Transcript Document 7451904

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
1
OUTLINE
• PROJECT DESCRIPTION
– TIMELINE
– PARTICIPANTS
• CONCEPT OF OPERATIONS
– BASIC PRODUCTS
– END PRODUCTS
• PLANS
– TIGGE / GIFS CONNECTIONS
2
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)
3
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
4
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
• Climate anomaly forecasts
• 2007-2008, MSC, NWS
– Follow-up implementations
• Improved and expanded product suite
5
NAEFS ORGANIZATION
Meteorological Service of Canada
National Weather Service, USA
MSC
NWS
PROJECT OVERSIGHT
Michel Beland, Director, ACSD
Louis Uccellini (Director, NCEP/NWS)
Angele Simard, Director, AEPD
Greg Mandt (Director, OST/NWS)
Gilbert Brunet, MRB
Steve Lord, EMC
PROJECT CO-LEADERS
Louis Lefaivre (Implementation)
Zoltan Toth (Science)
Peter Houtekamer (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 O’Lenic, 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
•
Systematic Error
Before Bias Correction
Variables added on annual basis, GRIB2,
direct link
After Bias Correction
•
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/
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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
RPSS
After Bias Correction
Before Bias Correction
» 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
8
ENSEMBLE-GRAMS
Total Cloud Cover
12-hr Accumulated Precipitation
10-m Wind Speed
2-m Temperature
9
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
10
North American Ensemble Forecast System
2 m Temperature
8 to 14 Day Outlook
00z forecast [EXPERIMENTAL]
Landshut
Probability of week-2 mean
2m temperature being in
lower (shades of blue) or
upper (shades of red)
climate tercile
Valid: Dec 09 - 15, 2006, Issued: Dec 01, 2006
11
•
Basic products
–
PRODUCT GENERATION PLANS
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
•
•
•
Systematic error in 1x1 lat/lon U wind forecast
on 5x5 km grid, 24-hr lead
Before downscaling
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
After downscaling
Effect of downscaling on
Systematic Error
Before
Bias corr.
After
Bias corr.
After
Downscaling
12
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
THORPEX Interactive Grand
Global Ensemble (TIGGE)
Transfers
New methods
Articulates
operational needs
NAEFS – Global Interactive
Forecast System (GIFS)
• 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
13
HURRICANE WILMA
STRIKE PROBABILITY
Probability of storm within 65 nm vicinity of any point on map
- Forecast track
- Observed track
Strike probability =>
14
ENSEMBLE-BASED
May 4th
Mississippi, River
Vicksburg, MS
The Large Basin
Total Cloud Cover
----- GEFS members
----- GEFS ens. mean
----- GEFS control
----- GFS high resolution
----- NLDAS
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.
0
2
April 1st
4
6
8
Lead Time (days)
10
12
14
16
•
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
COMPONENT
Characteristic
OPERATIONS
RAW DATA
Schedule
Exchange
Algorithm
Generation
Location
Based on
Software
Generation
Location
BASIC PRODUCTS
END PRODUCTS
•
FUTURE –
TIGGE2/GIFS
User driven
As needed
Shared
Centralized
Distributed
All centers
Generating center
Basic products
Separate
Shared
Centralized
Distributed
Individual centers
Web based
CURRENT NAEFS
Clock driven
All
Prototypes for TIGGE-2 / GIFS
– Science / mechanics
•
NAEFS
– IT Technology
•
NOMADS
– Flexible access to distributed data
– Web-based product generation
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NOAA NCDC Ensemble Archive TIGGE Goals: Want Data?
Backup for Phase-1; Operational server for Phase-2 & GIFS
Seamless access across real-time to historical
NOMADS Design
NCDC
Archive
NCEP Dual Ingest & QC
NOAA-wide LAS
“sister-servers”
Direct Client Access
NCEP R/T NOMADS
GDS
and TDS
Live Access
Portals
Server
Founding Member
Grid
GrADS, Ferret, MatLab, IDL,
IDV, Web browsers or any
OPeNDAP enabled client
PROPOSED:
National Ensemble
and Reanalysis
Archive and
Diagnostics facility
Exploratory Grid
Projects w/ Globus
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.
