Simulation%20of%20track%20and%20intensity%20of%20the%20Bay%20of%20Bengal%20cyclones%20using%20mesoscale%20models

1
Simulation of track and intensity of the Bay of
Bengal cyclones using mesoscale models
U.C. Mohanty
Centre for Atmospheric Sciences, Indian
Institute of Technology, Delhi India
2
Acknowledgements M. Mandal Sujata Pattanayak
CAS, IIT Delhi S. G. Gopalakrishnan, Naomi Surgi
Steve Lord NCEP/EMC/NOAA, Washington D. C.
3
PRESENTATION

• Introduction
• Uniqueness of Bay of Bengal cyclones Genesis
  and movement
• Simulation of Orissa super cyclone with improved
  initial condition Satellite observation and
  synthetic vortex
• Implementation of HWRF model A case study of
  Bay of Bengal cyclone (MALA)
• Summary Conclusion
• Future work plan

4
TROPICAL CYCLONE RESEARCH AT CAS, IIT DELHI
Observational aspects of the Bay of Bengal
cyclones based on 104 years database
(1891-1994). Simulation of severe cyclones over
the Bay of Bengal using global and regional
models. Generation of synthetic vortex for
initial vortex specification over data sparse
oceanic region. Important aspects viz.,
nonhydrostatic dynamics, model resolution,
parameterization of physical processes and
improvement in model initial condition for
mesoscale simulation of severe Bay of Bengal
cyclones .
5
(Continued ..)

• Role of sea surface temperature (SST) in
  modulating the track and intensity of Bay of
  Bengal cyclones
• Implementation of HWRF model of NCEP, USA
  for track and intensity prediction of Bay of
  Bengal cyclones
• Extensive study on prediction of storm surges
  and coastal inundation associated with tropical
  cyclones over Indian Seas. The real-time storm
  surge prediction system is presently in
  operational use by India Meteorological
  Department

6
INTRODUCTION

• In last two decades, there have been significant
  improvement in numerical prediction of
  tropical cyclones and is attributed mainly to
• the increase in model resolution
• improvement in model physics
• improvement in analysis and assimilation
  techniques
• emergence of super computing facilities
• Hence ,in recent years the focus is on high
  resolution non-hydrostatic mesoscale models

7
OBJECTIVE
To study some of the important aspects
(resolution, physics and initial condition) to
improve the forecast skill of a numerical model
towards prediction of track, intensity (in terms
of central pressure, surface wind and
precipitation) and landfall of Bay of Bengal
cyclones.
8
UNIQUENESS OF BAY OF BENGAL CYCLONES GENESIS
MOVEMENT
9
Tropical cyclone basins
10
UNIQUENESS OF INDIAN REGION

• ONLY 7 OF WORLDS TOTAL CYCLONE PRODUCTION BUT
  HIGHEST NUMBER OF HUMAN DEATHS
• TWO CYCLONE SEASONS PRE-MONSOON(APRIL-MAY)
  POST MONSOON(OCT-DEC) TWO SEAS OF FORMATION BAY
  OF BENGAL ARABIAN SEA
• CYCLONES ARE OF RELATIVELY MODERATE INTENSITY AS
  COMPARED TO WEST PACIFIC TYPHOONS AND WEST
  ATLANTIC HURRICANES
• CERTAIN SEGMENTS OF INDIAN COAST ARE HIGHLY
  VULNERABLE TO STORM SURGE DUE TO SHALLOW
  BATHEMETRY (RELATIVELY HIGHEST SURGE 8-12 m)
• LARGE STRETCH OF LOW LYING DELTA REGION (CLUSTER
  OF ISLANDS) IN THE CYCLONE PRONE EAST COAST OF
  INDIA WITH PRESENCE OF RELATIVELY LARGE NUMBER OF
  RIVER SYSTEMS
• COASTAL POPULATION IN THE REGION IS ONE OF THE
  DENSEST IN THE WORLD

