Precipitation nowcasting is of great importance for disaster prevention and mitigation.However,precipitation is a complex spatio-temporal phenomenon influenced by various underlying physical factors.Even slight change...Precipitation nowcasting is of great importance for disaster prevention and mitigation.However,precipitation is a complex spatio-temporal phenomenon influenced by various underlying physical factors.Even slight changes in the initial precipitation field can have a significant impact on the future precipitation patterns,making the nowcasting of short-term high-resolution precipitation a major challenge.Traditional deep learning methods often have difficulty capturing the long-term spatial dependence of precipitation and are usually at a low resolution.To address these issues,based upon the Simpler yet Better Video Prediction(SimVP)framework,we proposed a deep generative neural network that incorporates the Simple Parameter-Free Attention Module(SimAM)and Generative Adversarial Networks(GANs)for short-term high-resolution precipitation event forecasting.Through an adversarial training strategy,critical precipitation features were extracted from complex radar echo images.During the adversarial learning process,the dynamic competition between the generator and the discriminator could continuously enhance the model in prediction accuracy and resolution for short-term precipitation.Experimental results demonstrate that the proposed method could effectively forecast short-term precipitation events on various scales and showed the best overall performance among existing methods.展开更多
以2022年9月影响东北地区的台风“梅花”残余系统降水为例,采用SAL(structure amplitude and location)空间检验方法对5种中国气象局数值业务模式(CMA模式)降水最强日(2022年9月16日08时至17日08时,热带低压)累计24 h降水预报进行空间...以2022年9月影响东北地区的台风“梅花”残余系统降水为例,采用SAL(structure amplitude and location)空间检验方法对5种中国气象局数值业务模式(CMA模式)降水最强日(2022年9月16日08时至17日08时,热带低压)累计24 h降水预报进行空间检验、偏差成因及预报调整分析。结果表明:此次过程中各模式在降水位置方面预报均较好,CMA-GFS在降水结构分布、强度方面预报表现最佳,除CMA-BJ外其他模式对于降水极值预测均偏小。CMA-GFS预报效果最佳的原因是其对于台风变性、850 hPa低空急流及暖式切变线位置、强度以及移速预报更准确,CMA-TYM预报较差主要是未能预报出台风变性,对切变线预报速度过快而导致暴雨落区偏大。CMA-GFS随着预报时效的临近,预报效果越来越好,优势主要体现在临近时效内,但在长时效预报中几乎无预报能力,CMA-TYM虽然在临近时段结构与强度预报效果较差,但在长预报时效是最早指示出强降水大致落区与量级的模式。展开更多
为了评估改进的动力统计相似集合预报登陆台风降水模型(Dynamical-Statistical-Analog Ensemble Forecast model for Landfalling Typhoon Precipitation,DSAEF_LTP)在2023年第5号超强台风“杜苏芮”影响福建地区的表现,对其预报的台风...为了评估改进的动力统计相似集合预报登陆台风降水模型(Dynamical-Statistical-Analog Ensemble Forecast model for Landfalling Typhoon Precipitation,DSAEF_LTP)在2023年第5号超强台风“杜苏芮”影响福建地区的表现,对其预报的台风过程降水量进行常规检验和空间检验,并与欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)数值预报降水产品(以下简记为“ECMWF”)、福建省气象局最优TS(threat score)评分订正法(optimal TS,OTS)订正降水产品(以下简记为“FZECMOS”)结果进行对比。分析表明:(1)DSAEF_LTP模型对福建沿海强降水落区和东北部强降水中心的预报接近实况,100 mm及以上和250 mm及以上等极端降水量TS评分比ECMWF和FZECMOS提升明显,但DSAEF_LTP模型存在特大暴雨预报范围显著偏小等缺点。(2)在100 mm及以上和250 mm及以上量级,MODE(Method for Object-based Diagnostic Evaluation)空间检验显示,DSAEF_LTP模型在整体相似度上明显优于ECMWF和FZECMOS,尤其在对孤立小区域强降水的预报性能方面表现出色。(3)随着降水检验量级的增加,DSAEF_LTP模型预报产品与实况重叠面积之比也增大,表明DSAEF_LTP模型在极端降水方面的预报效果更加突出。(4)DSAEF_LTP模型还能够根据最新的相似路径实况和预报,调整筛选历史相似台风,合理保留相似台风及其降水分布,使得集合预报效果得以改善。展开更多
