1 citations · 2 across the 3 of their papers we have counts for
5 papers
Supporting renewable energy planning and operation with data-driven high-resolution ensemble weather forecast
Jingnan Wang, Jie Chao, Shangshang Yang +10
The planning and operation of renewable energy, especially wind power, depend crucially on accurate, timely, and high-resolution weather information. Coarse-grid global numerical w…
Fusion of multi-source precipitation records via coordinate-based generative model
Sencan Sun, Congyi Nai, Baoxiang Pan +5
Precipitation remains one of the most challenging climate variables to observe and predict accurately. Existing datasets face intricate trade-offs: gauge observations are relativel…
Generative assimilation and prediction for weather and climate
Shangshang Yang, Congyi Nai, Xinyan Liu +11
Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on t…
Boosting weather forecast via generative superensemble
Congyi Nai, Xi Chen, Shangshang Yang +3
Accurate weather forecasting is essential for socioeconomic activities. While data-driven forecasting demonstrates superior predictive capabilities over traditional Numerical Weath…
DRUM: Diffusion-based runoff model for probabilistic flood forecasting
Zhigang Ou, Congyi Nai, Baoxiang Pan +7
Extreme floods pose escalating risks in a changing climate, yet forecasting remains challenging due to peak flow underestimation and high uncertainty. We introduce DRUM, a diffusio…