Annual Joint WMO Technical Progress Report on the Global
Data processing and Forecasting System (GDPFS) including
Numerical Weather Prediction (NWP) Research Activities
March 2010
(A) Contribution of India Meteorological Department
1. Summary of highlights
The main mandate of the Numerical Weather Prediction NWP
Group at the
Head Quarters (New Delhi) of IMD has been to support for the day to day
operational weather forecasts in the time scale of nowcasting to medium range.
This
involves
processing
of
observations
received
through
Global
Telecommunication System (GTS) and automatic preparation of various
sysnoptic charts, operational run of NWP models and preparation of various
graphics products, dissemination, validation of model performance and R&D,
data updates for IMD Web site, archival of all data and forecast outputs.
India Meteorological Department operationally runs number of regional models,
namely, Limited Area Model (LAM), MM5, WRF and Quasi-Lagrangian Model
(QLM)
for short range prediction.
The MM5 model is run at the horizontal
resolution of 45 km with 23 sigma levels in the vertical and the integration is
carried up to 72 hours over a single domain covering the area between lat. 30 o S
to 45 o N long 25 o E to 125 o E. Initial and boundary conditions are obtained from
the National Centre for Medium Range Weather Forecasting (NCMRWF) T-254
Global Forecast System (GFS). The boundary conditions are updated at every
six hours interval. WRF is run for 72 hours forecast at the horizontal resolution of
27 km and 38 vertical levels domain covering the area between lat. 30
o
S to 45 o
N long 25 o E to 125 o E with NCEP initial and boundary condition. The resolution
1
of inner domain is 9 km covering India. The QLM model is used for cyclone track
prediction in case of cyclone situation in the Arabian Sea or Bay of Bengal.
Considering need of farming sector, India Meteorological Department
(IMD) has upgraded the Agro-Meteorological Advisory Service from agro climate
zone to district level. As a major step, IMD started issuing district level weather
forecasts from 1 June 2008 for meteorological
parameters such as rainfall,
maximum and minimum temperature, relative humidity, surface wind and cloud
octa up to 5 days in quantitative terms. These forecasts are generated through
Multi-Model Ensemble (MME) system making use of model outputs of state of
the art global models from the leading global NWP centres. These forecasts are
made available on the national web site of IMD. The method has been further
updated
for monsoon 2009 from the use of five NWP models
namely, (i)
NCMRWF T-254, (ii) ECMWF T799, (iii) JMA T859, (iv) UKMO and (v) NCEP
GFS T-382.
These NWP products are disseminated to the operational forecasters at various
IMD Forecast Centres/Offices through a ftp connectivity.
With the commissioning of High Performance Computing System (HPCS) at
IMD H/Q in January 2010, IMD has been in the process of expanding NWP
activities to meet the growing operational demands of multiscale forecasts
ranging from nowcastng to medium range and extended range. Global, regional
and mesosale NWP models with state of the art data assimilation procedure are
expected to be made operational at H/Q with in a few moths. At twelve other
regional centres, very high resolution mesoscale models will be made operational
under the guidance of H/Q. From the ongoing modernization programme of IMD,
observations
(both conventional and non conventional)
are expected to be
available on the mesoscale both in space and time by means of Doppler Weather
Radar (DWR), Satellites (INSAT Radiance), Wind Profilers, meso-network
2
(Automatic Weather Stations), buoys and aircrafts in the real time mode with the
use of advanced telecommunication system.
2. Equipment in use at the Centre
High Performance Computing System (HPCS) with peak speed 14,2 Tera
Flop was commissioned in IMD New Delhi in January 2010. High end servers at
12 different locations across the country (Pune; Regional Met. Centres Delhi,
Kolkata, Chennai, Mumbai, Guwawati and Nagpur; Met. Centres Ahmedabad,
Bangalore, Chandigarh, Bhubaneswar and Hyderabad) are installed (10
completed and 2 under progress).
Computing Racks with peak Power : Peak Speed 14. 4 Tera FLOPS
28 Nodes : POWER-6, 4.7 GHz Processors &128 Giga Bytes Memory per Node
Storage :
300 Tera Bytes (100 TB online and 200 TB near online)
Archival :
200 Tera Bytes
Operating Environment : IBM-AIX 5.3 with Parallel Computation Support
Network Bandwidth : 10 Gbps for Switching (Clustering)
4 High End Servers with a total Computing Power (134 GF x 4) = 536 G FLOFS
8 Racks for Storage
1 Rack of Robotic Tape Library
Computer System: (a) Altix- 350
(b)
0rigin 200 and (c) IBM P5/595 (64
processors).
3. Data and Products from GTS in use
Data management at IMD H/Q is comprising of four stages namely, (a) Reception of
data through the Global Telecommunication System (GTS) from RTH (b) Processing
of observations for various operational use (c) Disposal of final products and (d) Archival
of data. Reception of data includes of real time global weather observations and NWP
outputs of other operational NWP centres, llike ECMWF and JMA (in the GRIB format)
3
(a) Reception:
Meteorological
observational data
received on line at Regional
Telecom Hub (RTH) round the clock are being used in Northern Hemisphere Analysis
Centre (NHAC) at the H/Q of IMD in two different channels namely: (i) Manual Plotting
of operational
Synoptic Weather Charts, (ii) Processing of data (Decoding, Quality
Control) in the NHAC Computer system for Automatic Plotting of weather charts for
synoptic use and ingesting of data in the assimilation cycle of NWP models. Manual
synoptic weather charts are preserved
(b) Processing of data in NHAC Computer System:
Automatic Plotting of synoptic
charts are done at every three hours interval where as plotting of AWS observations are
done at hourly interval. Synoptic Observations are also used for preparation of various
display tables (like Current Weather
) and Graphics as required by the IMD web site,
which are made updated on the basis of latest observation. At IMD H/Q, NWP models
are run based on 00 UTC and 12 UTC observations. These models are run based on
the initial and boundary conditions from NCEP GFS (received online through the
Internet)/NCMRWF T-254 (received online through RTH). Based on the model outputs
various NWP graphics products are prepared as required for the operational forecasting.
ECMWF model outputs are decoded and then graphics product are prepared.
(c) Disposal of Final Product: All the automatic plots of weather charts and NWP
graphics outputs are used for the operational forecasting at NHAC. Display tables and
graphics product (based on observations as well as NWP outputs) are used for IMD
web site. Some of the products are kept in IMD ftp site for access to the field forecasters
at MC/RMC/MO.
(d) Archival data: Following data are being archived at NHAC (Computer): (i) GTS
observations (ii) Decoded synoptic and upper air observations (iii) NWP outputs both
graphics and digital data and (iv) Automatic Plots of weather charts. Manual synoptic
charts are also preserved at NHAC.
Data received from regional telecommunication hub (RTH) to NHAC computer through
GTS channel are WMO specific code format. They are further processed for plotting as
well as used for model input. Besides these data, initial and boundary fields are regularly
downloaded from NCEP/NCMRWF site to run LAM and MM5. NCEP data are also used
to run QLM to give cyclone track prediction up to 72hr. JMA and ECMWF outputs are
also being achieved.
Graphic products of all model output are uploaded to web server of IMD
4
( www.imd.gov.in) besides other routine observational data in tabular form. Hourly plots
of Automatic Weather Station (AWS) data, 3 hourly surface data & 12 hourly upper air
plotting are regularly uploaded to ftp server of IMD. Five days forecast products from
UKMET office & T254L64 products received from NCMRWF are also uploaded to ftp
server of IMD to facilitate field forecasting offices on day to day basis.
4. Forecasting System
(a) The High Performance Computing System (HPCS) Data Flow Diagrams :
The HPCS at IMD HQs receives the entire data including manual and
automatic devices from across the globe, processes it and generates global and
regional forecasts for the purpose of

