Hurricane/Typhoon Forecasts at Florida State University

Department of Meteorology, Florida State University,

Tallahassee, FL 32306, USA.

T. N. Krishnamurti

International Conference on South Atlantic Cyclones, Track Prediction and Risk Evaluation, Rio de Janeiro, May 19-21, 2008

Outline

Track and intensity forecasts over the Atlantic basin: Superensemble methodology A walk through a superensemble forecast Skills during 2004, 2005 Case studies on Katrina, Rita

Tropical storm forecasts using mesoscale superensemble. Tropical Storm frequency forecast a season in advance:

Methodology Application to north Atlantic basin tropical storms:

Using four models Using superensemble

Tropical storms in single, double and triple CO2 scenarios. Conclusions and future work.

Hurricane Track and Intensity Forecasts over the North Atlantic Basin

This technique demonstrates a way to post-process a set of multi-model forecasts to produce a new "optimal" forecast.

• 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.

Description of FSU Superensemble

Multimodel FSU Conventional Superensemble

The superensemble forecast is constructed as,

N

i

iii OFFaS1

)(

are the ith model forecasts.

are the mean of the ith model forecasts over the training period.

is the observed mean of the training period.

are the regression coefficient obtained by a minimization procedure during the training period. Those may vary in space but are constant in time.

is the number of forecast models involved.

iFiF

O

ia

where,

N

The coefficients ai are derived from estimating the minimum of function,

2)1( i

train

i ON

iSG

the mean square error.

N

i

ii OFFN

E1

)(1 Multimodel bias removed ensemble is defined as,

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.

Model 3

Model 1

Model 4

Model N

Model 2

Tra

inin

g T

ime

Serie

s

2)1( i

train

i ON

iSG

Multiple Linear Regression:

Training Phase

N

i

iii OFFaS1

)(

Fo

reca

sts

a1

a2

a3

a4

aN

Forecast Phase

Minimization of error term G.

Fo

reca

st T

ime

Serie

s

Weights

Statistical weights obtained in the training phase are passed on to the forecast phase.

Superensemble Forecasts:

F => Forecasts O=>Observations ai => Weights. Overbar represents climatology.

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.

n

iiii FtFOtS

1

)()(

S(60 72 hrs) = 0.44 +

0.45 (1.10 – (0.38)) Model 1

+ -0.07 (0.40 – (0.62)) Model 2

+ 0.08 (1.00 – (0.55)) Model 3

+ 0.40 (1.20 – (0.41)) Model 4

+ 0.26 (0.90 – (0.45)) Model 5

S(60 72 hrs) = 1.24

S(at 72 hours) = S(at 60 hours) + S(60 72hours)

= 79.30 + 1.24 = 80.54

Observed (at 72 hours) = 80.40

A Walk Through a Superensemble Forecast

1. OFCI – Previous cycle OFCL (Official NHC forecast) interpolated

2. GFDI – Previous cycle GFDL (Geophysical Fluid Dynamics Laboratory model) interpolated

3. GFSI – Previous cycle GFS interpolated

4. UKMI – Previous cycle UKM (United Kingdom Met Office model) interpolated

5. NGPI – Previous cycle NGPS (Navy Operational Global Atmospheric Prediction System) interpolated

6. GUNA – Mean consensus of GFDI, UKMI, NGPI, and GFSI

Models used in the construction of the track FSU Superensemble

1. OFCI – Previous cycle OFCL (Official NHC forecast) interpolated

2. GFSI – Previous cycle GFS interpolated

3. UKMI – Previous cycle UKM (United Kingdom Met Service model) interpolated

4. SHF5 – SHIFOR5 (Climatology and Persistence model)

5. DSHP – SHIPS with inland decay algorithm

Models used in the construction of the intensity FSU Superensemble

NHC’s Official annual average track errors (nm) in Atlantic basin for hurricanes and tropical storms

Official Intensity skill trend with respect to SHIFOR5 (in %) in Atlantic Basin from 1990

Current Operational Skills

Mean absolute track errors (in nm) for 2004 season

Mean absolute intensity errors (in kts) for 2004.

