数值天气预报的准确性极大地依赖于模式初始化场的质量及其平衡收敛过程,而这一过程在地形复杂、海陆交互显著的热带岛屿区域显得尤为关键。本研究基于WRF模式针对海南岛区域开展了不同分辨率初始场对模式平衡收敛特征的系统研究。采用E...数值天气预报的准确性极大地依赖于模式初始化场的质量及其平衡收敛过程,而这一过程在地形复杂、海陆交互显著的热带岛屿区域显得尤为关键。本研究基于WRF模式针对海南岛区域开展了不同分辨率初始场对模式平衡收敛特征的系统研究。采用ERA5 (0.25˚)和ERA-Interim (0.75˚)再分析资料作为初始场,通过设计短期和长期平行对比试验,分析了2米温度(T2)、2米比湿(Q2)及10米风场(U10、V10)等近地面要素的平衡收敛特征。研究发现,高分辨率初始场显著提升了模式的平衡收敛效率,ERA5驱动的模拟在长期积分中温度场平均收敛时间较ERA-Interim缩短2.7小时(17.4 vs 20.1小时),比湿场缩短3.3小时(18.1 vs 21.4小时),风场缩短3.0-3.5小时(U10:20.2 vs 23.2小时,V10:21.1 vs 24.6小时)。短期模拟结果表明,不同物理量具有显著的时间依赖特征:温度场的平均收敛时间为2.8小时,比湿场为3.3小时,风场则需要3.7~4.0小时。特别是在18时起报的预报中,ERA5温度场的动态时间规整(Dynamic Time Warping, DTW)相关系数达到最高值0.93,而ERA-Interim降至0.87,表明ERA5在处理日落前后的温度变化方面具有独特优势。基于研究结果,ERA5在各物理量的预报中均表现出更快的收敛速度和更高的预报准确性,这对提升热带海岛地区数值预报水平具有重要的参考价值。The accuracy of numerical weather prediction heavily depends on the quality of model initialization fields and their spin-up process, which is particularly crucial in tropical island regions characterized by complex terrain and significant land-sea interactions. This study systematically investigated the impact of initial fields with different resolutions on model spin-up characteristics over Hainan Island using the WRF model. Using ERA5 (0.25˚) and ERA-Interim (0.75˚) reanalysis data as initial fields, we analyzed the spin-up characteristics of near-surface variables including 2-meter temperature (T2), 2-meter specific humidity (Q2), and 10-meter wind fields (U10, V10) through both short-term and long-term parallel experiments. Results demonstrated that high-resolution initial fields significantly enhanced model spin-up efficiency. In long-term simulations, ERA5-driven experiments showed shorter convergence times compared to ERA-Interim: temperature field convergence time decreased by 2.7 hours (17.4 vs 20.1 hours), specific humidity field by 3.3 hours (18.1 vs 21.4 hours), and wind fields by 3.0~3.5 hours (U10: 20.2 vs 23.2 hours, V10: 21.1 vs 24.6 hours). Short-term simulation results revealed distinct temporal dependencies among different physical variables: temperature field averaged 2.8 hours for convergence, specific humidity field required 3.3 hours, while wind fields needed 3.7~4.0 hours. Notably, in forecasts initialized at 18, ERA5 temperature field achieved the highest DTW correlation coefficient of 0.93, while ERA-Interim dropped to 0.87, indicating ERA5’s superior performance in capturing temperature variations during sunset transitions. Based on these findings, ERA5 demonstrated superior performance in both convergence speed and forecast accuracy across all physical variables, providing valuable insights for improving numerical weather prediction capabilities in tropical island regions.展开更多
The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of ...The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.展开更多
Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was...Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.展开更多
可靠的次季节温度预测对极端温度事件的防灾减灾至关重要。然而,现有的次季节温度预测动力模型常受到初值问题和边值问题的影响,导致其预报能力相对薄弱。尽管近年来机器学习模型在次季节预测中逐渐展示出超越动力模型的潜力,但中国次...可靠的次季节温度预测对极端温度事件的防灾减灾至关重要。然而,现有的次季节温度预测动力模型常受到初值问题和边值问题的影响,导致其预报能力相对薄弱。尽管近年来机器学习模型在次季节预测中逐渐展示出超越动力模型的潜力,但中国次季节温度预测仍主要依赖于动力学模型。鉴于此,本研究基于Lasso (Multi-task Lasso)机器学习算法,构建了覆盖中国所有格点的次季节温度预测模型,并采用余弦相似度指标评估Lasso和CFSv2 (The Climate Forecast System version 2)动力模型在2018~2022年测试期内的预测性能表现。结果表明:Lasso在整体预测技能上显著优于CFSv2,其在未来3~4周和5~6周的平均余弦相似度较CFSv2分别提升了0.33和0.34;并且,在常规温度情景下,Lasso能够更精准地捕捉温度变化的规律,80%以上月份的平均CS高于CFSv2;其仅在极端低温情景下存在一定局限性,预测技能略逊于CFSv2。Reliable subseasonal temperature forecasting plays an important part in extreme temperature events prevention and mitigation. However, current dynamical models for subseasonal temperature forecasting are often influenced by initial value and boundary value problems, resulting in relatively weak forecasting performance. Although machine learning models have shown potential in surpassing dynamical models for subseasonal forecasting in recent years, subseasonal temperature forecasting in China still mainly relies on dynamical models. Under this background, the study constructs a subseasonal temperature forecasting model covering 957 grid points across