Enhanced Predictability During Extreme Winter Flow Regimes
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1 Enhanced Predictability During Extreme Winter Flow Regimes Ryan N. Maue (WeatherBELL Analytics - Atlanta) maue@weatherbell.com ECMWF UEF 2016 Reading, UK June 6 9, 2016
2 Where does forecast verification occur today? Internal teams within NWP centers Media: TV broadcasters, print, web Social Media, Twitter / Facebook Who is doing model forecast verification? Trained forecasters, gov t & commercial Your neighbor High impact events
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10 Comparison of Operational Suites HRES Global model 0-10 days (2x) EPS Ensembles 0-15 days, 46 days Seasonal 7-months (1 per mon) GFS global model 0-16 days (4x daily) GEFS ensembles 0 16 days (4x daily) NAM (12-km) 0-84 hours (4x), NAM-RR NAM 4-km Nest 0-60 hours (4x) RAP 12-km (24x) HRRR 3-km (24x), HRRR-E CFSv2 climate forecast days (4x) HIRESW 4-km WRF-windows (2x) SREF ensemble 16-km (4x) RTMA/URMA analysis (24x) WW3 Wave model (4x) RTOFS global model (1x) NDFD 2.5 km (48x) National Water Model, Aerosols HWRF hurricane model (4x) WPC, SPC, NWS, NSIDC, OPC, Space
11 Research Objectives Framework & Goals Use current NWP deterministic & ensemble systems to analyze large-scale flow patterns and relate to medium-range forecast skill dropouts Diagnose causes of lowpredictability flow regimes including dropouts: inadequate observations, large analysis uncertainty, and/or model error growth Link to skill of Teleconnection Indices such as AO/NAO, EPO, WPO and PNA Definitions Forecast skill metrics: 5 or n-day 500 hpa geopotential anomaly correlation (NH: N), Forecast dropout: an individual or collection of several consecutive forecasts that have significantly lower 500 hpa geopotential anomaly correlation skill compared to monthly/seasonal mean (AC < 0.8) Low-predictability regime: particular hemispheric-scale configuration of upper-level flow that leads to below average forecast skill
12 Anomaly Correlation: Definition 500-hPa geopotential height Northern Hemisphere N Forecast anomaly from climatology* at each grid point (m) Analysis anomaly The AC is common forecast skill metric used by operational centers Forecasts with AC > 0.6 are considered as providing potential positive skill Not perfect metric, but used in concert w/ e.g. mean squared error * ERA-Interim (reanalysis) climatology
13 Archive of Analysis and Forecast Fields Historical record of analysis and forecasts from current NWP deterministic and ensemble systems Key is archive and/or real-time access to forecast products Valuable resource includes the EPS Reforecasts / Hindcasts or any frozen forecast model for post-processing purposes e.g. EFI, M-Climate MODEL TIME PERIOD GRID/FIELDS SOURCE ECMWF HRES & EPS Oct 2006-present T799-T1279L137 Tco1279L137 ~9/18 km WeatherBELL ECMWF MARS NCEP GFS GEFS Feb 2004-present T382-T574L64 T1534L NOMADS/NCDC NAVY NAVGEM Jan 2004-present T239-T319-T NRL MONTEREY *Forecasts verified against own analysis
14 Anomaly Correlation: Forecast skill ECMWF GFS Seasonal AC scores are highly correlated NH Winter skill >> Summer Skill. Both models showed record-high skill during winter
15 Anomaly Correlation: Forecast skill ECMWF GFS Summer skill improvements Sustained improvement skill jumps mainly due to major model configuration changes: Jan 26, 2010: ECMWF T799 T1279 March 2016: 41r2 July 28, 2010: GFS T382 T574 and physics major upgrade T1534 4D-Hybrid EnVar May 11, 2016
16 Anomaly Correlation: Forecast skill 5-DAY NH Geopotential Height ECMWF vs GFS calendar year Model Wars in U.S.A. media YEAR GFS ECMWF YTD m YTD m NCEP May Upgrade: 4D-Var Hybrid EnKF GFS gains have leveled out Cycle 42r1 significant Improvement Gap widened by 20% On pace for 0.92
17 Predictive Skill Regimes From N Hemi AC, ECMWF & GFS: Centered means of 45-days and 7-days are calculated The 7-day minus 45-day mean represents a seasonally adjusted measure of skill Above-average skill GFS ECMWF Below-average skill Models Anom tend Corr to have / predictive low and skill high is strongly skill dependent the same on forecast(s)- atmospheric but flow ECMWF regime: skill different models drops off dropout less than on the GFSsame cases
18 Anomaly Correlation: GFS recent skill Periods of significant skill dropout & success Jan 2016 March 2016 June 2016
19 Anomaly Correlation: HRES ECMWF 5-day Nov 2013 Dec 2013 Jan 2013
20 Anomaly Correlation: ECMWF 6-day Compare to 5-day: Jan 2016 Mar 2016 Jun 2016
21 Anomaly Correlation: ECMWF 10d Nov 2013 Dec 2013 Jan 2014
22 Anomaly Correlation: ECMWF 10d Oct 2015 Jan 2016 Jun 2016
23 Example: Arctic Oscillation AO is the first EOF of sea-level pressure (1000 hpa geopotential height) variations north of 20 N latitude How do NWP systems perform during the + and phase of the AO, as well as through transitions [e.g. Archambault et al. 2010] during the Northern Hemisphere cold season? POSITIVE PHASE NEGATIVE PHASE J. Wallace, University of Washington
24 Arctic Oscillation Winter Sustained Negative AO
25 Arctic Oscillation Winter November January Negative AO, followed by strong Positive Major Transitions forecast
26 AC Arctic Oscillation ECMWF Winter hpa NH Anomaly Correlation Arctic Oscillation Index Transitions AO Dec 2009 Feb 2010
27 Arctic Oscillation AC Lower Skill ECMWF Winter hpa NH Anomaly Correlation Arctic Oscillation Index Very High Skill Lower Skill AO Dec 2010 Feb 2011
28 Visualizing Arctic Oscillation Forecast Index January 8, 2016
29 Visualizing Arctic Oscillation Forecast Index Bubbles Analyzed AO, 10-day forecast bubbles INIT: January 8, z HRES 41r2
30 Visualizing Arctic Oscillation Forecast Index Box & Whisker Forecast AO, 15-day EPS forecast, ensemble mean & distribution + control
31 Visualizing Arctic Oscillation Forecast Index
32 Visualizing PNA Forecast Index
33 Summary and Future Directions A decade archive of high-resolution operational center deterministic forecasts has been developed to study dropouts in medium-range forecast skill The models tend to dropout during the same forecasts, in lowerpredictability flow regimes The Arctic Oscillation (AO) index is (to some extent) anti-correlated with medium-range forecast skill, as measured by the 5-day anomaly correlation of 500mb height Value of frozen model w/many years of data (~ 20) to evaluate model performance during particular large-scale flow regimes Value of multi-model & ensemble forecast-forecast correlations for medium-range extreme events
34 Acknowledgements WeatherBELL Analytics [2012-present] Data: ECMWF, NCEP/NOMADS, NRL-Monterey NRC Postdoc at NRL Monterey [ ] advisor: Dr. Rolf H. Langland Updated research work based on Langland and Maue (Tellus A, 2012): Recent Northern Hemisphere mid-latitude medium-range deterministic forecast skill Appreciation and Thanks to Dr. Ghelli and ECMWF!
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