# Presentation: 668957

## Performance of the FSU Hurricane Superensemble during 2005 Atlantic Season

- Mrinal K. Biswas*, Brian P. Mackey and T. N. Krishnamurti
- Florida State University
- Tallahassee, FL

## Outline 

-  Formulation of the Superensemble
-  Overview of Operations
-  Models used in Superensemble
-  Key storms of 2005
-  Training Issues
-  Conclusions

## FSU Superensemble Description

-  It operates by applying unequal weights to each model for each forecast lead time.  It differs from the simple ensemble mean, which assigns a weight of 1/N to each of the N multi-models.
-  In the end, the Superensemble tends to reduce the individual model biases while giving more weight to the more accurate member forecasts.
-  For this purpose, the Superensemble requires a training period made of up previous forecast cases in order to produce the regression coefficients (weights) for each model.

**FSU Superensemble Description**

## Superensemble Methodology

## Formulation of the Superensemble

-  The superensemble forecast is constructed as,

- are the ith model forecasts increments.
- are the mean of the ith model forecasts increments over the training period.
- is the observed increments mean of the training period.
- are the regression coefficient obtained by a minimization procedure during the training period.
- is the number of forecast models involved.

- where,

## Formulation of Superensemble....Contd

-  In addition to removing the bias, the Superensemble scales the individual model forecasts contributions according to their relative performance in the training period in a way that, mathematically, is equivalent to weighting them.
-  Bias removed ensemble mean utilizes equal weights (= 1/N, N being the total number of models). Thus poorer models are assumed to be equal in strength to the best models after bias removal.

-   Multimodel bias removed ensemble is defined as,

## Overview of operations

- During 2005 approximately 400 Superensemble forecasts were disseminated to the National Hurricane Center 6 times a day (0000 UTC, 0600 UTC, 1200 UTC, 1800 UTC)
- This year the forecasts were sent in a timely fashion to NHC
- Superensemble forecasts are run as long as a system maintains minimum tropical storm intensity till its landfall/ becomes extratropical

## Models used in the construction of the track FSU Superensemble

-  **OFCI **– Previous cycle OFCL (Official NHC forecast) interpolated
-  **GFDI** – Previous cycle GFDL (Geophysical Fluid Dynamics Laboratory model) interpolated
-  **GFSI** – Previous cycle GFS interpolated
-  **UKMI** – Previous cycle UKM (United Kingdom Met Office model) interpolated
-  **NGPI** – Previous cycle NGPS (Navy Operational Global Atmospheric Prediction System) interpolated
-  **GUNA** – Mean consensus of GFDI, UKMI, NGPI, and GFSI

## Models used in the construction of the intensity FSU Superensemble

-  **OFCI** – Previous cycle OFCL (Official NHC forecast) interpolated
-  **GFSI **– Previous cycle GFS interpolated
-  **UKMI** – Previous cycle UKM (United Kingdom Met Service      model) interpolated
-  **SHF5** – SHIFOR5 (Climatology and Persistence model)
-  **DSHP** – SHIPS with inland decay algorithm

## Performance of FSSE

- Dennis (4–13 July 2005)
- Emily (10-21 July 2005)
- Katrina (23-30 August 2005)
- Rita (17-26 September 2005)
- Wilma (15-25 October 2005)

## Hurricane Dennis

- 4th storm of the season (July)

- Landfall on Santa Rosa Island, FL

- $2.23 bn damage

## Realtime forecasts of Hurricane Dennis 

- Realtime forecasts of Hurricane Dennis

- Superensemble was able to predict landfall quite accurately almost 84 hr in advance

## Track and Intensity forecast verification during Dennis

- Track forecasts comparable up to 60 hour of forecast

- Intensity errors are small through 72 hour of forecast

## Hurricane Emily

- Earliest forming cat 5 hurricane

- Landfall near San Fernando, Mexico

## Slide 15

## Hurricane Katrina

- Costliest and one of the five deadliest hurricanes

- First landfall near the border of Miami-Dade county and Broward county

- Final landfall near Louisiana / Mississippi border

- Around 1400 fatalities

## Hurricane Katrina

- 23 Superensemble forecasts were made during Katrina
- Predicted landfall 60 hours in advance
- Did not predict the intensification when it was crossing Florida, GFDL did a nice job

