It was presented at the Dept. Of Atmospheric Sciences for the award of M.Tech degree. It is all about the research in high resolution ARW model for tropical cyclones simulations.

1. THE IMPACT OF MICROPHYSICAL SCHEMES ON THE SKILLTHE IMPACT OF MICROPHYSICAL SCHEMES ON THE SKILL
OF FORECASTING THE TRACK AND INTENSITY OFOF FORECASTING THE TRACK AND INTENSITY OF
TROPICAL CYCLONES USING ARW MODELTROPICAL CYCLONES USING ARW MODEL
PRESENTED BYPRESENTED BY
DEVANIL CHOUDHURYDEVANIL CHOUDHURY
M.TECH. ATMOSPHERIC SCIENCESM.TECH. ATMOSPHERIC SCIENCES

2. OUTLINEOUTLINE
✔ OBJECTIVEOBJECTIVE
✔ BACKGROUNDBACKGROUND
✔ MICROPHYSICAL SCHEMESMICROPHYSICAL SCHEMES
✔ THE TROPICAL CYCLONESTHE TROPICAL CYCLONES
✔ EXPERIMENTAL SETUPEXPERIMENTAL SETUP
✔ MODEL CONFIGURATIONMODEL CONFIGURATION
✔ MODEL INITIAL AND BOUNDARY CONDITIONSMODEL INITIAL AND BOUNDARY CONDITIONS
✔ VERIFICATIONAL METHODVERIFICATIONAL METHOD
✔ PRECIPITATION ANALYSISPRECIPITATION ANALYSIS
✔ WIND ANALYSISWIND ANALYSIS
✔ VERTICAL CROSS-SECTIONSVERTICAL CROSS-SECTIONS
✔ VALUE OF HYDROMETEORSVALUE OF HYDROMETEORS
✔ TAYLOR DIAGRAMSTAYLOR DIAGRAMS
✔ VERIFICATION OF MSWVERIFICATION OF MSW
✔ CYCLONE TRACKSCYCLONE TRACKS
✔ AVERAGED DPEAVERAGED DPE
✔ RMSE of MSWRMSE of MSW
✔ THE SKILL ON TRACKTHE SKILL ON TRACK
✔ THE SKILL IN INTENSITYTHE SKILL IN INTENSITY
✔ SUMMARYSUMMARY
✔ CONCLUSIONCONCLUSION
✔ RMC-DELHIRMC-DELHI
✔ ACKNOWLEDGEMENTACKNOWLEDGEMENT
✔ REFRENCESREFRENCES
✔ THANKS GIVINGTHANKS GIVING

3. OBJECTIVEOBJECTIVE
The main objectives of this study are -
➔ to use a regional cloud-scale model with very high resolution
WRF-ARW model.
➔ to simulate the tropical cyclones 'Hudhud' (2014), 'Lehar' (2013),
'Phailin' (2013).
➔ to investigate the impact of microphysical schemes on the track
and intensity of the tropical cyclones over the Bay of Bengal.
➔ to find the skill of forecasting the track and intensity of the
tropical cyclones.

4. BACKGROUNDBACKGROUND
Few modeling studies have investigated microphysics in tropical cyclones and
hurricanes using high-resolution ( about 5 km or less) numerical models. In general,
all of the studies show that microphysics schemes do not have a major impact on
track forecasts but do have more of an effect on the simulated intensity (Tao et al.
2010).
Based on a study of impact of cloud microphysics on hurricane Charley, Pattnaik
and Krishnamurti (2007) reported that the microphysical parameterization schemes
have strong impact on the intensity prediction of hurricane but have negligible impact
on the track forecast.
Yang and Ching also concluded that the MRF in PBL and Grell in convective
parameterization scheme (CPS) combined with the Goddard Graupel in cloud
microphysics scheme give the best performance in the study of typhoon Toraji
(2001) (Yang and Ching, 2005).
Srinivas et al (2011) found from 65 sensitivity experiments for five severe tropical
cyclones over the BoB that the combinations of Kain-Fritsch (KF) convection, Yonsei
University (YSU) planetary boundary layer (PBL), LIN explicit microphysics and
NOAA land surface schemes provide the best simulations for intensity and track
prediction.

