Best track parameters of tropical cyclones over the North Indian Ocean: a review

India Meteorological Department has the responsibility of monitoring and prediction of cyclonic disturbances (CDs) including tropical cyclone (TC) and depression, collection, processing and archival of all data pertaining to CDs and preparation of best track data over the North Indian Ocean (NIO). The process of post-season analysis of CDs to determine the best estimate of a CD??s position and intensity along with other characteristics during its lifetime is described as ??best tracking??. The best tracking procedure has undergone several changes world-over including NIO due to change in definition and classification of TCs, monitoring and analysis tools and procedure and physical understanding of TCs. There have been a few attempts to document the temporal changes in the best track procedure including changes in observational network, monitoring technique, area of responsibility for monitoring, terminology and classification of the TCs over the NIO. Hence, a study has been undertaken to review the temporal variations in all the above aspects of best tracking procedure and its impact on quality of best track parameters over the NIO. The problems and prospective with the best track data over the (NIO) have been presented and discussed. Based on quality and availability, the whole period of best track information may be broadly classified into four phases, viz. (i) pre-1877, (ii) 1877?C1890, (iii) 1891?C1960 and (iv) 1961?C2010. The period of 1961?C2010 may be further classified into (a) 1961?C1973, (b) 1974?C1990 and (c) 1991?C2010. As optimum observational network including satellite leading to better estimation of location and intensity without missing of CDs was available since 1961, the climatology of genesis, location, intensity, movement (track) and landfall can be best represented based on the data set of 1961?C2010. The best track parameters need to be reanalysed since 1891, based on the present criteria/classification of CDs to develop a digital data set of every six hourly position, intensity and other characteristics throughout the life period of each recorded CD over the NIO to meet the world standard. At least attempt should be made from 1974 when all types of major data including satellite, radar, surface and upper air observations are available for best track analysis. The reanalysis of best track parameters can help in better understanding and prediction of CDs and address the issues related to climate change aspects over the NIO region.     

Customization of WRF-ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean

The convection and planetary boundary layer (PBL) processes play significant role in the genesis and intensification of tropical cyclones (TCs). Several convection and PBL parameterization schemes incorporate these processes in the numerical weather prediction models. Therefore, a systematic intercomparison of performance of parameterization schemes is essential to customize a model. In this context, six combinations of physical parameterization schemes (2 PBL Schemes, YSU and MYJ, and 3 convection schemes, KF, BM, and GD) of WRF-ARW model are employed to obtain the optimum combination for the prediction of TCs over North Indian Ocean. Five cyclones are studied for sensitivity experiments and the out-coming combination is tested on real-time prediction of TCs during 2008. The tracks are also compared with those provided by the operational centers like NCEP, ECMWF, UKMO, NCMRWF, and IMD. It is found that the combination of YSU PBL scheme with KF convection scheme (YKF) provides a better prediction of intensity, track, and rainfall consistently. The average RMSE of intensity (13?hPa in CSLP and 11?m?s^?1 in 10-m wind), mean track, and landfall errors is found to be least with YKF combination. The equitable threat score (ETS) of YKF combination is more than 0.2 for the prediction of 24-h accumulated rainfall up to 125?mm. The vertical structural characteristics of cyclone inner core also recommend the YKF combination for Indian seas cyclones. In the real-time prediction of 2008 TCs, the 72-, 48-, and 24-h mean track errors are 172, 129, and 155?km and the mean landfall errors are 125, 73, and 66?km, respectively. Compared with the track of leading operational agencies, the WRF model is competing in 24?h (116?km error) and 72?h (166?km) but superior in 48-h (119?km) track forecast.     

Predictability of landfall location and surge height of tropical cyclones over North Indian Ocean (NIO)

Natural Hazards - Forecasting, with precision, the location of landfall and the height of surge of cyclonic storms prevailing over any ocean basin is very important to cope with the associated...     

Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification

The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T??number. The result of the study reveals that the forecast error with MLP model is minimum (4.70?%) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62?%. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8?%, respectively. The models provide the forecast beyond 72?h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models.     

