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Chun-Chieh Wu
,
Tzu-Hsiung Yen
,
Yi-Hsuan Huang
,
Cheng-Ku Yu
, and
Shin-Gan Chen

Abstract

This study utilizes data compiled over 21 years (1993–2013) from the Central Weather Bureau of Taiwan to investigate the statistical characteristics of typhoon-induced rainfall for 53 typhoons that have impacted Taiwan. In this work the data are grouped into two datasets: one includes 21 selected conventional weather stations (referred to as Con-ST), and the other contains all the available rain gauges (250–500 gauges, mostly automatic ones; referred to as All-ST). The primary aim of this study is to understand the potential impacts of the different gauge distributions between All-ST and Con-ST on the statistical characteristics of typhoon-induced rainfall. The analyses indicate that although the average rainfall amount calculated with Con-ST is statistically similar to that with All-ST, the former cannot identify the precipitation extremes and rainfall distribution appropriately, especially in mountainous areas. Because very few conventional stations are located over the mountainous regions, the cumulative frequency obtained solely from Con-ST is not representative. As compared to the results from All-ST, the extreme rainfall assessed from Con-ST is, on average, underestimated by 23%–44% for typhoons approaching different portions of Taiwan. The uneven distribution of Con-ST, with only three stations located in the mountains higher than 1000 m, is likely to cause significant biases in the interpretation of rainfall patterns. This study illustrates the importance of the increase in the number of available stations in assessing the long-term rainfall characteristic of typhoon-associated heavy rainfall in Taiwan.

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Ming Cai
,
Yueyue Yu
,
Yi Deng
,
Huug M. van den Dool
,
Rongcai Ren
,
Suru Saha
,
Xingren Wu
, and
Jin Huang

Abstract

Extreme weather events such as cold-air outbreaks (CAOs) pose great threats to human life and the socioeconomic well-being of modern society. In the past, our capability to predict their occurrences has been constrained by the 2-week predictability limit for weather. We demonstrate here for the first time that a rapid increase of air mass transported into the polar stratosphere, referred to as the pulse of the stratosphere (PULSE), can often be predicted with a useful degree of skill 4–6 weeks in advance by operational forecast models. We further show that the probability of the occurrence of continental-scale CAOs in midlatitudes increases substantially above normal conditions within a short time period from 1 week before to 1–2 weeks after the peak day of a PULSE event. In particular, we reveal that the three massive CAOs over North America in January and February of 2014 were preceded by three episodes of extreme mass transport into the polar stratosphere with peak intensities reaching a trillion tons per day, twice that on an average winter day. Therefore, our capability to predict the PULSEs with operational forecast models, in conjunction with its linkage to continental-scale CAOs, opens up a new opportunity for 30-day forecasts of continental-scale CAOs, such as those occurring over North America during the 2013/14 winter. A real-time forecast experiment inaugurated in the winter of 2014/15 has given support to the idea that it is feasible to forecast CAOs 1 month in advance.

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I.-I. Lin
,
Robert F. Rogers
,
Hsiao-Ching Huang
,
Yi-Chun Liao
,
Derrick Herndon
,
Jin-Yi Yu
,
Ya-Ting Chang
,
Jun A. Zhang
,
Christina M. Patricola
,
Iam-Fei Pun
, and
Chun-Chi Lien

Abstract

Devastating Japan in October 2019, Supertyphoon (STY) Hagibis was an important typhoon in the history of the Pacific. A striking feature of Hagibis was its explosive rapid intensification (RI). In 24 h, Hagibis intensified by 100 knots (kt; 1 kt ≈ 0.51 m s−1), making it one of the fastest-intensifying typhoons ever observed. After RI, Hagibis’s intensification stalled. Using the current typhoon intensity record holder, i.e., STY Haiyan (2013), as a benchmark, this work explores the intensity evolution differences of these two high-impact STYs. We found that the extremely high prestorm sea surface temperature reaching 30.5°C, deep/warm prestorm ocean heat content reaching 160 kJ cm−2, fast forward storm motion of ∼8 m s−1, small during-storm ocean cooling effect of ∼0.5°C, significant thunderstorm activity at its center, and rapid eyewall contraction were all important contributors to Hagibis’s impressive intensification. There was 36% more air–sea flux for Hagibis’s RI than for Haiyan’s. After its spectacular RI, Hagibis’s intensification stopped, despite favorable environments. Haiyan, by contrast, continued to intensify, reaching its record-breaking intensity of 170 kt. A key finding here is the multiple pathways that storm size affected the intensity evolution for both typhoons. After RI, Hagibis experienced a major size expansion, becoming the largest typhoon on record in the Pacific. This size enlargement, combined with a reduction in storm translational speed, induced stronger ocean cooling that reduced ocean flux and hindered intensification. The large storm size also contributed to slower eyewall replacement cycles (ERCs), which prolonged the negative impact of the ERC on intensification.

