# Presentation: 814402

## An Inside Look at CPC’s Medium and Long-Range Forecasts

- <date/time>

- An Inside Look at CPC’s Medium and Long-Range Forecasts

- Ed O’Lenic
- NOAA-NWS-Climate Prediction Center
- Camp Springs, Maryland
- _[ed.olenic@noaa.gov](http://www.cpc.ncep.noaa.gov/products/predictions/schedule.html)_
- 301-763-8000, ext 7528

## WEATHER  vs.  CLIMATE 

**WEATHER  vs.  CLIMATE**** **

- Smooth curve = 30 year mean (climatology)

- Wildly oscillating curve = daily “weather”

**Notes:**
Weather and climate are different parts of the same thing and are dependent upon one another.

## 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

**Notes:**
Generic forecast process.  Basic elements of ANY forecast system.

## CPC Forecast System Schematic

**Notes:**
CPC forecast system  breaks down into Seasonal and Extended ranges, with Weather-Climate links emphasizing extreme events.
Inputs to the forecasts ranges from decadal through inter-annual to intra-seasonal time scales.
We model these time scales using a combination of dynamical and statistical models.
Foundation of the system is research done by CPC with NOAA and other partners.

## Generic Seasonal Climate Forecast Map

- B

- EC

- EC

- A

- 33

- 33

- 40

- 40

- 50

- 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**

**Notes:**
Seasonal forecast maps are currently probability ANOMALIES for three categories (A, B, N), each of which occurs 1/3 of the time in the long run.  Categories which are predicted to occur with greater than the climatological likelihood (1/3) are indicated by contours, which show how much more likely than 1/3 the category is, and by colors, blue for colder and red for warmer.  The sum of the probabilities of all the categories add to 100 everywhere on the map.  So, in regions where above or below normal is the favored category with a probability anomaly of 5, the likelihood of below or above in that region is reduced by 5.  Should near normal be predicted (very rare) the likelihood of that category rises while that of each of the extreme categories declines by half the amount of the increase in normal.  In EC regions, the probability of each of the three categories is 1/3.

## 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.

## NCEP Two-Tier Climate Modeling System

**INTEGRATED OCEAN MODEL-DATA ASSIMILATION SYSTEM**

**COUPLED OCEAN-ATMOSPHERE GCM**

**AGCM FORECASTS**

**STATISTICAL TOOLS: CCA, CA**

**STATISTICAL TOOLS**

**SST**

**TOPEX**

**XBT**

**TAO**

**OCEAN INITIAL CONDITIONS**

- STRESS

- EVAP-PRECIP FLUX

**SSTA**

- SSMI/ERS-2

- HEAT FLUXES

**OFFICIAL SST FCST**

**OFFICIAL PROBABILISTIC T,P OUTLOOKS**

**FORECASTERS**

**SURFACE T, P ANOMALIES**

- IRI,CDC

- CDC

**Notes:**
Two-tier system.  Tier 1 is most complex – ocean data assimilation system has to blend many observing systems with differing error characteristics.  Tier 2 is labeled “AGCM FORECASTS”.  Grey and white boxes show other parts of the system and their relationship to the two tiers.

## Forecast tools web page

**Notes:**
This is how forecasters view the tools.

## Global SST 

**Notes:**
SSTs are the single most important variable for the forecast, especially the status of the SSTs in the tropical Pacific.

## TAO Ocean T Obs

**Notes:**
Sub-surface ocean temperatures from the TAO array are extremely useful for diagnosing ocean dynamics related to ENSO and MJO.

## OLR

**Notes:**
CDC has developed a CCA rendering of 6 forecast models, using SSTa as a predictor.  So, while these are not actual output from the models, they are what the models “should” predict, based on an extensive set of prior forecasts and SSTs. 500 hPa anomalies shown.

## SST Consolidation

**Notes:**
CPC’s consolidation forecast for nino 3.4 SST

## SST Plume

**Notes:**
NCEP/CM  graph of nino 3.4 forecasts from ensemble of 16 forecasts.

## SFM 500 MB Z

**Notes:**
Same as previous, but for Precipitation.

## NDJ

## DJF

## Skill-Masked Seasonal Forecast Tools

- forecasters through station forecasts plotted in a coded format.  Plotted
- numerical values give the forecast anomaly in tenths of standard
- deviations, at stations.  If the known skill (anomaly correlation, c,
- between forecasts and observations) is:
-                  c < 0.30    only the sign is indicated, red for +, blue for -,
- 	 0.30 < c < 0.44,    a small-sized font is used,  e.g,       5
-      0.45 <  c < 0.59,   a medium-sized font is used, e.g.,   5
- 	0.60 <  c            ,   a large-sized font is used, e.g,        5

**Notes:**
How to read CPC seasonal forecast tools maps.

## CCA T NDJ

**Notes:**
Same as previous, but for Temperature.

## CCA T DJF

## OCN T NDJ

## OCN T DJF

## NCEP AGCM Forecasts for DJF 2000-01, 2001-02, and 2002-03

- 2000-01

- 2001-02

- 2002-03

**NCEP AGCM Forecasts for DJF 2000-01, ****2001-02, and 2002-03**

- SST Forcing

- Global and NOAM T Fcst

**Notes:**
ASIDE:  A mystery:  Why are these forecasts (left two columns) so similar, given very different SSTa forcing???  This shows how difficult it can be to use what should be the “most sophisticated” of forecast tools.

## ENSO Composites given CPC consolidated SST forecast

**Notes:**
ENSO composites conditioned on the NCEP consolidated SST nino 3.4 forecast.

## OFFICIAL NDJ, DJF 2003 T Outlook

- NDJ

- DJF

**Notes:**
The forecasters take on the tools.

## U.S. Temperature Skill

- SS1= ((c-e)/(t-e))*100

- SS2= ((c+(1/3)*cl - e)/(t-e))*100

- Mean ss1

- Mean ss2

**0**

**Notes:**
Skill for non-EC (red line) and all stations (blue line) forecasts.  Horizontal lines are means.  Lower bar diagram shows % covered.

## Precipitation Skill

**Notes:**
Precipitation skill.  How low can you go?  Skill only during ENSO (for the most part).

## The End