Despite decades of research, gaps still exist in our knowledge of tropical cyclones (TCs), in particular how they form. This impacts both our ability to forecast these systems, as well as our projections of TC frequency in future climates. I have used both high-resolution numerical models and observations to study several characteristics of TCs.
Tropical cyclones generally form from some pre-existing disturbance. To study this process as holistically as possible, we use cloud-resolving model simulations initialized from radiative-convective equilibrium (RCE), an approximation for the tropical atmosphere in which radiative cooling is balanced by heating from moist convection. This allows us to simulate an atmosphere in which convection operates "on its own free will", in the absence of external forcing.
Interactions between clouds, water vapor, radiation, and circulations help to organize convection (more on that in the "Tropical Convection" page). Similar feedback processes, such as the difference in radiative cooling between cloudy and cloud-free regions, are directly relevant to TC genesis and intensification! In this way, we can move one step back in the process of studying their life cycle: by identifying how the disturbances preceding TCs form when left to their own devices.
I published a paper on this topic in the Journal of Advances in Modeling Earth Systems (JAMES) in May 2020. This is an open-access journal, so head to this link to download and read it if you'd like!
Moist Static Energy Variability
Continuing with the intricate interplay of convection and tropical cyclones, we can quantify the feedback processes highlighted above by considering how they influence the spatial variance of moisture. For example, enhanced surface heat and moisture fluxes in a tropical cyclone eyewall will only increase the anomalously high moisture in that area, and likely contribute to intensification.
Using moist static energy (MSE) as a proxy for moisture, I am beginning to use dropsonde data from NOAA Hurricane Hunter aircraft reconnaissance to study how well we can capture these feedbacks with a limited set of observations. The cloud-resolving model simulations I've built to study convection and TCs are useful here as a "proof of concept". With data available there at high resolution, we can choose individual grid points to serve as simulated dropsondes, and develop our own hypothetical flight patterns. We can thus test how well these perform at capturing the variability in MSE and the feedbacks that influence it, compared to what we would find if we had a larger sampling of data.
As of early May 2022, a paper on this work is under review in GRL.
Hurricane Laura in the western Gulf of Mexico on August 26, 2020.
Vortex and moisture pathways to tropical cyclogenesis in Carstens and Wing (2020). The left panels represent the "high-f" pathway, and right panels represent the "low-f" pathway.
A skew-T log-P diagram from a dropsonde launched on the periphery of Hurricane Teddy (2020). This upper-air reconnaissance mission supports the 2020 NOAA Hurricane Field Program.
Integrated Kinetic Energy
Hurricane intensity is usually assessed by the Saffir-Simpson Hurricane Wind Scale, which considers the maximum 1-minute sustained wind speed in the storm. To account for the full tropical storm and hurricane-force wind field, Integrated Kinetic Energy (IKE) was developed by Powell and Reinhold (2007). It simply accumulates the kinetic energy of the winds in the 4 quadrants of the hurricane - NE, NW, SE, and SW. My undergraduate thesis was the first work computing this metric for East Pacific tropical cyclones, building off of an update to the HURDAT archive which includes the radii of 34, 50, and 64-knot winds in the 4 quadrants necessary to perform the calculation.
While IKE presents its communication challenges relative to simpler tools like the SSHWS or Accumulated Cyclone Energy (ACE), it is a more complete metric that may better inform of risk and expected impacts away from the center of a storm.