Using Anomalies to Forecast High Impact Events

David L. Beachler NOAA/National Weather Service Forecast Office Chicago IL

14-16 Mar 2012 Great Lakes Operational Meteorology Workshop Chicago IL

An anomalous outline…

• Salient Points:– Anomalies– EPS

• Cases:– Eastern United States Winter Storm: 1-2Feb2011– Western Great Lakes Flood (Ridge Roller): 21-

23July2010

Why such an outlier?• Outliers in a data set can pose significant

problems– Visually examine data…aids in identifying outliers

• Envelope of possibilities• Clustering

– NWP outliers at times are difficult

• Operational Solutions at times offer chaos– EPS at times offers focus & generally less chaos

• Once identified then what? – Forecasters leverage the significance

• Tempted to eliminate…to simplify the forecast

Outliers have their place• Forecasters hone their skills along the fringes

– Live on the edge through NWP forecasts• Aid in fine-tuning EPS• Eliminate the noise

EPS: Big Picture• EPS provides the forest amongst the trees!– PATTERN RECOGNITION

• Quick assessment of basic parameters– U-wind :FGEN/Trowal/Potential temp gradient

• East-West– Positive between 180-360 C– Negative between 360-180 C

– V-wind :Moisture transport• North-South

– Positive between 90-270 C– Negative between 270-90 C

– PWAT– MFLUX

Anomalous U-windStrengthening Easterly Jet

Anomalous U-windStrengthening Easterly Jet

GEFS: 24-hr QPF

SREF: Snowfall Threats

Impacts…

• Heavy Snow from OK to MI– Chicago 21.2” (72hr total)

• South of Low Ice– Southern Plains/New England– ~0.5” Ice Indiana

• Anomalous LLVL Easterly Jet• Inertial Gravity Wave (IGW)?– Likely resulted in Thunder/Blizzard conds from MO/IL• 15Dec1987 Observed IGW similar to 31Jan-

2Feb2011

Western Great Lakes Flood: 21-23July2010

• Dominant Subtropical Ridge– Galarneau & Bosart 2006 coined “ridge rollers”

• Flooding aligned with Anomalous PWAT/Ridge– 23-24July2010: Heavy Rainfall (flooding)

IA > 15”• Failure of Lake Delhi Dam (Eastern IA)

Composite: 17-26July2010

Plume Forecast

• Offer point based analysis • Probabilistic approach to DSS • Visualize clustering and outliers with

mean

Service Assessment: Record Floods Middle Tennessee & Western Kentucky 1-4 May

2010

• At the time of this report, a paper under peer review entitled “The Devastating Mid-Mississippi Valley Floods of 1-2 May 2010”, by Richard H. Grumm, National Weather Service, noted the following:“The event of 1-2 May 2010 had …key ingredients for a significant heavy rainfall event (Doswell 1996), and for historic events (Bodner 2011); This case is a classic case on the value ofanomalies in identifying a potentially significant heavy rainfall event... [which] clearly defined a threat of a Maddox Synoptic event type with a strong southerly jet and a surge of high PW [precipitable water] air into the region.”

• As the NWS focuses increasingly on Decision Support Services, offices and operational staff at all levels should place more attention on anomalies and pattern recognition in advance of potentially significant events.

Closing…

• All events demonstrate some anomaly• Noise amongst EPS suggests lower

confidence– Forecasters live on the edge and can improve

through operational envelope of solutions

• Minimal spread provides better vision into the future– Ability to provide high-confidence forecast for

DSS

References

• Web resources: http://cms.met.psu.edu/sref/– SREF/GEFS options and Ensemble Threats Page

• Forsythe, J.M., S.Q. Kidder, S.J. Kusselson, A.S. Jones, T.H. Vonder Haar, 2009: Increasing the land coverage of blended multisensory total precipitable water products for weather analysis. 16th Conference on Satellite Meteorology and Oceanography, Phoenix, Arizona. http://ams.confex.com/ams/89annual/techprogram/paper 149348.htm

• Grumm, R.H. and R. Hart. 2001: Standardized Anomalies Applied to Significant Cold Season Weather Events: Preliminary Findings. Wea. and Fore., 16,736–754.

• Hart, R. E., and R. H. Grumm, 2001: Using normalized climatological anomalies to rank synoptic scale events objectively. Mon. Wea. Rev., 129, 2426–2442.

• Junker, N. W., R. H. Grumm, R. Hart, L. F. Bosart, K. M. Bell, and F. J. Pereira, 2008: Use of standardized anomaly fields to anticipate extreme rainfall in the mountains of northern California. Wea. Forecasting,23, 336–356.

• Kusselson, S.J., S.Q. Kidder, J.M. Forsythe, A.J Jones, L. Zhao, 2009: An update on the operational implementation of blended total precipitable water products. 23rd Conference on Hydrology, Phoenix, AZ. http://ams.confex.com/ams/89annual/techprogram/paper_142967.htm

• http://www.nws.noaa.gov/os/assessments/pdfs/Tenn_Flooding.pdf NWS Service Assessment Tennessee Flood

• THE USE OF ENSEMBLE AND ANOMALY DATA TO ANTICIPATE EXTREME FLOOD EVENTS IN THE NORTHEASTERN U.S. – Neil A. Stuart(1), Richard H. Grumm(2), John Cannon(3), and Walt Drag(4)