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Abstract Cloud radiative feedback impacts early tropical cyclone (TC) intensification, but limitations in existing diagnostic frameworks make them unsuitable for studying asymmetric or transient radiative heating. We propose a linear Variational Encoder‐Decoder (VED) framework to learn the hidden relationship between radiative anomalies and the surface intensification of realistic simulated TCs. The uncertainty of the VED model identifies periods when radiation has more importance for intensification. A close examination of the radiative pattern extracted by the VED model from a 20‐member ensemble simulation on Typhoon Haiyan shows that longwave forcing from inner core deep convection and shallow clouds downshear contribute to intensification, with deep convection in the downshear‐left quadrant having the most impact overall on the intensification of that TC. Our work demonstrates that machine learning can aid the discovery of thermodynamic‐kinematic relationships without relying on axisymmetric or deterministic assumptions, paving the way for the objective discovery of processes leading to TC intensification in realistic conditions.
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Acceleration of tropical cyclone development by cloud-radiative feedbacks
Abstract A complete understanding of the development of tropical cyclones (TC) remains elusive and forecasting TC intensification remains challenging. This motivates further research into the physical processes that govern TC development. One process that has, until recently, been under-investigated is the role of radiation. Here, the importance of radiative feedbacks in TC development and the mechanisms underlying their influence is investigated in a set of idealized convection-permitting simulations. A TC is allowed to form after initialization from a mesoscale warm, saturated bubble on an f -plane, in an otherwise quiescent and moist neutral environment. Tropical storm formation is delayed by a factor of two or three when radiative feedbacks are removed by prescribing a fixed cooling profile or spatially homogenizing the model-calculated cooling profiles. The TC’s intensification rate is also greater when longwave radiative feedbacks are stronger. Radiative feedbacks in the context of a TC arise from interactions between spatially and temporally varying radiative heating and cooling (driven by the dependence of radiative heating and cooling rate on clouds and water vapor) and the developing TC (the circulation of which shapes the structure of clouds and water vapor). Further analysis and additional mechanism denial experiments pinpoint the longwave radiative feedback contributed by ice clouds as the strongest influence. Improving the representation of cloud-radiative feedbacks in forecast models therefore has the potential to yield critical advancements in TC prediction.
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- Award ID(s):
- 1830724
- PAR ID:
- 10351609
- Date Published:
- Journal Name:
- Journal of the Atmospheric Sciences
- ISSN:
- 0022-4928
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/JAS-D-21-0227.1
