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1 Mt. Mansfield, VT. Aka….New England Days 4-7 Forecast Test Paul A. Sisson NOAA/NWS, Weather Forecast Office, Burlington, Vermont (BTV) Joseph Dellicarpini.

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Presentation on theme: "1 Mt. Mansfield, VT. Aka….New England Days 4-7 Forecast Test Paul A. Sisson NOAA/NWS, Weather Forecast Office, Burlington, Vermont (BTV) Joseph Dellicarpini."— Presentation transcript:

1 1 Mt. Mansfield, VT

2 Aka….New England Days 4-7 Forecast Test Paul A. Sisson NOAA/NWS, Weather Forecast Office, Burlington, Vermont (BTV) Joseph Dellicarpini NOAA/NWS, Weather Forecast Office, Taunton, Massachusetts (BOX) Michael Ekster NOAA/NWS, Weather Forecast Office, Gray, Maine (GYX) Todd Foisy NOAA/NWS, Weather Forecast Office, Caribou, Maine (CAR) David Radell and Jeff Waldstreicher NOAA/NWS, Eastern Region Headquarters, Bohemia, New York (ERH) 2 Mt. Mansfield, VT

3 The real workers Matt Belk BOX Conor Lahiff BTV Margaret Curtis GYX Roman Berdes CAR Acknowledgements The NWS Central Region Blender team Tim Barker, SOO, BOI - BOIVerify 3

4 Outline Background Motivation The Experiment Verification Results Summary 4 “The Blend is your friend”

5 Background Many Studies on forecast/model consensus in general agreement that the consensus forecast tends to be the best forecast –Gyakum (1986), Fritsch et al (2000), Roebber (2014) etc. Baars and Mass (2005) Consensus/Weighted MOS – “competitive or superior to human forecasts at nearly all locations” “Human forecasts are most skillful compared to MOS during the first forecast day and for periods when temperatures differ greatly from climatology.” 5

6 Motivation Work efficiently Improve Accuracy Improve Consistency Focus on what we do best –(sig departures from climo and short term) Note: Don’t reinvent the wheel 6 “The Blend is your friend”

7 Consistency Problems 7

8 The Days 4-7 Forecast Experiment Oct Mar 2014 All offices start with same model initializations and blends Forecaster Surveys and Verification Does blend outperform Gridded MOS? Is the forecast more consistent? Does the method allow forecasters to be more efficient and allow time to do important things? 8

9 Nomenclature CONS = Consensus A consensus data set is calculated by combining/averaging a “list” of guidance data sources. 9

10 BC = Bias Correction Bias corrections are run on individual guidance sources using BOIVerify software. BCCONS data set is generated by bias correcting the individual components before forming the consensus. Using previous 14 day period BC 10

11 BLENDS Blends are a combination of various datasets of various weights. 11

12 NameDescription CMCnhCanadian Meteorological Center Global modelRaw Model output GFS40NWS Global Forecast System (GFS: 40km)Raw Model output ECMWFEuropean Centre Medium Range Forecast modelRaw Model output SREFNWS Short Range Ensemble Forecast (mean) Raw Model output NAM12NWS North American Model (NAM: 12km) Raw Model output NAMDNG5NWS NAM downscaled to 5km grid Raw Model output ADJMEX GFS40km ADJusted with GFS extended MOS point forecasts Raw Model background with MOS ADJMEN GFS40km ADJusted with GFS ensemble mean MOS pt forecasts Raw Model background with MOS ADJECEECMWF ADJusted with ECMWF MOS pt forecasts Raw Model background with MOS ADJECM ECMWF ADJusted with ECMWF ensemble mean MOS pt forecasts Raw Model background with MOS MOSG25GFS Gridded Model Output Statistics (2.5km) GFS MOS *Note: all forecasts mapped/downscaled to 2.5km grid Forecast Databases

13 NameDescriptionDatabases CONSAll Consensus Raw Models and MOS CMCnh, GFS40, ECMWF, SREF, NAM12, NAMDNG5, MOSG25, ADJMEX, ADJMEN, ADJECE, ADJECM (equal weights) BCCONSAll Consensus of Bias- corrected Raw Model and MOS databases CMCnhBC, GFS40BC, ECMWFBC, SREFBC, NAM12BC, NAMDNG5BC, MOSG25BC, ADJMEXBC, ADJMENBC, ADJECEBC, ADJECMBC HPCGuide Human Forecast by Weather Prediction Center Human adjusted Blend Official Previous Weather Forecast Office forecast Human adjusted Blend Forecast Databases

14 SuperBlend Previous Forecast + latest blends Official (25%), HPCGuide (25%) CONSALL (25%) BCCONSALL (25%) 50/50 Man Machine mix 14

15 Verification Results MaxT, MinT, T, Td, Wind Speed, PoP Use Real-Time Mesoscale Analysis adjusted by Observation as verification and for bias-correction 15

