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North Atlantic seasonal hurricane prediction: underlying science and an evaluation of statistical models

Klotzbach, PJ; Saunders, MA; Bell, GD; Blake, ES; (2017) North Atlantic seasonal hurricane prediction: underlying science and an evaluation of statistical models. In: Wang, S-Y and Yoon, J-H and Funk, CC and Gillies, RR, (eds.) Climate Extremes: Patterns and Mechanisms, Geophysical Monograph 226. (pp. 315-328). American Geophysical Union (AGU) / Wiley Green open access

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Klotzbach, Saunders, Bell and Blake_Chapter 19 in Climate Extremes Patterns and Mechanisms, Geophysical Monograph 226_in press (2017).pdf - Accepted Version

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Abstract

Statistically-based seasonal hurricane outlooks for the North Atlantic were initiated by Colorado State University (CSU) in 1984, and have been issued every year since that time by CSU. The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center and the UK-based Tropical Storm Risk (TSR) have the next longest records (1998-present) of continuous outlooks. This chapter describes how these three forecasts have evolved with time, and documents the approaches, the environmental fields, and the lead times which underpin the models’ operation. Some of the environmental parameters used in early seasonal outlooks are no longer employed, but new predictive fields have been found which appear to be more important for seasonal hurricane prediction. An assessment is made of the deterministic skill of the seasonal hurricane outlooks issued in real-time by CSU, NOAA, and TSR between 2003 and 2014. All methods show moderate-to-good skill for early August outlooks (prior to the most active portion of the hurricane season), low-to-moderate skill for outlooks issued in early June, and lesser skill for outlooks issued in early April. Overall, the TSR model has the most skillful predictions of Accumulated Cyclone Energy (ACE), while NOAA has the best named storm predictions issued in early August.

Type: Book chapter
Title: North Atlantic seasonal hurricane prediction: underlying science and an evaluation of statistical models
ISBN-13: 9781119067849
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/9781119068020.ch19
Publisher version: http://dx.doi.org/10.1002/9781119068020.ch19
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Atlantic Multidecadal Oscillation; climate extremes; El Nino-Southern Oscillation; NOAA Climate Prediction Center; North Atlantic seasonal hurricane prediction; Statistical Model Evaluation; UK-based Tropical Storm Risk
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics
URI: https://discovery.ucl.ac.uk/id/eprint/1558808
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