1. 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

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

3. 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

4. Preliminary Results
Ocean
Land

5. PSDs
1 December 2015

6. Preliminary Results

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

8. Nov 12,2015

9. December 1st, 2015

10. 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

11. 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

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

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

14. 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

15. 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

16. 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

19. 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

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

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

22. Matching Radar/In-Situ Data
1 December 2015