Generating forecast guidance for operational offices

Generating the initial conditions for feeding very high resolution models
run at regional meteorological centres
METEOROLOGICAL OBSERVATIONS
x
x
IMD PUNE
CLIMATE MODELS
(1.0 TFlops)
HPCS DELHI
GLOBAL/MESOSCALE
MODELS
(14.4 TFlops)
ANALYSIS
&
FORECAST
RMC
RMC
RMC
MC
MESOSCALE
MODELS
(134 GFlops)
MESOSCALE
MODELS
(134 GFlops)
GFlops)
MESOSCALE
MODELS
(134 GFlops)
GFlops)
MESOSCALE
MODELS
(134 GFlops)
GFlops)
ANAL & F/C
ANAL & F/C
ANAL & F/C
ANAL & F/C
PRODUCTION
END USER DISSEMINATION NETWORK
5
(b) The HPCS Connectivity :
 At the incoming end the HPCS is connected to the central message
switching computer called “TRANSMET”.
 The products are seamlessly connected to the operational forecasting
system of IMD called “SYNERGEE”. It directly flows through the manual
value addition stages to product generation platforms which create the
dissemination products.
 HPCS server feeds regional servers through automated ftp via VPN circuits.
Data and products are exchanged with other national users like Indian
Navy, Indian Air force etc.
System run schedule and forecast ranges
Medium Range Forecast System (4-10 days)
Implementation of Global Forecast System (GFS)
Global Forecast System (GFS, based on NCEP) at T382L64 resolution has
been implemented at NHAC, IMD HQ on IBM based High Power Computing
Systems (HPCS). In horizontal, it resolves 382 waves ( 35 Km) in spectral
triangular truncation representation (T382), for which the Gaussian grid of 1152 x
576 dimensions are used. The model has 64 vertical levels (hybrid; sigma and
pressure).
The GFS is running in experimental real-time mode since 15th
January 2010. This new higher resolution global forecast model and the
corresponding assimilation system are adopted from NCEP, USA. The horizontal
representation of model variables are in spectral form (spherical harmonic basis
functions) with transformation to a Gaussian grid for calculation of nonlinear
quantities and physics. The GFS at IMD Delhi involves 4 steps as given below :
6
Step 1 - Data Decoding and Quality Control: First step of the forecast system
is data decoding. It runs 48 times in a day on half-hourly basis, as soon as GTS
data files are updated at regional telecom hub (RTH) of global telecom system
(GTS), at IMD, New Delhi.
Steps 2 - Preprocessing of data (PREPBUFR) : Runs 4 times a day at 0000,
0600, 1200 & 1800 UTC. List of data presently being pre-processed for Global
Forecast System are :
1. Upper air sounding – TEMP, GPS & PILOT
2. Land surface – SYNOP, SYNOP MOBIL & AWS
3. Marine surface - SHIP
4. Drifting buoy - BUOY
5. Sub-surface buoy - BATHY
6. Aircraft observations - AIREP & AMDAR
7. Automated Aircraft Observation - BUFR (ACARS)
8. Airport Weather Observations - METAR
9. Satellite winds - SATOB
10. High density satellite winds - BUFR (EUMETSAT & Japan)
11. Wind profiler observations - BUFR (US/Europe)
12. Surface pressure Analysis - PAOB (Australia)
13. Radiance (AMSU-A, AMSU-B, HIRS-3 and HIRS-4, MSU, IASI, SSMI,
AIRS, AMSRE, GOES, MHS)
14. GPS Radio occultation
15. Rain Rate (SSMI and TRMM)
Step 3 - Global Data Assimilation (GDAS) cycle :
The Global Data Assimilation (GDAS) cycle runs 4 times a day (00, 06, 12 and
18 UTC). The assimilation system is a global 3-dimensional variational
technique, based on NCEP’s Grid Point Statistical Interpolation (GSI) scheme,
which is the next generation of Spectral Statistical Interpolation (SSI).
7
Step 4 – Forecast Integration for 7 days
The analysis and forecast for 7 days is performed using the HPCS
installed in IMD Delhi. One GDAS cycle and seven day forecast (168 hour) run
takes about 30 minutes on IBM Power 6 (P6) machine using 24 nodes with 7
tasks (7 processors) per node.
Operationally available Numerical Weather Prediction (NWP)
Products
IMD also
makes use of NWP global model forecast products of other
operational centres, like NCMRWF T-254, ECMWF, JMA, NCEP and UKMO
to meet the operational requirements of day to day weather forecasts in the
short to medium range time scale. Under a joint collaborative research project
IMD has been receiving global model outputs (in the GRIB format) of ECMWF
and JMA. The outputs (GRIB) of NCEP GFS are available freely from the
Internet. The model outputs of these models are post processed using GRIB
decoder and various graphics products are generated operationally in the real
time mode.
These NWP products are disseminated to the operational
forecasters at various IMD Forecast Centres/Offices through a ftp
connectivity. IMD receives NCMRWF T-254 and UKMO model outputs online
from NCMRWF, Noida
Operational techniques for application of NWP products.
IMD implemented a Multi-model Ensemble (MME)
based district level
quantitative forecasts in the operational mode since 1 June 2008, as required for
the Integrated Agro-advisory Service of India. The forecasts prepared daily are
also made available in the IMD web site (www.imd.ernet.in or www.imd.gov.in).
Five NWP models considered for this development work are: (i) National Centre
for Medium Range Weather Forecasting (NCMRWF) T-254, (ii) ECMWF T799,
8
(iii) JMA T899, (iv) UKMO and (v) NCEP GFS T-254.
As the model outputs
available are at different resolutions, in the first step,
model outputs of the
constituent models are interpolated at the uniform grid resolution of 0.25 oX0.25o
lat/long.
In the second step, the weight for each model at each grid is
determined objectively by computing the correlation co-efficient between the
predicted rainfall and observed rainfall. High resolution gridded rain-gauge data
produced operationally at National Centre of IMD Pune are used for development
and validation of the forecasts. The weight (W i,j,k) for each member model (k)
at each grid (i,j) is obtained from the following equation:
Wi , j , k , =
C i , j ,k
5
C
k 1
,
i = 1, 2, ….., 161; j=1,2,....,161
……… (1)
i , j ,k
Ci,j,k = Correlation co-efficient between rainfall analysis and forecast rainfall for
the grid (i,j) of model (k). For the computational consistency, C i,j,k is taken as
0.0001 in case Ci,j,k is less than or equal to 0.
The ensemble forecasts (day 1 to day 5 forecasts) are generated at the
0.25ox0.25o resolution. The ensemble forecast fields are then used to generate
district level forecasts by taking average value of all grid points falling in a
particular district.
4.2.3 Research Performed in this field
Performance skill of forecasts by these models in short to medium range time
scale during summer monsoon 2009 will be presented in the AMR meeting of
2010. Some of the important verification results are given below:

Various need based R & D activities such as, model validation, impact of
new conconventional data and model resolution, various diagnostic
9
studies, customization of NWP outputs for operational forecast are being
carried out. Recently two significant outcomes of these R & D activities
are:

A multi-model ensemble technique has been developed for five days
weather forecasts making use of state of the art global model outputs.
The method is made operational from 1 June 2008 for district level
Integrated Agro-advisory services.

Development and implementation of a Statistical Dynamical method for
cyclone genesis and intensity prediction
4.3 Short Range forecasting system (0 – 72 hours)
4.3.2 Model
NWP Models operational at IMD New Delhi are:

The Limited Area Model (LAM) forecast is being produced regularly in
respect of 00 UTC and 12 UTC observations for day-to-day operational
use. The operational forecasting system known as Limited Area Forecast
System (LAFS), is a complete system consisting of data decoding and
quality control procedures, 3-D multivariate optimum interpolation scheme
for objective analysis and a semi-implicit semi-Lagrangian multi-layer
primitive equation model. The horizontal resolution of the model is 0.75
ox0.75 olat.

