Uncertainty in rainfall prediction of land-falling tropical cyclones over India:

Impact of data assimilation

School of Earth Ocean and Climate Sciences

Indian Institute of Technology, Bhubaneswar

India

Krishna K. Osuri, Raghu Nadimpalli

S.G. Gopalakrishnan,

HRD, AOML

U. C. Mohanty

OUTLINE

Introduction

Objective

Results • Realtime prediction of TC and associated inland rainfall

• Mesoscale data assimilation:

– Satellite derived wind data assimilation

– DWR reflectivity and radial wind observations

Conclusions

Coastal regions of India (particularly East coast) frequently receive heavy to very heavy rainfall due to landfalling Tropical Cyclones (TCs).

Precipitation is one of the difficult parameters to forecast/ simulate with NWP models.

In recent years, there have been significant improvement in prediction of track over NIO basin, but skill of numerical models need to be improved in

o Intensity prediction o Structure prediction o Inland rainfall prediction

This can be achieved some extent by o increased model resolution (at cloud resolving) o Improved model parameterization schemes o High resolution data assimilation

INTRODUCTION

Studies showed that the meso-scale/convective scale features are well resolved by improving the model initial condition with high spatial and temporal observations like DWR, satellite products, etc.

The ability to anticipate the formation, intensity and rainfall structure of landfalling TCs remains a major challenge for researchers as well as forecasters.

Objective

To improve the forecast skill of high resolution model for heavy rainfall associated with Landfalling tropical cyclones

Real time prediction of Tropical cyclones over North Indian Ocean

Basin Name (Intensity) Simulations period in 12-hr interval Observed Landfall No. of forecasts

Arabian

Sea

Cyclones

(5 TCs)

Gonu (SuCS) 00 UTC 2 – 12 UTC 5 June 2007 03UTC 6 June (over Oman) 8

Yemyin (CS) 00 and 12 UTC 25 June 2007 03 UTC 26 June 2

Phyan (CS) 12 UTC 9 – 00 UTC 11 Nov 2009 Between 10-11 UTC 11 Nov 4

Phet (VSCS) 12 UTC 31 May – 00 UTC 6 June 2010 12 UTC 6 June (LF-2) 12

Murjan (CS) 00 UTC 23 – 25 October 2012 18 UTC 25 October 2012 5

Bay of

Bengal

cyclones

(20 TCs)

Akash (CS) 12 UTC 13 – 12 UTC of 14 May 2007 00 UTC 15 May 3

Sidr (VSCS) 12 UTC 11 – 00 UTC 15 Nov 2007 15 UTC 15 Nov 8

Nargis (VSCS) 12 UTC 27 April – 00 UTC 2 May 2008 12 UTC 2 May 10

Rashmi (CS) 00 UTC 25 – 12 UTC 26 Oct 2008 00 UTC 27 Oct 4

KhaiMuk (CS) 12 UTC 13 – 12 UTC 15 Nov 2008 00 UTC 16 Nov 5

Nisha (CS) 12 UTC 25 – 26 Nov 2008 00 UTC 27 Nov 3

Bijli (CS) 12 UTC 14 – 00 UTC 17 Apr 2009 15 UTC 17 April 6

Aila (SCS) 12 UTC 23 – 00 UTC 25 May 2009 9 UTC 25 May 4

Ward (CS) 12 UTC 10 – 12 UTC 13 Dec 2009 9 UTC 14 Dec 7

Laila (VSCS) 12 UTC 17 – 19 May 2010 12 UTC 20 May 5

Giri (VSCS) 12 UTC 20 – 00 UTC of 22 Oct 2010 14 UTC 22 Oct 4

Jal (VSCS) 00 UTC of 4 – 7 Nov 2010 16 UTC 7 Nov 7

Thane (VSCS) 00 UTC 26 – 12 UTC 29 Dec 2011 00 UTC 30 Dec 8

Nilam (CS) 00 UTC 28 – 12 UTC 31 Oct 2012 15 UTC 31 Oct 2012 6

Mahasen (CS) 00 UTC 10 – 12 UTC 16 May 2013 9UTC 16 May 2013 13

PHAILIN (VSCS) 7-12 Oct 2013 17 UTC 12 Oct 2013 9

(SCS) Helen 19 – 22 Nov 2013 9 UTC 22 Nov 2013 6

Lehar (VSCS) 25 – 28 Nov 2013 9 UTC 28 Nov 2013 6

HudHud (VSCS) 7 – 12 Oct 2014 9 UTC 12 Oct 2014 10

Total Number of cyclones during 2007 – 13 164

TCs during 2007 – 2014 (Total 172 cases)

