Huug van den Dool CPC CPC June 23 2011 Oct 2011 Feb 15 2012 UoMDMay22012 Aug2012 Dec122012UoMDApril242013 May222013 2 Assorted Underlying Issues Which tools are used How do these tools work ID: 683303
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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/Slide2
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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 attributionsSlide3
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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’)
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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EXAMPLE
P
U
B
L
I
C
L
Y
I
S
S
UED
“
O
F
F
I
C
I
A
L
”
F
O
R
E
C
A
S
TSlide5
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From an internal CPC Briefing packageSlide8
8
EMP
EMP
EMP
EMP
EMP
DYN
DYN
CON
CON
N/ASlide9
9
9
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
CONSlide10
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Element
US-T US-P SST US-soil moisture
Method:
CCA X X X
OCN X X
CFS X X X X
SMLR X X
ECCA X X
Consolidation X X X Constr Analog X X X XMarkov 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 = ConsolidationSlide11
11Slide12
12Slide13
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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 better3) somewhat shorter averages are better (for T)see Huang et al 1996. J.Climate. 9, 809-817.Slide15
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OCN has become the bearer of most of the skill, see also EOCN method (Peng et al)Slide16
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G
H
C
N
-
C
A
M
S
F
A
N
2008Slide18
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 alCFSv1, Aug 2004, Saha et al 2006. Almost global oceanCFSR, Saha et al 2010CFSv2, 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)Slide22
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Source Peitao Peng
After the fact…..Slide23
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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)Slide24
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ConsolidationSlide25
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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)
Slide27
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CFS v1 skill 1982-2003Slide28
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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 1956-2005Slide29
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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’?Slide30
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(Delsole 2007)Slide32
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3CVRE
SEC
SEC and CVSlide33
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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. Slide41
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Empirical tools can be comprehensive! (Thanks to reanalysis, among other things).
And very economic.
Constructed Analogue
(next 2 slides)Slide42
Given an Initial Condition, SST
IC (s, t0) at time t0 . We express SST
IC
(s, t
0
) as a linear combination of all fields in the historical library, i.e.
2010
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 SSTIC , to within a tolerance ε. Slide43
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.Slide44
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SST
Z500
Precip
T2m
CASlide46
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SST
Z500
Precip
T2m
CFS
Source: Wanqiu WangSlide47
Physical attributions of Forecast SkillGlobal 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/
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Please attendFriday 2pm June 14
Tuesday 1:30pm June 18Two meetings to Discuss the Seasonal Forecast.
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