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
19
PARADIGM SHIFT IN FORECASTING
PRIMARY OBJECTIVE
DERIVED QUANTITY
•
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
•
Must move to new paradigm to
•
•
–
Improve skill
Potentially enlarge customer base
Ensemble mean is better than control BECAUSE
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
•
•
•
PROBABILISTIC APPROACH
Operational requirements / routine engrained in traditional paradigm
–
•
NEW
Probabilistic Forecast
Single Value & Other
Distinguish clearly between
–
•
TRADITIONAL
Single Forecast
Forecast Uncertainty
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
20
BACKGROUND
21
Single value
Distribution
PROPAGATING FORECAST UNCERTAINTY
z
Ensemble Forecasting:
Central role – bringing the pieces together
CONFIGURATION, OUTPUT CHARACTERISTICS
2005, 2006, 2007, 2008
2005, 2006, 2007, 2008
SPECIFICS
Model resolution
ENSEMBLE CONFIGURATION
OUTPUT FREQUENCY
Forecast length
Cycles per day
Membership
Spatial grid (lat/lon, GRIB)
Temporal frequency (hrs)
MSC
SEF: ?
GEM: 1.2
lat/lon
240, 384
hrs
1, 2
16, 20
1.2x1.2
6
NCEP
GFS: T126L28, T170L64 or
T126L64+hind-casts
386 hrs
4
10, 14, 20
1x1
6
23
RAW DATA & BASIC PRODUCT AVAILABILITY
2005, 2006, 2007, 2008
PARAMETER
LEVEL/SPECIFICS
RAW
DATA
BIAS
CORRECTION
WEIGHT
CLIMATE
ANOMALIES
Model
topography
To facilitate post-processing
Operational
-
-
-
GZ
1000, 925, 850, 700, 500, 250,
200
Operational
Operational
TT
2m, 1000, 925, 850, 700, 500,
250, 200
Operational
Operational
U, V
10m, 1000, 925, 850, 700, 500,
250, 200
Operational
Operational
RH
MSLP
SP
PR
Tmax, Tmin
Precipitation
NT
IH
CAPE
CIN
WAM
2m, 1000, 925, 850, 700, 500,
250, 200
Mean Sea Level Pres.
Surface pressure
2m, 6-hrly
6-hr, by types: liquid, frozen,
snow
Total cloud cover
Total precip. water
Convective inhibition, 0-0-6, 0-1,
0-3 km
Ocean Wave parameters
Operational
Operational
Operational
Operational
Operational
Operational
Operational
Operational
Operational
1000, 700, 500, 250
hPa
Operational
2m, 850, 500, 250
hPa
Operational
10m, 850, 500, 250
hPa
March
06
Operational
Operational
Operational
Operational
Operational
Operational
Requested
Planned
24
THORPEX GOAL
•
IMPROVEMENT IN PROBABILISTIC
SKILL OVER PAST 4 YEARS
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
Impact of Models on Day 1 Precipitation Scores
THORPEX – NAEFS
TO DOUBLE RATE OF
IMPROVEMENT
Forecasters Add Value
0.35
Threat Score
0.3
0.25
Human(HPC)
0.2
ETA
Linear
0.15
1(Human(HPC))
2
Linear (ETA)
0.1
1.5-day extension of skill in 4 yrs
Models provide basis
for improvement
0.05
20
03
20
01
19
99
19
97
19
95
19
93
19
91
0
NORTH AMERICAN ENSEMBLE
FORECAST SYSTEM (NAEFS) 3
• Operational multi-center ensemble system
• Significant acceleration in skill
–
Joint ensemble research
•
–
4
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
25
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
26
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
27
BACKGROUND
28
Ensemble Mean Forecast & Bias Before/After RTMA Downscaling
Before
After
Before
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
29
INAUGURATION
CEREMONY
30
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%
31
BACKGROUND
32
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
33
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 level34
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
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?