The number of cases is shown on the top of the illustration

2004 Mean Absolute Track Errors

0

50

100

150

200

250

300

350

400

12 24 36 48 60 72 84 96 108 120Forecast Hour

Mea

n A

bso

lute

Err

or

(nm

)

OFCI AVNI GFDI

NGPI UKMI GUNA

GUNS ENSM FSSE

241 219 203 184 167 151 131 110 97 86

(number of cases)

2004 Mean Absolute Intensity Errors

0

5

10

15

20

25

30

35

40

12 24 36 48 60 72 84 96 108 120

Forecast Hour

Mea

n A

bso

lute

Err

or

(kts

)OFCI AVNI GFDI

GFNI UKMI SHF5

DSHP ENSM FSSE

241 219 203 184 167 151 131 110 97 86(number of cases)

2005 Mean Absolute Track Errors

0

50

100

150

200

250

300

350

400

450

500

12 24 36 48 60 72 84 96 108 120

Forecast Hours

Me

an

Ab

so

lute

Err

or

(nm

)

OFCI GFSI

GFDI NGPI

UKMI GUNA

BAMS CLP5

FSSE

355 320 285 244 204 170 140 117 84 65

Number of Cases

2005 Mean Absolute Intensity Errors

0

5

10

15

20

25

30

35

12 24 36 48 60 72 84 96 108 120

Forecast Hours

Me

an

Ab

so

lute

Err

or

(kts

)

OFCI GFSI

GFDI UKMI

SHF5 DSHP

ENSM FSSE

355 320 285 244 204 170 140 117 84 65

Number of Cases

Mean absolute track errors (in nm) for 2005

Mean absolute intensity errors (in kts) for 2005.

The number of cases is shown on the top of the illustration.

Official homogeneous comparisons for selected Atlantic basin early track guidance models for 2004 with respect to the CLIPER5

Official homogeneous comparisons for selected Atlantic basin early intensity guidance models for 2004 with respect to the SHIFOR5

Official homogeneous comparisons for selected Atlantic basin early track guidance models for 2005 with respect to the CLIPER5

Official homogeneous comparisons for selected Atlantic basin early intensity guidance models for 2005 with respect to the SHIFOR5

2004 Mean Absolute Intensity Errors for Forecast Hour 72

0

10

20

30

40

50

Charley Danielle Frances Ivan Jeanne Karl Lisa

Storm Name

Mea

n A

bsol

ute

Err

or (k

ts)

OFCI AVNI

GFDI UKMI

SHF5 DSHP

ENSM FSSE

8 14 38 42 40 17 21(number of cases)

2004 Mean Absolute Intensity Errors for Forecast Hour 120

0

10

20

30

40

50

60

Frances Ivan Jeanne Karl

Storm Name

Mea

n A

bso

lute

Err

or

(kts

) OFCI AVNI

GFDI UKMI

SHF5 DSHP

ENSM FSSE

30 35 32 10(Number of cases)

Mean absolute intensity errors in kts for (c) forecast hour 72 for Charley, Danielle, Frances, Ivan Jeanne, Karl and Lisa (in kts)

Mean absolute intensity errors in kts for forecast hour 120 for Frances, Ivan, Jeanne and Karl (in kts)

MAE for 96-hr fcstis 69 n mi (34 cases)

MAE for 96-hr fcst is 89 n mi (34 cases)

MAE for 96-hr fcstis 85 n mi (34 cases)

MAE for 96-hr fcst is 178 n mi (34 cases)

Landfall

120 hour track forecasts for hurricane Katrina with initial time at 27 August 2005 00 UTC

84 hour track forecasts for hurricane Katrina with initial time at 27 August 2005 18 UTC.

The red, blue and green lines show the superensemble track, the observed best track and the member model forecast tracks at 12 hour intervals

60 hour intensity forecasts for Katrina at initial time 26 August 2005 18 UTC. The red line indicates the superensemble forecast and the blue line denotes the observed at 12 hour intervals.

120 hour track forecasts for hurricane Rita with initial time at 19 September 12 UTC. The red, blue and green lines show the superensemble track, the observed best track and the member model forecast tracks at 12 hour intervals

120 hour intensity forecasts (in mph) for Katrina at initial time 19 September 2005 06 UTC. The red line indicates the superensemble forecast and the blue line denotes the observed at 12 hour intervals.

