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), 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
), 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
( 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
( 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
, 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
, 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
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
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
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
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
likely than in an environment that may have the same EHI value but more SRH and less CAPE. Similarly, SCP is not based on a proven physical process but on the observations of supercells which leads to short comings in certain situations. For example, since each term in SCP is multiplied together a low value of SCP would result from an environment with high shear and low cape, although supercells occur in such environments ( Sherburn and Parker 2014 ). Machine learning has the ability to generate
likely than in an environment that may have the same EHI value but more SRH and less CAPE. Similarly, SCP is not based on a proven physical process but on the observations of supercells which leads to short comings in certain situations. For example, since each term in SCP is multiplied together a low value of SCP would result from an environment with high shear and low cape, although supercells occur in such environments ( Sherburn and Parker 2014 ). Machine learning has the ability to generate
.e., modeled proximity soundings) and passed to various machine learning classification algorithms for training, testing, and cross-validation. Machine learning approaches for severe convective weather diagnostic and prognostic applications have garnered significant attention in the last five years, and their value added is demonstrable (e.g., Gagne et al. 2017 ; Lagerquist et al. 2017 ; McGovern et al. 2017 ; Czernecki et al. 2019 ; Gagne et al. 2019 ; Mostajabi et al. 2019 ; Burke et al. 2020
.e., modeled proximity soundings) and passed to various machine learning classification algorithms for training, testing, and cross-validation. Machine learning approaches for severe convective weather diagnostic and prognostic applications have garnered significant attention in the last five years, and their value added is demonstrable (e.g., Gagne et al. 2017 ; Lagerquist et al. 2017 ; McGovern et al. 2017 ; Czernecki et al. 2019 ; Gagne et al. 2019 ; Mostajabi et al. 2019 ; Burke et al. 2020
use statistical techniques but further include atmospheric variables provided by dynamical models ( DeMaria et al. 2005 ). Last, ensemble models combine the forecasts made by multiple runs of a single model ( Cangialosi 2020 ). Moreover, consensus models typically combine individual operational forecasts with a simple or weighted average ( Sampson et al. 2008 ; Simon et al. 2018 ; Cangialosi 2020 ; Cangialosi et al. 2020 ). In addition, recent developments in deep learning (DL) enable machine
use statistical techniques but further include atmospheric variables provided by dynamical models ( DeMaria et al. 2005 ). Last, ensemble models combine the forecasts made by multiple runs of a single model ( Cangialosi 2020 ). Moreover, consensus models typically combine individual operational forecasts with a simple or weighted average ( Sampson et al. 2008 ; Simon et al. 2018 ; Cangialosi 2020 ; Cangialosi et al. 2020 ). In addition, recent developments in deep learning (DL) enable machine
1. Introduction Many scientific domains have seen an unprecedented growth of machine learning tools and applications during the last decade. The toolbox of machine learning that allows learning complex nonlinear dynamics from data is also promising for many application areas within the Earth system sciences. This domain is data-rich—the European Centre for Medium-Range Weather Forecasts (ECMWF) has, for example, hundreds of petabytes of Earth-system-related data stored in its archive
1. Introduction Many scientific domains have seen an unprecedented growth of machine learning tools and applications during the last decade. The toolbox of machine learning that allows learning complex nonlinear dynamics from data is also promising for many application areas within the Earth system sciences. This domain is data-rich—the European Centre for Medium-Range Weather Forecasts (ECMWF) has, for example, hundreds of petabytes of Earth-system-related data stored in its archive
transfer simulations for a limited amount of frequency bands, the aim of this study is to explore the entire spectral information available from spaceborne sensors. To capture different, yet presumably distinct, radiative signatures that are characteristic for various fog, cloud or clear-sky scenes, a machine learning technique is applied to recognize the relevant patterns. Machine learning techniques are becoming increasingly popular in remote sensing and earth observation (e.g., Gardner and Dorling
transfer simulations for a limited amount of frequency bands, the aim of this study is to explore the entire spectral information available from spaceborne sensors. To capture different, yet presumably distinct, radiative signatures that are characteristic for various fog, cloud or clear-sky scenes, a machine learning technique is applied to recognize the relevant patterns. Machine learning techniques are becoming increasingly popular in remote sensing and earth observation (e.g., Gardner and Dorling
