Research
AI for Climate Modeling
Numerical weather and climate models simulate the physics governing the Earth system to generate real-time weather forecasts and long-term climate projections. Since the mid-20th century, numerical weather prediction has been the foundation of weather forecasting, providing enormous societal benefits. Climate models have also been instrumental in understanding the impact of anthropogenic emissions on our current and future climate and informed mitigation and adaptation strategies. Although the accuracy of weather and climate models has improved in recent decades, they have become increasingly complex and computationally expensive. Advances in machine learning techniques, the availability of high-performance computing, and the emergence of large high-dimensional meteorological datasets have made deep learning-based weather and climate models increasingly attractive. My research aims to leverage explainable, physics-informed deep learning techniques to improve existing weather and climate models.Relevant Publications
Polar Climate
The Arctic and Antarctic are a fundamental part of the climate system, and short-term and long-term changes in their climate can influence Earth's global radiative budget, atmospheric and oceanic circulation, meridional heat transport and extreme weather in the midlatitudes. I am broadly interested in sea ice variability and predictability and better understanding the coupled interactions between sea ice the atmosphere and the ocean.Relevant Publications
Climate Dynamics and Climate Change
Global climate is controlled by complex interactions between the atmosphere, ocean, crysophere, and land. Disentangling the relative contribution of anthropogenic warming and multidecadal internal variability is challenging due to this complex coupling, limited observational data, incorrect climate models. My research aims to better understand the physics controlling recent and projected climate change. My recent work examines the impact of cloud feedbacks on multidecadal internal variability in the tropical Eastern Pacific.Relevant Publications
Education
Professional Experience
- Studied the impact of marine boundary layer clouds on historical East Pacific Ocean cooling
- Studied the impact of climate change on annual precipitation in the Amazon Rainforest
- Developed a machine learning parameterization of gravity wave in a global climate model (Sheshadri Group)
- Publication in Geophysical Research Letters - Espinosa, Zachary I., et al (2022)
- Developed in-house machine learning pipeline for research & development. Introduced pipeline testing
- Built an open-source python framework for quantum networking (QN) simulations called netQuil, designed to support the implementation of canonical QN protocol
- Designed, built, and launched electric vehicle endpoints for Smartcar API
- Maintained python, node.js, and java SDKs. Contributed to OAuth2 pipeline.
- Built first iteration MVP mobile app for iOS and Android using React Native, Firebase, Heroku, and AWS RDS.
Awards & Fellowships
Teaching Experience
Talks & Presentations
Service & Leadership
Projects
Drivers of the Seasonal Delay of Rainfall in the Amazon Rainforest
The findings of this work were presented at the Pacific Northwest National Laboratory (PNNL) August 2021 Intern Symposium
Tropical precipitation has a distinct annual cycle characterized by an amplitude, the range between wet and dry seasons, and phase, their onset timing. Previous studies have reported a seasonal delay in the onset of precipitation in observations over the northern tropical land driven by changes in greenhouse gases (GHG) and anthropogenic aerosols (AER). Though it is well known that the phase of the annual precipitation cycle is projected to be delayed over land under global warming, it is unclear whether changes in GHG and AERs are the dominate external forcings for different regions. Due to its outsized importance in the global Earth system, changes in the precipitation cycle in the Amazon are of particular interest. Here, we use multi-model output of historical and individual forcing simulations to show that the seasonal delay of precipitation in the Amazon rainforest cannot be fully explained by changes in GHG and AER. We examine the impact of land use and land cover change (LULCC; e.g. deforestation) on the seasonal delay of rainfall by examining the historical and future simulations without changes in LULCC, and we perform an atmospheric energy budget analysis for the Amazon Basin.
This work was done at PNNL in collaboration with Dr. L. Ruby Leung and Dr. Fengfei Song.
NetQuil: A Quantum Playground for Distributed Quantum Computing Simulations
NetQuil is an open-source Python framework for quantum networking simulations built on the quantum computing framework pyQuil, by Rigetti Computing. NetQuil is built for testing ideas in quantum network topology and distributed quantum protocol. It allows users to create multi-agent networks, connect parties through classical and quantum channels, and introduce noise. NetQuil also makes running multiple trials for non-deterministic experiments, reviewing traffic in real-time, and synchronizing agents based on local and master clocks simple and easy. We provide an overview of the state of distributed quantum protocol and a basic introduction to netQuil's framework. Finally, we present several demonstrations of canonical quantum information protocols built using netQuil's distributed quantum gates and pyQuil.
This project was completed with Matthew Radzihovsky and Yewon Gim at AT&T Foundry, Palo Alto.
Henry's Fork Foundation Water Quality Monitoring Network
While at the Henry's Fork Foundation (HFF), I helped build an automated data-collection network of sensors in the Henry’s Fork Water Shed near Yellowstone National Park. To accompany this data-collection network, I then led an effort to build and published a real-time water quality monitoring website. Since 2018, the website has been frequently used by local scientists to study the health of the Henry's Fork hydrological system and has served as the cornerstone of HFF’s scientific communication mission.
This project was completed with Melissa Muradian and Justin Appleby.