Joint PhD Position in Hydroclimate and Data Science
Application deadline: January 15, 2024
The Department of Environmental Sciences invites applications for a PhD position under the supervision of Professors Kathleen Schiro and Antonios Mamalakis. The student will enroll full-time in the graduate program of the Department of Environmental Sciences in the Graduate School of Arts and Sciences. The student is expected to take all necessary courses/credits towards completion of their degree. The graduate student is also expected to carry out research related to hydroclimate and data science. This includes but is not limited to understanding predictability of hydroclimate across different timescales, using AI (Artificial Intelligence) to improve predictive skill, enhance understanding of climate change impacts and attribution of hydroclimatic extremes, and other related questions.
Bachelor’s degree in Atmospheric/Climate Science, Earth System Science, Computer Science, Engineering, or other related fields
Solid scientific background and academic performance (e.g., as may be indicated by GPA)
Proficiency in English
Experience in Python, MatLab, or R Experience in working with large datasets
Proficiency in Python, MatLab, or R
Passion for climate science and big data
Strong presentation skills (written/oral)
Previous experience in presenting at scientific conferences or/and publishing in scientific journals Independent thinking and creativity
Applicants should send an email including a CV (2 pages maximum) and 1-page cover letter to firstname.lastname@example.org and email@example.com. The cover letter should solidify the applicant’s interest in the position and discuss qualifications. The applicants may be contacted by the PIs to discuss further details about their application before being encouraged to apply to the graduate program.
Sayali advances to PhD Candidacy
Congrats to Sayali on the successful defense of her PhD thesis proposal, titled, “Investigating Environmental Controls on Tropical Mesoscale Convective Systems Lifecycles.”
Logan successfully defends MS thesis proposal
Congrats to Logan on successfully defending his MS thesis proposal titled, “Examining dynamical controls on tropical low clouds in present and future climate.”
Welcome, Swatah, Jun-Jie, and Rebecca!
In August (2023), we welcomed three new lab members: Jun-Jie Chang, Rebecca Weinstein, and Swatah Borkotoky. Rebecca is a PhD student and recent graduate of Virginia Tech, Jun-Jie is a PhD student co-advised by Prof. Kevin Grise coming to us from the National Taiwan University, and Swatah is an EI Climate Fellow co-advised by Prof. Grise who recently graduated from Cornell University in their Dept. of Earth and Atmospheric Sciences. A warm welcome to all!
Emma successfully defends MS thesis proposal
In August (2023), Emma Dawson successfully defended her MS thesis proposal titled, “Mean State Tropical High Cloud Relationship to Equilibrium Climate Sensitivity.” Way to go, Emma!
Sayali and Emma win EVSC awards!
Congratulations to Emma Dawson and Sayali Kulkarni on their awards at the 2023 UVA Dept. of Environmental Sciences awards ceremony! Sayali won the Michael Garstang award for exploratory research in the amount of $1500, and Emma Dawson won the Graduate Atmospheric Sciences award for her research on high cloud feedbacks. Way to go, ladies!
Two fully-funded graduate student opportunities available!
***Application deadline for admission in Fall 2023 is January 15, 2023. For those interested in admission in Spring 2023, please contact firstname.lastname@example.org ASAP.
Our group has an opening for two graduate students to study tropical deep convection at a process-level as part of two newly funded opportunities. Please contact email@example.com for more information about the projects and admissions, if interested.
Project 1: Characterizing interactions between tropical deep convection and the environment using a buoyancy framework (funded by the National Science Foundation)
Our overarching goal is to work towards identifying a conceptual model for tropical deep convection that can be used to explain precipitation variability across scales (diurnal, seasonal, intraseasonal, and interannual), building on our knowledge of relationships between precipitation and estimates of entraining plume buoyancy (Ahmed and Neelin 2018; Schiro et al. 2018; Schiro and Neelin 2019; Ahmed et al. 2020; Adames et al. 2021; Wolding et al. 2022) hereafter referred to as the “precipitation-buoyancy (P-B) framework.” Our main research questions are (1) to what extent do “grid-scale” O(100 km) integrated measures of convective instability accurately assess the capacity of the atmosphere to support deep convection, and (2) how can we refine grid-scale average instability measures to account for variability and dynamics across scales? We will address these questions with four proposal objectives. Our first objective is to characterize how the precipitation-buoyancy (“P-B”) relationship varies as a function of convective variability across spatial scales. Our second objective is to examine how P-B thresholds change as a function of mesoscale convective system evolution, estimating the sensitivity of deep convection to its thermodynamic environment throughout different lifecycle stages. Our third objective is to characterize how dynamical interactions – mass flux/entrainment, wind shear, vorticity, cold pools – modify thermodynamic controls on deep convective evolution. Our fourth objective will be to put our new buoyancy framework to the test: to apply the refined P-B framework to study diurnal, regional, and intraseasonal precipitation variability across the tropics. Using new mesoscale convective tracking databases and cold pool gradient feature detection algorithms, long-standing radar data, field campaign data, and satellite observations, combined with novel compositing techniques, the proposal team is uniquely positioned to study thermodynamic controls on tropical deep convection in a new way and thus to refine a buoyancy framework with significant potential to improve prediction of future hydroclimatological changes and extremes.
Project 2: Thermodynamic and Non-thermodynamic Controls on Deep Convection in ARM Observations (funded by the Department of Energy)
Deep convection remains one of the most challenging processes to represent in weather and climate models, partly because of multiple controlling factors. We propose to create a framework based on Atmospheric Radiation Measurement Program (ARM) observations, with which to assess the relative influence of thermodynamic, dynamic and aerosol effects on deep convection. This framework will expand upon a previously identified empirical buoyancy measure that constrains the thermodynamic influence on deep convective precipitation in the tropics. We also propose to integrate this precipitation-buoyancy framework into the ARM model diagnostics package (ARM-DIAGS).
Prior work with observations from DOE ARM sites from the Tropical Western Pacific (TWP) and Green Ocean Amazon (GoAmazon2014/5) helped establish the empirical precipitation- buoyancy framework, which constrains the thermodynamic influences on deep convection. This project will expand the buoyancy framework using ARM site observations across a wider range of environments (tropical and sub-tropical; oceanic and continental). We will examine data from recent ARM field campaigns: Cloud, Aerosol, and Complex Terrain Interactions (CACTI), GoAmazon2014/5, and Tracking Aerosol Convection Interactions Experiment (TRACER). We will also use long-term measurements from the Southern Great Plains (SGP) and TWP sites. We will extend the buoyancy framework by accounting for non-thermodynamic controls on convection, which include dynamical (e.g., wind shear, orography, sea-breeze convergence) and microphysical (aerosol) effects. By controlling for thermodynamic factors, we propose to use the precipitation-buoyancy framework to more precisely evaluate the dynamical and aerosol effects on convection. We will employ multi-instrument ARM observations, including radiosondes, surface meteorological instruments, precipitation and cloud radars, radar wind profilers, and aerosol observing systems. We will also quantify how the environmental influence on convection changes as a function of the cloud-lifecycle and convective organization. These efforts will allow us to construct a broader framework for representing deep convection dependence on its environment. To aid climate model development, we will also integrate the observed precipitation-buoyancy relationship into the ARM model diagnostics package (ARM- DIAGS). We also propose to use the buoyancy framework to identify optimal parameter regimes for development versions of DOE’s Energy Exascale Earth System Model (E3SM). These efforts will identify convection-related process errors in climate models. Our team has the required expertise in ARM instrumentation, convective physics, aerosol-cloud interactions and climate model diagnostics to achieve the proposed targets.