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S.K. Roy Bhowmik NWP, IMD, New Delhi Regional NWP Modelling at IMD Overview Assimilation of Doppler Weather RADAR (DWR) Observation Processing for Nowcasting Applications Ingest into assimilation cycle of NWP models Parameters: radial wind, reflectivity and spectrum width

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s k roy bhowmik nwp imd new delhi
S.K. Roy Bhowmik

NWP, IMD, New Delhi

Regional NWP Modelling at IMD

slide3

Assimilation of Doppler Weather RADAR (DWR) Observation

  • Processing for Nowcasting Applications
  • Ingest into assimilation cycle of NWP models

Parameters: radial wind, reflectivity and spectrum width

DWR Stations: Chennai, Machalipatnam, Vishakapatnam and Kolkata, Sriharikota (ISRO)

slide4

NHEC (Telecom server,IMD)

DWR Net-work for data processing

NCMRWF

slide5

A RADAR mosaic creation from reflectivity observations

DWR Chennai and Machhilipatnam of 28 September 2005

slide6

Nov’08 Ccyclone “Khai Muk”

A RADAR mosaic creation from reflectivity observations

Well marked on 13 Nov 2008 over south Bay of Bengal, concentrated into a depression in the evening. Moved in a northwesterly direction, intensified into a intensified into a cyclonic storm, “Khai Muk” It reached its maximum intensity near near lat. 14.5° N and long. 83.0° E around 0230 hours IST of 15th with estimated sustained maximum wind speed of 40 knots and estimated central pressure of 994 hpa.

slide7

Example of the 30 minute rainfall (mm) estimates for the rainstorm of 2 September 2005 from a single DWR at Chennai and the corresponding automatic rain gauges used to validate the data.

slide9

Numerical experiments for assimilation of DWR (radial wind and reflectivity) data of Chennai with ARPS model for cycloneOgni of October 2006

Cyclone Ogni of Oct’06

slide10

Simulation of Bay Cyclone Ogni of October 2006

Impact of DWR Chennai data in the

ARPS Analysis and forecast

9-km assimilation:

GFS model provide background and boundary conditions

0000Z

30 OCT 06

0000Z

29 OCT 06

0030Z

0230Z

0300Z

0130Z

0200Z

0100Z

forecast

forecast

forecast

forecast

forecast

forecast

Forecast

(21 hrs)

ADAS

ADAS

ADAS

ADAS

ADAS

ADAS

ADAS

IMDS.20061029.0004

IMDS.20061029.0304

Background and boundary values from GFS model into the ARPS grid. The Diagram is showing ½ hourly assimilation cycle ( first 3 hours) & then 21 hours ARPS Model forecast -

slide11

Contd.

88D2ARPS -

  • Doppler Weather Radar data in up8 format has been collected from IMD Chennai.
  • DWR data in up8 format has been converted in to netcdf format.
  • 88D2ARPS has been used for remapping IMD Chennai radar data ( Radial Wind &

Reflectivity) to a Cartesian grid .

  • For three hourly Data assimilation , half hourly Radar data file containing both reflectivity & radial velocity, starting from 00 UTC, has been generated. Ex -

Name of file Time duration of Data

(1) IMDS.20061029.0004 23:46 UTC 28-10- 2006 TO 00:15 UTC 29-10-2006

(2) IMDS.20061029.0034 00:16 UTC 29-10- 2006 TO 00:45 UTC 29-10-2006

(3) IMDS.20061029.0104 00:46 UTC 29-10- 2006 TO 01:15 UTC 29-10-2006

(4) IMDS.20061029.0134 01:16 UTC 29-10- 2006 TO 01:45 UTC 29-10-2006

(5) IMDS.20061029.0204 01:46 UTC 29-10- 2006 TO 02:15 UTC 29-10-2006

(6) IMDS.20061029.0234 02:16 UTC 29-10- 2006 TO 02:45 UTC 29-10-2006

(7) IMDS.20061029.0304 02:46 UTC 29-10- 2006 TO 03:15 UTC 29-10-2006

experiments with wrf var assimilation system
Experiments with WRF-Var Assimilation System
  • Model: WRF-ARW Model
  • Assimilation: 3DVAR
  • Data:
    • Observation (Synop, Temp, Pilot, Buoy, Ship and CMVs)
    • First Guess and Boundary NCEP GFS
    • Resolution (30 km / L51)