Additional basic GUI functionalities:
- Ability to manually select/identify members
- Ability to weight selected members Sept. 2005
LOCALLY
GENERATED
INTERACTIVE
ACCESS
Potentially useful functionalities that need further development:
- Mean/Spread/Median/Ranges for amplitude of specific features 35
- Mean/Spread/Median/Ranges for phase of specific features
ENSEMBLE PRODUCT REQUEST LIST
NCEP SERVICE CENTERS, OTHER PROJECTS
FUNCTIONALITY
CENTRALLY MADE PRODUCTS
DOMAIN
Mean
PMSL
Z: 500mb
Z: 500mb
T (K): 500mb
T (K): 700mb
T (K): 850mb
Wind: 500mb
Wind: 700mb
Wind: 850mb
Z: 700mb
Z: 850mb
Wind: 10 m
pmsl: lows/troughs/mins & highs/ridges/maxes
T (K): 300mb
Wind: 10 m
Wind: 250mb
Wind: 300mb
Wind: 925mb
Wind: 500mb
Wind: 850mb
Wind: 925mb
Z: 700mb
Z: 850mb
AVOR: 500mb
AVOR: 850mb
CAPE
QPF
NH,NA,SA,CA,AF,global
NH,NA,SA,CA,AF,global
NH,NA,SA,CA,AF, global
NH,NA,AF,global
NH,NA,AF,global
NH,NA,AF,global
NH,NA,AF,global
NH,NA,AF,global
NH,NA,AF,global
NH,NA,AF,global
NH,NA,AF,global
NH, NA,AF,global
NH, global,NA,SA,CA
NH,AF, global
NH, NA,AF,global
NH,NA,AF,global
NH,AF, global,NA
NH,NA,AF, global
NH,NA,AF, global
NH,NA,AF, global
NH,NA,AF, global
NH,AF, global
NH,AF, global
NA,SA,CA
NA,SA,CA
NA,AF
NA,SA,CA,AF
Mean
Spread
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Spread
Grouping
Mean
Mean
Mean
Mean
Mean
Spread
Spread
Spread
Spread
Spread
Mean
Mean
Mean
Mean
CENTER #'s CENTER
6
6
6
5
5
5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
4
4
4
3
3
3
3
AMMA, HPC,LAP,OPC,SPC,TPC
AMMA,HPC,LAP,OPC,SPC,TPC
AMMA,HPC,LAP,OPC,SPC,TPC
AMMA,HPC,OPC,SPC,TPC
AMMA,HPC,OPC,SPC,TPC
AMMA,HPC,OPC,SPC,TPC
AMMA,HPC,OPC,SPC,TPC
AMMA,HPC,OPC,SPC,TPC
AMMA,HPC,OPC,SPC,TPC
AMMA,HPC,OPC,SPC,TPC
AMMA,HPC,OPC,SPC,TPC
AWC,OPC,TPC,AMMA,SPC
HPC,LAP,OPC,TPC
AMMA,OPC,SPC,TPC
AMMA,OPC,SPC,TPC
AMMA,HPC,OPC,TPC
AMMA,OPC,SPC,TPC
AMMA,OPC,SPC,TPC
AMMA,OPC,SPC,TPC
AMMA,OPC,SPC,TPC
AMMA,OPC,SPC,TPC
AMMA,OPC,SPC,TPC
AMMA,OPC,SPC,TPC
HPC,LAP,SPC
HPC,LAP,SPC
AMMA,HPC,SPC
AMMA,HPC, LAP
36
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
37
Climate percentile (0 = 50 percentile)
38
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)
Transfers
New methods
Articulates
operational needs
North American Ensemble
Forecast System (NAEFS)
39
40
• Data exchange
DETAILS
– 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) 41
• Product distribution
DETAILS - 2
– 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)
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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
1. BMA method: only tested for surface temperature
2. Use frequency of “best member of ensemble” statistics
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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
44
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
45