Seasonal forecasting of tropical cyclogenesis over the North Indian Ocean

Over the North Indian Ocean (NIO) and particularly over the Bay of Bengal (BoB), the post-monsoon season from October to December (OND) are known to produce tropical cyclones, which cause damage to life and property over India and many neighbouring countries. The variability of frequency of cyclonic disturbances (CDs) during OND season is found to be associated with variability of previous large-scale features during monsoon season from June to September, which is used to develop seasonal forecast model of CDs frequency over the BoB and NIO based on principal component regression (PCR). Six dynamical/thermodynamical parameters during previous June–August, viz., (i) sea surface temperature (SST) over the equatorial central Pacific, (ii) sea level pressure (SLP) over the southeastern equatorial Indian Ocean, (iii) meridional wind over the eastern equatorial Indian Ocean at 850 hPa, (iv) strength of upper level easterly, (v) strength of monsoon westerly over North Indian Ocean at 850 hPa, and (vi) SST over the northwest Pacific having significant and stable relationship with CDs over BoB in subsequent OND season are used in PCR model for a training period of 40 years (1971–2010) and the latest four years (2011–2014) are used for validation. The PCR model indicates highly significant correlation coefficient of 0.77 (0.76) between forecast and observed frequency of CD over the BoB (NIO) for the whole period of 44 years and is associated with the root mean square error and mean absolute error ≤ 1 CD. With respect to the category forecast of CD frequency over BoB and NIO, the Hit score is found to be about 63% and the Relative Operating Curves (ROC) for above and below normal forecast is found to be having much better forecast skill than the climatology. The PCR model performs very well, particularly for the above and below normal CD year over the BoB and the NIO, during the test period from 2011 to 2014.     

Identification of clusters in tropical cyclone tracks of North Indian Ocean

Tropical cyclones are a key climate-related hazard in South Asia. Assessment of the risk of cyclone impacts requires a comprehensive characterization of historical cyclone climatology. This study analyzes the tracks of tropical cyclones in the North Indian Ocean. Based on their spatial characteristics, cyclone tracks appear to be grouped into five well-defined clusters. These clusters correspond to distinct regions of cyclonic activity and exhibit differences in characteristics such as genesis location, probability of landfall, duration, and maximum intensity. Some of the identified clusters appear particularly important with regard to impacts because events in these clusters have greater landfall probability and are more intense. The clustering approach is likely to provide useful insights for the characterization of cyclone risk.     

Temporal variation of tropical cyclones in the North Atlantic Basin

A total of 269 tropical storms and hurricanes originated in the North Atlantic basin from 1960–1989. Of these, 76 made landfall on the continental United states. This study divides the 76 tropical storms into their month of formation. Seasonal shifts in the principal areas of tropical cyclone formation over the Atlantic basin have been recognized for many decades. The results of the study suggest that the early and late season tropical cyclones develop in areas which are first affected by the position of the sun, resulting in an increase in water temperatures. These cyclones normally make landfall along the Gulf Coast and usually are of low intensity. Formation areas shift eastward in mid-summer with a slight increase in intensity. By late August and early September, the formation areas have extended to the Cape Verde Islands. These storms tend to strike the east coast of the US and are normally more intense. By the end of the hurricane season, the primary formation area has shifted back to the Gulf of Mexico, with low intensity storms affecting the Gulf Coast.     

Air-sea interaction over the tropical Indian Ocean during several contrasting monsoon seasons

Monthly mean anomaly fields of various parameters like sea surface temperature, air temperature, wind stress, effective radiation at the surface, heat gain over the ocean and the total heat loss between a good and bad monsoon composite and the evaporation rates over the Arabian Sea and southern hemisphere have been studied over the tropical Indian Ocean. The mean rates of evaporation on a seasonal scale over the Arabian Sea during a good and bad monsoon composites were equal (about 2·48 × 10¹⁰ tons/day). The evaporation rates over the southern hemisphere were greater during all the months. The mean evaporation rates over the southern hemisphere on a seasonal scale for the good and bad monsoon composites were 4·4 × 10¹⁰ and 4·6 × 10¹⁰ tons/day respectively. The maximum evaporation rates over the southern hemisphere were observed in August. The anomalies of wind stress, effective radiation at the surface and the heat gain over the ocean also exhibit large variations in August, as compared to other monsoon months.     

