4.2 Cloud Thickness and Precipitation Rate Relationship in the Arctic

Monday, 9 July 2018: 3:45 PM
Regency D (Hyatt Regency Vancouver)
Kyle E. Fitch, University of Utah, Salt Lake City, UT; and T. J. Garrett

Much uncertainty remains regarding the relationship between cloud properties and snow properties, particularly in the Arctic where few direct measurements of frozen hydrometeors are available. Such a lack of observations makes it difficult to constrain microphysical parameters used in weather and climate model predictions of precipitation amount, location and duration. Many previous studies have derived relationships between precipitation rate, cloud droplet number concentration, and either liquid water path or cloud thickness (Pawlowska and Brenguier, 2003; Comstock et al., 2004; VanZante et al., 2005). Kostinski (2008) used dimensional and physical reasoning to derive a particularly elegant power law relationship between the drizzle rate and geometric thickness of mid-latitude, marine stratocumulus clouds. The formula employs volume water fraction and hydrometeor fall speed, and is therefore easily extended for other cloud and precipitation types. The model was tested for Arctic stratus near Barrow, Alaska, with a smaller power law exponent resulting from fall speed regimes characterized by slower-falling ice crystals generating wake turbulence (Wang and Garrett, 2013). While fall speeds were not measured directly in this prior study, the Multi-Angle Snowflake Camera (MASC), located at the Department of Energy’s Atmospheric Radiation Measurement Mobile Facility at Oliktok Point, Alaska, has measured the fall speed of millions of particles since November 2014. Co-located with numerous advanced instruments used for cloud boundary retrieval, the location and instrumentation are ideal for more rigorous testing of the power law’s parameters. Early results from a subset of over 60,000 particles indicate that a smaller exponent is indeed representative of Arctic precipitation rates. Further analyses will help to constrain an appropriate parameterization scheme to improve modeling of precipitation rates in the Arctic.
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