China based on the Lasso (Multi-task Lasso) machine learning algorithm and uses the cosine similarity metric to evaluate the performance between the Lasso and CFSv2 (The Climate Forecast System version 2) dynamic model during the test period from 2018 to 2022. The results show that the Lasso significantly outperforms CFSv2 in overall forecasting performance. The average cosine similarity of the Lasso is 0.33 and 0.34 higher than the CFSv2 at the forecast horizon of weeks 3~4 and 5~6, respectively. Moreover, in normal temperature scenarios, the Lasso can more accurately capture temperature variation patterns with the average cosine similarity for over 80% of the months higher than that of the CFSv2. However, the Lasso has some limitations in forecasting extreme low temperature scenarios, where its forecasting skill is slightly inferior to that of the CFSv2.展开更多
Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly effi...Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.展开更多
Accurate subseasonal forecasting of East Asian summer monsoon(EASM)precipitation is crucial,as it directly impacts the livelihoods of billions.However,the prediction skill of state-of-the-art subseasonal-to-seasonal(S...Accurate subseasonal forecasting of East Asian summer monsoon(EASM)precipitation is crucial,as it directly impacts the livelihoods of billions.However,the prediction skill of state-of-the-art subseasonal-to-seasonal(S2S)models for precipitation remains limited.In this study,the authors developed a convolutional neural network(CNN)regression model to enhance the prediction skill for weekly EASM precipitation by utilizing the more reliably predicted circulation fields from dynamic models.The outcomes of the CNN model are promising,as it led to a 14%increase in the anomaly correlation coefficient(ACC),from 0.30 to 0.35,and a 22%reduction in the root-mean-square error(RMSE),from 3.22 to 2.52,for predicting the weekly EASM precipitation index at a leading time of one week.Among the S2S models,the improvement in prediction skill through CNN correction depends on the model’s performance in accurately predicting circulation fields.The CNN correction of EASM precipitation index can only rectify the systematic errors of the model and is independent of whether the each grid point or the entire area-averaged index is corrected.Furthermore,u200(200-hPa zonal wind)is identified as the most important variable for efficient correction.展开更多
Moisture conditions are crucial for the maintenance and development of severe convection.In the indirect assimilation of radar reflectivity,hydrometeors and water vapor retrieved from reflectivity are assimilated to a...Moisture conditions are crucial for the maintenance and development of severe convection.In the indirect assimilation of radar reflectivity,hydrometeors and water vapor retrieved from reflectivity are assimilated to avoid the nonlinearity issues associated with the observation operator.In a widely applied water vapor retrieval scheme,a cloud is assumed to be saturated when the radar reflectivity exceeds a certain threshold.This study replaces the traditional retrieval scheme with the“Z-RH”(radar reflectivity and relative humidity)linear statistical relationship for estimating the water vapor content,which is implemented to reduce the uncertainty caused by empirical relationships.The“Z-RH”relationship is statistically obtained from the humidity and the observations for rainfall rate at different temperature intervals with the use of the Z-R(radar reflectivity-rain rate)relationship.The impacts of these two retrieval approaches are investigated in the analyses and forecasts based on the radar reflectivity.The results suggest that both water vapor retrieval schemes yield similar reflectivity analyses,with“Z-RH”showing slightly stronger reflectivity intensities.Utilizing a“Z-RH”scheme contributes significantly to the improved analyses and forecasts of humidity and wind fields,resulting in more reasonable thermodynamic and dynamic structures.As the“Z-RH”relationship obtained by real-time statistics in a specific area provides a scientific basis for the retrieval of water vapor,a“Z-RH”scheme is beneficial to obtain more accurate reflectivity forecasts.The overall scores for the predicted precipitation of a“Z-RH”scheme are roughly 10%-20%higher compared to those of the traditional scheme.展开更多
