# Presentation: 177784

## THE NORTH AMERICAN ENSEMBLE FORECAST SYSTEM:

**AN OPERATIONAL MULTI-CENTER FORECAST SYSTEM**

*http://wwwt.emc.ncep.noaa.gov/gmb/ens/index.html*

**Zoltan Toth (NWS), Louis Lefaivre (MSC), Michel Rosengaus (NMSM)**
- Acknowledgements: Philippe Bougeault, David Parsons, Lawrence Wilson

## OUTLINE

**PROJECT DESCRIPTION**
**TIMELINE**
**PARTICIPANTS**
**CONCEPT OF OPERATIONS**
**BASIC PRODUCTS**
**END PRODUCTS**
**PLANS**
**TIGGE / GIFS CONNECTIONS**

## PROJECT DESCRIPTION

***International project to produce operational multi-center ensemble products***
- Combines global ensemble forecasts from Canada & USA
-  32 members per cycle, 2 cycles per day from MSC & NWS
- 6-hourly output frequency
- Forecasts out to 16 days
- Generates products for
- Weather forecasters
- E.g., NCEP Service Centers (US NWS)
- Specialized users
- E.g., hydrologic applications in all three countries
- End users
- E.g., forecasts for public distribution in Canada (MSC) and Mexico (NMSM)
- Operational outlet for THORPEX research using TIGGE archive
- Prototype ensemble component of THORPEX Global Interactive Forecast System (GIFS)

## BENEFITS

**Improves probabilistic forecast performance**
- Earlier warnings for severe weather
- Lower detection threshold due to more ensemble members
- Uncertainty better captured via analysis/model/ensemble diversity (assumed)
**Provides Seamless suite of forecasts** across
- International boundaries
- Canada, Mexico, USA
- Different time ranges (1-14 days)
**Saves development costs** by
-  Sharing scientific algorithms, codes, scripts
- Accelerated implementation schedule
- Low-cost diversity via multi-center analysis/model/ensemble methods
-  Exchanging complementary application tools
- MSC focus on end users (public)
- NWS focus on intermediate user (forecaster)
**Saves production costs** by
- Leveraging computational resources
- Each center needs to run only fraction of total ensemble members
-  Providing back-up for operations in case of emergencies
- Use nearly identical operational procedures at both centers to provide basic products
- Offers as default basic products based on unaffected center’s ensemble

## PROJECT HISTORY & MILESTONES

- February 2003, Long Beach, CA
- NOAA / MSC high level agreement about joint ensemble research/development work (J. Hayes, L. Uccellini, D. Rogers, M. Beland, P. Dubreuil, J. Abraham)
- May 2003, Montreal (MSC)
- 1st NAEFS Workshop, ***planning started***
- November 2003, MSC & NWS
- 1st draft of NAEFS Research, Development & Implementation Plan complete
- May 2004, Camp Springs, MD (NCEP)
- Executive Review
- September 2004, MSC & NWS
***Initial Operational Capability*** implemented at MSC & NWS
- November 2004, Camp Springs
- Inauguration ceremony & 2nd NAEFS Workshop
- Leaders of NMS of Canada, Mexico, USA signed memorandum
- 50 scientists from 5 countries & 8 agencies
- May 2006, Montreal
- 3rd NAEFS Workshop
- May-Oct 2006, MSC & NWS
***1******st****** Operational Implementation***
- Bias correction
- Climate anomaly forecasts
- 2007-2008, MSC, NWS
- Follow-up implementations
- Improved and expanded product suite

## NAEFS ORGANIZATION

***Meteorological Service of Canada******	******National Weather Service, USA***
***	******	******MSC ******	******	******	******	******	******NWS******	***

- _Louis Lefaivre (Implementation)_
- Peter Houtekamer (Science)

- _Zoltan Toth (Science)_
- David Michaud / Brent Gordon (Impl.)