5. MICROPHYSICAL SCHEMESMICROPHYSICAL SCHEMES
The representation of cloud microphysical process is a key component of the NWP
models, and during the past decade both research and operational NWP models have
started using more complex microphysical schemes.
Microphysics includes explicitly resolved water vapor, cloud, and precipitation
processes.
The microphysical schemes were originally developed for high resolution cloud
resolving models (CRM). CRMs explicitly simulate complex dynamical and
microphysical processes associated with deep, precipitating atmospheric convection.
The explicit and realistic representation of microphysics in the high-resolution numerical
weather forecast model is crucial for accurate prediction of the tropical mesoscale
convective systems and the intensity and track of tropical cyclones.
The microphysical schemes which are included in ARW framework are Kessler(1),
Lin(2), WSM3(3), WSM5(4), Eta(5), WSM6(6), Goddard(7), Thompson(8), Milbrandt (9),
Morrison(10), SBU-YLin(13), WDM5(14), WDM6(16), NSSL-2 moment(17), NSSL 2-
moment+CCN(18), NSSL 1-moment(19).

6. THE TROPICAL CYCLONESTHE TROPICAL CYCLONES
In this study three Very Severe
Cyclonic Storms (VSCS) 'Hudhud'
(6-13 Oct. 2014), 'Phailin' (8-14
Oct. 2013) and 'Lehar' (23-29
Nov. 2013) are selected for
conducting the experiment.
VSCS LEHAR (23-29 Nov. '13) VSCS PHAILIN (8-14 Oct. '13)
VSCS HUDHUD (6-13 Oct. '14)
Source : CIRA

7. EXPERIMENTAL SETUPEXPERIMENTAL SETUP
Expt. No. mp_physics cu_physics bl_pbl_physics sf_surface_physics
1.
(CONTROL)
LIN (2) KF (1) YSU (1) NOAA (2)
2.
(LIN)
LIN (2) X YSU (1) NOAA (2)
3.
(NSSL)
NSSL (17) X YSU (1) NOAA (2)
4.
(THOMPSON)
THOMPSON
(8)
X YSU (1) NOAA (2)
5.
(GODDARD)
GODDARD
(7)
X YSU (1) NOAA (2)
Using the best combination of schemes suggested by Srinivas et al. (2011) as
control run (CONTROL) four other microphysical schemes selected to carry out the
experiment to investigate the impact of microphysics on the forecasting skill for 9 km
and 3 km two-way nested ARW simulations.
The physics schemes for nested domain
(parent domain is same as the control run for all the experiments)

8. MODEL CONFIGURATIONMODEL CONFIGURATION
DOMAIN DETAILSDOMAIN DETAILS
●
Map projection : Mercator
●
Reference latitude :
22.273˚ N
● Reference longitude :
83.08˚ E
● No. of domains : 2
● No. of vertical layers :
27 sigma levels
●
Horizontal grid distance :
9 km and 3 km
●
Time steps : 45 s
●
No. of grid points :
e_we = 373, 634
e_sn = 418, 565
●
Resolution of geographical data : 10' , 2'
●
Nesting : two-way interactive
Experimental Domain

9. MODEL INITIAL AND BOUNDARYMODEL INITIAL AND BOUNDARY
CONDITIONSCONDITIONS
Cyclones Start Date,
Time
End Date,
Time
Hudhud 08-10-
2014_00
13-10-
2014_00
Phailin 09-10-
2013_00
14-10-
2013_00
Lehar 24-11-
2013_00
29-11-
2013_00
Computing resource 'Aditya HPCS'Computing resource 'Aditya HPCS'
790+ TeraFlops, 38,144 Intel Sandy790+ TeraFlops, 38,144 Intel Sandy
Bridge processors and 149 TB ofBridge processors and 149 TB of
memorymemory
Initial and boundary conditions : 0.25°
resolution GFS real time prediction from
IMD
Lateral boundary conditions : 06 hourly
intervals.
Forecast period : 120 hours.