Hurricane Rita Mean Absolute Intensity Errors (kts)

0

10

20

30

40

50

60

70

12 24 36 48 60 72 84 96 108 120

Forecast Hours

Err

or

(kts

)OFCI

AVNI

GFDI

UKMI

SHF5

DSHP

FSSE

Mean absolute intensity errors (in kts) for Hurricane Rita

Mesoscale Superensemble Forecasts of Tropical Storms

A suite of mesoscale models

Member models for the mesoscale superensemble: HWRF MM5 WRFA WRFB GFDL DSHIP

Global Spectral

Model T255 28 Layer

Meso scale Data Assimilation

Meso scaledata sets

HWRF

WRFA

WRFB

MM5

GFDL

DSHP

Multi model mesoscale

superensemble

MicrophysicsIce, graupel, snow,

cloud, liquid water, rain water,

hail

Angular momentu

m perspectiv

e

Scale interaction

between hurricane and cloud

scale

Intensity forecasts

and interpretatio

n of skills

LATERAL

BOUNDARY CONDITIONS

MEMBER MODELS

Sensitivity Studies

PostProcessing

The NMM-WRF Modeling System

Regional-Scale, Moving Nest, Atmospheric Modeling System.

Non-Hydrostatic system of equations formulated on a rotated latitude-longitude, Arakawa E-grid and a vertical, pressure hybrid (sigma_p-P) coordinate.

Advanced HWRF,3D Variational analysis that includes vortex relocation and adjustment to actual storm intensity.

Uses SAS convection scheme, GFS/GFDL surface, boundary layer physics, GFDL/GFS radiation and Ferrier Microphysical Scheme.

Ocean coupled modeling system (POM GFDL).

HWRF Grid Configuration

Arakawa E-grid

HWRF GFDL Grid configuration 2-nests (coincident) 3-nests(not coincident)

Nesting Force-feedback Interaction thru intra-nest fluxes

Ocean coupling POM (atlantic only) POMConvective parameterization

SAS mom.mix. SAS mom.mix.

Explicit condensation Ferrier FerrierBoundary layer GFS non-local GFS non-localSurface layer GFDL ..(Moon et. al.) GFDL ..(Moon et. al.)

Land surface model GFDL slab GFDL slabDissipative heating Based on D-L Zhang Based on M-Y tke 2.5

Radiation GFDL (cloud differences) GFDL

Source: James Franklin, NHC

Storm cases used for computing mesoscale superensemble

Helene Intensity Helene Sea Level Pressure

Mesoscale Superensemble Track Errors

Mesoscale superensemble intensity errors

Seasonal Forecasts of Hurricane Frequency

The notion of seasonal tropical storm frequency prediction using global models or reanalysis data sets is based on the fact that large scale models carry the signature of a tropical storm in several of its forecasted atmospheric parameters.

Identification of these parameters and their threshold values to come up with an algorithm that identifies tropical storm in a large scale model is the purpose of this section of the talk.

A goal of this study is also to construct superensemble forecasts for the seasonal frequencies.

Model Description

Florida State University (FSU) coupled global spectral model (GSM) was used for our study.

Atmospheric Model: The atmospheric model had T63 horizontal resolution (~1.875o). It had 14 vertical sigma levels. Global clouds in the radiative transfer are defined using a diagnostic

cloud algorithm, which is based on the difference between the model’s initial relative humidity, and certain threshold predefined relative humidity, Krishnamurti et al. (1990).

Ocean Model: The ocean model was an extended version of the model developed by

Ernst Maier-Reimer, also called the HOPE model. It is a primitive equation model using the Boussinesq and hydrostatic

approximation on a horizontally staggered Arakawa E-grid. The meridional resolution was highest near the equator (0.5o) and

decreases to 5.0o near the south and north boundaries.. The zonal resolution was 5.0o constant. 17 irregularly spaced levels. 10 levels within upper 300 meters.

Continued…

…Continued

Four versions of the model were used in our study. These four versions were the combinations of two different

cumulus parameterization schemes and two different radiative transfer schemes:

Radiative Transfer Schemes

New (Band Model)

Old (Emissivity-absorptivity

based)

Cumulus Parameterization Schemes

Arakawa-Schubert

(Simplified)

ANR AOR

Kuo (Modified)

KNR KOR

Coupled Assimilation Procedure

In this study we perform data assimilation using Newtonian relaxation technique. That constitutes the coupled assimilation of our model. Within the coupled model the SST field is continually assimilated toward the observed fields of Reynolds and Smith (1994).

