Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements

Mace, Gerald G.

doi:https://doi.org/10.5194/amt-17-3679-2024

Articles | Volume 17, issue 12

https://doi.org/10.5194/amt-17-3679-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-17-3679-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 12

Research article

|

19 Jun 2024

Research article |

| 19 Jun 2024

Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements

Gerald G. Mace

Data sets

RV Investigator BOM Atmospheric Data Overview (2016 onwards) Alain Protat et al. https://doi.org/10.25919/5f688fcc97166

MODIS atmosphere L2 cloud product (06_L2), Terra Steve Platnick et al. https://doi.org/10.5067/MODIS/MOD06_L2.006

MODIS atmosphere L2 cloud product (06_L2), Aqua Steve Platnick et al. https://doi.org/10.5067/MODIS/MYD06_L2.006

Cloud Condensation Nuclei Particle Counter (AOSCCN1COLAVG) Annette Koontz et al. https://doi.org/10.5439/1255094

MWR Retrievals (MWRRET1LILJCLOU) Damao Zhang https://doi.org/10.5439/1027369

Balloon-Borne Sounding System (SONDEWNPN) Evan Keeler et al. https://doi.org/10.5439/1595321

Microwave Radiometer (MWRLOS) Maria Cadeddu and Matt Tuftedal https://doi.org/10.5439/1999490

Marine W-Band (95 GHz) ARM Cloud Radar (MWACR) Iosif Lindenmaier et al. https://doi.org/10.5439/1973911

Micropulse Lidar (MPLPOLFS) Paytsar Muradyan et al. https://doi.org/10.5439/1320657

Marine Surface Meteorological Instrumentation (AADMET) Erol Cromwell and Mike Reynolds https://doi.org/10.5439/1593144

Navigational Location and Attitude (NAV) Scott Walton https://doi.org/10.5439/1974348

Camera That Monitors a Site Area (CAMSEASTATE), Martin Stuefer et al. https://doi.org/10.5439/1971078

Cloud mask from Micropulse Lidar (30SMPLCMASK1ZWANG) C. Sivaraman et al. https://doi.org/10.5439/1508389

NSF/NCAR GV HIAPER Raw 2D-S Imagery, Version 1.0 UCAR/NCAR - Earth Observing Laboratory https://doi.org/10.26023/M3KV-1SS0-DF10

SOCRATES: High Rate (HRT - 25 sps) Navigation, State Parameter, and Microphysics Flight-Level Data, Version 1.0 UCAR/NCAR - Earth Observing Laboratory https://doi.org/10.26023/K5VQ-K6KY-W610

Short summary

The number of cloud droplets per unit volume, N_d, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving N_d is very uncertain, although a synergistic algorithm seems to produce useful characterizations of N_d and effective particle size.