Simulation of Storm Surges in the Bay of Bengal Using One-Way Coupling Between NMM-WRF and IITD Storm Surge Model

The storm surge associated with severe tropical cyclones (TCs) in the Bay of Bengal (BoB) is a serious concern along the coastal regions of India, Bangladesh, Myanmar, and Sri Lanka. It is one of the most hazardous elements associated with landfalling TCs other than strong winds and heavy precipitation and about 75% of the casualities in this region are attributed to storm surges. Therefore, it is highly essential to predict the storm surges with greater accuracy at least 2 days in advance for effective evacuation. In the present study, an attempt is made to simulate the storm surges associated with severe TCs in the BoB using one-way coupling of the Non-hydrostatic Mesoscale Model core of Weather Research and Forecasting (NMM-WRF) system with the two-dimensional finite-difference storm surge model developed at the Indian Institute of Technology Delhi (IITD). The NMM-WRF model simulated track, pressure drop, and radius of maximum wind are used to calculate the wind-stress through Jelesnianski wind formulation. The results are compared with the observed/estimated values as provided by the operational/meteorological agencies of India, Bangladesh, and Myanmar. This study suggests that using simulated surface meteorological fields of a high-resolution mesoscale model, the storm surge can be predicted at least 2 days in advance of the actual landfall of TCs with reasonable accuracy. This approach will be helpful in providing disastrous storm warning well in advance in a coastal region, which will help with rapid evacuation from the vulnerable coastal region, relocation as well as protection of valuables, disaster mitigation, and coastal zone management.     

Experimental Calibration of ISRM Suggested Fracture Toughness Measurement Techniques in Selected Brittle Rocks

Summary A wide variety of specimen types and methods are employed in fracture toughness measurement of rocks, which result in scattered values for the same rock type. In order to provide some consistency to the values, the International Society for Rock Mechanics (ISRM) recommended three suggested methods using core based specimens, the Chevron Bend (CB) test, the Short Rod (SR) test and the Cracked Chevron Notch Brazilian Disc (CCNBD) test. This standardization helped obtain more consistent values but still a variation of 20–30% was observed in the values of fracture toughness obtained with the CB and SR methods. The values obtained with the CCNBD method were found to be consistently lower (30–50%) than those of the other two methods (CB and SR). Many reasons have been offered to explain this deviation. These include size of the specimen, anisotropy of rock, a dimensionless parameter in the fracture toughness calculation equation for the CCNBD test, etc. A comprehensive test program was initiated to identify the cause of these discrepancies between the CB and CCNBD methods. Three brittle rock types were selected for the study and more than 200 tests were conducted to measure the values of fracture toughness. A rigorous statistical analysis was carried out to determine the confidence level and find the significance of the test results. It was found that the CB and CCNBD methods were very comparable provided the correct equation for fracture toughness calculation was used for the CCNBD method and the size of the specimens was selected carefully. The error in the ISRM 1995 formula of fracture toughness for the CCNBD method could be the major factor responsible for the consistently lower values obtained with the method.     

Vulnerability from storm surges and cyclone wind fields on the coast of Andhra Pradesh,India

The results presented here are from a study conducted for the government of the state of Andhra Pradesh (GOAP) in India, as part of a World Bank project on cyclone mitigation. A set of detailed maps were prepared depicting the Physical Vulnerability (PV), specifically storm surge inundation zones are shown for frequent occurrence, 50-year return period, likely scenario for global warming and extreme global warming. Similarly vulnerable areas from strong wind field from tropical cyclones (TCS) are also presented for the same four parameters. Vulnerability zones are presented from a social point of view also based upon certain socio-economic parameters that were included in determining the overall vulnerability of each Mandal in a coastal district (a Mandal represents a group of villages and towns) include: population, senior citizens, women, children under different age groups, type of housing, income level, cyclone shelters, hospitals and medical centres, schools and caste based population. The study is about scenarios that could happen if global warming and the predicted intensification of TCS actually occur as predicted by some numerical models.     

