A method for retrieving the cumulus entrainment rate from ground based observations.

摘要

The entrainment of drier environmental air into cumulus clouds affects the impact that these clouds have on the environment by modifying their radiative, microphysical, and thermodynamic characteristics. Entrainment is a difficult parameter to observe directly, and heretofore has been obtained from occasional aircraft penetrations.;To increase the number of cumulus entrainment rate observations under a wide range of atmospheric conditions, an algorithm for retrieving the cumulus entrainment rate from ground-based remote sensing observations has been developed. This algorithm, called the Entrainment Rate In Cumulus Algorithm (ERICA), uses the suite of instruments at the Southern Great Plains (SGP) site of the United States Department of Energy's Atmospheric Radiation Measurement (ARM) Climate Research Facility as inputs into a Gauss-Newton optimal estimation scheme. The forward model in this algorithm is the Explicit Mixing Parcel Model (EMPM), a cloud parcel model that treats entrainment as a series of discrete mixing events. Output from EMPM is used to calculate quantities that can be observed from the surface, including effective radius and liquid water path. The entrainment rate in EMPM is adjusted iteratively until the modeled output converges to the observations. Sensitivity testing and error and information content analysis show that ERICA is a robust method for obtaining accurate estimates of entrainment rate without the drawbacks of aircraft observations.;Results from a three-month trial of ERICA show significant variability of the entrainment rate of clouds in a single day and from one day to the next. The mean value from this analysis corresponds well with prior knowledge of the entrainment rate.