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2.3.3 ADVANCES IN INTENSITY GUIDANCE
Rapporteurs: Buck Sampson (NRL – USA) and John Knaff (NESDIS – USA)
Working group: Joe Courtney (BOM - AUS), Brian Strahl (JTWC – USA), Tsukasa Fujita (JMA – Japan), Naohisi Koide (JMA – Japan), Olivier Bousquet (METEO-FRANCE/LACY),Thierry Dupont (METEO-FRANCE/DIRRE/CYC – La Reunion), Mike Brennan (NHC – USA), Vijay Tallapragada (NCEP – USA), Tim Marchok (GFDL – USA), Sundararaman G. Gopalakrishnan (HRD – USA), Baode Chen (CMA – PRC), M. Mohapatra (IMD-India), S. D. Kotal (IMD-India) and U. C. Mohanty (IMD – India), Mike Fiorino (ESRL – USA), Jim Doyle (NRL – USA), Russ Elsberry (NPS – USA)
Jeju Island, Korea December 2014IWTC VIII
Eighth WMO International Workshop on Tropical Cyclones2
Guidance history in terms of MAE
Track MAE reduced dramatically over last 15 years
Official forecasts have improved very little
Intensity guidance has improved 1% to 2% per year (MAE)
Very recently intensity guidance is beginning to improve the official forecasts
N. West Pacific
S. Hemisphere
DeMaria et al. (2014)
Eighth WMO International Workshop on Tropical Cyclones3
How intensity forecasts are made…
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NHC Forecasting
Perform analysis Dvorak, ADT, A/C, dropsondes,
SFMR, radar, AMSU, scatt
Display aids (see graphic) Use global models for
environmental factors Intensity trends, extratropical
transition
Check ocean fields SST, Ocean Heat Content, land
interaction
Use other guidance SHIPS/LGEM, consensus, SHIPS-RI
Eighth WMO International Workshop on Tropical Cyclones5
La Reunion Forecasting
Perform analysis Dvorak, …
Base forecast mostly on human expertise
Check global environmental factors Vertical windshear, low/mid- level
humidity, upper level divergence, outflow patterns, low level inflow
Intensity trends, extratropical transition
Check ocean fields SST, ocean heat content fields,
land interaction Maximum potential intensity
Use objective aids and NWP V and MSLP trends,
ensembles, STIPS
Current domain of Arome-OI (yellow) overlying ALADIN-Réunion operational domain (area of responsibility of RSMC La Réunion; white)
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Tokyo Forecasting
Do Analysis Dvorak, …
Check global environmental factors e.g. vertical windshear
Check ocean fields SST, ocean heat content fields
Use objective guidance Climatic guidance, global
atmospheric model
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New Delhi Forecasting
Do analysis Satellite, radar, …
Check NWP models 11 of them!
Check statistical models SCIP (like SHIPS) RI probabilities Landfall model for slow
decay over flat land
Use SCORPIO web page
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BoM Forecasting
Do analysis Satellite, radar, …
Do NWP model assessment How are they performing? Are they consistent?
Check environmental factors Vertical wind shear, upper
level flow, low- to mid-level moisture, low-level inflow, proximity to land
View guidance STIPS, SHIPS/LGEM Consensus
Check forecast consistency
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JTWC Forecasting
Perform analysis Satellite, radar, environment, … One Dvorak T-no/day
Do NWP model assessment How are they performing? Are they consistent?
Check environmental factors Vertical wind shear, upper level
flow, outflow channels, low- to mid-level moisture, low-level inflow, proximity to land, SST, OHC, TUTT
Check internal factors Eyewall replacement, annular
Check statistical aids, consensus STIPS, SHIPS/LGEM, GPCE
Look for forecast consistency
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Forecasting Similarities
Analysis is important Environmental factors explored using global models
Vertical wind shear, upper level flow, outflow channels, low- to mid-level moisture, low-level inflow, proximity to land, SST, OHC
Subjective analysis still plays large role Statistical models are still important Mesoscale and regional models are becoming more
skillful RI guidance is still limited
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Guidance
Statistical models Statistical-dynamical models NWP models Ensembles Consensus Probabilistic guidance Issues
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Statistical Models
Served as short range guidance in early years May be used as guidance in longer ranges Serve as skill baselines for other methods Examples: Intensity CLIPER (e.g., ST5D, SHF5)
persistence, analog (e.g., WANI)
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Statistical-Dynamical Models
Regression based on parameters we know matter Been around since 1991 Show skill relative to Intensity CLIPER out to about 72 h Upgraded to include many parameters with incremental
increases in skill Basis for other guidance (e.g., RI, annular storms,