11
GENESIS OF TROPICAL CYCLONES

• Tropical cyclone is a global phenomenon. It
  occurs in tropical ocean belts (30N - 30S)
  except a narrow equatorial belt (5N-5S).
• Total number of tropical cyclones over
• the global tropical ocean per year
• Number of cyclones over Bay of Bengal and Arabian
  Sea (Indian Seas) per year
• Annual sever tropical cyclones over Indian seas
  total cyclones

• 80-100
• 5-6 (6-7
• Global data)
• 2-3 (40-50 of the area)

12
(No Transcript)
13
Percentage Distribution of the Occurrence of
Tropical disturbances (TD), Tropical Storm (TS)
and Severe Tropical Storms (STS) relative to
calendar year
14
Monthly Variation of Initial Location of Cyclonic
Disturbances Intensified into Tropical Cyclone
over Bay of Bengal
15
Number of Tropical Cyclones Crossing Different
Countries Bordering Bay of Bengal (1891-1994)
16
PROBABLE MAXIMUM STORM SURGE (M) FOR THE INDIAN
AND BANGLADESH COASTLINES
17
Total Number of Cyclones Crossing Different
Maritime States of India During 1890-1993
18
Combined Surge, Tide and Wave Set-Up
(metres above mean sea level)
19
Movement of Bay of Bengal cyclones
20
Monthly frequency of dissipation of cyclones in
the Bay of Bengal (1891-1994)
21
(No Transcript)
22
Simulation of Orissa super cyclone with improved
initial condition Satellite Observation and
Synthetic Vortex
23
SATELLITE DATA USED
QSCAT Surface wind speed MSMR - Surface
Wind speed SSM/I - Surface Wind speed
METEOSAT Cloud Motion Wind and Temperature
(mostly at upper levels)
24
SYNTHETIC VORTEX

• Hollands (1980) empirical relation is used to
  generate synthetic within a radius of 500 km
  around the storm.
• The value of the empirical constant B in
  Holland vortex is calibrated using SSM/I
  conservations.
• The upper level winds are derived using wind
  reduction factors by McBride (McBride, 1981). Low
  level convergence and upper level divergence is
  incorporated using inflow (lower levels) and
  outflow (upper levels) angles at different
  levels.

25
SYNTHETIC VORTEX (Continued)

• The sectorial asymmetry in the flow pattern is
  achieved incorporating large-scale steering
  current based on last 12 hours movement of the
  storm following Chan and Williams (1982).
• The data generated synthetically at 80 points
  around the storm in 10 concentric circles.

26
VORTEX INITIALIZATION

• High resolution reanalysis is prepared using
  Cressman analysis technique with NCEP reanalysis
  as large-scale first guess field and synthetic
  vortex generated at 80 points around the storm as
  additional observations.
• The model is initialized with Holland vortex
  using 12 hours analysis nudging (nudging to high
  resolution reanalysis) before the start of the
  actual forecast period.

27
Schematic Diagram of 80 points
28
Numerical Experiments

• Control Run NCEP reanalysis (2.5ox2.5o)
  initialized with 12 hours (12 UTC 25th 00 UTC
  26th) analysis nudging.
• Enhanced Analysis High-Resolution Reanalysis
  (HRR) is prepared for three times (12 UTC 25th,
  18 UTC 25th and 00 UTC 26th) with insertion of
  the data sets mentioned into the NCEP reanalysis
  as first guess and model initial condition is
  improved with 12 hours nudging to the prepared HRR

