Wind Analysis of Cyclone Neelam using Remote Sensing and Reanalysis Data

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1 The Eighth Asia-Pacific Conference on Wind Engineering, December 10 14, 2013, Chennai, India Wind Analysis of Cyclone Neelam using Remote Sensing and Reanalysis Data Krushna Chandra Gouda 1, Nagaraj Bhat 2, Prashant Goswami 2. 1 Scientist, CSIR Fourth Pradigm Institute, Bangalore, India, kcgouda@cmmacs.ernet.in 2 Project Asst., CSIR Fourth Pradigm Institute, Bangalore, India, 3 Chief Scientist and Group Co-ordinator (CEMP), CSIR Fourth Pradigm Institute, Bangalore, India ABSTRACT Tropical Cyclones are the very prominent disaster events along the coastal Indian region during pre and post monsoon season. As strong winds and heavy rainfall are usually associated with the tropical cyclones so they cause major devastation. It is now very important to monitor and model the cyclones properly so that these events can be predicted in advance for the better mitigation of the disaster due to the extreme events. With growing of remote sensing satellite data and high performance computing enabled numerical modeling approach the cyclone studies can be performed in real time to assess and forecast the track and intensity in terms of wind. In the present study the wind and surface pressure during the cyclone Neelam is studied using the various satellite remote sensing data and reanalysis data. This study also highlights the techniques to predict the track using the low level wind circulations which turn in to tropical cyclone in later stage using the scatterometer wind data. Keywords: Wind analysis, tropical cyclone, cyclone Neelam, remote sensing Introduction India usually experience extreme weather due to Tropical Cyclones during pre and post monsoon season. The strong winds and heavy rainfall are usually associated with the tropical cyclones. Generally these events are governed by meso-scale atmospheric dynamics, operating on horizontal scales ranging from a few to several hundred kilometers. It is now very important to monitor and model the cyclones properly so that these events can be predicted in advance for the better mitigation of the disaster due to the extreme events. With growing of remote sensing satellite data and high performance computing enabled numerical modeling approach the cyclone studies can be performed in real time to assess and forecast the track and intensity in terms of wind. During the 1980s the forecasting of the track and intensity of tropical cyclones was mainly based on statistical (regression) methods using the meteorological data. Later, in the early 1990s, remote sensing techniques were used successfully for short range cyclone forecasting (Marshall et al., 2002). Later on generation of high resolution atmospheric motion vectors (AMVs) from satellite images, and four dimensional variational assimilation (4D- VAR) have reduced the error in forecasting a cyclone s track and intensity (Marshall, et al., 2002). In recent times numerical models are also adopted and the cyclone are being well predicted more time in advance. General circulation models are also used for the prediction of cyclones and several studies are available (Goswami et al., 2005, Goswami et al.,2011) Images data from different channels obtained from weather satellites have their specialized use in track and intensity forecasting of tropical cyclones. Satellite images within the thermal infrared (IR) band can be used to forecast and analyze the cyclone s intensity Proc. of the 8th Asia-Pacific Conference on Wind Engineering Nagesh R. Iyer, Prem Krishna, S. Selvi Rajan and P. Harikrishna (eds) Copyright c 2013 APCWE-VIII. All rights reserved. Published by Research Publishing, Singapore. ISBN: doi: /

2 (Kossin, 2003). In addition, Lau and Crane (1997) and Kishtawal et al. (2005) measured the intensity of tropical cyclones based on satellite data from a Thermal Microwave Imager using non-linear data fitting. Present techniques can be developed further, as data from Advanced Microwave Sounder Units having better horizontal resolution and vertical temperature sounding abilities provide an improved basis for temperature estimation compared to conventional Microwave Sounding Units and IR satellite images (Knaff et al., 2000). Study of Neelam Cyclone The cyclonic storm, NILAM crossed Tamilnadu coast near Mahabalipuram (south of Chennai) in the evening of 31st October 2012 with a sustained maximum wind speed of knots ( kmph). It followed a unique track with many rapid changes in direction of movement. It initially moved westwards, remained practically stationary for quite some time near Sri Lanka coast and then moved north-northwestwards till landfall. It moved westnorthwestwards initially over land up to south interior Karnataka and then moved northwest and northwards. The remnant low pressure area moved northeastwards. The track of the cyclone obtained from IMD observation is presented in figure 1. On the day of landfall, (31 st October 2012), the cyclone moved very faster resulting intense rainfall activities over the land surface i.e. over Karnataka and the cloud mass was significantly sheared to the northeast of system centre during the dissipation stage leading to rainfall activity over entire Andhra Pradesh and adjoining Odisha. The maximum rainfall occurred over southwest sector of the system centre and heavy to very heavy rainfall extended up to 300 km. Fig. 1 Track of Neelam Cyclone from IMD observation For the analysis of the cyclone track the wind parameter is considered and the location of maximum wind are being observed and analyzed using the satellite wind data and also to fill the missing wind values the NCEP data are interpolated and finally a merged wind analysis is carried out and the track of cyclone is computed from the merged data. Using the satellite image data i.e Oceansat derived daily wind and the interpolated NCEP reanalysis wind products the high intense wind points are extracted and the track are generated and presented in figure 2, which clearly shows the track so obtained is well matches with that of the IMD observed or announced track. This analysis shows that the cyclone track can be used for the disaster management by monitoring the real time satellite data. 1102

