arXiv subjects

Chanh Kieu

Publications and source records attributed to Chanh Kieu.

8 recordsLinked to original sources

arXiv2026

A note on the shear-forced dynamics of tornadoes

This note presents an axisymmetric model of a tornado-like vortex with internal vertical wind shear. By employing a prescribed incompressible circulation-strain flow that captures the horizontal variation of the vertical wind, we show that the model admits an exact viscous Feynman-Kac representation for the tornado structure. In the presence of vortex vertical shear, the tilting term induces radial phase mixing that rapidly deforms the vortex structure, thus causing the radius of the maximum azimuthal wind to broaden and shift upward. At the long-time limit, the vortex approaches the Burgers radial scale while both the wind and vorticity decay exponentially. This class of solutions may help explain why tornadoes often weaken rapidly once an internal vertical shear structure emerges after touchdown.

physics.ao-ph↗

arXiv2025

Reconstructing Pre-Satellite Tropical Cyclogenesis Climatology Using Deep Learning

A reliable tropical cyclone (TC) climatology is the key to assessing historical and future changes in TC activities. While global TC records have been systematically maintained since the early 1940s, substantial uncertainties remain for the pre-satellite era during which TC observations relied mostly on scattered aircraft reconnaissance and sporadic ship reports. This study presents a deep learning (DL) approach to reconstruct historical TC activity in the western North Pacific (WNP) basin, with a main focus on the pre-satellite era. Using data feature enrichment tailored for tropical cyclogenesis (TCG), we demonstrate that DL can effectively capture the main characteristics and changes in TCG climatology during the post-satellite era. With additional cross-validations, the reconstruction of TCG climatology is then extended to a pre-satellite period (1940-1960) during which TC base-track datasets are most uncertain. Our DL reconstruction reveals a significant missing of TCG in the current best-track data between September and November during the pre-satellite era. Such a TCG undercount in the best track data occurs mainly around 10-15$^\circ$N in the central WNP, while coastal regions show better consistency with DL reconstruction. These findings not only highlight the potential of DL for improving historical assessments of TC activity, but also advance our understanding of TCG processes by identifying key environmental conditions conducive to TC formation. The DL approach presented herein can be applied to other ocean basins, climate proxies, or reanalysis datasets for future TC climate studies.

physics.ao-ph↗

arXiv2025

Retrieving Tropical Cyclone Intensity from Climate Reanalysis using Deep Learning

Traditional methods for improving tropical cyclone (TC) intensity from climate model outputs or projections have primarily relied on either dynamical or statistical downscaling. With recent advances in deep learning (DL) techniques, an important question is how DL can provide an alternative approach for enhancing TC intensity and structure retrieval from climate data. Using a common DL architecture based on convolutional neural networks (CNN) and a set of key environmental features relevant to TCs, we show that TC intensity and size can be effectively retrieved from climate reanalysis data without requiring super-resolution enhancement as in previous studies, even when applied to coarse-resolution climate data. This approach allows for retrieving TC intensity metrics and size that are dynamically constrained by the data, rather than estimating these quantities independently. Our results highlight that TC intensity and size are governed not only by TC internal processes but also by local environments during TC development for which DL models can learn and capture. The performance of our DL model depends on several factors such as season, the stage of TC development, or ocean basins, with root-mean-square errors ranging from $\approx$8-10 m s$^{-1}$ for the maximum 10-m wind, $\approx$10-13 hPa for minimum central pressure, and $\approx$13-21 km for the radius of the maximum wind. Although these errors are better than any direct vortex detection or statistical downscaling methods applied to the same data, their wide ranges also suggest that a 0.5$^\circ$-resolution climate data may contain limited TC information for DL models to learn from, regardless of model optimizations or architectures. Possible improvements and challenges in addressing the lack of fine-scale TC information in coarse-resolution climate reanalysis datasets are discussed.

physics.ao-ph↗

arXiv2025

Deep Learning Reconstruction of Tropical Cyclogenesis in the Western North Pacific from Climate Reanalysis Dataset

This study presents a deep learning (DL) architecture based on residual convolutional neural networks (ResNet) to reconstruct the climatology of tropical cyclogenesis (TCG) in the Western North Pacific (WNP) basin from climate reanalysis datasets. Using different TCG data labeling strategies and data enrichment windows for the NASA Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2) dataset during the 1980-2020 period, we demonstrate that ResNet can reasonably reproduce the overall TCG climatology in the WNP, capturing both its seasonality and spatial distribution. Our sensitivity analyses and optimizations show that this TCG reconstruction depends on both the type of TCG climatology that one wishes to reconstruct and the strategies used to label TCG data. Of interest, analyses of different input features reveal that DL-based reconstruction of TCG climatology needs only a subset of channels rather than all available data, which is consistent with previous modeling and observational studies of TCG. These results not only enhance our understanding of the TCG process but also provide a promising pathway for predicting or downscaling TCG climatology based on large-scale environments from global model forecasts or climate output. Overall, our study demonstrates that DL can offer an effective approach for studying TC climatology beyond the traditional physical-based simulations and vortex-tracking algorithms used in current climate model analyses.

