arXiv subjects

Daosheng Xu

Publications and source records attributed to Daosheng Xu.

4 recordsLinked to original sources

arXiv2026

Butterfly Effect Confirmed in Global AI Weather Models: Evidence from Tropical Cyclone Forecasting

A paradox recently emerged in artificial intelligence (AI) weather prediction research. While some claim AI weather models cannot simulate atmospheric butterfly effect, this conflicts with AI models' limited predictability and advances in AI ensemble forecasting. This study demonstrates via counterexamples that the butterfly effect does exist in AI weather predictions. For Super Typhoon Khanun, AI predictions are constrained by a double-attractor system. Minor initial perturbations confined to two regions trigger state transitions between two local attractors, causing a 1006-km difference in the predicted storm position on Day 7. This behavior is consistent with numerical weather prediction models and observed in ~12% of tropical cyclones in the past 5 years. These findings verify AI's ability to capture atmospheric chaos and provide the physical basis for AI ensemble forecasting.

physics.ao-ph↗

arXiv2026

Recent Sharp Rise in Inhomogeneous Hydrological Extremes Stress Vegetation Growth in China

The intensifying spatial inhomogeneity of rainfall under greenhouse warming implies that more inhomogeneous hydrological extremes (IHEs), i.e., coexistence of extreme rainfall or drought, may be triggered. While vegetation growth in China is sensitive to hydrological hazards, the variability of IHEs and their ecological impacts remain underexplored. Here, we find a significant increase in IHEs during China's growing season since 2000 (+2.1 events or +14.52 days per decade), with a rapid sharp rise to an annual average of 6.4 events or 42.0 days in the past five years. The primary driver is the enhanced inhomogeneity of moisture-dynamic coupled weather conditions, overlapping with a northward shift of climatological precipitation distribution. This results in a "Wet-North and Dry-South" pattern of IHE impacts, which poses severe and asymmetric threats to vegetation growth in China, with the expansion of drought areas exerts stronger stress on vegetation than the compensatory effects of rainfall. Our findings suggest that the sharp rises of IHEs tend to yield net negative impacts on vegetation growth, highlighting the need for stronger hydrological management to reduce future risks.

physics.ao-ph↗

arXiv2025

Questioning whether seasonal advance of intense tropical cyclones since the 1980s truly exists

Shan et al. (2023) recently reported significant seasonal advances of intense tropical cyclones (TCs) in both the Northern Hemisphere (NH) and Southern Hemisphere (SH) since the 1980s, and emphasized the data insensitivity of this conclusion, based on the Advanced Dvorak Technique-Hurricane Satellite (ADT-HURSAT) and the International Best Track Archive for Climate Stewardship (IBTrACS) datasets. However, this conclusion contradicts our recent findings. Following the procedures outlined in Shan et al., our analysis reveals that the seasonal advancing trend of intense TCs does not pass the significance test in the SH. Meanwhile, for the NH, the trend is statistically significant only when using the ADT-HURSAT, but not when using the IBTrACS. These discrepancies may be due to flaws in the calculations performed by Shan et al. The above findings raise doubts about the reproducibility and validity of Shan et al.'s conclusions regarding the global seasonal advance of intense TCs. We argue that the reported seasonal advance of intense TCs since the 1980s is inconclusive, and further investigations are needed.

physics.ao-ph↗

arXiv2025

AI Models Still Lag Behind Traditional Numerical Models in Predicting Sudden-Turning Typhoons

Given the interpretability, accuracy, and stability of numerical weather prediction (NWP) models, current operational weather forecasting relies heavily on the NWP approach. In the past two years, the rapid development of Artificial Intelligence (AI) has provided an alternative solution for medium-range (1-10 days) weather forecasting. Bi et al. (2023) (hereafter Bi23) introduced the first AI-based weather prediction (AIWP) model in China, named Pangu-Weather, which offers fast prediction without compromising accuracy. In their work, Bi23 made notable claims regarding its effectiveness in extreme weather predictions. However, this claim lacks persuasiveness because the extreme nature of the two tropical cyclones (TCs) examples presented in Bi23, namely Typhoon Kong-rey and Typhoon Yutu, stems primarily from their intensities rather than their moving paths. Their claim may mislead into another meaning which is that Pangu-Weather works well in predicting unusual typhoon paths, which was not explicitly analyzed. Here, we reassess Pangu-Weather's ability to predict extreme TC trajectories from 2020-2024. Results reveal that while Pangu-Weather overall outperforms NWP models in predicting tropical cyclone (TC) tracks, it falls short in accurately predicting the rarely observed sudden-turning tracks, such as Typhoon Khanun in 2023. We argue that current AIWP models still lag behind traditional NWP models in predicting such rare extreme events in medium-range forecasts.

physics.ao-ph↗