Our atmospheric Science work concentrates on two main subjects, Cloud microphysics, cloud patterns and aggregation, and Hurricane dynamics and thermodynamics. Below our activities in each of them is briefly described.

Cloud Microphysics

Cloud processes act on a whole range of scales, from submillimeter cloud droplet formation to large 1000 km cloud systems. Their evolution influences other processes over this scale range, all the way to planet scale. It is, for instance a large player, and at the same time, a large unknown in climate change.

One of the least understood cloud processes is drizzle. Drizzle formation modulates cloud properties and evolution, and affects the water cycle of the Earth. Since drizzle formation involves cloud droplets of all sizes, it requires extensive computational time. Hence, we often use simplified methods called parameterizations in weather and climate prediction models to obtain a bulk estimate of how fast and how many cloud droplets collide with each other or collide with bigger drops to form drizzle. However, many models continue to have inadequate representation of drizzle formation, calling for the need to improve these parameterizations.

In a coordinated effort with several researchers, lead by Christine Chiu, we have developed three 3 new parameterizations for autoconversion, the process by which two cloud droplets collide to form one rain (drizzle) drop, see this open access paper. To this end we combined aircraft measurements and results from a detailed bin-microphysics model that models the collision processes in clouds. Firstly, we have build an extremely accurate machine learned model for autoconversion and accretion process parameterization. We also used the extensive data set to derive new classic parameterizations where the autoconversion rate was assumed to have a power=law dependence on several cloud properties. Both methods found a strong dependence of the autoconversion process on the drizzle number concentration, a new finding that is missing in existing parameterizations. To understand this better, we developed new analytical expressions for autoconversion and accretion based on the stochastic collection equation.

This finding is unexpected, because the autoconversion process represents the coalescence between cloud droplets and is causally only related to cloud properties. However, drizzle number concentration does contain information on the width and evolution of the drop size distribution and hence indirectly on the autoconversion rate. In the past parameterizations have been proposed that include rain water concentration, but rain water is directly affected by the accretion process, while the rain drop concentration is not, and hence is mostly affected by autoconversion.

In a second project we apply nonlinear causal discovery to aerosol-cloud-precipitation interactions, using land-based remote sensing instrumentation from the Eastern North Atlantic (ENA) site and the ARM Mobile Facility deployments at the Azores. PhD student Michael DeCaria is leading this work, in collaboration with Prof Sonia Kreidenweis, Prof Chiu, and Dr. Ching-Shu Hung.

Cloud patterns and aggregation

When smaller clouds form they tend to form in distinct patterns, e.g. small puffs, or larger flower-like structures, or structures with a directional component. To contribute to our understanding of why this happens we start with a very simple cloud column model, which we can analyze completely, such that we understand their complete bifurcation structure. Then we couple many of these simple columns together and study their collective behavior, looking for emerging structures that resemble what we see in nature. Since we understand the simple models, the hope is that we can shed light on their combined evolution, eventually leading to understanding of cloud patttern formation. PhD student Michelle Kanipe is the main investigator in this work.

In a second project, we investigate why and how clouds often tend to aggregate in bigger cloud complexes. PhD student Michelle Kanipe is the main scientists on this project.

Hurricane dynamics and thermodynamics

Our main goal is to further understanding of Hurricane intensification. To this end we use nonlinear data assimilation as a tool to combine information from detailed Hurricane models and detailed Hurricane observations. We study both the change that the assimilation makes to the pure model forecasts, and the assimilated model evolution to infer what drives Hurricane intensification.

Recent work includes assimilating drop-sonde and Doppler radar observations in Hurricane Patricia (2015), with main researcher Dandan Tao. We showed that assimilating inner-core drop-sonde and aircraft-based radar observations produces a stronger initial vortex and significantly improves the prediction of RI. Analysis of observation sensitivity experiments shows that the deep-layer dropsonde observations have high impact on both the primary and secondary circulations for the entire troposphere while the radar observations have the most impact on the primary circulations near aircraft flight level. A wide range of intensification scenarios are obtained through two sets of ensemble forecasts initialized with and without assimilating the data prior to the RI onset. Verification of the ensemble forecasts against the observations during the RI period shows that forecast errors toward later stages can originate from two different error sources at early stages of the vortex structure: One is a timing error from a delayed vortex development such that the TC evolution is the same but shifted in time; the other is due to a totally different storm such that there is no moment in time the simulated storm can obtain a correct Hurricane structure.

We are embarking on a study in which we will assimilate aircraft-based radar Doppler observations from the marine boundary layer of Hurricane Teddy (2020), in conjunction with drop-sonde, aircraft-based mid-level Doppler-radar observations, and newly developed high-resolution satellite observations of convective structures (Vortical Hot Towers). PhD student Yu-An Chen is leading this effort.