/long. with 16 sigma levels in the vertical.
The Quasi-lagrangian Model (QLM) model is
run to produce track
forecasts based on the initial conditions of each day based on 00 UTC and
12 UTC observations when the disturbance is in cyclonic storm stage. The
QLM is a multilevel fine-mesh primitive equation model with a horizontal
resolution of 40 km and 16 sigma levels in the vertical. The integration
domain consists of 111x111 grid points in a 4440x4440 km 2 domain that
is centred on the initial position of the cyclone. Very recently, model has
been updated (from 36 to 72 hours) to get six hourly track forecasts valid
up to 72 hours.
10

The non hydrostatic mesoscale model MM5 is run at the resolution of 45
km daily with
00 UTC initial and boundary condition of NCEP GFS
(National Centre for Environmental Prediction, USA; Global Forecast
System).

A multi-model ensemble technique has been developed for five days
weather forecasts making use of state of the art global model outputs.
The method is made operational from 1 June 2008 for district level
Integrated Agro-advisory services.

The mesoscale model WRF has been implemented with the assimilation
of local observations.

The storm scale model ARPS (Advanced Regional Prediction System)
has been experimented at the horizontal resolution of 9 km with the
assimilation of Doppler Weather observations.

Development and implementation of a Statistical Dynamical method for
cyclone genesis and intensity prediction

For Storm Surge Prediction, Dynamical Storm Surge model of IIT Delhi
has been made operational.
Graphics product of all these models are available in the IMD web site.
Meso-Scale Assimilation System (WRF-VAR)
Recently, the regional mesoscale analysis system WRF-Var is installed on
High HPCS at Head Quarter, IMD, Delhi with its all components i.e.
preprocessing programs (WPS and REAL), observation assimilation program
(WRF-Var), boundary condition updation (update_bc) and forecasting model
(WRF).
The pre-processed observational data from GTS and other sources
prepared for the Global Forecast System in the BURF format (PREPBUFR of
step 2 in GFS) is also used in case of WRF assimilation.
11
In the WRF-Var assimilation system, all conventional observations over a
domain (200S to 450N; 400E to 1150E) which merely cover RSMC, Delhi region
are considered to improve the first guess of GFS analysis. Assimilation is done
with 27 km horizontal resolution and 38 vertical eta levels. The boundary
conditions from GFS forecasts run at IMD are updated to get a consistency with
improved mesoscale analysis. WRF model is then integrated for 75 hours with a
nested configuration (27 km mother and 9 km child domain) and with full physics
(including cloud microphysics, cumulus, planetary boundary layer and surface
layer parameterization). The post-processing programs ARWpost and WPP are
also installed on HPCS to generate graphical plots and grib2 out for MFISYNERGIE system respectively.
Outer and inner domain of WRF model at 27 km and 9 km
12
4.3.3 Operationally available NWP Products
4.3.4 Operational Techniques for application of NWP Products
Various stages of cyclone forecasting are: (a) Genesis, (b) Track, (c)
Intensity and (d) Decay after landfall.
During 2008-09, IMD used an objective
numerical method for the operational cyclone forecasting work. The method
comprises of four forecast components, namely (a) cyclone genesis potential
parameter (GPP), (b) Multi-model Ensemble (MME) technique for track
prediction, (c) cyclone intensity prediction (SCIP) model and (d) predicting
decaying intensity after the landfall.
Genesis Potential Parameter (GPP)
Genesis Potential Parameter (GPP) is defined as:
GPP =

