Climate Modelling

Dr. Dhanyalekshmi K. Pillai is working on understanding the Carbon Cycle Dynamics in a changing Climate Scenario, which involves quantifying the geospatial distribution of sources and sinks of greenhouse gases at the scales relevant to policymakers. This requires implementaion of a chain of data assimilation procedures, utilizing heterogeneous data from muti-data streams as well as devising of modeling tools that are capable of resolving spatiotemporal scales on which the carbon-climate system operates and mitigation efforts matters. As for an example, it involves an efficient blending of huge sets of remote sensing data products from different satellite instruments to detect and quantify the biomass burning emission from the agricultural stubble burning over the Northwestern states of India. These remote-sensing products need to be evaluated and calibrated with ground truth datasets and farmer surveys. Additionally, these efforts can be properly implemented and facilitated by the atmospheric trajectory simulating models which again requires consistent assimilation of various data.

Dr. Pankaj Kumar is in the process of developing a Decision Support Systems (DSS) using Artificial Intelligence and Big-Data techniques to predict severe weather (e.g., heavy rainfall events, urban floods, heat and cold waves), monsoon active and break spells, tropical cyclones and associated damages well in advance with the land-fall errors. The recent increase in the frequency of extreme events such as severe thunderstorms, cyclones, heatwaves, urban flash flood etc. has forced scientists across the globe to try and develop reliable, dependable and robust techniques to predict their occurrences. According to Weather Analytics, adverse weather changes affect more than 33% of worldwide GDP, impacting numerous industries. Advancement in their real-time prediction can significantly reduce the associated infrastructural and mortal damages. The plethora of readily available data-sets is being used to deduce climate change signals and unearth significant information on issues like an increase in global temperature, emission rate of GHGs, and areas prone to natural calamities.

Dr. Sanjeev Jha is working on hydrological modelling, streamflow forecasting, and assessing the impact of climate change and variability on surface and ground water resources. In recent years, we have developed methodologies and algorithms aimed at quantifying spatial uncertainty and assimilating data in a variety of contexts. This includes for example: fusing river bathymetry measurements where the variety of sensors available results in a combination of low- and high-resolution data; modeling three-dimensional subsurface reservoirs and complex geomorphological patterns where the information available is very noisy and incomplete; spatial prediction of soil properties in case of data scarcity; downscaling land-atmospheric variables using data from remote sensing, satellites and general circulation models.