Coupled Assimilation Methodology

FSUCoupledGlobalModels

AtmosphericModel I

OceanModel

Coupled AssimilationAtmospheric

Model III

AtmosphericModel II

AtmosphericModel IV

11 Year Spin-up

Forecasts

Model I

Model II

Model III

Model IV

Ensemble Mean and

Superensemble Forecasts

Model I

Model II

Model II

Model IV

Forecasts

Ensemble Mean and

Superensemble Forecasts

Other Models

Other Models

IC-1

IC-2

Seasonal Forecast Experiments

Three-months long seasonal forecasts experiments were performed with the four versions of the FSU CGCM for the summer seasons of the years 1987 to 2001.

The model was initiated at the middle and end of every month during this period (24 forecast experiments per year) and was integrated up to 90 days.

In this study we use forecasts starting on 29 July of every month.

6-hourly model outputs were considered for our analysis.

Previous Studies on Calculation of Storm Frequency from Large Scale Data Sets (Observations or Models)

(from Walsh et al. 2007)

Basin Domains

Basin domains used to calculate global average wind speed at 1000 hPa to construct the wind speed threshold.

Red: After Camargo and Zebiak (2002); Black: This study.

Configuration-dependent thresholds for the Atlantic basin

U (m/s) ζ (s-1) (K)

ANR 9.98 3.20e-05 1.32

AOR 7.09 2.11e-05 0.69

KNR 10.20 3.42e-05 1.32

KOR 7.46 2.31e-05 0.75

'T̂

1. The 850-mb relative vorticity exceeds the vorticity threshold.

2. The maximum 1000 mb wind speed in a centered 7 by 7 box exceeds the wind speed threshold.

3. The sea level pressure is the minimum in a centered 7 by 7 box.

4. The vertically-integrated temperature anomaly at the grid point exceeds the temperature anomaly threshold.

5. The mean wind speed averaged over a 7 by 7 box is larger at 850 mb than at 300 mb.

6. If there exists a grid point at a following timestep that satisfies these criteria within two grid points of latitude and longitude (approximately 3.75°) of the original point, then they are connected.

7. If the storm lasts at least one day (5 consecutive grid points), then it is identified as a model tropical cyclone.

Conditions for Detection of a Tropical Strom

Relative Vorticity at 850 hPa vs. Vertical Integrated Temperature Anomaly, KNR

Relative Vorticity (x 10-5 s-1)

Vertical Integrated T

emperature

Anom

aly (K)

Thresholds are shown in Red line.

Structure of a Strong Storm in KNR

Wind at 1000 hPa Vertically integrated temperature anomaly (shaded) and sea level

pressure (contour)

Relative vorticity at 850 hPa (shaded) and sea

level pressure (contour)

Latitude-vertical cross section of wind speed

Longitude-vertical cross section of wind speed

Latitude-vertical cross section of temperature anomaly (shaded) and total temperature (contour)

Longitude-vertical cross section of temperature anomaly (shaded) and total temperature (contour)

1. The strongest storms in KNR and KOR possess similar surface wind characteristics as the strongest storms in ANR and AOR.

2. The strongest winds of each are located generally to the north, northeast, and east of the center of circulation.

3. The strong KNR storm is the strongest storm in any configuration of the model, and it is one the looks the most realistic.

There is a bulls-eye structure in the pressure field around the center, and the lowest closed isobar, 974 mb, is by far the strongest out of any of the other storms.

The vertical wind profile and the vertical temperature structures also look very realistic, especially the heating maximum’s location at 300 mb.

The strongest storm in the KOR does show a pressure drop as well at the center. The near-zero wind speed is still present at the core.

The level of maximum heating, however, is not 300 mb. Although the vertical structure of the heating is very uniform and not sheared, the anomaly maximum is located at 500 mb, which is below observational records.

Tropical Cyclone Frequency, July 29 to October 27

During 1987 to 2001, the model with Kuo convection scheme and new radiation scheme (KNR) performs the best compared to the other three models in the suite.

Tropical Cyclone Frequency, August 13 to October 27

If we exclude first 45 days from our analysis, the prediction of number of tropical storms during the next 45 days of seasonal forecasts also shows that KNR performs better than rest of the models. This shows that initial condition does not have an impact on the predictability of seasonal storm frequency with these large scale coupled models.

Superensemble Prediction of Hurricane Frequency on the Atlantic Basin

The RMS error of frequency forecasts decreases with the use of the superensemble.