850
xMxI
S
=0
if 850 > 0, M > 0 and I > 0
if 850 ≤ 0, M ≤ 0 or I ≤ 0
Where , 850 = Low level relative vorticity (at 850 hPa) in 10-5 s-1
S = Vertical wind shear between 200 and 850 hPa (knots)
= Middle troposphere relative humidity
Where, RH is the mean relative humidity between 700 and 500 hPa
I = (T850 – T500) °C = Middle-tropospheric instability (Temperature difference
between 850 hPa and 500 hPa). All the variables are estimated by averaging of
all grid points over an area of radius 2.5o around the centre of cyclonic systems
using model analysis field.
GPP values for developing and non-developing systems are shown in Table 1.
Table 1. Genesis potential parameter (GPP) for Developing Systems and NonDeveloping Systems.
13
GPP (x10-5) 
T.No. 
1.0
1.5
2.0
2.5
3.0
Developing
11.1
12.3
13.3
13.5
13.6
Non-Developing
3.4
4.2
4.6
2.7
-
Various thermo-dynamical parameters, which are used for real time
analyzing Genesis Potential Parameter (GPP) for cyclonic storms over the Bay of
Bengal during 2008-2009, are derived from the operational model analysis of the
limited area model (LAM) of India Meteorological Department (IMD), New Delhi.
Track : Multimodel Ensemble (MME) Technique
A multimodel ensemble (MME) technique is developed using cyclone
data of 2008. The technique is based on a linear statistical model. The predictors
(shown in Table 2) selected for the ensemble technique are forecasts latitude
and longitude position at 12-hour interval up to 72-hour of five operational
models. In the MME forecasts, model-forecast latitude position and longitude
position of the member models are linearly regressed against the observed
latitude position and longitude position respectively for each forecast time at 12hours intervals for the forecast up to 72-hour. Multiple linear regression technique
is used to generate weights (regression coefficients) for each model for each
forecast hour (12hr, 24hr, 36 hr, 48hr, 60hr, 72hr). These coefficients are then
used as weights for ensemble forecasts.
12-hourly forecast latitude (LATf) and longitude (LONf) positions by multiple linear
regression technique is defined as:
LATft = ao+ a1ECMWFtlat + a2NCEP tlat +a3JMAtlat + a4MM5tlat + a5QLMtlat
LONft = a’o+ a’1ECMWFtlon + a’2NCEPtlon +a’3JMAtlon + a’4MM5tlon +
a’5QLMtlon
14
for t = forecast hour 12, 24, 36, 48, 60 and 72
The dependent variable latitude (LATf) in °N and longitude (LONf) in °E.
The detailed of model predictors are given in Table 3.
Table 3. Model Parameters
S.No. Member models
1.
2.
3.
4.
5.
European Centre for MediumRange Weather Forecasts
(ECMWF),
GFS of National Centers for
Environmental Prediction (NCEP)
Japan Meteorological Agency
(JMA)
MM5 Model
Quasi-Langrangian model (QLM)
Symbol of Predictors
Latitude
Longitude
position
position
lat
ECMWF
ECMWFlon
NCEPlat
NCEPlon
JMAlat
JMAlon
MM5lat
QLMlat
MM5lon
QLMlon
Intensity prediction
A Statistical Cyclone Intensity Prediction (SCIP) model for the Bay of Bengal for
predicting 12 hourly cyclone intensity (up to 72 hours), applying multiple linear
regression technique using various dynamical and physical parameters as
predictors. The model equation is given as:
dvt = ao+ a1 IC12 + a2 SMS +a3 VWS+ a4 D200+ a5 V850+a6 ISL+ a7 SST+ a8 ISI
for t= forecast hour 12, 24, 36, 48, 60 and 72
dvt = Intensity change during the time interval t
The detailed of model predictors are given in Table 4.
15
Table 4 Model parameters
S.No.
1.
2.
3.
4.
5.
6.
7.
8.
Predictors
Symbol
Predictors
Intensity change during last 12
IC12
hours
Vorticity at 850 hPa
V850
Storm motion speed
SMS
Divergence at 200 hPa
D200
Initial Storm intensity
ISI
Initial Storm latitude position
ISL
Sea surface temperature
SST
Vertical wind shear
VWS
of
Unit
Knots
x 105 s-1
ms-1
x105 s-1
Knots
°N
°C
Knots
Decay of intensity after the Landfall
The forecast of inland wind after the landfall of a cyclone is of great
concern to disaster management agencies. To address this problem, an
empirical model for predicting 6-hourly maximum sustained surface winds
(intensity) was developed. The maximum sustained surface wind speed (MSSW)
after the landfall at time t is given by:
Vt+6 = Vb+(Vt-Vb)*R1, for t=0
= Vb+(Vt-Vb)*R2, for t=6,12,18 and 24
Where, reduction factors
R1 = exp(-a1*6.0)
and, R2 = exp(-a2*6.0)
Decay constant a1 for the first six hours after the landfall (for t= 0 to 6) is given
by:
a1 = [ln {(Vo –Vb)/(V6-Vb))}]/6
The decay constant a2 for the remaining 12 hours (for t= 6 to 18 hours) is taken
as:
a2 = [ln {(V6 –Vb)/(V18-Vb))}]/12
16
Regression equation relating R1 and R2 as given below:
R2
= 0.982*R1 –0.081
Where, V0 is the maximum sustained surface wind speed at the time of
landfall, Vt is the wind speed at time t after the landfall and Vb is the background
wind speed. After landfall, tropical cyclone decays to some background wind
speed.
4.4 Nowcasting and very short range forecasting systems (0-6
hours)
