High-resolution numerical simulations of lake-effect snowstorms: Investigating physics sensitivity, multi-scale predictability, and model performance UAlbany: Massey Bartolini, Justin Minder (lead faculty), Ryan Torn, Dan Keyser NWS Focal Points: David Zaff (BUF), Joseph Villani (ALY) NOAA-ESRL: Stan Benjamin (GSD, HRRR dev.), Joseph Olson (GSD, HRRR dev.)

Overall Research Goals Project: High-resolution numerical simulations of lake-effect snowstorms Physics sensitivity Model performance Multi-scale predictability Physics: test microphysics, PBL, and surface layer schemes to find out which best predicts lake-effect snow and/or isolate a component (or components) of the parameterization(s) that can be improved to get better results Multi-scale predictability: understand the sensitivity of the simulations to synoptic-scale (e.g. shortwave trough influences) and meso/microscale (e.g., lake-breeze fronts) features – get more robust results by looking at multiple cases and analyzing an ensemble of simulations for each case Model performance: using extensive observational datasets gathered during OWLeS to verify and understand simulation results

10-12 December 2013 Case Study (OWLeS IOP2b) Major lake-effect snow event for the typical snow belts downwind of Lake Erie and Lake Ontario Snow accumulations as much as 101.5 cm on the Tug Hill Plateau 500 hPa winds (barbs), vorticity (fill), and heights (contours) Surface / 850 hPa winds (black/blue barbs), surface convergence (contours), and surface–850 hPa temperature difference (fill)

10-12 December 2013 Case Study (OWLeS IOP2b) Major lake-effect snow event for the typical snow belts downwind of Lake Erie and Lake Ontario Snow accumulations as much as 101.5 cm on the Tug Hill Plateau 500 hPa winds (barbs), vorticity (fill), and heights (contours) Surface / 850 hPa winds (black/blue barbs), surface convergence (contours), and surface–850 hPa temperature difference (fill)

10-12 December 2013 Case Study (OWLeS IOP2b) Probably use internet to show loops if possible here

10-12 December 2013 Case Study (OWLeS IOP2b) KTYX radar-estimated liquid precipitation equivalent (LPE) from 0000 UTC 11 December 2013 to 0000 UTC 12 December 2013, Campbell et al. 2016 Probably use internet to show loops if possible here

10-12 December 2013 Case Study (OWLeS IOP2b) KTYX radar-estimated liquid precipitation equivalent (LPE) from 0000 UTC 11 December 2013 to 0000 UTC 12 December 2013, Campbell et al. 2016 Probably use internet to show loops if possible here Time series of automated weighing precipitation gauge LPE measurements

WRF Configuration: 10–12 December 2013 Case Study Triple-nested two-way WRF simulations (12-, 4-, and 1.33-km horizontal grid spacing) Initialized at 1200 UTC 10 December 2013, run for 42 hours WRF v3.7.1, experimental HRRR version from Joseph Olson (ESRL)

WRF Configuration: 10–12 December 2013 Case Study Model physics: Experimental HRRR (Sept. 2016 version) Initial/boundary conditions: RAP, augmented with NAM soil data and Great Lakes lake-surface temperature and ice cover analyses

Control Simulation Results

Control Simulation Results: 0300 UTC, 11 December 2013 WRF Simulated Reflectivity Correct position and morphology Observed Reflectivity

Control Simulation Results: 1200 UTC, 11 December 2013 WRF Simulated Reflectivity Correct position, wrong morphology Observed Reflectivity

Control Simulation Results: 2000 UTC, 11 December 2013 WRF Simulated Reflectivity Correct morphology, wrong position Observed Reflectivity

Control Simulation Results: Accumulated Precipitation (snow + graupel) Observed LPE (radar-derived), Campbell et al. 2016 WRF Control LPE (Exp. HRRR)

Microphysics Ensemble

Accumulated Precipitation (snow + graupel) Observed LPE (radar-derived), Campbell et al. 2016 WRF Control LPE, MP= Thompson aerosol-aware (THOM AERO) WRF LPE, MP=Goddard (GDRD) WRF LPE, MP=Morrison (MORR) WRF LPE, MP=NSSL 2-moment WRF LPE, MP= Milbrandt-Yau (MYAU)

