一种2 m温度误差订正方法在复杂地形区数值预报中的应用
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摘要
利用模式三维预报变量,结合地面要素预报产品,采用2 m温度三维插值方法进行地形订正,以确保预报与观测三维空间上的一致性,在地形订正基础上,利用历史月均预报误差作为参考误差,剔除模式系统性误差,获取具备日变化特征的预报产品。基于陕西地区复杂地形条件下的典型观测站点,利用2016年8月28日48 h预报个例进行对比分析发现,三维插值方法有效改善了地形差异引起的评估误导问题,但无法改进模式预报的日变化趋势,进一步采用系统性误差订正后,日变化特征明显改善,特别是前24 h预报效果体现出与实况良好的一致性及更佳的预报技巧。通过2016年夏季统计评估表明,误差订正后的2 m温度预报产品有效改善了周期性误差振荡,均方根误差稳定在2 K左右,显示出明显的改进优势。
The inherent differences between observational topography and model terrain have seriously affected the verification accuracies of 2 m temperature levels.The traditional two-dimensional interpolation schemes are only able to ensure the forecasting elements and observational consistency in latitude and longitude locations of two-dimensional spaces,while ignoring the vertical direction consistency.This has the effect of the forecasting and observational verification results not originating from the same spatial positions,thereby causing misleading evaluations.The diurnal cycles are important features of the 2 m temperatures.However,due to the limitations of the physical processes(such as radiation),large bias have consistently appeared in the diurnal cycle forecasts.In this research study,three-dimensional forecast variables were combined with the near-surface elements of the forecasting products,and an advanced three-dimensional interpolation scheme was developed in order to ensure a consistency with the observations in the three-dimensional spatial forecasting processes.Then,based on topography correction methods,the monthly forecasting errors were used as reference bias products for the purpose of eliminating systematic errors and obtaining forecasting products with characteristic diurnal cycles.The abnormal datasets were rejected using a significance test in order to ensure the validity of the samples.In this study,using a classification analysis based on 27 typical observational gauges selected in the complex terrain of Shanxi Province,six major gauge stations were selected which were known to have different height biases between the model terrain and observational heights.The 48-hour forecasting products in August of 2016 were used for this study’s comparison process.It was found that the three-dimensional interpolation scheme effectively solved the misleading evaluations caused by the height bias between the model terrain and observation topography,regardless of whether the large height bias gauge stations or small height bias gauge stations were examined.However,it was observed that the scheme had not effectively improved the diurnal cycle trends of 2 m temperature forecasting.Therefore,it was determined that the three-dimensional interpolation scheme could only modify the overall bias magnitude,and could not improve the forecasting abilities of the diurnal cycles.However,it was observed that after systematic error corrections were adopted,the diurnal cycle forecasting features had been obviously improved.In particular,it was found that a better consistency with the observations had been attained,as well as higher skill scores,particularly in the first 24hours.The results of the seasonal statistical evaluation of the summer of 2016 indicated that,after the bias corrections,the 2 m temperatures could be effectively improve the oscillation of the periodic errors.Furthermore,the RMSE had been maintained at approximately 2 K,which indicated the obvious advantages of the improvements.This study focused on the effectiveness of the bias correction method,and was most concerned with the improvement trends.The systematic errors required monthly forecasting data for many years as reference errors,and the number of forecasting samples was found to restrict the bias correction effects to some extent.Therefore,it was concluded in this study that by increasing the forecasting samples,more reference samples could be added to ensure the error correction methods were perfected.In this way,the proposed bias correction effects could potentially be more significant in the future.At the same time,some of the related operational models have been running for long periods of time(such as the NCEPGFS,ECMWF,T639,and so on).A more ideal reference data base could be obtained using these long period forecasting products,which would potentially display superior effectiveness and applicability in 2 m temperature bias corrections in future studies.
