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Hot Towers and Hurricanes: Early Observations, Theories, and Models

2003/01/01 by Richard A. Anthes · 1 citation
Earth and Planetary Sciences · Environmental Science · #Tropical and Extratropical Cyclones Research #Wind and Air Flow Studies #Meteorological Phenomena and Simulations #Tropical cyclone #Climatology #Meteorology #Environmental science #Storm #Convection #Geography #Geology

paper · doi:10.1007/978-1-878220-63-9_10

openalex publication_date 2003/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

This paper describes the exciting period of discovery in the 1950s and 1960s in tropical meteorology, and the important role played by Joanne Malkus (Simpson) in her studies of cumulus convection and tropical cyclones. A key concept developed by Joanne, with Herbert Riehl, was that of the “hot tower.” Hot towers were deep tropical cumulonimbus clouds whose cores were undiluted by entrainment and thus carried heat and water vapor from the boundary layer to high in the troposphere. Joanne’s observational work led to a major effort by a number of theoreticians and modelers in the 1960s and 1970s to incorporate the effects of the relatively small-scale but energetically important cumulus clouds in numerical models of tropical cyclones. The important theory of conditional instability of the second kind , or CISK, and its contribution to tropical cyclone theory and modeling, is summarized. The CISK theory envisioned a cooperation between the tropical cyclone-scale circulation and the much smaller-scale convective clouds, including hot towers, that caused tropical cyclones to form and intensify. Although the CISK and hot tower theories were misunderstood and misused by some, they both contributed much to the development of tropical cyclone models and scientific understanding of these violent storms, and their general concepts and importance remain valid today. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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