3 Fig. 2 Tracking the Neelam Cyclone using merged remote sensing and reanalysis wind product Fig. 3 Wind structure of the cyclone Neelam captured by Oceansat scatterometer. A variable resolution General Circulation model is also used to simulate the cyclone Neelam and presented. The detailed formulation and procedures of the model is explained in the earlier works by Goswami et al., (2005 and 2011). In this analysis the 25 th October 2012 initial condition are used to generate the forecast of cyclone and the track of cyclone is obtained by using the simulations at an interval of 6hrs. Figure 4 presents the simulated cyclone track. As a sample case of the surface wind study the Oceansat data is being analyzed and the wind structure at 31 st October 2012 is presented in figure 3 below, which clearly shows the cyclonic and intense wind structure near the Chennai coast of India. 1103

4 Fig. 4 Tracking the Neelam Cyclone using the simulation from a general circulation model The comparison of the observed track, estimated track using remote sensing and GCM simulated track are being presented in the figure 5 below and it indicates there are little error in the land fall of about 20-40km. Fig. 5 Comparison of track of Neelam Cyclone estimated from satellite data, simulated from GCM and IMD observation. 1104

5 (a) Average SLP Before Cyclone (22 to ) (b) Average SLP During Cyclone (28 oct to 02 nov-2012 Fig. 6 Surface Level Pressure derived from Satellite Data (a) before cyclone and (b) during cyclone. The red line represents the track of cyclone Neelam 1105

6 To investigate the physical feature of the cyclone Neelam the sea level pressure (SLP) is also analyzed and reported in figure 6. As the wind distribution causes the surface pressure drop due to shear and divergence field so the surface pressure before and during the cyclone are presented and the clear difference in the pressure drop is well captured in the reanalysis data and model simulation. The top panel presents the average SLP before the cyclone i.e 24 to 26 Oct 2012 (figure 6a) and the bottom panel presents the average SLP during the cyclone period (figure 6b), which shows a clear indication of the lowest pressure during the cyclone. The surface pressure and wind intensity associated with cyclone are controlled by the thermo dynamics of the ocean atmosphere interaction. Similar way the NCEP reanalysis wind are analyzed and presented in figure 7. The wind with respect to the evolution of the cyclone is also discussed in the present work. The vertical profile of wind also indicated an unique feature associated with the cyclone which after landfall also persisted over the land for more than 18 hours. 1106

7 (a) Average Wind Speed before Cyclone (22 to 26 Oct 2012) (b)average Wind Speed during Cyclone (28 Oct to 02 Nov-2012 Fig. 7 Surface resultant wind derived from Satellite Data (a) before cyclone and (b) during cyclone. The red line represents the track of cyclone Neelam Conclusions Apart from the monitoring the physical processes associated with cyclones it is very important to predict the cyclones. Large scale wind over the land and ocean during the passage of cyclone is compared with the non-cyclone period and the wind anomaly computed which can be used as an indicator for the rainfall and storm activities. This study also highlights the techniques to predict the track using the low level wind circulations which turn in to tropical cyclone in later stage using the scatterometer wind data. A more detailed study needs to be done using the in situ observed data which can further refine the understanding of the cyclone. 1107

8 References Goswami, P., S. Mallick, K. C. Gouda, 2011: Objective Debiasing for Improved Forecasting of Tropical Cyclone Intensity with a Global Circulation Model. Mon. Wea. Rev., 139, doi: Goswami,P., A. Mandal, H.C. Upadhyaya and F. Hourdin, 2005: Advance forecasting of Cyclone track over north Indian Ocean using a global circulation model. Mausam, 57,1 (January 2006), Kishtawal, C. M., Patadia F., Singh R., Basu S., Narayanan M. S., and Joshi P. C., 2005, Automatic estimation of tropical cyclone intensity using multi-channel TMI data: A genetic algorithm approach,geophysical Research Letters, vol 32. Knaff, J. A., Zehr, R. M., Goldberg, M. D., 2000, An example of temperature structure differences in two cyclone systems derived from the advanced microwave sounder unit. Weather and Forecasting:15, 4, Kossin J., 2003, A user s guide to the UW-CIMSS Tropical Cyclone Intensity Estimation (TIE) Model.Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison. Marshall, J. F. L., Leslie, L. M., Abbey Jr. R. F., and Qi, L., 2002, Tropical cyclone track and intensity prediction: The generation and assimilation of high-density, satellite-derived data. Meteorology and Atmospheric Physics, 80, 1-4,