Student Opportunities

Contacting the Faculty

Though it can be intimidating to do so – as we can speak to ourselves! – we highly encourage all prospective graduate students to reach out to the faculty members who have student research opportunities (listed below) in the areas of greatest interest to you. This does not have to be very formal: an e-mail that introduces yourself and lets us know that you are interested in learning more about our student research opportunities is a great starting point! We can strike up a deeper conversation from there. We promise to promptly respond to all student inquiries!

Prof. Evans | Prof. Kahl | Prof. Kravtsov | Prof. Roebber | Prof. Shen

Mesoscale and Tropical Meteorology
Dr. Clark Evans

Thank you for your interest in graduate research opportunities in our group! We currently expect to have one opportunity for a new graduate student to join our group in Fall 2023, working on one of the following two projects:

  • The impacts of climate change on downstream development driven by the extratropical transition of tropical cyclones: This study will use a combination of idealized and real-data numerical weather prediction simulations to quantify how our changing climate may influence downstream development – the amplification of the synoptic-scale trough/ridge pattern – that is triggered by the extratropical transition of tropical cyclones. This project would be conducted in collaboration with Prof. Sergey Kravtsov at UWM.
  • How do people perceive their individual hurricane-related risks, and how do their perceptions compare to their actual risks? This study will leverage observations from decades of landfalling Atlantic tropical cyclones to quantify wind-, rain-, and inundation-related risks on a county-by-county basis across the southeastern United States, which will then be compared to survey and social-media data regarding what people perceive to be their risks collected by collaborating researchers. This project would be conducted in collaboration with Prof. Kim Wood at Mississippi State University.

We’re happy to help prospective students shape these or related ideas in support of applications to graduate fellowship programs (such as those offered by NSF and NASA) where applicable – if you’re interested in anything we’re working on, let’s talk!

Air Pollution and Microscale Meteorology
Dr. Jonathan Kahl

We seek graduate students that are broadly interested in meteorological aspects of air pollution and/or micrometeorology to join our research group beginning in Fall 2023. The specific research focus is open and to be decided by the student in consultation with Professor Kahl.

Examples of recent projects include the development of models to predict peak wind gusts, the development of a combined air quality and heat/humidity index, the assessment of the environmental impact of fine particulates (PM2.5) generated by firework displays, and and the evaluation of the accuracy of the popular Pasquill stability classification scheme.

For more information or to discuss specific research ideas, please contact Professor Kahl.

Climate Dynamics
Dr. Sergey Kravtsov

We seek one graduate student to join our research group in Fall 2023. We particularly welcome contact with prospective students at either level (M.S. or Ph.D.) who are interested in one of the following research areas:

Empirical Climate Modeling: Preliminary results demonstrated that it is possible to generate synthetic highly resolved data sets of the select observed fields (sea-level pressure, vector wind etc.) that closely mimic these fields’ detailed observed statistics. Such climate emulators may be used for statistical prediction, climate model error estimation, climate downscaling and many other applications. This basic strategy is proposed to be augmented to include the dependence of the empirical models on the external variables (sea-surface temperature, greenhouse gas forcing and so on). This will allow development of hybrid statistical-dynamical schemes for future climate prediction.

Synoptic eddies as building blocks of large-scale low-frequency variability in the atmosphere: Previous work attributed a surprisingly large fraction of large-scale low-frequency variability such as the North Atlantic Oscillation to ultra-low-frequency redistribution of synoptic storm tracks and suggested that synoptic eddies play a primary dynamical role in defining what’s traditionally referred to as the “mean flow.” A numerical strategy is proposed to model the observed life cycles of eddies and their long-range interactions to analyze the resulting kinematics and dynamics of the midlatitude climate.

Climate networks: A useful novel way to analyze climate variability is by regarding the climate system as a network of interacting climate subsystems represented by their respective index time series. We are working on developing and analyzing conceptual models of such networks, which may dynamically rationalize some of the observed low-frequency organization properties behind climate regime shifts.

Mesoscale air–sea interaction: The theme of mesoscale ocean–atmosphere coupling and its large-scale climate repercussions has been drawing much attention recently. This project will utilize an intermediate-complexity eddy-resolving coupled model (Q-GCM: to systematically explore the effect of increasing atmospheric-model resolution on the simulated climate variability.

Role of quasi-biennial (QB) variability in the dynamics of El Nino/Southern Oscillation (ENSO): ENSO is the leading mode of climate variability in the tropics, with teleconnections extending all over the globe resulting in numerous socioeconomic repercussions. ENSO events peak in winter and tend to occur quasi-regularly with an interval between consecutive events of 3-6 years. Recent diagnostic work utilizing tools from information theory identified a particularly pronounced role of QB variability in ENSO dynamics, despite generally small contributions of QB modes to the overall energy spectrum. The goal of this project is to resolve this paradox using advanced statistical analysis of the observed QB variability and its connections with ENSO.

