Abstract
Abstract. Supersaturation, crucial for cloud droplet activation and condensational growth, varies in clouds at different spatial and temporal scales. In-cloud supersaturation is poorly known and rarely measured directly. On the scale of a few tens of meters, supersaturation in clouds has been estimated from in situ measurements assuming quasi-steady-state supersaturation. Here, we provide a new method to estimate supersaturation using ground-based remote-sensing measurements, and results are compared with those estimated from aircraft in situ measurements in a marine stratocumulus cloud during the Aerosol and Cloud Experiment (ACE-ENA) field campaign. Our method agrees reasonably well with in situ estimations, and it has three advantages: (1) it does not rely on the quasi-steady-state assumption, which is questionable in clean or turbulent clouds, (2) it can provide a supersaturation profile, rather than just point values from in situ measurements, and (3) it enables building statistics of supersaturation in stratocumulus clouds for various meteorological conditions from multi-year ground-based measurements. The uncertainties, limitations, and possible applications of our method are discussed.
Highlights
Cloud forms under supersaturated conditions when the air contains more water vapor than it can retain
probability density functions (PDFs) are compared from the 105 km in-cloud flight leg with those retrieved at the flight level from 7 h of ground-based measurements, between 15:00 and 22:00 UTC
The fundamental idea is that the difference in LWC between two cloud layers is the result of condensation or evaporation of cloud droplets; the gradient of LWC together with w can be used to estimate the mean supersaturation between the two layers
Summary
Cloud forms under supersaturated conditions when the air contains more water vapor than it can retain. Where sqs is the quasi-steady-state supersaturation, A is a parameter that depends on temperature and pressure (see Eq A1 in Appendix), w is vertical air velocity, Nd is the cloud droplet number concentration, and r is the mean cloud droplet radius. This method relies on the quasi-steady-state assumption in which the source or sink of water vapor in an adiabatic air parcel due to vertical motion is roughly balanced with the source or sink of water vapor due to condensation or evaporation. The uncertainties, limitations, and applications of our method are discussed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