The inherent differences between observational topography and model terrain have seriously affected the verification accuracies of 2 m temperature levels.The traditional two-dimensional interpolation schemes are only able to ensure the forecasting elements and observational consistency in latitude and longitude locations of two-dimensional spaces,while ignoring the vertical direction consistency.This has the effect of the forecasting and observational verification results not originating from the same spatial positions,thereby causing misleading evaluations.The diurnal cycles are important features of the 2 m temperatures.However,due to the limitations of the physical processes(such as radiation),large bias have consistently appeared in the diurnal cycle forecasts.In this research study,three-dimensional forecast variables were combined with the near-surface elements of the forecasting products,and an advanced three-dimensional interpolation scheme was developed in order to ensure a consistency with the observations in the three-dimensional spatial forecasting processes.Then,based on topography correction methods,the monthly forecasting errors were used as reference bias products for the purpose of eliminating systematic errors and obtaining forecasting products with characteristic diurnal cycles.The abnormal datasets were rejected using a significance test in order to ensure the validity of the samples.In this study,using a classification analysis based on 27 typical observational gauges selected in the complex terrain of Shanxi Province,six major gauge stations were selected which were known to have different height biases between the model terrain and observational heights.The 48-hour forecasting products in August of 2016 were used for this study’s comparison process.It was found that the three-dimensional interpolation scheme effectively solved the misleading evaluations caused by the height bias between the model terrain and observation topography,regardless of whether the large height bias gauge stations or small height bias gauge stations were examined.However,it was observed that the scheme had not effectively improved the diurnal cycle trends of 2 m temperature forecasting.Therefore,it was determined that the three-dimensional interpolation scheme could only modify the overall bias magnitude,and could not improve the forecasting abilities of the diurnal cycles.However,it was observed that after systematic error corrections were adopted,the diurnal cycle forecasting features had been obviously improved.In particular,it was found that a better consistency with the observations had been attained,as well as higher skill scores,particularly in the first 24hours.The results of the seasonal statistical evaluation of the summer of 2016 indicated that,after the bias corrections,the 2 m temperatures could be effectively improve the oscillation of the periodic errors.Furthermore,the RMSE had been maintained at approximately 2 K,which indicated the obvious advantages of the improvements.This study focused on the effectiveness of the bias correction method,and was most concerned with the improvement trends.The systematic errors required monthly forecasting data for many years as reference errors,and the number of forecasting samples was found to restrict the bias correction effects to some extent.Therefore,it was concluded in this study that by increasing the forecasting samples,more reference samples could be added to ensure the error correction methods were perfected.In this way,the proposed bias correction effects could potentially be more significant in the future.At the same time,some of the related operational models have been running for long periods of time(such as the NCEPGFS,ECMWF,T639,and so on).A more ideal reference data base could be obtained using these long period forecasting products,which would potentially display superior effectiveness and applicability in 2 m temperature bias corrections in future studies.
引文
Degratano A T,Belcher B N,2007.Spatial interpolation of daily maximum and minimum air temperature based on meteorological model analyses and independent observations[J].J Appl Meteor,46:1981-1992.
Engel C,Ebert E,2012.Gridded operational consensus forecasts of 2-m temperature over Australia[J].Wea Forecasting,27:301-322.
Fan Y,Huug V D,2011.Bias correction and forecast skill of NCEP GFS ensemble week-1 and week-2 precipitation,2-m surface air temperature,and soil moisture forecasts[J].Wea Forecasting,26:354-370.
傅娜,陈葆德,谭燕,等,2014.上海自动站气温资料的空间质量控制与特征分析[J].大气科学学报,37(2):199-207.Fu N,Chen B D,Tan Y,et al.,2014.Spatial quality control and characteristic analysis of AWS temperature data in Shanghai[J].Trans Atmos Sci,37(2):199-207.(in Chinese).
黄利萍,苗峻峰,刘月琨,2012.天津城市热岛效应的时空变化特征[J].大气科学学报,35(5):620-632.Huang L P,Miao J F,Liu Y K,2012.Spatial and temporal variation characteristics of urban heat island in Tianjin[J].Trans Atmos Sci,35(5):620-632.(in Chinese).