For nowcasting purposes, application software called “Warning Decision Support
System Integrated Information (WDSS-II)”, developed by National Severe Storm Lab,
USA has been used in experimental mode. For mesoscale forecasting, radar data has
been assimilated into the ARPS mesoscale model. With the ingesting of Indian DWR
observations, the
application software
is
capable of detecting and removing
anomalous propagation echoes. The application software could successfully track storm
cells and meso-cyclones through successive scans. Radar reflectivity mosaics are
created for the recent November 2009 Bay of Bengal cyclone “Khaimuk” using
observations from three DWR stations namely, Visakhapatnam, Machilipatnam and
Chennai. Positive impact of the radar observations in a very high resolution NWP model
(ARPS) have been demonstrated for land falling cyclones.
Figure : displays a sequence of mosaic images of the tropical cyclone Khaimukh of 14
November 2008, which was tracked by the three radars at Chennai, Machhilipatnam and
Visakhapatnam.
17
arps_ref
arps_vel
A
A
c
B
c
B
(b)
(a)
arps_both
arps_con
A
A
c
c
B
B
(c)
(d)
A
Fig. 8 (a-e
Fig.: Inter-comparison of reflectivity fields of various
simulation experiments against the observed field valid
c
(e
e)
B
)
at 0600 UTC of 27 November 2008 for Bay of Bengal
Cyclone Nisha:
(a) Reflectivity by arps_ref experiment
(b) Reflectivity by arps_vel experiment
(e)
(c) Reflectivity by arps_both experiment
(d) Reflectivity by arps_con experiment
(e) Observed field from the radar station
18
4.5 Specialized numerical prediction ( sea wave, storm surge etc.)
For the storm surge prediction IMD has been using a dynamical model
developed by Indian Institute of Technology, Delhi. IMD also uses
nomogrrams in conjunction with the dynamical model for storm surge
prediction.
4.6 Extended range Forecast (10-30 days) (Model, ensemble,
Methodology)
Extended range forecast products generated from NCEP (CFS) and ECMWF
(Ensemble) are currently used for extended range forecasts over the Indian
region
4.7 Long range forecast
IMD uses a statistical model for the long range forecasting of Indian
monsoon.
Dynamical
model
outputs
meteorological institutes are also
generated
from
other
national
used in conjunction with the statistical
model for the long range forecast of Indian monsoon.
5. Verification of prognostic products
Before one uses outputs of a NWP model in preparation of final operational
forecast, adequate knowledge on the performance skill of the model is a prerequisite. Towards this direction, continues efforts are being made by the
NWP group of IMD to document performance skill of operational NWP
models. These documents would contribute in updating knowledge of
operational forecaster for judicious use of
NWP products for delivering
improved operational forecasts.
19
For example, track forecast error (km) of the multi-model ensemble forecast and
member models during the year 2009 is given below.
HOUR ECMWF
12 hr
72
24 hr
111
36 hr
114
48 hr
93
60 hr
168
72 hr
217
GFS
83
191
193
117
126
151
JMA
86
167
142
86
85
152
MM5
153
234
320
246
351
415
QLM
77
124
143
242
447
577
MME
70
90
147
199
242
293
The State-wise performance of district level rainfall forecasts for some selective
states for day 5 forecasts has been presented in a Fig. given below.
DAY-5 :MON-2009
ORISSA
1
RAJASTHAN
MAHARASTRA
0.9
KERALA
0.8
GUJARAT
0.7
MADHYA PRADESH
POD
0.6
0.5
0.4
0.3
0.2
0.1
0
NO RAIN
LIGHT RAIN
MOD RAIN
HEAVY RAIN
6. Plans for the future
Under the modernization programme, IMD is in the process of commissioning a
state of
the art High Performance Computing (HPC)
system with a peak
performance of 15 TF at IMD HQ., 1 TF at IMD Pune along with high end servers
of 100 GF capacities to each in major meteorological centers viz. Delhi, Mumbai,
20
Chennai, Nagpur, Kolkata, Guwahati, Ahmedabad, Bangalore, Bhubaneswar,
Chandigarh, Hyderabad and Pune for global and regional NWP modeling,
particularly for the regional database management, mesoscale data assimilation
and high resolution local area model. From the ongoing modernization programme
of IMD. observations (both conventional and non conventional) are expected to
be available on the mesoscale both in space and time by means of Doppler
Weather Radar (DWR), Satellites (INSAT Radiance), Wind Profilers, meso-network
(Automatic Weather Stations), buoys and aircrafts in the real time mode with the
use of advanced telecommunication system.
In view of growing operational requirements from various user agencies,
there is a need for a seamless forecasting system covering now-casting to medium
range user specific forecasts. There is also need for the improved extended range
and long range forecasts, particularly for the agricultural requirements.
Future
Weather Forecasting System of IMD would be as briefly given below:
(a) Now-casting and Mesoscale Forecasting System (valid for half hour to 24
hours)

