PERTURBATION VS. ERROR CORRELATION ANALYSIS (PECA)

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THE NORTH AMERICAN ENSEMBLE FORECAST SYSTEM
      
Zoltan Toth (NWS) Andre Methot (MSC) Ackn.
Michel Rosengaus (NMSM), Philippe Bougeault
(ECMWF)
http//wwwt.emc.ncep.noaa.gov/gmb/ens/index.html
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OUTLINE

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

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PROJECT DESCRIPTION

• International project to produce operational
  multi-center ensemble products
• Combines global ensemble forecasts from Canada
  USA
• 40 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 for
• National Unified Operational Prediction
  Capability (NUOPC)
• THORPEX Global Interactive Forecast System (GIFS)

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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

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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
• 1st Operational Implementation
• Bias correction

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NAEFS ORGANIZATION
Meteorological Service of Canada National Weather
Service, USA MSC NWS
PROJECT OVERSIGHT
Angele Simard, DG, WEOD Gilbert Brunet, MRD
Louis Uccellini (Director, NCEP/NWS) Steve Lord,
EMC
PROJECT CO-LEADERS
Andre Methot (CMC Development) Peter Houtekamer
(Science)
Zoltan Toth (Science) Chris Caruso / Brent Gordon
(Impl.)
JOINT TEAM MEMBERS
Meteorological Research Division MRD Lawrence
Wilson, Vincent Fortin Canadian Meteorological
Center CMC Gilles Verner, Yves Pelletier,
Stephane Beauregard, Norman Gagnon, Lewis
Poulin, Jacques Hodgson
Environmental Modeling Center EMC Yuejian Zhu, Bo
Cui, Richard Wobus, Dingchen Hou, Malaquias Pena,
M. Charles NCO Scott Jacobs HPC Keith
Brill Storm Prediction Center David
Bright Climate Prediction Center CPC Ed OLenic,
David Unger, Dan Collins NWS Richard Grumm, Fred
Branski
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
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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 algorithm (Bayesian)
• Anomalies (19 variables)
• Forecasts expressed as percentiles compared to
  climatological distribution
• Allows downscaling by adding local climatological
  distribution
• Generation

Systematic Error
Before Bias Correction
After Bias Correction
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CONCEPT OF OPERATIONS - 2

• 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

RPSS
After Bias Correction
Before Bias Correction

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

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ENSEMBLE-GRAMS
Total Cloud Cover
12-hr Accumulated Precipitation
10-m Wind Speed
2-m Temperature
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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
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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
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DEC 2007 NCEP PRODUCT GENERATION UPDATE

• Bias correct high resolution forecast
• Same method as used for ensemble
• 1x1 lat/lon grid
• Combine NCEP highres ensemble fcsts
• Heuristic method to be replaced by Bayesian
• 1x1 lat/lon grid
• NAEFS summary products based on 41 members
• NAEFS ensemble mean, spread, mode climate
  anpomalies
• 10, 50 (median), 90 probability climate
  anomalies
• 1x1 lat/lon grid
• Statistical downscaling
• Based on stat. relationship between 1x1 lat/lon
  5x5km analysis
• Sp, 2m temp, u/v winds
• 5x5 km National Digital Forecast Database (NDFD)
  grid

7-day downscaled NAEFS 2m temp. forecast valid
00Z 5 June 2008
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STATISTICAL PROCESSING

• 2-step statistical correction
• 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 based on statistically corrected
  fields
• New applications Tropical cyclones, hydrology,
  etc
• Decision Support System collaboration

Systematic error in 1x1 lat/lon U wind forecast
on 5x5 km grid, 24-hr lead
Before downscaling
Effect of downscaling on
After downscaling
Systematic Error
Before Bias corr.
After Bias corr.
After Down-scaling
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NCEP/GEFS raw forecast
8 days gain
NAEFS final products
From Bias correction (NCEP, CMC) Dual-resolution
(NCEP only) Down-scaling (NCEP,
CMC) Combination of NCEP and CMC
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NAEFS PROCESSING PRODUCTS

• Operationally available at both NCEP MSC
  (except as noted)
• Prototype for future NUOPC
• Process Bias correct individual members,
  including hires forecast
• In future, this step done at producing center
  only, bias corrected data exchanged
• Product Bias corrected individual members (35
  variables now, all variables by 2010)
• Process Combine NCEP ensemble with hires GFS
  control (only at NCEP)
• Add weighted difference between high low
  resolution controls to each ensemble member
• Products Enhanced NCEP ensemble member forecasts
  