10. VERIFICATIONAL METHODVERIFICATIONAL METHOD
ARW Model (version 3.6.1) with two-way interactive nested grid is used on the
'Aditya HPC' to study the effects of microphysical schemes on track and intensity of
these cyclones.
Direct Positional Error (DPE) is taken as track errors. DPE was calculated from
haversine formula.
haversine formula gives great-circle distances between two points on a sphere
from their longitudes and latitudes.
haversin (d/r) = haversin ( φ2
- φ1
) + cos(φ1
) cos(φ2
) haversin (λ2
– λ1
)
haversin(θ) = sin2
(θ/2) = 1-cos(θ)/2
d = 2r arcsin ( √ ( haversin ( φ2
- φ1
) + cos(φ1
) cos(φ2
) haversin (λ2
– λ1
) ) )
[d = distance between two points , r = radius of the earth , 6378 km]
For track gain(loss) in skill in terms of DPE =
For intensity gain(loss) in skill in terms of MSW =
Bias/Error = IMD Observations – Model Forecasts => +ve (Underestimate)
-ve (Overestimate)
CONTROL DPE−DPE
CONTROL DPE
×100
CONTROL RMSE−RMSE
CONTROL RMSE
×100

11. PRECIPITATION ANALYSISPRECIPITATION ANALYSIS
(THOMPSON)(THOMPSON)
HUDHUD
LEHAR
PHAILIN
mp_physics = THOMPSON (8)
LEHAR
PHAILIN

12. WIND ANALYSISWIND ANALYSIS
(GODDARD)(GODDARD)
HUDHUD
LEHAR
PHAILIN
mp_physics = GODDARD (8)

13. VERTICAL CROSS-SECTION:VERTICAL CROSS-SECTION:
HUDHUDHUDHUD
1.GODDARD 2. LIN 3. NSSL
4. CONTROL 5. THOMPSON
Meridional wind (v_wind) cross-section at time and position of peak intensity based on minimum CSLP

14. VERTICAL CROSS-SECTION:VERTICAL CROSS-SECTION:
PHAILINPHAILIN
1. GODDARD 2. LIN 3. NSSL
4. CONTROL 5. THOMPSON
Meridional (v_wind) wind cross-section at the time and position of peak intensity based on minimum CSLP

15. VERTICAL CROSS-SECTION:VERTICAL CROSS-SECTION:
LEHARLEHAR
1. GODDARD 2. LIN 3. NSSL
4. CONTROL 5. THOMPSON
Meridional wind (v_wind) cross-section at the time and position of peak intensity based on minimum CSLP

16. VALUE OF HYDROMETEORSVALUE OF HYDROMETEORS
Schemes Rainfall
(mm)
Hail (mm) Graupel
(mm)
Max. dBZ
GODDARD 251.75 0 0 50
LIN 233.02 0 0 40
NSSL 172.24 0.017 0.042 35
CONTROL 105.71 0 0 48
THOMPSON 200.23 0 0 55
GODDARD 203.71 0 0 40
LIN 210.74 0 0 60
NSSL 172.59 0.11 0.10 53
CONTROL 182.46 0 0 58
THOMPSON 310.62 0 0 60
GODDARD 232.05 0 0 60
LIN 207.13 0 0 58
NSSL 152.43 0.002 0.13 60
CONTROL 258.75 0 0 60
THOMPSON 237.07 0 0 55
1. HUDHUD
2. PHAILIN
3. LEHAR

17. TAYLOR DIAGRAMTAYLOR DIAGRAM
● The Taylor diagrams are drawn based
on standard deviation and correlation
of CSLP and MSW from the sensitivity
experiments at 72h forecast using
NCAR Command Language (NCL).
Taylor diagram summarizes multiple
aspects of model performance in a
single diagram.
2.PHAILIN 3.LEHAR
1.HUDHUD