黄利萍,苗峻峰,刘月琨,等,2013.天津地区夏季海陆风对城市热岛日变化特征影响的观测分析[J].大气科学学报,36(4):417-425.Huang LP,Miao J F,Liu Y K,et al.,2013.Observational analysis of influence of sea-land breeze on diurnal characteristics of urban heat island in Tianjin during summer[J].Trans Atmos Sci,36(4):417-425.(in Chinese).
Kann A,Wittmann C,Wang Y,et al.,2009.Calibrating 2-m temperature of limited-area ensemble forecasts using high-resolution analysis[J].Mon Wea Rev,137:3373-3387.
李新,程国栋,卢玲,2000.空间内插方法比较[J].地球科学进展,15(3):260-265.Li X,Cheng G,Lu L,2000.Comparison of spatial interpolation methods[J].Advance in Earth Sciences,15(3):260-265.(in Chinese).
林春泽,刘琳,林文才,等,2016.湖北省夏季降水日变化特征[J].大气科学学报,39(4):490-500.Lin C Z,Liu L,Lin W C,et al.,2016.Characteristics of summer precipitation diurnal variations in Hubei Province[J].Trans Atmos Sci,39(4):490-500.(in Chinese).
刘还珠,赵声蓉,陆志善,等,2004.国家气象中心气象要素的客观预报-MOS系统[J].应用气象学报,15(2):181.Liu H J,Zhao S R,Lu Z S,etal.,2004.Objective element forecasts at NMC-A MOS system[J].J APPL Meteor Sci,15(2):181.(in Chinese).
刘宇,陈泮勤,张稳,等,2006.一种地面气温的空间插值方法及其误差分析[J].大气科学,30(1):146-152.Liu Y,Chen P Q,Zhang W,et al.,2006.A spatial interpolation method for surface air temperature and its error analysis[J].Chin J Atmos Sci,30(1):146-152.(in Chinese).
Lussana C,Uboldi F,Salvati MR,2010.A spatial consistency test for surface observations from mesoscale meteorological networks[J].Quart J Roy Meteor Soc,136:1075-1088.
Murphy A H,Epstein E S,1989.Skill scores and correlation coefficients in model verification[J].Mon Wea Rev,117:572-581.
North G R,Wang J,Genton MG,2011.Correlation models for temperature fields[J].J Climate,24:5850-5862.
潘旸,沈艳,宇婧婧,等,2015.基于贝叶斯融合方法的高分辨率地面-卫星-雷达三源降水融合试验[J].气象学报,73(1):177-186.Pan Y,Shen Y,Yu J J,et al.,2015.An experiment of high-resolution gauge-radar-satellite combined precipitation retrieval based on the Bayesian merging method[J].Acta Meteor Sinica,73(1):177-186.(in Chinese).
Pitman A J,Perkins S E,2009.Global and regional comparison of daily 2-m and 1000-hPa maximum and minimum temperatures in three global reanalyses[J].J Climate,22:4467-4681.
Shen Y,Xiong A Y,Wang Y,et al.,2010.Performance of high-resolution satellite precipitation products over China[J].J Geophys Res,115(2):355-365.
Shen Y,Zhao P,Pan Y,et al.,2014.A high spatiotemporal gauge satellite merged precipitation analysis over China[J].J Geophys Res,119(6):3063-3075.
佟华,郭品文,朱跃建,等,2014.基于大尺度模式产品的误差订正与统计降尺度气象要素预报技术[J].气象,40(1):66-75.Tong H,Guo P W,Zhu Y J,et al.,2014.Bias correction and statistical downscaling meteorological parameters forecast technique based on large-scale numerical model products[J].Meteor Mon,40(1):66-75.(in Chinese).