• Process Combine NCEP CMC ensembles (currently,
  equal weighting)
• Products Combined ensemble mean, spread, mode,
  10, 50, 90 percentile forecast
• Process Bias correct combined distribution
• Under development - Bayesian processor - will
  also address weights above
• Products Bias free products as listed above
• Process Compare forecasts with climate
  distribution

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NCEP ENSEMBLE CONFIG. UPGRADE JAN 09

• Increased horizontal resolution
• T190/L28 for first 7.5 days (instead of T126/L28)
• Horizontal diffusion
• 8th order (instead of 4th order)
• Stochastic perturbations
• Exchange information among all ensemble members
• Integrate all members concurrently under ESMF
  framework

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NAEFS PLANS

• Data exchange
• FNMOC data exchange with NCEP operational (as of
  Aug. 2008)
• IOC with FNMOC
• Add 15 new variables (Jan 09?)
• Switch to use of GRIB2 (Jan 09?)
• Add new variables on annual basis
• Bias correction of individual members
• Evaluate value added from bias corrected FNMOC /
  ECMWF data (July 2009)
• Need to define metric
• Need to discuss data availability to CMC
• Bias correct only at producing center (2009)
• Avoid duplication
• Keep using identical methods
• Improve method (2009)
• Mini-Bayesian method
• All model variables on native model grid
  (2010-11)
• Including precipitation
• Use new NCEP reanalysis

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NAEFS PLANS - 2

• Bias correction of ensemble distribution (2010)
• Introduce full Bayesian method (currently
  statistically not corrected)
• Combination of forecast info from all sources
  (2011)
• Initial state (obs or analysis)
• Regional SREF ensemble
• High resolution forecasts, etc
• Downscaling
• Add new variables (Dec 08)
• Wind speed/direction, min/max/dewpoint temp
• Add new domains (on continual basis)
• Alaska (Dec. 08), Puerto Rico, Hawaii, Guam
• Add precipitation (2010)
• Add additional derived variables (2010-11)
• Use SMARTINIT tool on existing downscaled vars
  plus bias corrected fields
• Most NDFD variables in probabilistic / ensemble
  mode

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NAEFS PLANS - 3

• Product generation
• FNMOC ECMWF data included (July 2010)
• Depending on added value
• Some products may, others may not use new data
  source?
• ECMWF data has restrictions
• Week-2 precipitation (2010)
• Tropical cyclone related products
• Hydro-meteorological products
• River stage prediction
• Joint work with
• Office of Hydrologic Development
• Hydro-meteorological Testbed (HMT)

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

• Forecast track
• Observed track

Strike probability gt
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ENSEMBLE-BASED
May 4th
Mississippi, River Vicksburg, MS The Large
Basin May 4th 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.
Total Cloud Cover
----- GEFS members ----- GEFS ens. mean -----
GEFS control ----- GFS high resolution ----- NLDAS
0 2 4 6
8 10 12 14
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Lead Time (days)
April 1st
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STREAMFLOW FORECAST EXAMPLE12-DAY LEAD-TIME
(APRIL 1, 2006)
Analysis (NLDAS)
Ensemble Mean
Ensemble mean similar to analysis Error 10
of flow Ensemble spread comparable to error
in ensemble mean
ErrorEns. Mean - analysis
Ensemble Spread
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FORECAST ANALYSIS TIME SERIES
Positive correlation between forecasts analyses
for all lead times
Lower Mississippi River Very Large Basin
Trend is predicted well even at 15-day lead
15-day lead
Merrimack- Concord River, Lowell, MA Medium Basin
May 2006 New England Flood correctly predicted,
some minor events indicated 5 days in advance
----- GEFS members ----- GEFS ens. mean -----
GEFS control ----- GFS high resolution -----
NLDAS Analysis
5-day lead
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NAEFS EXPANSION

• NUOPC
• Formalized collaboration
• FNMOC, AFWA to join
• Follow/improve NAEFS protocols
• Build on existing / planned architecture /
  software
• Build on THORPEX research
• Make NUOPC the prime US THORPEX research to
  transition project
• Other centers that may be interested joining
  NAEFS
• Somewhat looser collaboration?
• CMA, UK Metoffice
• Global Interactive Forecast System (GIFS)
• Based on volunteer contributions
• International collaboration WMO/WWRP/THORPEX
• 10 NWP centers