Track prediction of Indian Ocean cyclones using Lagrangian advection model

A new model has been developed for track prediction of Indian Ocean cyclones. The model utilizes environmental steering flow using the forecasts from a high-resolution global model and the effect due to earth??s rotation (the beta-effect) to determine the future movement of cyclone. A new approach based on vertical profile of potential vorticity is used to determine weights for different vertical levels for computation of mean steering current. Despite the fact that the model is based on the dynamical framework, the operational cost and time for running the model is only a fraction of what is needed by a normal numerical weather prediction model. This new approach will enhance flexibility in defining the initial position of the cyclone in the model, and also, it is possible to create a large ensemble of predicted tracks to assess the impact of the uncertainty of initial cyclone position on the predicted tracks. The performance of the model for ten cyclones, viz. GONU (02?C08 Jun, 2007), SIDR (11?C16 November, 2007), NARGIS (27 Apr?C04 May, 2008), RASHMI (25?C27 October, 2008) KHAI-MUK (14?C16 November, 2008), NISHA (25?C27 November, 2008), SEVEN (04?C08 December, 2008), BIJLI (14?C18 April, 2009), AILA (23?C26 May, 2009), and PHYAN (09?C11 November, 2009), have been tested in the present study. The forecast errors of the present model have been computed with respect to the Joint Typhoon Warning Center best track analysis positions. The forecast skill improvement (mean of ten cyclones) of the model with respect to the Climatology and Persistence (CLIPER) statistical model varies from 7 to 67?% between 12 and 72?h.     

Sensitivity of inland decay of North Atlantic tropical cyclones to soil parameters

Using the HURDAT best track analysis of track and intensity of tropical cyclones that made landfall over the continental United States during the satellite era (1980?C2005), we analyze the role of land surface variables on the cyclone decay process. The land surface variables considered in the present study included soil parameters (soil heat capacity and its surrogate soil bulk density), roughness, topography and local gradients of topography. The sensitivity analysis was carried out using a data-adaptive genetic algorithm approach that automatically selects the most suitable variables by fitting optimum empirical functions that estimates cyclone intensity decay in terms of given observed variables. Analysis indicates that soil bulk density (soil heat capacity) has a dominant influence on cyclone decay process. The decayed inland cyclone intensities were found to be positively correlated with the cube of the soil bulk density (heat capacity). The impact of the changes in soil bulk density (heat capacity) on the decayed cyclone intensity is higher for higher intensity cyclones. Since soil bulk density is closely related to the soil heat capacity and inversely proportional to the thermal diffusivity, the observed relationship can also be viewed as the influence of cooling rate of the land surface, as well as the transfer of heat and moisture underneath a land-falling storm. The optimized prediction function obtained by statistical model processes in the present study that predicts inland intensity changes during 6-h interval showed high fitness index and small errors. The performance of the prediction function was tested on inland tracks of eighteen hurricanes and tropical storms that made landfall over the United States between 2001 and 2010. The mean error of intensity prediction for these cyclones varied from 1.3 to 15.8 knots (0.67?C8.12?m?s^?1). Results from the data-driven analysis thus indicate that soil heat flux feedback should be an important consideration for the inland decay of tropical cyclones. Experiments were also undertaken using Weather Research Forecasting (WRF) Advanced Research Version (ARW ver 3.3) to assess the sensitivity of the soil parameters (roughness, heat capacity and bulk density) on the post-landfall structure of select storms. The model was run with 1-km grid spacing, limited area single domain with boundary conditions from the North American Regional Reanalysis. Of different experiments, only the surface roughness change and soil bulk density (heat capacity) change experiments showed some sensitivity to the intensity change. The WRF results thus have a low sensitivity to the land parameters (with only the roughness length showing some impact). This calls for reassessing the land surface response on post-landfall characteristics with more detailed land surface representation within the mesoscale and hurricane modeling systems.     

Seasonal cycle of surface circulation in the coastal North Indian Ocean

Monthly-mean winds and currents have been used to identify the driving mechanisms of seasonal coastal circulation in the North Indian Ocean. The main conclusions are: (i) the surface circulation off Arabia is typical of a wind-driven system with similar patterns of longshore current and wind stress; (ii) circulation off the west coast of India is consistent with the dynamics of a wind-driven eastern boundary current only during the southwest monsoon. During the northeast monsoon it is possible that the influence of the interior flow is important. (iii) There are at least three mechanisms that influence the surface circulation off the east coast of India: wind-stress, influence of fresh-water run off and contribution of the interior flow. It is difficult at present to assess the relative importance of these three processes.     

