PERTURBATION VS' ERROR CORRELATION ANALYSIS PECA

1
CONCEPT OF MULTI-CENTER ENSEMBLE
FORECASTING CURRENT OPERATIONS NORTH AMERICAN
ENSEMBLE FORECAST SYSTEM (NAEFS) RESEARCH
DATABASE THORPEX INTERACTIVE GRAND GLOBAL
ENSEMBLE (TIGGE) FUTURE OPERATIONS GLOBAL
INTERACTIVE FORECAST SYSTEM (GIFS)
      
Zoltan Toth (NOAA) Acknowledgements Philippe
Bougeault (ECMWF), David Parsons (NCAR), Louis
Lefaivre (MSC), Michel Rosengaus (NMSM), Lawrence
Wilson (Environment Canada)
http//wwwt.emc.ncep.noaa.gov/gmb/ens/index.html
2
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

3
PROPAGATING FORECAST UNCERTAINTY
z
Distribution
Single value
Ensemble Forecasting Central role bringing the
pieces together
4
THE NORTH AMERICAN ENSEMBLE FORECAST SYSTEM AN
OPERATIONAL MULTI-CENTER FORECAST SYSTEM
      
Zoltan Toth (NWS), Louis Lefaivre (MSC), Michel
Rosengaus (NMSM) Acknowledgements Philippe
Bougeault, David Parsons, Lawrence Wilson
http//wwwt.emc.ncep.noaa.gov/gmb/ens/index.html
5
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)

6
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

7
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

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

9
CONFIGURATION, OUTPUT CHARACTERISTICS
2005, 2006, 2007, 2008
10
RAW DATA BASIC PRODUCT AVAILABILITY
2005, 2006, 2007, 2008
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ENSEMBLE-GRAMS
Total Cloud Cover
12-hr Accumulated Precipitation
10-m Wind Speed
2-m Temperature
12
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
13

• TIGGE
• The THORPEX Interactive Grand Global Ensemble

Philippe Bougeault, ECMWF Zoltan Toth,
NOAA Co-chairs, GIFS-TIGGE WG
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TIGGE

• What is TIGGE?
• Archive of global ensemble forecasts generated at
  10 operational NWP centers
• Major goals
• Support THORPEX research in
• Ensemble forecasting (statistical
  post-processing bias removal, combining
  ensembles)
• Predictability (limiting role of observing, DA,
  numerical modeling systems)
• Support demonstration projects with near real
  time forecast data
• Field campaigns (T-PARC, IPY)
• Forecast demonstration projects (Beijing
  Olympics, African Health Initiative)
• Lead to development of prototype new Global
  Interactive Forecast System
• Adaptive use of observational, DA, ensemble
  forecasting, application systems
• New level of international coordination among NWP
  centers
• Phases
• Phase 1 Being implemented
• Data archived centrally at 3 locations ECMWF,
  NCAR, CMA
• Common data format
• Archive specific data access mechanism at 3
  archive centers
• Phase 2 Preliminary planning phase
• Distributed archiving at generating centers

15
TIGGE infrastructure Phase 1

• Data collected in near-real time (via internet
  ftp) at central TIGGE data archives
• Can be implemented now at little cost
• Can handle current data volumes (estimated 200
  Gb/day) within available network and storage
  capabilities

Predictability science
Real-world applications
NHMS
academic
End user
TIGGE Centre A
TIGGE Centre B
EPS 1
EPS 2
EPS n
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Content upper air fields

• 5 parameters on 8 pressure levels 1000, 925,
  850, 700, 500, 300, 250 and 200 hPa.
• Geopotential height on 50 hPa as well.
• 41 fields in all.

