by Huug van den Dool

Report
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/ 1
May22,2013/
Assorted Underlying Issues
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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
2
Menu of CPC predictions:
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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/9
0day/tools/briefing/index.pri.html
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EXAMPLE
P
U
B
L
I
C
L
Y
I
S
S
U
E
D
“
O
F
F
I
C
I
A
L
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F
O
R
E
C
A
S
T
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6
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From an internal CPC Briefing package
EMP
EMP
EMP
N/A
DYN
EMP
DYN
CON
EMP
CON
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SMLR
CCA
OCN
LAN
OLD-OTLK
CFSV1
LFQ
ECP
IRI
ECA
CON
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(15 CASES: 1950, 54, 55, 56, 64, 68, 71, 74, 75, 76, 85, 89, 99, 00, 08)
Element 
US-T
Method:
CCA
X
OCN
X
CFS
X
SMLR
X
ECCA
X
Consolidation
X
US-P
X
X
X
X
X
X
SST
US-soil moisture
X
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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Long Lead Predictions of US Surface
Temperature using Canonical Correlation
Analysis. 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
4X652=2608 predictors
one predictand
period
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 2012 (K=10)
= (Jan02+Jan03+... 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)
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see Huang et al 1996. J.Climate. 9, 809-817.
OCN has become the bearer of
most of the skill, see also EOCN
method (Peng et al)
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G
H
C
N
C
A
M
S
F
A
N
2
0
0
8
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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.
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NCEP’s Climate Forecast
System, now called CFS v2
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Major Verification Issues
• ‘a-priori’ verification (used to be
rare)
• After the fact (fairly normal)
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After the fact…..
Source Peitao Peng
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(Seasonal) Forecasts are useless
unless accompanied by a reliable apriori 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
28 to
reduce noise.
CA skill 1956-2005
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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SEC
SEC and CV
3CVRE
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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 economic.
Constructed Analogue(next 2 slides)
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• 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.
2010
• SSTIC (s, t0) ~= SSTCA(s) = Σ α(t) SST(s,t)
t=1956
(CA=constructed Analogue)
(1)
• 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 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
2010
• XF (s, t0+Δt) = Σ α(t) X(s, t +Δt)
t=1956
(2)
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 XIC (s, t0) will evolve over Δt.
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SST
CA
Z500
T2m
Precip
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SST
Z500
CFS
Precip
T2m
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Source: Wanqiu Wang
Physical attributions of Forecast
Skill
• Global SST, mainly ENSO. Teleconnections needed.
• Trends, mainly (??) global change
• Distribution of soil moisture anomalies
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Website for display of
NMME&IMME
NMME=National Multi-Model Ensemble
IMME=International Multi-Model Ensemble
• http://origin.cpc.ncep.noaa.gov/products/N
MME/
Please attend
• Friday 2pm June 14
• Tuesday 1:30pm June 18
Two meetings to Discuss the Seasonal
Forecast.
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