29
MM5 model configuration used in this present study
Model Fifth-generation Pen State / NCAR mesoscale model (MM5) version 2.0
Dynamics Non-hydrostatic with 3-D Coriolis force
Main prognostic variables u, v, w, T, p',q
Map projection Mercator
Central point of the domain 11.5ºN / 85.0ºE
Horizontal grid distance 30 km (10ºS - 30ºN, 55ºE - 110ºE)
Number of vertical level 23 sigma levels
Horizontal grid scheme Arakawa C-grid
Time integration scheme Leap-frog scheme with time splitting technique
Initial Lateral boundary condition NCEP reanalysis (2.5º X 2.5º)
Radiation scheme CCM2
Planetary boundary layer parameterization scheme MRF
Cumulus parameterization scheme Grell
Topography 30 s elevation data (USGS)
SST Surface parameters NCEP reanalysis
30
(No Transcript)
31
(No Transcript)
32
(No Transcript)
33
Model simulated 24 hours accumulated rainfall
Day 3 (29.10.99)
Day 4 (30.10.99)
Day 5 (31.10.99)
34
Displacement errors in track forecast and
percentage of improvements w.r.t. CONTROL
simulation
Time SAT Simulation SAT Simulation SYNTHETIC simulation SYNTHETIC simulation
Time Error Improvement Error Improvement
Day-1 165 15 31 84
Day-2 104 34 31 80
Day-3 124 37 33 83
Day-4 99 29 39 72
Day-5 94 62 52 83
35
TRACK OF ORISSA SUPER CYCLONE
36
(No Transcript)
37
Surge Envelope Scenario Actual Landfall
Distance along East Coast of India (km)
38
Error in landfall point and time in Orissa super
cyclone
Experiments Error in location of landfall (in km) Delay in landfall (in hours)
30-km 120 6.5
60-km 172 6.5
90-km 180 21.0
30-km-satellite 92 3.5
30-km-synthetic vortex 12 2.0
39
CONCLUSIONS

• Inclusion of synthetic vortex in the
  high-resolution reanalysis and model initial
  condition with analysis nudging results a little
  strong initial vortex. Most importantly, the
  location of the vortex is found to coincide with
  the observed one.
• There is consistent and very significant
  improvement in simulating the track of the storm
  with inclusion of synthetic vortex.
• Most importantly, the landfall point and time of
  the storm is simulated almost accurately with
  vortex initialization and can be very useful in
  disaster mitigation and management.

40
PREDICTION SEVERE CYCLONES DURING 1995-1999
41
The cyclonic storms with forecast length
Storms Initialized at stage (T) Time of initialization Forecast length
25-31 October 1999 (Case-1) 2.0 00 UTC 26 Oct. 120 hours
15-19 October 1999 (Case-2) 2.0 00 UTC 16 Oct. 72 hours
19-23 November 1998 (Case-3) 2.0 00 UTC 20 Nov. 72 hours
13-16 November 1998 (Case-4) 1.5 00 UTC 14 Nov. 48 hours
15-20 May 1997 (Case-5) 2.0 00 UTC 16 May 96 hours
4-7 November 1996 (Case-6) 3.0 12 UTC 05 Nov. 36 hours
22-26 November 1995 (Case-7) 3.0 00 UTC 23 Nov. 72 hours
7-10 November 1995 (Case-8) 2.5 00 UTC 08 Nov. 48 hours
42
Initial positional error before and after vortex
initialization
Storms Initialized at stage (T) Initial positional error in large scale analysis (in km) Initial positional error after vortex initialization (in km)
25-31 October 1999 2.0 122 00
15-15 October 1999 2.0 584 104
19-23 November 1998 2.0 133 39
13-16 November 1998 1.5 559 31
15-20 May 1997 2.0 347 81
4-7 November 1996 3.0 358 00
22-26 November 1995 3.0 165 00
7-10 November 1995 2.5 545 22
43
Observed and model simulated MSLP
Storm Initialized at stage (T) Day-1 (24 hours) Day-1 (24 hours) Day-2 (48 hours) Day-2 (48 hours) Day-3 (72 hours) Day-3 (72 hours)
Storm Initialized at stage (T) Observed Forecast Observed Forecast Observed Forecast
Case-1 2.0 998 995 986 982 912 968
Case-2 2.0 988 986 994 987 1004 998
Case-3 2.0 996 997 984 986 998 995
Case-4 1.5 992 993 994 999 - -
Case-5 2.0 998 997 986 970 968 960
Case-6 3.0 988 998 - - - -
Case-7 3.0 976 991 970 979 1004 1004
Case-8 2.5 984 995 1002 1000 - -
44
TRACK OF ORISSA SUPER CYCLONE DURING 26-31 Oct.
1999 (Case-1)
45
TRACK OF ORISSA CYCLONE DURING 16-19 Oct. 1999
(Case-2)
46
TRACK OF WEST BENGAL CYCLONE DURING 20-23 Nov.
1998 (Case-3)
47
TRACK OF ANDHRA CYCLONE DURING 14-16 Nov. 1998
(Case-4)
48
TRACK OF BANGLADESH CYCLONE DURING 16-20 May
1997 (Case-5)
49
TRACK OF ANDHRA CYCLONE DURING 05-07 Nov. 1996
(Case-6)
50
TRACK OF BANGLADESH CYCLONE DURING 23-26 Nov.
1995 (Case-7)
51
TRACK OF ANDHRA CYCLONE DURING 08-10 Nov. 1995
(Case-8)
52
Vector displacement error in track forecast
Time ? Case ? 00 hours 12 hours 24 hours 36 hours 48 hours
Case-1 00 20 31 31 31
Case-2 104 120 146 144 137
Case-3 39 56 46 306 350
Case-4 31 86 128 136 150
Case-5 81 126 156 307 329
Case-6 0 32 86 115 -
Case-7 22 56 46 112 201
Case 8 0 33 35 115 248
Average 35 66 84 158 207
53
Average error in track forecast (of eight Bay of
Bengal cyclones during 1995-99)

