REGIONAL-SCALE ENSEMBLE FORECASTS OF THE 7 FEBRUARY 2007 LAKE EFFECT SNOW EVENT Justin Arnott and Michael Evans NOAA/NWS Binghamton, NY Richard Grumm NOAA/NWS.

Slides:



Advertisements
Similar presentations
New Remote Sensing Technologies NOAA’s Cooperative Institute for Meteorological Satellite Studies (CIMSS),
Advertisements

Ensembling Mesoscale Model Data National Weather Service, Cleveland, OH Christopher Mello.
Tropical Cyclone-frontal interactions Lee and Eloise Richard H. Grumm National Weather Service State College PA
“Where America’s Climate, Weather and Ocean Services Begin” NCEP CONDUIT UPDATE Brent A Gordon NCEP Central Operations January 31, 2006.
Life on the edge Patterns and Probabilities of heavy rainfall Richard H. Grumm National Weather Service Office State College PA
An Assessment of CMAQ with TEOM Measurements over the Eastern US Michael Ku, Chris Hogrefe, Kevin Civerolo, and Gopal Sistla PM Model Performance Workshop,
Correlations Between Observed Snowfall and NAM Forecast Parameters : Part 2 – Thermodynamic Considerations Michael L. Jurewicz, Sr. NOAA/NWS Binghamton,
Enhanced seasonal forecast skill following SSWs DynVar/SNAP Workshop, Reading, UK, April 2013 Michael Sigmond (CCCma) John Scinocca, Slava Kharin.
Analysis of Precipitation Distributions Associated with Two Cool-Season Cutoff Cyclones Melissa Payer, Lance F. Bosart, Daniel Keyser Department of Atmospheric.
The Inland Extent of Lake Effect Snow (LES) Bands Joseph P. Villani NOAA/NWS Albany, NY Michael L. Jurewicz, Sr. NOAA/NWS Binghamton, NY Jason Krekeler.
Forecasting the Inland Extent of Lake-Effect Snow (LES) Bands: Application and Verification for Winter Joseph P. Villani NOAA/NWS Albany, NY.
Anticipating Mesoscale Band Formation in Winter Storms David Novak, Jeff Waldstreicher NWS Eastern Region, Scientific Services Division, Bohemia, NY Lance.
Lessons in Predictability: Part 2 The March 2009 “Megastorm” Michael J. Bodner, NCEP/HPC Camp Springs, MD Richard H. Grumm, NWS WFO State College, PA Neil.
An Investigation of Cool Season Extratropical Cyclone Forecast Errors Within Operational Models Brian A. Colle 1 and Michael Charles 1,2 1 School of Marine.
Correlations between observed snowfall and NAM forecast parameters, Part I – Dynamical Parameters Mike Evans NOAA/NWS Binghamton, NY November 1, 2006 Northeast.
The Collaborative Effort Between Stony Brook University and the National Weather Service Part 3 – Integration of Mesoscale Models in Operational Weather.
The Use of Ensemble and Anomaly Data during the May 2006 New England Record Rain Event Neil A. Stuart Richard Grumm Walter Drag NOAA/NWS Albany,
Use of the Nondivergent Wind for Diagnosing Banded Precipitation Systems Thomas J. Galarneau, Jr., and Daniel Keyser Department of Earth and Atmospheric.
The 10th annual Northeast Regional Operational Workshop, Albany, NY Northeast Regional Ensemble Simulations of the 10 February 2008 Lake Effect Snow Event:
Univ of AZ WRF Model Verification. Method NCEP Stage IV data used for precipitation verification – Stage IV is composite of rain fall observations and.
Development of an eastern Great Lakes sub-regional WRF ensemble for lake effect snow prediction.
FORECASTING EASTERN US WINTER STORMS Are We Getting Better and Why? Jeff S. Waldstreicher NOAA/NWS Eastern Region Scientific Services Division – Bohemia,