Processing of Doppler Weather Radar (DWR) observations at a central
location (NHAC) to generate 3 D mosaic and other graphics products for
nowcasting applications.

Enhancing mesoscale forecasting capability of local severe weather by
providing 3 hourly area specific rainfall and wind forecasts (up to 24
hours) at the resolution of 3 km from ARPS (Advanced Regional
Prediction System) with the assimilation (hourly intermediate cycle) of
DWR, AWS, Wind profilers and other conventional and non-conventional
observations.

UKMO based nowcast system
Nowcast and mesoscale forcast system would be expanded for major cities of
India
21
(b) Regional Models for Short Range Forecasting System ( valid up to 3
days)

72 hours forecasts from WRF model with 3 nested domains (at the
resolution of 27 km, 9 km and 3 km). The nested model at the 3 km
resolution would be operated at the Regional/State Met Centres at 6 hours
interval with 3 DVAR data assimilation.

For Cyclone Track Prediction, 72 hours forecast from Quasi Lagrangian
Model (QLM) at 40 km resolution at six hours interval; WRF (NMM) at 27
km resolution with assimilation package of Grid Statistical Interpolation
(GSI).

For Cyclone
track and intensity prediction:
multimodel ensemble
technique and application of dynamical statistical approach for 72 hours
forecasts, forecast would be updated at 12 hours interval.

Development of multimodel ensemble technique for probabilistic forecasts
of district level heavy rainfall events.
(c) Global model for Medium range Forecasting (valid up to 7 days)

Global Data Assimilation System (GDAS), six hourly cycle
with GSI (Grid Statistical Interpolation).

Global Forecast System (GFS) T-382

Global Ensemble Prediction System
(d) Extended range forecast for rainfall and temperature

