Advanced Search
HAO Cui, ZHANG Yingxin, XU Luyang, XING Nan, DAI Yi, LI Jing. 2022: Verification of improved analog ensemble methods for forecasting extreme temperature and wind speed in Beijing. Torrential Rain and Disasters, 41(4): 467-476. DOI: 10.3969/j.issn.1004-9045.2022.04.011
Citation: HAO Cui, ZHANG Yingxin, XU Luyang, XING Nan, DAI Yi, LI Jing. 2022: Verification of improved analog ensemble methods for forecasting extreme temperature and wind speed in Beijing. Torrential Rain and Disasters, 41(4): 467-476. DOI: 10.3969/j.issn.1004-9045.2022.04.011

Verification of improved analog ensemble methods for forecasting extreme temperature and wind speed in Beijing

More Information
  • Received Date: July 01, 2021
  • Accepted Date: April 06, 2022
  • Available Online: November 03, 2022
  • Published Date: July 31, 2022
  • Upon the current requirement of the extreme weather forecast and service, we developed two improved prediction schemes (that is, schemeⅠ and scheme Ⅱ) based on the ECMWF-IFS model (EC model) and the model output statistics (MOS) method on the basis of the Anolog Ensemble (AnEn) method. First, taking the EC model forecasts from 2016 to 2018 and their corresponding observations as the training dataset, the overall performance of schemeⅠ, scheme Ⅱ, and AnEn for the extreme temperature and wind speed in Beijingfrom January 1 to December 31 in 2019 is tested and evaluated against the observations at 364 stations. The results show that the prediction accuracy of schemeⅠ and scheme Ⅱ is better than that of AnEn for both extreme temperature (T) and wind speed (VM), particularly for scheme Ⅱ. Second, according to the 2nd and 98th percentiles, the thresholds of extreme low temperature (Tm) and extreme high temperature (TM) at the different stations in Beijing are -22.3 ℃ and 38.8 ℃, respectively. The overall prediction results of schemeⅠ and scheme Ⅱ for T in this region show that the two schemes are significantly improved compared to AnEn, and their mean absolute errors (EMA) are reduced by 11.90% and 21.43%, respectively. Similarly, according to the 98th percentile, the VM threshold of each station in Beijing is set at 20.3 m·s-1, and the EMA of VM forecast with schemeⅠ and scheme Ⅱ is reduced by 23.08% and 26.52%, respectively, compared with AnEn. Finally, the prediction results of Tm, TM and VM at each station in Beijing show that schemeⅠ and scheme Ⅱ have improved in T and VM on the basis of AnEn, and more than 94% of stations show that scheme Ⅱ has better performance. In addition, the spatial distributions of prediction accuracy of T and VM show that the two improved schemes have better performance on the prediction of T and VM in the mountainous areas than in the plain areas.