***PROJECT OVERSIGHT***

***JOINT TEAM MEMBERS***

*Meteorological Research Branch MRB*
- Pierre Gauthier, Lawrence Wilson, Vincent Fortin, Guillem Candille
*Canadian Meteorological Center CMC*
- Richard Verret, Yves Pelletier, Gerard
-  Pellerin, Stephane Beauregard, Norman
-  Gagnon, Lewis Poulin,  Jacques Hodgson

*Environmental Modeling Center EMC*
- Yuejian Zhu, Bo Cui, Richard Wobus, Dingchen Hou, Malaquias Pena
*NCO *John Huddleston  *HPC  Keith Brill*
*Storm Prediction Center *David Bright
*Climate Prediction Center CPC*
- Ed O’Lenic, David Unger, Dan Collins
*NWS* Richard Grumm, Fred Branski

- Michel Beland, Director, ACSD
- Angele Simard, Director, AEPD
- Gilbert Brunet, MRB

- Louis Uccellini (Director, NCEP/NWS)
- Greg Mandt (Director, OST/NWS)
- Steve Lord, EMC

***PROJECT CO-LEADERS***

***National Meteorological Service of Mexico (NMSM) - Rene Lobato***
***Fleet Numerical Meteorology & Oceanography Center (FNMOC) – Michael Sestak***
- Acknowledgements to: J. Whitaker, T. Hamill, Y. Gel, R. Krzysztofowicz

## CONCEPT OF OPERATIONS

**Data exchange**
- Current status
- ~50 selected variables, GRIB1, ftp
- Subset of TIGGE variables
- Plan
- Variables added on annual basis, GRIB2,
- 	direct link
**Basic products**
- Types of products
- Bias corrected fields (35 variables)
- Reduce systematic error
- Combined ensemble (all variables)
- Based on weights (equal weights currently) or other algorithms (Bayesian)
- Anomalies (19 variables)
- Forecasts expressed as percentiles compared to climatological distribution
- Allows downscaling by adding local climatological distribution
- Generation
- Use same algorithms/codes at both MSC and NWS
- Duplicate procedures provide full backup in case of problems at either end
- If one component of ensemble missing, products based on rest of ensemble
- Distribution
- Ftp – e.g., *http://nomad5.ncep.noaa.gov/ncep_data/*

**Systematic Error**

- Before Bias Correction

- After Bias Correction

## End products
Types of products
Site specific
Ensemble-grams (MSC)
Geographically distributed
Host of probabilistic products for various regions (NCEP)
Temporal mean
Week-2 temperature
First joint end product
Generation
Based on common set of basic products

- Types of products
- Site specific
- Ensemble-grams (MSC)
- Geographically distributed
- Host of probabilistic products for various regions (NCEP)
- Temporal mean
- Week-2 temperature
- First joint end product
- Generation
- Based on common set of basic products

- Ensures consistency among end products whether generated
- Jointly or by individual centers
- Distribution
- Web, e.g., *http://meteo.ec.gc.ca/ensemble/index_naefs_e.html*; ftp
**Evaluation / Outreach goals**
- Verification using same algorithms
- Link with Decision Support Systems
- User feedback for improvements

**RPSS**

- Before Bias Correction

- After Bias Correction

**CONCEPT OF OPERATIONS - 2**

## ENSEMBLE-GRAMS

**ENSEMBLE-GRAMS**

**Total Cloud Cover**

**12-hr Accumulated Precipitation**

**2-m Temperature**

**10-m Wind Speed**

## Probability of precipitation over 10 mm at least one day 

**Probability of precipitation over 10 mm at least one day**
- 12-16 December 2006

- Any other end product can be generated based on basic products

## North American Ensemble Forecast System

- 2 m Temperature
- 8 to 14 Day Outlook
- 00z forecast [EXPERIMENTAL]
- Valid: Dec 09 - 15, 2006, Issued: Dec 01, 2006

- Probability of week-2 mean 2m temperature being in lower (shades of blue) or upper (shades of red) climate tercile

*Landshut*

## PRODUCT GENERATION PLANS

**PRODUCT GENERATION PLANS**

- Basic products
**Bias correction on model grid** against analysis
- Remove lead-time dependent behavior - Cheap
- Add more variables (especially precipitation)
- Develop / test new techniques
- Bayesian methods - Weighting
- Introduce **downscaling onto fine resolution grid**
- After bias correction – Can use more expensive methods
- Independent of forecasts– Low to fine resolution analysis
- NN, MOS, etc (including hires LAM NWP)
- End products
- New applications – Tropical cyclones, hydrology, etc
- Decision Support System collaboration

- Before
- Bias corr.

