Photo: Siddharth Kankaria / Research Matters
The high-impact extreme weather, such as severe thunderstorms, plays havoc with normal life. In India, thunderstorms kill and injure more than 500 people every year, and cause severe damage to livestock and crops. Although there exist models to predict severe weather phenomena, like thunderstorms, they either fail to warn the concerned people early enough or do so with very less accuracy. Researchers around the world have been trying to develop better models to help avert major disasters and a recent joint venture among Indian Institute of Technology (IIT) Bhubaneswar, National Institute of Technology (NIT), Rourkela and Purdue University lead to the creation of a model that can better predict severe thunderstorms. For the first time, they have tried to improve upon the existing forecast models for the Indian Monsoon Region (IMR), to specifically predict severe thunderstorms.
"Severe thunderstorms are a dominant weather feature during the pre-monsoon season over eastern and north-eastern parts of India (Gangetic West Bengal, Jharkhand, Odisha, Bihar, and Assam). These storms generally move from the northwest to the southeast and disrupt everyday life and cause widespread loss of human and animal life”, explains Prof. U C Mohanty from IIT- Bhubaneswar and a co-author of this work.
The researchers have used actual land surface parameters like the amount of vegetation, land use, and land cover patterns, along with atmospheric data like temperature, humidity, wind speeds, surface pressure, elevation and total rainfall rate. These parameters, the researchers believe, can lead to better prediction of the onset and propagation of a thunderstorm. The researchers have also addressed a major challenge often faced in running a weather prediction model -- the unavailability of proper in-situ data. They have generated a high-resolution dataset from HRLDAS (High-Resolution Land Data Assimilation System) model, which can provide the necessary parameters for the model.
The proper selection of model domain also played a key role in the study. Based on the Severe Thunderstorms—Observations and Regional Modelling (STORM) programme initiated by the Department of Science and Technology (DST) and Ministry of Earth Sciences (MoES), locations like Ranchi and Kharagpur were identified as important sites for the genesis and peak intensity during the life cycle of a thunderstorm as it moves from north-westward to south-eastwards. In addition, these two locations are equipped with highly sensitive instruments, which are able to track thunderstorms/rainfall associated with severe weather. The main modeling and analysis was conducted for the pre-monsoon month May of 2007, 2009 and 2010 since six major thunderstorms were detected in the study area during this time.
The results of the study have major ramifications over the IMR. In general, the authors were able to conclude that due to the application of surface variables, the subsequent forecasts could be improved significantly. The inclusion of those variables allowed HRLDAS to accurately predict the path of a storm and also the amount of rainfall (precise to within ~10 mm) it would bring. As of now, a limited study has been performed using HRLDAS-derived land surface fields, but, it is proposed to run for a larger area using several other climatological parameters, across a large part of the Indian subcontinent.
"The HRLDAS model was initially integrated experimentally for the chosen study area. Later, we developed these products for the entire country at 4 km resolution for the period 2000 – 2014. A rigorous verification has also been done to assess the reliability of these models over the IMR under the on-going National Monsoon Mission (NMM) project initiated by the Ministry of Earth Sciences (MoES), Government of India", explains Prof. Mohanty.
The study is one of the several attempts that are focused on improved prediction of the weather – a challenge posed by Nature – and thus saving many lives. The future may not be far when we can claim victory in this challenge, armed with better models and technology.