More Like this

1. Effect of Storm Size on Sea Surface Cooling and Tropical Cyclone Intensification in the Western North Pacific

   https://doi.org/10.1175/JCLI-D-22-0949.1  

   Liu, Yuhao; Guan, Shoude; Lin, I-I; Mei, Wei; Jin, Fei-Fei; Huang, Mengya; Zhang, Yihan; Zhao, Wei; Tian, Jiwei (October 2023, Journal of Climate)

   Abstract The effect of tropical cyclone (TC) size on TC-induced sea surface temperature (SST) cooling and subsequent TC intensification is an intriguing issue without much exploration. Via compositing satellite-observed SST over the western North Pacific during 2004–19, this study systematically examined the effect of storm size on the magnitude, spatial extension, and temporal evolution of TC-induced SST anomalies (SSTA). Consequential influence on TC intensification is also explored. Among the various TC wind radii, SSTA are found to be most sensitive to the 34-kt wind radius (R34) (1 kt ≈ 0.51 m s⁻¹). Generally, large TCs generate stronger and more widespread SSTA than small TCs (for category 1–2 TCs, R34: ∼270 vs 160 km; SSTA: −1.7° vs −0.9°C). Despite the same effect on prolonging residence time of TC winds, the effect of doubling R34 on SSTA is more profound than halving translation speed, due to more wind energy input into the upper ocean. Also differing from translation speed, storm size has a rather modest effect on the rightward shift and timing of maximum cooling. This study further demonstrates that storm size regulates TC intensification through an oceanic pathway: large TCs tend to induce stronger SST cooling and are exposed to the cooling for a longer time, both of which reduce the ocean’s enthalpy supply and thereby diminish TC intensification. For larger TCs experiencing stronger SST cooling, the probability of rapid intensification is half of smaller TCs. The presented results suggest that accurately specifying storm size should lead to improved cooling effect estimation and TC intensity prediction. 

   more » « less
2. Track‐Dependent Links Between Tropical Cyclones and Extratropical Predictability in Physical and AI Models

   https://doi.org/10.1029/2026GL122172  

   Zhang, Gan (July 2026, Geophysical Research Letters)

   Abstract Global medium‐range weather forecasts suffer occasional failures (“busts”) linked to tropical cyclones (TCs). We investigate TC influences on extratropical predictability by comparing forecasts from a physics‐based model (ECMWF‐IFS) and an AI‐hybrid model (Google‐NGCM) initialized near TC genesis. Analyzing 108 out‐of‐sample Northern Hemisphere cases reveals similar extratropical error growth patterns and comparable performance between the models. This suggests that the Neural General Circulation Model (NGCM) is capable of predicting the bulk upscale effects of tropical convection without directly representing convective processes. Leveraging the NGCM's computational efficiency, we compare forecasts initialized with and without TC genesis to isolate track‐dependent forecast impacts. For Week‐2 extratropical forecasts, TC impacts are highly time‐, metric‐, and track‐dependent. The analysis confirms that some poleward‐moving TCs degrade Week‐2 US and European forecasts and suggests significant impacts from westward‐moving TCs. The findings highlight the utility of the AI‐hybrid model in predictability research and complex tropical‐extratropical teleconnections that warrant future research. 

   more » « less
3. An Observational Study of the Symmetric Boundary Layer Structure and Tropical Cyclone Intensity

   https://doi.org/10.3390/atmos11020158  

   Ren, Yifang; Zhang, Jun A.; Vigh, Jonathan L.; Zhu, Ping; Liu, Hailong; Wang, Xiang; Wadler, Joshua B. (February 2020, Atmosphere)

   This study analyses Global Positioning System dropsondes to document the axisymmetric tropical cyclone (TC) boundary-layer structure, based on storm intensity. A total of 2608 dropsondes from 42 named TCs in the Atlantic basin from 1998 to 2017 are used in the composite analyses. The results show that the axisymmetric inflow layer depth, the height of maximum tangential wind speed, and the thermodynamic mixed layer depth are all shallower in more intense TCs. The results also show that more intense TCs tend to have a deep layer of the near-saturated air inside the radius of maximum wind speed (RMW). The magnitude of the radial gradient of equivalent potential temperature (θe) near the RMW correlates positively with storm intensity. Above the inflow layer, composite structures of TCs with different intensities all possess a ring of anomalously cool temperatures surrounding the warm-core, with the magnitude of the warm-core anomaly proportional to TC intensity. The boundary layer composites presented here provide a climatology of how axisymmetric TC boundary layer structure changes with intensity. 

   more » « less
4. Effect of Varying Wind Intensity, Forward Speed, and Surface Pressure on Storm Surges of Hurricane Rita

   https://doi.org/10.3390/jmse9020128  

   Musinguzi, Abram; Akbar, Muhammad K. (February 2021, Journal of Marine Science and Engineering)

   null (Ed.)

   Hurricane storm surges are influenced by several factors, including wind intensity, surface pressure, forward speed, size, angle of approach, ocean bottom depth and slope, shape and geographical features of the coastline. The relative influence of each factor may be amplified or abated by other factors that are acting at the time of the hurricane’s approach to the land. To understand the individual and combined influence of wind intensity, surface pressure and forward speed, a numerical experiment is conducted using Advanced CIRCulation + Simulating Waves Nearshore (ADCIRC + SWAN) by performing hindcasts of Hurricane Rita storm surges. The wind field generated by Ocean Weather Inc. (OWI) is used as the base meteorological forcing in ADCIRC + SWAN. All parameters are varied by certain percentages from those in the OWI wind field. Simulation results are analyzed for maximum wind intensity, wind vector pattern, minimum surface pressure, forward speed, maximum water elevation, station water elevation time series, and high water marks. The results for different cases are compared against each other, as well as with observed data. Changes in the wind intensity have the greatest impact, followed by the forward speed and surface pressure. The combined effects of the wind intensity and forward speed are noticeably different than their individual effects. 

   more » « less
5. A Generative Super‐Resolution Model for Enhancing Tropical Cyclone Wind Field Intensity and Resolution

   https://doi.org/10.1029/2024JH000375  

   Lockwood, Joseph W; Gori, Avantika; Gentine, Pierre (December 2024, Journal of Geophysical Research: Machine Learning and Computation)

   Abstract Extreme winds associated with tropical cyclones (TCs) can cause significant loss of life and economic damage globally, highlighting the need for accurate, high‐resolution modeling and forecasting for wind. However, due to their coarse horizontal resolution, most global climate and weather models suffer from chronic underprediction of TC wind speeds, limiting their use for impact analysis and energy modeling. In this study, we introduce a cascading deep learning framework designed to downscale high‐resolution TC wind fields given low‐resolution data. Our approach maps 85 TC events from ERA5 data (0.25° resolution) to high‐resolution (0.05° resolution) observations at 6‐hr intervals. The initial component is a debiasing neural network designed to model accurate wind speed observations using ERA5 data. The second component employs a generative super‐resolution strategy based on a conditional denoising diffusion probabilistic model (DDPM) to enhance the spatial resolution and to produce ensemble estimates. The model is able to accurately model intensity and produce realistic radial profiles and fine‐scale spatial structures of wind fields, with a percentage mean bias of −3.74% compared to the high‐resolution observations. Our downscaling framework enables the prediction of high‐resolution wind fields using widely available low‐resolution and intensity wind data, allowing for the modeling of past events and the assessment of future TC risks. 

   more » « less