Bay Cyclone Rashmi of October 2008

forecast vorticity 10 5 s 1
Forecast Vorticity (10-5 s-1 )

24 hour forecast valid at 00 UTC of 25-10-2008

WRF-VAR (var) experiment

48 hour forecast valid at 00 UTC of 26-10-2008

meridional cross section of vertical velocity cms 1
Meridional Cross Section of Vertical Velocity (cms-1 )

24 hour forecast valid at 00 UTC of 25-10-2008

WRF-VAR (var) experiment

No observation (cntl) experiment

48 hour forecast valid at 00 UTC of 26-10-2008

slide24

Performance of operational NWP models for Cyclone Track Prediction

A depression over the SE Bay of Bengal at 0300 UTC of 27th April 200812.00 N and long. 87.00 E., intensified into a cyclonic storm and lay at 0000 UTC of 28th , a severe cyclonic storm at 0900 UTC of 28th and into a very severe cyclonic storm at 0300 UTC of 29th. It moved in easterly direction while intensifying further and crossed southwest coast of Myanmar between 1200 to 1400 UTC of 2nd May near lat. 16.00 N

VSCS Nargis of April 08

slide25

ECMWF

MM5

QLM

72 hours Forecast

Initial Condition 29 April 00 UTC

UKMO

WRF

slide26

MM5

ECMWF

QLM

WRF

48 Hrs Forecast

Initial condition 30 April 00UTC

UKMO

slide27

QLM

ECMWF

MM5

UKMO

WRF

24 hours forecast

Initial Condition 1 May 00 UTC

QLM with initial condition 2 May 00 UTC

slide31

Cyclone Genesis Parameter

  • Two Dynamical variables
  • Low level relative vorticity (850)
  • Vertical wind shear (S)
  • Two Thermo dynamical variables
  • (i) Middle troposphere relative humidity (M)
  • (ii) Middle-trpospheric instability (I)

Mausam (2003), Nat. Hazards (2008)

slide32

GPP =  850*M*I/S if  850 > 0, M > 0 and I > 0

= 0 if  850≤ 0, M ≤ 0 or I ≤ 0

Where ,

 850= Low level relative vorticity (at 850 hPa) in 10-5 s-1

S = Magnitude of Vertical wind shear between 200 and 850 hPa (ms-1)

[ RH - 40 ]

M = -------------- = Middle tropospheric

30 relative humidity

Where RH is the mean relative humidity between 700 and 500 hPa

I = (T850 – T500) °C = Middle-trpospheric instability (Temperature difference between 850 hPa and 500 hPa)

slide33

VSCS SIDR of Nov 2007

Comparison of composite Genesis potential parameter (GPPx10-5) and Genesis potential parameter of Very Severe Cyclonic Storm (SIDR) over the Bay of Bengal of 11-15 November 2007. (T=6.0).

slide35

Comparison of composite Genesis potential parameter (GPPx10-5) and Genesis potential parameter of Cyclonic Storm over the Bay of Bengal of 15-19 October 2000. (T=2.5).

The initial low-pressure system formed over Central Bay of Bengal and intensified into depression (T.No. 1.5) on 0000 UTC of 15 October. The system persisted over the Bay of Bengal for more than four days and traveled more than 700 km (14.5/88.5 to 14.5/82.0), but maximum intensity never exceeded T.No. 2.5. Finally it dissipated over the Sea.

slide36

Statistical Tropical Cyclone Intensity Prediction (SCIP) Model

62 sample cases of Tropical Cyclones (TCs) those formed over the Bay of Bengal during the period 1981 to 2000. Fifteen independent cyclones were used to test the model those formed over the Bay of Bengal during the period 2000 to 2007.