54
Error in landfall point and time
Storm Initialized at Stage (T) Error in location of landfall (in Km) Delay in landfall (in hours)
Case-1 2.0 12 2.0
Case-2 2.0 54 -08
Case-3 2.0 308 11
Case-4 1.5 44 0.5
Case-5 2.0 22 0.5
Case-6 3.0 21 10
Case-7 2.5 55 1
Case-8 3.0 91 18
55
Average error in track forecast (of all severe
cyclonic storms during 1995-1999, in km), NCMRWF
T-80 model, error in operational limited area
model and FDL
12 hours 24 hours 36 hours 48 hours
Average error in track forecast in present study (all severe cyclonic storms during 1995-1999) 66 84 158 207
NCMRWF T80 model with synthetic vortex (three storms during 1995-1999) - 155 - 466
Average error in track forecast in operational limited area model (three storms during 1998) - 169 - 254
Forecast difficulty level in North Indian seas 60 117 176 230
56
SUMMARY

• Initialization with 12 hours nudging to high
  resolution reanalysis through insertion of
  synthetic vortex is very useful in improving
  model initial condition.
• Though there is some improvement in track
  prediction with the utilization of satellite
  datasets still there is need to use synthetic
  vortex.
• There is consistent and very significant
  improvement in simulating the track and landfall
  (location) of the storms with inclusion of
  synthetic vortex though there is delay in
  landfall time.
• Intensity of the storms are better simulated in
  case the storm is initialized at less intense
  stage and has relatively longer path over the
  ocean. Sharp intensification and dissipation of
  the explosively deepening storms are not well
  captured by the model.

