Mesoscale and Tropical Meteorology
Dr. Clark Evans
Are you interested in studying severe storm environments, thunderstorm dynamics, or tropical cyclone intensity change? Our research group has openings for three graduate students to join us in Fall 2019. While we are open to discussing any potential research topic of interest, specific research projects that prospective graduate students in our group may become involved with include:
Evaluating Severe Storm Environment Predictions from Next-Generation Global Models: Forecasters extensively use model-derived soundings to help predict severe storm occurrence, severity, and mode. In collaboration with the Storm Prediction Center in Norman, OK, this project seeks to quantify how well NCEP’s next-generation FV3 global model can forecast thunderstorm-supporting environments. The student working on this project will have the opportunity to lead the research in the NOAA Hazardous Weather Testbed for 2-4 weeks per year and will be supported by up to two years of research assistant funding.
Overland Tropical Cyclone Reintensification: Previous research by Prof. Evans and others has quantified the importance of surface enthalpy fluxes over strongly-heated wet land surfaces to overland tropical cyclone reintensification for events such as Tropical Storm Erin over Oklahoma in 2007. This project seeks to reconcile competing theories as to the physical processes that allow for tropical cyclone intensity to be maintained over land.
Lake-Crossing Mesoscale Convective System Predictability: The proposed Michigan Thunderstorm and Marine Experiment seeks to collect observations in the Lake Michigan environment to improve understanding of MCSs that interact with large water bodies. This project seeks to quantify the extent to which MCS structure (particularly the rear-inflow jet) and propagation mechanism (cold-pool vs. bore) in the near- and over-lake environment are predictable. This project is contingent on funding support; the first semester of study would be as a teaching assistant, whereas subsequent semesters would be supported as a research assistant.
Students in our group have the opportunity to present their research at one or more AMS conferences per year and to publish their research findings in AMS journals. Group alumni have a strong track record of post-graduation employment across the field. We’re happy to help prospective students shape these or related ideas in support of applications to graduate fellowship programs. To express interest in or for more information about these opportunities, please contact
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 2019. The specific research focus is open and to be decided by the student in consultation with Professor Kahl. Current projects include the development of a combined air quality and heat/humidity index, a predictive model for fine particulates (PM2.5) in Mexico City, and the assessment of environmental impacts associated with the construction of the New Port of Veracruz, Mexico.
Examples of recent student-led research include the development of improved fine particulate matter models utilizing remotely-sensed observations of aerosol optical depth; the development of an empirical model to forecast winds and peak wind gusts atop the roof of Miller Park; 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.
Dr. Sergey Kravtsov
We seek one graduate student to join our research group in Fall 2019. We particularly welcome contact with prospective students at either level 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: http://www.q-gcm.org) 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.
Dr. Vincent Larson
Our research group seeks a student who is interested in numerical modeling.
The goal of our research group is to develop a unified parameterization of subgrid variability in atmospheric models. A unified parameterization represents all cloud types with a single equation set. In this way, a unified approach differs from the separate-schemes-for-separate-regimes approach used in current-generation weather and climate models.
In the coming years, we plan to improve our parameterization’s representation of turbulence, which will involve the numerical solution of partial differential equations. The parameterization’s treatment of precipitation will also be revised; this research will involve Monte Carlo integration.
All members of our research group – postdocs, graduate students, and undergrads – develop the parameterization code base and have the opportunity to co-author papers. All newcomers to the group should expect to be challenged!
We seek up to one graduate student to join our group in Fall 2019.
For more information, please contact Professor Larson.
Synoptic Meteorology and Numerical Methods
Dr. Paul Roebber
We are seeking one graduate student, at either the M.S. or Ph.D. level, to join our research group in Fall 2019. We particularly welcome contact with prospective students at either level who are interested in one of the following research areas:
Systems Modeling and Analysis: Our group has conducted NSF, NOAA, and UCAR funded and peer-reviewed studies using a variety of modeling tools, including traditional NWP, 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, and the impact of wide adoption of green roof architecture on urban climates. 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). A current collaboration with a major power company provides exceptionally high-quality wind power data to advance research into improved wind-power predictions.
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 and precipitation 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.