Editor's choices & News
 
1. Research reveals the main patterns and predictive models of summer vegetation variations in eastern Siberia  (2025-04-07)
2. Increase in hot–dry events in China with a high mortality risk over the past two decades  (2025-03-26)
3. Regional role in the discrepancy of Hadley circulation intensity trends between reanalysis data and models  (2025-03-26)
4. High-skill information in subseasonal ensemble forecasting  (2025-03-21)
5. Deep learning technique enhances lightning risk prediction for power grids  (2025-03-14)
6. New parameterization schemes enhance the prediction accuracy of typhoon intensity  (2025-03-03)
7. Effects of cold pool dynamics and vertical motion on the convergence of mountainous downslope and plain thunderstorm clusters  (2025-02-21)
8. Data-driven networks influence convective-scale ensemble weather forecasts  (2025-02-12)
9. Unravelling how meteorological conditions cause changes in atmospheric fine-particle concentration  (2025-01-06)
10. Vector winds forecast by numerical weather prediction models still in need of optimization  (2024-12-23)
11. China focuses on improving air quality via the coordinated control of fine particles and ozone  (2024-11-26)
12. New Model Combines Data to Improve Typhoon Forecasting  (2024-11-25)
13. How many typhoons will make landfall on Taiwan Island this year?  (2024-10-30)
14. Satellite data fusion enhances the early detection of convective clouds  (2024-09-26)
15. The largest and highest laboratory in the world—how the Tibetan Plateau is helping us to understand the current and future climate  (2024-09-13)
16. Weather pattern as a major contributing factor to complex air pollution  (2024-08-19)
17. Continuing climate warming trend and pronounced interannual variability in precipitation in the Three Gorges Region in 2022–2023  (2024-08-09)
18. Variation in the permafrost active layer over the Tibetan Plateau since 1980  (2024-07-12)
19. China’s 2023 annual temperature hit a new high with serious floods and droughts  (2024-07-03)
20. NUIST constructed a cross-component background error covariance for strongly coupled land-atmosphere data assimilation   (2024-06-28)

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