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An Inside Look at CPC’s Medium and Long-Range Forecasts
Ed O’Lenic
NOAA-NWS-Climate Prediction Center
Camp Springs, Maryland
301-763-8000, ext 7528
WEATHER vs. CLIMATE
•Smooth curve = 30 year mean (climatology)
•Wildly oscillating curve = daily “weather”
Forecast Process Schematic
Dynamical model forecasts/multi-
model ensembles
Recent observations Historical observations..
Verifications/Statistical tools Downscaling, Analogs, Composites
WEB PAGES/AUTOMATED DATABASES
Peer-reviews of the forecast tools and of the penultimate forecast via web/telephone conference with partners and through local discussions
(map discussions,sanity check, conference calls, etc…)
Forecaster-created or automated products
Dissemination to public
Applied Research, Diagnostics and Forecast ToolsCollaborators: EMC, TPC, CDC, GFDL, IRI, Scripps, COLA, U. Wash.
Inter-Annual Variability
- ENSO
Decadal Variability
- PDO- AO/NAO- Global Warming
Intra-seasonal Variability
- Tropical MJO- Blocking- AO/NAO/NPO/PNA
SeasonalExtended Range
Climate Prediction Center Forecast System Schematic
HighFrequency:Interannual
Low-Frequency:
Trend
U.S.Threats Assessment
6-10 Day
Week Two
Monthly
International Threats
Dynamical/statistical models
- Real-Time Diagnostics- Model Simulations- Ensembles- Verification
Weather/climate links
- Composites- Teleconnections- Extreme events- Tropical storms- Drought/Floods- Climate/Weather Monitoring
B
EC
EC
A
0
05
510
EC=Equal Chances for the tercile categories, 33-1/3 each.
Contours are labeled with the deviation from EC for the indicated category.
Generic Seasonal Climate Forecast Map
Part 1. Long-Lead Seasonal Forecasts
Forecast Maps and Bulletins
•Each month, on the Thursday between the 15th 21st, CPC issues a set of 13 seasonal outlooks.
•There are two maps for each of the 13 leads, one for temperature and one for precipitation for a total of 26 maps.
•Each outlook covers a 3-month “season”, and each forecast overlaps the next and prior season by 2 months.
•Bulletins include: the prognostic discussion for the seasonal outlook over North America, and, for Hawaii.
•The monthly outlook is issued at the same time as the seasonal outlook. It consists of a temperature and precipitation outlook for a single lead, 0.5 months, and the monthly bulletin.
•All maps are sent to AWIPS, Family of Services and internet.
Statistical Prediction Tools
• Multiple Linear Regression:
- Predicts a single variable from historical and recent observations of two or more predictors.
• Canonical Correlation Analysis (CCA):
– Uses recent and historical observations of Northern Hemisphere circulation (Z), global sea surface temperature (SST), US surface T (Tus) to create a set of 5 or 6 EOFs of predictors and predictands.
– Looks at cross-correlations between time series of predictors and predictands.
– Predicts temporal and spatial patterns from patterns.
Statistical Prediction Tools
• Constructed Analogs (CA)– Uses recent observations (base) of a single
variable and historical observations, to construct a weighted mean of all prior years which best explains the base data. Assumes the evolution to subsequent seasons is also best explained by the weights used to construct the analog to the base.
• Optimal Climate Normals (OCN)– Uses the difference between the most recent 10
(15) years of temperature (precipitation) observations and the 30-year climatology (i.e., the trend) for a given season as the prediction for future occurrences of that season.
Detailed operations concept for NCEP’s ocean-atmosphere model
An ensemble of 16 ocean SST forecasts are created using a coupled
GCM. The average of these is used as the lower boundary for…
An AGCM, along with 20 different sets of initial conditions, to create
a set of 20 ensemble atmosphere forecasts out to 9 months. A 20-year
AMIP run of the AGCM is made each month for use as the climatology
to create anomalies/remove model bias.
In collaboration with partners (CDC, IRI), forecasters use the NCEP
model tools, together with other model tools to subjectively create
outlook maps of the probability of monthly and seasonal mean
temperature and total precipitation category.