For more information, please contact Professor Kravtsov.

Multiscale Meteorology and Numerical Methods
Dr. Paul Roebber

We are seeking one M.S. level graduate student to join our research group in Fall 2023. We particularly welcome contact with prospective students who are interested in one of the following research areas:

Systems Modeling and Analysis: Our group has conducted NSF, NOAA, and UCAR funded, peer-reviewed studies using a variety of modeling tools, including traditional numerical weather prediction, multiple linear and logistic regression, artificial neural networks, agent-based models, and evolutionary programming. Subjects of these studies overlap extensively with the topics below, but outside of these, have also included operations of a large state university system, the performance of peer review, the impact of wide adoption of green roof architecture on urban climates, and flood inundation mapping. Ongoing research in this area seeks to extend these tools in pursuit of both operational forecast challenges and fundamental research questions (e.g., new methods of ensemble forecast generation and calibration).

Multiscale Predictability and Forecast Studies: Past studies have included the following broad areas: convective initiation, heavy rainfall, severe storms, wind-power forecasting, snow depth, ice storms, rapid cyclogenesis, landfalling cyclones, heavy rainfall and large-scale flow regime change. In this area, new forecast verification tools have also been developed during the progression of this work.

Air-Water Interactions: Students in our group have investigated wave generation on large freshwater lakes, carbon cycling between the air and water in such systems, improving predictions of deep lake circulations from better representation of atmospheric driving, and long-term periodicities in Great Lakes water levels owing to climate-linked changes in precipitation and evaporative driving.

For more information, please contact Professor Roebber.

Physical Hydrology and Ecohydrology
Dr. Xinyi Shen

We are seeking one or two funded students at a Ph.D. level and thesis-based master level students, to join our research group from Spring to Fall 2023. Currently, our group is primarily funded by NOAA, partially by local flood managers, NASA and NSF, and has published peer-reviewed articles. We particularly welcome contact with prospective students who are interested in one of the following research areas:

Near real-time (NRT) flood observation using Synthetic Aperture Radar (SAR) Satellites: We developed the only operational and national-scale inundation mapping system in the world using SAR data and are now deploying this system to NOAA with further improvements. SAR data has the advantages of high spatial resolution (from <1 m to 30 m), and close to 100% weather penetration, which makes it the most-reliable sensor for flood-inundation mapping. Recently, public agency-owned and commercial satellites can provide sub-daily to 6-day revisiting intervals, which is a game-changer for flood observation. This unique product has been used for validating and calibrating dynamic models and rapid disaster response. Based on existing radar physics and statistics, we keep on advancing satellite-based inundation mapping methods by incorporating emergent big datasets, and deep learning approaches.

Flood risk analysis and prediction by dynamic modeling: We developed fully distributed hydrological models for long-term water cycle simulation, flood risk analysis, and seasonal and short-term flood forecasts. Our hydrological models have been applied by users over all continents. Currently, we attempt to integrate more artificial intelligence (AI) techniques and big data from autonomous systems and satellites into these models, to improve their applicability to ungauged locations.

Analysis of flood impact and drivers: Floods threaten human society in many aspects, including residential and food security while flood severity is primarily determined by weather, topography, and the built environment (including infrastructure). Through building AI models and generating some big datasets, we attempt to answer the following questions: 1) To what extent the damages could future climate causes through floods? 2) how do different backgrounds (e.g., socioeconomic status, education level, etc.) and law enforcement affect a homeowner’s perspective on purchasing flood insurance? And 3) is social equity the key to reducing flood vulnerability?

Climate change and anthropogenic activities on biodiversity: This is a highly collaborative area being contributed to by my group and other ecological and biogeographic groups. Based on the abundance of remote sensing data, AI techniques, and quantitative techniques, we try to first identify the distribution of species’ population and/or estimate their physiological traits, then model their vulnerability to climate extremes and to human activities.

We are seeking graduate students with strong programming capabilities and the following skill sets. We are also enthusiastic to support prospective undergraduates who are interested in related studies. We will also help students with fellowship applications (federal/university level).

  • Physical hydrology (Student 1): with strong hydrological and hydraulic modeling background, remote sensing data handling experience is preferred.
  • Ecohydrology (Student 2): with hydroclimatic and AI/statistic learning background, ecology and watercolor remote sensing backgrounds are preferred. The study area will include but is not limited to the Great Lake areas.

Please contact Professor Shen for details.