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1 How Does NCEP/CPC Make Operational Monthly and Seasonal Forecasts? Huug van den Dool (CPC) CPC, June 23, 2011/ Oct 2011/ Feb 15, 2012 / UoMDMay,2,2012/

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Presentation on theme: "1 How Does NCEP/CPC Make Operational Monthly and Seasonal Forecasts? Huug van den Dool (CPC) CPC, June 23, 2011/ Oct 2011/ Feb 15, 2012 / UoMDMay,2,2012/"— Presentation transcript:

1 1 How Does NCEP/CPC Make Operational Monthly and Seasonal Forecasts? Huug van den Dool (CPC) CPC, June 23, 2011/ Oct 2011/ Feb 15, 2012 / UoMDMay,2,2012/ Aug2012/ Dec,12,2012/UoMDApril24,2013/ May22,2013/

2 2 Assorted Underlying Issues Which tools are used… How do these tools work? How are tools combined??? Dynamical vs Empirical Tools Skill of tools and OFFICIAL How easily can a new tool be included? US, yes, but occasional global perspective Physical attributions

3 3 Menu of CPC predictions: 6-10 day (daily) Week 2 (daily) Monthly (monthly + update) Seasonal (monthly) Other (hazards, drought monitor, drought outlook, MJO, UV-index, degree days, POE, SST) (some are ‘briefings’) Informal forecast tools (too many to list) 0day/tools/briefing/index.pri.html 0day/tools/briefing/index.pri.html


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7 7 From an internal CPC Briefing package


9 99 SMLR CCA OCN LAN LFQ (15 CASES: 1950, 54, 55, 56, 64, 68, 71, 74, 75, 76, 85, 89, 99, 00, 08) OLD-OTLK CFSV1 ECP IRI ECA CON

10 10 Element  US-TUS-P SSTUS-soil moisture Method: CCA X X X OCN X X CFS X X XX SMLR X X ECCA X X Consolidation X X X Constr Analog X X X X Markov X ENSO Composite X X Other (GCM) models (IRI, ECHAM, NCAR,  N(I)MME): X X CCA = Canonical Correlation Analysis OCN = Optimal Climate Normals CFS = Climate Forecast System (Coupled Ocean-Atmosphere Model) SMLR = Stepwise Multiple Linear Regression CON = Consolidation

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14 14 About OCN. Two contrasting views: - Climate = average weather in the past - Climate is the ‘expectation’ of the future 30 year WMO normals: ; ; etc OCN = Optimal Climate Normals: Last K year average. All seasons/locations pooled: K=10 is optimal (for US T). Forecast for Jan 2012 (K=10) = (Jan02+Jan Jan11)/10. – WMO-normal plus a skill evaluation for some 50+ years. Why does OCN work? 1) climate is not constant (K would be infinity for constant climate) 2) recent averages are better 3) somewhat shorter averages are better (for T)  see Huang et al J.Climate. 9,

15 15 OCN has become the bearer of most of the skill, see also EOCN method (Peng et al)

16 16


18 NCEP’s Climate Forecast System, now called CFS v2 MRFb9x, CMP12/14, 1995 onward (Leetmaa, Ji etc). Tropical Pacific only. SFM 2000 onward (Kanamitsu et al CFSv1, Aug 2004, Saha et al Almost global ocean CFSR, Saha et al 2010 CFSv2, March Global ocean, interactive sea-ice, increases in CO2. 18

19 NCEP’s Climate Forecast System, now called CFS v2 19

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21 21 Major Verification Issues ‘a-priori’ verification (used to be rare) After the fact (fairly normal)

22 22 Source Peitao Peng After the fact…..

23 23 (Seasonal) Forecasts are useless unless accompanied by a reliable a- priori skill estimate. Solution: develop a 50+ year track record for each tool present. (Admittedly we need 5000 years)

24 24 Consolidation

25 OUT TO 1.5 YEARS 

26 26 OFFicial Forecast(element, lead, location, initial month) = a * A + b * B + c * C + … Honest hindcast required 1950-present. Covariance (A,B), (A,C), (B,C), and (A, obs), (B, obs), (C, obs) allows solution for a, b, c (element, lead, location, initial month)

27 27 CFS v1 skill

28 28 Fig.7.6: The skill (ACX100) of forecasting NINO34 SST by the CA method for the period The plot has the target season in the horizontal and the lead in the vertical. Example: NINO34 in rolling seasons 2 and 3 (JFM and FMA) are predicted slightly better than 0.7 at lead 8 months. An 8 month lead JFM forecast is made at the end of April of the previous year. A smoothing was applied in the vertical to reduce noise. CA skill

29 29 M. Peña Mendez and H. van den Dool, 2008: Consolidation of Multi-Method Forecasts at CPC. J. Climate, 21, 6521–6538. Unger, D., H. van den Dool, E. O’Lenic and D. Collins, 2009: Ensemble Regression. Monthly Weather Review, 137, (1) CTB, (2) why do we need ‘consolidation’?

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31 31 (Delsole 2007)

32 32 3CVRE SEC SEC and CV

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40 40 See also: O’Lenic, E.A., D.A. Unger, M.S. Halpert, and K.S. Pelman, 2008: Developments in Operational Long-Range Prediction at CPC. Wea. Forecasting, 23, 496–515.

41 41 Empirical tools can be comprehensive! (Thanks to reanalysis, among other things). And very economic. Constructed Analogue (next 2 slides)

42 Given an Initial Condition, SST IC (s, t 0 ) at time t 0. We express SST IC (s, t 0 ) as a linear combination of all fields in the historical library, i.e SST IC (s, t 0 ) ~= SST CA (s) = Σ α(t) SST(s,t) (1) t=1956 (CA=constructed Analogue) The determination of the weights α(t) is non-trivial, but except for some pathological cases, a set of (55) weights α(t) can always be found so as to satisfy the left hand side of (1), for any SST IC, to within a tolerance ε.

43 Equation (1) is purely diagnostic. We now submit that given the initial condition we can make a forecast with some skill by 2010 X F (s, t 0 +Δt) = Σ α(t) X(s, t +Δt) (2) t=1956 Where X is any variable (soil moisture, temperature, precipitation ) The calculation for (2) is trivial, the underlying assumptions are not. We ‘persist’ the weights α(t) resulting from (1) and linearly combine the X(s,t+Δt) so as to arrive at a forecast to which X IC (s, t 0 ) will evolve over Δt.

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45 45 SST Z500 Precip T2m CA

46 46 SSTZ500 Precip T2m CFS Source: Wanqiu Wang

47 Physical attributions of Forecast Skill Global SST, mainly ENSO. Tele- connections needed. Trends, mainly (??) global change Distribution of soil moisture anomalies 47

48 Website for display of NMME&IMME NMME=National Multi-Model Ensemble IMME=International Multi-Model Ensemble MME/ MME/

49 Please attend Friday 2pm June 14 Tuesday 1:30pm June 18 Two meetings to Discuss the Seasonal Forecast. 49

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