Model WRF model configuration

Dynamics Non-hydrostatic

Horizontal Resolution 27 km / 18 km / 9 km

Domain 3 N – 28 N and 78 E – 103 E (BOB)

5 N – 30 N and 48 E – 78 E (AS)

Time step 30s

Map projection Mercator

Horizontal Grid System Arakawa C- Grid

Vertical co-ordinates Terrain following hydrostatic pressure co-ordinates (51 levels)

Radiation Dudhia scheme for long and short wave radiation

Surface Layer Thermal Diffusion scheme

Cumulus scheme Kain Fristch

PBL scheme Yonsui University scheme

Micro physics WSM-3

Initial and boundary conditions

GFS model analysis and forecast fields (0.5 0.5 resolution)

TCs over Bay of Bengal Longitude: 77 E – 102 E Latitude : 3 N – 28 N Resolution : 9 km

TCs over Arabian Sea Longitude : 48 E – 78 E Latitude : 5 N – 28 N Resolution : 9 km IC & BC : GFS model (0.5x0.5)

WRF model configuration for cyclone prediction

TC Phailin (08 – 12 October 2013)

TC Phailin (96 hour) Forecast

based on 12 UTC of 8 October 2013

TC Phailin (72 hour) Forecast

based on 12 UTC of 9 October 2013

Landfall point error is 29 km

Time error 5 hrs ahead

Landfall point error is 16 km

Time error 2 hrs ahead

White line is

IMD OBS track

RED track is

Model Predicted

10

Comparison of Track

Prediction of TC PHAILIN With

Different operational Models

TC PHAILIN with different

initial conditions

Forecast length (hour)

Mean Track errors (km)

Time series of pressure drop at Gopalpur (landfall point)

Observed

Model averaged value

24-hr accumulated rainfall (cm) during landfall day for TC PHAILIN

(Number represents IMD OBSERVED RAINFALL at 108 stations)

72 hour forecast 24 hour forecast

Though model-predicted track was good, rainfall distribution and intensity is not realistic.

Observed peak rain

(~40 cm) is over

Northern parts of

Odisha

Model showed peak rainfall over South Odisha and Andhra Pradesh

Modelled

rainfall over

South

Odisha

Modelled

rainfall over

South

Odisha

0

5

10

15

20

25

30

35

40

Air

akhol

Anandpur

Ath

malik

Balik

uda

Banarp

al

Banpur

Barm

ul

Bijepur

Chaib

asa

Chandik

hol

Daitari

Daspalla

G U

dayag

iri

Hin

dol

Jaip

ur

Jhum

pura

K N

uag

aon

Keonjh

ag…

Kore

i

Madanpur …

Mohana

Nara

j

Nayag

arh

Nim

para

Odag

aon

Papunki

Phir

ing

ia

Puri

Rag

hunat…

Rajg

hat

Ram

pur

Sale

pur

Sukin

da

Telk

oi

Tig

iria

Tir

ing

OBS IMD

Model

24-hr accumulated rainfall (cm) during landfall day for TC PHAILIN

(Verification at 108 stations)

RMSE=7 cm

At most of the stations, model underestimated the rainfall. When compared with TRMM rainfall, Model overestimates the rainfall

IIT-BBS Storm Surge Prediction for TC PHAILIN

(84 Hours in Advance of Landfall)

Time series of Surge at different location along the

coast (TC PHAILIN)

15

PEAK SURGE ELEVATIONS AT THE BOUNDARY (IC:2013100912)

0

0.5

1

1.5

2

2.5

3

3.5

Vis

ha

kh

ap

atn

am

Srik

ak

ulu

m

La

nd

fall

Go

pa

lpu

r

Pu

ri

Pa

rad

ip

Dh

am

ara

Ch

an

dip

ur

§

2.24

2.933.18

2.55

-1

0

1

2

3

60 70 80 90

Time (hrs)

Sea s

urf

ace e

levati

on

(m

)

Left_LF

Landfall

Gopalpur

Puri

Visakhapatnam Airport

Disaster of “TC Hudhud” in

Visakhapatnam, India

Mean Track (km) and Intensity (Knots) Errors M

ea

n t

rac

k e

rro

rs (

km

)