A subseasonal-to-seasonal(S2S) forecast system(FS) has recently been released based on the fully coupled Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM). This study evaluated the subseasonal ...A subseasonal-to-seasonal(S2S) forecast system(FS) has recently been released based on the fully coupled Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM). This study evaluated the subseasonal prediction skill of this system via a 21-year hindcast experiment for the period 2000–20 with eight ensemble members.Results showed moderate-to-high skill for the primary atmospheric variables. The most accurate predictions emerged in the cold season but were largely confined within tropical bands as the forecast lead time was increased. Compared with the NCEP S2S FS, the CAMS-CSM S2S FS showed comparable subseasonal skill for 500-h Pa geopotential height, but slightly higher(lower) skill for precipitation(2-m temperature). The skillful lead time in the CAMS-CSM S2S FS for the Madden–Julian Oscillation and North Atlantic Oscillation reached 20 and 10 days, respectively, consistent with the NCEP S2S FS. Consequently, these findings guide future research on subseasonal predictability based on the CAMS-CSM S2S FS, and where efforts should be focused to improve the prediction system.展开更多
文摘数值天气预报的准确性极大地依赖于模式初始化场的质量及其平衡收敛过程,而这一过程在地形复杂、海陆交互显著的热带岛屿区域显得尤为关键。本研究基于WRF模式针对海南岛区域开展了不同分辨率初始场对模式平衡收敛特征的系统研究。采用ERA5 (0.25˚)和ERA-Interim (0.75˚)再分析资料作为初始场,通过设计短期和长期平行对比试验,分析了2米温度(T2)、2米比湿(Q2)及10米风场(U10、V10)等近地面要素的平衡收敛特征。研究发现,高分辨率初始场显著提升了模式的平衡收敛效率,ERA5驱动的模拟在长期积分中温度场平均收敛时间较ERA-Interim缩短2.7小时(17.4 vs 20.1小时),比湿场缩短3.3小时(18.1 vs 21.4小时),风场缩短3.0-3.5小时(U10:20.2 vs 23.2小时,V10:21.1 vs 24.6小时)。短期模拟结果表明,不同物理量具有显著的时间依赖特征:温度场的平均收敛时间为2.8小时,比湿场为3.3小时,风场则需要3.7~4.0小时。特别是在18时起报的预报中,ERA5温度场的动态时间规整(Dynamic Time Warping, DTW)相关系数达到最高值0.93,而ERA-Interim降至0.87,表明ERA5在处理日落前后的温度变化方面具有独特优势。基于研究结果,ERA5在各物理量的预报中均表现出更快的收敛速度和更高的预报准确性,这对提升热带海岛地区数值预报水平具有重要的参考价值。The accuracy of numerical weather prediction heavily depends on the quality of model initialization fields and their spin-up process, which is particularly crucial in tropical island regions characterized by complex terrain and significant land-sea interactions. This study systematically investigated the impact of initial fields with different resolutions on model spin-up characteristics over Hainan Island using the WRF model. Using ERA5 (0.25˚) and ERA-Interim (0.75˚) reanalysis data as initial fields, we analyzed the spin-up characteristics of near-surface variables including 2-meter temperature (T2), 2-meter specific humidity (Q2), and 10-meter wind fields (U10, V10) through both short-term and long-term parallel experiments. Results demonstrated that high-resolution initial fields significantly enhanced model spin-up efficiency. In long-term simulations, ERA5-driven experiments showed shorter convergence times compared to ERA-Interim: temperature field convergence time decreased by 2.7 hours (17.4 vs 20.1 hours), specific humidity field by 3.3 hours (18.1 vs 21.4 hours), and wind fields by 3.0~3.5 hours (U10: 20.2 vs 23.2 hours, V10: 21.1 vs 24.6 hours). Short-term simulation results revealed distinct temporal dependencies among different physical variables: temperature field averaged 2.8 hours for convergence, specific humidity field required 3.3 hours, while wind fields needed 3.7~4.0 hours. Notably, in forecasts initialized at 18, ERA5 temperature field achieved the highest DTW correlation coefficient of 0.93, while ERA-Interim dropped to 0.87, indicating ERA5’s superior performance in capturing temperature variations during sunset transitions. Based on these findings, ERA5 demonstrated superior performance in both convergence speed and forecast accuracy across all physical variables, providing valuable insights for improving numerical weather prediction capabilities in tropical island regions.
