Explicit Prediction of Supercooled Liquid Water Application to Aircraft and Ground Icing Problems Gregory Thompson, Roy Rasmussen, Trude Eidhammer WRF User’s Workshop 27 Jun 2012, Boulder, CO

Motivation Aircraft Icing Ground Icing

Coming soon … aerosols & droplet number activated fraction depends on: 1)aerosol concentration 2)updraft velocity 3)temperature 4)hygroscopicity (kappa) 5)aerosol mean radius implementation details: 1)nearest neighbor T, , r = )interpolated N a, w 3)grid-scale vertical velocity only 4)negative w uses 1 cm s -1 5)added 2 components to WRF scalar array (N c, N ccn ) potential improvements: 1)vertical velocity variance 2)variable kappa and mean size depend on ocean vs. land 3)connect to radiation 4)separate sulfates, sea salts, other aerosol species 5)couple with WRF-CHEM

Cloud droplets (size) Continental more drops smaller mean size Maritime fewer drops larger mean size Liquid water content = 0.25 g m –3 (1) (2) (3) (4) Affects “autoconversion ” 3 characteristic diameters considered when converting cloud water to rain Affects accretion due to changes in MVD Affects droplet freezing larger drops more likely to freeze than small drops

Aerosol testing Typical vs. polluted conditions Typical marine aerosols result in low cloud droplet concentrations (about drops per cc) Polluted aerosols result in high cloud droplet concentrations (about drops per cc)

Resulting LWC & MVD Continental more drops much smaller mean size more liquid water content delayed drizzle/rain onset alters upper cloud Maritime fewer drops much largermean size less liquid water content more drizzle or light rain

CO Headwaters (WRF simulations) References:Ikeda et al, 2010 Rasmussen et al, 2011 Liu et al, 2011 Advantages:  8 years  same code  high-resolution (4km)  project leveraging  excellent QPF (5-10%)  prototype HRRR model Disadvantages:  low air traffic  infrequent FZDZ/FZRA  “reanalysis” system Purdue-Lin WSM5/WSM6/WDM6,Goddard Thompson, Morrison Observations Data and analysis by Changhai Liu & Kyoko Ikeda

WRF’s LWC v. MVD Plot by Nick Ledru

CO Headwaters: icing pilot report comparison 7,430 with yes icing (excluding Jun, Jul, Aug & 36km WRF perimeter) 26,269 with clear-sky assumed “No” icing, 65 with explicit “No” icing ( ) 1222 MCI UA /OV MCI /TM 1222 /FLUNKN /TP MD82 /SK OVCUNKN-TOP030 /IC MOD RIME /RM /TA UNKN

Results: Can the model capture icing? Probability of Detection (PoD) Reference: Wolff and McDonough, 2010

Ice accretion application where: M = mass   = collision efficiency = velocity (89.4m/s = 200mph) A = cross-section area D = diameter (3-inch cylinder) and: K is “Stokes number” Re is “Reynolds number”  is “Langmuir’s parameter”  a is dynamic viscosity  w is density of water  a is density of air References: Finstad et al, 1988; Makkonen, 2000

Final application of predicted ice load on TV tower Provided by Bjorn Egil Nygaard

Summary and future plans  Microphysics scheme produces physically reasonable characteristic water contents and droplet size.  Initial tests with variable aerosols produce well correlated cloud droplet concentrations and resulting precip stays within bounds of constant/imposed droplet number tests.  Explicit forecasts of supercooled water capture significant portion of pilot-reported icing.  Continue testing and advancing the “aerosol-aware” scheme.  Directly utilize explicit SLW forecasts into future FAA icing end- user applications (eventual replacement of CIP/FIP).  Incorporate Thompson et al (2008) scheme into H-WRF and NMM-B

Thank you This research is in response to requirements and funding of the Federal Aviation Administration. The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA. especially Dave Gill, Jimy Dudhia and the entire WRF model development community.