1, Yulan Hong1, Bastiaan van Diedenhoven4, Brian Cairns5, Mikhail D. Alexandrov5, Paul Lawson6, Sarah Woods6, Simone Tanelli7, Ousmane O. Sy7, Sebastian Schmidt8,9, Chris A. Hostetler10, and Amy Jo Scarino11 Dongwei Fu et al.
  • 1Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
  • 2Cooperative Institute for Severe and High Impact Weather Research and Operations, The University of Oklahoma, Norman, Oklahoma, USA
  • 3School of Meteorology, The University of Oklahoma, Norman, Oklahoma, USA
  • 4SRON Netherlands Institute for Space Research, Leiden, The Netherlands
  • 5NASA Goddard Institute for Space Studies, New York City, New York, USA
  • 6Stratton Park Engineering Company, Inc., Boulder, Colorado, USA
  • 7Jet Propulsion Laboratory, Pasadena, California, USA
  • 8Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
  • 9Laboratory for Atmospheric and Space Physics, University of Colorado Boulder, Boulder, Colorado, USA
  • 10NASA Langley Research Center, Hampton, Virginia, USA
  • 11Science Systems and Applications, Inc, Hampton, Virginia. USA

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.

Dongwei Fu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-73', Anonymous Referee #1, 07 Mar 2022
  • RC2: 'Comment on acp-2022-73', Anonymous Referee #2, 24 Mar 2022
  • AC1: 'Response to referee comments on acp-2022-73', Dongwei Fu, 04 May 2022

Dongwei Fu et al.

Data sets

MODIS Level 1B Calibrated radiances at 500m MODIS Characterization Support Team https://doi.org/10.5067/MODIS/MOD02HKM.061

MODIS bias-adjusted Re correction factors Dongwei Fu https://doi.org/10.17632/j4r72zxc6g.2

AHI official cloud property product Husi Letu; Kun Yang; Takashi Y. Nakajima; Hiroshi Ishimoto; Takashi M. Nagao; Jérôme Riedi; Anthony J. Baran; Run Ma; Tianxing Wang; Huazhe Shang; Pradeep Khatri; Liangfu Chen; Chunxiang Shi; Jiancheng Shi https://www.eorc.jaxa.jp/ptree/index.html

MODIS Level 1B Calibrated radiances at 250m MODIS Characterization Support Team https://doi.org/10.5067/MODIS/MOD02QKM.061

MODIS Collection 6.1 cloud products Steven Platnick; Kerry G. Meyer; Michael D. King; Galina Wind; Nandana Amarasinghe; Benjamin Marchant; G. Thomas Arnold; Zhibo Zhang; Paul A. Hubanks; Robert E. Holz; Ping Yang; William L. Ridgway; Jérôme Riedi https://doi.org/10.5067/MODIS/MOD06_L2.061

CAMP2Ex datasets CAMP2Ex Science Team https://www-air.larc.nasa.gov/cgi-bin/ArcView/camp2ex

Dongwei Fu et al.

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Short summary
Satellite-retrieved cloud microphysics is widely used in climate research because of their central role in water and energy cycles. Here we provide the first detailed investigation on retrieved cloud drop sizes from in situ and various satellite and airborne remote sensing techniques applied to real cumulus cloud fields. We conclude that the most widely used passive remote sensing technique used in climate research produces high biases of 6 to 8 µm (60–80 %) caused by 3D radiative effects.
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