eyewall replacement cycles) Examples:
SHIPS LGEM STIPS SCIP
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Dynamical Models
Now routinely available and skillful! Most effort spent, most improvement obtained http://www.emc.ncep.noaa.gov/HWRF/IWTC_VIII/IWTC.html
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Global Examples
Global ModelsModel Name Information NotesARPEGE (France;Action de Recherche Petite Echelle Grande Echchelle)
http://www.cnrm-game.fr/spip.php?article121
GFS (USA;Global Forecast System)
http://www.emc.ncep.noaa.gov/GFS/doc.php
GDPS(Canada; Global Deteministic Prediction System)
http://weather.gc.ca/model_forecast/global_e.html
Met Office(UK; Met Office)
http://research.metoffice.gov.uk/research/nwp/numerical/operational/
Also known as the unified model
NAVGEM (USA; NAVy Global Environmental Model)
http://www.nrlmry.navy.mil/metoc/nogaps/ Replaces NOGAPS
NGFS (India:GFS run at NCMRWF)
http://www.ncmrwf.gov.in/#
ECMWF (Europe; European Center for Medium Range Weather Forecasts)
http://www.ecmwf.int/en/forecasts/documentation-and-support
ACCESS (Australia; Australian Community Climate and Earth-System Simulator)
http://www.bom.gov.au/australia/charts/about/about_access.shtm
GSM (Japan; Global Spectral Model) CMA-GSM(China; Global Spectral Model run at NMC/CMA)
http://www.jma.go.jp/jma/en/Activities/nwp.html Yu et al. (2013)
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Regional ExamplesRegional Models
Model Name Information NotesGFDL (USA; Geophysical Fluid Dynamics Lab)
http://www.gfdl.noaa.gov/operational-hurricane-forecasting
Running in West Pacific
GFDN (USA; run off of NAVGEM by the US Navy)
Development behind GFDL
ALADIN (France/Consortium)
http://www.cnrm.meteo.fr/aladin/?lang=en
AROME-IO(France; AROME-Indian Ocean)
http://www.cnrm.meteo.fr/spip.php?article120
AROME will replace ALADIN in 2015
HWRF (USA; Hurricane Weather and Research Forecast Model)
http://www.emc.ncep.noaa.gov/?branch=HWRF
Coupled,3km inner meshAlso run in W. Pacific and SH
NHWRF (India; Hurricane Weather and Research Forecast Model run at NCMRWF)
COAMPS-TC (USA)
http://www.nrlmry.navy.mil/coamps-web/web/tc?&spg=2&scl=3
Coupled, big improvements in 2014 thus far.
ACCESS-TC(Australia; TC centric)
http://www.bom.gov.au/australia/charts/about/about_access.shtml
Replaces TC-LAPS, 3 re-locatable domains
ACCESS-R (Australia; Regional)
http://www.bom.gov.au/australia/charts/about/about_access.shtml
GRAPES-TCM (China; GLOBAL/REGIONAL ASSIMILATION AND PREDICTION SYSTEM –TC Model) GRAPES-TCM (China; GLOBAL/REGIONAL ASSIMILATION AND PREDICTION SYSTEM –Tropical Model)
http://www.ral.ucar.edu/hurricanes/repository/models/grapestcm.php Yu et al. (2013)
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GFDL Improvement
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H-WRF Improvement
Left: Non-homogenous comparison of the absolute track forecast errors between the 2012 HWRF version during 2012 (blue columns) and the 2013 HWRF version during 2013 (red columns), Right: similar to (a) but for the absolute intensity forecast errors.
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AROME Improvement
Intense TC Béjisa intensity (central pressure, HPa) simulated by ECMWF (red), AROME OI (brown) and AROME-OI coupled with an oceanic model (blue) NWP systems. Left panel: intensity analysis (left panel) and 72-h intensity forecast starting at 12 UTC, 30 Dec 2013 (centre panel) and 12 UTC, 02 January 2014 (right panel). The best track produced by RSMC La Réunion is shown in black.
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IMD H-WRF
Mean intensity forecast error (kts) of HWRF during 2013 (total number of forecasts verified is shown in the bracket)
21.9
16.1
9.811.1
13.3
18.3
25.4
18.3
26.3
43
23.1
17.4
11.4 11.915.1
20.8
26.4
18.5
26.5
43
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
12(23) 24(21) 36(18) 48(16) 60(12) 72(10) 84(8) 96(6) 108(4) 120(2)
Forecast Lead Time(h)
Inte
nsi
ty fo
reca
st e
rro
rs (k
t)
AAE
RMSE
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COAMPS-TC Improvement
Track and intensity verification results for the 2013 Western Pacific basin season, for a homogeneous comparison of the control version (used in 2013) and a new version of COAMPS-TC. The intensity results show mean absolute error (solid) and mean error (dashed), while the track results show mean absolute error.
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Ensembles
Ensemble averages can reduce mean error
2013 HWRF Experimental ensemble track and intensity forecast performance for the NATL basin. Red lines show the operational HWRF and blue the 20-member HWRF ensemble mean (HWMN). The ensemble model was identical to the operational HWRF model run at 3km resolution.