57
IMPLEMENTATION OF HWRF MODEL A case study of
Bay of Bengal cyclone (MALA)
58
HWRF model configuration used in this present
study
Model Hurricane WRF of NCEP/ NOAA, Version 2.1
Dynamics Non-hydrostatic with terrain following hybrid pressure sigma vertical co-ordinate
Map projection Mercator
Central point of the domain 11.5ºN / 85.0ºE
Horizontal grid distance 27 km (10ºS - 30ºN, 55ºE - 110ºE), 9 km (Movable)
Number of vertical level 31 sigma levels
Horizontal grid scheme Arakawa E-grid
Time integration scheme Horizontal Forward-backward scheme Vertical Implicit scheme
Initial Lateral boundary condition GFS Analysis Forecast
Radiation scheme Long wave GFDL scheme Short wave GFDL scheme
Planetary boundary layer parameterization scheme NCEP Global Forecast System scheme
Land Surface NMM Land Surface scheme
Cumulus parameterization scheme Simplified Arakawa-Schubert scheme
Microphysics Ferrier scheme
Topography 30 s elevation data (USGS)
SST Surface parameters GFS Analysis Forecast
59
Observed Simulated Central Pressure (hPa) and
Wind Speed (kts)
60
Observed Simulated Central Pressure and
Pressure droop
Date Time Observed Observed Simulated Simulated
Date Time Central Pressure (hPa) Pressure Droop (hPa) Central Pressure (hPa) Pressure Droop (hPa)
2612 994 6
2618 994 6 1000 6
2700 994 6 1000 6
2706 990 10 998 6
2712 984 10 996 8
2718 984 22 994 10
2800 984 22 991 14
2806 964 40 981 22
2812 954 52 971 33
2818 954 52 960 43
2900 954 52 965 39
2906 966 52 955 48
2912 945 59
2915 943 61
2918 945 59
3000 967 39
3006 983 21
3012 991 15
61
Observed Simulated Surface Wind (kts)
Date Time Surface Wind (kts) Surface Wind (kts)
Date Time Observed Simulated
2612 40 29.70
2618 40 30.41
2700 40 33.94
2706 50 38.41
2712 65 48.16
2718 65 53.15
2800 65 62.9
2806 90 87.7
2812 100 95.0
2818 100 105.8
2900 100 97.3
2906 50 123.0
2912 140.0
2918 125.4
3000 90.5
3006 62.0
3012 47.63
62
Proj Mercator
63
DOMAIN 1 SIMULATION
DOMAIN 2 SIMULATION
64
(No Transcript)
65
TRACK OF CYCLONE MALA (26 30 April 2006)
66
(No Transcript)
67
(No Transcript)
68
Vector displacement errors by different models
Date Time HWRF UKMO IMD
2600 263.4
2612 65.0
2700 101.1 287.4 50.0
2712 99.7 102.9
2800 124.0 348.5 263.9
2812 189.1 357.3
2900 239.0 260.4 557.0
2912 325.4
69
CONCLUSION

• The model could able to capture the
  intensification and dissipation of the storm
  though there is a much intensification in the
  model simulation.
• The surface wind is also well simulated by the
  model.
• With the high resolution moving grid structure,
  the track of the storm can be better predicted.
• The model result can be better simulated with
  high resolution and sophisticated data
  assimilation technique.

70
FUTURE COLLABORATIVE RESEARCH

• Simulation / prediction of Bay of Bengal cyclones
  using coupled (ocean) HWRF model and associated
  storm surges.
• Extensive numerical experiments with a number of
  recent tropical cyclones with HWRF system.
• Operational implementation of HWRF by National
  Meteorological Services of India .
• Joint RD work with HWRF System for tropical
  cyclones over Indian seas.
• Mesoscale data assimilation using 3D/4D
  variational assimilation technique and vortex
  initialization for providing better initial
  conditions for HWRF.

71
Thanks
72
SUMMARY (continued.)

• The intensity of such explosively deepening
  storms is expected to be better simulated with
  further improvement in model physics,
  particularly in convection and ice physics. Use
  of coupled mesoscale model can also improve
  intensity forecast.
• The model initial condition can be further
  improved with use of sophisticated assimilation
  technique and inclusion of satellite, drop-sonde,
  deep ocean bouys Doppler radar observations
• The forecast of these severe cyclonic storms can
  be further improved with use of multi-model
  mesoscale super-ensemble technique which reduces
  the uncertainty associated with a single model
  forecast.

73
HOLLANDS VORTEX
In this vortex pressure profile is given by

• Pr Po (Pe-Po) exp (-A/rB)
• Using gradient wind balance, the wind profile can
  be deduced as,
• Vr A.B (Pe-Po) exp (-A/rB) / ?rB (r2-f2) / 4
  ½ r.f/2
• Where Pe and Po are peripheral and central
  pressures. ??? is the air density, f is the
  Coriolis parameter and A B are the empirical
  constants.