Me

an

In

ten

sit

y E

rro

rs (

Kn

ots

)

HUDHUD Forecast (12 UTC 8 – 12 UTC 11 Oct 2014)

Landfall between 6-9 UTC 12 Oct 2014

with intensity of very severe cyclonic

storm (100 knots)

source: IMD

Based on IC: 12 UTC 09 Oct 2014 (96 hr forecast)

Landfall between 6-9 UTC 12 Oct 2014

with intensity of very severe cyclonic

storm (100 knots)

source: IMD

Blue line represent time of landfall

based on IC: 00 UTC 11 Oct 2014 (36 hr forecast)

24-hr accumulated rainfall prediction in real time

TRMM rainfall

Model predictions

96h fcst 72h fcst 48h fcst 24h fcst

24-hr accumulated rainfall (cm) during landfall day for HudHud

(Verification at 103 stations)

RMSE: 8 cm CC : 0.25

Track prediction from different initial conditions for TC LEHAR

Model predicted weakening of Lehar into depression when approaching land

as observed. Model simulated showers/low rainfall as observed.

Possible reasons are

1. Dry air incursion along the southwestern periphery of the low level

circulation (SSMIS 91 GHZ MICROWAVE IMAGE)

2. Cooler sea surface (passage of prior cyclone, Helen)

3. Increased vertical wind shear

TC Lehar (60 hour) Forecast based on 00 UTC

of 26 November 2013

TC

Leh

ar

(60 h

ou

r) F

ore

ca

st

bas

ed

on

00 U

TC

of

26 N

ov 2

01

3

VSCS “Madi” over Bay of Bengal (8 – 12 Dec 2013)

TC Madi (96 hour) Forecast based

on 12 UTC of 08 December 2013

Model predicted sudden return and weakening of Madi. It intensified to very severe cyclonic storm stage (64 – 119 knots) while moving northward till 12UTC 10 Dec.2013 After that, it suddenly returned back and started moving in southwest ward. It started loosing its intensity and reached to depression stage (<33knots) within one day.

Madi's pole ward track was explained by

the strong subtropical ridge located to the

east of the system.

Another subtropical ridge located over

India had steered the system

southwestward. On December 11, Madi's

LLCC became clearly exposed after dry air

wrapped around the southern part of the

system. This weakened Madi into a

Cyclonic Storm

OBSERVATIONAL facts

Mean track errors for NIO cyclones during 2007 - 2011

(under operational setup)

These error statistics are based on 100 TC cases

Initial cyclone vortex position error is

about 60 km

Recent cyclone Giri (20-22 Oct 2010)

Observed TC Location

Model TC Location

Mean Intensity Errors (10m winds m/s) Mean Errors for NIO TCs with different resolutions

11

3

14

0

18

6

24

8

31

6

37

5

10

6

12

9

16

9

22

2

29

1

35

9

10

4

11

5

14

1

20

4

27

4

32

9

0

100

200

300

400

500

12 24 36 48 60 72Forecast length (hour)

Me

an

DP

E (

km

)

27 km 18 km 9 km

Mean Errors for NIO TCs with different resolutions

-10

-11

-12

-13

-13

-12

-11

-10 -9 -8 -6 -6-8 -7 -8 -8 -8 -7 -7 -6 -6 -5 -4 -4

23

38

36 4

0

39 42

39 42

40 42

38

36

-25

-20

-15

-10

-5

0

5

10

6 12 18 24 30 36 42 48 54 60 66 72

Forecast length (hour)

Me

an

DP

E (

km

)

-55

-45

-35

-25

-15

-5

5

15

25

35

45

18 km 9 km % of improvement

Osuri et al. 2013, JAMC

Errors for recurving TCs

74

13

1

16

4

23

1

30

8

38

2

44

0

69

.03

12

8

14

9

22

0

28

4

34

7

41

3

65

10

9

12

8

19

3

24

5

33

0

39

0

0

100

200

300

400

500

600

700

0 12 24 36 48 60 72Forecast length (hour)

Me

an

DP

E (

km

)

27 km 18 km 9 km

Mean errors w.r.t Intensity at initialization at 27 km

resolution

81

13

1

17

6

21

1

30

0

33

4

42

1

60

11

8

14

6

20

1

28

5

32

9

41

2

51

92

12

9

16

5

23

3

26

4

33

0

0

100

200

300

400

500

600

700

0 12 24 36 48 60 72

Forecast length (hour)

Me

an

DP

Es

(k

m)

DD CS SCS

Mean errors for

recurving TCs

Mean track errors w.r.t

intensity at initialization

Improvement is significant

with high resolution for

recurving TCs.

