Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational...Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.展开更多
This review summarizes the scientific and technical progress in atmospheric modeling in China since 2011,including the dynamical core,model physics,data assimilation,ensemble forecasting,and model evaluation strategie...This review summarizes the scientific and technical progress in atmospheric modeling in China since 2011,including the dynamical core,model physics,data assimilation,ensemble forecasting,and model evaluation strategies.In terms of the dynamical core,important efforts have been made in the improvement of the existing model formulations and in exploring new modeling approaches that can better adapt to massively parallel computers and global multiscale modeling.With regard to model physics,various achievements in physical representations have been made,especially a trend toward scale-aware parameterization for accommodating the increase of model resolution.In the field of data assimilation,a 4D-Var system has been developed and is operationally used by the National Meteorological Center of China,and its performance is promising.Furthermore,ensemble forecasting has played a more important role in operational forecast systems and progressed in many fundamental techniques.Model evaluation strategies,including key performance metrics and standardized experimental protocols,have been proposed and widely applied to better understand the strengths and weaknesses of the systems,offering key routes for model improvement.The paper concludes with a concise summary of the status quo and a brief outlook in terms of future development.展开更多
Atmospheric variability is driven not only by internal dynamics,but also by external forcing,such as soil states,SST,snow,sea-ice cover,and so on.To investigate the forecast uncertainties and effects of land surface p...Atmospheric variability is driven not only by internal dynamics,but also by external forcing,such as soil states,SST,snow,sea-ice cover,and so on.To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction,we added modules to perturb soil moisture and soil temperature into NCEP's Global Ensemble Forecast System(GEFS),and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation.It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes.Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously,as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts.Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere,thus reflecting interactions and the response to atmospheric external forcing.Various verification scores were calculated in this study.The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability,a noticeable reduction in forecast error,as well as an increase in ensemble spread in an under-dispersive system.This paper provides a preliminary evaluation of the effects of land surface processes on predictability.Further research using more complex and suitable methods is needed to fully explore our understanding in this area.展开更多
Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of ac...Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study confirmed that the T_(2m)data assimilation scheme that we implemented in the kilometer-scale CMA-MESO 3DVAR system is effective.展开更多
Numerical weather prediction(NWP) is a core technology in weather forecast and disaster mitigation. China’s NWP research and operational applications have been attached great importance by the meteorological communit...Numerical weather prediction(NWP) is a core technology in weather forecast and disaster mitigation. China’s NWP research and operational applications have been attached great importance by the meteorological community.Fundamental achievements have been made in the theories, methods, and NWP model development in China, which are of certain international impacts. In this paper, the scientific and technological progress of NWP in China since1949 is summarized. The current status and recent progress of the domestically developed NWP system-GRAPES(Global/Regional Assimilation and Pr Ediction System) are presented. Through independent research and development in the past 10 years, the operational GRAPES system has been established, which includes both regional and global deterministic and ensemble prediction models, with resolutions of 3-10 km for regional and 25-50 km for global forecasts. Major improvements include establishment of a new non-hydrostatic dynamic core, setup of four-dimensional variational data assimilation, and development of associated satellite application. As members of the GRAPES system, prediction models for atmospheric chemistry and air pollution, tropical cyclones, and ocean waves have also been developed and put into operational use. The GRAPES system has been an important milestone in NWP science and technology in China.展开更多
A method has been presented to improve ensemble forecast by utilizing these initial members generated by four-dimensional variational data assimilation (4-D VDA), to conquer limitation of those initial members generat...A method has been presented to improve ensemble forecast by utilizing these initial members generated by four-dimensional variational data assimilation (4-D VDA), to conquer limitation of those initial members generated by Monte Carlo forecast (MCF) or lagged average forecast (LAF). This method possesses significant statistical characteristic of MCF, and by virtue of LAF that contains multi-time information and its initial members are harmonic with展开更多
基金This work was jointly supported by the National Natural Science Foundation of China(Grant Nos.41975137,42175012,and 41475097)the National Key Research and Development Program(Grant No.2018YFF0300103).