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GIFS CONCEPT OF OPERATIONS FOR PRODUCTS

• Ensemble data access
• Real time, directly from ensemble producing
  centers
• For product generating centers
• Flexible processing methods to handle missing
  data
• Product generation
• Distributed coordinated among ensemble
  producing centers and RSMC (DCPCs)
• Major challenge control change process, etc
• Product distribution
• Common web interface using WIS concepts (GISCs)
• Ensemble data
• Real time
• Archived
• Probabilistic forecast products
• Predesigned
• On demand
• User applications

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PROPOSED REGIONAL APPROACH FOR GIFS

• Concurrent GIFS development in 4 regions
• Form separate subgroups in 4 regions from
• 10 Global and RSMC(s) in each region
• Use identical data from 10 Global centers
• Inter-comparability
• Develop products specifically tailored for each
  region
• Periodically exchange experience to
  cross-fertilize efforts
• Grant real time data access to GIFS partners to
• Global ensemble data
• Products for
• Testing operational data feed
• Engaging forecasters and other experts at global
  centers regions
• Engage Global centers in Product development
• Provide regular feedback from RSMCs on product
  design/quality
• Contribute to forecaster training
• Ensures fast and high quality product development
  for all 4 regions
• Best use of regional data sources

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PROTOTYPES FOR GIFS

• Tropical cyclone forecasting CXML data from
  multiple centers
• Focus group involvement
• Common web interface for CXML data
• Access
• Display
• Combination / product generation
• Probabilistic precipitation forecasting
• Subgroups to address special product needs in
  each region
• Regional observationally based analysis
• Real time data/product exchange among
  participants requested
• Product development / testing in parallel with
• Focus group technical developments
• Probabilistic 10m winds, 2m temperature next

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NAEFS THORPEX

• NAEFS expands international collaboration
• Mexico joined in November 2004
• FNMOC to join by 2010
• Other centers also expressed interest
• NAEFS provides framework for transitioning
  research into operations
• Prototype for Global Interactive Forecast System
  (GIFS)
• Transition all T-PARC research into NAEFS
  operations
• Observing, DA, ensemble, application systems
• Measure of success for THORPEX

RESEARCH
THORPEX
THORPEX Interactive Grand Global Ensemble (TIGGE)
RESEARCH
Articulates operational needs
Transfers New methods
North American Ensemble Forecast System (NAEFS)
OPERATIONAL
LEGACY (GIFS)
OPERATIONS
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NAEFS WORKSHOP OUTCOME

• Estimate expected growth in data exchange volume
• Work under EC NOAA big pipe
• Alternative until EC-NOAA pipe
• Continue using ftp (2-3 yrs?)
• Set priorities for different data types (NA
  first)
• Establish high level (EC-NOAA) targets for
  ensemble resolution
• Coordinate operational timelines at CMC NCEP
• Use downscaling methods
• For US highly populated portions of Canada
  Mexico, RTMA applications
• Joint development of observationally based precip
  and other analyses
• Collaborate on algorithms/software for worded
  uncertainty info generator for public
• Invitation from Mexico for next NAEFS workshop to
  be held there
• Possibility of holding jointly with THORPEX NA
  Regional Committee meeting

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NAEFS WORKSHOP OUTCOME - 2

• Detailed request from Mexico for improved
  products / collaboration
• Explore joint aviation related product
  development
• Connection with NEXGEN
• 5D-cube
• Statistically reliable ensemble database
• Interrogation tool set to aswser questions
• Pursue hydrological applciations
• Comparisons and complementary coverage of NA
  domain
• Pursue joint wave ensemble application
• NCEP, FNMOC uses WAVEWATCH-3
• NCEP to provide FNMOC with wind bias correction
  algorithm
• NCEP to consider running WAVEWATCH-3 with bias
  corrected CMC ensemble
• Until EC develops ows wave model
• Collaborate in development of meteorological and
  hydrologic verification systems