Stronger cyclones can be

tracked with minimum errors

compared to marginal

cyclones or depressions.

0

0.05

0.1

0.15

0.2

0.25

0

2

4

6

8

10

12

14

16

25 50 75 100 125 150 175 200

ET

S

Bia

s

Thresholds (mm)

ETSBias

Mean ETS and Bias of 24-hr accumulated rainfall

during landfall day (in real time)

Model is overestimating rainfall (comparing with TRMM observations). Skill (ETS) is good up to 5 cm rainfall, thereafter decreasing sharply.

Impact of Satellite derived winds

Assimilation on Track and

Intensity

Derived winds of

QSCAT (wind speed and direction)

SSM/I (wind speed) and

Kalpana water vapor wind and CMVs

Cyclone Location

GONU (2 – 7 June 2007) Arabian Sea

SIDR (12 – 16 Nov 2007) Bay of Bengal

NARGIS (27 Apr – 3 May 2008) Bay of Bengal

KhaiMuk (11 – 15 Nov 2008) Bay of Bengal

Aila (22-25 May 2009) Bay of Bengal

Laila (17 – 21 May 2010) Bay of Bengal

Data and Experiments

o 2851 SSMI 2008042812

(b)

3374 QSCAT 2008042812 .

Satellite derived wind ingested into the model initial condition of TC NARGIS

o 377 Kalpana WV and CMVs

SSMI, QSCAT and Kalpana winds for 12 UTC of 28 April 2008

. QSCAT 5794 2007060200 o SSMI 2701 2007060200

Satellite derived wind ingested into the model initial condition of TC GONU

o Kalpana 33

SSMI, QSCAT and Kalpana winds for 00UTC of 2 June 2007

CNTL NARGIS 3DVAR

CNTL GONU 3DVAR

IC: 00UTC of 2 June 2007

Forecast length (hour) 2

4 c

ase

s M

ea

n V

DE

s (

km

)

120 1

76

199

291

261

327 366 4

25

99 1

32

157

228

169

176

199

224

0

50

100

150

200

250

300

350

400

450

12 24 36 48 60 72 84 96

CasesM

ea

n V

DE

s (

km

)

CNTL

3DVAR

1

0 m

win

d (

m s

-1)

IC: 00UTC of 2 June 2007

0

10

20

30

40

50

60

70

0200 0312 0500 0612 0800

Time (datehour)

10

m w

ind

(m

s-1

)

Time (ddhh)

GONU case IC: 12UTC of 28 April 2008

930

950

970

990

1010

28_12 29_12 30_12 01_12 02_12

Time (date_hour)

MS

LP

(h

Pa)

IMD

CNTL

3DVAR

Wind field at 925 hPa

Osuri et al., 2012, IJRS

Impact of Satellite winds on track and intensity of cyclones

An average 8 m/s error is improved in 3DVAR run

compared to CNTL run.

24-hr accumulated rainfall valid for landfall day (2-3 May 2008) for TC NARGIS

IC: 12UTC of 28 April

(c) 3DVAR (b) CNTL

(a) TRMM

30

24

18

12

8

4

1

cm

Improved track leads to improvement in rainfall structure with satellite data

Mean at landfall

0

1

2

3

4

5

6

7

25 50 75 100 125 150 175

Thresholds (mm)

Bia

s

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

ET

S

CNTL(Bias)

3DVAR(Bias)

CNTL(ETS)

3DVAR(ETS)

Mean (of 5 cases) ETS and Bias of 24-hr accumulated rainfall (mm) valid for landfall day

ETS - Lines Bias - histograms

24-hr accumulated rainfall valid for landfall day for SuCS GONU

Lan

dfa

ll o

ver

Om

an

6 –

7 J

un

e 2

00

7

Lan

dfa

ll o

ver

Iran

7

– 8

Ju

ne

20

07

30

24

18

12

8

4

1

cm

GONU has two landfalls 1) over Oman and 2) over Iran

0

5

10

15

20

25

25 50 75 100 125 150 175 200

Thresholds (mm)

Bia

s

0

0.1

0.2

0.3

0.4

ET

S

CNTL (Bias)

3DVAR (Bias)

CNTL (ETS)

3DVAR (ETS)

Mean (of 5 cases) ETS and Bias of 24-hr accumulated rainfall (mm) valid for landfall

ETS - Lines Bias - histograms

TRMM CNTL 3DVAR

24-hr accumulated rainfall valid for landfall day for SIDR (15 – 16 Nov 2007)

In Sidr case, the initial vortex is completely covered by QSCAT path.