74
In the regions of high wind speed, the Coriolis
term can be neglected, and the vortex is assumed
to be in cyclostropic balance. These winds are
given by,
The radius of maximum wind is obtained by setting
dVc/dr 0 in the above equation. Thus
75
FUTURE COLLABORATIVE RESEARCH

• Coupling of tropical cyclone prediction model
  with storm surge model (one way).
• Preparation of mesoscale reanalysis over the
  Indian region for the period 1979-2004 using
  3D/4D VAR with utilization of all available
  conventional and non-conventional observations.

76
UPPER LEVEL WINDS

• The strength of wind (upper level) decreases
  with increasing height by a factor w called
  wind reduction factor and are generated from
  surface wind. The reduction factors for differ
  levels as determined by McBride (1981) based on
  rawinsonde composite is given in the table below

Levels (in hPa) 850 700 500 400 300
Reduction factor 1.0 0.95 0.85 0.65 0.35
77
INFLOW AND OUTFLOW ANGLES

• In order to generate, proper convergence and
  divergence at different levels, many modelers
  introduce inflow and outflow angles to winds.
  The inflow and outflow angles normally used are
  given in the table below and are based on the
  composite profiles of radial components of winds
  by Frank (1977).

Levels 100 850 700 500 400 300
Inflow angle 30? 20? 10? 0? -10? -20?
78
TEMPERATURE HUMIDITY PROFILE

• Temperature is generated through the relation
• t(r,k) tmean (k) tanol(k) t (k) exp
  (-A/rB)
• Where k stands for vertical level, r is the
  distance from centre of the storm and Tanol(k) is
  temperature anomaly at level k.
• rh (r,k) rhenv (k) (r/R) r/anol (k) for
  r ltR
• rh (r,k) rhenv (k) (r/R)-? ? rhanol(k) for
  r ?R
• where rhenv(k) is environmental relative
  humidity at level ?k? and rhanol(k) is relative
  humidity anomaly at level ?k?.

79
Probability of Landfall of Cyclones per Year
80
DYNAMICAL ASPECTS MODEL RESOLUTION
81
EXPERIMENTAL DESIGN

• Resolution The mesoscale model MM5 at 90 km, 60
  km and 30 km resolutions is used to produce 5 day
  simulation of Orissa super cyclone (1999).
• Initial and boundary condition is taken from
  NCEP/NCAR reanalysis.
• Initialization 12 hours analysis nudging before
  the start of actual forecast period.

82
(No Transcript)
83
(No Transcript)
84
TRACK OF ORISSA SUPER CYCLONE
85
Observed and model simulated pressure drop
Time\ Experiment 30 km   60 km 90 km Observed
Day-1 10 10 09 10
Day-2 23 20 14 20
Day-3 45 31 25 98
Day-4 25 19 20 14
Day-5 15 12 11 12
86
Observed and model simulated maximum surface wind
Time\ Experiment 30 km 60 km 90 km Observed
Day-1 35 35 33 45
Day-2 55 55 41 65
Day-3 78 68 53 140
Day-4 51 43 51 65
Day-5 37 33 33 18
87
Displacement error in track forecast compared to
the observed track
Time\Experiment 30 km 60 km 90 km
Day-1 196 181 155
Day-2 157 167 283
Day-3 197 155 272
Day-4 137 191 197
Day-5 302 323 402
88
CONCLUSIONS

• Model horizontal resolution has significant
  impact on the simulation of intensity of the
  storm. The impact is relatively less pronounced
  on track of the storm.

89
SENSITIVITY STUDIES ON PARAMETERIZATION OF
PHYSICAL PROCESSES
90
Schemes considered in the study

• Cumulus Convection
• 1. Anthes-Kuo
• 2.   Grell
• 3.   Betts-Miller
• 4.   Kain-Fritsch
• Planetary Boundary Layer
• 1.   Blackadar
• 2.   NCAR MRF
• Radiation
• 1.   Dudhias short wave-long wave
• 2. NCAR CCM2

91
EXPERIMENTAL DESIGN

• Stage-I Eight experiments with eight possible
  combinations of four convection and two PBL
  parameterization schemes (all these experiments
  are carried out with CCM2 radiation scheme)
• Stage-II One experiment with possible best
  combination of convection and PBL schemes (from
  stage-I) with Dudhias (1989) long- and
  short-wave radiation scheme
• Stage-III Eight experiments (four each at 90 km
  and 30 km model resolutions) with four CPSs in
  combination with MRF PBL and CCM2 radiation
  parameterization schemes