CSTAR Update: New Tools for More Efficient Use of Ensembles in Operations Brian A. Colle, Minghua Zheng, and Edmund K.M. Chang, School of Marine and Atmospheric.
The 10th annual Northeast Regional Operational Workshop, Albany, NY Verification of SREF Aviation Forecasts at Binghamton, NY Justin Arnott NOAA / NWS.
Ensemble Post-Processing and it’s Potential Benefits for the Operational Forecaster Michael Erickson and Brian A. Colle School of Marine and Atmospheric.
Anticipating Structure in Lake-Effect Snow Events (Updated Results) Michael L. Jurewicz, Sr. NOAA/NWS, Binghamton, NY Justin Arnott NOAA/NWS, Gaylord,
Patterns of Historic River Flood Events in the Mid-Atlantic Region Richard H. Grumm NOAA/NWS Weather Forecast Office, State College, Pennsylvania and Charles.
Improving Medium-Range Ensemble-Based QPF over the Western United States Trevor Alcott and Jon Rutz NOAA/NWS WR-STID Jim Steenburgh University of Utah.
Climatology and Predictability of Cool-Season High Wind Events in the New York City Metropolitan and Surrounding Area Michael Layer School of Marine and.
Verification of the Cooperative Institute for Precipitation Systems‘ Analog Guidance Probabilistic Products Chad M. Gravelle and Dr. Charles E. Graves.
Verification Summit AMB verification: rapid feedback to guide model development decisions Patrick Hofmann, Bill Moninger, Steve Weygandt, Curtis Alexander,
Towards an object-oriented assessment of high resolution precipitation forecasts Janice L. Bytheway CIRA Council and Fellows Meeting May 6, 2015.
SWFDP-Eastern Africa November 2011 NOAA/NCEP African Desk Product Surfing Presented by Hamza Kabelwa Prepared by Richard H. Grumm Contributions by Vadlamani.
The Inland Extent of Lake- Effect Snow (LES) Bands Joe Villani NOAA/NWS Albany, NY Michael L. Jurewicz, Sr. NOAA/NWS Binghamton, NY Jason Krekeler State.
Ensemble Forecasting and You The very basics Richard H. Grumm National Weather Service State College PA
1 Climate Test Bed Seminar Series 24 June 2009 Bias Correction & Forecast Skill of NCEP GFS Ensemble Week 1 & Week 2 Precipitation & Soil Moisture Forecasts.
An Experiment to Evaluate the Use of Quantitative Precipitation Forecasts from Numerical Guidance by Operational Forecasters Joshua M. Boustead and Daniel.
Isentropic Analysis of January Snowstorm Across Eastern Virginia and Lower Maryland Tim Gingrich and Brian Hurley NOAA/NWS Wakefield VA Isentropic.
1 Results from Winter Storm Reconnaissance Program 2008 Yucheng SongIMSG/EMC/NCEP Zoltan TothEMC/NCEP/NWS Sharan MajumdarUniv. of Miami Mark ShirleyNCO/NCEP/NWS.
The Similar Soundings Technique For Incorporating Pattern Recognition Into The Forecast Process at WFO BGM Mike Evans Ron Murphy.
Mike Evans NWS Binghamton, NY. Outline The checklist Example – April 28, 2011 Verification Summary / Conclusion.
Using Ensemble Probability Forecasts And High Resolution Models To Identify Severe Weather Threats Josh Korotky NOAA/NWS, Pittsburgh, PA and Richard H.
Use of Mesoscale Ensemble Weather Predictions to Improve Short-Term Precipitation and Hydrological Forecasts Michael Erickson 1, Brian A. Colle 1, Jeffrey.
Lake Effect Snow Forecast Tools at NWS Gaylord, MI Part 1: The Similar Sounding Approach Justin Arnott Science and Operations Officer NWS Gaylord, MI.
