Transcript May 2006 Upgrade of the NCEP Global Ensemble Forecast System (NAEFS)

May 2006 Upgrade of the
NCEP Global Ensemble Forecast System
(NAEFS)
Yuejian Zhu,
Zoltan Toth, Richard Wobus, Mozheng Wei and Bo Cui
Environmental Modeling Center
NOAA/NWS/NCEP
Acknowledgements:
DingChen Hou and Ken Campana EMC
David Michaud, Brent Gorden and Luke Lin NCO
Planned Changes - Summary
1. Increasing the number of perturbation runs
•
14 (20 later) perturbation runs for each cycle
2. Adding control runs for 0600, 1200 and 1800
cycles.
3. Use Ensemble Transform (ET) breeding method
instead of breeding method
•
ET breeding method will create initial orthogonal
vectors instead of independent vectors
4. NAEFS new products from NCEP-CMC joint
ensemble
•
•
•
Bias corrected forecast
Forecast anomalies
Weights
GEFS configurations
Current
Plan
Model
GFS
GFS
Initial uncertainty
BV
ETBV
Model uncertainty
None
None
Tropical storm
Relocation
same
Daily frequency
00,06,12 and 18UTC
same
Hi-re control
(GFS)
T382L64 (d0-d7.5)
T190L64 (d7.5-d16)
same
Low-re control
(ensemble control)
T126L28 (d0-d16)
00UTC only
T126L28 (d0-d16)
00,06,12 and 18UTC
Perturbed members
10 for each cycle
14 (20) for each cycle
Forecast length
16 days (384 hours)
same
Implementation
August 17th 2005
May 30th 2006
Planned Changes - 1
• Increasing the number of perturbation runs
– This change is intended to improve ensemble
based probabilistic forecast over all and to
support NAEFS (North American Ensemble
Forecast System) project.
– Results:
• Improving probabilistic skills
• Slightly improving ensemble mean skills (seasonal
dependent)
Planned Changes - 2
• Adding control runs for 0600, 1200 and
1800 cycles.
– This change is intended to enable for relocation
of perturbed tropical storm, to respond the
ensemble initial perturbation changes from pairs
to one side only. After this implementation,
there will be complete ensemble package with
control for each cycle. It is useful to compare
different cycles and leg forecast.
Planned Changes - 3
• Use Ensemble Transform (ET) breeding
method instead of breeding method
– There is no pair anymore after this
implementation (see next slide for details)
– This change is intended to improve probabilistic
forecast skills for all lead-time.
– Results:
• Slightly reducing ensemble mean track errors
(retrospective runs) for 12-96 hours
• Improving probabilistic forecast skills
Bred Vector
(Current)
Ensemble Transform Bred Vector
(Plan)
Rescaling
Rescaling
P1 forecast
P2 forecast
P1
ANL
ANL
N1
P3 forecast
t=t0
t=t1
t=t2
P4 forecast
t=t0
t=t1
t=t2
P#, N# are the pairs of positive and negative
P1, P2, P3, P4 are orthogonal vectors
P1 and P2 are independent vectors
No pairs any more
Simple scaling down (no direction change)
To centralize all perturbed vectors (sum of all
vectors are equal to zero)
P2
Scaling down by applying mask,
The direction of vectors will be tuned by ET.
ANL
N2
Planned Changed 4 – NAEFS Post Products
• NAEFS basic product list
– Bias corrected members of joint MSC-NCEP ensemble
• 35 of NAEFS variables
• 32(00Z), 15(06Z), 32(12Z) and 15(18Z) joint ensemble members
• Bias correction against each center’s own operational analysis
– Weights for each member for creating joint ensemble (equal weights right now)
• Weights don’t depend on the variables
• Weights depend on geographical location (low precision packing)
• Weights depend on the lead time
– Climate anomaly percentiles for each member
• 19 of NAEFS variables
• 32(00Z), 15(06Z), 32(12Z) and 15(18Z) joint ensemble members
• Use NCEP/NCAR 40-year reanalysis
• Considering the difference between current analysis and reanalysis
• Non-dimensional unit, allows downscaling of scalar variables to any local
climatology
List of Variables for Bias Correction, Weights
and Forecast Anomalies for CMC & NCEP Ensemble
NCO parallel
• Start from 01/15/2006
– 14 perturbed runs and control for each cycle
– With new file structures
• Start from 02/01/2006
– Adding ET scheme (03/07/2006)
– Tuning TS relocation (?)