The predictors:

(a) Persistence:

(i) Initial storm intensity (ISI)

(ii) Previous 12 hours change in the intensity (IC12)

(b) Thermodynamical factors :

(i) Storm motion speed (SMS)

(ii) Sea surface temperature (SST)

(c) Dynamical factors :

(i) Initial storm latitude position (ISL)

(ii) Vertical wind shear (850-200) hPa averaged along storm track (VWS)

(iii) Vorticity at 850 hPa (V850)

(iv) Divergence at 200 hPa (D200)

Natural Hazards (2007) ; J. Earth Sys. Sci. (2008), Geofizika (2008)

slide37

Formulation of the model:

The model is developed using multiple linear regression technique

y = ao+ a1x1+ a2x2+ ……….+ anxn

Where y is the dependent variable (predictant) and x1, x2, …...…. xn are independent variables (predictors). The regression coefficients a1, a2, …...…. an are determined using a large data set (62 cyclones).

The SCIP model estimates changes of intensity at 12, 24, 36, 48, 60 and 72 hours. Six separate regression analyses are carried out for forecast interval 12, 24, 36, 48, 60 and 72 hour.

12 hours intensity change by multiple linear regression technique is defined as:

dvt = ao+ a1 IC12+ a2SMS+a3VWS+ a4D200+ a5V850+a6ISL+ a7SST+ a8ISI

for t = forecast hour 12, 24, 36, 48, 60 and 72

slide39

Twelve-hourly Intensity Prediction up to 36 hours for cyclone SIDR Nov 2007

Based on 14/00 UTC

Based on 15/00 UTC

slide41

Performance of the model:

For dependent sample of 62 cyclones (1981-2000):

For independent sample of 15 cyclones (2000-2007):

slide42

IMD Multimodel Ensemble Technique

Generation of Multi-Analysis Weights

Step-1

NCEP

JMA

ECMWF

Observed Gridded Field

Weight for each grid of each Model (W)

slide43

Generation of Multi-model Forecasts

Step-2

NCEP

JMA

ECMWF

Forecast (F)= WiFi + D

D= Value addition

slide44

Multi-model Ensemble at 0.25o resolution

  • Member Models
  • ECMWF at 0.25o resolution
  • JMA at 1.2o resolution
  • NCEP at 1.0o resolution
slide45

MME:ECMWF, JMA, NCEP

Roy Bhowmik and Durai, 2008, Atmosfera, 21(3), 225-239

slide46

Performance Evaluation of MME Forecasts during Monsoon 2008

The results of spatial correlation coefficient for day1 to day 5 forecasts illustrating the superiority of the MME technique over the member models ( ECMWF, JMA,NCEP)

ECMWF

NCEP

The results of anomaly correlation coefficient for day 3 forecast showing superiority of MME

JMA

MME

slide47

Sate-wise performance Day 3 Rainfall Forecast

Over-all performance of MME district level forecasts over some major states. Performance index is defined as the % of total districts with threat score more than 0.5 for different rainfall thresholds. Threat score is defined as number of correct forecasts divided by total forecast. The threat score ranges between 0 and 1

slide48

Near Future Plan: Now-casting

  • and mesoscale forecasting
  • Real-time radar (DWR) mosaic creation
  • Operation of ARPS model at 3 km resolution with
  • assimilation of DWR data for local severe weather
  • City forecast for Delhi as required for the Commonwealth
  • Games 2010
  • Implementation of dynamical Fog prediction model for visibility forecasting at the major airports of India.
slide49

Near Future Plan:Regional Models

  • 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.
  • MM5 model with 2 nested domains (at the resolution of 27 km and 9 km) at 12 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.
slide50

Proposed triple nesting WRF model (27, 9, 3 km) with flexible fixing of inner most domain

immediate short range forecasting strategy at rmc and mc in imd
RMC-MC:

Very high-resolution (~3km) double nest operational model forecast generation for 2 days

Strom scale model with ~ 1km resolution for 24 hours forecast

3 hourly cycle for specific event

Assimilation of region specific special observations e.g. DWR

Immediate Short-range Forecasting Strategy at RMC and MC in IMD