To implement a statistical dynamical model
22
7. References
Roy Bhowmik S.K. and Durai V.R., 2010, Application of multi-model ensemble
technique for real-time district level forecasts over Indian region in short range
time scale, Meteorl. Atmos. Phy., 106, 19-35
Kotal, S.D., Roy Bhowmik S.K. and Mukhopadhaya, B, 2010, Real-time
forecasting of Bay of Bengal Cyclonic Storm Rashmi of October 2008 – A
statistical dynamical approach, Mausam, 61, 1-10
Sen Roy Soma, Roy Bhowmik, SK, Lakshmanan, V, and . Thampi S.B., 2010,
Doppler Radar-based Nowcasting of the Bay of Bengal Cyclone – Ogni of
October 2006, J., Earth SCI. Sys (to appear)
Srivastava Kuldeep, Roy Bhowmik, S.K., Sen Roy, S., Thampi S.B. and .
Reddy Y.K., 2010, Simulation of high impact convective events over Indian
region by ARPS model with assimilation of Doppler Weather Radar radial velocity
and reflectivity , Atmosfera, 23, 53-74
Roy Bhowmik S.K.,Shakar Nath, Mitra, A and Hatwar, H.R., 2009, Application of
neural network technique to improve the location specific forecast of Delhi from
MM5 Model, MAUSAM, 60, 11-24
Srivastava Kuldeep, Roy Bhowmik S. K., Hatwar H.R., 2009, Evaluation of
different Convective schemes on simulation of thunderstorm event over Delhi by
ARPS Model, Mausam, 60(2), 123-136
Kotal, S.D., Kundu P.K. and Roy Bhowmik S.K. , 2009, An analysis of cyclogenesis parameter for developing and non-developing low pressure systems
over the Indian Sea, Natural Hazards, 50,389-402
Roy Bhowmik, S.K. and Prasad K, 2008 Improving IMD operational limited area
model forecasts , Geofizika, 25(2), 87-108
Roy Bhowmik S.K. and V.R. Durai, 2008, Multi-model Ensemble Forecasting of
rainfall over Indian monsoon region, Atmosfera , 21(3), 225-239
Kotal, S.D., Roy Bhowmik S.K.. P.K. Kundu and Das Ananda K., 2008, A
Statistical Model for Cyclone Intensity Prediction, Earth Sc. System, 117(2),
157-168
23
Kotal, S.D., Roy Bhowmik S.K. and Kundu Prabir, 2008,“Application of Statistical
- Dynamical scheme for real time forecasting of the Bay of Bengal very severe
cyclonic storm SIDR of November 2007, Geofizika, 25 (2), 139-158
Srivastava Kuldip, Roy Bhowmik, S.K., Hatwar, H.R. Ananda K. Das and Kumar
Awadsesh, 2008, “ Simulation of mesoscale structure of thunderstorm using
ARPS model, Mausam, 59 (1), 1-14
Roy Bhowmik S.K., Joardar, D. and Hatwar H.R. 2007, An evaluation of
precipitation prediction skill of IMD operational NWP system, Meteorl Atmos
Phy, 95(3) ,205-221
Roy Bhowmik S.K. and Das Ananda K., 2007, Rainfall Analysis for Indian
monsoon region from merged dense raingauge observations and satellite
estimates – Evaluation of monsoon rainfall features, Journal of Earth System
Science System, 116(3), 187-198
Roy Bhowmik, S.K., Joardar, D, Das, Ananda .K., Rama Rao Y.V and Hatwar
H.R., 2006, Impact of KALPANA-1 CMV data in the analysis and forecast of IMD
operational NWP system, Mausam, 57, 319-331
Lal, B., Singh, O.P., Prasad, O., Roy Bhowmik, S.K., Kalsi, S.R. and
Subramanian, S.K., 2006, District Level value added dynamical Ensemble
Forecast, Mausam,57, 209-220
7.2 Meteorological Monograph/Science Reports
Kotal S.D., Roy Bhowmik S.K. and Mukhopadhaya B., 2009, Performance of
IMD NWP based Objective Cyclone Forecast System during 2008-2009, IMD
Met Monograph No. Cyclone Warning 4/2009
Roy Bhowmik S.K., Durai, V.R., Das Ananda K and Mukhopadhaya B., 2009,
Performance of IMD Multi-model ensemble based district level forecast system
during summer monsoon 2008, IMD Met Monograph No. 8/2009
Roy Bhowmik S.K., Durai, V.R., Das Ananda K and Mukhopadhaya B., 2009,
Evaluation of Prediction skill of ECMWF forecasts over Indian monsoon region in
medium tange time scale during summer monsoon 2008, IMD Met Monograph
No. 7/2009
24
Roy Bhowmik S.K. and Hatwar H.R., 2008, Performance of operational NWP
short-range forecast - Monsoon 2007 Report, p. 78-92, IMD Met Monograph
No. Synoptic Meteorology 6/2008.
Kalsi et al. (Roy Bhowmik as co-author), 2007, Probable maximum storm
surge heights for the maritime districts of India, IMD. Met Monographh No.
Synoptic Meteorology No. 5/2007
25
(B) Contribution of National Centre for Medium Range Weather
Forecasting (NCMRWF), Noida (UP), India
1.
Summary of Highlights :
The operational forecast suite at NCMRWF is the Global Forecast System
(GFS, adapted from NCEP, USA) forecasting suite at T254L64 resolution
The Grid point Statistical Interpolation (GSI) Analysis Scheme has been made
operational in the GFS from 1 Jan 2009.
2.
Equipment used in the centre :
Cray X1E (64 Processor 1.1TF
PARAM PADMA (IBM-p5) (64 proc 0.5TF)
LAN (1000mbps backbone)
Internet Leased Line (8 mbps)
3.
Data and products in use
from GTS :
Surface observations:
METAR(~10,000),
SYNOP ( ~40,000), SHIP(~2500),
BUOY (~30,000)
Upper-air observations:
TEMP(~1230) , PILOT(~300),
Wind profiler (BUFR) (~90)
Air-craft observations:
AIREP, AMDAR, ACARS(BUFR) (total ~10,000)
Satellite winds (in BUFR): METEOSAT -7, 9, GMS (total ~2,50,000)
from NESDIS site (through ftp)
Satellite Radiance :
AMSU-A/B, HIRS, MHS – from NOAA-17/18, METEOP
QSCAT winds, GOES winds (BUFR)
from NCEP site ((through ftp) : Analysed SST , snow etc.
4.
4.1
Forecasting System :
System run schedule and forecast ranges:
The GFS forecasting suite at T254L64 resolution runs up to 7 days based
on 0000UTC initial condition of every day. (Starts at 0500 UTC everyday, takes
about 4hours for the whole suite including data assimilation)
26
WRF model (Nested 27 km resolution, 38 levels) runs everyday based on
0000UTC for 72 hours. (Starts at 1000 UTC everyday, takes about 2 hours for
the whole suite, including high resolution runs at 9 km resolution for select
locations )
Medium range forecasting system :
4. 2.1 Data Assimilation :
4.2.1.1 In Operation: Six hourly intermittent 3D-VAR assimilation system
based on Grid point Statistical Interpolation (GSI) Analysis Scheme, along
with GFS (T254L64)
4.2.1.1 Research performed: Observation impact studies using various
conventional as well as non-conventional observations
4.2.2. Model :
4.2.2.1 In Operation: GFS at T254L64 resolution
4.2.2.2 Research Performed: Sensitivity experiments with different
physical parameterisation scheme
4.2.3
Operationally available NWP products
Analysed and predicted (up to 168 hr) wind, geopotential height, at
various atmospheric levels and precipitation based on every day
0000 UTC initial condition are available on the web in real time. In
addition to these fields, various other anaylsed and predicted fields
are provided to the users as per specific demands.
Location specific predictions for surface winds, maximum, minimum
temperature, cloudiness, humidity, mean sea level pressure, rainfall
etc. for 70 major cities of India.
4.2.4
Operational techniques for application of NWP products
4.2.4.1 In operation : PPM
4.2.4.2 Research : KF
4.2.5
Ensemble Prediction
4.2.5.1 In operation: Multi-model Ensemble (MME) forecasts of rainfall
during the monsoon season using predicted rainfall from four
models ( viz. NCMRWF, NCEP, UKMO and JMA)
27
4.2.5.2
Research: Experimental Ensemble Prediction System (EPS)
with 8 members of T80L18 model, with perturbed initial state using
breeding method
4.2.5.3
Operationally available EPS products : NIL
4.3 Short-range forecasting system (0-72hr)
4.3.1 Data Assimilation method
4.3.1.1 In operation : WRF-3DVAR
4.3.1.2 Research: Assimilation of Indian Radar observations, Impact
of background error covariance etc.
4.3.2 Model
4.3.2.1
In operation : WRF-ARW at 27km resolution with 38
vertical levels over Indian region
4.3.2.2
Research Preformed : Very high resolution (9km and 3km)
nested WRF model integrations for exclusive case studies of
severe weather, such as tropical cyclone ,very intense rainfall
etc.
4.3.3 Operationally available NWP products
Analysed and predicted (up to 72 hrs) wind, geopotential height at
various atmospheric levels and precipitation based on every day
0000 UTC initial condition are available on the web in real time. In
addition to these fields, various other anaylsed and predicted fields
are provided to the users as per specific demands.
4.3.4 Operational techniques : NIL
4.3.5 EPS : NIL
4.4 Nowcasting : NIL
4.5 Specialised NWP :
4.5.2 Specific Models :
4.5.2.1 In Operation: Wave model: WAVEWATCH -III Model (Version
2.22) The model is run for 00z cycle only, and starts with a 6-hr
hindcast to assure continuity of swell. Spatial resolution is 10 x 10
longitude-latitude grid extending from 77.50S to 77.50N.
4.6
Extended Range :
28
4.6.1 In operation: The real-time seasonal prediction for monsoon rainfall is
being carried out at NCMRWF using a two-tier approach. In this approach, the
predicted Sea Surface Temperatures (SST) from NCEP CFS are provided as
input to the global atmospheric model.
4.7
Long range :
4.7.1 In operation: The real-time seasonal prediction for monsoon rainfall is
being carried out at NCMRWF using a two-tier approach. In this approach, the
predicted Sea Surface Temperatures (SST) are provided as input to the global
atmospheric model. These SST data are obtained from IRI, USA and these
contain multi-model ensemble monthly SST predictions and an uncertainty
factor. Three SST scenarios are prepared based on these datasets and are
used as input to the model. Several ensemble integration runs for the
monsoon season are made and a probabilistic prediction is prepared by taking
into account the bias of the model. First set of predictions are made in midApril and predictions are updated in mid-May.
5.
Verification
Objective verification scores against the analysis and observations are computed
every day valid for 00UTC at standard pressure levels for different areas as
recommended by the CBS, WMO. Monthly averages are then computed from the
daily values of all forecasts verifying within the relevant month. The scores are
shared with other operational NWP centres.
6.
Plans for future :
6.1.1 Next year
It is proposed to increase the resolution of the current operational GFS to
T382L64 and assimilate the Indian satellite observations (INSAT-3d) and
OCEANSAT)
6.1.2 Next four years
It is planned to implement the U. K. Met Office, Unified Model and 4-D Var
assimilation system and make it operational by next year. A coupled oceanatmosphere assimilation-forecast system will also be implemented for
development of unified prediction suite for different space-time scales (up to a
season), with emphasis on predictions from days to weeks in advance initially.
29