Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics

Faranda, Davide; Messori, Gabriele; Bourdin, Stella; Vrac, Mathieu; Thao, Soulivanh; Riboldi, Jacopo; Fromang, Sébastien; Yiou, Pascal

doi:10.1007/s00382-023-06794-8

Cited by 5 publications

(2 citation statements)

References 79 publications

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“…The authors found that this is generally the case for most (yet not all) of the Euro‐Atlantic regimes, and concluded that this provided strong evidence for the regimes being physically‐meaningful. This argument rests on a growing body of literature that has shown how d and θ may be used to characterize both large‐scale and synoptic‐scale atmospheric flows (e.g., Faranda, Messori, Alvarez‐Castro, & Yiou, 2017; Faranda, Messori, Bourdin, et al., 2023; Faranda, Messori, & Yiou, 2017; Faranda, Messori, Yiou, et al., 2023; Hochman et al., 2019; Messori et al., 2017, 2021; Nabizadeh et al., 2022; Platzer et al., 2023). We nonetheless note that correspondence with low d and θ states can be considered a sufficient, but not a necessary, condition for the physical existence of a regime, and that other approaches having been proposed to identify physically‐meaningful weather regimes (Strommen et al., 2023).…”

Section: Introductionmentioning

confidence: 99%

The Dynamical Footprint of Year‐Round North American Weather Regimes

Lee,

Messori

2024

Geophysical Research Letters

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Weather regimes have been defined over multiple regions and used in a range of practical applications, including subseasonal‐to‐seasonal forecasting and climate model evaluation. Despite their widespread use, the extent to which regimes reflect physical modes of the atmosphere is seldom investigated. Here, we adopt a year‐round classification of four North American weather regimes, with a fifth “no regime” class, and leverage dynamical systems theory to investigate their dynamical properties. We find that when the atmospheric flow is assigned to a regime, it displays persistent characteristics and a lifecycle‐like temporal evolution. We further find that, regardless of season, these characteristics are enhanced when the atmospheric flow displays a comparatively strong projection onto the cluster‐mean of the regime to which it is assigned (while the reverse is true for a weaker projection). We interpret these results as evidence that the four North American weather regimes are physically‐meaningful, with a clear dynamical footprint.

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Section: Introductionmentioning

confidence: 99%

The Dynamical Footprint of Year‐Round North American Weather Regimes

Lee,

Messori

2024

Geophysical Research Letters

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“…Here, we aim to correct the intensity in the ERA5 reanalysis using the true intensity in IBTrACS as a reference. There are some related works to correct the intensity in operational forecasting [48][49][50]. Differently, we develop an adaptive learning approach based on deep neural networks and domain adaptation, which helps to solve the issues of data quality and weak model generalisability.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Learning Approach for Tropical Cyclone Intensity Correction

Chen,

Toumi,

Shi

et al. 2023

Remote Sensing

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Tropical cyclones (TCs) are dangerous weather events; accurate monitoring and forecasting can provide significant early warning to reduce loss of life and property. However, the study of tropical cyclone intensity remains challenging, both in terms of theory and forecasting. ERA5 reanalysis is a benchmark data set for tropical cyclone studies, yet the maximum wind speed error is very large (68 kts) and is still 19 kts after simple linear correction, even in the better sampled North Atlantic. Here, we develop an adaptive learning approach to correct the intensity in the ERA5 reanalysis, by optimising the inputs to overcome the problems caused by the poor data quality and updating the features to improve the generalisability of the deep learning-based model. Specifically, we use understanding of TC properties to increase the representativeness of the inputs so that the general features can be learned with deep neural networks in the sample space, and then use domain adaptation to update the general features from the known domain with historical storms to the specific features for the unknown domain of new storms. This approach can reduce the error to only 6 kts which is within the uncertainty of the best track data in the international best track archive for climate stewardship (IBTrACS) in the North Atlantic. The method may have wide applicability, such as when extending it to the correction of intensity estimation from satellite imagery and intensity prediction from dynamical models.

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Toward the reliable use of reanalysis data as a reference for bias correction in climate models: A multivariate perspective

de Padua,

Ahn

2024

Journal of Hydrology

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Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics

Cited by 5 publications

References 79 publications

The Dynamical Footprint of Year‐Round North American Weather Regimes

The Dynamical Footprint of Year‐Round North American Weather Regimes

An Adaptive Learning Approach for Tropical Cyclone Intensity Correction

Toward the reliable use of reanalysis data as a reference for bias correction in climate models: A multivariate perspective

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