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A Technique for Estimating Liquid Droplet Diameter and Liquid Water Content in Stratocumulus Clouds Using Radar and Lidar Measurements1. Introduction Microphysical parameterization, namely, the effective radius of droplets and liquid water content (LWC) are used in modeling stratocumulus clouds for quantifying cloud–climate feedback. Accurate representation of microphysical parameterization using only the airborne in situ probe observations is incomplete as the observations of a small concentration of large droplets that represent drizzle in drop size distribution (DSD) spectra are not always detected. The presence of a few
1. Introduction Microphysical parameterization, namely, the effective radius of droplets and liquid water content (LWC) are used in modeling stratocumulus clouds for quantifying cloud–climate feedback. Accurate representation of microphysical parameterization using only the airborne in situ probe observations is incomplete as the observations of a small concentration of large droplets that represent drizzle in drop size distribution (DSD) spectra are not always detected. The presence of a few
Merged Cloud and Precipitation Dataset from the HIAPER GV for the Cloud System Evolution in the Trades (CSET) Campaign-level sampling legs. As Doppler spectra are desirable for the ongoing research purposes, and there is considerable debate about the usefulness of the width of Doppler spectrum calculated from the pulse-pair technique for performing cloud and drizzle property retrievals (discussed later), Doppler spectra were computed by performing fast Fourier transforms (FFTs) of windowed versions of the aforementioned I and Q signal data streams. The moments of the Doppler spectra were computed from the Doppler spectra
-level sampling legs. As Doppler spectra are desirable for the ongoing research purposes, and there is considerable debate about the usefulness of the width of Doppler spectrum calculated from the pulse-pair technique for performing cloud and drizzle property retrievals (discussed later), Doppler spectra were computed by performing fast Fourier transforms (FFTs) of windowed versions of the aforementioned I and Q signal data streams. The moments of the Doppler spectra were computed from the Doppler spectra
; Bretherton et al. 2007 ). Precipitation can also hasten the stratocumulus-to-cumulus transition ( Yamaguchi et al. 2017 ; Abel et al. 2017 ). Both the cloud depth and the aerosol cloud condensation nuclei (CCN) concentrations can impact the initiation of precipitation. Thus, entrainment growth of the boundary layer depth is a factor in the development of drizzle. The subsequent removal of aerosols by the precipitation maintains an environment of enhanced precipitation susceptibility. This feedback
; Bretherton et al. 2007 ). Precipitation can also hasten the stratocumulus-to-cumulus transition ( Yamaguchi et al. 2017 ; Abel et al. 2017 ). Both the cloud depth and the aerosol cloud condensation nuclei (CCN) concentrations can impact the initiation of precipitation. Thus, entrainment growth of the boundary layer depth is a factor in the development of drizzle. The subsequent removal of aerosols by the precipitation maintains an environment of enhanced precipitation susceptibility. This feedback
( Schwartz et al. 2019 ) from the GV aircraft provide profiles of hydrometeor fraction, precipitation fraction (defined as Z > −10 dB Z , i.e., including drizzle) and conditional averages of radar reflectivity where precipitation is present. In this paper, a newly calibrated dataset ( V. Ghate 2020 , personal communication) is used, which is based on Ghate and Schwartz (2020) . The radar and lidar on board the GV switched from downward- to upward-pointing during the flight depending on the GV
( Schwartz et al. 2019 ) from the GV aircraft provide profiles of hydrometeor fraction, precipitation fraction (defined as Z > −10 dB Z , i.e., including drizzle) and conditional averages of radar reflectivity where precipitation is present. In this paper, a newly calibrated dataset ( V. Ghate 2020 , personal communication) is used, which is based on Ghate and Schwartz (2020) . The radar and lidar on board the GV switched from downward- to upward-pointing during the flight depending on the GV
the Atlantic Stratocumulus Transition Experiment (ASTEX) in 1992 provide one of the most widely used datasets for understanding the SCT and are perhaps the first measurements of cloud microphysical properties in the SCT. The two Lagrangian transition experiments carried out in ASTEX showed that drizzle occurred with sufficient frequency and rate to be important in MBL energy and moisture budgets ( Bretherton and Pincus 1995 ; Bretherton et al. 1995 ). Surface-based radar measurements in ASTEX
the Atlantic Stratocumulus Transition Experiment (ASTEX) in 1992 provide one of the most widely used datasets for understanding the SCT and are perhaps the first measurements of cloud microphysical properties in the SCT. The two Lagrangian transition experiments carried out in ASTEX showed that drizzle occurred with sufficient frequency and rate to be important in MBL energy and moisture budgets ( Bretherton and Pincus 1995 ; Bretherton et al. 1995 ). Surface-based radar measurements in ASTEX
