Data-driven Discovery of Predictive Spatiotemporal Patterns leading to Tropical Cyclogenesis

The early intensification (genesis) of tropical cyclones (TCs) is challenging to predict accurately in operational settings. The difficulty in predicting TC genesis stems from an insufficient understanding of the thermodynamic-kinematic characteristics involved in the multiscale interaction between clouds and TC circulations leading to genesis. Cloud-radiative feedback (CRF) has been shown to play a critical role in accelerating intensification during genesis by initiating secondary circulations that drive moisture and momentum convergence. However, it is still challenging to identify the exact pattern in radiation that could benefit genesis the most. Traditional diagnostic approaches to isolate CRF, such as the Sawyer-Eliassen Equation, require steady-state, axisymmetric thermal forcing. As such, these diagnostics methods are likely suboptimal in studying the response of weak TCs to intermittent, spatially asymmetric thermal forcing. 

 

This presentation utilizes novel data-driven methodologies to identify complex three-dimensional radiative patterns and approximate the thermodynamic-kinematic feedback between such patterns and early TC intensification. Specifically, we tasked a stochastic Variational Encoder-Decoder (VED) framework to discover different predictive patterns in radiative heating and quantify how these patterns affect early TC intensification. Applying the proposed framework to ensemble WRF simulations of Typhoon Haiyan (2013), longwave radiation anomalies in the downshear quadrants of Haiyan are shown to be particularly relevant to the early intensification of that TC. The extracted patterns provide new insights into how deep convective and shallow clouds should distribute spatially to best accelerate genesis. Apart from analyzing the extracted pattern, the stochastic nature of the proposed ML architecture brings additional insights into the radiatively-driven TC genesis research problem. We can use uncertainty in the prediction of intensification rates to track the time evolution of the relevance of radiation in tropical cyclone intensification. Furthermore, the uncertainty in the extracted pattern allows us to pinpoint trustworthy regions in the discovered predictive patterns for scientific interpretation.

 

Our study underscores the potential use of data-driven methodologies to quantify the impact of asymmetric radiative forcing on early TC formation without relying on axisymmetric or steady-state assumptions. The successful application of VED in this presentation reveals a promising way to use data-driven methods to uncover new knowledge in weather dynamics.

Reference:

Iat-Hin Tam, F., Beucler, T., & Ruppert, J. H., Jr. (2024). Identifying three-dimensional radiative patterns associated with early tropical cyclone intensification. Journal of Advances in Modeling Earth Systems, 16, e2024MS004401. https://doi.org/10.1029/2024MS004401