Accumulated Precipitation (snow + graupel) Observed LPE (radar-derived), Campbell et al. 2016 WRF Control LPE, MP= Thompson aerosol-aware (THOM AERO) WRF LPE, MP= Thompson, no aerosols (THOM ORIG) WRF LPE, MP=WDM6 WRF LPE, MP=Stony Brook Univ. – Lin (SBUL) WRF LPE, MP=WSM6

Precipitation Time Series

Precipitation Time Series

Orographic Enhancement, Downstream Hydrometeor Advection More Tug Hill Precipitation (e.g., WDM6) Lake Ontario Tug Hill Plateau Adirondack Mountains Less Tug Hill Precipitation (e.g., GDRD) More posing a hypothesis here, haven’t done enough analysis yet to confirm or deny this as Reeves and Dawson (2009) did Lake Ontario Tug Hill Plateau Adirondack Mountains

Lake Ontario Snow Band Precipitation Statistics Showing area of integration for next two slides For every single hour, I’m going to sum the total amount of precipitation that falls within this box

Lake Ontario Snow Band Precipitation Statistics “Hotter” schemes (WSM, WDM) aren’t producing much larger amounts of total precipitation…

Lake Ontario Snow Band Precipitation Statistics …but they do have much larger maximum precip intensities which fluctuate (cue Reeves and Dawson hydrometeor in-cloud residence time discussion), could relate this to orographic enhancement??

Multi-scale Uncertainty due to Initial/Boundary Conditions

Hypotheses for IC/BC Sensitivity Slight variations in the position and timing of shortwave trough features, modulating LeS band position Resolution of lake-breeze front location and strength, perhaps a critical mechanism for focusing axis of strongest convection in some LeS cases (e.g., 10-12 Dec. 2013) Land/lake temperature contrast, shoreline resolution 10-12 Dec. 2013 lake-breeze front schematic, Steenburgh and Campbell 2017

Future Work Project: High-resolution numerical simulations of lake-effect snowstorms Physics sensitivity Continue analysis of microphysics ensemble experiments, test PBL/surface physics Model performance Use additional OWLeS datasets to verify simulation results Multi-scale predictability Work with NCAR Ensemble simulations, June 2017 Physics: test microphysics, PBL, and surface layer schemes to find out which best predicts lake-effect snow and/or isolate a component (or components) of the parameterization(s) that can be improved to get better results Multi-scale predictability: understand the sensitivity of the simulations to synoptic-scale (e.g. shortwave trough influences) and meso/microscale (e.g., lake-breeze fronts) features – get more robust results by looking at multiple cases and analyzing an ensemble of simulations for each case Model performance: using extensive observational datasets gathered during OWLeS to verify and understand simulation results

NCAR Ensemble Simulations Working with Craig Schwartz (NCAR) to analyze retroactive NCAR Ensemble simulations for the entire OWLeS field campaign (December 2013 – January 2014) Hope to understand range of synoptic and mesoscale lake-effect snowstorm uncertainty for several case studies

Extra Slides

WRF Control Simulation Initial/boundary conditions: RAP, augmented with NAM soil data and Great Lakes lake-surface temperature and ice cover analyses NCEI NOMADS server archive is missing soil data (temperature and moisture), so I had to fill these fields from the corresponding NAM files Modified Great Lakes lake-surface temperature (“SKINTEMP”) and fractional ice cover (“SEAICE”) variables in met_em files Used Great Lakes Coastal Forecasting System analysis dataset (as in Campbell et al. 2016)

Modified Lake Surface Temperatures

WRF Control Simulation Physics WRF Control: Experimental HRRR, Sept. 2016 version Copied all namelist.input physics and dynamics settings from Joe Olson’s code repository Original WRF v3.7.1 MYNN (option 60) performs slightly better than Joe’s MYNN modifications as of Sept. 2016, for this case study Namelist Parameter Option mp_physics 28 (Thompson aerosol-aware) ra_lw_physics 4 (RRTMG) ra_sw_physics sf_sfclay_physics 60 (MYNN v3.6) sf_surface_physics 3 (RUC LSM) bl_pbl_physics cu_physics 1 (Kain-Fritsch, only for 12-km domain) Original MYNN (option 60) performs better than Sept. 2016 MYNN (option 5) for this case study

Synoptic-scale Uncertainty: 1800 UTC, 11 December 2013 RAP: 500 hPa Vorticity NARR: 500 hPa Vorticity

Mesoscale Uncertainty, 10-12 December 2013 Case Study Steenburgh and Campbell 2017 (MWR, Early Online Release)