Quantifying Impacts of Environmental Moisture Spatial Patterns on Tropical Cyclones Via Observational Datasets

Tropical cyclones (TCs), known as tropical storms and hurricanes in the North Atlantic and eastern North Pacific basins, tend to exist in environments characterized by an asymmetric distribution of moisture around the storm. However, there is a lack of conceptual models that explain the processes by which these moisture configurations impact TC intensity and structure. For example, dry air intrusions in the mid-levels via ventilation are known to negatively impact a TC, yet the nature and severity of these intrusions depend on the TC (e.g., its current structure, inertial stability) and the location of the dry air as well as the direction and magnitude of vertical wind shear. Here, we examine TCs in low-to-moderate (≤ 20 kt) shear environments with intensities of at least 65 kt whose centers have not interacted with land to focus on mature cases in varying moisture environments.

Using previously-identified cases that are representative of dominant environmental moisture patterns, this study investigates mechanisms by which the spatial distribution of moisture at a particular time affects the TC’s intensity and structure through a storm-centered framework. Passive microwave observations from the Global Precipitation Measurement (GPM) mission constellation reveal TC convective structure below the cloud tops, and the Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) provides 2-km resolution (at nadir) full-disk infrared (IR) imagery every 15 minutes in 2018 and every 10 minutes since 2019. The Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset provides 30-minute precipitation estimates at 0.1° spatial resolution to supplement microwave overpasses, and the NOAA/NASA Merged IR (MERGIR) 4-km, 30-minute dataset enables assessment of cloud-top temperature, a proxy for deep convection, for cases prior to 2018. Additional factors such as sea surface temperature (SST) and ocean heat content can be evaluated using NOAA’s daily, 0.25° optimum interpolation SST and nearby Argo float observations, respectively. Python packages that facilitate efforts for this project include xarray, Pydap, SciPy, MetPy, argopy, NumPy, and matplotlib. Beyond the research findings, this presentation will highlight the applicability of these approaches to real-time assessment and include links to relevant code on GitHub.