Constraints on a priori assumptions and microphysical properties in precipitation from in situ measurements in GPM-GV field campaigns: regime dependence and impact on retrievals

Tuesday, 16 December 2014: 2:25 PM
Stephen W Nesbitt1, Daniel S Harnos1, Kirstin Gleicher1, Kimberly A Reed1, George Duffy1, Greg M McFarquhar1, Simone Tanelli2, Christopher R Williams3, Benjamin T Johnson4, Walter Arthur Petersen5, Ali Tokay6, Ana Paula Barros7 and Anna M Wilson7, (1)University of Illinois at Urbana Champaign, Atmospheric Sciences, Urbana, IL, United States, (2)Jet Propulsion Laboratory, Pasadena, CA, United States, (3)University of Colorado Boulder, Boulder, CO, United States, (4)University of Maryland Baltimore County / JCET, Bowie, MD, United States, (5)NASA GSFC/WFF Code 610.W, Wallops Island, VA, United States, (6)NASA, Greenbelt, MD, United States, (7)Duke University, Civil and Environmental Engineering, Durham, NC, United States
Active and passive physical precipitation retrieval algorithms are tasked to retrieve precipitation across a wide variety of precipitation types and environments, however, there is presently little knowledge as to how characteristics of precipitation, some of which are retrieved and some assumed a priori, vary across the diverse precipitation profiles on earth, particular in the vertical. GPM-Ground Validation (GV) has collected a broad range of microphysical observations both on the ground and through airborne campaigns. For retrieval algorithm a priori assumptions, which must reliably represent the natural variability of cloud properties, statistical characterization of in situ measurements of parameters that algorithms retrieve or assume are known to vary in meteorological regimes and must be characterized as well as their uncertainties reported in order to aid in algorithm accuracy and uncertainty characterization.

In this study, we will use data collected from in situ aircraft and ground based sensors, as well as remote sensing retrievals from GPM field campaigns across meteorological regimes to characterize the statistical relationships among a priori assumptions as a function of height as well as meteorological regime. Parameters that will be investigated include the variability of parameters such as cloud liquid water, effective mass-diameter relationships, as well as parameterized hydrometeor size distribution characteristics. Joint probability distributions of these parameters will be examined across campaigns as a function of height to understand the variability in these parameters for constraining algorithm assumptions. Variations in these parameters will be propagated through a dual-wavelength precipitation retrieval algorithm to assess their impacts on retrievals in warm and cold season precipitation. Results will consider how these parameters to what degree these parameters should be allowed to vary in global retrieval algorithms.