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  • Author or Editor: Gregory R. Foltz x
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Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
John Kaplan
,
Wenwei Xu
,
Nicolas Reul
, and
Bertrand Chapron

Abstract

Tropical cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon–Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical–dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near–real time.

Free access
Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
John Kaplan
,
Wenwei Xu
,
Nicolas Reul
, and
Bertrand Chapron
Full access
Janet Sprintall
,
Victoria J. Coles
,
Kevin A. Reed
,
Amy H. Butler
,
Gregory R. Foltz
,
Stephen G. Penny
, and
Hyodae Seo
Full access
Janet Sprintall
,
Victoria J. Coles
,
Kevin A. Reed
,
Amy H. Butler
,
Gregory R. Foltz
,
Stephen G. Penny
, and
Hyodae Seo

Abstract

Process studies are designed to improve our understanding of poorly described physical processes that are central to the behavior of the climate system. They typically include coordinated efforts of intensive field campaigns in the atmosphere and/or ocean to collect a carefully planned set of in situ observations. Ideally the observational portion of a process study is paired with numerical modeling efforts that lead to better representation of a poorly simulated or previously neglected physical process in operational and research models. This article provides a framework of best practices to help guide scientists in carrying out more productive, collaborative, and successful process studies. Topics include the planning and implementation of a process study and the associated web of logistical challenges; the development of focused science goals and testable hypotheses; and the importance of assembling an integrated and compatible team with a diversity of social identity, gender, career stage, and scientific background. Guidelines are also provided for scientific data management, dissemination, and stewardship. Above all, developing trust and continual communication within the science team during the field campaign and analysis phase are key for process studies. We consider a successful process study as one that ultimately will improve our quantitative understanding of the mechanisms responsible for climate variability and enhance our ability to represent them in climate models.

Free access
Samson Hagos
,
Gregory R. Foltz
,
Chidong Zhang
,
Elizabeth Thompson
,
Hyodae Seo
,
Sue Chen
,
Antonietta Capotondi
,
Kevin A. Reed
,
Charlotte DeMott
, and
Alain Protat
Free access
Chidong Zhang
,
Gregory R. Foltz
,
Andy M. Chiodi
,
Calvin W. Mordy
,
Catherine R. Edwards
,
Christian Meinig
,
Dongxiao Zhang
,
Edoardo Mazza
,
Edward D. Cokelet
,
Eugene F. Burger
,
Francis Bringas
,
Gustavo J. Goni
,
Hristina G. Hristova
,
Hyun-Sook Kim
,
Joaquin A. Trinanes
,
Jun A. Zhang
,
Kathleen E. Bailey
,
Kevin M. O’Brien
,
Maria Morales-Caez
,
Noah Lawrence-Slavas
,
Richard Jenkins
,
Shuyi S. Chen
, and
Xingchao Chen

Abstract

On 30 September 2021, a saildrone uncrewed surface vehicle (USV) was steered into category 4 Hurricane Sam, the most intense storm of the 2021 Atlantic hurricane season. It measured significant wave heights up to 14 m (maximum wave height = 27 m) and near-surface winds exceeding 55 m s−1. This was the first time in more than seven decades of hurricane observations that in real time a USV transmitted scientific data, images, and videos of the dynamic ocean surface near a hurricane’s eyewall. The saildrone was part of a five-saildrone deployment of the NOAA 2021 Atlantic Hurricane Observations Mission. These saildrones observed the atmospheric and oceanic near-surface conditions of five other tropical storms, of which two became hurricanes. Such observations inside tropical cyclones help to advance the understanding and prediction of hurricanes, with the ultimate goal of saving lives and protecting property. The 2021 deployment pioneered a new practice of coordinating measurements by saildrones, underwater gliders, and airborne dropsondes to make simultaneous and near-collocated observations of the air–sea interface, the ocean immediately below, and the atmosphere immediately above. This experimental deployment opened the door to a new era of using remotely piloted uncrewed systems to observe one of the most extreme phenomena on Earth in a way previously impossible. This article provides an overview of this saildrone hurricane observations mission, describes how the saildrones were coordinated with other observing platforms, presents preliminary scientific results from these observations to demonstrate their potential utility and motivate further data analysis, and offers a vision of future hurricane observations using combined uncrewed platforms.

Open access