physics.ao-ph↗

arXiv2025

NWP-based deep learning for tropical cyclone intensity prediction

Global artificial intelligence (AI) models are rapidly advancing and beginning to outperform traditional numerical weather prediction (NWP) models across metrics, yet predicting regional extreme weather such as tropical cyclone (TC) intensity presents unique spatial and temporal challenges that global AI models cannot capture. This study presents a new approach to train deep learning (DL) models specifically for regional extreme weather prediction. By leveraging physics-based NWP models to generate high-resolution data, we demonstrate that DL models can better predict or downscale TC intensity and structure when fine-scale processes are properly accounted for. Furthermore, by training DL models on different resolution outputs from physics-based simulations, we highlight the critical role of fine-scale processes in larger storm-scale dynamics, an aspect that current climate datasets used to train most global DL models cannot fully represent. These findings underscore the challenges in predicting or downscaling extreme weather with data-driven models, thus proposing the new role of NWP models as data generators for training DL models in the future AI model development for weather applications.

physics.ao-ph↗

arXiv2024

Predictability of Global AI Weather Models

This study examines the predictability of artificial intelligence (AI) models for weather prediction. Using a simple deep-learning architecture based on convolutional long short-term memory and the ERA5 data for training, we show that different time-stepping techniques can have a strong influence on the model performance and weather predictability. Specifically, a small-step approach for which the future state is predicted by recursively iterating an AI model over a small time increment displays strong sensitivity to the type of input channels, the number of data frames, or forecast lead times. In contrast, a big-step approach for which a current state is directly projected to a future state at each corresponding lead time provides much better forecast skill and a longer predictability range. In particular, the big-step approach is very resilient to different input channels, or data frames. In this regard, our results present a different method for implementing global AI models for weather prediction, which can optimize the model performance even with minimum input channels or data frames.

physics.ao-ph↗

arXiv2021

Detecting chaos in hurricane intensity

Determining the maximum potential limit in the accuracy of hurricane intensity prediction is important for operational practice. Using the phase-space reconstruction method for hurricane intensity time series, here we found that hurricane dynamics contain inherent low-dimensional chaos at the maximum intensity equilibrium. Examination of several chaotic invariants including the largest Lyapunov exponent, the Sugihara-May correlation, and the correlation dimension consistently captures an intrinsic dimension of the hurricane chaotic attractor in the range of 4-5. In addition, the error doubling time is roughly 1-5 hours, which accords with the decay time obtained from the Sugihara-May correlation. The confirmation of hurricane chaotic intensity as found in this study suggests a relatively short limit for intensity predictability of $\sim$18-24 hours after reaching the maximum intensity stage. So long as the traditional metrics for hurricane intensity such as the maximum surface wind or the minimum central pressure is used for intensity forecast, our results support that hurricane intensity forecast errors will not be reduced indefinitely in any modelling systems, even in the absence of all model and observational errors. As such, the future improvement of hurricane intensity forecast should be based on different intensity metric beyond the absolute intensity errors as in the current practice of intensity verification.

physics.ao-ph↗

arXiv2021

Hitting Time of Rapid Intensification Onset in Hurricane-like Vortices

Predicting tropical cyclone (TC) rapid intensification (RI) is an important yet challenging task in current operational forecast due to our incomplete understanding of TC nonlinear processes. This study examines the variability of RI onset, including the probability of RI occurrence and the timing of RI onset, using a low-order stochastic model for TC development. Defining RI onset time as the first hitting time in the model for a given subset in the TC-scale state space, we quantify the probability of the occurrence of RI onset and the distribution of the timing of RI onset for a range of initial conditions and model parameters. Based on asymptotic analysis for stochastic differential equations, our results show that RI onset occurs later, along with a larger variance of RI onset timing, for weaker vortex initial condition and stronger noise amplitude. In the small noise limit, RI onset probability approaches one and the RI onset timing has less uncertainty (i.e., a smaller variance), consistent with observation of TC development under idealized environment. Our theoretical results are verified against Monte-Carlo simulations and compared with explicit results for a general 1-dimensional system, thus providing new insights into the variability of RI onset and helping better quantify the uncertainties of RI variability for practical applications.

math.PR↗