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Ayrton Zadra
,
Keith Williams
,
Ariane Frassoni
,
Michel Rixen
,
Ángel F. Adames
,
Judith Berner
,
François Bouyssel
,
Barbara Casati
,
Hannah Christensen
,
Michael B. Ek
,
Greg Flato
,
Yi Huang
,
Falko Judt
,
Hai Lin
,
Eric Maloney
,
William Merryfield
,
Annelize Van Niekerk
,
Thomas Rackow
,
Kazuo Saito
,
Nils Wedi
, and
Priyanka Yadav
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Greg M. McFarquhar
,
Christopher S. Bretherton
,
Roger Marchand
,
Alain Protat
,
Paul J. DeMott
,
Simon P. Alexander
,
Greg C. Roberts
,
Cynthia H. Twohy
,
Darin Toohey
,
Steve Siems
,
Yi Huang
,
Robert Wood
,
Robert M. Rauber
,
Sonia Lasher-Trapp
,
Jorgen Jensen
,
Jeffrey L. Stith
,
Jay Mace
,
Junshik Um
,
Emma Järvinen
,
Martin Schnaiter
,
Andrew Gettelman
,
Kevin J. Sanchez
,
Christina S. McCluskey
,
Lynn M. Russell
,
Isabel L. McCoy
,
Rachel L. Atlas
,
Charles G. Bardeen
,
Kathryn A. Moore
,
Thomas C. J. Hill
,
Ruhi S. Humphries
,
Melita D. Keywood
,
Zoran Ristovski
,
Luke Cravigan
,
Robyn Schofield
,
Chris Fairall
,
Marc D. Mallet
,
Sonia M. Kreidenweis
,
Bryan Rainwater
,
John D’Alessandro
,
Yang Wang
,
Wei Wu
,
Georges Saliba
,
Ezra J. T. Levin
,
Saisai Ding
,
Francisco Lang
,
Son C. H. Truong
,
Cory Wolff
,
Julie Haggerty
,
Mike J. Harvey
,
Andrew R. Klekociuk
, and
Adrian McDonald

Abstract

Weather and climate models are challenged by uncertainties and biases in simulating Southern Ocean (SO) radiative fluxes that trace to a poor understanding of cloud, aerosol, precipitation, and radiative processes, and their interactions. Projects between 2016 and 2018 used in situ probes, radar, lidar, and other instruments to make comprehensive measurements of thermodynamics, surface radiation, cloud, precipitation, aerosol, cloud condensation nuclei (CCN), and ice nucleating particles over the SO cold waters, and in ubiquitous liquid and mixed-phase clouds common to this pristine environment. Data including soundings were collected from the NSF–NCAR G-V aircraft flying north–south gradients south of Tasmania, at Macquarie Island, and on the R/V Investigator and RSV Aurora Australis. Synergistically these data characterize boundary layer and free troposphere environmental properties, and represent the most comprehensive data of this type available south of the oceanic polar front, in the cold sector of SO cyclones, and across seasons. Results show largely pristine environments with numerous small and few large aerosols above cloud, suggesting new particle formation and limited long-range transport from continents, high variability in CCN and cloud droplet concentrations, and ubiquitous supercooled water in thin, multilayered clouds, often with small-scale generating cells near cloud top. These observations demonstrate how cloud properties depend on aerosols while highlighting the importance of dynamics and turbulence that likely drive heterogeneity of cloud phase. Satellite retrievals confirmed low clouds were responsible for radiation biases. The combination of models and observations is examining how aerosols and meteorology couple to control SO water and energy budgets.

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