文摘Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.
基金supported by the National Key R&D Program of China(Grant No.2017YFC1502202)the National Science Foundation of China(Grant Nos.41675075 and41875135)the National Key R&D Program of China(Grant No.2016YFA0602101)
文摘This review summarizes the scientific and technical progress in atmospheric modeling in China since 2011,including the dynamical core,model physics,data assimilation,ensemble forecasting,and model evaluation strategies.In terms of the dynamical core,important efforts have been made in the improvement of the existing model formulations and in exploring new modeling approaches that can better adapt to massively parallel computers and global multiscale modeling.With regard to model physics,various achievements in physical representations have been made,especially a trend toward scale-aware parameterization for accommodating the increase of model resolution.In the field of data assimilation,a 4D-Var system has been developed and is operationally used by the National Meteorological Center of China,and its performance is promising.Furthermore,ensemble forecasting has played a more important role in operational forecast systems and progressed in many fundamental techniques.Model evaluation strategies,including key performance metrics and standardized experimental protocols,have been proposed and widely applied to better understand the strengths and weaknesses of the systems,offering key routes for model improvement.The paper concludes with a concise summary of the status quo and a brief outlook in terms of future development.
基金supported by the National Fundamental(973) Research Program of China(Grant No.2013CB430100)the Special Fund for Meteorological Scientific Research in the Public Interest(Grant No.GYHY201506005)the National Natural Science Foundation of China(Grant Nos.41475097,41075079,41275065 and 41475054)
文摘Atmospheric variability is driven not only by internal dynamics,but also by external forcing,such as soil states,SST,snow,sea-ice cover,and so on.To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction,we added modules to perturb soil moisture and soil temperature into NCEP's Global Ensemble Forecast System(GEFS),and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation.It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes.Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously,as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts.Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere,thus reflecting interactions and the response to atmospheric external forcing.Various verification scores were calculated in this study.The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability,a noticeable reduction in forecast error,as well as an increase in ensemble spread in an under-dispersive system.This paper provides a preliminary evaluation of the effects of land surface processes on predictability.Further research using more complex and suitable methods is needed to fully explore our understanding in this area.
基金Supported by the National Key Research and Development Program of China(2018YFF0300103)。
文摘Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study confirmed that the T_(2m)data assimilation scheme that we implemented in the kilometer-scale CMA-MESO 3DVAR system is effective.
基金Supported by the National Key Research and Development Program of China(2017YFC1501900)Middle-and Long-term Development Strategic Research Project of the Chinese Academy of Engineering(2019-ZCQ-06)。
文摘Numerical weather prediction(NWP) is a core technology in weather forecast and disaster mitigation. China’s NWP research and operational applications have been attached great importance by the meteorological community.Fundamental achievements have been made in the theories, methods, and NWP model development in China, which are of certain international impacts. In this paper, the scientific and technological progress of NWP in China since1949 is summarized. The current status and recent progress of the domestically developed NWP system-GRAPES(Global/Regional Assimilation and Pr Ediction System) are presented. Through independent research and development in the past 10 years, the operational GRAPES system has been established, which includes both regional and global deterministic and ensemble prediction models, with resolutions of 3-10 km for regional and 25-50 km for global forecasts. Major improvements include establishment of a new non-hydrostatic dynamic core, setup of four-dimensional variational data assimilation, and development of associated satellite application. As members of the GRAPES system, prediction models for atmospheric chemistry and air pollution, tropical cyclones, and ocean waves have also been developed and put into operational use. The GRAPES system has been an important milestone in NWP science and technology in China.
文摘A method has been presented to improve ensemble forecast by utilizing these initial members generated by four-dimensional variational data assimilation (4-D VDA), to conquer limitation of those initial members generated by Monte Carlo forecast (MCF) or lagged average forecast (LAF). This method possesses significant statistical characteristic of MCF, and by virtue of LAF that contains multi-time information and its initial members are harmonic with