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NAEFS WORKSHOP OUTCOME - 3

• Consider extension to regional ensemble
  forecasting
• CMC parallel testing to start in 2009
• Experimental data exchange / evaluation
• Evaluate potential for operational implementation
  (2010-11?)
• Collaborate on intra-seasonal forecasting
• Weeks 3-4 temp/precip
• MJO
• Consider extending NAEFS integrations to 30 days
  (2009-10?)
• Establish NAEFS standards for
• Quality verification metrics against
• Observations analyses
• Computational efficiency
• Operational considerations ease of
  implementation / maintenance
• Etc
• Test / evaluate FNMOC inclusion into NAEFS
• Test by Aug 2009

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BACKGROUND
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SENSITIVITY CALCULATIONS, DATA ASSIMILATION,
NUMERICAL FORECASTING, EVALUATION

• SENSITIVITY CALCULATIONS
• Adapt and use ECMWF PREVIEW software
• Web interface
• Allows multiple remote users input/out
  functionalities
• Recurvature, ET, Winter groups encouraged to
  mutually contribute to each others objectives
• DATA ASSIMILATION
• New methods tested
• Eg, Szunyogh et al, Bishop et al.
• Operational use of T-PARC obs with existing data
  types
• NUMERICAL FORECASTING
• Forecast tools to use in field work
• NAEFS, PREVIEW, TIGGE
• Web access
• Research/evaluation applications
• Global and Limited Area Models
• Ensemble methods (Based on real time data access

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NAEFS ACCOMPLISHMENTS / PLANS

• Configuration
• NCEP membership increased to 20 March 07
• Canadian major upgrade (incl. 20 members) July 07
• Downscaled forecasts Dec 07
• FNMOC ensemble to be evaluated Apr 09
• If there is added value, operationally
  implement Apr 10
• Product development
• Products on Canadian / NCEP web page
• First joint product Week-2 temperature
• Statistical post-processing
• Enhancements under development
• Bayesian bias correction R. Krzysztofowicz
• Precipitation included DJ. Seo, P. Schultz
• Enhanced downscaling
• GSD contributions P. Schultz
• Training / outreach
• NA Regional Committee meeting

7-day downscaled NAEFS 2m temperature forecast
valid 00Z 5 June
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NAEFS RESULTS

• 8 days total gain in skill
• Downscaling more important than bias correction
• Less need for hindcasting?
• Need for local observationally based analysis
• Multicenter approach adds 1-2 days skill

NCEP biascorrected-downscaled
NCEP raw-downscaled
NCEP raw
NAEFS final
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STAGES OF TIGGE-GIFS

• TIGGE
• Phase-1 (2005-2007) - Completed
• Ensemble data archives at 3 centers
• 100 and growing number of users
• Need to broaden user base
• Phase-2 (2008-2012) Under planning
• Develop infrastructure for
• Real time data access directly from producing
  centers
• Derived product generation
• Common web interface for data/product access
• GIFS
• GIFS Products (2013-)
• Real time product generation
• End-to-End GIFS (2013-)
• Adaptive use of ovserving, DA, NWP/ensemble, and
  application systems

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TIGGE DATA FLOW
TIGGE PHASE-2 OPENED IN SPRING 2008
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TIGGE DATA PROVIDERS
BOM CMA CMC CPTEC ECMWF JMA KMA MF NCEP UKMO
Standard Fields (Out of 73) 55 60 56 55 70 61 46 62 59 70
Ensemble Members 33 15 21 15 51 51 17 11 21 24
Forecast Length (Day) 10 10 16 15 15 9 10 2.5 16 15
Forecast cycles per Day 2 2 2 2 2 1 2 1 4 2
May 1, 2008
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TIGGE DATA PORTALS

• Location
• CMA http//wisportal.cma.gov.cn/tigge/
• ECMWF http//tigge-portal.ecmwf.int/
• NCAR http//tigge.ucar.edu
• Functions
• Register, search, discover, select, check volume,
  download
• Data selection criteria
• Initial date/time
• Forecast lead time
• Spatial sub-domain
• Data provider
• Atmospheric variable / level
• Options at NCAR
• Output format (GRIB, netcdf)
• Native or regular lat/lon grid

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TIGGE PHASE-2 / GIFS FUTURE DEVELOPMENTS

