Operational Perspectives on Tropical Cyclone Intensity Change Part 1: recent advances in intensity guidance

Courtney, Joseph B.; Langlade, Sébastien; Sampson, Charles R.; Knaff, John A.; Birchard, Thomas; Barlow, Stephen; Kotal, S. D.; Kriat, Tarik; Lee, Woojeong; Pasch, Richard J.; Shimada, Udai

doi:10.1016/j.tcrr.2019.10.002

Cited by 24 publications

(14 citation statements)

References 18 publications

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“…Tropical cyclones (TCs) are one of the most devastating global natural disasters, inducing large economic losses as well as fatalities for various coastal regions. Among TC metrics, TC intensity change has long been regarded as a major challenge for both the scientific research and operational forecasting communities (Courtney et al, 2019;Hendricks et al, 2019). TC intensity change is the result of a complex interaction between various internal influences that are related to the structure and internal processes of the TC itself and various external influences that are controlled by the largescale atmospheric and oceanic environment (Hendricks et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Trends in Western North Pacific Tropical Cyclone Intensity Change Before Landfall

Song

Klotzbach

2021

Front. Earth Sci.

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This study investigates the long-term trend in the average 24-h intensity change (ΔV24) of western North Pacific (WNP) tropical cyclones (TCs) before landfall during June-November for the period from 1970–2019. We find a significant increasing trend in basin-averaged ΔV24 during 1970–2019. The increase in ΔV24 is significant over the northern South China Sea (17.5°-25°N, 107.5°-120°E) and to the east of the Philippines (7.5°-15°N, 122.5°-132.5°E), implying a slower weakening rate before landfall for the South China Sea and an increased intensification rate before landfall for the region east of the Philippines. We find a significant linkage between changes in ΔV24 and several large-scale environmental conditions. The increased ΔV24 before landfall in the above two regions is induced by a warmer ocean (e.g., higher sea surface temperatures, maximum potential intensity and TC heat potential) and greater upper-level divergence, with a moister mid-level atmosphere also aiding the ΔV24 increase east of the Philippines. Our study highlights an increasing tendency of ΔV24 before landfall, consistent with trends in ΔV24 over water and over land as found in previous publications.

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Section: Introductionmentioning

confidence: 99%

Trends in Western North Pacific Tropical Cyclone Intensity Change Before Landfall

Song

Klotzbach

2021

Front. Earth Sci.

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“…Tropical cyclones (TCs), which develop over warm tropical oceans, are one of the most serious natural disasters, especially for coastal regions in the western North Pacific (e.g., Japan, Korea, China, and the Philippines) [1][2][3]. Forecasting skills for TC intensity have remained largely static in recent decades, e.g., [4,5]. This is a result of incomplete comprehension of the physical features of TC intensification.…”

Section: Introductionmentioning

confidence: 99%

Effects of Environmental Relative Vorticity and Seasonal Variation on Tropical Cyclones over the Western North Pacific

Chen

Zhou

et al. 2022

Atmosphere

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An improved understanding of the environmental factors influencing tropical cyclones (TCs) is vital to enhance the accuracy of forecasting TC intensity. More than half of TCs that were substantially affected by environmental factors were predominantly affected by low-level environmental relative vorticity (hereafter, VOR TCs). In this study, the seasonal variation and related physical features of VOR TCs from 2003–2017 during TC seasons in summer and autumn over the western North Pacific were analyzed. Autumn VOR TCs exhibited the strongest intensity among all TCs over the western North Pacific. The enhanced environmental relative vorticity during the TC intensification period was larger and more favorably distributed for VOR TC development in autumn. The vorticity diagnostic analysis showed that the convergence was the positive source of environmental relative vorticity of VOR TCs, while the contribution of convergence was larger in autumn than in summer. The increased convergence was related to seasonal variation in larger-scale systems, especially the higher environmental pressure gradient, which reflected the larger subtropical high and the compressed East Asian summer monsoon trough in autumn. In addition, the East Asian summer monsoon trough was also somewhat stronger during the intensification period of VOR TCs, especially in autumn.

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“…At the same time, dynamical intensity models still have some limitations, such as an incomplete understanding of air-sea interaction processes (Lloyd and Vecchi 2011), the underestimation of convection and convective cloud processes (Fovell and Bu 2015), and the lack of real-time inner core TC observations (Shimada et al 2018). Hence, statistical models and statisticaldynamical models remain competitive and are included in official intensity forecasts worldwide (Cangialosi 2019;Courtney et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Experiments for Tropical Cyclone Intensity Forecasts

Balaguru

August

et al. 2021

Weather and Forecasting

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Reducing tropical cyclone (TC) intensity forecast errors is a challenging task that has interested the operational forecasting and research community for decades. To address this, we developed a deep learning (DL)-based Multilayer Perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic Basin. In the first experiment, a 24-hour forecast period was considered. To overcome sample size limitations, we adopted a Leave One Year Out (LOYO) testing scheme, where a model is trained using data from all years except one and then evaluated on the year that is left out. When tested on 2010–2018 operational data using the LOYO scheme, the MLP outperformed other statistical-dynamical models by 9-20%. Additional independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model again outperformed the statistical-dynamical models by 5-22% and achieved comparable results as HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-hour intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic Basin. Therefore, the MLP-based approach has the potential to improve operational TC intensity forecasts, and will also be a viable option for generating synthetic TCs for climate studies.

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Operational Perspectives on Tropical Cyclone Intensity Change Part 1: recent advances in intensity guidance

Cited by 24 publications

References 18 publications

Trends in Western North Pacific Tropical Cyclone Intensity Change Before Landfall

Trends in Western North Pacific Tropical Cyclone Intensity Change Before Landfall

Effects of Environmental Relative Vorticity and Seasonal Variation on Tropical Cyclones over the Western North Pacific

Deep Learning Experiments for Tropical Cyclone Intensity Forecasts

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