Beyond Weather Timescale Prediction of

Hurricane Sandy and Super Typhoon Haiyan

Tim Li

University of Hawaii

Acknowledgement: B.-Q. Xiang and M. Zhao (GFDL)

Xiang, B., S.-J. Lin, M. Zhao, S. Zhang, G. Vecchi, T. Li, X. Jiang, L. Harris,

J.-H. Chen, 2015: Beyond weather time scale prediction for Hurricane Sandy

and Super Typhoon Haiyan in a global climate model, Monthly Weather

Review, 143, 524-535.

Construction of a 24-48-hr TC genesis forecast model

for JTWC (An ONR project)

3-8-day filtered 850 mb relative vorticity in

2004 (5oN-15oN in N. Atlantic)

Red dots are TC formations.

Satellite image on Oct 17,2007

(two days before19W Kajiki

formed in WNP)

Many disturbances exist in the

tropics, but only a few of

them develop to TCs.

Tim

e (

Th

ree m

on

ths)

Longitude 10E100W

Sample composites (2003-2005)

850mb relative humidity(%) (In a 20x20 degree box centered at the disturbance)

N AtlanticWNP

Find the statistically significant difference between developing

and non-developing disturbance groups

A Box Difference Index (BDI) Method

NONDEVDEV

NONDEVDEV MMBDI

0

0.1

0.2

0.3

0.4

0.5

1000 950 900 850 800 750 700 600 500 400 300 (hPa)

BDI na_rhum_20x20 na_rhum_10x10 Calculated BDI for relative

humidity in NATL averaged over

two horizontal domains at different

vertical levels

Peng et al. 2012, Fu et al. 2012,

MWR

The BDI methodology can quantitatively measure which parameter at which level

is best in distinguishing developing and non-developing disturbance groups.

In-sample

2004-2008Hindcast

2009-2012

Hit: 78.2%

False alarm: 23.4%

Hit: 72.1%

False alarm: 18.2%

24-48h Prediction model for WNPThe optimal model includes 3 predictors:

1. Maximum relative vorticity at 800mb

2. Vertically integrated du/dy

3. SST

NOGAPS 850 mb vorticity

analysis (20100809)

GPI=0.60, 0.01, 0.01, 0.00

Disturbance in red box developed

to TD-5 24 hours later.

NOGAPS 850 mb vorticity

analysis (20100922)

GPI=0.85, 0.10, 0.11

Disturbance in red box developed

to TS Matthew 24 hours later.

7

24-48-hr forecast of TC genesis appears

quite challenging. To what extend can we

predict cyclogenesis in extended range

(10-30-day)?

What is the predictability source of 10-

30-day cyclogenesis forecast?

Predictability source for extended-range TC forecast: MJOCamargo et al. 2009, JAS

GP (colors) and OLR (contours) anomaly composites for different MJO phases

Wave energy

accumulation

Both divergent and rotational

ISO flows contribute to enhanced

CK during the wet phase

C Barotropic energy conversion between eddy and ISO (Hsu and Li 2011, JC)

Through what process does MJO influence TC genesis?

(1) Barotropic energy conversion

MSLP

WRF model experiments to reveal relative role of ISO moisture versus

circulation fields on TC formation

1. CTL: resting mean state

2. NOSH: ISO circulation only, no specific humidity

3. SH: ISO specific humidity field only, no circulation

4. Red: both ISO circulation and specific humidity fields

Time evolution of (a) minimum sea level pressure (unit: hPa) in the four experiments

2:110

ACV_NOSH

ACV_SH

CTL

Cao, Li, et al.

2014, JAS

Through what process does MJO influence TC genesis?

(2) Change of background moisture and vorticity

Triply nested. Horizontal resolution of 27, 9 and 3 km.

Beta-plane (15◦N) and a quiescent environment with constant SST (29◦C).

A fixed lateral boundary condition.

A weak initial balanced axisymmetric vortex. Vm= 8 m/s, RMW= 150 km

The vorticity maximum is at the surface and decreases upward (Wang 1995, 2001)

Vt T

Div Sh

Active: solid

Inactive: dashed

Reanalysis

data

11

WRF model experimental design —— a initial bogus placed in

MJO active or inactive 3D field

Group 1 Group 2 Group 3

Beta plane CTL ACV AC IACV IAC

MT ISO No Active Active Inactive Inactive

Vortex Yes Yes No Yes No

List of numerical experiments

To examine “pure” vortex evolutions, background ISO fields need to be removed.

ACV:

ISO AC

+ Vortex

IACV:

ISO IAC

+ Vortex

12

Time evolution of (a) the minimum sea level pressure (unit: hPa) and (b) the maximum azimuthal

mean wind speed (unit: m s-1) at 10 m in the three experiments CTL (black solid line), ACV (red dashed

line) and IACV (blue dotted line).

