Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-ra...Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.展开更多
To represent model uncertainties more comprehensively,a stochastically perturbed parameterization(SPP)scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics,convection...To represent model uncertainties more comprehensively,a stochastically perturbed parameterization(SPP)scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics,convection,boundary layer,and surface layer parameterization schemes,as well as the stochastically perturbed parameterization tendencies(SPPT)scheme,and the stochastic kinetic energy backscatter(SKEB)scheme,is applied in the Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System(GRAPES-REPS)to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes.Six experiments are performed for a summer month(1-30 June 2015)over China and multiple verification metrics are used.The results show that:(1)All stochastic experiments outperform the control(CTL)experiment,and all combinations of stochastic parameterization schemes perform better than the single SPP scheme,indicating that stochastic methods can effectively improve the forecast skill,and combinations of multiple stochastic parameterization schemes can better represent model uncertainties;(2)The combination of all three stochastic physics schemes(SPP,SPPT,and SKEB)outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill;(3)Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed.SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields,and it contributes most to the improvement in spread and outliers for wind;(4)The introduction of SPP has a positive added value,and does not lead to large changes in the evolution of the kinetic energy(KE)spectrum at any wavelength;(5)The introduction of SPPT and SKEB would cause a 5%-10%and 30%-80%change in the KE of mesoscale systems,and all three stochastic schemes(SPP,SPPT,and SKEB)mainly affect the KE of mesoscale systems.This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.展开更多
Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial pertur...Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.展开更多
Given the chaotic nature of the atmosphere and inevitable initial condition errors,constructing effective initial perturbations(IPs)is crucial for the performance of a convection-allowing ensemble prediction system(CA...Given the chaotic nature of the atmosphere and inevitable initial condition errors,constructing effective initial perturbations(IPs)is crucial for the performance of a convection-allowing ensemble prediction system(CAEPS).The IP growth in the CAEPS is scale-and magnitude-dependent,necessitating the investigation of the impacts of IP scales and magnitudes on CAEPS.Five comparative experiments were conducted by using the China Meteorological Administration Mesoscale Numerical Weather Prediction System(CMA-MESO)3-km model for 13 heavy rainfall events over eastern China:smaller-scale IPs with doubled magnitudes,larger-,meso-,and smaller-scale IPs;and a chaos seeding experiment as a baseline.First,the constructed IPs outperform unphysical chaos seeding in perturbation growth and ensemble performance.Second,the daily variation of smaller-scale perturbations is more sensitive to convective activity because smaller-scale perturbations during forecasts reach saturation faster than meso-and larger-scale perturbations.Additionally,rapid downscaling cascade that saturates the smallest-scale perturbation within 6 h for larger-and meso-scale IPs is stronger in the lower troposphere and near-surface.After 9-12 h,the disturbance development of large-scale IPs is the largest in each layer on various scales.Moreover,thermodynamic perturbations,concentrated in the lower troposphere and near-surface with meso-and smaller-scale components being dominant,are smaller and more responsive to convective activity than kinematic perturbations,which are concentrated on the middle-upper troposphere and predominantly consist of larger-and meso-scale components.Furthermore,the increasing magnitude of smaller-scale IPs enables only their smaller-scale perturbations in the first 9 h to exceed those of larger-and meso-scale IPs.Third,for forecast of upper-air and surface variables,larger-scale IPs warrant a more reliable and skillful CAEPS.Finally,for precipitation,larger-scale IPs perform best for light rain at all forecast times,whereas meso-scale IPs are optimal for moderate and heavy rains at 6-h forecast time.Increasing magnitude of smaller-scale IPs improves the probability forecast skills for heavy rains during the first 3-6 h.展开更多
To meet the demands for seamless medium-and short-range weather forecasting during the Beijing Winter Olympics(2022),the Winter Olympics research team at the Earth System Modeling and Prediction Centre(CEMC)of the Chi...To meet the demands for seamless medium-and short-range weather forecasting during the Beijing Winter Olympics(2022),the Winter Olympics research team at the Earth System Modeling and Prediction Centre(CEMC)of the China Meteorological Administration(CMA)developed an integrated global and regional numerical weather prediction(NWP)model system.In support of the Winter Olympics,the system focuses on key short-and medium-range deterministic and ensemble forecast technologies for complex terrain.By introducing a three-dimensional reference atmosphere and a predictor-corrector iterative algorithm into