This document summarizes an internship project using the WRF-ARW model to simulate a tropical cyclone. The internship involved learning UNIX, WRF installation, configuration and running a simulation of Tropical Cyclone HudHud without data assimilation. The results showed the model predicted the cyclone track closely compared to observations. Future work proposed using more cyclones and assimilating Doppler radar data to evaluate impact on track and intensity forecasts. The internship provided valuable experience in operational forecasting and research using numerical weather prediction models.

1. A Simulation Using WRF-ARW
Model
A Internship Report Presentation
Presented by - Devanil Choudhury
Intern at – India Meteorological Department

2. Content

Introduction

Learning Objectives

Evaluation of the Internship

Project topic

Scientific Background

State of Art

About Data

WRF-ARW Model

The experimental configuration

The case event

The Results

Future works

Conclusion

References

Thanks Giving

3. Introduction

The Internship was carried out at NWP Division, India
Meteorological Department – New Delhi

Under the guidance of Dr. Someswar Das (Sc-G/ Project
Director)

First phase was all about on learning Installation and
simulation using WRF-ARW model

It was a very useful experience working with several
scientists and engineers at IMD

4. Learning Objectives

To learn UNIX and AIX (Advanced Interactive eXecutive)
operating system

To learn shell scripting (bash/c-shell) and vi editor

To learn the WRF-ARW installation on the server

To know briefly about WRF-ARW model for using and
running

To learn NCL (NCAR Coded Language) and GrADS for
visualizing the output

5. Evaluation of Internship
Unix envioronment WRF installed on Laptop
Test experiment visualization with GrADS
WRF installation on RMC Delhi's Server for real experiment
Remotely acessed through telnet service
Working with IBM-AIX OS
Chose the case for experiment (HudHud)
Learnt the entire domain settings and initial configuration for the model run
Transferred GFS data for the initial condition
Ran the model for three days forecast
Graphical analysis with NCAR Coded Language (NCL)
Made the Animation

6. Project Topic

Simulation of Tropical Cyclone and impact of Doppler Weather
Radar (DWR) data assimilation on the skill of track and
intensity forecasting based on the WRF-ARW model

Project consists two part of works
1) Simulation of some tropical cyclone using various scheme of
WRF-ARW without data assimilation
2) Simulation of some tropical cyclone with DWR data
assimilation

7. Scientific Background

The ARW has a good overall capability to predict TCs over
the NIO basin (Osuri et al.) but
still there is huge scope for further reduction in track forecast
error including 1)further improvement in initial condition 2)
assimilation of more data (DWR) etc. (Mohapatra et al.)

Synoptic studeies shown that assimilation of DWR can
improve NWP models (Liu et al. 2013)

DWR data may improve weather analysis and forecasts
because of their high temporal and spatial resolution (Maielno
et al.)

8. State of Art

The work was carried out using WRF-ARW model at NWP
division of IMD-Delhi

The High Performance Computing System (HPCS) with 16
processors, 1 node and IBM-AIX parallel computing environment
was used to carry out the works

The telnet service, ftp (file transfer protocol) service, internal LAN
connection etc assisted this work

WRF with ARW core model version 3.3.1 was used for the
simulation

9. About Data

The initial and lateral boundary condition for the WRF-ARW model are
obtained from analysis and forecast field of the NCEP-GFS model.

The lateral boundary condition are updated in 6-h interval with a fixed
sea-surface temperature throughout the model integration, with no
regional data assimilation used in this study.

The land surface boundary condition are taken from the U.S. Geological
survey data with a horizontal grid spacing of 10 min. To simulate the full
genesis of the experimental event five day

(7-10-2014 to 11-10-2014 ) 72 hrs GFS (Global Forecasting System)
analysis data at 00 UTC are taken for the initial condition of the model

10. WRF-ARW Model

Weather Research and Forecasting (WRF) – Advanced Research WRF
(ARW) model was developed by NCAR and UCAR and is a
community effort to develop a next generation mesoscale model and
data assimilation system.

This mesoscale model suitable for use in
1) Idealized simulations
2) Parameterization research
3) Data assimilation research
4) Forecast research
5) Real-time NWP
6) Hurricane research ....

11. Flowchart of WRF-ARW
Source : http://www.mmm.ucar.edu

12. WRF Components
1. WRF Preprocessing System (WPS)
2. ARW Dynamics Solver
3. WRF-DA (Data Assimilation)
4. Graphics (NCL)

13. WPS
Source:http://www2.mmm.ucar.edu/wrf/users/docs/user_guide/users_guide_chap3.html
EXTERNAL
DATA
SOURCE

14. ARW Dynamics Solver
Source:http://www.meted.ucar.edu
Primitive Equation

15. WRF-DA & VISUALIZATION

WRF-DA is a data assimilation system for the WRF model on the basis of 3D
and 4D-var, ensemble and hybrid methods.

A wide variety of data from conventional observation, radar velocity and
reflectivity, satellite (radiance and derived data) and accumulated precipitation
can be handled by WRF-DA

NCL (NCAR Command Language) was used for visualization of wrfoutput

NCL is an interpreted language designed specifically for scientific data analysis
and visualization.

NCL can read many data formats like NetCDF, HDF, GRIB, ASCII, Binary and
more.

