CV

Education

• Ph.D in Climate System from Pusan National University, 2023
• M.S. in Ocean and Atmospheric Sciences from University of Hyderabad, 2016
• B.S. in Physics (Honors) from Delhi University, 2014

Research experience

• Postdoctoral Researcher, Earth Commons Institute, Georgetown University
  • Developing advanced machine learning algorithms to predict long-term changes in precipitation patterns and assess climate risk.
• Ph.D. Researcher, Earth System Predictability Lab, IBS Center for Climate Physics, South Korea
  • Investigated atmospheric rivers and their responses to a changing climate using ultra-high-resolution CESM climate models.
  • Developed a novel algorithm for detecting extreme weather events in large-scale climate datasets.
  • Co-developed machine learning tools to improve climate data downscaling for higher-resolution projections.
• Research Assistant, National Institute of Oceanography, Goa, India
  • Mapped Indian Ocean currents using data from NOAA and INCOIS, creating a high-resolution climatology of ocean currents.
  • Applied artificial neural networks to fill gaps in sea surface temperature data and predict sea surface temperature fronts.
• Summer Intern, Indian Academy of Sciences, India
  • Analyzed CODAR (Coastal Ocean Dynamics Application Radar) data from the Gulf of Khambhat, off the Gujarat coast.
  • Conducted harmonic analysis to improve tide prediction accuracy.
• Research Intern, Interdisciplinary Innovation Project, Delhi University
  • Conducted data analysis and field studies to assess the energy efficiency, light characteristics, and economic feasibility of LEDs.

Skills

• Python
  • Feature detection from large climate data
  • Machine learning tools for climate downscaling and future projections
• MATLAB: Developed artificial neural network (ANN) interpolation methods for observational data.
• R: Built an R-based codebase for satellite data interpolation.
• FORTRAN: Developed algorithms for harmonic analysis and climate model simulations.
• Computational Environments: Experienced in virtual machines (VMs) and containerized environments (e.g., Docker, Singularity) for scalable scientific computing.
• High Performance Computing: Developed computational and data analysis tools for HPC environments, with experience in Shell and Bash scripting.

Publications

Atmospheric river detection under changing seasonality and mean‐state climate: ARTMIP tier 2 paleoclimate experiments

Rush, William Davis, Juan Manuel Lora, C. B. Skinner, S. A. Menemenlis, Christine A. Shields, P. Ullrich, Travis Allen O’Brien et al. "Atmospheric river detection under changing seasonality and mean‐state climate: ARTMIP tier 2 paleoclimate experiments." Journal of Geophysical Research: Atmospheres 130, no. 1 (2025): e2024JD042222.

Anthropogenically Driven Changes in the Carbon to Phosphorus Ratio of Marine Dissolved Organic Matter

Sreeush, Mohanan Geethalekshmi, Eun Young Kwon, Sun‐Seon Lee, and Arjun Babu Nellikkattil. "Anthropogenically driven changes in the carbon to phosphorus ratio of marine dissolved organic matter." Global Biogeochemical Cycles 38, no. 6 (2024): e2023GB008069.

Scalable Feature Extraction and Tracking (SCAFET): a general framework for feature extraction from large climate data sets

Nellikkattil, Arjun Babu, Danielle Lemmon, Travis Allen O`Brien, June-Yi Lee, and Jung-Eun Chu. "Scalable Feature Extraction and Tracking (SCAFET): a general framework for feature extraction from large climate data sets." Geoscientific Model Development 17, no. 1 (2024): 301-320.

Increased amplitude of atmospheric rivers and associated extreme precipitation in ultra-high-resolution greenhouse warming simulations

Nellikkattil, Arjun Babu, June-Yi Lee, Bin Guan, Axel Timmermann, Sun-Seon Lee, Jung-Eun Chu, and Danielle Lemmon. "Increased amplitude of atmospheric rivers and associated extreme precipitation in ultra-high-resolution greenhouse warming simulations." Communications Earth & Environment 4, no. 1 (2023): 313.

Future sea-level projections with a coupled atmosphere-ocean-ice-sheet model

Park, Jun-Young, Fabian Schloesser, Axel Timmermann, Dipayan Choudhury, June-Yi Lee, and Arjun Babu Nellikkattil. "Future sea-level projections with a coupled atmosphere-ocean-ice-sheet model." Nature Communications 14, no. 1 (2023): 636.

The East Asian summer monsoon response to global warming in a high resolution coupled model: mean and extremes

Liu, Zhen, Sun-Seon Lee, Arjun Babu Nellikkattil, June-Yi Lee, Lan Dai, Kyung-Ja Ha, and Christian LE Franzke. "The East Asian summer monsoon response to global warming in a high resolution coupled model: mean and extremes." Asia-Pacific Journal of Atmospheric Sciences 59, no. 1 (2023): 29-45.

Downscaling earth system models with deep learning

Park, Sungwon, Karandeep Singh, Arjun Nellikkattil, Elke Zeller, Tung Duong Mai, and Meeyoung Cha. "Downscaling earth system models with deep learning." In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp. 3733-3742. 2022.

Aiding the Earth System Models with Super Resolution Deep Learning

Mai, Tung-Duong, et al. "Aiding the Earth System Models with Super Resolution Deep Learning." 2021 한국소프트웨어종합학술대회. 한국정보과학회, 2021.

Prediction of daily sea surface temperature using artificial neural networks

Aparna, S. G., Selrina D’souza, and N. B. Arjun. "Prediction of daily sea surface temperature using artificial neural networks." International Journal of Remote Sensing 39, no. 12 (2018): 4214-4231.

Talks

Contrasting the Atmospheric River Responses to greenhouse gas warming with Orbital changes

December 01, 2023

Invited Talk at Moscone Center, San Francisco, San Francisco, California

Projected Changes in Atmospheric Rivers in Response to Greenhouse Warming

January 26, 2022

Talk at George Brown Convention Center, Texas, Houston TX, USA

Service and leadership

• Headed the Social Service League (SSL) for the academic year 2013-14, at the St. Stephens college, Delhi University organizing year round events.