Impact of DWR reflectivity and radial wind

on the rainfall prediction of

landfalling Tropical Cyclones over Bay of Bengal

Experiments and Data used

Three numerical experiments are carried out

CNTL - With out Data Assimilation

GTS - With Assimilation of GTS data

DWR - Assimilation of GTS + DWR data

GTS includes : SYNOP, AWS, SHIP, TEMP, PILOT,

BUOYS, SATOB, SATEM, AIREP etc.

DWR includes : Reflectivity and Radial velocity of

Kolkata DWR

Cyclone

Name Cases Initial conditions

Data

density

Radar

Coverage

SIDR (Very

severe

cyclone)

Case–1 00 UTC of 13 Nov 2007 1911

Kolkata

DWR

Case–2 12 UTC of 13 Nov 2007 1868

Case–3 00 UTC of 14 Nov 2007 2702

Case–4 12 UTC of 14 Nov 2007 2657

Aila (Severe

cyclone)

Case–5 00 UTC of 23 May 2009 1995

Kolkata

DWR

Case–6 12 UTC of 23 May 2009 2467

Case–7 00 UTC of 24 May 2009 1937

Case–8 12 UTC of 24 May 2009 1869

Laila

(Severe

cyclone)

Case–9 12 UTC of 17 April 2010 1800

Chennai

DWR

Case–10 00 UTC of 18 April 2010 2556

Case–11 12 UTC of 18 April 2010 2532

Case–12 00 UTC of 19 April 2010 2300

Case–13 12 UTC of 19 April 2010 2433

Jal (Severe

cyclone)

Case–14 00 UTC of 6 Nov 2010 1250 Chennai

DWR Case–15 12 UTC of 6 Nov 2010 2364

Case–16 00 UTC of 7 Nov 2010 1950

80 E 90 E 100 E

30 N

20 N

10 N

EQ

2187 QSCAT

80 E 90 E 100 E

3239 SSMI

80 E 90 E 100 E

45 AIREP

80 E 90 E 100 E

54 SOUND

80 E 90 E 100 E

38 METAR

30 N

20 N

10 N

EQ 80 E 90 E 100 E

178 SYNOP

25 N

24 N

23 N

22 N

21 N

20 N

19 N 85E 86E 87E 88E 89E 90E 91E 92E

Kolkata DWR

Data distribution (of SYNOP, AIREP, SOUND, METAR, QSCAT, SSMI and Kolkata DWR respectively) for Case-1 (TC Sidr) at 00 UTC 13 Nov 2007.

80 E 90 E 100 E

30 N

20 N

10 N

EQ

1923 SSMI

80 E 90 E 100 E

736 SATOB

80 E 90 E 100 E

33 AIREP

80 E 90 E 100 E

54 SOUND

80 E 90 E 100 E

41 METAR

30 N

20 N

10 N

EQ 80 E 90 E 100 E

204 SYNOP

16 N

15 N

14 N

13 N

12 N

11 N

10 N

9 N 77E 78E 79E 80E 81E 82E 83E 84E

Chennai DWR:

Data distribution (of SYNOP, AIREP, SOUND, METAR, QSCAT, SSMI and Kolkata DWR respectively) for Case-14 (TC Jal) at 00 UTC 6 Nov 2010.

As the rainfall structure and intensity prediction of Sidr is poorly predicted by the ARW model, this case is taken up to study the impact of DWR observations.

The cloud bands are observed by DWR Kolkata though it is far away from the DWR station and of course, when it approaches, the inner-core details are observed by the DWR.