16. MODEL SUMMARY

Fully compressible Euler non-hydrostatic equations with hydrostatic option ;

scaler-conserving flux form for prognostic variables ;

complete coriolis and curvature terms ;

nesting : one way , two way with multiple nests and moving nests ;

mass based terrain folloing co-ordinate ; vertical grid spacing can vary with height ;

mapping to sphere : 3 map projections are supported for real data simulations (coriolis term
included) : polar stereographic , lambert conformal , mervator ;

Arakawa C-grid staggering ;

Runge-Kutta 2 nd and 3 rd order time step options ;

2 nd to 6 th order advection options (horizontal and vertical );

time-split small step for acoustic and gravity-wave modes;

lateral boundary conditions:
Idealized cases: periodic, symmetric, and open radiative;
Real cases: specified with relaxation zone;

full physics options for land-surface, PBL, radiation, microphysics and cumulus

Parameterization. (Source – Research & Forecasting Model WRF by Laura Bianco)

17. The Experimental Configuration
For the first simulation of the VSCS
HudHud :

WRF-ARW nesting was taken as
shown in figure

Horizontal resolution – 27 km and 9
km

Vertical level – 28

Time step for integration – 90 sec

Run days – 3

Cumulus parameterization – Betts-
Miller-Janjic scheme

Initial data – GFS gridded data

18. The Case Event
Track of HudHud (07-14 october)
Source: Joint Research Centre of
the European Commission (JRC)
HudHud VSCS taken as the case event

19. The Salient Features of HudHud

HudHud is the first cyclone that crossed Visakhapattanam coast in
the month of October after 1985 and it made landfall on the same
day as VSCS Phailin did in 2013.

At the time of landfall on 12 th October, the estimated maximum
sustained surface wind speed in association with the cyclone was
about 100 kts.

The estimated central pressure was 950 hpa with a pressure drop of
54 hpa at the centre compared to surroundings.

It caused very heavy to extremely heavy rainfall over North Andhra
Pradesh and South Odisha and strong gale winds leading to large
scale structural damage

Maximum 24 hour cumulative rainfall of 38 cm ending at 0830 hrs
IST of 13 th october was reported from Gantyada (Vizianagaram) in
AndhraPradesh. Source : IMD Report

20. The Results
INSAT-3D vis cloud image on 10-10-
2014
Forecast valid for 00 UTC of
10-10-2014 based on 00
UTC of 7-10-2014

21. The Results
INSAT-3D vis cloud image on 11-10-
2014
Forecast valid for 00 UTC of
11-10-2014 based on 00 UTC
of 8-10-2014

22. The Results
INSAT-3D vis cloud image on 12-10-
2014
Forecast valid for 00 UTC of 12-
10-2014 based on 00 UTC of 9-
10-2014

23. The Results
INSAT-3D vis cloud image on 13-
10-2014
Forecast valid for 00 UTC of 13-
10-2014 based on 00 UTC of 10-
10-2014

24. The Results
Forecast on 9-10-2014
Based on 7-10-2014
Forecast on 11-10-
2014 based on 8-10-
2014
Forecast on 13-10-
2014 based on 10-10-
2014
Some forecast graphics based on different initial condition of parent domain

25. The Results
The Animation of parent and nested domain based forecast
Parent domain animation from
7-9th
Oct. Model run based on
7th
oct.
Nested domain animation
from 10-12th
oct. Model run
based on 10th
oct.

26. The Results
Full genesis of HudHud : A
animation from INSAT-3D vis cloud
image (07-14th
October)
A simulation from 09-11th
October : Forecast based on the
initial condition of 00 UTC 9-10-
2014
Comparison between observation and simulation

27. Future Works

The study of skill of WRF by taking more tropical
cyclones

The last possible work is to learn DWR data
assimilation technique and assimilate on the skill of
track and intensity forecast based on the WRF-ARW
model

28. Conclusion

The deatail investigation and many more experiment are needed
for better forecasting

Based on the initial configuration WRF-ARW predicted
HudHud closely compared to the observation

This internship was very useful experience for me by gaining
new knoledges, skills

Got the opprotunity to observe the operational forecasting and
professional practice at IMD

Helped me to define what skills and knowledge I have
to improve i the ensuing time.

Lastly It opened for me a new path to reach the higher research
in development of NWP models.

29. References

Liu et al. , A study on WRF radar data assimilation for hydrological
rainfall predictions; 2013: HESS

Maielno et al., Impact of radar data assimilation for the simulation
of a heavy rainfall case in central Italy using WRF-3D var; 2014:
AMT

Mohapatra et al. Evaluation of official tropical cyclone track
forecast over north Indian ocean issued by IMD, 2013; JESS

Osuri et al. Real-time track prediction of tropical cyclone over north
Indian ocean using ARW, 2013:JAM

VSCS, HUDHUD over BOB (07-14 October 2014) : A Report
by IMD

30. Thanks Giving
I would like to thank Dr. Someshwar Das (Sc-
G/Project Director) for giving this opportunity;
Mr. A. K. Das (Sc-D) and Mr. V. R. Durai (Sc-D)
for their enomormous support at IMD
&
THANK YOU ALL for your PATIENCES