92
72 hours forecast, MSLP valid at 00 UTC 29.10.99
93
Model simulated central pressure drop
Experiments Day-1 (24 hours) Day-2 (48 hours) Day-3 (72 hours) Day-4 (96 hours) Day-5 (120 hours)
Observed 10 20 98 14 12
M-AK-CCM2 07 10 15 09 08
M-BM-CCM2 07 12 24 34 17
M-GR-CCM2 10 20 31 19 12
M-KF-CCM2 06 11 28 25 16
B-AK-CCM2 05 05 05 02 01
B-BM-CCM2 08 12 22 32 20
B-GR-CCM2 07 11 25 24 17
B-KF-CCM2 06 08 12 18 19
M-GR-SC 09 17 30 15 08
94
24 hours accumulated rainfall valid at 03 UTC
30.10.99
95
TRACK OF THE STROM WITH FOUR CONVECTION SCHEMES
96
Displacement errors in track forecast
Experiments Day-1 (24 hours) Day-2 (48 hours) Day-3 (72 hours) Day-4 (96 hours) Day-5 (120 hours)
M-AK-CCM2 176 156 69 86 474
M-BM-CCM2 208 196 291 177 122
M-GR-CCM2 181 167 155 191 323
M-KF-CCM2 144 177 208 146 147
B-AK-CCM2 165 000 108 374 762
B-BM-CCM2 211 291 289 196 203
B-GR-CCM2 111 105 187 196 235
B-KF-CCM2 211 291 289 196 203
M-GR-SC 167 139 294 306 623
97
TRACK OF THE STROM WITH TWO PBL SCHEMES
98
CONCLUSIONS

• The forecast skill of the model at the
  resolutions used in the present study is
  sensitive to the cumulus convection and planetary
  boundary layer parameterization schemes. The
  radiation parameterization is also found to have
  perceptible impact on model simulation.
• The Grell cumulus parameterization scheme with
  MRF PBL scheme along with NCAR CCM2 radiation
  parameterization scheme provides the optimal
  combination of the schemes for simulation of the
  Bay of Bengal cyclones.
• The Anthes-Kuo scheme is more suitable at coarse
  resolution. With Grell and Betts-Miller scheme,
  the forecast skill of the model is found to
  improve with increasing resolution.

99
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 reallocation and adjustment to
  actual storm intensity.
• Uses SAS convection scheme, GFS surface, boundary
  layer physics, GFDL/GFS radiation and Ferrier
  Microphysical Scheme.
• Ocean coupled modeling system.

100
Continued

• Time stepping method
• fast waves forward-backward
• vertically propagating sound waves implicit
• Advection
• horizontal Adams-Bashforth for U,V and
  T (and
• Coriolis)
• vertical Crank-Nicholson for U,V and
  T
• forward, flux-corrected
  for q and water
• species
• Horizontal diffusion
• forward, 2nd order Smagorinsky-type

101
Salient Features Telescopic E-Grid

• All interpolations are done on a rotated lat-lon,
  E-grid with the reference lat-lon located at the
  centre of the parent domain.
• Consequently the nested domain can be freely
  moved anywhere within the grid points of the
  parent domain, yet the nested domain lat-lon
  lines will coincide with the lat-lon lines of
  the parent domain at integral parent-to-nest
  ratio.
• This coincidence of grid points between the
  parent and nested domain eliminates the need for
  more complex, generalized remapping calculations
  in the WRF Advanced Software Framework and is
  expected to aid better distributed memory
  performance, and portability of the modeling
  system.

102

• Nonhydrostatic model equations for simplicity,
  inviscid, adiabatic, sigma

? F, w and e not independent, watch for
overspecification!
Janjic, et al., Mon. Wea. Rev., 2001
Janjic, Meteor. Atmos. Phys., 2002)
103

JTWC track for the storm MALA (April 2006)