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Qidong Yang
,
Chia-Ying Lee
,
Michael K. Tippett
,
Daniel R. Chavas
, and
Thomas R. Knutson

), which impacts TC wind structure to certain degree. Land–sea roughness contrast ( Wong and Chan 2007 ) and air–sea interaction ( Lee and Chen 2012 , 2014 ), as well as the interactions between TCs and midlatitude circulations ( Komaromi and Doyle 2018 ), can influence TC surface wind structure as well. In recent years, machine learning (ML) has made incremental progress in geophysical sciences. Several recent works have applied ML methods in TC studies. Racah et al. (2017) and Kim et al. (2019

Full access
Wei-Yi Cheng
,
Daehyun Kim
,
Scott Henderson
,
Yoo-Geun Ham
,
Jeong-Hwan Kim
, and
Rober H. Holzworth

comparable to NLDN as the spatial correlation of lightning flashes between the WWLLN and the NLDN is as high as 0.75 in 2008–09 ( Abarca et al. 2010 ). The advance in the quality and the quantity of lightning observation data opens a new pathway to improve the representation of lightning in numerical models through data-driven approaches. In particular, machine learning (ML) can be used to train an algorithm to extract desired patterns from the data, which can then be used for the prediction with

Open access
Azusa Takeishi
and
Chien Wang

turbulence effects. Likewise, Chen et al. (2018a) introduced turbulent collision efficiencies based on their DNS results. They later found that the narrowing of drop size distributions by condensation sets up a condition for an effective enhancement of collision–coalescence by turbulence ( Chen et al. 2018b ). Over the past few years, the applicability of machine learning (ML) techniques to atmospheric science research has been progressively explored. Some studies used simulations of cloud

Open access
Bu-Yo Kim
,
Miloslav Belorid
, and
Joo Wan Cha

, and ensemble prediction to reduce the uncertainty of the NWP models and increase its prediction accuracy ( Zhou et al. 2012 ; Cornejo-Bueno et al. 2017 ; D. J. Kim et al. 2020 ). Nevertheless, the NWP models are still disadvantaged by a low prediction accuracy and high computational cost ( Zong et al. 2020 ). Therefore, studies are increasingly using linear or nonlinear relationships between weather variables and visibility or employing machine learning (ML) to predict visibility with higher

Open access
Fraser King
,
George Duffy
, and
Christopher G. Fletcher

( Pettersen et al. 2020 ; McIlhattan et al. 2020 ). Traditional algebraic methods for precipitation retrievals can become impractical if we consider the full set of variables present in a typical radar retrieval. Machine learning (ML) algorithms, however, are unrestricted by algebra and can incorporate a virtually unlimited number of variables to derive complex and nonintuitive relationships between the provided predictors and response ( Karpatne et al. 2019 ). Previous studies have used ML to

Open access
Randy J. Chase
,
David R. Harrison
,
Amanda Burke
,
Gary M. Lackmann
, and
Amy McGovern

1. Introduction The mention and use of machine learning (ML) within meteorological journal articles is accelerating ( Fig. 1 ; e.g., Burke et al. 2020 ; Hill et al. 2020 ; Lagerquist et al. 2020 ; Li et al. 2020 ; Loken et al. 2020 ; Mao and Sorteberg 2020 ; Muñoz-Esparza et al. 2020 ; Wang et al. 2020 ; Bonavita et al. 2021 ; Cui et al. 2021 ; Flora et al. 2021 ; Hill and Schumacher 2021 ; Schumacher et al. 2021 ; Yang et al. 2021 ; Zhang et al. 2021 ). With a growing

Open access
Daniela de Oliveira Maionchi
,
Adriano Carvalho Nunes e Araújo
,
Walter Aguiar Martins Jr.
,
Junior Gonçalves da Silva
, and
Danilo Ferreira de Souza

strategies to prevent such accidents ( de Souza et al. 2022 ). In this approach, machine learning can be used to assist in understanding the mortality patterns related to lightning strikes, as well as identifying the most relevant set of input variables to predict the number of weekly deaths. Machine learning techniques, more specifically, gradient boosting, were applied to analyze and extract valuable insights from the available data. This enabled the most significant factors contributing to lightning

Restricted access
Amy McGovern
,
Randy J. Chase
,
Montgomery Flora
,
David J. Gagne II
,
Ryan Lagerquist
,
Corey K. Potvin
,
Nathan Snook
, and
Eric Loken

developing artificial intelligence (AI) and machine learning (ML), referred to as AI/ML throughout the paper, techniques to improve prediction and understanding of convective weather and its associated hazards including tornadoes, wind, hail, and lightning. The prediction of convective hazards cannot be optimized solely by improving existing numerical weather prediction (NWP) models. Thunderstorms and their hazards are not fully resolved at the resolutions achievable by real-time models in the

Open access
G. Eli Jergensen
,
Amy McGovern
,
Ryan Lagerquist
, and
Travis Smith

dataset (2004–11). These difficulties highlight the benefits of machine learning (ML), which can achieve human-level accuracy in image classification in a small fraction of the time ( Quartz 2017 ). The success of ML-based image classification in meteorology has been less dramatic, likely because definitions of meteorological phenomena (e.g., linear hybrids) are less objective. Nonetheless, it has achieved notable successes in meteorology as well. For example, Wang et al. (2016) use ML to detect sea

Open access
Alexander J. DesRosiers
and
Michael M. Bell

; Bell et al. 2013 ) but existing techniques provide a suboptimal classification of weather and nonweather echoes, either removing too much weather data in real-time applications or requiring additional time-consuming manual QC to produce high-quality wind analyses for research. In the current study, we improve this process through the use of complex, automated decision-making available via machine learning techniques. Although recent progress has been made with QC techniques for ground

Open access