MSLP

MAMWDefine:15 m/s

t = 99h CTL

t = 72h ACV

13

CTL

ACV

IACV

ISO impact on vortex development

Cao, Li, et al.

2014, JAS

1h 3h 5h

Black: active

Red : inactive1000 hPa

Radial wind

Tangential

wind

Geopotentia

l

14

7h

ISO impact on vortex development

1h 3h 5h

Active

Inactive

Radial wind

An overbar denotes ISO wind; a prime denotes perturbation wind. 15

ISO impact on vortex development

7h

' ' ' ' ' ' ' ' '' ' ' '

'2 ' '' '

0

( ) ( ) ( )

2( ) u

u u u u v u v u v u u u uu u u w w w

t r r r r r r p p p

v v vf v F

r r r

5.5h 6h 6.5h

Active

16

ISO impact on vortex development

Radial

wind

Div

W

heating

5.5h 6h 6.5h

Inactive

17

ISO impact on vortex development

Radial

wind

Div

W

heatin

g

MSLP

“NOSH” denotes

prescribed ISO

dynamic fields but

no moisture field;

“SH” denotes

prescribed ISO

moisture field but

no dynamic fields.

Time evolution of (a) the minimum sea level pressure (unit: hPa) in the four experiments CTL, ACV, ACV_SH and ACV_NOSH.

2:1

Group 1 Group 2

Beta plane ACV_NOSH AC_NOSH ACV_SH AC_SH

MT ISO Active Active Active Active

Variables u, v, ps, T, hgt u, v, ps, T, hgt sh sh

Vortex Yes No Yes No

The list of sensitivity experiments

18

ACV_NOSH

ACV_SH

CTL

Relative roles of ISO dynamic and thermodynamic impacts

Cao, Li, et al.

2014, JAS

Sandy (Oct 2012) Haiyan (Nov 2013)

Genesis on Oct 22, Genesis on Nov 4,

landfall on Oct 29 landfall on Nov 7

Beyond weather timescale prediction of Hurricane

Sandy and Super Typhoon Haiyan using HiRAM

GFDL High-Resolution Atmosphere Model (HiRAM)

• Designed for resolution between 1– 50 km, capable of direct cloud simulation

• Non-hydrostatic finite-volume dynamical core on the cubed-sphere

• A “6-category cloud micro-physics” with high-order vertical sub-grid reconstruction allowing vertically & horizontally sub-grid cloud formation

• A “non-intrusive” shallow convective parameterization (Bretherton scheme modified by Zhao et al. 2009), and recently further modified with a double-plume convective scheme

• Options to couple with ocean/wave models (Fan et al, 2012)

HiRAM simulated TC tracks (1979-2008)

Observation

HiRAM (50-

km grid)

AMIP-type

simulation

North Atlantic

East Pacific

West Pacific

corr=0.83

corr=0.62

corr=0.52

HiRAM simulated TC annual cycle and interannual variability/trend

Seasonal hurricane predictions

1990-2010 (J-A-S-O-N)

0.940.78

0.88

(Chen and Lin 2012)

Forecast starting date: Jul 1 No information past the forecast date used

Improvement of HiRAM model physics

24

HIRAM simulated well the mean climate when forced by observed SSTs.

However, when coupled with ocean, it produced significant cold/dry bias in the

equatorial Pacific, negatively affecting ENSO simulation. To reduce the biases,

a modified convection scheme was recently developed:

An additional plume was introduced to represent deep/organized convection

with entrainment rate dependent on ambient RH.

This new scheme incorporates recent findings on key processes for

modeling MJO convection (including shallow cumulus moistening

ahead of deep organized convection, cold pools due to precipitation

re-evaporation)

The modified scheme is called double plume (DP) scheme, which can

significantly reduce the equatorial Pacific cold/dry bias

improve simulated precipitation and cloud response to ENSO

maintain competitive simulation of global TC statistics

improve MJO simulation

Left: OBS middle: DP right: Non-DP

Power Spectrum of OLR over Indian Ocean

OBS DP Non-DP

Wavenumber- frequency analysis of OLR anomalies at [10S-10N] for

boreal winter (upper) and boreal summer (bottom)

OBS DP Non-DP

Evolution of composite OLR anomaly field (northern winter)

OBS DP Non-DP

Evolution of composite OLR anomaly field (northern summer)

From ISVHE project

(Korea)

(Australia) (Japan) (Korea)

(Canada) (Hawaii)

MJO simulations in 20-yr coupled runs

HiRAM (DPC)

The bivariate ACC RMSE

0.5

RMSE= ~ R=0.5

Lin et al. 2008; Rashid et al. 2011:

HiRAM 10-yr (2003-2013) MJO Forecast Skill

RMM skill: 27 days

Published Results ISVHE (unpublished)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 165

10

15

20

25

30

Pre

dic

tio

n S

kill (d

ays)

one G

FD

L

CC

Cm

a

GE

M

CFS

v1P

OA

MA

1.5

bE

CM

WF

CFS

v2

CFS

v1P

OA

MA

1.5

EC

JMA

SN

U

UH

CFS

v2

AB

OM

EC

MW

F

MJO Skill Comparison

Methodology

Initial Condition:Nudging (U, V, SLP, HGT, Temperature + SST) toward NCEP

FNL

TC tracker:Lucas Harris’s simply tracker

Definition of ‘correct’ forecast range:Genesis during one day before and after the observed

genesis (3-day window) within radius of 1100 km

24 ensemble forecast members each day

Genesis forecast of Sandy & Haiyan

Blue lines represent

the observed TC

track. Grey lines

denote the predicted

tracks.