Clarifying Remotely Retrieved Precipitation of Shallow Marine Clouds from the NSF/NCAR Gulfstream Vimplemented into Eq. (2) and the iteration continues until a D o and μ g are established. The γ ′ and lidar ratio S parameter appropriate to the retrieved D o are thereafter extracted from a lookup table constructed a priori using Stamnes et al. (1988) . Once D o , μ g , γ ′, and S are established, a normalized and total drizzle number concentration ( N w and N t ) can be calculated, along with a D eff and the rain rate ( R ) (see the data availability statement below for a link to a
implemented into Eq. (2) and the iteration continues until a D o and μ g are established. The γ ′ and lidar ratio S parameter appropriate to the retrieved D o are thereafter extracted from a lookup table constructed a priori using Stamnes et al. (1988) . Once D o , μ g , γ ′, and S are established, a normalized and total drizzle number concentration ( N w and N t ) can be calculated, along with a D eff and the rain rate ( R ) (see the data availability statement below for a link to a
lie between them, with a similar east–west gradient. We conclude that CSET provides a climatologically representative sample of data across the northeast Pacific Sc–Cu transition. b. Liquid water Figure 10a shows lon′– z composites of area-averaged “cloud water” content in droplets of less than 25 μ m radius, the size range measured by the CDP and consistent with the 20–30- μ m droplet radius threshold for rapid further collision–coalescence growth into a drizzle or rain drop ( Rogers and Yau
lie between them, with a similar east–west gradient. We conclude that CSET provides a climatologically representative sample of data across the northeast Pacific Sc–Cu transition. b. Liquid water Figure 10a shows lon′– z composites of area-averaged “cloud water” content in droplets of less than 25 μ m radius, the size range measured by the CDP and consistent with the 20–30- μ m droplet radius threshold for rapid further collision–coalescence growth into a drizzle or rain drop ( Rogers and Yau
upward (but not simultaneously). The radar sensitivity is −39.6 dB Z at a range of 1 km. The 532-nm wavelength HSRL can also be oriented upward or downward, with a 4° zenith angle offset to minimize specular reflection. The HSRL beam is severely attenuated in cloud and drizzle columns, making it well-suited to sense the near-lidar cloud boundary. This is particularly useful for cloud base, for which precipitation will mask the cloud base to the radar. The HCR/HSRL datasets have been placed on a
upward (but not simultaneously). The radar sensitivity is −39.6 dB Z at a range of 1 km. The 532-nm wavelength HSRL can also be oriented upward or downward, with a 4° zenith angle offset to minimize specular reflection. The HSRL beam is severely attenuated in cloud and drizzle columns, making it well-suited to sense the near-lidar cloud boundary. This is particularly useful for cloud base, for which precipitation will mask the cloud base to the radar. The HCR/HSRL datasets have been placed on a
Ultraclean Layers and Optically Thin Clouds in the Stratocumulus-to-Cumulus Transition. Part II: Depletion of Cloud Droplets and Cloud Condensation Nuclei through Collision–Coalescenceapplied in the model can be found in the appendix of Korolev and Mazin (2003) . There is no artificial separation between unactivated aerosols, activated cloud droplets, and drizzle. The nucleation of unactivated aerosol (i.e., haze particles) is explicitly calculated by solving the droplet growth equations with κ –Kohler parameterization for aerosol hygroscopicity developed by Petters and Kreidenweis (2007 ; the details regarding the size distribution and κ value of aerosol are given later
applied in the model can be found in the appendix of Korolev and Mazin (2003) . There is no artificial separation between unactivated aerosols, activated cloud droplets, and drizzle. The nucleation of unactivated aerosol (i.e., haze particles) is explicitly calculated by solving the droplet growth equations with κ –Kohler parameterization for aerosol hygroscopicity developed by Petters and Kreidenweis (2007 ; the details regarding the size distribution and κ value of aerosol are given later
. Pincus , 1995 : Cloudiness and marine boundary layer dynamics in the ASTEX Lagrangian experiments. Part I: Synoptic setting and vertical structure . J. Atmos. Sci. , 52 , 2707 – 2723 , https://doi.org/10.1175/1520-0469(1995)052<2707:CAMBLD>2.0.CO;2 . 10.1175/1520-0469(1995)052<2707:CAMBLD>2.0.CO;2 Bretherton , C. S. , P. H. Austin , and S. T. Siems , 1995a : Cloudiness and marine boundary layer dynamics in the ASTEX Lagrangian experiments. Part II: Cloudiness, drizzle, surface fluxes
. Pincus , 1995 : Cloudiness and marine boundary layer dynamics in the ASTEX Lagrangian experiments. Part I: Synoptic setting and vertical structure . J. Atmos. Sci. , 52 , 2707 – 2723 , https://doi.org/10.1175/1520-0469(1995)052<2707:CAMBLD>2.0.CO;2 . 10.1175/1520-0469(1995)052<2707:CAMBLD>2.0.CO;2 Bretherton , C. S. , P. H. Austin , and S. T. Siems , 1995a : Cloudiness and marine boundary layer dynamics in the ASTEX Lagrangian experiments. Part II: Cloudiness, drizzle, surface fluxes