Therefore, the experiments are conducted in two scenarios:

1. Impact of TC environment observed by the DWR on TC prediction

2. TC Inner-core observations on TC prediction

CN

TL (

No

Ass

imila

tio

n

GTS

dat

a A

ssim

ilati

on

DW

R+G

TS A

ssim

ilati

on

IMD Best track

CASE-1

TC-SIDR

10

15

20

25

30

35

40

45

50

55

60

1300 1312 1400 1412 1500 1512 1600 1612

Forecast hour (hrs)

10-m

win

d (

m/s

)

OBS CNTLGTS DWR

10m wind speed (m/s)

Time (date hour)

Assimilation of DWR TC environment observations

cm (b)

(c)

(d)

(a) TRMM CNTL GTS DWR

SIDR (15–16 Nov 2007)

AILA (25–26 May 2009)

Laila (20–21 May 2010)

Jal (7–8 Nov 2010

24-hr accumulated rainfall valid for landfall day

CASE – 1 (SIDR)

CASE – 5 (Aila)

Model simulated Reflectivity along with Observed

reflectivity

Assimilation of Inner-core observations from DWRs

CNTL DWR

TC symbol: Observed location of TC Laila at 12UTC 19 May 2010

Plus symbol: 6-hr cycled model generated initial vortex

CNTL simulated cyclone is continuously intensifying without landfall

While, DWR system showed improvement in track and landfall

Laila cyclone: Initial vortex position and structure

10-m winds (m/s)

winds

W vel &

Temp Anamoly

Titled vortex is improved with DWR observations, while both CNTL and GTS runs could not.

Warm core structure is also improved with DWR observations

CNTL GTS DWR

Laila cyclone: Inner-core improvements with DWR obs

TRMM CNTL GTS DWR

Laila cyclone: 3hrly rain rate and track (Initial condition 12UTC 19 May 2010 (60 hr forecast)

CNTL GTS DWR

RMSE (cm) 22 19 12

Correlation Coeff

0.28 0.47 0.71

Based on 76 IMD station rainfall

Impact of Microphysics: Inner-core Reflectivity

assimilation on Hydrometeors structure

WarmRain_Microphysics IcePhase_Microphysics

CNTL (no-data assimilation) Inner-core Reflectivity assimilation

contours (cloud water Mixing ratio); shaded (Rain water mixing ratio)

Impact of Microphysics: Inner-core Reflectivity

assimilation on Rainfall structure

CNTL

Reflectivity

Assimilation

CNTL

Reflectivity

Assimilation

TRMM Rainfall

WarmRain_Microphysics IcePhase_Microphysics

WarmRain_Microphysics IcePhase_Microphysics

Statistics based on 76 rainfall stations Warm-CNTL Ice-CNTL Warm-DWR Ice-DWR

Mean RMSE 17.4 15.0 13.5 8.05

Correlation -0.28 0.14 -0.21 0.44

Station Name Lat Long OBS Rain Warm-CNTL Ice-CNTL Warm-DWR Ice-DWR

Addanki 15.8 80.0 52 0 0 19 20

Bollapalli 15.9 80.1 20 0 0 16 14

Chimakurty 15.6 79.9 27 0 0 14 27

Darsi 15.8 79.7 18 0 0 11 20

Jarugaumalli 15.3 80.0 15 0 0 17 14

Kondepi 15.4 79.9 16 0 0 13 21

Korisapadu 15.8 80.0 15 0 1 17 16

Kothapatnam 15.4 80.2 26 0 0 15 18

Kurichedu 15.9 79.6 15 0 0 14 21

Maadipadu 15.6 80.0 28 0 1 16 24

Machavaram 16.5 80.7 19 0 0 5 16

Mangalagiri 16.4 80.6 15 0 0 4 10

N. G. Padu 15.6 80.1 17 0 0 16 16

Nakirekkallu 17.2 79.4 17 0 0 10 11

S.N. Padu 15.5 79.9 27 0 0 14 17

Savalyapuram 16.1 79.8 23 0 0 18 21

Tadepalle 16.8 81.5 18 0 2 0 9

Tallur 16.5 80.1 26 0 0 13 16

Vinukonda 16.0 79.7 25 0 0 20 23

Phialin cyclone: Real-time HWRF model rainfall guidance

(3 km nest)

Predicted rainfall peaks over Northern parts of Odisha (close to observed)

HWRF modeling system: • 6-hr cyclic runs with vortex

relocation and initialization • No-data assimilation

• Numbers represents IMD

station rainfall (showed >15 cm rainfall)

The NWP model performance for the prediction of heavy rainfall due to landfalling Cyclones can be improved with mesoscale data assimilation Assimilation of DWR reflectivity and radial wind showed maximum impact on heavy rainfall simulation.

Improved track prediction correct the rainfall structure up to some extent, Needs further investigation.

Cloud resolving model is certainly a solution. Multi DWR data assimilation and Improved land surface conditions can further improve rainfall prediction

CONCLUSIONS

Thank You