Post on 31-Dec-2015
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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,/Nov20,2013/April,23,2014/
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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
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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’)
• Operational forecasts (‘OFFICIAL’) and informal forecast tools (too many to list)
• http://www.cpc.ncep.noaa.gov/products/predictions/90day/tools/briefing/index.pri.html
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EXAMPLEPUBLICLY
ISSUED
“OFFICIAL”FORECAST
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7From an internal CPC Briefing package
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EMP EMP EMP EMP
EMPDYN
DYN
CONCON
N/A
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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
ECPIRI
ECA CON
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Element US-T US-P SST US-soil moisture
Method:CCA X X XOCN X X CFS X X X XSMLR 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 X
CCA = Canonical Correlation AnalysisOCN = Optimal Climate NormalsCFS = Climate Forecast System (Coupled Ocean-Atmosphere Model)SMLR = Stepwise Multiple Linear RegressionCON = Consolidation
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Long Lead Predictions of US SurfaceTemperature using Canonical CorrelationAnalysis. Barnston(J.Climate, 1994, 1513)
Predictor - Predictand Configuration
Predictors Predictand
* Near-global SSTA
* N.H. 700mb Z * US sfc T
* US sfc T
four predictor “stacked” fields one predictandperiod
4X652=2608 predictors 102 locations
Data Period 1955 - last month
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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 2015 (K=10) = (Jan05+Jan06+... Jan14)/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 better3) somewhat shorter averages are better (for T)see Huang et al 1996. J.Climate. 9, 809-817.
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OCN has become the bearer of most of the skill, see also EOCN method
(Peng et al), or other alternatives of projecting normals forward.
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1.huug.vandendool@noaa.gov
Preview of 2010s, 4 years only
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 2006. Almost global
ocean• CFSR, Saha et al 2010• CFSv2, March 2011. Global ocean, interactive
sea-ice, increases in CO2. Saha et al 2014.19
NCEP’s Climate Forecast System, now called CFS v2
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<--Out of date diagram.Still instructive
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Major Verification Issues
• ‘a-priori’ verification (used to be rare)
• After the fact (fairly normal and traditional)
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Source Peitao Peng
After the fact…..
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(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)
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Consolidation
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--------- OUT TO 1.5 YEARS -------
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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)
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CFS v1 skill 1982-2003
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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 1956-2005
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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, 2365-2379.
(1) CTB, (2) why do we need ‘consolidation’?
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(Delsole 2007)
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3CVRE
SEC
SEC and CV
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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.
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Empirical tools can be comprehensive! (Thanks to
reanalysis, among other things).
And very economical.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.
2012 or 2013• SSTIC (s, t0) ~= SSTCA(s) = Σ α(t) SST(s,t) (1)
t=1956 or 1957 (CA=constructed Analogue)
• The determination of the weights α(t) is non-trivial, but except for some pathological cases, a set of (57) weights α(t) can always be found so as to satisfy the left hand side of (1), for any SSTIC , to within a tolerance ε.
• Equation (1) is purely diagnostic. We now submit that given the initial condition we can make a forecast with some skill by
2012 or 2013• XF (s, t0+Δt) = Σ α(t) X(s, t +Δt) (2)
t=1956 or 1957Where 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 XIC (s, t0) will evolve over Δt.
Year Wgt Year Wgt Year Wgt Year Wgt Year Wgt Year Weigt
1956 -5 1966 -10 1976 -4 1986 5 1996 2 2006 2
1957 12 1967 0 1977 0 1987 -9 1997 9 2007 2
1958 3 1968 1 1978 -3 1988 -10 1998 -11 2008 2
1959 13 1969 -6 1979 -3 1989 0 1999 -2 2009 11
1960 -7 1970 -4 1980 8 1990 5 2000 -17 2010 6
1961 -2 1971 2 1981 -7 1991 14 2001 3 2011 -1
1962 5 1972 4 1982 -12 1992 -3 2002 -2 2012 12
1963 5 1973 10 1983 -7 1993 -4 2003 20 2013 7
1964 -8 1974 6 1984 3 1994 -7 2004 -1 2014 NA
1965 -9 1975 -2 1985 2 1995 -1 2005 7 Xx
CA-weights inMarch 2014
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SST Z500
PrecipT2m
CA
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SST Z500
PrecipT2m
CFS
Source: Wanqiu Wang
Physical attributions of Forecast Skill
• Global SST, mainly ENSO. Tele-connections needed.
• Trends, mainly (??) global change
• Distribution of soil moisture anomalies
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Website for display of NMME&IMME
NMME=National Multi-Model EnsembleIMME=International Multi-Model Ensemble
• http://origin.cpc.ncep.noaa.gov/products/NMME/
Please attend
• Friday 2pm June 14
• Tuesday 1:30pm June 18
Two meetings to Discuss the Seasonal Forecast.
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