王思维,刘勇,朱超洪,等,2011.青海省逐日地面气温数据不同插值方法的对比[J].高原气象,30(6):1640-1646.Wang S W,Liu Y,Zhu C H,et al.,2011.Contrast on different spatial interpolation methods of daily surface temperature data in terrain complex area,Qinghai Province[J].Plateau Meteor,30(6):1640-1646.(in Chinese).
王智,师庆东,常顺利,等,2012.新疆地区平均气温空间插值方法研究[J].高原气象,31(1):201-208.Wang Z,Shi Q D,Chang S L,et al.,2012.Study on spatial interpolation method of mean air temperature in Xinjiang[J].Plateau Meteor,31(1):201-208.(in Chinese).
Yuan W,Rucong Y U,Jian L I,2013.Changes in the diurnal cycles of precipitation over Eastern China in the past 40 years[J].Adv Atmos Sci,30(2):461-467.
张海东,张昕璇,孙照渤,等,2010.中国近50 a来度日变化的研究[J].大气科学学报,33(5):593-599.Zhang H D,Zhang X X,Sun Z B,et al.,2010.A study on degree days change in China in the past fifty years[J].Trans Atmos Sci,33(5):593-599.(in Chinese).
赵滨,李子良,张博,2016.三维插值方法在2 m温度评估中的应用[J].南京信息工程大学学报(自然科学版),8(4):343-355.Zhao B,Li Z L,Zhang B,2016.Application of a 3D interpolation scheme for 2 meter temperature verification[J].Journal of Nanjing University of Information Science&Technology(Natural Science Edition),8(4):343-355.(in Chinese).
Engel C,Ebert E,2012.Gridded operational consensus forecasts of 2-m temperature over Australia[J].Wea Forecasting,27:301-322.
Fan Y,Huug V D,2011.Bias correction and forecast skill of NCEP GFS ensemble week-1 and week-2 precipitation,2-m surface air temperature,and soil moisture forecasts[J].Wea Forecasting,26:354-370.
傅娜,陈葆德,谭燕,等,2014.上海自动站气温资料的空间质量控制与特征分析[J].大气科学学报,37(2):199-207.Fu N,Chen B D,Tan Y,et al.,2014.Spatial quality control and characteristic analysis of AWS temperature data in Shanghai[J].Trans Atmos Sci,37(2):199-207.(in Chinese).
黄利萍,苗峻峰,刘月琨,2012.天津城市热岛效应的时空变化特征[J].大气科学学报,35(5):620-632.Huang L P,Miao J F,Liu Y K,2012.Spatial and temporal variation characteristics of urban heat island in Tianjin[J].Trans Atmos Sci,35(5):620-632.(in Chinese).
黄利萍,苗峻峰,刘月琨,等,2013.天津地区夏季海陆风对城市热岛日变化特征影响的观测分析[J].大气科学学报,36(4):417-425.Huang LP,Miao J F,Liu Y K,et al.,2013.Observational analysis of influence of sea-land breeze on diurnal characteristics of urban heat island in Tianjin during summer[J].Trans Atmos Sci,36(4):417-425.(in Chinese).
Kann A,Wittmann C,Wang Y,et al.,2009.Calibrating 2-m temperature of limited-area ensemble forecasts using high-resolution analysis[J].Mon Wea Rev,137:3373-3387.
李新,程国栋,卢玲,2000.空间内插方法比较[J].地球科学进展,15(3):260-265.Li X,Cheng G,Lu L,2000.Comparison of spatial interpolation methods[J].Advance in Earth Sciences,15(3):260-265.(in Chinese).
林春泽,刘琳,林文才,等,2016.湖北省夏季降水日变化特征[J].大气科学学报,39(4):490-500.Lin C Z,Liu L,Lin W C,et al.,2016.Characteristics of summer precipitation diurnal variations in Hubei Province[J].Trans Atmos Sci,39(4):490-500.(in Chinese).