the regional model's dynamical framework,the team enhanced the spatial accuracy and temporal integration stability of the high-resolution regional model.The team also developed data assimilation techniques for dense surface automatic weather stations and high spatiotemporal resolution imagery from China's Fengyun satellites,improving the monitoring and application capability of unconventional observations for the Winter Olympics.Furthermore,they established a3 km high-resolution regional ensemble prediction system by advancing multiscale hybrid initial perturbation techniques and stochastic perturbation methods for physical processes with spatiotemporal correlations,suitable for complex terrain.To enhance deterministic and probabilistic forecasts at grid and station scales over complex terrain,the team studied bias correction techniques across different resolutions and developed methods for rapidly and effectively extracting key forecast information from large volumes of model output.In particular,machine learning-based approaches were employed to process and fuse massive forecast products containing probabilistic information.These efforts led to the development of a seamless Winter Olympics meteorological forecasting system covering a lead time of 0–15 days and the entire competition zone,featuring forecast updates every hour within 24 h,every 3 h within 24–72 h,and every 12 h within 72–360 h.These products were applied comprehensively in real-time operations during the winter training,test events,and the Olympic and Paralympic Games,representing the highest level of China's independently developed NWP systems in meteorological support for major events.The integrated technological achievements have since been incorporated into the national operational NWP system,and they continue to play a vital role in daily forecasting services,disaster prevention and mitigation,and support for major events.展开更多
How to construct appropriate perturbations for convection-permitting ensemble prediction systems(CPEPSs)is a critical issue awaiting urgent solutions.As two common perturbations,initial perturbations(IPs)and lateral b...How to construct appropriate perturbations for convection-permitting ensemble prediction systems(CPEPSs)is a critical issue awaiting urgent solutions.As two common perturbations,initial perturbations(IPs)and lateral boundary perturbations(BPs)interact with each other,affecting the model error growth,especially in mesoscale models.Using the China Meteorological Administration(CMA)-CPEPS,this study tries to elucidate how BPs interact with matched and mismatched IPs under varied large-scale weather conditions/forcings.Seven groups of experiments were conducted for strong-forcing and weak-forcing weather regimes over southern China:three with single IPs,one with single BPs,and three with combined perturbations.It is found that the perturbation magnitudes were dominated by meso-α-scale components,and IPs under weak forcing exhibited more pronounced effects than under strong forcing;whereas BPs exerted more pronounced effects under strong forcing than weak forcing regimes.Furthermore,it lasts longer for high-level variables when the perturbation energy from BPs is higher than that from IPs,compared to low-level variables.Moreover,for precipitation and dynamic variables,IPs and BPs can mutually reinforce.The source of these perturbations,and their specific vertical levels,do not alter the extent of their interactions.Nevertheless,the weather regime and the scales of the perturbations influence the strength of their mutual reinforcement.In particular,the weak-forcing regimes exhibit a more pronounced reinforcing effect,and meso-α-scale perturbations are more conducive to fostering interactions compared to meso-β-scale ones.Ultimately,it is the perturbation magnitude inherent in the initial perturbation itself that determines the interactions between IPs and BPs.展开更多
This paper describes the GRAPES Evaluation Tools based on Python(GetPy),a community verification and diagnostic toolfor the evaluation of numerical models.The traditional statistical verification with confidence level...This paper describes the GRAPES Evaluation Tools based on Python(GetPy),a community verification and diagnostic toolfor the evaluation of numerical models.The traditional statistical verification with confidence level test,the comprehensivescorecard,the precipitation skill score such as TS,ETS,diagnostic score SEEPS and the spatial verification techniques areused as verification modules.The Error tracing techniques conducted on the performance with different scales by waveletanalysis.The diurnal cycle of precipitation can also be calculated by Precipitation frequency-intensity method.Based onsimple script architecture GetPy also includes a revised and simplified installation procedure and interactive display system.Users can easily access graphic products and carry out evaluation applications.展开更多
基金supported by the joint funds of the Chinese National Natural Science Foundation(NSFC)(Grant No.U2242213)the funds of the NSFC(Grant No.42341209)+2 种基金the National Key Research and Development(R&D)Program of the Ministry of Science and Technology of China(Grant No.2021YFC3000902)the National Science Foundation for Young Scholars(Grant No.42205166)the Joint Research Project for Meteorological Capacity Improvement(Grant No.22NLTSQ008)。
文摘Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.
