by Huug van den Dool

January 14, 2018 | Author: Anonymous | Category: Science, Health Science, Pediatrics
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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/ 1 May22,2013/

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 2

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/9 0day/tools/briefing/index.pri.html 3

EXAMPLE P U B L I C L Y I S S U E D

“ O F F I C I A L ” F O R E C A S T

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From an internal CPC Briefing package

EMP

EMP

EMP

N/A

DYN

EMP

DYN CON

EMP

CON 8

SMLR

CCA

OCN

LAN

OLD-OTLK

CFSV1

LFQ

ECP

IRI ECA

CON 9

(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 10

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) 14 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. 18

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 22

(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) 26

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’? 29

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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 46

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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