不同时间尺度的MOS方法对宁夏气温预报的影响
|
摘要
掌握NMC和本地客观产品的气温预报能力,对提高宁夏城镇天气预报质量和全国排名有重要的意义。基于2009年1月～2014年12月T639和ECMWF两种模式产品及宁夏20个国家级观测站气温资料,采用资料按月和季划分的2种MOS方法,预报未来168 h最高、最低气温,运用宁夏现行的检验评估方法对2015年1月～2016年6月的预报效果进行检验,并与预报员(YBY)和中央气象台指导产品(NMC)对比,结果表明:MOS方法预报宁夏气温,整体预报效果优于NMC,资料按月划分的MOS预报效果优于资料按季划分,ECMOS月和T639MOS月是最高和最低气温的最优客观产品;最低气温预报中YBY优于T639MOS月,最高气温预报中ECMOS月优于YBY;两种最优客观预报产品一致的物理量为集中在中低层或地面的相当位温或假相当位温、温度、湿位涡、高度等,但各自还有一些起关键作用的入选因子影响最高、最低气温的预报。
To master the temperature forecast capabilities of NMC,local subjective and objective products in temperature forecasting is of great significance to improve the quality of urban weather forecasting in Ningxia Province,China and its national ranking in weather forecasting. Based on the two model products of T639 and ECMWF with the temperature data of 20 national observatories in Ningxia from January 2009 to December 2014,the highest and lowest temperatures of the coming 168 hours were predicted by adopting two MOS methods which grouped the data by month or by season. The verification was done on the forecasted results for the time period from January 2015 to June 2016 using the current inspection and evaluation method adopted in Ningxia,and the comparisons with YBY and NMC were also conducted. The results show that the whole effect of the MOS method is superior to the NMC,and its forecast effect with monthly grouped data is superior to that when the data was grouped in season. The T639 MOS by month and ECMOS by month is the optimal objective products of the highest and lowest temperature.For predicting the lowest temperature,compared with the optimal objective product,the forecast by YBY is better than that of T639 MOS by month. For predicting the highest temperature,the forecast of ECMOS by month is better than that by YBY. The physical quantities of the two best objective predictive products are the equivalent temperature,or the potential pseudo-equivalent temperature,the temperature,the wet potential vorticity and the height,which are concentrated in the middle and lower layers or the ground. However,there are some factors that play a key role in the forecast of the highest and lowest temperatures.
To master the temperature forecast capabilities of NMC,local subjective and objective products in temperature forecasting is of great significance to improve the quality of urban weather forecasting in Ningxia Province,China and its national ranking in weather forecasting. Based on the two model products of T639 and ECMWF with the temperature data of 20 national observatories in Ningxia from January 2009 to December 2014,the highest and lowest temperatures of the coming 168 hours were predicted by adopting two MOS methods which grouped the data by month or by season. The verification was done on the forecasted results for the time period from January 2015 to June 2016 using the current inspection and evaluation method adopted in Ningxia,and the comparisons with YBY and NMC were also conducted. The results show that the whole effect of the MOS method is superior to the NMC,and its forecast effect with monthly grouped data is superior to that when the data was grouped in season. The T639 MOS by month and ECMOS by month is the optimal objective products of the highest and lowest temperature.For predicting the lowest temperature,compared with the optimal objective product,the forecast by YBY is better than that of T639 MOS by month. For predicting the highest temperature,the forecast of ECMOS by month is better than that by YBY. The physical quantities of the two best objective predictive products are the equivalent temperature,or the potential pseudo-equivalent temperature,the temperature,the wet potential vorticity and the height,which are concentrated in the middle and lower layers or the ground. However,there are some factors that play a key role in the forecast of the highest and lowest temperatures.
引文
[1]薛志磊,张书余.气温预报方法研究及其应用进展综述[J].干旱气象,2012,30(3):459-464.[XUE Zhilei,ZHANG Shuyu.Advance in research and application about temperature forecast method[J]. Arid Meteorology,2012,30(3):459-464.]
[2]罗菊英,周建山,闫永财.基于数值预报及上级指导产品的本地气温MOS预报方法[J].气象科技,2014,42(3):443-450.[LUO Juying,ZHOU Jianshan,YAN Yongcai. Local temperature MOS forecast method based on numerical forecast products and superior guidance[J]. Meteorological Science and Technology,2014,42(3):443-450.]
[3]刘还珠,赵声蓉,陆志善,等.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,2004,15(2):181-191.[LIU Huanzhu,ZHAO Shengrong,LU Zhishan,et al. Objective element forecasts AT NMC:A MOS system[J]. Quarterly Journal of Applied Meteorology,2004,15(2):181-191.]
[4]陈豫英,陈楠,王素艳,等. MOS方法在动力延伸期候平均气温预报中的应用[J].应用气象学报,2011,22(1):86-95.[CHEN Yuying,CHEN Nan,WANG Suyan,et al. Application of MOS method on average temperature prediction in dynamical extended range[J]. Journal of Applied Meteorological Science,2011,22(1):86-95.]