基金National Key Research and Development(R&D)Program of China,(Grant No.2018YFC1507405).
文摘To represent model uncertainties more comprehensively,a stochastically perturbed parameterization(SPP)scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics,convection,boundary layer,and surface layer parameterization schemes,as well as the stochastically perturbed parameterization tendencies(SPPT)scheme,and the stochastic kinetic energy backscatter(SKEB)scheme,is applied in the Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System(GRAPES-REPS)to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes.Six experiments are performed for a summer month(1-30 June 2015)over China and multiple verification metrics are used.The results show that:(1)All stochastic experiments outperform the control(CTL)experiment,and all combinations of stochastic parameterization schemes perform better than the single SPP scheme,indicating that stochastic methods can effectively improve the forecast skill,and combinations of multiple stochastic parameterization schemes can better represent model uncertainties;(2)The combination of all three stochastic physics schemes(SPP,SPPT,and SKEB)outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill;(3)Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed.SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields,and it contributes most to the improvement in spread and outliers for wind;(4)The introduction of SPP has a positive added value,and does not lead to large changes in the evolution of the kinetic energy(KE)spectrum at any wavelength;(5)The introduction of SPPT and SKEB would cause a 5%-10%and 30%-80%change in the KE of mesoscale systems,and all three stochastic schemes(SPP,SPPT,and SKEB)mainly affect the KE of mesoscale systems.This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.
基金supported by the Joint Funds of the Chinese National Natural Science Foundation (NSFC)(Grant No.U2242213)the National Key Research and Development (R&D)Program of the Ministry of Science and Technology of China(Grant No. 2021YFC3000902)the National Science Foundation for Young Scholars (Grant No. 42205166)。
文摘Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.
基金Supported by the National Natural Science Foundation of China(U2242213)National Science Foundation for Young Scholars of China(42205166).
文摘Given the chaotic nature of the atmosphere and inevitable initial condition errors,constructing effective initial perturbations(IPs)is crucial for the performance of a convection-allowing ensemble prediction system(CAEPS).The IP growth in the CAEPS is scale-and magnitude-dependent,necessitating the investigation of the impacts of IP scales and magnitudes on CAEPS.Five comparative experiments were conducted by using the China Meteorological Administration Mesoscale Numerical Weather Prediction System(CMA-MESO)3-km model for 13 heavy rainfall events over eastern China:smaller-scale IPs with doubled magnitudes,larger-,meso-,and smaller-scale IPs;and a chaos seeding experiment as a baseline.First,the constructed IPs outperform unphysical chaos seeding in perturbation growth and ensemble performance.Second,the daily variation of smaller-scale perturbations is more sensitive to convective activity because smaller-scale perturbations during forecasts reach saturation faster than meso-and larger-scale perturbations.Additionally,rapid downscaling cascade that saturates the smallest-scale perturbation within 6 h for larger-and meso-scale IPs is stronger in the lower troposphere and near-surface.After 9-12 h,the disturbance development of large-scale IPs is the largest in each layer on various scales.Moreover,thermodynamic perturbations,concentrated in the lower troposphere and near-surface with meso-and smaller-scale components being dominant,are smaller and more responsive to convective activity than kinematic perturbations,which are concentrated on the middle-upper troposphere and predominantly consist of larger-and meso-scale components.Furthermore,the increasing magnitude of smaller-scale IPs enables only their smaller-scale perturbations in the first 9 h to exceed those of larger-and meso-scale IPs.Third,for forecast of upper-air and surface variables,larger-scale IPs warrant a more reliable and skillful CAEPS.Finally,for precipitation,larger-scale IPs perform best for light rain at all forecast times,whereas meso-scale IPs are optimal for moderate and heavy rains at 6-h forecast time.Increasing magnitude of smaller-scale IPs improves the probability forecast skills for heavy rains during the first 3-6 h.