• Start from 04/01/2006
– Use GEFS new system (May implementation)
– Create bias correction forecast
– Forecast anomalies
• Evaluations
Statistic results
• NCO real-time parallel verification statistics are
posted at:
– http://wwwt.emc.ncep.noaa.gov/gmb/yzhu/html/opr/prx
_daily.html (available now)
– Updated every morning
• Retrospective experimental verification statistics
are posted at:
– http://www.emc.ncep.noaa.gov/gmb/yzhu/html/opr/et14
m_daily.html (available now)
– Updated as required
Northern Hemisphere
Southern Hemisphere
Early studies for ET
Winter of 2002-2003
ROC scores for 32 cases
Tropical
ENS-o  control runs
ENS-s  ET-20 members
ENS-x  ET-10 members
Summary of Retrospective Runs
• Period: 08/20/2005 – 09/30/2005
• Statistics for
– Hurricane track errors
• Atlantic-, East Pacific-, West Pacific- basins, total basins
–
–
–
–
AC scores (Northern Hemisphere)
RMS errors (Southern Hemisphere)
Outlier for Northern Hemisphere
ROC scores for NH and SH
• Conclusion
– Tropical – mean of track error (slightly improved)
• Improved (48-, 72-, 96-hours over all)
– NH – mean (improved), probabilistic (improved)
– SH – mean (slightly), probabilistic (improved)
Hurricane Track Errors (Atlantic Basin: 08/20-09/30/2005)
AEMN
350
ETIM
AVNO
AEMN-operational ensemble
300
ETIM-retrospective runs
250
ETIM is better than AEMN
200
150
100
50
0
Hours
12
24
36
48
72
96
120
Cases
174
157
141
128
101
75
48
Hurricane Track Errors (East Pacific Basin: 08/20-09/30/2005)
AEMN
ETIM
AVNO
350
300
Ensemble need to improve in
East Pacific Basin in the future
250
200
150
100
50
0
Hours
12
24
36
48
72
96
Cases
181
165
149
135
109
85
120
69
Hurricane Track Errors (West Pacific Basin: 08/20-09/30/2005)
AEMN
250
ETIM
AVNO
ETIM is better than AEMN
200
150
100
50
0
Hours
12
24
36
48
72
96
120
Cases
177
161
145
129
101
66
44
Hurricane Track Errors (All Basins: 08/20-09/30/2005)
AEMN
ETIM
AVNO
300
250
200
Overall, ensemble mean beat GFS
from/after 72 hours for hurricane
tracks, it is similar to NH/SH
500hPa height rms errors
150
100
50
0
Hours
12
24
36
48
72
96
120
Cases
532
483
435
392
311
226
161
Improving skills from/after day 3
65% AC scores – useful skill
Ens. extended another 20 hours
Much better than GFS
after 72 hours
ENS_s – operational ensemble
ENS_x – retrospective runs
Reduced errors after day 6
Reduced initial spread
Growing faster than operational
Outlier zero is perfect
Due to reduce
initial perturbation
Better after day 4
ENS_s – operational ensemble
ENS_x – retrospective runs
Improved ROC scores for
Northern Hemisphere
Improved ROC scores for
Southern Hemisphere
Summary of NCO Parallel
• Period: 03/01/2006-current (more than 50 days)
• Statistics for
–
–
–
–
RMS errors (Northern Hemisphere)
AC scores (Southern Hemisphere)
AC scores for tropical
ROC scores for NH, SH and tropical
• Conclusion
– Tropical – no significant changes
– NH – mean (even), probabilistic (improved)
– SH – mean (improved), probabilistic (improved)
Rms errors are slightly better
for short lead time
Less spread at initial,
but growing faster
Significant improvement for
Southern Hemisphere
Ensemble mean beat
GFS from/after day 3
There is no big effect in Tropical
by apply ET in generally
Improving for all lead time
Most considerable
improvement for medium range
Improvement for all lead time
It is very similar to NH
There is no big difference for
Tropical region
Perturbation versus Error Correlation Analysis (PECA)
Retrospective runs
NCO parallel runs
Summary of NAEFS new products
- Post process
• Early studies
• NCO parallel: 04/01/2006-current
• No new stats yet
Ensemble size = 10 members
ENSEMBLE 10-, 50- (MEDIAN) & 90-PERCENTILE FORECAST VALUES (BLACK
CONTOURS) AND CORRESPONDING CLIMATE PERCENTILES (SHADES OF COLOR)
Example of
percentile forecast
in terms of climate
percentiles
Proposal
future NDGD
products
Background !!!!!
RAW & 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
May 06
TT
2m, 1000, 925, 850, 700, 500,
250, 200
Operational
May 06
U, V
10m, 1000, 925, 850, 700, 500,
250, 200
Operational
May 06
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
May 06:
1000, 700, 500, 250
hPa
May 06:
2m, 850, 500, 250
hPa
May 06:
10m, 850, 500, 250
hPa
Operational
Operational
Operational
Operational
Operational
Operational
Operational
Operational
Operational
Requested
Planned
May 06
May 06
May 06
May 06
May 06
May 06
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
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
- 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
NDGD FORECAST UNCERTAINTY - DOWNSCALING
• Ensemble uncertainty information
– Sent on NDGD grid for convenience (if no big overhead)
– Valid on model grids (32km for regional, 110 km for global ensemble)
– How to bridge gap between model and NDGD grids?
• Anomaly uncertainty information – proposed methodology
– Establish reanalysis climatology
• In progress for global (NAEFS), methods can be transferred to regional
reanalysis
– Bias correct ensemble forecasts (wrt operational analysis)
– Take 10-50-90 percentile values from bias corrected ensemble
– (For establishing anomaly forecasts, adjust 10-50-90 percentile values to
look like re-analysis)
– Check climatological percentile corresponding to 10-50-90 forecast
percentiles
• Provide climatological percentiles corresponding to 10-50-90
percentile forecast values as second set of guidance products
Hurricane Track Errors (Atlantic+East Pacific Basins: 08/20-09/30/2005)
AEMN
ETIM
AVNO
350
300
250
200
150
100
50
0
Hours
12
24
36
48
72
96
120
Cases
355
322
290
263
210
160
117
Track errors and spreads
2004 Atlantic Basin (8/23-10/1)
opr-errors
400
exp-errors
opr-spread
exp-spread
From Timothy Marchok (GFDL)
350
300
250
200
150
100
Reduced mean track
errors and spreads
50
0
24h
48h
72h
96h
120h
Hurricane track errors
2 basins (Atlantic and e-Pacific)
GFS
OPR
REL
GFS
REL
24
48
15
350
300
10
250
200
5
150
0
100
-5
50
-10
0
0
24
48
96
0
96
Percentage improvement to
Track errors (miles)
operational ensemble
Period: 20040824-20040930 (53-103 cases)