Below highlights research projects supported by NSF HDR iHARP Grant 2118285. iHARP research is guided by a specific focus area with a senior faculty member serving as lead.
Data
Chhaya Kulkarni, UMBC Research Assistant, is analyzing snow and ice conditions in Southeast Greenland to understand the patterns of snowmelt and how patterns have changed over the years. She uses satellite observations and ERA5 reanalysis data covering more than 17 years. Her goal is to validate and improve the accuracy of modeled climate data. The researchers believe that this study will help improve our understanding of climate shift and its effects on the Arctic region.
Data & Modeling
Akila Sampath, UMBC Research Assistant, is developing a physics-informed machine learning model to predict snow depth, which is an important factor in Arctic sea ice loss. And sea ice loss in the Arctic is a major sign of rapid environmental change. Traditional climate models have had trouble capturing the complex interactions between the atmosphere, ocean, and sea ice. Machine learning can analyze large datasets and find patterns, but most models in this area don’t use domain knowledge from physics. Akila’s research fills this gap. This makes it more accurate and helps us understand seasonal changes in snow depth better. Her work aims to improve how we understand climate shifts and support better decision-making for climate policy.
Prediction