Alex Matus

Weather Analyst / ML Researcher

About Me

I am an Atmospheric Scientist and Machine Learning Researcher at NASA Goddard Space Flight Center / UMBC. My work focuses on pushing the limits of weather, climate, and environmental predictability by combining massive multi-sensor satellite observations, high-performance computing (HPC), and advanced machine learning architectures.

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Education

PhD Atmospheric and Oceanic Sciences

2013-08-01
2017-08-01

University of Wisconsin-Madison

MS Atmospheric and Oceanic Sciences

2011-08-01
2013-08-01

University of Wisconsin-Madison

BS Meteorology

2005-09-01
2009-05-01

Penn State University

Interests

Remote Sensing Machine Learning Predictive Modeling Applied Research

📚 Research

Welcome to my research portfolio. I specialize in parsing highly chaotic earth systems and translating raw, multi-dimensional environmental data into high-precision, actionable risk assessments.

My research bridges the gap between physical atmospheric modeling and data-driven statistical engineering. Using advanced data assimilation, neural networks, and high-performance computing workflows (Python/Dask/SLURM), I build scalable pipelines to optimize predictive accuracy, quantify structural uncertainty, and evaluate the global energy budget.

Beyond core climate modeling, I am deeply interested in applied engineering solutions for renewable energy forecasting (wind/solar) and real-time environmental monitoring.

Featured Publications

Atmospheric Energy Budget

The Role of Precipitation Variability in Closing the Global Atmospheric Energy Budget

By evaluating three generations of GPCP data alongside CERES observations, this study reveals that while GPCP v3.3 reconciles the global atmospheric energy budget within 98%, this …

Alex Matus

• May 24, 2026 • 1 min read

Spaceborne Lidar

Enhancing Surface PM2.5 Air Quality Estimates in GEOS Using CATS Lidar Data

This study introduces a 1-D ensemble-based variational technique (1-D EnsVar) to fuse 1064-nm backscatter lidar data from NASA's CATS with GEOS model priors, significantly …

Alex Matus

• May 1, 2025 • 1 min read

Radiative Kernels

Observation-Based Radiative Kernels From CloudSat/CALIPSO

This study introduces the first observation-based climate radiative kernels derived directly from vertical active sensor tracks (CloudSat/CALIPSO), bypassing traditional GCM …

ryan-j.-kramer

• May 31, 2019 • 1 min read

Cloud Radiative Effects

The role of cloud phase in Earth's radiation budget

Utilizing 5 years of spaceborne active sensor data from CloudSat and CALIPSO, this study quantifies the individual global radiative contributions of liquid, ice, and mixed-phase …

Alex Matus

• Mar 16, 2017 • 1 min read

Aerosol Direct Radiative Effects

The Role of Clouds in Modulating Global Aerosol Direct Radiative Effects in Spaceborne Active Observations and the Community Earth System Model

By combining active A-Train spaceborne observations (CloudSat/CALIPSO) with CESM1 model simulations, this study quantifies how cloud overlapping structures mask, alter, and …

Alex Matus

• Apr 15, 2015 • 1 min read

Recent Publications

Alex Matus, Ryan J. Kramer, Lazaros Oreopoulos, Nayeong Cho (2026). The Role of Precipitation Variability in Closing the Global Atmospheric Energy Budget. Under review in GRL.

PDF

Alex Matus, Edward P. Nowottnick, John E. Yorks, Arlindo M. da Silva (2025). Enhancing Surface PM2.5 Air Quality Estimates in GEOS Using CATS Lidar Data. ESS.

Link Preprint DOI

Ryan J. Kramer, Alex Matus, Brian J. Soden, Tristan S. L'Ecuyer (2019). Observation-Based Radiative Kernels From CloudSat/CALIPSO. JGR Atmospheres.

Link DOI

Alex Matus, Tristan S. L'Ecuyer (2017). The role of cloud phase in Earth's radiation budget. JGR Atmospheres.

Link DOI

Alex Matus, Tristan S. L'Ecuyer, Jennifer E. Kay, Jean-François Lamarque, C. Hannay (2015). The Role of Clouds in Modulating Global Aerosol Direct Radiative Effects in Spaceborne Active Observations and the Community Earth System Model. J. Climate.