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Weekly TC forecasts in the Southern Hemisphere
Anne Leroy (Météo France)
Matthew Wheeler (CAWCR/BOM)
John McBride (CAWCR/BOM)
funded by the Indian Ocean Climate Initiative
May 2009
Introduction
• Potential for TC genesis and occurrence predictions at intraseasonal leadtime based on the MJO state
From Leroy and Wheeler 2008From Leroy and Wheeler 2008
Plan
• Previous method : description and weakness
• Modifications to the method
• Description of the new models
• Exemple of forecats : 1984-1985
• Evaluation : Brier skill score, reliability diagram and economic value
• Conclusion
Former version of the method• Use of logistic regression to forecast both TC genesis and occurrence probability• Predictors :
- Daily climatological probability of genesis/occurrence- Interannual predictors : SST1/SST2 to take into account the ENSO state and
possibility the interannual variability in the Indian Ocean- Intraseasonnal predictors : RMM1/RMM2 to take into account the MJO state
• Described in Leroy and Wheeler 2008• Operational forecast from 2006/07 to 2008/2009 on http://www.meteo.nc/espro/
previcycl/cyclA.php
Genesis probability forecasted in 2008/2009 in z3
Logistique regression
Probability P(x)
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Z3occ.htm
Z4occ.htm
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doc.pdf
• Total number of click during TC season : 3500
• web page in english more visited than the one in french
• users seems to become faithful (more click at the end of the season than at the beginning)
• Forecasts of all the 4 areas are regurlarly visited
Nb of click to zoom on the curvesNb of click to zoom on the curves
Nb of click on the 3 more Nb of click on the 3 more visited pagesvisited pages
Web statistic in 2008/2009 Statistiques du web
FR page
ENG page
Doc
Weakness of this product
• Regions too large for most users : forecasts over smaller regions may have less skill but more usefullness
• Boundary in the Pacific not optimized for ENSO
From Camargo et al. 2007From Camargo et al. 2007
Weakness of this product
• Trends in predictors appeared in recent years => trend in predicted TC probability
• Artificial skill
Changes to the method
• Changes in areas
• Use of other interannual predictors
• Take into account the seasonality of the relationship between ENSO and TC (finally not implemented)
Changes in areas
• Reduce the size of areas : a compromise between size and TC number
Number of TC genesis
Number of TC day (occurrence)
Numbers of TC in overlapping boxes
20 º
15 º
Southward tracks
Seasonal cycle
• FFT filtering to calculate the probability of TC occurrence during a week
Late season
Long season
Changes in areas
• there are more occurrence events than genesis => the statistical model should perform well over more areas to predict occurrence
• Occurrence is really linked to the impact of TC for users
=> Focus on occurrence forecast
Changes in interannual predictors
• Like SST1 and SST2, we are looking for :– uncorrelated indices,– Indices taking into account the Pacific and
Indian Ocean variability,– Indices available in real-time,– Indices without trend.
New interannual predictors
• Nino3.4• trans-Nino Index: TNI =
Nino1+2-Nino4. This index is complementary to Nino3.4 to characterize ENSO (Trenberth and Stepaniak 2000)
• Indian Ocean Dipole index : DMI= SST in tropical W Indian Ocean – SST in SE Indian Ocean (defined by Saji et al 2000). Chan and Liu showed the DMI is related to seasonal TC numbers . Area used to define the DMIArea used to define the DMI
New interannual predictors
• Use of 2-month average of indices• One month lag introduced to simulate real-time• Uncorrelated ?
NINO3.4 TNI DMI NINO3.4 TNI DMININO3.4 1 -0.18 0.48 NINO3.4 1 -0.18 0.67TNI 1 0.05 TNI 1 0.09DMI 1 DMI 1
Correlation between monthly indices
September to March September to December
NINO3.4 and TC occurrence
All season
Early season
Nb of TC day per 20 * 15 degree boxes
Late season
No evidence that the impact of NINO3.4 on TC depends on the time within the season
El Nino – La Nina
TNI and TC occurrence
All season
Early season
Late season
DMI and TC occurrence
All season
Early season
Late season
Weak signal
Looks like an ENSO pattern
A signal different from the signal of ENSO ?
Predictor selection
• ascending predictor selection procedure
• But include predictors with a more strict criterion (pe=0.001) than in Leroy and Wheeler (2008)
Predictors selection
• W1 (first week of forecasts) : selection order
Predictors selection
• W2 (second week of forecasts) : selection order
Predictors selection
• W3 (third week of forecasts) selection order
Predictors selection
• W4 (fourth week of forecasts): selection order
Coefficients of the model
• W1
Predictors have been standardized so that coefficients of different predictors can be compared
The method to fit the logistic model to the data does not always converge in region where very few TC occurs
Coefficients of the model
• W2
Coefficients of the model
• W3
Coefficients of the model
• W4
Example of forecasts
• W1 during 1984-1985
• Strong MJO : in the Indian Ocean in early Feb moving to Western Pacific by mid-march
From http://www.bom.gov.au/bmrc/clfor/cfstaff/matw/maproom/RMM/phasediag.list.htm
Example of forecasts
• W1 during 1984-1985• Strong MJO : in the Indian Ocean in early Feb
moving to Western Pacific by mid-march
W1 forecasted probability
Anomaly against daily climatological probability
Score
• Brier Skill score
• Cross validation : the statistical model is validated over a period different from its learning period => no artificial skill
Brier Score :
Expressed as a percentage of improvement over a reference strategy
Brier Skill score :
Here, the reference strategy is the one that forecasts the seasonal mean climatology over each area.
Cross-validated Brier skill score
• W1
The reference forecast used to calculate Brier skill score is the mean seasonal climatology.
The skill comes mainly from the daily climatology
Cross-validated Brier skill score
• W1
The skill comes mainly from the daily climatology, then from the MJO and ENSO. Weak impact of TNI and DMI.
Cross-validated Brier skill score
• W2
The improvement brought by the MJO is not as important as the one at W1.Impact of Nino3.4 maintains.
Cross-validated Brier skill score
• W3
The MJO still brings an improvement.
Cross-validated Brier skill score
• W4
No skill comes from the MJO (except ?)
For operational products
• Over regions where our model shows little skills, our forecasts are replaced by the daily climatological forecast.
• little skills at a given lead time = if the Brier score of our forecast is higher than the Brier score of the daily clim at this lead time and the next one.
For operational products
Areas where our forecasts is replaced by the daily climatology.Hatched areas become larger with lead time as the predictability decreases.
Reliability diagram
W1
1/10 of the data in each dot
• The pairs (forecast, observation) are ordered by increasing value of the forecast and then grouped into 10 groups
• The average of observed probability (1, 0, 0, 1…) for each group is then calculated
Perfect forecast
Reliability diagramsW1
W2
W3
Reliability diagramsW1
W2
W3
Reliability diagramsW1
W2
W3
Economic Value
• Cost/loss model : a particular user will take actions when the forecated probability is over a threshold value (Paction)
Cost of the action
Cost of the loss
Yes
No
Event
Yes No
Economic Value
• Expense (E) generated by different strategies and economic value :
•Perfect forecast => V=100 %•Forecast as good as climatology => V=0
Paction
Eclimate - Eforecast
Eclimate - Eperfect
Economic ValueW1
W2
W3
Economic ValueW1
W2
W3
Economic valueW1
W2
W3
Conclusion
• SST1/SST2 have been removed from the scheme
• Large impact of Nino3.4, low impact of TNI and DMI
• Smaller regions with reasonably good skill, probably more useful
• Plans to use the new scheme for real-time forecasts during 2009/2010 TC season
