The assimilation of dual-polarization(dual-pol)radar data plays a crucial role in enhancing the simulation of hydrometeors and improving the short-term precipitation forecasts of numerical weather prediction(NWP)model...The assimilation of dual-polarization(dual-pol)radar data plays a crucial role in enhancing the simulation of hydrometeors and improving the short-term precipitation forecasts of numerical weather prediction(NWP)models.However,existing dual-pol radar data assimilation(DA)methods exhibit limitations in terms of computational efficiency and data utilization.In this study,a new dual-pol radar DA approach is developed that utilizes a UNet-based model to retrieve mixing ratio information for four hydrometeor species from dual-pol radar data.The validation results for the UNet-based model indicate that the distributions of the retrieved hydrometeor mixing ratios provided by the model align well with the labeled data,yielding a reasonable range of root mean square errors(RMSEs).On this basis,the hydrometeor analysis increments retrieved by the UNet-based model are incorporated into the model integration process through the incremental analysis update(IAU)scheme,establishing a complete dual-pol radar DA framework for the CMA-MESO model.To evaluate the efficacy of this DA scheme,comparative simulation experiments were conducted for Typhoon Lekima(2019).Verification results indicate that using the hydrometeor DA scheme generally improves the threat scores(TSs)for 3-hour accumulated precipitation during medium-and heavy-rainfall events.Additionally,the 24-hour accumulated rainfall TSs for the medium-,heavy-,and extreme-precipitation categories in the DA experiment are all superior to those in the control experiment.The DA method also yields superior predictions of the spatial distribution of extremerainfall events.These results demonstrate that the proposed dual-pol radar DA approach effectively enhances the precipitation forecasting capabilities of numerical weather models.展开更多
精准识别与划定可能受到洪水影响的地区对于规划与实施防洪措施十分重要。针对洪水区域识别过程中易受到地形、气候、光照以及数据不平衡等因素影响的问题,提出一种基于改进UNet++的洪水区域图像分割算法。该算法以UNet++分割网络为基...精准识别与划定可能受到洪水影响的地区对于规划与实施防洪措施十分重要。针对洪水区域识别过程中易受到地形、气候、光照以及数据不平衡等因素影响的问题,提出一种基于改进UNet++的洪水区域图像分割算法。该算法以UNet++分割网络为基础框架,在每层解码器的第一个卷积单元后嵌入空洞空间金字塔池化以得到新的解码特征;同时通过密集连接和跳跃连接将不同解码路径的特征融合,采用Lovasz Hinge Loss损失函数来获取全局最优并采用数据增强的方式对原有数据进行扩充。实验结果表明,该算法在Flood Area Segmentation数据集上的IOU值达到80.53%,与目前流行的图像分割算法DeepLabv3、UNet、FCN(Res18)、PspNet、FCN(Res50)、UNet++相比,依次高出7.41、6.21、3.81、3.70、1.92、1.82个百分点。该算法具有较高的分割精度和良好的稳定性,整体性能优异,为实际防洪监测提供了技术支撑。展开更多
基金Major Key Project of PCL(PCL2025A10)Open Research Project of the China Meteorological Administration Hydro-Meteorology Key Laboratory(23SWQXM036)+2 种基金National Natural Science Foundation of China(42375160)Project of the Key Laboratory of Atmospheric Sounding of China Meteorological Administration(2022KLAS06M)Science and Technology Research Project of the Guangdong Provincial Meteorological Bureau(GRMC2024M04)。
文摘The assimilation of dual-polarization(dual-pol)radar data plays a crucial role in enhancing the simulation of hydrometeors and improving the short-term precipitation forecasts of numerical weather prediction(NWP)models.However,existing dual-pol radar data assimilation(DA)methods exhibit limitations in terms of computational efficiency and data utilization.In this study,a new dual-pol radar DA approach is developed that utilizes a UNet-based model to retrieve mixing ratio information for four hydrometeor species from dual-pol radar data.The validation results for the UNet-based model indicate that the distributions of the retrieved hydrometeor mixing ratios provided by the model align well with the labeled data,yielding a reasonable range of root mean square errors(RMSEs).On this basis,the hydrometeor analysis increments retrieved by the UNet-based model are incorporated into the model integration process through the incremental analysis update(IAU)scheme,establishing a complete dual-pol radar DA framework for the CMA-MESO model.To evaluate the efficacy of this DA scheme,comparative simulation experiments were conducted for Typhoon Lekima(2019).Verification results indicate that using the hydrometeor DA scheme generally improves the threat scores(TSs)for 3-hour accumulated precipitation during medium-and heavy-rainfall events.Additionally,the 24-hour accumulated rainfall TSs for the medium-,heavy-,and extreme-precipitation categories in the DA experiment are all superior to those in the control experiment.The DA method also yields superior predictions of the spatial distribution of extremerainfall events.These results demonstrate that the proposed dual-pol radar DA approach effectively enhances the precipitation forecasting capabilities of numerical weather models.
文摘精准识别与划定可能受到洪水影响的地区对于规划与实施防洪措施十分重要。针对洪水区域识别过程中易受到地形、气候、光照以及数据不平衡等因素影响的问题,提出一种基于改进UNet++的洪水区域图像分割算法。该算法以UNet++分割网络为基础框架,在每层解码器的第一个卷积单元后嵌入空洞空间金字塔池化以得到新的解码特征;同时通过密集连接和跳跃连接将不同解码路径的特征融合,采用Lovasz Hinge Loss损失函数来获取全局最优并采用数据增强的方式对原有数据进行扩充。实验结果表明,该算法在Flood Area Segmentation数据集上的IOU值达到80.53%,与目前流行的图像分割算法DeepLabv3、UNet、FCN(Res18)、PspNet、FCN(Res50)、UNet++相比,依次高出7.41、6.21、3.81、3.70、1.92、1.82个百分点。该算法具有较高的分割精度和良好的稳定性,整体性能优异,为实际防洪监测提供了技术支撑。