  • 陈凯. 2014. 基于加权KNN算法的降水相似预报方法研究与实现[D]. 南京: 南京航空航天大学
    戴翼, 何娜, 付宗钰, 等. 2019. 北京智能网格温度客观预报方法(BJTM)及预报效果检验[J]. 干旱气象, 37(2): 339-344 https://www.cnki.com.cn/Article/CJFDTOTAL-GSQX201902018.htm
    季崇萍, 张迎新, 乔林, 等. 2020. 北京天气预报手册[M]. 北京: 气象出版社
    丁一汇, 张锦, 宋亚芳. 2002. 天气和气候极端事件的变化及其与全球变暖的联系——纪念2002年世界气象日"减低对天气和气候极端事件的脆弱性"[J]. 气象, 28(3): 1-5 https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200203001.htm
    高丽, 任宏利, 郑嘉雯, 等. 2019. 基于NCEP-GEFS回算资料的我国极端温度变化特征研究[J]. 大气科学学报, 42(1): 58-67 https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201901007.htm
    郝翠, 张迎新, 王在文, 等. 2019. 最优集合预报订正方法在客观温度预报中的应用[J]. 气象, 45(8): 1085-1092 https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201908005.htm
    胡志华. 1991. Weibull分布及其在风能计算中的应用[J]. 云南师范大学学报: 自然科学版, 11(1): 50-53 https://www.cnki.com.cn/Article/CJFDTOTAL-YNSK199101006.htm
    居丽丽, 史军, 张敏. 2020.1961—2015年华东地区极端气温变化研究[J]. 沙漠与绿洲气象, 14(3): 112-121 https://www.cnki.com.cn/Article/CJFDTOTAL-XJQX202003014.htm
    任宏利, 丑纪范. 2005. 统计-动力相结合的相似误差订正法[J]. 气象学报, 63(6): 988-993 doi: 10.3321/j.issn:0577-6619.2005.06.015
    陶祖钰, 赵翠光, 陈敏. 2016. 谈谈统计预报的必要性[J]. 气象科技进展, 6(1): 6-13 https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201601006.htm
    王在文, 陈敏, Luca Delle Monache, 等. 2019. 相似集合预报方法在北京区域地面气温和风速预报中的应用[J]. 气象学报, 77(5): 869-884 https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201905006.htm
    吴启树, 韩美, 郭弘, 等. 2016. MOS温度预报中最优训练期方案[J]. 应用气象学报, 27(4): 426-434 https://www.cnki.com.cn/Article/CJFDTOTAL-YYQX201604005.htm
    张琳娜, 郭锐. 2014. 2012年冬季北京三种高影响天气的关联与成因分析[J]. 气象, 40(5): 598-604 https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201405010.htm
    翟盘茂, 潘晓华. 2003. 中国北方近50年温度和降水极端事件变化[J]. 地理学报, 58(增刊): 1-10 https://www.cnki.com.cn/Article/CJFDTOTAL-DLXB2003S1000.htm
    周海. 2009. 动态相似统计方法的改进及其在温度预报中的应用[D]. 兰州: 兰州大学
    智协飞, 王田, 季焱. 2020. 基于深度学习的中国地面气温的多模式集成预报研究[J]. 大气科学学报, 43(3): 435-446 https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX202003002.htm
    智协飞, 黄闻. 2019. 基于卡尔曼滤波的中国区域气温和降水的多模式集成预报[J]. 大气科学学报, 42(2): 197-206 https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201902004.htm
    智协飞, 赵欢, 朱寿鹏, 等. 2016. 基于CMIP5多模式回报资料的地面气温超级集合研究[J]. 大气科学学报, 39(1): 64-71 https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201601008.htm
    Alessandrini S, Davò F, Sperati S, et al. 2014. Comparison of the economic impact of different wind power forecast systems for producers [J]. Advances in Science and Research, 11(1): 49-53 doi: 10.5194/asr-11-49-2014
    Alessandrini S, Dell Monache L, Sperati S, et al. 2015. An analog ensemble for short-term probabilistic solar power forecast [J]. Applied Energy, 157: 95-110 doi: 10.1016/j.apenergy.2015.08.011
    Alessandrini S, Sperati S, Dell Monache L. 2019. Improving the analog ensemble wind speed forecasts for rare events [J], Monthly Weather Review, 147(7): 2677-2692 doi: 10.1175/MWR-D-19-0006.1
    Clemins P J, BuciniG, Winter J M, et al. 2019. An analog approach for weather estimation using climate projections and reanalysis data [J]. Journal of Applied Meteorology and Climatology, 58(8): 1763-1777 doi: 10.1175/JAMC-D-18-0255.1
    Monache L D, Nipen T, Liu Y B, et al. 2011. Kalman filter and analog schemes to postprocess numerical weather predictions [J]. Monthly Weather Review, 139(11): 3554-3570 doi: 10.1175/2011MWR3653.1
    Hamill, T M, Scheuerer M, Bates G T. 2015. Analog probabilisticprecipitation forecasts using GEFS reforecasts and climatology calibrated precipitation analyses [J]. Monthly Weather Review, 143: 3300-3309 doi: 10.1175/MWR-D-15-0004.1
    Lorenz E N. 1969. Atmospheric predictability as revealed by naturally occurring analogues [J]. Journal of the Atmospheric Sciences, 26: 636-646 doi: 10.1175/1520-0469(1969)26<636:APARBN>2.0.CO;2
    Nagarajan B, Dell Monache L, Hacker J P, et al. 2015. An evaluation of analog-based postprocessing methods across several variables and forecast models [J]. Weather and Forecasting, 30(6): 1623-1643 doi: 10.1175/WAF-D-14-00081.1
    Panziera L, Germann U, Gabella M, et al. 2011. NORA-nowcasting of orographic rainfall by means of analogues [J]. Quartely Journal of the Royal Meteorological Society, 137(661): 2106-2123 doi: 10.1002/qj.878
    Iris O P, Luca D M, Kristian H, et al. 2018. Deterministic wind speed predictions with analog-basedmethods over complex topography [J]. Journal of Applied Meteorology and Climatology, 57: 2047-2070
    TothZ. 1989. Long-range weather forecasting using an analog approach [J]. Journal of Climate, 2(6): 594-607 doi: 10.1007/BF01447939
    Van den Dool H M. 1989. A new look at weather forecasting through analogues [J]. Monthly Weather Review, 117(10): 2230-2247 https://ui.adsabs.harvard.edu/abs/1989MWRv..117.2230V/abstract

Catalog

    Article views (78) PDF downloads (142) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return