基金supported by the National Natural Science Foundation of China(NSFC)Major Program(Grant No.42090032)NSFC Projects(Grant Nos.42475169,42175012)the Science and Technology Winter Olympics Special Subject(Grant No.2018YFF0300103)。
文摘To meet the demands for seamless medium-and short-range weather forecasting during the Beijing Winter Olympics(2022),the Winter Olympics research team at the Earth System Modeling and Prediction Centre(CEMC)of the China Meteorological Administration(CMA)developed an integrated global and regional numerical weather prediction(NWP)model system.In support of the Winter Olympics,the system focuses on key short-and medium-range deterministic and ensemble forecast technologies for complex terrain.By introducing a three-dimensional reference atmosphere and a predictor-corrector iterative algorithm into the regional model's dynamical framework,the team enhanced the spatial accuracy and temporal integration stability of the high-resolution regional model.The team also developed data assimilation techniques for dense surface automatic weather stations and high spatiotemporal resolution imagery from China's Fengyun satellites,improving the monitoring and application capability of unconventional observations for the Winter Olympics.Furthermore,they established a3 km high-resolution regional ensemble prediction system by advancing multiscale hybrid initial perturbation techniques and stochastic perturbation methods for physical processes with spatiotemporal correlations,suitable for complex terrain.To enhance deterministic and probabilistic forecasts at grid and station scales over complex terrain,the team studied bias correction techniques across different resolutions and developed methods for rapidly and effectively extracting key forecast information from large volumes of model output.In particular,machine learning-based approaches were employed to process and fuse massive forecast products containing probabilistic information.These efforts led to the development of a seamless Winter Olympics meteorological forecasting system covering a lead time of 0–15 days and the entire competition zone,featuring forecast updates every hour within 24 h,every 3 h within 24–72 h,and every 12 h within 72–360 h.These products were applied comprehensively in real-time operations during the winter training,test events,and the Olympic and Paralympic Games,representing the highest level of China's independently developed NWP systems in meteorological support for major events.The integrated technological achievements have since been incorporated into the national operational NWP system,and they continue to play a vital role in daily forecasting services,disaster prevention and mitigation,and support for major events.
基金Supported by the National Natural Science Foundation of China(U2242213 and 42105154).
文摘How to construct appropriate perturbations for convection-permitting ensemble prediction systems(CPEPSs)is a critical issue awaiting urgent solutions.As two common perturbations,initial perturbations(IPs)and lateral boundary perturbations(BPs)interact with each other,affecting the model error growth,especially in mesoscale models.Using the China Meteorological Administration(CMA)-CPEPS,this study tries to elucidate how BPs interact with matched and mismatched IPs under varied large-scale weather conditions/forcings.Seven groups of experiments were conducted for strong-forcing and weak-forcing weather regimes over southern China:three with single IPs,one with single BPs,and three with combined perturbations.It is found that the perturbation magnitudes were dominated by meso-α-scale components,and IPs under weak forcing exhibited more pronounced effects than under strong forcing;whereas BPs exerted more pronounced effects under strong forcing than weak forcing regimes.Furthermore,it lasts longer for high-level variables when the perturbation energy from BPs is higher than that from IPs,compared to low-level variables.Moreover,for precipitation and dynamic variables,IPs and BPs can mutually reinforce.The source of these perturbations,and their specific vertical levels,do not alter the extent of their interactions.Nevertheless,the weather regime and the scales of the perturbations influence the strength of their mutual reinforcement.In particular,the weak-forcing regimes exhibit a more pronounced reinforcing effect,and meso-α-scale perturbations are more conducive to fostering interactions compared to meso-β-scale ones.Ultimately,it is the perturbation magnitude inherent in the initial perturbation itself that determines the interactions between IPs and BPs.
基金funded by the National Key Technologies Research and Development Program of Anhui Province of China grant number(2017YFA0604502).
文摘This paper describes the GRAPES Evaluation Tools based on Python(GetPy),a community verification and diagnostic toolfor the evaluation of numerical models.The traditional statistical verification with confidence level test,the comprehensivescorecard,the precipitation skill score such as TS,ETS,diagnostic score SEEPS and the spatial verification techniques areused as verification modules.The Error tracing techniques conducted on the performance with different scales by waveletanalysis.The diurnal cycle of precipitation can also be calculated by Precipitation frequency-intensity method.Based onsimple script architecture GetPy also includes a revised and simplified installation procedure and interactive display system.Users can easily access graphic products and carry out evaluation applications.