- After
- Bias corr.

- After Down-scaling

**Systematic error in 1x1 lat/lon U wind forecast**
** ****on 5x5 km grid, 24-hr lead**

*Before downscaling*

*After downscaling*

**Systematic Error**

**Effect of downscaling on**

## Meteorological application example
Tropical cyclone forecasting
Link with IWTC – Recent meeting in Costa Rica (Nov. 2006)
Great interest in ensemble / probabilistic forecasting

“Downstream” application example
Hydrological forecasting
Link with HEPEX – June 2007 meeting in Italy
NWS/OHD Ensemble Workshop
Great interest in ensemble forecasting

Decision Support Systems
Feed statistically corrected ensemble trajectories into decision systems
Links with SERA

Training
Strong need on all fronts
Link with WMO/CBS Expert Team on Ensemble Prediction – Meeting in February 2006 

- Tropical cyclone forecasting
- Link with IWTC – Recent meeting in Costa Rica (Nov. 2006)
- Great interest in ensemble / probabilistic forecasting
- “Downstream” application example
- Hydrological forecasting
- Link with HEPEX – June 2007 meeting in Italy
- NWS/OHD Ensemble Workshop
- Great interest in ensemble forecasting
- Decision Support Systems
- Feed statistically corrected ensemble trajectories into decision systems
- Links with SERA
- Training
- Strong need on all fronts
- Link with WMO/CBS Expert Team on Ensemble Prediction – Meeting in February 2006

**FUTURE APPLICATIONS – NAEFS – TIGGE2 - GIFS**

**THORPEX Interactive Grand**
**Global Ensemble (TIGGE)**

**NAEFS – Global Interactive Forecast System (GIFS)**

*OPERATIONS*

*RESEARCH*

*Transfers*
*New methods*

*Articulates*
*operational needs*

## HURRICANE WILMA

**HURRICANE WILMA**
*STRIKE PROBABILITY*
*Probability of storm within 65 nm vicinity of any point on map*

* **Forecast track*
* **Observed track*

*Strike probability =>*

## ENSEMBLE-BASED

**Mississippi, River**
**Vicksburg, MS**
**The Large Basin**
**May 4****th**** case**
- A major mid-range event well   predicted
- Significant spread in
- extended range
**April 1st case**
- Without  a major event,
- all simulations are similar and spread is small.
- Trend and events** **picked up.
- Short lead time dominated
-    by initial condition, showing
-    little spread.
- Spread Increases with time.

**May 4th**

**April 1st**

**----- GEFS members**
**----- GEFS ens. mean**
**----- GEFS control**
**----- GFS high resolution**
**----- NLDAS**

- 0           2              4             6             8            10           12           14           16
-                                        Lead Time  (days)

**ENSEMBLE-BASED**

**Total Cloud Cover**

## EXPANSION PLANS – LINK WITH TIGGE-2 / GIFS

**Other centers**
- FNMOC to join by 2008
- After a 1-year experimental data exchange, subject to evaluation
- UK Metoffice, KMA, CMA considers participation
- No detailed plans
- JMA, CPTEC expressed an interest
- ECMWF, NCMRWF want to be informed
**Alternate concept of operations**
- Current operational concept may not suite all parties
**Prototypes for TIGGE-2 / GIFS**
- Science / mechanics
- NAEFS
- IT Technology
- NOMADS
- Flexible access to distributed data
- Web-based product generation

## NOMADS  Design

*NOMADS  Design*

- _PROPOSED:_

- Seamless access across real-time to historical

- Want Data?

**NOAA NCDC Ensemble Archive TIGGE Goals:**
**Backup for Phase-1; Operational server for Phase-2 & GIFS**

## Screen shot of the web page containing prompts and user 

*Screen shot of the web page containing prompts and user *
*entered responses for the probability of frost at day 4 1⁄2.*

- Global Ensemble model data
- is from both the “a” and “b” files
- since June/2006.

