Presentation is loading. Please wait.

Presentation is loading. Please wait.

High-resolution numerical simulations of lake-effect snowstorms: Investigating physics sensitivity, multi-scale predictability, and model performance.

Published byGeorgina Freeman Modified over 6 years ago

Similar presentations

Presentation on theme: "High-resolution numerical simulations of lake-effect snowstorms: Investigating physics sensitivity, multi-scale predictability, and model performance."— Presentation transcript:

1 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.)

2 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

3 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 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)

4 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 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)

5 10-12 December 2013 Case Study (OWLeS IOP2b)
Probably use internet to show loops if possible here

6 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

7 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

8 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)

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

10 Control Simulation Results

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

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

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

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

15 Microphysics Ensemble

16 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)

17 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

18 Precipitation Time Series

19 Precipitation Time Series

20 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

21 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

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

23 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??

24 Multi-scale Uncertainty due to Initial/Boundary Conditions

25 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., Dec. 2013) Land/lake temperature contrast, shoreline resolution 10-12 Dec lake-breeze front schematic, Steenburgh and Campbell 2017

26 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

27 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

28 Extra Slides

29 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)

30 Modified Lake Surface Temperatures

31 WRF Control Simulation Physics
WRF Control: Experimental HRRR, Sept 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 MYNN (option 5) for this case study

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

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

Download ppt "High-resolution numerical simulations of lake-effect snowstorms: Investigating physics sensitivity, multi-scale predictability, and model performance."

Similar presentations

Some questions on convection that could be addressed through another UK field program centered at Chilbolton Dan Kirshbaum 1.

Some questions on convection that could be addressed through another UK field program centered at Chilbolton Dan Kirshbaum 1.
Meteorologisches Institut der Universität München

Meteorologisches Institut der Universität München
UU Unitah Basin WRF Model Configuration (same as DEQ) See Alcott and Steenburgh 2013 for further details on most aspects of this numerical configuration:

UU Unitah Basin WRF Model Configuration (same as DEQ) See Alcott and Steenburgh 2013 for further details on most aspects of this numerical configuration:
Toward Improving Representation of Model Microphysics Errors in a Convection-Allowing Ensemble: Evaluation and Diagnosis of mixed- Microphysics and Perturbed.

Toward Improving Representation of Model Microphysics Errors in a Convection-Allowing Ensemble: Evaluation and Diagnosis of mixed- Microphysics and Perturbed.
Advancing Numerical Weather Prediction of Great Salt Lake-Effect Precipitation John D McMillen.

Advancing Numerical Weather Prediction of Great Salt Lake-Effect Precipitation John D McMillen.
The Persistence and Dissipation of Lake Michigan-Crossing Mesoscale Convective Systems Nicholas D. Metz* and Lance F. Bosart # * Department of Geoscience,

The Persistence and Dissipation of Lake Michigan-Crossing Mesoscale Convective Systems Nicholas D. Metz* and Lance F. Bosart # * Department of Geoscience,
Effects of the Great Salt Lake’s Temperature and Size on the Regional Precipitation in the WRF Model Joe Grim Jason Knievel National Center for Atmospheric.

Effects of the Great Salt Lake’s Temperature and Size on the Regional Precipitation in the WRF Model Joe Grim Jason Knievel National Center for Atmospheric.
WRF Physics Options Jimy Dudhia. diff_opt=1 2 nd order diffusion on model levels Constant coefficients (khdif and kvdif) km_opt ignored.

WRF Physics Options Jimy Dudhia. diff_opt=1 2 nd order diffusion on model levels Constant coefficients (khdif and kvdif) km_opt ignored.
Observed and Simulated Multi-bands in Northeast U.S. Winter Storms S ARA A. G ANETIS 1, B RIAN A. C OLLE 1, S ANDRA E. Y UTER 2, AND N ICOLE C ORBIN 2.

Observed and Simulated Multi-bands in Northeast U.S. Winter Storms S ARA A. G ANETIS 1, B RIAN A. C OLLE 1, S ANDRA E. Y UTER 2, AND N ICOLE C ORBIN 2.
Rapid Refresh and RTMA. RUC: AKA-Rapid Refresh A major issue is how to assimilate and use the rapidly increasing array of off-time or continuous observations.

Rapid Refresh and RTMA. RUC: AKA-Rapid Refresh A major issue is how to assimilate and use the rapidly increasing array of off-time or continuous observations.
Issues in Very High Resolution Numerical Weather Prediction Over Complex Terrain in Juneau, Alaska Don Morton 1,2, Delia Arnold 3,4, Irene Schicker 3,

Issues in Very High Resolution Numerical Weather Prediction Over Complex Terrain in Juneau, Alaska Don Morton 1,2, Delia Arnold 3,4, Irene Schicker 3,
Convection-permitting forecasts initialized with continuously-cycling limited-area 3DVAR, EnKF and “hybrid” data assimilation systems Craig Schwartz and.

Convection-permitting forecasts initialized with continuously-cycling limited-area 3DVAR, EnKF and “hybrid” data assimilation systems Craig Schwartz and.
What we have learned about Orographic Precipitation Mechanisms from MAP and IMPROVE-2: MODELING Socorro Medina, Robert Houze, Brad Smull University of.

What we have learned about Orographic Precipitation Mechanisms from MAP and IMPROVE-2: MODELING Socorro Medina, Robert Houze, Brad Smull University of.
Warm-Season Lake-/Sea-Breeze Severe Weather in the Northeast Patrick H. Wilson, Lance F. Bosart, and Daniel Keyser Department of Earth and Atmospheric.

Warm-Season Lake-/Sea-Breeze Severe Weather in the Northeast Patrick H. Wilson, Lance F. Bosart, and Daniel Keyser Department of Earth and Atmospheric.
The Effect of the Terrain on Monsoon Convection in the Himalayan Region Socorro Medina 1, Robert Houze 1, Anil Kumar 2,3 and Dev Niyogi 3 Conference on.

The Effect of the Terrain on Monsoon Convection in the Himalayan Region Socorro Medina 1, Robert Houze 1, Anil Kumar 2,3 and Dev Niyogi 3 Conference on.
Warm-Season Lake-/Sea-Breeze Severe Weather in the Northeast Patrick H. Wilson, Lance F. Bosart, and Daniel Keyser Department of Earth and Atmospheric.

Warm-Season Lake-/Sea-Breeze Severe Weather in the Northeast Patrick H. Wilson, Lance F. Bosart, and Daniel Keyser Department of Earth and Atmospheric.
Rapid Update Cycle Model William Sachman and Steven Earle ESC452 - Spring 2006.

Rapid Update Cycle Model William Sachman and Steven Earle ESC452 - Spring 2006.
Roll or Arcus Cloud Supercell Thunderstorms.

Roll or Arcus Cloud Supercell Thunderstorms.
Transitioning unique NASA data and research technologies to the NWS 1 Evaluation of WRF Using High-Resolution Soil Initial Conditions from the NASA Land.

Transitioning unique NASA data and research technologies to the NWS 1 Evaluation of WRF Using High-Resolution Soil Initial Conditions from the NASA Land.
1 st UNSTABLE Science Workshop 18-19 April 2007 Science Question 3: Science Question 3: Numerical Weather Prediction Aspects of Forecasting Alberta Thunderstorms.

1 st UNSTABLE Science Workshop April 2007 Science Question 3: Science Question 3: Numerical Weather Prediction Aspects of Forecasting Alberta Thunderstorms.

Similar presentations

Ads by Google