• TIGGE Phase-2 - Developments for GIFS
• Complex tasks
• Real time data access
• Multicenter product generation
• Common web interface for data/product access
• GIFS Products - Operations
• Major challenges for international
• Data sharing policy issues
• Coordination of derived product generation
• Prototypes
• Tropical cyclone related data
• Small amount of data new CXML format
• Highest impact/priority T-PARC support, Aug 08
  Mar 09
• Link with WWRP/TC program
• Precipitation
• End-to-End GIFS - Conceptual planning
• Coordinated use of additional major resources

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NAEFS RESULTS

• 8 days total gain in skill
• Downscaling more important than bias correction
• Less need for hindcasting?
• Need for local observationally based analysis
• Multicenter approach adds 1-2 days skill

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CONFIGURATION, OUTPUT CHARACTERISTICS
2005, 2006, 2007, 2008
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RAW DATA BASIC PRODUCT AVAILABILITY
2005, 2006, 2007, 2008
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ENSEMBLE PRODUCTS - FUNCTIONALITIES
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)
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?
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
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ENSEMBLE PRODUCT REQUEST LIST NCEP SERVICE
CENTERS, OTHER PROJECTS
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ENSEMBLE 10-, 50- (MEDIAN) 90-PERCENTILE
FORECAST VALUES (BLACK CONTOURS) AND
CORRESPONDING CLIMATE PERCENTILES (SHADES OF
COLOR)
Example of probabilistic forecast in terms of
climatology
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BACKGROUND
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FUTURE APPLICATIONS NAEFS TIGGE2 - GIFS

• 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

RESEARCH
THORPEX Interactive Grand Global Ensemble (TIGGE)
Transfers New methods
Articulates operational needs
NAEFS Global Interactive Forecast System (GIFS)
OPERATIONS
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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

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Want Data?
NOAA NCDC Ensemble Archive TIGGE Goals Backup
for Phase-1 Operational server for Phase-2 GIFS
Seamless access across real-time to historical
NOMADS Design
PROPOSED
56
Time for coordinated planning for TIGGE Phase-2
Screen shot of the web page containing prompts
and user entered responses for the probability
of frost at day 4 ½.
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.
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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

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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

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GIFS PLANNING DOCUMENT

• Sept 08 (THORPEX Workshop, Geneva)
• GIFS Plan accepted by GIFS-TIGGE WG
• Two focus groups form
• Oct 08
• Input from THORPEX WGs and WWRP
• Nov 08 (ICSC meeting, Geneva)
• ICSC input / approval
• Mar 09 (THORPEX Symposium, Monterey)
• TIGGE User Workshop
• Focus group meetings
• Open issue
• Maintain centralized archive and/or introduce
  distributed archiving?
• Combination of two approaches may be realistic
• Central role taken by producing centers (in place
  of archive centers)

60
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

Systematic error in 1x1 lat/lon U wind forecast
on 5x5 km grid, 24-hr lead
Before downscaling
Effect of downscaling on
After downscaling
Systematic Error
Before Bias corr.
After Bias corr.
After Down-scaling
61
PROPAGATING FORECAST UNCERTAINTY
z
Distribution
Single value
Ensemble Forecasting Central role bringing the
pieces together
62
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

THORPEX NAEFS TO DOUBLE RATE OF IMPROVEMENT
2
1
1.5-day extension of skill in 4 yrs
NORTH AMERICAN ENSEMBLE FORECAST SYSTEM (NAEFS)
3
4

• 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

Close to 2-day extension of skill with first
NAEFS implementation
63
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

64
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

65
Ensemble Mean Forecast Bias Before/After RTMA
Downscaling
Before
Before
After

• 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

66
INAUGURATIONCEREMONY
67
Accumulated Bias Before/After RTMA Downscaling
black
red
blue
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
68
BACKGROUND
69
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 centers 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

70
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

71
Climate percentile (0 50 percentile)
72
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)

RESEARCH
THORPEX Interactive Grand Global Ensemble (TIGGE)
THORPEX
RESEARCH
Articulates operational needs
Transfers New methods
North American Ensemble Forecast System (NAEFS)
OPERATIONAL
LEGACY (GIFS)
OPERATIONS
73
DETAILS

• 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)

74
DETAILS - 2

• 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

75
BIAS CORRECTION WEIGHTING

• 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

76
CLIMATE ANOMALIES

• Express bias-corrected forecasts (each member) in
  terms of climate percentile
• Forecasts bias corrected wrt NCEP CMC oper.
  analysis
• 1.01.0 (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)
• 2.52.5 (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

77
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