- The purpose of this example is to show how to make queries to the server as well as show how to directly  obtain model values in an user application.  We click “yes” to show the temperature queries as they are made.

**Time for coordinated planning for TIGGE Phase-2**

## US CONTRIBUTIONS TO TIGGE - UPDATE

**PROVISION OF NCEP OPERATIONAL ENSEMBLE DATA**
- November 1 of 2006
- 41 of 71 TIGGE variables available
- Existing UCAR UNIDATA feed used
- NCAR changes “operational” to “TIGGE” header
- NCAR to transmit data with reformatted header to ECMWF
- Regular transmission is to be set up
- Reliability of transfer to be assessed
- September 2007
- 70 of 71 variables planned
- NCAR to process additional 30 operationally available variables into TIGGE-required format
- Subject to funding
- NCAR to transmit reformatted data to other archives
- September 2008
- 71 of 71 variables planned to be made available
**PROVISION OF FNMOC OPERATIONAL ENSEMBLE DATA**
- To be pursued next
**NCAR ARCHIVE CENTER**
- December 2006
- 2-3 wks on disk, rest in mass storage
- Access to archived data initially on original grid only
- NCEP, ECMWF, UK Met Office, JMA
- Future enhancements
- CPTEC data in the works, other centers to follow
- Regridding capability and other access software enhancements to follow

## PARADIGM SHIFT IN FORECASTING

- Distinguish clearly between
- Forecast process
- Single value focus OR
- Probability distribution focus AND
- Serving customers’ needs
- Many may not be ready for paradigm shift yet
- Optimal procedure for both
- Forecast process and
- Customer applications
- requires a           ***PROBABILISTIC APPROACH***
- Operational requirements / routine engrained in traditional paradigm
- Must move to new paradigm to
- Improve skill				***Ensemble mean is better than control BECAUSE***
- Potentially enlarge customer base		***Case dependent probability distribution is captured by ensemble***
- While maintaining ability to serve up traditional forecast products
- All existing and new products can be generated based on ensemble
- Numerical ensemble forecasting is favored approach
- Automated ensemble-based forecast process
- Systematic errors to be mitigated via statistical bias correction & downscaling
- Unified approach across scales / applications
- New, empowered role for forecasters
- Adjust entire ensemble (instead of single NWP forecast)
- Direct & quality control adaptive forecast process

## BACKGROUND

## PROPAGATING FORECAST UNCERTAINTY

- z

*Single value*

*Distribution*

- Ensemble Forecasting:
- Central role – bringing the pieces together

**PROPAGATING FORECAST UNCERTAINTY**

## CONFIGURATION, OUTPUT CHARACTERISTICS

**CONFIGURATION, OUTPUT CHARACTERISTICS**

*2005, **2006**, **2007**, **2008*

## RAW DATA & BASIC PRODUCT AVAILABILITY

**RAW DATA & BASIC PRODUCT AVAILABILITY**

*2005, **2006**, **2007**, **2008*

##     THORPEX GOAL	
Accelerate improvements in skill & utility of high impact forecasts
All improvements related to advances in NWP skill
We must accelerate improvements in NWP skill
Maintain/improve application procedures

**IMPROVEMENT IN PROBABILISTIC SKILL OVER PAST 4 YEARS**

**    ****THORPEX GOAL**
- Accelerate improvements in skill & utility of high impact forecasts
- All improvements related to advances in NWP skill
- We must accelerate improvements in NWP skill
- Maintain/improve application procedures

**NORTH AMERICAN ENSEMBLE**
**FORECAST SYSTEM (NAEFS)**

- Operational multi-center ensemble system
**Significant acceleration** in skill
- Joint ensemble research
- More achieved in one implementation than in previous 4 yrs
*Implementations at participating centers have immediate impact for all participants*
- Shortcutting the typical 2-3 year development path that takes to adapt changes internally

