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Brendan Wallace
Numerical Modeling | Climate Dynamics | Land Surface-Atmosphere Interactions
I am a weather enthusiast with a strong interest in climate science, big data and efficiency. Originally from Chicago, Illinois, I grew up fascinated by tornadoes and severe weather. I received my B.Sc in Meteorology with a concentration in Geographic Information Systems and Mathematics from Western Illinois University before later attending graduate school at the University at Albany where I received my M.S and PhD in Atmospheric Science.

Outside work, I enjoy hiking, board games, traveling, and archiving digital media.

Welcome#

Hello! I am a climate data scientist at Leidos. My work primarily focuses on the utilization of high-resolution regional climate datasets to address important weather and climate research questions. I have a strong interest in Python and leveraging distributed computing to efficiently process large climate datasets. More recently, I have taken an interest in alternative file storage formats and statistical downscaling techniques using machine learning.

Recent News#

See the news archive for a complete list of all posts.

  • 2024-02-01 - AMS 2024

    Our group at NIU recently presented results at AMS 2024 on changes in mesoscale convective system (MCS) behavior and spatial distribution in a set of continental-scale convection permitting regional climate model output.

    A link to the talk can be here (starts at 00:30:00).

    What distinguishes our results from past studies utilizing convection-permitting models to study changes in MCS is that we consider how changes in the large-scale circulation may influence the overall MCS response. Due to computational restraints, many studies looking at future changes in extreme precipitation in high resolution numerical models are primarily constrained to quantifying how changes in thermodynamics affect the overall response. We find a shift in the frequency of upper-tropospheric troughs that is overall supportive of an enhanced MCS signature in the eastern United States. However this signal is small relative to the changes in large-scale circulation due to interannual variability. Instead, we classify MCS based on the magnitude of their large-scale forcing at initiation time and find that an increase in MCS in weakly-forced synoptic environments can help to explain a non-negligible increase in the overall MCS frequency.

  • 2024-01-15 - Hello world!

    This website is (hopefully) live!

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