Aircraft PSD Studies Using UND Citation Data Greg McFarquhar, Randy Chase, Paloma Borque, Saisai Ding, Joe Finlon, Steve Nesbitt, Mike Poellot, Andrew Heymsfield and Aaron Bansemer

Outline Differences between ocean & land measurements Approaches for deriving mass from particle imagery Characterizing Particle Size Distributions (PSDs) Future Efforts

Hypotheses PSD will differ when separated by meteorological regime (stratiform, frontal, convective etc.) and by geographic location (ocean vs topographic) 2. In situ PSD parameters will show that current assumptions used by the GPM precipitation algorithms are incorrect for mid-latitude orographic precipitation

Preliminary Results Ocean Land

PSDs 1 December 2015

Preliminary Results

Assessing performance of Nevzorov probe during OLYMPEX IWC from PSDs computed using Heymsfield et al. (2004) appropriate for aggregates, m = .0061D2.05

Nov 12,2015

December 1st, 2015

Problems with our habit classification Classification designed for colder temperatures does not work as well at warmer temperatures of some of OLYMPEX observations Particle boundaries identification Currently working with Alexis Berne to develop better classification scheme for T > -10˚C

New Approach to Defining m-D Relation Derive most likely (a,b) by minimizing c2 difference between two observed moments and those derived from in-situ PSDs (i.e., bulk mass measured by Nevzorov probe and radar reflectivity measured by sum of Ku/Ka band radar) radar matched to locatoin of aircraft using Airborne Weather Observation Toolkit radar matching algorithm To give uncertainty, all c2 within Dc2 of minimum c2 are equally realizable solutions, Dc2 determined by variability and statistical uncertainty

New Approach to Defining m-D Relation Example of application of approach applied to data collected during MC3E

Future Work: Coincidence Points DC-8 Citation ER-2 Find Coincident observations: Airborne Radar to Optical Array Probes

Fits to PSDs GPM algorithms assume PSD follows 𝑵 𝑫 = 𝑵 𝟎 𝑫𝝁𝒆𝒙𝒑(−𝝀𝑫) 𝑫: Diameter 𝑵 𝟎 : Intercept 𝝁: Shape 𝝀: Slope IGF technique gives fit parameters (McFarquhar et al. 2015) DPR algorithm represents PSD in terms of 2 measurable quantities, normalized intercept (Nw) and mass-weighted mean diameter (Dm), and μ Retrieval algorithms sensitive to m

Background Ocean Topo 𝑵 𝟎 𝟏 𝟎 𝟒.𝟒𝟗 𝟏 𝟎 𝟏𝟎.𝟖 𝝁 𝟏.𝟎𝟗 −𝟏.𝟒 𝝀 𝟐.𝟕𝟓∗𝟏 𝟎 −𝟑 𝟓.𝟎𝟓∗𝟏 𝟎 −𝟒

Calculating m-Dm constraint Williams et al. (2014) method adds constraint on m using Dm and sm: m = Dm2/sm2 – b - 1 This allows better constraints on the next generation of retrieval algorithms to better perform across a wide variety of precipitation types and environments

Future Efforts Determine how PSD parameters and bulk properties vary across greater variety of meteorological conditions Develop new habit recognition scheme for particles closer to melting layer Apply new m-D approach to derive a/b parameters for OLYMPEX, determine dependence on environmental conditions Examine in-situ data coincidence with ER-2/DC-8 radar

Background Jackson et al. 2015 showed conceptually how these parameters change Adapted from Figure 1. Jackson et al. 2015

Overview of OLYMPEX Flights November – December 2015: 20 flights UND Citation

Matching Radar/In-Situ Data 1 December 2015