- 1.5-day extension of skill in 4 yrs

- Close to 2-day extension of skill with first NAEFS implementation

- THORPEX – NAEFS
- TO DOUBLE RATE OF IMPROVEMENT

***1***

***2***

***3***

***4***

## CONCEPT OF OPERATIONS

*CURRENT - NAEFS*
- Concept
- Schedule driven
- Central product generation
- Data access
- Exchange all data among participating centers
- Large data transfer volume
- Basic products
- Generate all basic products by all participating centers
- Share all algorithms
- End products
- Generation based on basic products
- Suite of joint and center-specific products

## ALTERNATE CONCEPT OF OPERATIONS

*FUTURE – TIGGE-2 / GIFS*
- Concept
- User driven
- Web-based product generation
- Data access
- Grab selectively only what is needed
- Basic products
- Basic products generated by producing center only
- Hind-casts included if needed
- Share all algorithms
- End products
- Generation based on basic products
- Jointly develop and maintain product generation toolbox
- Web-based product generation

## BACKGROUND

## Ensemble Mean Forecast & Bias Before/After RTMA Downscaling  

- 2% experiments
-  Left top: operational ens. mean and its bias
-  Right top: bias corrected ens. mean and its bias
-  Left bottom: bias corrected ens. mean after downscaling and its bias left toward RTMA** **
- After Downscaling
-  More detailed forecast information
-  Bias reduced, especially high topography areas** **

- Before

- Before

- After

## INAUGURATIONCEREMONY

## Accumulated Bias Before/After RTMA Downscaling  

**Black- operational ensemble mean, 2%**
**Pink- bias corrected ens. mean after downscaling, 5%**
**Red- NAEFS bias corrected ensemble mean, 2%**
**Blue-bias corrected ens. mean after downscaling, 2%**
**Yellow-bias corrected ens. mean after downscaling, 10%**

- black

- red

- blue

## BACKGROUND

## BASIC PRODUCTS

- NAEFS basic product list
- Bias corrected members of joint MSC-NCEP ensemble
- 40 members, 35 of NAEFS variables, GRIB2
- Bias correction against each center’s own operational analysis
- Weights for each member for creating joint ensemble
- 40 members, independent of variables, GRIB2
- Weights depend on geographical location (low precision packing)
- Climate anomaly percentiles for each member
- 40 members, 19 of NAEFS variables, GRIB2
- Non-dimensional unit, allows downscaling of scalar variables to any local climatology
- Issues – Products to be added in future years
- Bias correction on precipitation & some other variables not corrected yet)
*Use CMORPH satellite-based analysis of precipitation rates*
- CPC collaborators (J. Janowiak)
- Climate anomalies for missing variables
- Need to process reanalysis data to describe climatology for missing variables

## END PRODUCTS

- End product generation
- Can be center specific
- Need to conform with procedures/requirements established at different centers
- End products generated at NCEP
- Based on prioritized list of requests from NCEP Service Centers
- Graphical products (including Caribbean, South American, and AMMA areas)
- NCEP official web site (gif – NA, Caribbean, SA, AMMA)
- NCEP Service Centers (NAWIPS metafile)
- Gridded products
- NAWIPS grids
- NCEP Service Centers (list of 661 products)
- GRIB2 format
- Products of general interest (Possible ftp distribution, no decision yet on products)
- NDGD (10-50-90 percentile forecast value + associated climate percentile)
- End products generated at MSC
- TBD
- End products generated jointly
- Experimental probabilistic Week-2 forecast
- Fully automated, based on basic products: bias corrected, weighted climate anomalies
- Can become official product once performance reaches current operational level