Black stars (red

dots) denote the

observed (predicted)

genesis locations.

Sandy and Haiyan genesis is predictable at a lead time of 11 days

Red: possibility of detection (POD)

Blue: false alarm ratio (FAR)

The ‘correct’ prediction is counted by

the cyclogenesis within 1.5 days around

the observed genesis time (a 3-day

window) within 1100 km radius.

The false alarm is counted by cyclone

numbers 5 days before and 5 days after

the ‘correct’ prediction window within

1100 km radius of circle.

For example, for 5-day lead forecast, if 25 ensemble

members predict 12 cases during the 3-day ‘correct’

forecast window, and 8 cases during 5 days before

and after the ‘correct’ forecast window. Thus the

POD is 48% ( ) and the FAR is 16.7% . POD is above 70% for both Sandy and

Haiyan for 5- to 11- day lead.

Possible predictability source: MJO

Observation Prediction (10-day lead)

20-70-day filtered precipitation (color) and 850hPa wind (vector) fields prior to TC genesis

Sandy

Haiyan

Possible predictability source: Easterly waves

Observation Prediction (10-day lead)

Track forecast of Sandy

Track forecast of Sandy on

Oct 22, Oct 23. Landfall time:

Oct 29, 2012

7-day lead 700hPa geopotential height

forecasts (shading) and observational

validation (contours)

a)

b)

Observed and predicted Sandy rainfall and snowfall (7-day lead)

Obs

Pred

Conclusion

1) The GFDL HiRAM model with a new double-plume scheme was used to study the predictability of super storms Sandy and Haiyan. Results show that the genesis of both the storms can be well predicted at 11-day lead, and landfall timing can be well predicted one week ahead for Sandy and two weeks ahead for Haiyan.

2) The beyond weather timescale prediction of two TCs is mainly attributed to the successful prediction of MJO and easterly waves in the tropical Atlantic and Pacific Oceans.

3) The result suggests that HiRAM has a potential to bridge a gap between weather and climate scales.

ThanksDiamond Head

MJO hindcast experiments

• From 2004 to 2013, initiated at every 1st,

6th, 11th, 16th, 21st, 26th for each month

during NDJFMA. At each day we have 5

members so that we have totally

360*5=1800 forecast experiments.

• Based on this, we evaluate the MJO

prediction skill by using the bivariate

correlation method.

HiRAM captures the effect of ENSO on TC genesis frequency

(occurrence per 4x4 degree box per year)

42

El-Nino years minus La-Nina years

(observation)

El-Nino years minus La-Nina years

(HiRAM2.1)

Fig. 1 Prediction of

the genesis of

hurricane Sandy with

initial condition from

Oct 9 to Oct 17. Each

day has 24 ensemble

members and the

prediction results are

shown between Oct

21-23. Blue star and

red dots indicate the

observational and

predicted genesis

locations.

Within 10

degree

-5 days

Lead 5

daysLead 8

daysLead 11

days

6 days forecast of Hurricane Sandy from coupled model with HiRAM

(upper) and DPC (lower, a new version of GFDL atmospheric model)

30 ensemble members;

Red: observations

Blue dots: genesis location

Red dots: maximum wind speed

larger than 29 m/s

Name Averaged grid size (km) Notes

C48 188 IPCC AR5

Full chemistry + aerosols + deep

conv.; poor TC climatology

C90 100 Good TC climatology

C180 50 Excellent TC climatology

IPCC AR5

C360 25 Excellent TC climatology

IPCC AR5 time slice

C720 12.5 Next generation climate model

under development

C2560 3.5 Experimental global cloud-

resolving simulation/prediction

GFDL finite-volume “cubed sphere” models

• Model:

- GFDL HiRAM C360 (25 km)

• External forcing:

- climatology O3, aerosol, and green-house gases

• SST:

• Initial conditions: NCEP analysis

• Forecast starting date: Jul 1 for hurricane predictions

HiRAM seasonal TC prediction

)()()( 0yclimatolog ttSSTtSSTtSST anomaly

No information past the forecast date was used in any way.

Low Spatial False Alarm

Observed and Simulated TC genesis density (shading) and TC genesis

(black dots) during Oct. 15 - Nov. 14 from a 30-year simulation