Impact of sea surface temperature in modulating movement and intensity of tropical cyclones

It is well recognized that sea surface temperature (SST) plays a dominant role in the formation and intensification of tropical cyclones. A number of observational/empirical studies were conducted at different basins to investigate the influence of SST on the intensification of tropical cyclones and in turn, modification in SST by the cyclone itself. Although a few modeling studies confirmed the sensitivity of model simulation/forecast to SST, it is not well quantified, particularly for Bay of Bengal cyclones. The present study is designed to quantify the sensitivity of SST on mesoscale simulation of an explosively deepening storm over the Bay of Bengal, i.e., Orissa super cyclone (1999). Three numerical experiments are conducted with climatological SST, NCEP (National Center for Environmental Prediction) skin temperature as SST, and observed SST (satellite derived) toward 5-day simulation of the storm using mesoscale model MM5. At model initial state, NCEP skin temperature and observed SST over the Bay of Bengal are 1–2°C warmer than climatological SST, but cooler by nearly 1°C along the coastline. Observed SST shows a number of warm patches in the Bay of Bengal compared with NCEP skin temperature. The simulation results indicate that the sea surface temperature has a significant impact on model-simulated track and intensity of the cyclonic storm. The track and intensity of the storm is better simulated with the use of satellite-observed SST.     

Interannual variability of onset of the summer monsoon over India and its prediction

In this article, the interannual variability of certain dynamic and thermodynamic characteristics of various sectors in the Asian summer monsoon domain was examined during the onset phase over the south Indian peninsula (Kerala Coast). Daily average (0000 and 1200 UTC) reanalysis data sets of the National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) for the period 1948–1999 were used. Based on 52 years onset date of the Indian summer monsoon, we categorized the pre-onset, onset, and post-onset periods (each an average of 5 days) to investigate the interannual variability of significant budget terms over the Arabian Sea, Bay of Bengal, and the Indian peninsula. A higher difference was noticed in low-level kinetic energy (850 hPa) and the vertically integrated generation of kinetic energy over the Arabian Sea from the pre-onset, onset, and post-onset periods. Also, significant changes were noticed in the net tropospheric moisture and diabatic heating over the Arabian Sea and Indian peninsula from the pre-onset to the post-onset period. It appears that attaining the magnitude of 40 m² s⁻² and then a sharp rise in kinetic energy at 850 hPa is an appropriate time to declare the onset of the summer monsoon over India. In addition to a sufficient level of net tropospheric moisture (40 mm), a minimum strength of low-level flow is needed to trigger convective activity over the Arabian Sea and the Bay of Bengal. An attempt was also made to develop a location-specific prediction of onset dates of the summer monsoon over India based on energetics and basic meteorological parameters using multivariate statistical techniques. The regression technique was developed with the data of May and June for 42 years (1948–1989) and validated with 10 years NCEP reanalysis from 1990 to 1999. It was found that the predicted onset dates from the regression model are fairly in agreement with the observed onset dates obtained from the Indian Meteorology Department.     

Application of weather prediction models for hazard mitigation planning: a case study of heavy off-season rains in Senegal

Heavy off-season rains in the tropics pose significant natural hazards largely because they are unexpected and the popular infrastructure is ill-prepared. One such event was observed from January 9 to 11, 2002 in Senegal (14.00° N, 14.00°␣W), West Africa. This tropical country is characterized by a long dry season from November to April or May. During this period, although the rain-bearing monsoonal flow does not reach Senegal, the region can occasionally experience off-season rains. We conducted a numerical simulation of the January 9–11, 2002 heavy off-season rain using the Fifth-Generation NCAR/Pennsylvania State University Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) model. The objective was to delineate the meteorological set-up that led to the heavy rains and flooding. A secondary objective was to test the model’s performance in Senegal using relatively simpler (default) model configurations and local/regional observations. The model simulations for both MM5 and WRF agree satisfactorily with the observations, particularly as regards the wind patterns, the intensification of the rainfall, and the associated drop in temperatures. This situation provided the environment for heavy rainfall accompanied by a cold wave. The results suggest that off-the-shelf weather forecast models can be applied with relatively simple physical options and modest computational resources to simulate local impacts of severe weather episodes. In addition, these models could become part of regional hazard mitigation planning and infrastructure.     

