Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep...Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.展开更多
The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its l...The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its long-term forecasting capabilities.Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system,with specific attention to the structure of the initial errors in the tropical model.Three main types of experiments are explored:with random perturbations along the three Lyapunov vectors of the tropical model;along the two dominant Lyapunov vectors;and along the first Lyapunov vector only.When perturbations are introduced along all vectors,forecasting biases develop even if in a perfect model framework and with known initial uncertainty properties.Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector.Furthermore,this perturbation strategy allows a reduced mean square error to be obtained at long lead times of a few years,as well as reliable ensemble forecasts across the whole time range.These very counterintuitive findings further underline the importance of appropriately controlling the initial error structure in the tropics through data assimilation.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
高原低涡是造成青海暴雨和短时强降水的重要天气系统之一。基于1979—2021年高原低涡数据集、青海气象站点降水观测资料及欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)ERA5再分析资料,利用高原低...高原低涡是造成青海暴雨和短时强降水的重要天气系统之一。基于1979—2021年高原低涡数据集、青海气象站点降水观测资料及欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)ERA5再分析资料,利用高原低涡降水关联方法和动态合成分析方法,研究了青海高原低涡日占比、高原低涡降水及环境场特征。结果表明:青海高原低涡日占比呈东北向西南递增的空间分布特征,全年最大值为15.37%。高原低涡降水占总降水量的比例全年最大值为37.92%,高原低涡极端降水日数占总极端降水日数的比例全年最大值位于青海西南部(63.69%),高原低涡极端降水日数占高原低涡日数的比例全年最大值位于海西州东部到海南州南部地区(10.73%),这些地区高原低涡日数较少,但往往会引发较强降水。青海高原低涡日占比高值主要集中在4—10月,高原低涡东移过程中对降水的影响更显著。以高原低涡中心为原点动态合成的高原低涡大雨频次呈现纬向宽、经向窄的不对称分布,大雨落区集中分布在东北象限和东南象限,大雨发生频次最大出现在距离低涡中心0.50~1.25个纬距范围内。展开更多
基金supported by the National Natural Science Foundation of China[grant number 62376217]the Young Elite Scientists Sponsorship Program by CAST[grant number 2023QNRC001]the Joint Research Project for Meteorological Capacity Improvement[grant number 24NLTSZ003]。
文摘Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.
基金supported by the National Key R&D Program of China(Grant No.2023YFF0805100)。
文摘The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its long-term forecasting capabilities.Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system,with specific attention to the structure of the initial errors in the tropical model.Three main types of experiments are explored:with random perturbations along the three Lyapunov vectors of the tropical model;along the two dominant Lyapunov vectors;and along the first Lyapunov vector only.When perturbations are introduced along all vectors,forecasting biases develop even if in a perfect model framework and with known initial uncertainty properties.Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector.Furthermore,this perturbation strategy allows a reduced mean square error to be obtained at long lead times of a few years,as well as reliable ensemble forecasts across the whole time range.These very counterintuitive findings further underline the importance of appropriately controlling the initial error structure in the tropics through data assimilation.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
文摘高原低涡是造成青海暴雨和短时强降水的重要天气系统之一。基于1979—2021年高原低涡数据集、青海气象站点降水观测资料及欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)ERA5再分析资料,利用高原低涡降水关联方法和动态合成分析方法,研究了青海高原低涡日占比、高原低涡降水及环境场特征。结果表明:青海高原低涡日占比呈东北向西南递增的空间分布特征,全年最大值为15.37%。高原低涡降水占总降水量的比例全年最大值为37.92%,高原低涡极端降水日数占总极端降水日数的比例全年最大值位于青海西南部(63.69%),高原低涡极端降水日数占高原低涡日数的比例全年最大值位于海西州东部到海南州南部地区(10.73%),这些地区高原低涡日数较少,但往往会引发较强降水。青海高原低涡日占比高值主要集中在4—10月,高原低涡东移过程中对降水的影响更显著。以高原低涡中心为原点动态合成的高原低涡大雨频次呈现纬向宽、经向窄的不对称分布,大雨落区集中分布在东北象限和东南象限,大雨发生频次最大出现在距离低涡中心0.50~1.25个纬距范围内。