New evidence on the sequence of deglacial warming in the tropical Indian Ocean

The transition from the Last Glacial Maximum to the Holocene was an internal of climate variability that was characterised by large spatial and temporal variations. Here we show that deglaciation warming in the northern Indian Ocean was initiated ca. 19 ka, which is contemporary with deglaciation warming in the Antarctica and Southern Ocean. A gradual warming occurred during the glacial/Holocene transition in the northern Indian Ocean, unlike the two‐step warming seen in Greenland and the North Atlantic. Synchronous deglacial warming ca. 19 ka in Antarctica and the northern Indian Ocean suggests a strong connection in the propagation of climate signals between Antarctica and the Indian Ocean, probably through the Indonesian Throughflow and/or Subantarctic Mode Water. Copyright © 2010 John Wiley & Sons, Ltd.     

Altimeter data assimilation in the tropical Indian Ocean using water property conserving scheme

Altimeter data have been assimilated in an ocean general circulation model using the water property conserving scheme. Two runs of the model have been conducted for the year 2004. In one of the runs, altimeter data have been assimilated sequentially, while in another run, assimilation has been suppressed. Assimilation has been restricted to the tropical Indian Ocean. An assessment of the strength of the scheme has been carried out by comparing the sea surface temperature (SST), simulated in the two runs, with in situ derived as well as remotely sensed observations of the same quantity. It has been found that the assimilation exhibits a significant positive impact on the simulation of SST. The subsurface effect of the assimilation could be judged by comparing the model simulated depth of the 20°C isotherm (hereafter referred to as D20), as a proxy of the thermocline depth, with the same quantity estimated from ARGO observations. In this case also, the impact is noteworthy. Effect on the dynamics has been judged by comparison of simulated surface current with observed current at a moored buoy location, and finally the impact on model sea level forecast in a free run after assimilation has been quantified in a representative example.     

Validation of a satellite-based cyclogenesis technique over the North Indian Ocean

Indian region is severely affected by the tropical cyclones (TCs) due to the long coast line of about 7500 km. Hence, whenever any low level circulation (LLC) forms over the Indian Seas, the prediction of its intensification into a TC is very essential for the management of TC disaster. Satellite Application Centre (SAC) of Indian Space Research Organization (ISRO), Ahmedabad, has developed a technique to predict TCs based on scatterometer-derived winds from the polar orbiting satellite, QuikSCAT and Oceansat-II. The India Meteorological Department (IMD) has acquired the technique and verified it for the years 2010–2013 for operational use. The model is based on the concept of analogs of the sea surface wind distribution at the stage of LLC or vortex (T1.0) as per Dvorak’s classifications, which eventually leads to cyclogenesis (T2.5). The results indicate that the developed model could predict cyclogenesis with a probability of detection of 61% and critical success index of 0.29. However, it shows high over-prediction of the model is better over the Bay of Bengal than over Arabian Sea and during post-monsoon season (September–December) than in pre-monsoon season (March–June).     

Appraisal of the prevalence of severe tropical storms over Indian Ocean by screening the features of tropical depressions

Natural Hazards - Tropical cyclones are one of the nature’s most violent manifestations and potentially the deadliest of all meteorological phenomena. It is a unique combination of violent...     

Steroid geochemistry in the oxygen minimum zone of the eastern tropical North Pacific Ocean

Particulate matter was collected in the eastern tropical North Pacific Ocean, an area characterized by a shallow and intense oxygen minimum zone, in order to investigate the geochemistry of particulate organic matter in the ocean. Sinking, large particles and suspended, small particles were analyzed for sterols, 3-ketosteroids, 3-methoxy-steroids and sterenes. Vertical fluxes of steroid classes in sinking particles and concentrations of steroids in suspended particles decreased rapidly below the euphotic zone consistent with upper ocean sources and deep water decomposition. Significant compositional changes were observed with increasing depth and as a function of particle size. Sinking particles were enriched in C₂₇-Sterols but deficient in C₂₈ and C₂₉-sterols compared to the suspended particles. Suspended particles, especially in the oxygen minimum zone, were enriched in steroidal ketones and sterenes compared to sinking particles. Steroid distributions suggest that the oxygen minimum zone is the site of active “diagenetic” transformations, most likely microbially-mediated, of stenols to steroidal ketones, stanols and sterenes. These transformations occur preferentially in suspended particles relative to sinking particles.