Inter-annual variability of summer monsoon rainfall over Orissa (India) in relation to cyclonic disturbances

The summer monsoon rainfall over Orissa, a state on the eastern coast of India, is more significantly related than Indian summer monsoon rainfall (ISMR) to the cyclonic disturbances developing over the Bay of Bengal. Orissa experiences floods and droughts very often due to variation in the characteristics of these disturbances. Hence, an attempt was made to find out the inter-annual variability in the rainfall over Orissa and the frequencies of different categories of cyclonic disturbances affecting Orissa during monsoon season (June–September). For this purpose, different statistical characteristics, such as mean, coefficient of variation, trends and periodicities in the rainfall and the frequencies of different categories of cyclonic disturbances affecting Orissa, were analysed from 100 years (1901–2000) of data. The basic objective of the study was to find out the contribution of inter-annual variability in the frequency of cyclonic disturbances to the inter-annual variability of monsoon rainfall over Orissa. The relationship between summer monsoon rainfall over Orissa and the frequency of cyclonic disturbances affecting Orissa shows temporal variation. The correlation between them has significantly decreased since the 1950s. The variation in their relationship is mainly due to the variation in the frequency of cyclonic disturbances affecting Orissa. The variability of both rainfall and total cyclonic disturbances has been above normal since the 1960s, leading to more floods and droughts over Orissa during recent years. The inter-annual variability of seasonal rainfall over Orissa and the frequency of cyclonic disturbances affecting Orissa during monsoon season show a quasi-biennial oscillation period of 2–2.8 years. There is least impact of El Nino southern oscillation (ENSO) on inter-annual variability of both the seasonal rainfall over Orissa and the frequencies of monsoon depressions/total cyclonic disturbances affecting Orissa.     

An Objective Approach for Prediction of Daily Summer Monsoon Rainfall over Orissa (India) due to Interaction of Mesoscale and Large-scale Synoptic Systems

Orissa State, a meteorological subdivision of India, lies on the east coast of India close to north Bay of Bengal and to the south of the normal position of the monsoon trough. The monsoon disturbances such as depressions and cyclonic storms mostly develop to the north of 15° N over the Bay of Bengal and move along the monsoon trough. As Orissa lies in the southwest sector of such disturbances, it experiences very heavy rainfall due to the interaction of these systems with mesoscale convection sometimes leading to flood. The orography due to the Eastern Ghat and other hill peaks in Orissa and environs play a significant role in this interaction. The objective of this study is to develop an objective statistical model to predict the occurrence and quantity of precipitation during the next 24 hours over specific locations of Orissa, due to monsoon disturbances over north Bay and adjoining west central Bay of Bengal based on observations to up 0300 UTC of the day. A probability of precipitation (PoP) model has been developed by applying forward stepwise regression with available surface and upper air meteorological parameters observed in and around Orissa in association with monsoon disturbances during the summer monsoon season (June-September). The PoP forecast has been converted into the deterministic occurrence/non-occurrence of precipitation forecast using the critical value of PoP. The parameters selected through stepwise regression have been considered to develop quantitative precipitation forecast (QPF) model using multiple discriminant analysis (MDA) for categorical prediction of precipitation in different ranges such as 0.1–10, 11–25, 26–50, 51–100 and >100 mm if the occurrence of precipitation is predicted by PoP model. All the above models have been developed based on data of summer monsoon seasons of 1980–1994, and data during 1995–1998 have been used for testing the skill of the models. Considering six representative stations for six homogeneous regions in Orissa, the PoP model performs very well with percentages of correct forecast for occurrence/non-occurrence of precipitation being about 96% and 88%, respectively for developmental and independent data. The skill of the QPF model, though relatively less, is reasonable for lower ranges of precipitation. The skill of the model is limited for higher ranges of precipitation. accepted September 2006