A Rapid Intensification Deterministic Ensemble (RIDE) for the Joint Typhoon Warning Center’s Area of Responsibility

Author:

Knaff John A.1ORCID,Sampson Charles R.2,Brammer Alan3,Slocum Christopher J.1

Affiliation:

1. a NOAA/Center for Satellite Applications and Research, Fort Collins, Colorado

2. b Naval Research Laboratory, Monterey, California

3. c NOAA/Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado

Abstract

Abstract The Rapid Intensification Deterministic Ensemble (RIDE) is an operational method used to estimate the probability of tropical cyclone rapid intensification in the Joint Typhoon Warning Center’s area of responsibility. Inputs to RIDE are current intensity, storm latitude, intensity change forecasts from seven routinely available operational deterministic models of intensity change, and the number of those models exceeding their individual 90th percentile of intensity change. Deterministic model inputs come from four numerical weather prediction models, two statistical–dynamical models, and one purely statistical model. In RIDE, logistic regression combines the deterministic inputs to form a probabilistic rapid intensification forecast model. RIDE then also generates deterministic intensity forecasts from these probabilistic forecasts that serve as forecaster guidance and as input to intensity consensus aids. Results based on a year of independent verification suggest good reliability and discrimination with a general tendency to underpredict rapid intensification events, but with few false alarms. Significance Statement An operational tropical cyclone forecaster makes a forecast with deterministic and probabilistic intensity guidance tools at their disposal. These models have a varying degree of abilities for predicting both intensity change and rapid intensification. The forecaster faces a dilemma in how to combine this disparate guidance to anticipate rapid intensification events. Here, the RIDE model provides probability forecasts associated with rapid intensification at 12-, 24-, 36-, 48-, and 72-h lead times and associated deterministic forecasts. RIDE provides skillful rapid intensification forecasts and helps rectify this forecast dilemma.

Funder

Office of Naval Research

National Oceanic and Atmospheric Administration

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference35 articles.

1. Biswas, M. K., and Coauthors, 2018: Hurricane Weather Research and Forecasting (HWRF) model: 2018 scientific documentation. Developmental Testbed Center Doc., 112 pp., https://dtcenter.org/sites/default/files/community-code/hwrf/docs/scientific_documents/HWRFv4.0a_ScientificDoc.pdf.

2. CSIRO, 2022: Software from Alan J. Miller. Commonwealth Scientific and Industrial Research Organisation, accessed 1 June 2022, https://wp.csiro.au/alanmiller/.

3. Operational multivariate ocean data assimilation;Cummings, J. A.,2005

4. A simplified dynamical system for tropical cyclone intensity prediction;DeMaria, M.,2009

5. Further improvement to the Statistical Hurricane Intensity Prediction Scheme (SHIPS);DeMaria, M.,2005

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3