16 RTMA MaxT

17 Obs to ADJ RTMA MaxT

18 RESULTS 18

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21 Day 5 MinT

22 Day 4 PoP Reliability Under forecast Over forecast

23 Survey Results Compare Before, During, & After Before: Gridded MOS method After: SuperBlend method 23

24 Impact of SuperBlend on Days 4-8 Forecast Process MEAN – 4.69 Mid-Test SurveyPre-Test Survey (MOSGuide) MEAN – 4.48MEAN – 3.45

25 Forecaster Modifications What Doesn’t Work Well SuperBlend MOSGuide

26 SuperBlend Performance Regime Changes / Anomalous Conditions

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28 MEAN – 4.64 Mid-Test SurveyPre-Test Survey (MOSGuide) MEAN – 4.31MEAN – 3.16 Forecaster Overall Evaluation of Approach

29 Consistency Mar 2013 Less Than 80 % % % %100 % 29

30 QPF 30

31 SnowAmt 31

32 Summary Blends provide a more accurate and consistent starting point Forecasters survey show confidence in the method Allows more time for operations vs grid preparation 32

33 Bill Belichick, Head Coach New England Patriots Dec 14, Caveat: "Stats are for losers," "The final score is for winners."

34 Or….stated another way Caveat: Stats are helpful Clearly Communicating Accurate and Timely Weather Information is the goal. 34

35 The End 35

36 NameDescriptionDatabases CONSRawConsensus of Raw Models CMCnh, GFS40, ECMWF, SREF, NAM12, NAMDNG5 (equal weights) BCCONSRaw Consensus of Bias-corrected Raw Model databases CMCnhBC, GFS40BC, ECMWFBC, SREFBC, NAM12BC, NAMDNG5BC (equal weights) CONSMOS Consensus of MOS databases MOSG25, ADJMEX, ADJMEN, ADJECE, ADJECM, EKDMOS, ADJMAV, ADJLAV, ADJMET (equal weights) BCCONSMO S Consensus of Bias-corrected MOS databases MOSG25BC, ADJMEXBC, ADJMENBC, ADJECEBC, ADJECMBC, EKDMOSBC, ADJMAVBC, ADJLAVBC, ADJMETBC (equal weights) AllBlend 50/50 Blend of Official and CONSAll Official, CONSAll BCAllBlend 50/50 Blend of Official and BCCONSAll Official, BCCONSAll RawBlend 50/50 Blend of Official and CONSRaw Official, CONSRaw BCAllBlend 50/50 Blend of Official and BCCONSRaw Official, BCCONSRaw Forecast Databases

37 NameDescriptionDatabases SuperBlendPrevious Forecast + latest blendsOfficial (25%), HPCGuide (25%), CONSALL (25%), and BCCONSALL (25%) CONSAllConsensus Raw Models and MOS CMCnh, GFS40, ECMWF, SREF, NAM12, NAMDNG5, MOSG25, ADJMEX, ADJMEN, ADJECE, ADJECM,, HPCERP (equal weights) BCCONSAll Consensus of Bias-corrected Raw Model and MOS databases CMCnhBC, GFS40BC, ECMWFBC, SREFBC, NAM12BC, NAMDNG5BC, MOSG25BC, ADJMEXBC, ADJMENBC, ADJECEBC, ADJECMBC CONSRawConsensus of Raw ModelsCMCnh, GFS40, ECMWF, SREF, NAM12, NAMDNG5 (equal weights) BCCONSRaw Consensus of Bias-corrected Raw Model databases CMCnhBC, GFS40BC, ECMWFBC, SREFBC, NAM12BC, NAMDNG5BC (equal weights) CONSShort CONSRaw databases, Local WRF's and short term MOS CMCnh, GFS40, ECMWF, SREF, NAM12, NAMDNG5, RUC13BC, HIRESWarwBC, HIRESWnmmBC, BTV4, BTV12, BTV6, ADJMAV, ADJMET, ADJECE, ADJLAV (equal weights) BCCONSShort Consensus of Bias-corrected CONSShort databases CMCnhBC, GFS40BC, ECMWFBC, SREFBC, NAM12BC, NAMDNG5BC, RUC13BC, HIRESWarwBC, HIRESWnmmBC,BTV4BC, BTV12BC, BTV6BC, ADJMAVBC, ADJMETBC, ADJECEBC, ADJLAVBC (equal weights) CONSMOSConsensus of MOS databases MOSG25, ADJMEX, ADJMEN, ADJECE, ADJECM, EKDMOS, ADJMAV, ADJLAV, ADJMET (equal weights) BCCONSMOS Consensus of Bias-corrected MOS databases MOSG25BC, ADJMEXBC, ADJMENBC, ADJECEBC, ADJECMBC, EKDMOSBC, ADJMAVBC, ADJLAVBC, ADJMETBC (equal weights) AllBlend 50/50 Blend of Official and CONSAll Official, CONSAll BCAllBlend 50/50 Blend of Official and BCCONSAll Official, BCCONSAll RawBlend 50/50 Blend of Official and CONSRaw Official, CONSRaw BCAllBlend 50/50 Blend of Official and BCCONSRaw Official, BCCONSRaw

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