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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 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

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  1. S.K. Roy Bhowmik NWP, IMD, New Delhi Regional NWP Modelling at IMD

  2. Overview

  3. 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)

  4. NHEC (Telecom server,IMD) DWR Net-work for data processing NCMRWF

  5. A RADAR mosaic creation from reflectivity observations DWR Chennai and Machhilipatnam of 28 September 2005

  6. 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.

  7. 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.

  8. 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

  9. 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 -

  10. 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

  11. ARPS + Radar Data Assimilation GFS ANALYSIS

  12. ARPS + Radar Data Assimilation ARPS FORECAST

  13. ARPS + Radar Data Assimilation ARPS FORECAST

  14. ARPS + Radar Data Assimilation ARPS FORECAST

  15. ARPS + Radar Data Assimilation ARPS FORECAST

  16. Nov’08 Ccyclone “Khai Muk”

  17. Bay Cyclone Khai-mukh of Nov 2008Control ADAS

  18. Control Adas

  19. Control Adas

  20. 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

  21. 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

  22. 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

  23. 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

  24. ECMWF MM5 QLM 72 hours Forecast Initial Condition 29 April 00 UTC UKMO WRF

  25. MM5 ECMWF QLM WRF 48 Hrs Forecast Initial condition 30 April 00UTC UKMO

  26. QLM ECMWF MM5 UKMO WRF 24 hours forecast Initial Condition 1 May 00 UTC QLM with initial condition 2 May 00 UTC

  27. Inter-comparison of Model Performance for “Naargis” April’08

  28. Model Error statistics (km) for Nargis

  29. Statistical Dynamical model for Prediction of: • Cyclone genesis • Intensity

  30. 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)

  31. 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)

  32. 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).

  33. GENESIS PARAMETER OF TC “NISHA” of Nov 2008

  34. 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.

  35. 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)

  36. 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

  37. Twelve-hourly Intensity Prediction up to 36 hours for cyclone SIDR Nov 2007 Based on 14/00 UTC Based on 15/00 UTC

  38. INTENSITY PREDICTION OF TC “RASHMI” of October 2008

  39. Performance of the model: For dependent sample of 62 cyclones (1981-2000): For independent sample of 15 cyclones (2000-2007):

  40. 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)

  41. Generation of Multi-model Forecasts Step-2 NCEP JMA ECMWF Forecast (F)= WiFi + D D= Value addition

  42. Multi-model Ensemble at 0.25o resolution • Member Models • ECMWF at 0.25o resolution • JMA at 1.2o resolution • NCEP at 1.0o resolution

  43. MME:ECMWF, JMA, NCEP Roy Bhowmik and Durai, 2008, Atmosfera, 21(3), 225-239

  44. 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

  45. 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

  46. 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.

  47. 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.

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

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