How Does NCEP/CPC Make Operational Monthly and Seasonal Forecasts?

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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/. Assorted Underlying Issues. Which tools are used… How do these tools work? - PowerPoint PPT Presentation

Text of How Does NCEP/CPC Make Operational Monthly and Seasonal Forecasts?

Huug van den Dool Climate Prediction Center April, 30, 2009

1How 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/May22,2013/12Assorted Underlying IssuesWhich tools are usedHow do these tools work?How are tools combined???Dynamical vs Empirical ToolsSkill of tools and OFFICIALHow easily can a new tool be included?US, yes, but occasional global perspectivePhysical attributions3Menu 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)

http://www.cpc.ncep.noaa.gov/products/predictions/90day/tools/briefing/index.pri.html 4EXAMPLE

PUBLICLY

ISSUED

OFFICIALFORECAST5

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From an internal CPC Briefing package8

EMPEMPEMPEMPEMPDYNDYNCONCONN/A9

9SMLRCCAOCNLANLFQ(15 CASES: 1950, 54, 55, 56, 64, 68, 71, 74, 75, 76, 85, 89, 99, 00, 08)OLD-OTLKCFSV1ECPIRIECACON910 Element US-TUS-P SSTUS-soil moistureMethod:CCA X X XOCN X X CFS X X XXSMLR X XECCA X XConsolidation X X X

Constr Analog X X X XMarkov XENSO Composite X XOther (GCM) models (IRI, ECHAM, NCAR, N(I)MME): X XCCA = Canonical Correlation AnalysisOCN = Optimal Climate NormalsCFS = Climate Forecast System (Coupled Ocean-Atmosphere Model)SMLR = Stepwise Multiple Linear RegressionCON = Consolidation11

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14 About OCN. Two contrasting views:- Climate = average weather in the past- Climate is the expectation of the future

30 year WMO normals: 1961-1990; 1971-2000; 1981-2010 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+Jan03+... Jan11)/10. WMO-normalplus 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 better3) somewhat shorter averages are better (for T)see Huang et al 1996. J.Climate. 9, 809-817.15OCN has become the bearer of most of the skill, see also EOCN method (Peng et al)16

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GHCN-CAMS

FAN

2008NCEPs Climate Forecast System, now called CFS v2MRFb9x, CMP12/14, 1995 onward (Leetmaa, Ji etc). Tropical Pacific only.SFM 2000 onward (Kanamitsu et alCFSv1, Aug 2004, Saha et al 2006. Almost global oceanCFSR, Saha et al 2010CFSv2, March 2011. Global ocean, interactive sea-ice, increases in CO2.18NCEPs Climate Forecast System, now called CFS v219

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21Major Verification Issuesa-priori verification (used to be rare)After the fact (fairly normal)

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Source Peitao PengAfter the fact..23(Seasonal) Forecasts are useless unless accompanied by a reliable a-priori skill estimate.

Solution: develop a 50+ year track record for each tool. 1950-present.(Admittedly we need 5000 years)24Consolidation25

--------- OUT TO 1.5 YEARS -------26OFFicial 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

CFS v1 skill 1982-200328

Fig.7.6: The skill (ACX100) of forecasting NINO34 SST by the CA method for the period 1956-2005. 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 1-2-1 smoothing was applied in the vertical to reduce noise.CA skill 1956-200529M. Pea Mendez and H. van den Dool, 2008: Consolidation of Multi-Method Forecasts at CPC. J. Climate, 21, 65216538.

Unger, D., H. van den Dool, E. OLenic and D. Collins, 2009: Ensemble Regression.Monthly Weather Review, 137, 2365-2379.

(1) CTB, (2) why do we need consolidation?30

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(Delsole 2007)32

3CVRESECSEC and CV33

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See also:OLenic, E.A., D.A. Unger, M.S. Halpert, and K.S. Pelman, 2008: Developments in Operational Long-Range Prediction at CPC. Wea. Forecasting, 23, 496515. 41Empirical tools can be comprehensive! (Thanks to reanalysis, among other things).

And very economic.Constructed Analogue(next 2 slides)Given an Initial Condition, SSTIC (s, t0) at time t0 . We express SSTIC (s, t0) as a linear combination of all fields in the historical library, i.e. 2010SSTIC (s, t0) ~= SSTCA(s) = (t) SST(s,t) (1) t=1956 (CA=constructed Analogue)The determination of the weights (t) is non-trivial, but except for some pathological cases, a set of (55) weights (t) can always be found so as to satisfy the left hand side of (1), for any SSTIC , to within a tolerance . Equation (1) is purely diagnostic. We now submit that given the initial condition we can make a forecast with some skill by

2010XF (s, t0+t) = (t) X(s, t +t) (2) t=1956Where X is any variable (soil moisture, temperature, precipitation)The calculation for (2) is trivial, the underlying assumptions are not. We persist the weights (t) resulting from (1) and linearly combine the X(s,t+t) so as to arrive at a forecast to which XIC (s, t0) will evolve over t.44

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SSTZ500PrecipT2mCA46

SSTZ500PrecipT2mCFSSource: Wanqiu WangPhysical attributions of Forecast SkillGlobal SST, mainly ENSO. Tele-connections needed. Trends, mainly (??) global changeDistribution of soil moisture anomalies

47Website for display of NMME&IMMENMME=National Multi-Model EnsembleIMME=International Multi-Model Ensemble

http://origin.cpc.ncep.noaa.gov/products/NMME/ 48Please attendFriday 2pm June 14Tuesday 1:30pm June 18Two meetings to Discuss the Seasonal Forecast.

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