M. Mohapatra

Simulation of Bay of Bengal Tropical Cyclones with WRF Model: Impact of Initial and Boundary Conditions

An attempt is made to delineate the relative performances and credentials of GFS, FNL, and NCMRWF global analyses/forecast products as initial and boundary conditions (IBCs) to the WRF-ARW model in the simulation of four Bay of Bengal tropical cyclones (TCs). The results suggest that FNL could simulate horizontal advection of vorticity maxima at 850 hPa; hence, the tracks are more realistic with least errors as compared to GFS and NCMRWF. The mean landfall errors for 24-, 48-, and 72-hour forecasts are 73, 41, and 72 km, respectively. The TC intensity is well captured by NCMRWF IBCs, as it could predict 850 hPa vorticity maxima. The 24-hour accumulated rainfall is well simulated with FNL, and equitable threat score is more than 0.2 up to 100 mm with minimum bias.

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model

The performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007‐11. The analyses are carried out with respectto1)basinsofformation,2)straight-movingandrecurvingTCs,3)TCintensityatmodelinitialization, and 4) season of occurrence. The impact of high resolution (18 and 9km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375km (7%‐51%) for a 12‐72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9km) predictions yield an improvement in mean track error for the NIO Basin by about 4%‐10% and 8%‐24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ;13%‐28% and 5%‐15% when compared with the 27- and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%‐40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.

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) 1877–1890, (iii) 1891–1960 and (iv) 1961–2010. The period of 1961–2010 may be further classified into (a) 1961–1973, (b) 1974–1990 and (c) 1991–2010. 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 1961–2010. 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.

The impact of satellite-derived wind data assimilation on track, intensity and structure of tropical cyclones over the North Indian Ocean

In the present satellite era, remote-sensing data are more useful to improve the initial condition of the model and hence the forecast of tropical cyclones (TCs) when they are in the deep oceans, where conventional observations are unavailable. In this study, an attempt is made to assess the impact of remotely sensed satellite-derived winds on initialization and simulation of TCs over the North Indian Ocean (NIO). For this purpose, four TCs, namely, ‘Nargis’, ‘Gonu’, ‘Sidr’ and ‘KhaiMuk’, are considered, with 13 different initial conditions. Two sets of numerical experiments, with and without satellite-derived wind data assimilation, are conducted using a high-resolution weather research and forecasting (WRF) model. The inclusion of satellite-derived winds through a three-dimensional variational (3DVAR) data assimilation system improves the initial position in 11 cases out of 13 by 34%. The 24-, 48-, 72- and 96-hour mean track forecast improves by 28%, 15%, 41% and 47%, respectively, based on 13 cases. The landfall prediction is significantly improved in 11 cases by about 37%. The intensity prediction also improves by 10–20%. Kinematic and thermodynamic structures of TCs are also better explained, as it could simulate heat and momentum exchange between sea surface and upper air. Due to better simulation of structure, intensity and track, the 24-hour accumulated rainfall intensity and distribution are also well predicted with the assimilation of satellite-derived winds.

Some characteristics of very heavy rainfall over Orissa during summer monsoon season

Orissa is one of the most flood prone states of India. The floods in Orissa mostly occur during monsoon season due to very heavy rainfall caused by synoptic scale monsoon disturbances. Hence a study is undertaken to find out the characteristic features of very heavy rainfall (24 hours rainfall ≥125 mm) over Orissa during summer monsoon season (June–September) by analysing 20 years (1980–1999) daily rainfall data of different stations in Orissa. The principal objective of this study is to find out the role of synoptic scale monsoon disturbances in spatial and temporal variability of very heavy rainfall over Orissa.Most of the very heavy rainfall events occur in July and August. The region, extending from central part of coastal Orissa in the southeast towards Sambalpur district in the northwest, experiences higher frequency and higher intensity of very heavy rainfall with less interannual variability. It is due to the fact that most of the causative synoptic disturbances like low pressure systems (LPS) develop over northwest (NW) Bay of Bengal with minimum interannual variation and the monsoon trough extends in west-northwesterly direction from the centre of the system. The very heavy rainfall occurs more frequently with less interannual variability on the western side of Eastern Ghat during all the months and the season except September. It occurs more frequently with less interannual variability on the eastern side of Eastern Ghat during September. The NW Bay followed by Gangetic West Bengal/Orissa is the most favourable region of LPS to cause very heavy rainfall over different parts of Orissa except eastern side of Eastern Ghat. The NW Bay and west central (WC) Bay are equally favourable regions of LPS to cause very heavy rainfall over eastern side of Eastern Ghat. The frequency of very heavy rainfall does not show any significant trend in recent years over Orissa except some places in north-east Orissa which exhibit significant rising trend in all the monsoon months and the season as a whole.