文摘次季节预测在农业规划、防灾减灾和水资源管理等领域具有重要意义。基于人工智能的“风顺”次季节预测模型(CMA-AIM-S2S-Fengshun),结合自主研发的CRA-40再分析数据和FY-3E卫星数据,通过级联Swin Transformer模块和智能扰动生成技术,实现了气候多要素集合预测。对2017—2021年中国区域降水的历史回算检验表明,“风顺”在逐候平均降水预测中的表现显著优于欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)模式,整体技巧提升18.6%,其中华南地区提升41.2%,东部地区提升26.5%。在MJO(Madden-Julian Oscillation)预测方面,“风顺”将技巧保持时间延长至32 d(CRA-40驱动),超过ECMWF的30 d基准。个例分析显示,模型对2024年7月中旬华北强降水过程的落区和强度预测精度更高,提前3~4候捕捉到关键异常信号。
基金supported by the National Key Program for Developing Basic Science(Nos.2022YFF0801702 and 2022YFE0106600)the National Natural Science Foundation of China(Nos.42175060 and 42175021)the Jiangsu Province Science Foundation(No.BK20250200302).
文摘The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2242213,U2142213,42305167,42175105)。
文摘Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.
文摘可靠的次季节温度预测对极端温度事件的防灾减灾至关重要。然而,现有的次季节温度预测动力模型常受到初值问题和边值问题的影响,导致其预报能力相对薄弱。尽管近年来机器学习模型在次季节预测中逐渐展示出超越动力模型的潜力,但中国次季节温度预测仍主要依赖于动力学模型。鉴于此,本研究基于Lasso (Multi-task Lasso)机器学习算法,构建了覆盖中国所有格点的次季节温度预测模型,并采用余弦相似度指标评估Lasso和CFSv2 (The Climate Forecast System version 2)动力模型在2018~2022年测试期内的预测性能表现。结果表明:Lasso在整体预测技能上显著优于CFSv2,其在未来3~4周和5~6周的平均余弦相似度较CFSv2分别提升了0.33和0.34;并且,在常规温度情景下,Lasso能够更精准地捕捉温度变化的规律,80%以上月份的平均CS高于CFSv2;其仅在极端低温情景下存在一定局限性,预测技能略逊于CFSv2。Reliable subseasonal temperature forecasting plays an important part in extreme temperature events prevention and mitigation. However, current dynamical models for subseasonal temperature forecasting are often influenced by initial value and boundary value problems, resulting in relatively weak forecasting performance. Although machine learning models have shown potential in surpassing dynamical models for subseasonal forecasting in recent years, subseasonal temperature forecasting in China still mainly relies on dynamical models. Under this background, the study constructs a subseasonal temperature forecasting model covering 957 grid points across China based on the Lasso (Multi-task Lasso) machine learning algorithm and uses the cosine similarity metric to evaluate the performance between the Lasso and CFSv2 (The Climate Forecast System version 2) dynamic model during the test period from 2018 to 2022. The results show that the Lasso significantly outperforms CFSv2 in overall forecasting performance. The average cosine similarity of the Lasso is 0.33 and 0.34 higher than the CFSv2 at the forecast horizon of weeks 3~4 and 5~6, respectively. Moreover, in normal temperature scenarios, the Lasso can more accurately capture temperature variation patterns with the average cosine similarity for over 80% of the months higher than that of the CFSv2. However, the Lasso has some limitations in forecasting extreme low temperature scenarios, where its forecasting skill is slightly inferior to that of the CFSv2.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.41930971,42330111,and 42405061)the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(Earth Lab).
文摘Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.