刘还珠,赵声蓉,陆志善,等,2004.国家气象中心气象要素的客观预报-MOS系统[J].应用气象学报,15(2):181.Liu H J,Zhao S R,Lu Z S,etal.,2004.Objective element forecasts at NMC-A MOS system[J].J APPL Meteor Sci,15(2):181.(in Chinese).
刘宇,陈泮勤,张稳,等,2006.一种地面气温的空间插值方法及其误差分析[J].大气科学,30(1):146-152.Liu Y,Chen P Q,Zhang W,et al.,2006.A spatial interpolation method for surface air temperature and its error analysis[J].Chin J Atmos Sci,30(1):146-152.(in Chinese).
Lussana C,Uboldi F,Salvati MR,2010.A spatial consistency test for surface observations from mesoscale meteorological networks[J].Quart J Roy Meteor Soc,136:1075-1088.
Murphy A H,Epstein E S,1989.Skill scores and correlation coefficients in model verification[J].Mon Wea Rev,117:572-581.
North G R,Wang J,Genton MG,2011.Correlation models for temperature fields[J].J Climate,24:5850-5862.
潘旸,沈艳,宇婧婧,等,2015.基于贝叶斯融合方法的高分辨率地面-卫星-雷达三源降水融合试验[J].气象学报,73(1):177-186.Pan Y,Shen Y,Yu J J,et al.,2015.An experiment of high-resolution gauge-radar-satellite combined precipitation retrieval based on the Bayesian merging method[J].Acta Meteor Sinica,73(1):177-186.(in Chinese).
Pitman A J,Perkins S E,2009.Global and regional comparison of daily 2-m and 1000-hPa maximum and minimum temperatures in three global reanalyses[J].J Climate,22:4467-4681.
Shen Y,Xiong A Y,Wang Y,et al.,2010.Performance of high-resolution satellite precipitation products over China[J].J Geophys Res,115(2):355-365.
Shen Y,Zhao P,Pan Y,et al.,2014.A high spatiotemporal gauge satellite merged precipitation analysis over China[J].J Geophys Res,119(6):3063-3075.
佟华,郭品文,朱跃建,等,2014.基于大尺度模式产品的误差订正与统计降尺度气象要素预报技术[J].气象,40(1):66-75.Tong H,Guo P W,Zhu Y J,et al.,2014.Bias correction and statistical downscaling meteorological parameters forecast technique based on large-scale numerical model products[J].Meteor Mon,40(1):66-75.(in Chinese).
王思维,刘勇,朱超洪,等,2011.青海省逐日地面气温数据不同插值方法的对比[J].高原气象,30(6):1640-1646.Wang S W,Liu Y,Zhu C H,et al.,2011.Contrast on different spatial interpolation methods of daily surface temperature data in terrain complex area,Qinghai Province[J].Plateau Meteor,30(6):1640-1646.(in Chinese).
王智,师庆东,常顺利,等,2012.新疆地区平均气温空间插值方法研究[J].高原气象,31(1):201-208.Wang Z,Shi Q D,Chang S L,et al.,2012.Study on spatial interpolation method of mean air temperature in Xinjiang[J].Plateau Meteor,31(1):201-208.(in Chinese).
Yuan W,Rucong Y U,Jian L I,2013.Changes in the diurnal cycles of precipitation over Eastern China in the past 40 years[J].Adv Atmos Sci,30(2):461-467.
张海东,张昕璇,孙照渤,等,2010.中国近50 a来度日变化的研究[J].大气科学学报,33(5):593-599.Zhang H D,Zhang X X,Sun Z B,et al.,2010.A study on degree days change in China in the past fifty years[J].Trans Atmos Sci,33(5):593-599.(in Chinese).
赵滨,李子良,张博,2016.三维插值方法在2 m温度评估中的应用[J].南京信息工程大学学报(自然科学版),8(4):343-355.Zhao B,Li Z L,Zhang B,2016.Application of a 3D interpolation scheme for 2 meter temperature verification[J].Journal of Nanjing University of Information Science&Technology(Natural Science Edition),8(4):343-355.(in Chinese).