[5]陈豫英,刘还珠,陈楠,等.基于聚类天气分型的KNN方法在风预报中的应用[J].应用气象学报,2008,19(5):564-572.[CHEN Yuying,LIU Huanzhu,CHEN Nan,et al. Application of KNN to wind forecast based on clustering synoptic patterns[J].Journal of Applied Meteorological Science,2008,19(5):564-572.]
[6]陈豫英,陈晓光,马金仁,等.风的精细化MOS预报方法研究[J].气象科学,2006,26(2):210-216.[CHEN Yuying,CHEN Xiaoguang,MA Jinren,et al. A study on subtle MOS forecasting method of wind[J]. Journal of the Meteorological Sciences,2006,26(2):210-216.]
[7]陈豫英,陈晓光,马筛艳,等.精细化MOS相对湿度预报方法研究[J].气象科技,2006,34(2):143-146.[CHEN Yuying,CHEN Xiaoguang,MA Shaiyan,et al. Fine MOS forecasting method of relative humidity[J]. Meteorological Science and Technology,2006,34(2):143-146.]
[8]陈豫英,陈楠,张磊,等.概率回归方法在宁夏分级沙尘天气预报中的应用检验[J].干旱区地理,2016,39(4):704-711.[CHEN Yuying,CHEN Nan,ZHANG Lei,et al. Application verification of the regression estimation of event probabilities in graded sand prediction[J]. Arid Land Geography,2016,39(4); 704-711.]
[9]梁立为,尹洁,马振富,等.三种非线性回归逐时气温预报比较订正[J].气象科技,2015,43(6):1116-1120.[LIANG Liwei,YIN Jie,MA Zhenfu,et al. Comparative correction of three nonlinear regressive methods of hourly surface temperature forecasting[J]. Meteorological Science and Technology,2015,43(6):1116-1120.]
[10]陈丽娟,李维京.月动力延伸预报产品的评估和解释应用[J].应用气象学报,1999,10(4):486-490.[CHEN Lijuan,LI Weijing. The score skill and interpretation of monthly dynamic extended range forecast[J]. Journal of Applied Meteorological Science,1999,10(4):486-490.]
[11]王娜,方建刚.月动力延伸预报产品在陕西月降水预测中的释用[J].气象科技,2009,37(5):520-522.[WANG Na,FANG Jiangang. Interpretation and application of monthly dynamical extended range forecast products in Shaanxi Province[J]. Meteorological Science and Technology,2009,37(5):520-522.]
[12]卞正奎,朱寿鹏,胡航菲,等.泰州市气温多模式集成预报系统的建立与评估[J].气象科技,2016,44(4):605-611.[BIAN Zhengkui,ZHU Shoupeng,HU Hangfei,et al. Establishment and evaluation of a multi-model ensemble forecast system of temperature for Taizhou[J]. Meteorological Science and Technology,2016,44(4):605-611.]
[13]董全,代刊,陶亦为,等. ECMWF集合预报的极端气温预报产品应用和检验[J].气象,2017,43(9):1095-1109.[DONG Quan,DAI Kan,TAO Yiwei,et al. Application and verification of extreme weather forecast products of ECMWF weather forecast prediction system[J]. Meteorological Monthly,2017,43(9):1095-1109.]
[14]王莹,李永生,段春锋.多模式对东北地区月气温的预测性能对比评估[J].气象科技,2016,44(5):749-753.[WANG Ying,LI Yongsheng,DUAN Chunfeng. Contrast and evaluation of multi-model forecast performance of monthly temperature over Northeast China[J]. Meteorological Science and Technology,2016,44(5):749-753.]
[15]李玲萍,尚可政,钱莉.最优子集神经网络高温预报模型[J].干旱区地理,2012,35(1):67-72.[LI Lingping,SHANG Kezheng,QIAN Li. High temperature prediction modeling of neural network using the optimal subset[J]. Arid Land Geography,2012,35(1):67-72.]
[16]吴爱敏.极端气温集成预报方法对比[J].气象科技,2012,40(5):772-777.[WU Aimin. Comparative analysis of consensus forecast for extreme temperature[J]. Meteorological Science and Technology,2012,40(5):772-777.]
[17]陈豫英,陈晓光,马金仁,等.基于MM5模式的精细化MOS温度预报[J].干旱气象,2005,23(4):52-56.[CHEN Yuying,CHEN Xiaoguang,MA Jinren,et al. Fine MOS temperature forecast based on MM5[J]. Journal of Arid Meteorology,2005,23(4):52-56.]
[2]罗菊英,周建山,闫永财.基于数值预报及上级指导产品的本地气温MOS预报方法[J].气象科技,2014,42(3):443-450.[LUO Juying,ZHOU Jianshan,YAN Yongcai. Local temperature MOS forecast method based on numerical forecast products and superior guidance[J]. Meteorological Science and Technology,2014,42(3):443-450.]
[3]刘还珠,赵声蓉,陆志善,等.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,2004,15(2):181-191.[LIU Huanzhu,ZHAO Shengrong,LU Zhishan,et al. Objective element forecasts AT NMC:A MOS system[J]. Quarterly Journal of Applied Meteorology,2004,15(2):181-191.]
