Our group specializes in using numerical simulations to better understand the dynamics, predictability, and multi-scale impacts of tropical cyclones, mesoscale convective systems, and severe convective weather. Some of our recent and ongoing research is detailed below. We gratefully acknowledge support from NSF, NOAA, and UWM for this research.
Tropical Cyclone-Midlatitude Flow Interactions
The interaction of a tropical cyclone’s diabatically driven outflow with the antecedent midlatitude waveguide can reconfigure the downstream flow over one or more synoptic-scale wavelengths. The specific outcome of this interaction is sensitive to the phasing between the cyclone and midlatitude waveguide and the structure of the midlatitude waveguide. Our group conducts research to document the extent to which the downstream response can modify the downstream tropical-to-subtropical environment, particularly as it relates to its favorability for tropical cyclone formation and maintenance, and thus better understand the role of tropical cyclones in the climate system.
Overland Tropical Cyclone Intensity Change
Tropical cyclones are primarily fueled by enthalpy fluxes from an underlying warm ocean. However, a subset of tropical cyclones has been observed to reintensify over land, even in non-baroclinic environments (i.e., absent large-scale forcing for ascent from an upstream midlatitude trough). While it is generally accepted that making the land-surface more water like is necessary to permit overland reintensification, the precise thermodynamic processes at the land-surface interface and within the upper soil that can subsequently result in a tropical cyclone intensifying over land are not yet agreed upon. Our group conducts research to better quantify these processes using both idealized and real-data numerical simulations.
Tropical Cyclone Intensity-Change Prediction
Although improved observational capabilities and data-assimilation methods have recently led to significant advances in high-resolution dynamical models’ ability to provide skillful tropical cyclone intensity forecasts, statistical-dynamical models like SHIPS and LGEM remain some of the most skillful intensity-change guidance. Our group is using a novel machine-learning approach called evolutionary programming to develop a skillful, minimally biased tropical cyclone intensity-change model for deterministic forecasts of cyclone intensity and probabilistic forecasts of rapid intensification and rapid weakening. Evaluation of this model within the Joint Hurricane Testbed is ongoing.
Mesoscale Convective Systems
Mesoscale convective systems are important contributors to the warm-season climatology of the central and eastern United States. We conduct research to better understand MCS dynamics, particularly rear-inflow jet structure and evolution, quantify the predictability of MCS maintenance, and to document how uncertainty in convection initiation forecasts influences MCS predictability.
Successful predictions of thunderstorms and their hazards hinge upon accurate convection initiation forecasts and the multi-scale physical processes that influence its occurrence. We conduct research to quantify convection initiation's predictability and to identify fundamental shortcomings, such as model representations of capping inversions, that impose limits upon its skillful prediction.
Ensemble Prediction and Utilization
Ensemble forecasting aids in quantifying weather prediction uncertainty and in weather event risk estimation. Much of our research makes extensive use of ensemble guidance. We have also studied varying methods for ensemble construction and how forecasters utilize ensemble guidance when preparing forecasts for high-impact meteorological events.
We actively welcome new collaboration with prospective graduate students and other scientists on topics of mutual interest. For a full publication listing with accessible summaries, please see the Publications page. A simple publication listing is available in Prof. Evans' Curriculum Vita. Citation information is available in Prof. Evans' Google Scholar profile.