beyond weather timescale prediction of hurricane sandy and ... · 2) the beyond weather timescale...
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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