forecast skill and major forecast failures over the northeastern pacific and western north america...
TRANSCRIPT
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Forecast Skill and Major Forecast Failures over the Northeastern
Pacific and Western North America
Lynn McMurdie
and Cliff Mass
University of Washington
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Why the North Pacific Ocean?
• The Pacific is one of the largest areas of sparse insitu observations in the world
• Uncertainty over the Pacific has a large impact on predictability over the North American continent and beyond
• There are often large initialization errors and short-term forecasts over the northern Pacific ocean.
• One symptom of such problems is that short-term forecast skill on the western side of North America is worse than in other areas.
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An example of a short-term forecast error
Eta 24-h
03 March 00UT 1999
Eta 48-h
03 March 00UT 1999
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And the public and media have noticed these failures….
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Seattle Times Eugene Register Guard
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Some applicable research in the literature
• Langland et al. (2002) – poor forecast of Jan 2000 storm on East Coast due in part to sensitivity over the Pacific.
• Bosart et al. (2002) – lack of convection over the midwest not represented in forecasts due to poorly initialized trough along west coast
• Schultz et al. (2005) – 70% of troughs arriving on the West Coast were underforecast, a portion of which continued to effect short-term forecasts across the North American continent.
• McMurdie and Mass (2004) – documented forecast failures over the eastern Pacific.
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This talk will …
• Demonstrate that large initial condition and short-term forecast errors still occur over the eastern Pacific and downstream
• Present a feature-based approach to monitoring errors in this region
• Discuss implications for THORPEX
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How frequent are large numerical forecast errors?
• Approach: compare buoys/coastal observations of sea level pressure (SLP) to NCEP’s Eta and GFS 00, 24, 36 and 48 hr forecasts.
• Error = Forecast SLP – Observed SLP• At each station, calculated average and
absolute error and the standard deviation using winter (Oct – Mar) data.
• Large Error = |Error| > (average error + 2 * SD)
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Station Locations
Tatoosh Is.
Cape Arago
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24 h
Large Errors
Tatoosh Is., WA
Cape Arago, OR
Inter-annual variability
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48-h Errors
48h errors much larger and more frequent than 24-h errors
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GFS vs. Eta 24-h errors
NCEP GFS better than Eta on average
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48-h errors
GFS over forecastsEta under forecasts
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GFS has more accurate SLP initializations and forecasts than Eta over the Northeast Pacific
• For 00-h forecasts (initial conditions), GFS has smaller mean absolute error (MAE) and standard deviation (SD) than Eta at all 17 stations
• For 24- and 36-h forecasts, GFS has smaller MAE and SD than Eta at 13/17 buoy and coastal stations
• For 48-h forecasts, GFS has smaller MAE and SD than Eta at 12/17 stations.
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Forecast Verification: The Need for Feature-Based Evaluation
• Examining statistics at observing sites is not sufficient for understanding the problems.
• Must also track features to gain an understanding of the deficiencies.
• Case studies of major failures should reveal important information.
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GFS
What are these large forecast errors associated with?
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How frequently do large forecast errors of synoptic events occur?
Number of Events/Season associated with Lows/Troughs/Highs
Season Total Low Trough High
1999 – 2000 21 12 7 2
2000 – 2001 19 12 4 2
2001 – 2002 16 14 2 0
2002 – 2003 16 11 5 0
(from McMurdie and Mass 2004)
Event = large error at 2 or more adjacent stations for 2 or more forecasts periods
Data shown for Eta model only
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Of the forecast failures associated with lows, what are the central pressure and cyclone position errors?
Ave SLP error = 3.4 mbSD = 8.7 mbAbsolute error = 7.5 mb
Ave position error = 453.8 kmSD = 260 km
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Recent examples of major forecast errors
• February 2002
• October 2003
• February 2004
• November 2004
• April 2005
• May 2005
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An example of a recent high-impact, poorly forecast storm
•Power outages, large trees uprooted in Eugene, OR
• Powerful, rapidly developing storm with strong winds (70 kts)
• Very poor short-term numerical guidance
L1008
L1004
L996
L1010
7 – 8 February 2002 Cyclone
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48-hr Forecasts Valid 00 UTC 8 February 2002
AVN
UKMO
ETA
NOGAPS
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24-Hr Forecasts Valid 00 UTC 8 February 2002
AVN
UKMO
ETA
NOGAPS
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Difference between UKMO and Eta 850 mb Temperature K
Valid 00 UTC 7 February 2002
Solid = UKMO, Dashed = ETA, Shades, blue = differences
L1010
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20 Oct 03
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Flood of 20 October 2003
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00hr GFS 24hr GFS 48hr GFS 00hr + 48hr GFS
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GFS Forecasts of 12-hr Precipitation
12h Forecast24h Forecast36h Forecast48h Forecast
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February 04
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00UTC 16 Feb 04 GFS 00-hr Forecast
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00 UTC 16 Feb 04 24-hr GFS
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00 UTC 16 Feb 04 48 hr GFS
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Apr 05
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24-hr forecast GFS Position error ~ 420km
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Large Short Term Forecast Errors Still Occur
• Number of slp errors > 10 mb continues to be 10 – 15 per winter (despite the ridge this year)
• Vast majority of large errors due to mispositioned or under (or over) forecast low centers (see McMurdie and Mass, 2004)
• For Feb 02 case, forecast errors were likely due to initial condition errors (McMurdie and Mass, 2004)
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Some Unanswered Questions• What are the origins of these short-term forecast
errors – initial condition/data assimilation errors, model errors?
• Are there particular flow patterns (or regimes) where short-term (or longer term) forecasts are less accurate (e.g., E-T transitions)?
• How do model sensitivity structures compare for major forecast failure cases? How do they project on obvious initialization problems? How do adjoint-based and ensemble-based sensitivities compare for such cases?
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Unanswered Questions continued
• What are the downstream implications for medium to long- range forecasts when initial condition errors are large over the Pacific? To what degree are downstream errors mitigated by greater data density over North America?
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Implications for THORPEX• Major forecast failures still occur, even at the short-
time ranges. So there is still work to be done!• It is important to monitor the quality of model
initializations and forecasts to know how well we are doing and where the failures are.
• Both statistical and feature-based approaches are needed to gain a full understanding of model failures.
• Case studies can provide important insights into forecast failures
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The END
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Recent trends in forecast accuracy
From Simmons and Hollingsworth (2002) Hemispheric r.m.s. error of SLP
Increased skill of 3-5 Day forecast skill of SLP (and 500 hghts) especially last 10 years.
Unable to discern forecast skill of storm systems in particular locations from these statistics
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Brief Outline
• Show statistics of short term errors along West Coast
• Highlight several examples of major forecast failures
• Briefly discuss the effect of uncertainty over the Pacific on longer term forecasts
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Adjoint Sensitivity wrt 850 mb Temperature
Area of forecast error projected onto sensitivities
Courtesy of Brian Ancell
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2004 17 Nov
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2004 17 Nov
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6-hr forecast Eta
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6-hr Forecast GFS