18. MSW VERIFICATIONMSW VERIFICATION
● Simulated Maximum Sustained
Wind (MSW) speed (m/s) from
model output is verified with
IMD observation (Ash line).
MSW is taken as Intensity skill
measurement.
Row 1 Row 2 Row 3 Row 4
0
2
4
6
8
10
12
Column 1
Column 2
Column 3
0 12 24 36 48 60 72 84 96 108 120
0
10
20
30
40
50
60
70
LEHAR CYCLONE
Maximum Sustained Wind (MSW)
GODDARD
LIN
NSSL
CONTROL
THOMPSON
IMD
Forecast Lead Time (h)
MSW(m/s)
0 12 24 36 48 60 72 84 96 108 120 132
0
10
20
30
40
50
60
70
PHAILIN CYCLONE
Maximum Sustained Wind (MSW)
GODDARD
LIN
NSSL
CONTROL
THOMPSON
IMD
Forecast Lead Time (h)
MSW(m/s)
0 12 24 36 48 60 72 84 96 108 120 132
0
10
20
30
40
50
60
70
HUDHUD CYCLONE
Maximum Sustained Wind Speed (MSW)
IMD
GODDARD
LIN
NSSL
CONTROL
THOMPSON
Forecast Lead Time (h)
MSW(m/s)

19. CYCLONE TRACKSCYCLONE TRACKS
Model simulated cyclone tracks varying with different microphysical
schemes along with India Met. Dept. observations (black line) for five days.
1. HUDHUD 2. PHAILIN 3. LEHAR
GODDARD (blue line) microphysical scheme produced consistently better
track of the each tropical cyclones.

20. AVERAGED DPEAVERAGED DPE
Row 1 Row 2 Row 3 Row 4
0
2
4
6
8
10
12
Column 1
Column 2
Column 3
24 h 48 h 72 h 96 h 120 h
0
100
200
300
400
500
600
700
Track Error for all the TCs
Based on 24 hourly averaged DPE
GODDARD
LIN
NSSL
CONTROL
THOMPSON
Forecast Lead Time (h)
TrackError(km)
24 hourly Average DPE (track errors) in km for all the tropical cyclones

21. RMSE of MSWRMSE of MSW
24 h 48 h 72 h 96 h 120 h
0
2
4
6
8
10
12
RMSE of Intensity for all the TCs
Based on 24 hourly averaged RMSE
GODDARD
LIN
NSSL
CONTROL
THOMPSON
Forecast Lead Time (h)
RMSE(m/s)
24 hourly averaged RMSE of MSW (m/s) for all the tropical cyclones

22. THE SKILL ON TRACKTHE SKILL ON TRACK
24 h 48 h 72 h 96 h 120 h
-50
-40
-30
-20
-10
0
10
20
30
Skill Score on Track for all the TCs
Based on 24 hourly averaged DPE
GODDARD
LIN
NSSL
THOMPSON
Forecast Lead Time (h)
SkillScore(%)

23. THE SKILL IN INTENSITYTHE SKILL IN INTENSITY
24 h 48 h 72 h 96 h 120 h
-30
-20
-10
0
10
20
30
40
50
Skill Score in Intensity for all the TCs
Based on 24 hourly averaged RMSE of MSW
GODDARD
LIN
NSSL
THOMPSON
Forecast Lead Time (h)
SkillScore(%)

24. SUMMARYSUMMARY
ARW model nested with 9 km and 3 km resolutions is evaluated
statistically for examining the impact of microphysical schemes on the
forecasting skill of track and intensity of very severe cyclonic storms by
considering Hudhud (7-13 October, 2014), Phailin (8 -14 October, 2013)
and Lehar (24 – 29 November, 2013). The results are summarized below.
1. 15 sensitivity experiments were carried out by varying the
microphysics for the three cyclones. The least track error is found to vary
from 45.76 km (at 24 h) to 325.16 km (at 120 h) using GODDARD
microphysics.
2. The NSSL scheme performed better for the case of Lehar for intensity.
All other schemes underestimated the central sea-level pressure (CSLP).
This underestimation may be due to imperfectly balanced initial state,
coarse grid resolution, and deficiency of model representation of physical
processes.