基金supported by a Guangdong Major Project of Basic and Applied Basic Research[grant number 2020B0301030004]the National Natural Science Foundation of China[grant number 42175061]。
文摘Accurate subseasonal forecasting of East Asian summer monsoon(EASM)precipitation is crucial,as it directly impacts the livelihoods of billions.However,the prediction skill of state-of-the-art subseasonal-to-seasonal(S2S)models for precipitation remains limited.In this study,the authors developed a convolutional neural network(CNN)regression model to enhance the prediction skill for weekly EASM precipitation by utilizing the more reliably predicted circulation fields from dynamic models.The outcomes of the CNN model are promising,as it led to a 14%increase in the anomaly correlation coefficient(ACC),from 0.30 to 0.35,and a 22%reduction in the root-mean-square error(RMSE),from 3.22 to 2.52,for predicting the weekly EASM precipitation index at a leading time of one week.Among the S2S models,the improvement in prediction skill through CNN correction depends on the model’s performance in accurately predicting circulation fields.The CNN correction of EASM precipitation index can only rectify the systematic errors of the model and is independent of whether the each grid point or the entire area-averaged index is corrected.Furthermore,u200(200-hPa zonal wind)is identified as the most important variable for efficient correction.
基金supported by the National Natural Science Foundation of China(Grant No.42192553,Grant No.41805070)Open Grants of the State Key Laboratory of Severe Weather(2024LASW-B05)+7 种基金Natural Science Fund of Anhui Province of China under grant(2308085MD127)the China Meteorological Administration Tornado Key Laboratory(TKL202306)Beijige Funding from Jiangsu Research Institute of Meteorological Science(BJG202503)the Open Grants of China Meteorological Administration Radar Meteorology Key Laboratory(2023LRM-B03)the Open Project Fund of China Meteorological Administration Basin Heavy Rainfall Key Laboratory(2023BHRY20)the Shanghai Typhoon Research Foundation(TFJJ202107)Innovation and Development Projects of Anhui Provincial Meteorological Bureau(CXM202205)the High Performance Computing Center of Nanjing University of Information Science&Technology for their support of this work.
文摘Moisture conditions are crucial for the maintenance and development of severe convection.In the indirect assimilation of radar reflectivity,hydrometeors and water vapor retrieved from reflectivity are assimilated to avoid the nonlinearity issues associated with the observation operator.In a widely applied water vapor retrieval scheme,a cloud is assumed to be saturated when the radar reflectivity exceeds a certain threshold.This study replaces the traditional retrieval scheme with the“Z-RH”(radar reflectivity and relative humidity)linear statistical relationship for estimating the water vapor content,which is implemented to reduce the uncertainty caused by empirical relationships.The“Z-RH”relationship is statistically obtained from the humidity and the observations for rainfall rate at different temperature intervals with the use of the Z-R(radar reflectivity-rain rate)relationship.The impacts of these two retrieval approaches are investigated in the analyses and forecasts based on the radar reflectivity.The results suggest that both water vapor retrieval schemes yield similar reflectivity analyses,with“Z-RH”showing slightly stronger reflectivity intensities.Utilizing a“Z-RH”scheme contributes significantly to the improved analyses and forecasts of humidity and wind fields,resulting in more reasonable thermodynamic and dynamic structures.As the“Z-RH”relationship obtained by real-time statistics in a specific area provides a scientific basis for the retrieval of water vapor,a“Z-RH”scheme is beneficial to obtain more accurate reflectivity forecasts.The overall scores for the predicted precipitation of a“Z-RH”scheme are roughly 10%-20%higher compared to those of the traditional scheme.
基金jointly funded by the National Key Research and Development Program (Grant No.2022YFC3004203)the National Natural Science Foundation of China (Grant No.42375033)the Basic Scientific Research and Operation Foundation of the Chinese Academy of Meteorological Sciences (Grant Nos.2023Z018, 2024KJ013, and 2023KJ036)。
文摘A subseasonal-to-seasonal(S2S) forecast system(FS) has recently been released based on the fully coupled Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM). This study evaluated the subseasonal prediction skill of this system via a 21-year hindcast experiment for the period 2000–20 with eight ensemble members.Results showed moderate-to-high skill for the primary atmospheric variables. The most accurate predictions emerged in the cold season but were largely confined within tropical bands as the forecast lead time was increased. Compared with the NCEP S2S FS, the CAMS-CSM S2S FS showed comparable subseasonal skill for 500-h Pa geopotential height, but slightly higher(lower) skill for precipitation(2-m temperature). The skillful lead time in the CAMS-CSM S2S FS for the Madden–Julian Oscillation and North Atlantic Oscillation reached 20 and 10 days, respectively, consistent with the NCEP S2S FS. Consequently, these findings guide future research on subseasonal predictability based on the CAMS-CSM S2S FS, and where efforts should be focused to improve the prediction system.