[4]陈豫英,陈楠,王素艳,等. MOS方法在动力延伸期候平均气温预报中的应用[J].应用气象学报,2011,22(1):86-95.[CHEN Yuying,CHEN Nan,WANG Suyan,et al. Application of MOS method on average temperature prediction in dynamical extended range[J]. Journal of Applied Meteorological Science,2011,22(1):86-95.]
[5]陈豫英,刘还珠,陈楠,等.基于聚类天气分型的KNN方法在风预报中的应用[J].应用气象学报,2008,19(5):564-572.[CHEN Yuying,LIU Huanzhu,CHEN Nan,et al. Application of KNN to wind forecast based on clustering synoptic patterns[J].Journal of Applied Meteorological Science,2008,19(5):564-572.]
[6]陈豫英,陈晓光,马金仁,等.风的精细化MOS预报方法研究[J].气象科学,2006,26(2):210-216.[CHEN Yuying,CHEN Xiaoguang,MA Jinren,et al. A study on subtle MOS forecasting method of wind[J]. Journal of the Meteorological Sciences,2006,26(2):210-216.]
[7]陈豫英,陈晓光,马筛艳,等.精细化MOS相对湿度预报方法研究[J].气象科技,2006,34(2):143-146.[CHEN Yuying,CHEN Xiaoguang,MA Shaiyan,et al. Fine MOS forecasting method of relative humidity[J]. Meteorological Science and Technology,2006,34(2):143-146.]
[8]陈豫英,陈楠,张磊,等.概率回归方法在宁夏分级沙尘天气预报中的应用检验[J].干旱区地理,2016,39(4):704-711.[CHEN Yuying,CHEN Nan,ZHANG Lei,et al. Application verification of the regression estimation of event probabilities in graded sand prediction[J]. Arid Land Geography,2016,39(4); 704-711.]
[9]梁立为,尹洁,马振富,等.三种非线性回归逐时气温预报比较订正[J].气象科技,2015,43(6):1116-1120.[LIANG Liwei,YIN Jie,MA Zhenfu,et al. Comparative correction of three nonlinear regressive methods of hourly surface temperature forecasting[J]. Meteorological Science and Technology,2015,43(6):1116-1120.]
[10]陈丽娟,李维京.月动力延伸预报产品的评估和解释应用[J].应用气象学报,1999,10(4):486-490.[CHEN Lijuan,LI Weijing. The score skill and interpretation of monthly dynamic extended range forecast[J]. Journal of Applied Meteorological Science,1999,10(4):486-490.]
[11]王娜,方建刚.月动力延伸预报产品在陕西月降水预测中的释用[J].气象科技,2009,37(5):520-522.[WANG Na,FANG Jiangang. Interpretation and application of monthly dynamical extended range forecast products in Shaanxi Province[J]. Meteorological Science and Technology,2009,37(5):520-522.]
[12]卞正奎,朱寿鹏,胡航菲,等.泰州市气温多模式集成预报系统的建立与评估[J].气象科技,2016,44(4):605-611.[BIAN Zhengkui,ZHU Shoupeng,HU Hangfei,et al. Establishment and evaluation of a multi-model ensemble forecast system of temperature for Taizhou[J]. Meteorological Science and Technology,2016,44(4):605-611.]
[13]董全,代刊,陶亦为,等. ECMWF集合预报的极端气温预报产品应用和检验[J].气象,2017,43(9):1095-1109.[DONG Quan,DAI Kan,TAO Yiwei,et al. Application and verification of extreme weather forecast products of ECMWF weather forecast prediction system[J]. Meteorological Monthly,2017,43(9):1095-1109.]
[14]王莹,李永生,段春锋.多模式对东北地区月气温的预测性能对比评估[J].气象科技,2016,44(5):749-753.[WANG Ying,LI Yongsheng,DUAN Chunfeng. Contrast and evaluation of multi-model forecast performance of monthly temperature over Northeast China[J]. Meteorological Science and Technology,2016,44(5):749-753.]
[15]李玲萍,尚可政,钱莉.最优子集神经网络高温预报模型[J].干旱区地理,2012,35(1):67-72.[LI Lingping,SHANG Kezheng,QIAN Li. High temperature prediction modeling of neural network using the optimal subset[J]. Arid Land Geography,2012,35(1):67-72.]
[16]吴爱敏.极端气温集成预报方法对比[J].气象科技,2012,40(5):772-777.[WU Aimin. Comparative analysis of consensus forecast for extreme temperature[J]. Meteorological Science and Technology,2012,40(5):772-777.]
[17]陈豫英,陈晓光,马金仁,等.基于MM5模式的精细化MOS温度预报[J].干旱气象,2005,23(4):52-56.[CHEN Yuying,CHEN Xiaoguang,MA Jinren,et al. Fine MOS temperature forecast based on MM5[J]. Journal of Arid Meteorology,2005,23(4):52-56.]