## ENSEMBLE PRODUCTS - FUNCTIONALITIES

|  | FUNCTIONALITY | CENTRALLY GENERATED | LOCALLY GENERATED | INTERACTIVE
ACCESS |
| --- | --- | --- | --- | --- |
| 1 | Mean of selected members  Done |  |  |  |
| 2 | Spread of selected members Done  |  |  |  |
| 3 | Median of selected values Sept. 2005 |  |  |  |
| 4 | Lowest value in selected members Sept. 2005 |  |  |  |
| 5 | Highest value in selected members Sept. 2005 |  |  |  |
| 6 | Range between lowest and highest values Sept. 2005 |  |  |  |
| 7 | Univariate exceedance probabilities for a selectable threshold value FY06? |  |  |  |
| 8 | Multivariate (up to 5) exceedance probabilities for a selectable threshold value FY06? |  |  |  |
| 9 | Forecast value associated with selected univariate percentile value Sept. 2005 - FY06? |  |  |  |
| 10 | Tracking center of maxima or minima in a gridded field (eg – low pressure centers) Sept. 2005, Data flow FY06? |  |  |  |
| 11 | Objective grouping of members FY08? |  |  |  |
| 12 | Plot Frequency / Fitted probability density function at selected location/time (lower priority) FY07? |  |  |  |
| 13 | Plot Frequency / Fitted probability density as a function of forecast lead time, at selected location (lower priority) FY07? |  |  |  |

- List of centrally/locally/interactively generated products required by NCEP Service Centers for each functionality are provided in attached tables (eg., *MSLP, Z,T,U,V,RH, etc, at 925,850,700,500, 400, 300, 250, 100, etc hPa)*

***Potentially useful functionalities that need further development:***
- - Mean/Spread/Median/Ranges for amplitude of specific features - Mean/Spread/Median/Ranges for phase of specific features

***Additional basic GUI functionalities:**** *- Ability to manually select/identify members - Ability to weight selected members *Sept. 2005*

## ENSEMBLE PRODUCT REQUEST LIST

- NCEP SERVICE CENTERS, OTHER PROJECTS

## Example of probabilistic forecast in terms of climatology

- ENSEMBLE 10-, 50- (MEDIAN) & 90-PERCENTILE FORECAST VALUES (BLACK CONTOURS) AND CORRESPONDING CLIMATE PERCENTILES (SHADES OF COLOR)

## Climate percentile (0 = 50 percentile)

## NAEFS & THORPEX

- Expands international collaboration
- Mexico joined in November 2004
- FNMOC to join in 2006
- UK Met Office may join in 2009
- Provides framework for transitioning research into operations
- Prototype for ensemble component of THORPEX legacy forecst system:   		***Global Interactive Forecast System (GIFS)***

**THORPEX Interactive Grand**
**Global Ensemble (TIGGE)**

**North American Ensemble**
**Forecast System (NAEFS)**

*OPERATIONS*

*RESEARCH*

*Articulates*
*operational needs*

*Transfers*
*New methods*

- THORPEX

- RESEARCH

- LEGACY (GIFS)

- OPERATIONAL

## Slide 40

## Data exchange
Coordination needed with Yves Pelletier from MSC (Brent Gordon)
Switch to GRIB2 format
New file structure (files containing NAEFS variables only)
Operational transmission arrangements
NCEP pushes its data to MSC
Basic products
Bias correction (Bo Cui, Dave Unger)
First moment method works, accepted for use by both parties
Second moment correction
Moment adjustment & Bayesian Model Averaging, BMA methods to be compared
May or may not be included in 1st operational implementation
Weighting (Bo Cui, Dave Unger)
Skill, Ridging, BMA methods to be compared
Climate anomalies (Yuejian Zhu)
Detailed algorithm to be developed
End product generation
One stream to generate multiple product formats (Dave Michaud)
Start with highest priority items from prioritized list from Service Centers (Z. Toth)
Required NAWIPS tools ready by Sept & Dec 2005 (Maxine Brown)
Default graphical setup to be developed & JIF’d for web display (Maxine Brown)
NAWIPS graphical products using web default display (Dave Michaud)
NAWIPS & GRIB2 product generation as part of one product stream (Dave Michaud)

- Coordination needed with Yves Pelletier from MSC (Brent Gordon)
- Switch to GRIB2 format
- New file structure (files containing NAEFS variables only)
- Operational transmission arrangements
- NCEP pushes its data to MSC
- Basic products
- Bias correction (Bo Cui, Dave Unger)
- First moment method works, accepted for use by both parties
- Second moment correction
- Moment adjustment & Bayesian Model Averaging, BMA methods to be compared
- May or may not be included in 1st operational implementation
- Weighting (Bo Cui, Dave Unger)
- Skill, Ridging, BMA methods to be compared
- Climate anomalies (Yuejian Zhu)
- Detailed algorithm to be developed
- End product generation
- One stream to generate multiple product formats (Dave Michaud)
- Start with highest priority items from prioritized list from Service Centers (Z. Toth)
- Required NAWIPS tools ready by Sept & Dec 2005 (Maxine Brown)
- Default graphical setup to be developed & JIF’d for web display (Maxine Brown)
- NAWIPS graphical products using web default display (Dave Michaud)
- NAWIPS & GRIB2 product generation as part of one product stream (Dave Michaud)