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Content single level fields

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Content single level fields

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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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Status of TIGGE Phase 1

• Data collection
• ECMWF, UKMO and JMA are now received daily at
  ECMWF and NCAR
• NCEP received at NCAR via CONDUIT data feed
• NCAR to reformat retransmit data to ECMWF CMA
• Meteo-France, CMA expected end 2006
• Data distribution
• Work in progress to deploy software for user
  access
• NCAR - Community Data Portal
• ECMWF - MARS
• Timeline
• Currently Beta testing
• Official opening expected in Spring 2007 with at
  least
• 49 (of 71) variables
• 2 (out of 3) archive centers
• 5 (out of 10) generating centers
• 95 data availability

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TIGGE-LAM

• Limited Area Ensembles will be important part of
  GIFS
• Work to be pursued in collaboration with THORPEX
  Regional Committees
• Priorities
• Facilitate exchange of initial and boundary
  conditions between ALL global ensembles and ALL
  LAM ensembles by agreed standard data formats and
  contents
• Agree on content and standard format of LAM
  ensemble data for wide exchange and evaluation
• Exchange meta-data through a common Internet site
• Explore which Centres are willing to take a
  leading role in offering routine boundary
  conditions, relocatable LAM ensembles, archiving
  services, dissemination, etc.. for a TIGGE-LAM
• Form TIGGE-LAM subgroup

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The GIFS-TIGGE WG

• Philippe Bougeault ECMWF
• Zoltan Toth NCEP
• Barbara Brown NCAR
• Chen De Hui CMA
• Beth Ebert BMRC
• Martin Ehrendorfer Innsbruck
• Mark Roulston Penn State
• Tom Hamill NOAA CDC

• Co-chairs
• Pedro Silva Dias CPTEC
• Richard Swinbank UKMO
• Warren Tennant Africa
• Laurie Wilson MSC
• Yoshiaki Takeuchi JMA
• Sang-Ok Han KMA

GIFS-TIGGE WG has met in November 2005 and March
2006 and will have its 3rd meeting tomorrow
GIFS-TIGGE is supported by an excellent
cooperation between the IT experts at NCAR and
ECMWF
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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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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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CRPSS of The Ensemble(Averaged for selected
ranges of Stream Flow)
After Bias-correction
Without Bias-correction
gt2000m3/s 1000-2000
gt2000m3/s 1000-2000
500-1000 300-500
500-1000 300-500
0
0

• Observations
• Positive skill for the large river basins.
• Improvement due to bias-correction.
• Positive skill for all river basins after bias
  correction

200-300 70-90 35-45 15-20
Ranges (m3/s) gt2000m 1000-2000 500-1000 300-50
0 200-300 70-90 35-45 15-20
200-300 70-90 35-45 15-20
Discussion Operationally practical
bia-correction algorithms have similar (although
less striking) effect. Already implemented in
NAEFS (Toth et al., 2006 Cui et al.,2006)
26
Correlation between Ens. Mean Fcst and
Analysis(averaged over each of the 20 ranges of
streamflow)
Ranges gt2000 m3/s 1000-2000 500-1000 300-500 200
-300 150-200 120-150 90-120 70-90 55-70 45-55 40-4
5 35-40 30-35 25-30 20-25 15-20 10-15 1-10 0-1
1 2 4
6 8 10
12 14 16

Lead Time (days)
27
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

28
BACKGROUND
29
OUTLINE

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

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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

31
NAEFS ORGANIZATION
Meteorological Service of Canada National Weather
Service, USA MSC NWS
PROJECT OVERSIGHT
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
Louis Lefaivre (Implementation) Peter Houtekamer
(Science)
Zoltan Toth (Science) David Michaud / Brent
Gordon (Impl.)
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 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
32
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
33
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
34
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
35
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
16
Lead Time (days)
April 1st
36
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
37
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.
38
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
39
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

40
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

41
BACKGROUND
42
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

43
INAUGURATIONCEREMONY
44
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
45
BACKGROUND
46
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

47
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

48
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)
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
49
ENSEMBLE PRODUCT REQUEST LIST NCEP SERVICE
CENTERS, OTHER PROJECTS
50
Climate percentile (0 50 percentile)
51
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
52
(No Transcript)
53
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)

54
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

55
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

56
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

57
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