25. SUMMARYSUMMARY
3. The GODDARD scheme consistently performed better and provided
highest gain of 14%, 27.54%, 25.72%, 22.54% and 29.78% at 24 h, 48 h,
72 h, 96 h, and 120 h forecasts respectively for the skill score of track.
4. The THOMPSON microphysical scheme provided the highest gain of
44.37%, 26.9%, and 11.91 % at 48 h, 72 h and 120 h forecast respectively
for the skill in intensity whereas the GODDARD scheme indicated the
highest gain of 18.59% and 8.21% at 24 h and 96 h forecast respectively.
5. The results indicated rapid deepening and over-intensification of tropical
cyclones. It may be because all hydrometeors were very large raindrops,
and fell out quickly at and near eye-wall region. This would hydrostatically
produce the lowest pressure.
6. The variations in cloud microphysics were found to have a significant
impact on inner core structure. Stronger storms tend to show more
compact eye-walls with heavier precipitation and more symmetric
structures in the warm cored eye and in the eye-wall. The vertical profiles
of cloud hydrometeors and horizontal distribution of rain bands can be
affected by the microphysics.

26. CONCLUSIONCONCLUSION
This study is aimed to investigate the impact of microphysics on the
track and intensity with explicitly resolved convection scheme as it is
desirable to resolve the convection with sufficiently high resolution and
with the use of explicit cloud physics.
The GODDARD one-moment bulk liquid-ice microphysical scheme
provided the highest skill on track.
The THOMPSON scheme indicated the highest skill in intensity at
48h, 96h and 120h whereas at 24h and 72h the GODDARD scheme
provided the highest skill in intensity.
This study suggests that the Goddard cumulus ensemble (GCE)
microphysical scheme is suitable for high resolution ARW simulation
for tropical cyclone's track and intensity over the Bay of Bengal. It is
useful for planning real-time predictions using ARW modeling system.
Additional case studies including more comprehensive microphysical
sensitivity testing and diagnostics will be considered in future
research.

27. RMC-DELHIRMC-DELHI
Automatize of WRF-ARW model in RMC-Delhi server with NCEP-GFS data
through UNIX-Shell Scripting. These scripts are created by Mr. Ananda Kr.
Das (2009, Sc-D). It are modified and implemented by Devanil Choudhury.

28. ACKNOWLEDGEMENTACKNOWLEDGEMENT
It gives me great pleasure to express my heartfelt and profound sense of
gratitude to my guide, Dr. Someshwar Das, for his invaluable guidance,
constant encouragement and motivation.
I express my sincere gratitude to the Director General of Meteorology Dr. L.
S. Rathore and and the Deputy Director General of Meteorology (NWP) Dr.
Y. V. Ramarao for providing all the facilities to carry out this research work.
I would also like to express my deep and sincere gratitude to Mr. Ananda
Kumar Das and Mr. V. R. Durai Without their additional guidance, this thesis
would have never come to fruition.
I would like to acknowledge all the faculty members of Department of
Atmospheric Sciences (CUSAT) and whole DAS-CUSAT family for their
support and help.
I express my special thanks to all of my friends and whole IMD-NWP division.
Last but not the least I would like to thank my parents, without you none of
this would indeed be possible.

29. REFERENCESREFERENCES
● Cooperative Institute for Research in the Atmosphere. Colorado State University.
https://www.cira.colostate.edu.
● Srinivas CV, Bhaskar Rao DV, Yesubabu V, Baskaran R, Venkatraman B. 2012.
Tropical cyclone predictions over the Bay of Bengal using the high-resolution
advanced research weather research and forecasting model. Q. J. R. Meteorol. Soc.
DOI:10.1002/qj.2064.
● Tao W. K., Shi J. J., Chen s. S., Lang S., Lin P. L., Hong S. Y., Peters-Lidard C., Hou
A. 2010. The Impact of Microphysical Schemes on Intensity and Track of Hurricane.
Special issue of the Asia-Pacific Journal of Atmospheric Sciences (APJAS).
● Yang, M.-J. and L. Ching, 2005: A modeling study of Typhoon Toraji (2001): Physical
parameterization sensitivity and topographic effect. TAO, 16, 177-213.

30. THANK YOU FOR YOUR PRECIOUSTHANK YOU FOR YOUR PRECIOUS
PRESENCE AND ATTENTIONPRESENCE AND ATTENTION