Planning & Mitigation | VOL. 16, ISSUE 97

Forecasting Tropical Cyclones

Cyclones, especially on the eastern seaboard of India, have always been a major cause of loss to lives and property. The accurate prediction of the path and landfall of a cyclone can bring a huge relief to coastal populations that are in its path.

The analysis and prediction of tropical cyclones (TCs) involves the process of blending conceptual, dynamic and statistical models, meteorological data sets, technology and expertise. A decision support system (DSS) in a digital environment is used to plot and analyse different weather parameters, satellite, radar and numerical weather prediction (NWP) data. In this hybrid system, a synoptic method could be overlaid on an NWP model supported by modern graphical and geographical information system (GIS) applications to produce high quality analyses and forecasting, prepare past and forecast tracks up to 120 hours, depict uncertainty in track forecast and to forecast wind directions and speed in the different sectors of a TC. Additional help is taken from the radar network of neighbouring countries, satellite imageries and analysis and forecast products from various national and international centres.

Of late, automation has increased the efficiency of these systems. Advances made in the recent decade are shown in Table 1.


The benefits of early warning and preparedness were evident when two successive cyclones hit the Indian coastline in the last two years. In October 2014, when powerful cyclone ‘HudHud’ struck Odisha and Andhra Pradesh on country’s eastern coast, there was widespread destruction and 46 people were reported dead. Almost exactly an year earlier, cyclone Phailin, the strongest cyclone to hit India in more than a decade swept across the Bay of Bengal and battered the same area. However, in comparison to past cyclones the death toll showed a dramatic drop. It was also indicative of timely preparedness measures put into place. For instance, four days before Phailin struck, the area was evacuated and a staggering 1.2 million people were moved to safer areas. Phailin’s devastating blow caused hundreds of millions of dollars in damages and affected the livelihood of millions of people but only 21 people were reported dead. In contrast, a 1999 cyclone in the same region had killed 10,000 people.

Prediction of cyclogenesis

If a depression with maximum surface wind (MSW) of 17-27 knots forms over the northern Indian Ocean (NIO), a cyclogenesis is said to have occurred. Prevailing environmental conditions such as sea surface temperature (SST), ocean thermal energy, vertical wind shear, low level vorticity, middle level relative humidity, upper level divergence, moist static stability, and low to upper level winds are considered for predicting cyclogenesis. These fields in the NWP model analyses, taken along with characteristic satellite and radar observations, are evaluated to monitor the cyclogenesis, step-wise.

A genesis potential parameter (GPP), for NIO is developed as the product of four variables—vorticity at lower levels, middle tropospheric relative humidity, middle tropospheric instability, and vertical wind shear. The GPP is operationally used for predicting cyclogenesis in the early stages. Operational probabilistic forecast is issued in terms of nil, fair, moderate, high and very high probability corresponding to 0, 1-25, 26-50, 51-75 and 76-100 per cent probability of occurrence for the next 72 hours.

TC track forecasting

After determining the initial location an attempt is made to forecast the track and intensity of the cyclone. While the synoptic, statistical and satellite/radar guidance help in short range forecast (upto 12/24 hours), NWP guidance is mainly used for 24-120 hour forecasts. Apart from deterministic NWP model, multi model ensemble and single model ensemble prediction systems (EPS) are used for track forecasting. Consensus forecasts that gather all or part of the numerical forecast and use synoptic and statistical guidance are utilised to ultimately issue the official forecast.

An example of Phailin (Fig. 1a) shows the ‘cone of uncertainty’ (CoU), also known as the ‘cone of death/probability/error’, representing the forecast track of the centre of a TC and the likely error in the forecast track based on past predictive skills.

22-27_Fig 1

22-27_Fig 2

22-27_Fig 3

TC intensity forecasting

A statistical-dynamical model is implemented for real time forecasting of 12 hourly intensities of up to 72 hours. For real-time forecasting, model parameters are derived based on the forecast fields of the India Meteorological Department (IMD) Global Forecast System (GFS) model. In the satellite method, a region of maximum reflectivity and mesoscale vortices are assumed to be associated with higher winds. In the radar technique, the direct wind observations are available through radial velocity measurements. The wind estimates from satellite, radar and other observations are extrapolated to forecast the wind. MSW is also available from other sources like scatterometry wind from satellite, buoy, and ships, apart from estimates by the Dvorak technique. Consensus forecasts that gather all or part of the numerical forecast and use synoptic and statistical guidance are utilised for the official forecast. An example of the official intensity forecast of cyclone is presented in Fig. 1b. It shows the radial extent of wind of 34, 50, and 64 knots in four geographical quadrants of the cyclone.

Among various NWP, a cyclone specific high resolution model, with hurricane weather research and forecast (HWRF) has been introduced for cyclone intensity forecasting in collaboration with the National Oceanic and Atmospheric Administration (NOAA), USA.

Adverse weather forecasting

In addition to the forecast of cyclone track, intensity and landfall location, IMD also provides specific forecasts of the following adverse weather phenomena associated with cyclones.

Heavy rainfall: The forecast/warning of heavy rainfall includes time of commencement, duration, area of occurrence and intensity of heavy rainfall. The methods for prediction of heavy rainfall include synoptic, climatological, satellite, radar, and NWP techniques. While the NWP models predict rainfall for different lead periods, satellite and radar provides short-term (3 to 12 hours) forecast guidance. In the synoptic and climatology method, rainfall intensity and spatial distribution are used. The final forecast is the consensus arrived at using all of the above.

Gale wind: The forecast of gale winds involve predicting time of commencement, duration, area of occurrence, and magnitude. Apart from the four methods mentioned above, prediction of gale winds include dynamical statistical techniques.

Storm surge: The storm surge depends on the pressure drop at the centre, radius of maximum wind, point of landfall and interaction with sea waves, astronomical tide, rainfall, river run-off, bathymetry, and coastal geometry. A storm surge forecast mentions the time of commencement, duration, area of occurrence, and magnitude (of the storm surge). Storm surge and coastal inundation is predicted by the consensus using IMD nomogram, the Indian Institute of Technology (Delhi) and the Indian National Centre for Ocean Information Services (INCOIS), Hyderabad models.


A range of technological advances has now made cyclone forecasting a lot more accurate, thus reducing risks. Along with cyclones, problems associated with cyclones such as rainfall, storm surge and coastal inundation can also be accurately forecast in terms of magnitude, time and place of occurrence. This has resulted in reduction in loss of lives, and brought down the costs involved in evacuation of population.

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