Spatio-temporal variability of summer monsoon rainfall over Orissa in relation to low pressure systems

The summer monsoon rainfall over Orissa occurs mostly due to low pressure systems (LPS) developing over the Bay of Bengal and moving along the monsoon trough. A study is hence undertaken to find out characteristic features of the relationship between LPS over different regions and rain-fall over Orissa during the summer monsoon season (June-September). For this purpose, rainfall and rainy days over 31 selected stations in Orissa and LPS days over Orissa and adjoining land and sea regions during different monsoon months and the season as a whole over a period of 20 years (1980-1999) are analysed. The principal objective of this study is to find out the role of LPS on spatial and temporal variability of summer monsoon rainfall over Orissa.The rainfall has been significantly less than normal over most parts of Orissa except the eastern side of Eastern Ghats during July and hence during the season as a whole due to a significantly less number of LPS days over northwest Bay in July over the period of 1980-1999. The seasonal rainfall shows higher interannual variation (increase in coefficient of variation by about 5%) during 1980-1999 than that during 1901-1990 over most parts of Orissa except northeast Orissa. Most parts of Orissa, especially the region extending from central part of coastal Orissa to western Orissa (central zone) and western side of the Eastern Ghats get more seasonal monsoon rainfall with the development and persistence of LPS over northwest Bay and their subsequent movement and persistence over Orissa. The north Orissa adjoining central zone also gets more seasonal rainfall with development and persistence of LPS over northwest Bay. While the seasonal rainfall over the western side of the Eastern Ghats is adversely affected due to increase in LPS days over west central Bay, Jharkhand and Bangladesh, that over the eastern side of the Eastern Ghats is adversely affected due to increase in LPS days over all the regions to the north of Orissa. There are significant decreasing trends in rainfall and number of rainy days over some parts of southwest Orissa during June and decreasing trends in rainy days over some parts of north interior Orissa and central part of coastal Orissa during July over the period of 1980-1999

Construction and Quality of Best Tracks Parameters for Study of Climate Change Impact on Tropical Cyclones over the North Indian Ocean during Satellite Era

India Meteorological Department (IMD) has the responsibility of monitoring and prediction of cyclonic disturbances (CDs) including tropical cyclone (TC) and depressions; collection, processing and archival of all data pertaining to CDs and preparation of best track data over the North Indian Ocean (NIO). A CD is classified based on the associated sustained surface wind (MSW) (IMD, 2003). The detailed classification over the NIO adopted by IMD is shown in Table 1. This classification has been used in this study for analyzing interannual variation of frequency and intensity of CDs over the NIO during satellite era (1961-2010).

Cyclone hazard proneness of districts of India

Hazards associated with tropical cyclones (TCs) are long-duration rotatory high velocity winds, very heavy rain, and storm tide. India has a coastline of about 7516 km of which 5400 km is along the mainland. The entire coast is affected by cyclones with varying frequency and intensity. Thus classification of TC hazard proneness of the coastal districts is very essential for planning and preparedness aspects of management of TCs. So, an attempt has been made to classify TC hazard proneness of districts by adopting a hazard criteria based on frequency and intensity of cyclone, wind strength, probable maximum precipitation, and probable maximum storm surge. Ninety-six districts including 72 districts touching the coast and 24 districts not touching the coast, but lying within 100 km from the coast have been classified based on their proneness. Out of 96 districts, 12 are very highly prone, 41 are highly prone, 30 are moderately prone, and the remaining 13 districts are less prone. This classification of coastal districts based on hazard may be considered for all the required purposes including coastal zone management and planning. However, the vulnerability of the place has not been taken into consideration. Therefore, composite cyclone risk of a district, which is the product of hazard and vulnerability, needs to be assessed separately through a detailed study.

Modeling Storm Surge and its Associated Inland Inundation Extent Due to Very Severe Cyclonic Storm Phailin

A hindcast simulation of storm surge and inundation from tropical cyclone Phalin, which made landfall at Odisha, India, on 12 October 2013, was carried out using ADCIRC model. Model-simulated inundation extent matched well with field surveys at Ganjam, Odisha, within a few days of landfall. Further, the model reproduced the temporal evolution of the surge residual with respect to observations from a tide gauge at Paradip (correlation 0.8, RMSE 0.26 m). However, the model marginally underestimated the magnitude with respect to observations, which can be attributed to the lack of wave setup in the model and uncertainty in wind and pressure information. The experiment also involved the use of two idealized scenarios, that is, variation of landfall timings with the ebbing and high tide phase. These scenarios were required for better understanding the sensitivity of inundation to the phase of tide in the model. Simulation with landfall at flooding (ebbing) tide showed greater (lower) inundation than the real scenario. Results from idealized scenarios confirmed the significance of the accuracy needed in forecasting landfall time. Our results clearly indicate that the overall performance of the model is good and therefore is of potential use as a tool to forewarn disaster management authorities.

A Great Escape from the Bay of Bengal ''Super Sapphire-Phailin'' Tropical Cyclone: A Case of Improved Weather Forecast and Societal Response for Disaster Mitigation

AbstractThe very severe cyclonic storm (VSCS) “Phailin (2013)” was the strongest cyclone that hit the eastern coast of the India Odisha state since the supercyclone of 1999. But the same story of casualties was not repeated as that of 1999 where approximately 10 000 fatalities were reported. In the case of Phailin, a record 1 million people were evacuated across 18 000 villages in both the Odisha and Andhra Pradesh states to coastal shelters following the improved operational forecast guidance that benefited from highly skillful and accurate numerical model guidance for the movement, intensity, rainfall, and storm surge. Thus, the property damage and death toll were minimized through the proactive involvement of three-tier disaster management agencies at central, state, and district levels.