Ensemble variability in rainfall forecasts of Hurricane Irene (2011) Molly Smith, Ryan Torn, Kristen Corbosiero, and Philip Pegion NWS Focal Points: Steve.
Printed by The Mechanisms and Local Effects of Heavy Snow in Interior Valleys of Northwest Californi a Matthew Kidwell, Senior Forecaster.
Object-oriented verification of WRF forecasts from 2005 SPC/NSSL Spring Program Mike Baldwin Purdue University.
Predictability of High Impact Weather during the Cool Season over the Eastern U.S: CSTAR Operational Aspects Matthew Sardi and Jeffrey Tongue NOAA/NWS,
Typhoon Forecasting and QPF Technique Development in CWB Kuo-Chen Lu Central Weather Bureau.
The Record South Carolina Rainfall Event of 3-5 October 2015: NCEP Forecast Suite Success story John LaCorte Richard H. Grumm and Charles Ross National.
Do the NAM and GFS have displacement biases in their MCS forecasts? Charles Yost Russ Schumacher Department of Atmospheric Sciences Texas A&M University.
Kelley Murphy Earth & Atmospheric Sciences Department State University of New York at OneontaPhoto of snow crystals collected by Univ. of Utah during the.
Northeast Regional Operational Workshop Annual Meeting University of Albany Tuesday, November 5, 2002.
Short-Wave Troughs in the Great Lakes Region and their Impacts on Lake-Effect Snow Bands Zachary S. Bruick 1, Nicholas D. Metz 2, and Emily W. Ott 2 1.
Exploring Multi-Model Ensemble Performance in Extratropical Cyclones over Eastern North America and the Western Atlantic Ocean Nathan Korfe and Brian A.
Fly - Fight - Win 2 d Weather Group Mr. Evan Kuchera HQ AFWA 2 WXG/WEA Template: 28 Feb 06 Approved for Public Release - Distribution Unlimited AFWA Ensemble.
Evolution in identifying High Impact Weather Events since February 1979 Richard H. Grumm National Weather Service State College, PA Contributions:
An Ensemble Primer NCEP Ensemble Products By Richard H. Grumm National Weather Service State College PA and Paul Knight The Pennsylvania State University.
An Overview of HPC Winter Weather Guidance for Three Warning Criteria Snowfall Events That Occurred During the Winter Season. A Review of the.
NCEP CMC ECMWF MEAN ANA BRIAN A COLLE MINGHUA ZHENG │ EDMUND K. CHANG Applying Fuzzy Clustering Analysis to Assess Uncertainty and Ensemble System Performance.
Brian Freitag 1 Udaysankar Nair 1 Yuling Wu – University of Alabama in Huntsville.
Application of the CRA Method Application of the CRA Method William A. Gallus, Jr. Iowa State University Beth Ebert Center for Australian Weather and Climate.
REGIONAL SCALE ENSEMBLE FORECAST OF THE LAKE EFFECT SNOW EVENT OF 7 FEBRUARY 2007 Justin Arnott and Michael Evans NOAA/NWS Binghamton, NY Richard Grumm.
West Virginia Floods June 2016 NROW 2016 Albany NY
The November 26, 2014 banded snowfall case in southern NY
FORECASTING EASTERN US WINTER STORMS Are We Getting Better and Why?
Michael L. Jurewicz, Sr. and Christopher Gitro
Warm Season Flash Flood Detection Performance at WFO Binghamton
Predictability of Snow Multi-Bands Using a 40-Member WRF Ensemble
North American Monsoon Rainfall Parameterization over the Southwest United States and Northwest Mexico: WRF Simulations using NAME IOP Data Stephen W.
Presentation transcript:

REGIONAL-SCALE ENSEMBLE FORECASTS OF THE 7 FEBRUARY 2007 LAKE EFFECT SNOW EVENT Justin Arnott and Michael Evans NOAA/NWS Binghamton, NY Richard Grumm NOAA/NWS State College, PA George Young Penn State University, University Park, PA NROW IX, 7-8 November 2007

Motivation Past LES Forecasting LES a Pattern Recognition Problem GFS unable to resolve bands GFS unable to resolve bands Rely on tools such as BUFKIT Rely on tools such as BUFKIT

Motivation, continued 12 km NAM grossly resolves lake-parallel bands 12 km NAM grossly resolves lake-parallel bands –Each NWS office can run a local version of this model Individual runs often have problems with band location/orientation Individual runs often have problems with band location/orientation Can multiple simulations of the NAM (an ensemble) provide added value? Can multiple simulations of the NAM (an ensemble) provide added value? –This question has prompted the development of the Northeast Regional Ensemble Present/Future LES Forecasting

What is the Northeast Regional Ensemble? 12 km Workstation WRF 12 km Workstation WRF –24-36 hr run length : 7-8 Members : 7-8 Members –2 CTP members –1 Operational Goal: Improve operational forecasts of lake effect snowfall Goal: Improve operational forecasts of lake effect snowfall

Case Day: 07FEB2007 Part of a ~10-day prolific lake effect snow event east of Lake Ontario Part of a ~10-day prolific lake effect snow event east of Lake Ontario Band moved significantly throughout the day Band moved significantly throughout the day –Excellent test for the ensemble

The Ensemble – 07FEB2007 OfficeCoreIC/BCsMicroCPS #Z Lev OperationalNMMNAMFerrierBMJ60 BGMARWNAMLinKF31 CLEARWGFSLinKF40 CTP-1NMMNAMLinBMJ31 CTP-2ARWNAMLinBMJ31 BTVNMMGFSFerrierBMJ31

07FEB2007 – Synoptic Setup

T LAKE : +4C

07FEB2007 – Synoptic Setup

Radar Loop

Operational NAM Performance

Captures basic band evolution Captures basic band evolution –Slow with initial southward band movement Problems with inland extent of the band Problems with inland extent of the band –Frequently too far inland Can the ensemble add value to this simulation? Can the ensemble add value to this simulation?

Ensemble Performance

All members able to simulate a band All members able to simulate a band Like NAM, ensemble successfully captures basic band evolution Like NAM, ensemble successfully captures basic band evolution Probability plots indicate operational NAM an outlier with inland extent Probability plots indicate operational NAM an outlier with inland extent –Ensemble provides added value

Individual Member Performance Quantitatively assess each ensemble member Quantitatively assess each ensemble member –Method: MODE pattern matching software (Davis et al. 2006) Identify precipitation “objects” in forecast/observations Identify precipitation “objects” in forecast/observations Match objects based on different attributes Match objects based on different attributes –Distance apart, similarity in area/orientation, overlap Precipitation Obs: NCEP Stage IV Analysis Precipitation Obs: NCEP Stage IV Analysis

Individual Member Performance Example: Example:

Individual Member Performance The Statistics: Primary Band Identification The Statistics: Primary Band Identification –POD/FAR/CSI MODELPODFARCSI NAM-NMM* BGM-ARW BTV-NMM CLE-ARW CTP-ARW CTP-NMM * 3 hourly time steps

Individual Member Performance The Statistics: Basic Position/Intensity The Statistics: Basic Position/Intensity Average Bias (fcst-obs) MODELArea(gdpts)Angle(degs) Area-Avgd Intensity (mm/grdpt) NAM-NMM* BGM-ARW BTV-NMM CLE-ARW CTP-ARW CTP-NMM * 3 hourly time steps

Conclusions Case study suggests ensemble approach to LES may be valuable Case study suggests ensemble approach to LES may be valuable –Hone in on high-probability impact areas –Highlight outlier (low-probability) outcomes Initial Quantitative Analysis shows diversity in “best member” for different variables Initial Quantitative Analysis shows diversity in “best member” for different variables –Ensemble mean likely to have increased skill over individual members

Contact Info/Acknowledgements Have Questions? Have Questions? Acknowledgements: Ensemble Participants Ensemble Participants –For agreeing on a common domain/sharing data Ron Murphy, ITO BGM Ron Murphy, ITO BGM –For gathering 7 February 2007 case data MODE Software designers MODE Software designers –