Superensemble Prediction of Hurricane Frequency on the Atlantic Basin after 3-year Smoothing

3-year running mean

Superensemble prediction of seasonal hurricane frequency reduces the overall RMS error over the best member model in the suite.

The Impact of Current and Possibly Future Sea Surface Temperature Anomalies on the Frequency of Atlantic Hurricanes

An example of hurricane formation in the FSU global model

SST anomaly fields for the hurricane season 1 June through 31 October obtained from NMC operational 10 day mean data sets. Units K.

1987, El Nino Year

1988, La Nina Year

SST anomaly fields for the for the double CO2 experiments. These are obtained by modifying the observed anomalies requiring a tropical oceanic warming of 0.5K.

1987, El Nino Year

1988, La Nina Year

SST anomaly fields from a triple CO2 coupled ocean-atmosphere model run made at the Max-Planck Institute at Hamburg.

Hurricane season mean flow field of 850hPa, average from 1 June through 31 October for mean data sets for 1988. Thick dashed line denote the Monsoon Trough

Hurricane season mean flow field of 850hPa, average from 1 June through 31 October for single CO2 experiment. Thick dashed line denote the Monsoon Trough

Hurricane season mean flow field of 850hPa, average from 1 June through 31 October for double CO2 experiment. Thick dashed line denote the Monsoon Trough

Double CO2 Experiment

A sequence of daily vector flow charts at 850hPa. Individual storms are identified by letters.

SINGLE CO2 Experiment

A sequence of daily vector flow charts at 850hPa. Individual storms are identified by letters.

El Nino Year

La Nina Year

Seasonal prediction of storm tracks for the current CO2 conditions

Seasonal prediction of storm tracks for the double CO2 conditions

El Nino Year

La Nina Year

Seasonal prediction of storm tracks for the triple CO2 conditions

Maximum intensity of individual hurricanes in each of the 5 simulations, 1 June -31 October; units are ms-1.

Mean seasonal precipitation (mm/day) for single CO2 experiment for an El Nino year

Mean seasonal precipitation (mm/day) for double CO2 experiment for an El Nino year

Mean seasonal precipitation (mm/day) for single CO2 experiment for a La Nina year

Mean seasonal precipitation (mm/day) for double CO2 experiment for a La Nina year

A x-t diagram of the shear of the zonal wind (200 hPa zonal wind minus 850hPa wind meridionally averaged from 5N to 20N for (a) single CO2 simulations for a La Nina year 9b) double CO2 simulations for a La Nina year.

(a) (b)

1. The direction of research by the community is clearly in the right direction.

2. We need data sets for the storm environment, for the inner core.

3. We need suites of global and mesoscale models.

4. Refinement of individual models is a necessity (Data assimilation, physics, nesting strategies, ensemble constructions).

5. Superensemble strategies help the reduction of collective systematic errors, further work is needed to improve these methods for operations.

6. Models can help on seasonal forecast issues. Clearly a sustained research effort is needed.

Conclusions and Future Work

Continued…

7. The track and intensity errors during 2004 and 2005 Atlantic hurricane seasons (those included hurricanes line Katrina and Rita) were substantially decreased with the use of the superensemble.

8. Prediction of the seasonal frequency of tropical storms was possible with a suite of four FSU large scale models. Construction of a superensemble on those model predicted frequencies further decreased the RMS error of tropical storm frequency during 1987-2001.

9. When we carry out season long integrations of our atmospheric model, we find that the number of hurricanes we have simulated for the current day CO2 conditions are somewhat less for El Nino year as compared to those for a La Nina year.

10. Under the assumption that El Ninos and La Ninas shall continue to occur under the enhanced CO2 conditions, we have constructed modeled SST anomalies assuming a 0.5K net global tropical oceanic warming. The number of hurricanes were nearly the same for the single and the double CO2 experiments of the EL Nino. However, we see a doubling in the number of hurricanes when the La Nina configured SST anomalies are used for the doubled CO2.

11. We extracted the SST anomaly fields from the triple CO2 coupled ocean-atmospheric model run of the Max Planck Institute of Hamburg. Using these SST anomalies, we carried out a hurricane season long integration with the FSU atmospheric global model. The results showed a slight enhancement in the number and an intensification of Atlantic hurricanes compared to the EL Nino experiments.