**5. Tropical cyclone climatology over NIO region**

Since hundreds of years, the Indian Ocean is a breeding basin for disastrous TCs associated with heavy rainfall, torrential wind and storm surges. The cyclones in 1970 and 1991 caused a loss of more than 400,000 lives. During the Odisha super cyclone (1999), more than 10,000 lives were lost and a destruction of 1.9 million houses occurred in 14 districts. Recently, Nargis (2008) caused ∼1 40,000 deaths in Myanmar. In 2015, cyclonic storm Komen caused a heavy loss throughout Bangladesh, Myanmar, northeast India and eastern parts of India although the loss of lives was very few as compared to previous cases because of the improvement in TC predictability. This was also realized in case of Phailin (2013) and Hudhud (2014).

the observations, while the single domain based simulations show the deviation of the track towards north. A more recent study by Osuri et al. [51] found that the use of high resolutions in operational ARW model improves the prediction of recurving TC tracks and their intensity. In a climatological framework, Community Atmospheric Model or CAM showed sensitiveness to the prediction of more number of intensified tropical cyclones over most of the global basins including NIO. Further, it also found that the duration of tropical storms would be much larger in high resolutions simulations. Thus, it is realized that the model horizontal grid resolution impacts significantly the TC track, intensity and duration besides other relevant meteorolog-

Most of the times, the use of data assimilation techniques in TC simulations helps in improving the model predictability. For this purpose, satellite-based observations, aircraft measurements and radar data are used besides the conventional data sets. The widely used data assimilation techniques are primarily based on either ensemble Kalman filter (EnKF) or variational techniques (3DVAR or 4DVAR). Most of the studies related to TC simulation were done using variational data assimilation techniques for improving the TC prediction over NIO region. For example, the studies such as [52, 69–71] used 3DVAR techniques for assimilating satellite, radar and conventional measurements for improving the initial and boundary conditions of MM5 and ARW mesoscale models in order to better predict TC structure, track, intensity and associated relevant meteorological variables including rainfall. In some situations, the improvement was not significantly noticed. For instance, the studies by Singh et al. [70] found that assimilation of SSM/I wind speed data resulted in simulating weak intensity and failed to

Although there are no significant studies related to the use of 4DVAR and EnKF techniques for simulating NIO TCs, there are literatures, which demonstrate the usage of four dimensional data assimilation (FDDA) nudging technique in order to improve the ARW model predictability. For example, [71–73] used FDDA nudging technique in order to improve ARW initial and boundary conditions for the simulation of several TCs over NIO region those occurred during 2007–2010. These studies primarily emphasized upon TC track and intensity forecasts. While some of them reported remarkable improvements in track prediction and landfall position with either 12- or 18-h of nudging yielding maximum impact [72, 74], some others noticed relatively less impact of FDDA observational nudging on intensity prediction [73].

Since hundreds of years, the Indian Ocean is a breeding basin for disastrous TCs associated with heavy rainfall, torrential wind and storm surges. The cyclones in 1970 and 1991 caused a loss of more than 400,000 lives. During the Odisha super cyclone (1999), more than 10,000 lives were lost and a destruction of 1.9 million houses occurred in 14 districts. Recently, Nargis (2008) caused ∼1 40,000 deaths in Myanmar. In 2015, cyclonic storm Komen caused a heavy loss

ical parameters.

**4.4. Significance of data assimilation**

204 Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection

make an impact on track prediction.

**5. Tropical cyclone climatology over NIO region**

TCs usually form over NIO basin in two seasons, that is, pre-monsoon (March-April-May) and post-monsoon (October–November–December) period. In total, about 1108 numbers of cyclonic systems are formed over NIO region (includes both BOB and Arabian Sea, AS) during 1891–2015. It includes depressions (or D), cyclonic storms (or CS) and severe cyclonic storms (or SCS). However, the cyclonic systems do not form each month of every year. If the average monthly distribution of these three types of cyclonic systems (**Figure 3**) is analysed, it is evident that maximum number of cyclones occur between the months of May to December. Maximum numbers of depressions are formed in August. Maximum numbers of CS are formed in the month of October, while November is the most favourable month for the formation of SCS. Though the number of total cyclonic systems in May is relatively less, ∼48.7% of cyclonic disturbances are transformed to very severe cyclonic storms. However, this transformation is found to be 43.9 and 41.7%, respectively, in the months of April and November. Annually the probability of intensification of depression to CS is ∼44.8%, depression to SCS is ∼21.3% and the probability of intensification of CS to SCS is ∼47.5%.

**Figure 3.** Monthly frequency of cyclonic disturbances in North Indian Ocean region during 1891–2015. Here depression signifies the low-pressure systems which do not transform to cyclonic storms; CS is for the cyclonic storms and SCS represents the severe cyclonic storms.

BOB contributes about 75% of TCs during cyclone seasons (pre- and post-monsoon periods) and the AS contributes ∼25% [75]. The possible reason could be that BOB is generally more stratified than AS because its upper-ocean part is relatively warmer resulting in higher SST. In addition, low flat coastal terrain and funnel shape, shallow water of BOB [76], monsoonal wind (trough), more middle tropospheric moisture availability and lower tropospheric westward travelling disturbances such as easterly waves (often serve as the 'seedling' circulations) play roles in generating more number of cyclonic systems over BOB. Most of the monsoon troughs generated because of re-intensification of westerly propagating disturbances or from in situ depressions help in the formation of cyclonic systems over this region as well. Boreal summer intraseasonal oscillation (BSISO) also modulates the topical cyclogenesis over BOB [77], and it may be noted that the genesis potential index is high during the active phase of the BSISO.