**DETAILS**

## Product distribution
NAEFS basic products (Brent Gordon) 
3 new data sets, in addition to raw NCEP global ensemble data
Use GRIB2, low precision (for weights & climate anomalies) to control resource requirements
Must be made available via ftp for
Community use
Real time forecasts
Archive for research (THORPEX-TIGGE)
Backup in case of problem at either generating center
Resource implications
HPSS disc storage
Ftp servers
NCDC is to post & keep ensemble data?

NAEFS end products
Supercede current global ensemble products based on NCEP ensemble only
As NAESFproducts are introduced, they replace current NCEP products
NCEP official web site 
Public
NAEFS partners/users
Central & South America
Africa (AMMA)
Polar regions (IPY)

- NAEFS basic products (Brent Gordon)
- 3 new data sets, in addition to raw NCEP global ensemble data
- Use GRIB2, low precision (for weights & climate anomalies) to control resource requirements
- Must be made available via ftp for
- Community use
- Real time forecasts
- Archive for research (THORPEX-TIGGE)
- Backup in case of problem at either generating center
- Resource implications
***HPSS disc storage***
- Ftp servers
***NCDC is to post & ***_***keep***_*** ensemble data?***
- NAEFS end products
- Supercede current global ensemble products based on NCEP ensemble only
- As NAESFproducts are introduced, they replace current NCEP products
- NCEP official web site
- Public
- NAEFS partners/users
- Central & South America
- Africa (AMMA)
- Polar regions (IPY)

**DETAILS - 2**

##   Bias correction  

- First moment correction
- choose a fixed weigh factor (2 % as a default), or vary it as a function of lead time and location ( how to determine variations?)
- apply bias correction scheme
- 35 variables ( NCEP & CMC )
- on 1 x1 degree ensemble data (NCEP & CMC )
- on 00z and 12Z (NCEP & CMC, 06 &18Z for NCEP )
- Second moment correction
- may not be included in next spring operational implementation
-   Weighting
- BMA method: only tested for surface temperature
- Use frequency of “best member of ensemble” statistics

- BIAS CORRECTION & WEIGHTING

## CLIMATE ANOMALIES

*Express bias-corrected forecasts (each member) in terms of climate percentile*
- Forecasts bias corrected wrt NCEP & CMC oper. analysis
- 1.0*1.0 (lat/lon) grid
- Climate based on NCEP/NCAR reanalysis data
- 4 cycles (00UTC, 06UTC, 12UTC and 18UTC) per day
- 40 years (Jan. 1st 1959 – Dec. 31th 1998)
- 2.5*2.5 (lat/lon) grid
- Need to consider the systematic difference between reanalysis and oper. analysis (NCEP & CMC respectively)
- Variables (possible to add more)
- Height: 1000hPa, 700hPa, 500hPa, 250hPa
- Temperature: 2m, 850hPa, 500hPa, 250hPa
- Wind: 10m, 850hPa, 500hPa, 250hPa
- PRMSL, max/min temperature

## CLIMATE ANOMALIES

*PROCEDURE** *
- Determine climatological distribution for each day using reanalysis data
- Use first few harmonics to describe annual variations
- Compute all stats for 4 times per day
- Estimate climate mean (first moment)
- Estimate distribution around mean
- Archive data to be used on daily basis
- Determine systematic difference between reanalysis and operational analysis fields
- Use standard NAEFS “bias estimation” method
- Adjust bias corrected NAEFS forecasts by systematic difference between reanalysis & oper. analysis
- Compare bias corrected & adjusted NAEFS forecasts to reanalysis distribution
- Express each forecast as percentile of climate distribution