Aneesh Subramanian

Aneesh Subramanian

Associate Professor
Associate Professor in the Department of Atmospheric and Oceanic Sciences at CU Boulder. Research spans weather and climate prediction, subseasonal-to-seasonal forecasting, atmospheric river dynamics, machine learning for Earth system modeling, and data assimilation in coupled ocean-atmosphere systems.

Towards Kriging-informed Conditional Diffusion for Regional Sea-Level Data Downscaling: A Summary of Results

Given coarser-resolution projections from global climate models or satellite data, the downscaling problem aims to estimate finer-resolution regional climate data, capturing …

subhankar-ghosh
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Time Series Classification of Supraglacial Lakes Evolution over Greenland Ice Sheet

The Greenland Ice Sheet (GrIS) has emerged as a significant contributor to global sea level rise, primarily due to increased meltwater runoff. Supraglacial lakes, which form on the …

emam-hossain
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Greenland Ice Sheet wide supraglacial lake evolution and dynamics: insights from the 2018 and 2019 melt seasons

Supraglacial lakes on the Greenland Ice Sheet (GrIS) can impact both the ice sheet surface mass balance and ice dynamics. Thus, understanding the evolution and dynamics of …

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Devon Dunmire
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Advancing Atmospheric River Science and Inspiring Future Development of the Atmospheric River Reconnaissance Program

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david-a-lavers
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STint: Self-supervised Temporal Interpolation for Geospatial Data

Supervised and unsupervised techniques have demonstrated the potential for temporal interpolation of video data. Nevertheless, most prevailing temporal interpolation techniques …

nidhin-harilal
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Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach

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subhankar-ghosh
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Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important …

subhankar-ghosh
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Quantifying Causes of Arctic Amplification via Deep Learning Based Time-Series Causal Inference

The warming of the Arctic, also known as Arctic amplification, is led by several atmospheric and oceanic drivers. However, the details of its underlying thermodynamic causes are …

sahara-ali
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Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference

The warming of the Arctic, also known as Arctic amplification, is led by several atmospheric and oceanic drivers. However, the details of its underlying thermodynamic causes are …

sahara-ali
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A Machine Learning Augmented Data Assimilation Method for High-Resolution Observation

The accuracy of initial conditions is an important driver of the forecast skill of numerical weather prediction models. Increases in the quantity of available measurements, in …

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Lucas Howard
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