## Slide 18

## Intensity forecasts covering landfall period

## Hurricane Katrina Track verification 48 hour before landfall

## Slide 21

## Hurricane Rita

- Third Cat 5 hurricane of 2005 season

## Economic impacts of Hurricane Rita

## Hurricane Rita

- FSU Superensemble gave the best guidance for intensity during hurricane Rita at most forecast times
- 120 hour intensity error was around 10 mph

## Obs

- 00     57.5
- 12     69
- 24     69
- 36     97.75
- 48   120.75
- 60   166.75
- 72   172.5
- 84   143.75
- 96   138
- 108 126.50
- 120 120.75

- FSSE
-   57.5
-   72.26
-   91.07
- 108.26
- 122
- 126
- 129
- 130
- 130
- 130
- 120.98

- 00   109.25
- 12   138
- 24   172.5
- 36   166.75
- 48   138.00
- 60   132.25
- 72   120.75

- FSSE
-   103.5
-   122.32
-   132.06
-   136.07
-   139.86
-   144.75
-   133.98

## Slide 26

## Uncertainties in track forecasts during Rita

- During Katrina the models shifted to the left while in Rita it did the opposite

## Slide 28

## Hurricane Wilma

- All time lowest central pressure 882 mb

- 60 kt tropical storm to a 150 kt Cat 5 hurricane in 24 hours

## Obs (mph)

- 00      74.75
- 12    149.5
- 24    172.5
- 36    155.25
- 48    143.75
- 60    149.5
- 72    143.75
- 84    138
- 96    115
- 108    97.75
- 120    97.75

- 00      80.5
- 12    172.5
- 24    161
- 36    149.5
- 48    149.5
- 60    138
- 72    126.5
- 84      97.75
- 96      97.75
- 108  103.5
- 120  120.75

- FSSE
-   74.75
-   90.47
-  106.53
-  121.06
-  130.41
-  132.76
-  136.40
-  137.99
-  120.40
-  105.61
-    90.39

- FSSE
-   80.5
-   97.82
- 116.59
- 129.56
- 137.19
- 140.86
- 142.20
- 139.75
- 131.92
-   90.66
-   80.04

## Slide 31

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## Slide 38

## Timing error corrections

- Timing errors are caused by the error in the forecasts of the translation velocity of the hurricane
- For a given hour of forecast the correction is obtained from training phase and it is applied in the forecast phase
- At present we are still testing it and if found consistent will be implemented in the next season

## Correction applied on around 400 forecasts on 2005 season realtime data sets. The track errors improved by as much as 60 km at 120 hour. 

- Correction applied on around 400 forecasts on 2005 season realtime data sets. The track errors improved by as much as 60 km at 120 hour.

## Training Issues

-  Training forms the backbone of the  Superensemble Technique
-  The larger the training data set, better the forecast in general
-  Model changes during later part of 2004 season and during the 2005 season, affected the Superensemble performance

## During the early part of the 2005 season the length of the modified training set was small making the forecast less spectacular as we saw in 2004 season

As the season progressed new data sets were included in the training set which helped in improving the Superensemble forecasts

- As the season progressed new data sets were included in the training set which helped in improving the Superensemble forecasts

## Conclusions	

- Superensemble did a fairly good job in predicting the tracks through 72 hour of forecast
- Superensemble produced the best intensity forecasts along with DSHP at most forecast times
- The performance was not so impressive as 2004 probably due to large scale model changes which seriously affected the training data sets
- Post realtime analysis with the correction in the timings improved the skill of the Superensemble and will be implemented in the next hurricane season

## Acknowledgements

-  NHC/TPC for providing the input member models in real-time
-  All of the dynamical modeling centers (NCEP, GFDL, FNMOC, UKMO) for providing hurricane track and intensity forecast data to NHC

## Thank You