The studies like that of [4] indicate that under the global warming scenario, the number and proportion of cyclones reaching SCS are increasing in almost all basins of the world especially indicating the impact of climate change. **Figure 4** shows the decadal variation of cyclonic disturbances and CSs over NIO, that is, over BOB and AS. It is clear from the curve that there is a significant decreasing trend in the number of cyclonic disturbances and CS. When the number of SCS are analysed, it shows a slight increase or may be considered as a constant trend in decadal scale (**Figure 4**). During 1961–1970 and 1971–1980, there was most number of SCS. Besides El-Nino Southern Oscillation (ENSO), MJO (Madden-Julian Oscillation) and IOD (Indian Ocean Dipole) may also play appreciable role in modulating the TC activity over NIO region [13, 16, 17, 77].

**Figure 4.** Variation of decadal frequency of cyclonic disturbances or depressions (D), cyclonic storms (CS) and severe cyclonic storms (SCS) over NIO region (smooth curved line). The bar diagrams represent SCS during 1891–2015. The dotted line indicates the moving trend and line shows the linear trend.

For the past three decades, the number of SCS has somehow decreased to a considerable value (**Figure 4**). However, Mohanty et al. [75] demonstrated that there is a considerable increase of SCS by about 65% during the warming period 1951–2007 by analysing the genesis and intensity of TCs over NIO basin in yearly scale. In the southern sector of BOB, a considerable increase of ∼71% in SCS is found in post-monsoon season. Rate of dissipation of SCS over BOB is also significantly reduced besides increase in mean SST in the warming scenario and these features contribute to increase in the number of SCS over NIO. In the western sector of AS, a significant increase in SCS is also observed in the warming conditions. Therefore, the intensity of the SCS is increasingly becoming significant in the changing climate scenario. When the 'T Numbers' of the cyclones are analysed in satellite era, it is found that the Odisha super cyclone (1999) was the strongest recorded CS in the NIO basin during 1990–2015.

Analysing the track of cyclones over BOB and AS from e-atlas available at IMD, New Delhi, it is observed that most of the cyclonic systems developing over the NIO basin move in a northwesterly direction. However, there are cases of recurvature towards the northeast or east to southwest. The frequency of recurvature is higher towards the northeast compared to southwest or east. The probability of recurvature is higher over the AS when the system moves to the north of 15°N increasing the possibility of landfall over Gujarat coast. Over BOB, there is no such preferred latitude/longitude for the recurvature prospects. On the other hand, the probability of recurvature towards northeast region is higher during the pre-monsoon season.

depressions help in the formation of cyclonic systems over this region as well. Boreal summer intraseasonal oscillation (BSISO) also modulates the topical cyclogenesis over BOB [77], and it may be noted that the genesis potential index is high during the active phase of the BSISO.

206 Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection

The studies like that of [4] indicate that under the global warming scenario, the number and proportion of cyclones reaching SCS are increasing in almost all basins of the world especially indicating the impact of climate change. **Figure 4** shows the decadal variation of cyclonic disturbances and CSs over NIO, that is, over BOB and AS. It is clear from the curve that there is a significant decreasing trend in the number of cyclonic disturbances and CS. When the number of SCS are analysed, it shows a slight increase or may be considered as a constant trend in decadal scale (**Figure 4**). During 1961–1970 and 1971–1980, there was most number of SCS. Besides El-Nino Southern Oscillation (ENSO), MJO (Madden-Julian Oscillation) and IOD (Indian Ocean Dipole) may also play appreciable role in modulating the TC activity over NIO

**Figure 4.** Variation of decadal frequency of cyclonic disturbances or depressions (D), cyclonic storms (CS) and severe cyclonic storms (SCS) over NIO region (smooth curved line). The bar diagrams represent SCS during 1891–2015. The

For the past three decades, the number of SCS has somehow decreased to a considerable value (**Figure 4**). However, Mohanty et al. [75] demonstrated that there is a considerable increase of SCS by about 65% during the warming period 1951–2007 by analysing the genesis and intensity of TCs over NIO basin in yearly scale. In the southern sector of BOB, a considerable increase of ∼71% in SCS is found in post-monsoon season. Rate of dissipation of SCS over BOB is also significantly reduced besides increase in mean SST in the warming scenario and these features contribute to increase in the number of SCS over NIO. In the western sector of AS, a significant increase in SCS is also observed in the warming conditions. Therefore, the intensity of the SCS is increasingly becoming significant in the changing climate scenario. When the 'T Numbers' of the cyclones are analysed in satellite era, it is found that the Odisha super cyclone (1999)

dotted line indicates the moving trend and line shows the linear trend.

was the strongest recorded CS in the NIO basin during 1990–2015.

region [13, 16, 17, 77].

Out of 1108 cyclones formed during last 124 years, 751 (68%) have crossed east coast of India, 214 (19.31%) Bangladesh, 57 (5.18%) Myanmar, 63 (5.68%) west coast of India and 26 (2.3%) numbers of cyclones crossed the coastal regions between India and Pakistan affecting the economy of both the countries. According to studies by Tyagi et al. [78], over 60% of TCs formed over BOB suffer landfall in different parts of east coast of India, 30% strike coasts of Bangladesh and Myanmar and about 10% dissipate over the sea itself. The differences in observed percentages are because of the obvious reason, that is, consideration of different time periods. However, it is evident that NIO basin is quite significant in view of the TC occurrence and highly populated and economically growing south Asian region.