NCEP Two-Tier Climate Modeling System
INTEGRATED OCEAN MODEL-
DATA ASSIMILATION
SYSTEM
COUPLED OCEAN-
ATMOSPHERE GCM
AGCM FORECAST
S
STATISTICAL TOOLS: CCA,
CA
STATISTICAL TOOLS
SST TOPEX XBT TAO
OCEAN INITIAL CONDITIONS
STRESSEVAP-
PRECIP FLUX
SSTA
SSMI/ERS-2
HEAT FLUXES
OFFICIAL SST FCSTOFFICIAL
PROBABILISTIC T,P OUTLOOKS
FORECASTERS
SURFACE T, P ANOMALIES
IRI,CDC
CDC
Forecast tools web page
Global SST
TAO Ocean T Obs
2000-01
2001-02
2002-03
NCEP AGCM Forecasts for DJF 2000-01, 2001-02, and 2002-03
SST ForcingGlobal and NOAM T Fcst
MAM 2003 NCEP AGCM T Forecast
CCA 0.5 Mo lead MAM T Outlook
OCN 0.5 Mo lead MAM T Outlook
ENSO Composites given CPC
consolidated SST forecast
OFFICIAL MAM 2003 T Outlook
SS1= ((c-e)/(t-e))*100
SS2= ((c+(1/3)*cl - e)/(t-e))*100
Mean ss1
Mean ss2
0
U.S. Temperature Skill
Precipitation Skill
The Forecast Forecast
• 1-3 years: Week 2 forecasts for the Pacific Region & Caribbean based on MJO, ENSO, Monsoon, improved dynamical & statistical models.
• 3-5 years: Week 3, 4 forecast based on NAM/AO relationships, MJO, ENSO, improved statistical & dynamical models.
• 5-10 years: Improved seasonal, monthly, week 2, 3-4 forecasts based on improved dynamical & statistical model prediction of NAM/AO, diurnal cycle of convection, MJO, ENSO, Monsoon, decadal oscillations, ocean-atmosphere coupling, MM ensembles, more & better observations, …
Part 2. Medium-Range Forecasts
6-10 day/week 2 process schematic
Multi-model ensemble 9:00 AM
Weighted average of model 500 hPa height
Downscale: get surface weather from 500 mb height via analogs, regression, neural network.
R R
Forecaster formulates maps of predicted T, P, PMD bulletin
Disseminate via web, AWIPS, FOS 3-4 PM
R = Forecaster reconciliation of tools required
Recent Changes to Procedures
• From 3 times/week to daily in October 2000• Automated weekend forecasts from October 2000• Percent probability format from October 2000• Alaska and week 2 added October 2000• Automated weekend forecasts improved October
2001—neural net tool omitted and consistency with weekday forecasts added
• Bias-corrected precipitation forecast tool and other improvements added in the fall of 2001
Forecast Maps and BulletinsEach day,between 3 and 4 PM Eastern Time, CPC issues a set of 6-10 day and week 2 outlooks. These are formulated by a forecaster (Monday through Friday) and are automated on weekends. There are two 500 mb height maps, two surface maps and a single bulletin.
Sample 6-10 day outlook 500 mb height and anomaly forecast map from CPC web page.
GFS Ensemble upper-air-height forecasts, analog
ECMWF upper-air-height forecasts, analog
Official 6-10 day 500 hPa forecast
Teleconnections (TC)Definition: Composite of
those maps, for a calendar month, with largest + (top 10%) or – (bottom 10%) 500 hPa height at a specified space point from 1950-1999 (~150 maps).
Forecaster computes TC on major anomaly centers (base points) of 500 hPa forecast maps.
Strong TC ~ large correlation values at the distant centers ~ frequent/persistent pattern
Weak TC ~ the pattern is probably transient and not as likely to be well predicted by the model as would a persistent pattern.
Teleconnection on 500 hPa center at 50N/140W (+) and 55N/90W (-)
Composite of observed T, P anomalies associated with teleconnecion on + 500 hPa anomalies at 56N 10W
ENSEMBLE T, P prediction analog maps ECMWF
6-10 DayMRF precipitation bias correction
8-14 Day
Official 6-10 day T forecast
Official 6-10 day P forecast
6-10 day Monthly Average Skill Scores
s=((c-e)/(t-e))*100
c = # hitse = # chance hitst = # forecasts
Skill of Official 6-10 day T, P, 500 hPa Ensemble Mean Z
Skill of 6-10 day T tools, 500 hPa Ensemble Mean Z
The Forecast Forecast
• 1-3 years: Week 2 forecasts for the Pacific Region & Caribbean based on MJO, ENSO, Monsoon, improved dynamical & statistical models.
• 3-5 years: Week 3, 4 forecast based on NAM/AO relationships, MJO, ENSO, improved statistical & dynamical models.
• 5-10 years: Improved seasonal, monthly, week 2, 3-4 forecasts based on improved dynamical & statistical model prediction of NAM/AO, diurnal cycle of convection, MJO, ENSO, Monsoon, decadal oscillations, ocean-atmosphere coupling, MM ensembles, more & better observations, …
The End