文摘针对2023年第6号台风“卡努”影响辽宁及周边地区产生的强降水,采用TS评分(threat score)检验法和多种空间检验法,检验分析CMA_MESO、CMA_GFS和EC_IFS模式的日降水24 h、48 h和72 h预报在强度、落区形态结构和位置3方面的偏差特征。(1)TS评分检验表明,CMA_MESO、CMA_GFS和EC_IFS模式在24 h预报时效下对大雨以下量级预报的TS评分分别为0.64、0.66和0.69,随着降水阈值的增大,TS评分值降低至0.4左右。EC_IFS的TS评分最好,CMA_MESO空报率最大。(2)SAL(structure,amplitude and location)空间检验表明,3个数值模式降水整体位置预报基本一致,但降水强度预报均偏弱,EC_IFS的预报落区结构预报与实况最相似,但对暴雨以上量级范围较实况偏大。(3)MODE(method for object based diagnostic evaluation)属性综合相似度评分表明,CMA_GFS模式对于此次台风降水预报不稳定;CMA_MESO模式预报稳定,但对于暴雨量级预报效果不理想;EC_IFS模式24 h预报时效下的相似度评分最高,对于此次台风降水预报可靠性最高。空间检验法相比传统TS评分,能更精准定位模式在暴雨量级上的结构偏差,并量化雨带位置偏移。在实际业务工作中,应根据不同需求选择不同的检验方式,同时也可将不同的检验方法相结合,从不同角度分析数值模式的预报性能,有助于提高相似台风降水预报的模式适用性。
基金Supported by the National Natural Science Foundation of China(No.42306214)the Postdoctoral Innovative Talents Support Program of Shandong Province(No.SDBX2022026)+1 种基金the China Postdoctoral Science Foundation(No.2023M733533)the Special Research Assistant Project of the Chinese Academy of Sciences in 2022。
文摘Precipitation nowcasting is of great importance for disaster prevention and mitigation.However,precipitation is a complex spatio-temporal phenomenon influenced by various underlying physical factors.Even slight changes in the initial precipitation field can have a significant impact on the future precipitation patterns,making the nowcasting of short-term high-resolution precipitation a major challenge.Traditional deep learning methods often have difficulty capturing the long-term spatial dependence of precipitation and are usually at a low resolution.To address these issues,based upon the Simpler yet Better Video Prediction(SimVP)framework,we proposed a deep generative neural network that incorporates the Simple Parameter-Free Attention Module(SimAM)and Generative Adversarial Networks(GANs)for short-term high-resolution precipitation event forecasting.Through an adversarial training strategy,critical precipitation features were extracted from complex radar echo images.During the adversarial learning process,the dynamic competition between the generator and the discriminator could continuously enhance the model in prediction accuracy and resolution for short-term precipitation.Experimental results demonstrate that the proposed method could effectively forecast short-term precipitation events on various scales and showed the best overall performance among existing methods.
文摘为了评估改进的动力统计相似集合预报登陆台风降水模型(Dynamical-Statistical-Analog Ensemble Forecast model for Landfalling Typhoon Precipitation,DSAEF_LTP)在2023年第5号超强台风“杜苏芮”影响福建地区的表现,对其预报的台风过程降水量进行常规检验和空间检验,并与欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)数值预报降水产品(以下简记为“ECMWF”)、福建省气象局最优TS(threat score)评分订正法(optimal TS,OTS)订正降水产品(以下简记为“FZECMOS”)结果进行对比。分析表明:(1)DSAEF_LTP模型对福建沿海强降水落区和东北部强降水中心的预报接近实况,100 mm及以上和250 mm及以上等极端降水量TS评分比ECMWF和FZECMOS提升明显,但DSAEF_LTP模型存在特大暴雨预报范围显著偏小等缺点。(2)在100 mm及以上和250 mm及以上量级,MODE(Method for Object-based Diagnostic Evaluation)空间检验显示,DSAEF_LTP模型在整体相似度上明显优于ECMWF和FZECMOS,尤其在对孤立小区域强降水的预报性能方面表现出色。(3)随着降水检验量级的增加,DSAEF_LTP模型预报产品与实况重叠面积之比也增大,表明DSAEF_LTP模型在极端降水方面的预报效果更加突出。(4)DSAEF_LTP模型还能够根据最新的相似路径实况和预报,调整筛选历史相似台风,合理保留相似台风及其降水分布,使得集合预报效果得以改善。
文摘对一种降水预报跨量级通用综合评价方法(precipitation accuracy score,PAS)进行了邻域法改进。改进方案通过预报与观测资料匹配技术,采用距离权重评分统计方法,旨在减轻双重惩罚问题,同时确保评分系统能合理表征位置预报的准确性。研究应用邻域PAS方法,基于2021年汛期中国气象局智能网格实况产品,对江苏本地精细化天气分析预报系统进行整体和典型个例检验,同时引入均方根误差(root mean square error,RMSE)、结构相似性(structural similarity,SSIM)、峰值信噪比(peak signal-to-noise ratio,PSNR)、概率空间中的稳定公平误差(stable equitable error in probability space,SEEPS)等跨量级检验指标进行了对比。结果表明,邻域PAS方法显著回避了原方法在位置预报上的双重惩罚问题,更符合预报人员的主观预期和预报应用服务的要求,具有明显优势。邻域PAS评分、PAS评分与现有多个跨量级指标均表现出良好的相关性,多方验证了方法的有效性。同时,相较于RMSE,该方法更有效地平衡了对不同量级降水的敏感性;而与SSIM和PSNR相比,则展现了更强的可解释性,多个个例显示评分结果更符合预报员的认知。邻域PAS方法相比于SEEPS技巧评分保留了对大量级降水的检验分辨能力,同时减轻了PAS方法检验大量级降水产生的严重双重惩罚,一定程度平衡了大量级降水检验的两难问题。