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Consensus
Equal weights Unequal weights (based on performance) User (human) selection Examples:
IVCN – NHC, equal weights, n>1ICON – NHC, equal weights, n=4FSSE – FSU, unequal weights, n>1S5XX – JTWC, equal weights, n>1S5YY – JTWC, equal weights, n>1
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Consensus Improvements over ST5D
More skillful models -> more independence -> more consensus skill
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Unequal or Equal Weights?
The weighted (FSSE) didn’t improve over equal weights (IVCN). There is work involved (bias correction and weight derivation) to do the weights.
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From Economics & Finance
“….combination methods have gained even more ground in the forecasting literature, largely because of the strength of the empirical evidence suggesting that these methods systematically perform better than alternatives based on forecasts from a single model. Stable, equal weights have so far been the workhorse of the combination literature and have set a benchmark that has proved surprisingly difficult to beat. This is surprising since−on theoretical grounds−one would not expect any particular combination scheme to be dominant, since the various methods incorporate restrictions on the covariance matrix that are designed to trade off bias against reduced parameter estimation error. The optimal bias can be expected to vary across applications, and the scheme that provides the best trade-off is expected to depend on the sample size, the number of forecasting models involved, the ratio of the variance of individual models’ forecast errors as well as their correlations and the degree of instability in the underlying data generating process.” - Timmermann (2005)
"At least since the publication of “The Combination of Forecasts” (Bates and Granger [1969]), economists have known that combining forecasts from different sources can both improve accuracy and reduce forecaster error. In the intervening years, numerous studies have confirmed these conclusions, outlined conditions under which forecast combinations are most effective, and tried to explain why simple equal weights work so well relative to more sophisticated statistical techniques.” – Jones (2014)
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Probabilistic Guidance
Ensemble spread (NWP models)RI probabilities (SHIPS-based, on text output)WANI (analog-based)GPCE (consensus-based)
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Guidance Issues?
Carl: “I'd keep playing. I don't think the heavy stuff's gonna come down for quite awhile.”
Caddy Shack (1980)
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Guidance Issues?
Carl: “I'd keep playing. I don't think the heavy stuff's gonna come down for quite awhile.”
A forecast bust …
Caddy Shack (1980)
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Forecast Issues: Genesis/Formation
Hurricane strike probability from EPS (European Centre Ensemble) 29 March 2014 at 12 UTC valid for the 48 hours period from 30 March at 12 UTC to 01 April at 12 UTC. The hurricane probability was still at zero on 29 March at 12 UTC (TC HELLEN was then a tropical storm), while TC HELLEN became a very intense cyclone 24 hours later (top right = Metop 2, 29 March at 0635 UTC; bottom right = Noaa 19, 30 March at 1042 UTC).
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Forecast Issues: Rapid Intensification
Selected intensity guidance (rainbow colors) initiated on August 1, 2014 at 12 UTC and verifying best track intensity (black typhoon symbols) for Halong (11W). Note that all the forecasts are well below the verifying intensity between 24 and 48 h.
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Verification
Courtesy: treeofwishes.com
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Verification Issues
Does MAE capture everything we want in verification? See Aberson (2008) for some issues and solutions.
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Conclusions
The guidance has improved
Agency procedures
Basic process the same
Plenty of subjectivity
Deterministic guidance appears to be of benefit
NWP models are now competitive with other techniques
Consensus reduces MAE
Forecast busts (e.g., genesis and RI) are still a problem
Use performance metrics with caution
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Recommendations
Increase sharing of data among forecast centers. More guidance has been developed since the last IWTC and sharing this guidance among the forecast agencies benefits all centers.
Continue development of NWP models, statistical models and probabilistic guidance with special focus on cases with large forecast errors.
Continue to increase the availability of TC intensity guidance by making methods developed in the eastern North Pacific and Atlantic available in other basins.
Investigate new and innovative verification metrics and methods to aid in the elimination of the occasional very poor intensity
forecasts. Encourage research activity to investigate and understand the
physical causes and the impact observation quality/availability on very poor intensity forecasts.
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Some Definitions SFMR – Special frequency mircowave imager
A/C – Aircraft
Scatt – Scatterometer
V – Intensity
STIPS – Statistical typhoon intensity prediction system
SHIPS – Statistical hurricane intensity prediction system
SCIP – Statistical cyclone intensity prediction system
GPCE – Goerss predicted consensus error
WANI – Analog model developed by Naval Postgraduate School
IVCN – Intensity variable consensus
ICON – Intensity consensus
FSSE – Florida State Superensemble
S5XX – Intensity consensus at JTWC, uses STIPS
S5YY – Intensity consensus at JTWC, uses SHIPS and H-WRF
RI – Rapid intensification
CLIPER – Climatology and persistence
SHF5 – CLIPER model for intensity in the Atlantic and eastern North Pacific
ST5D – CLIPER model for intensity in the western North Pacific, Indian Ocean and Southern Hemisphere
GFDL – NWP model developed by the GFDL
H-WRF – Hurricane WRF
AROME – NWP model run by METEO-France
COAMPS-TC – Tropical cyclone model developed by U.S. Navy
SCORPIO – IMD web page for satellite imagery and model runs