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Application of Rain and Snow Phase Criterion Based on Ice-phase Particle Content Forecast by CMA-MESO
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摘要: 利用中国气象局中尺度模式(CMA-MESO)云降水物理直接输出的水凝物混合比, 确定基于冰相水凝物占比的雨雪相态判据, 并应用于2023年1月14—15日我国大范围降水过程的雨雪相态判别。结果表明:该判据明显改善了基于温度和高度场的厚度判据对我国东部地区雨夹雪范围判别偏大、对分散性雨夹雪漏报的问题, 6~18 h时效雨夹雪预报TS评分较厚度判据提升75%~100%, 24 h时效降雪预报TS评分较厚度判据提升67%;对全国雨雪范围判别合理, 对小范围雨夹雪具有指示作用;对全国3~36 h时效降雨、降雪和雨夹雪预报TS评分为0.76~0.62, 0.69~0.63和0.11~0.08;对降雨和降雪存在一定空报和漏报, 对24 h时效雨夹雪空报明显;对相态转换过程有较好指示效果, 判别代表站相态转换开始时间误差为1~2 h, 对我国东部地区代表站的相态转换和雨夹雪持续时间判别优于厚度判据, 基于厚度判据雨夹雪预报持续时间偏长。研究结果可为雨雪相态业务预报提供客观预报产品参考。Abstract: The forecast of rain and snow phase is one of the difficulties in precipitation forecast, which is of great significance for disaster prevention and reduction. Rain or snow phase is mainly discriminated according to the traditional temperature-thickness criterion, or the combination of numerical model results with the judgment of forecasters on environmental conditions in present operatorial forecast. However, the determination of temperature-thickness criterions is subjective, complicated and various in different regions. The precipitation phase product of numerical model is based on temperature, humidity and liquid water content forecasts, resulting in errors of other variables besides microphysics introduced. Therefore, many uncertainties exist in the forecast of the transition of rain and snow, especially in the mixed phase. China Meteorological Administration mesoscale model (CMA-MESO) is a regional numerical model and has been applied to national operatorial weather forecast. Its precipitation phase is diagnosed using temperature, humidity and other basic atmospheric variables, including only rain, snow, freezing rain and hail, excluding mixed phase. Therefore, it is urgent to study on a more effective method for rain and snow, especially for the sleet forecast.A criterion for discriminating rain and snow phase is determined using the ice-phase particle content directly output from microphysics scheme of CMA-MESO, and applied to discriminate the range and transition of rain and snow in a widespread precipitation process in China during 14-15 January 2023. The proportion threshold of ice particles is firstly determined by the statistical threat scores. Results show that problems of larger range of sleet and underreporting scattered sleet in eastern China discriminated by traditional thickness criterion are obviously improved by ice-phase criterion. Threat scores for 6-18 h forecast of sleet increase by 75%-100%, and those for 24 h forecast of snow increase by 67% using ice-phase criterion compared with those using thickness criterion, respectively. Threat scores of 3-36 h forecast for rain, snow and sleet are 0.76 to 0.62, 0.69 to 0.63 and 0.11 to 0.08. There are false alarm and missing for rain and snow, respectively, and obvious false alarm for sleet within 24 h. The ice-phase criterion performs well on discriminating the transition process of rain and snow. The forecast error of phase transition start time at representative stations is about 1-2 h using ice-phase criterion, better than thickness criterion. Besides, the ice-phase criterion performs better in discriminating the duration of sleet for the representative station in eastern China too, while the thickness criterion will make forecast results longer than observations. These results could provide a more reliable and objective forecast product for the rain and snow phase forecast in operation.
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图 1 2023年1月14日08:00 500 hPa高度场(等值线，单位：dagpm)、850 hPa风场(风羽) 和850 hPa水汽通量(填色)
(＋为安徽青阳站和贵州绥阳站)
Fig. 1 500 hPa geopotential height (the contour, unit:dagpm), 850 hPa wind (the barb) and 850 hPa water vapor flux (the shaded)
(＋ denote locations of Qingyang Station of Anhui and Suiyang Station of Guizhou)
图 2 2023年1月14—15日观测和CMA-MESO 14日08:00起报的基于厚度判据和冰相水凝物判据判别的降水相态分布
Fig. 2 Precipitation phase in observations and CMA-MESO forecast discriminated by thickness criterion and ice-phase criterion initialized at 0800 BT 14 Jan 2023 and observations during 14-15 Jan 2023
图 3 CMA-MESO 2023年1月14日08:00起报的东部地区(25°~38°N，112°~122°E) 基于厚度判据和冰相水凝物判据的不同时刻降雨、降雪和雨夹雪预报TS评分和偏差评分
Fig. 3 Threat score and bias for rain, sleet and snow discriminated by thickness criterion and ice-phase criterion based on CMA-MESO forecast in eastern China (25°-38°N,112°-122°E) initialized at 0800 BT 14 Jan 2023
图 4 CMA-MESO 2023年1月14日08:00起报的不同时刻的基于冰相水凝物判据的降雨、雨夹雪和降雪预报TS评分和偏差评分
Fig. 4 Threat score and bias for rain, sleet and snow discriminated by ice-phase criterion based on CMA-MESO forecast initialized at 0800 BT 14 Jan 2023
图 5 CMA-MESO在2023年1月14日02:00—15日14:00逐3 h预报时效的基于冰相水凝物判据的降雨、雨夹雪和降雪预报平均TS评分和偏差评分
Fig. 5 Mean threat score and bias for rain, sleet and snow discriminated by ice-phase criterion based on CMA-MESO forecast of every 3 hours from 0200 BT 14 Jan to 1400 BT 15 Jan in 2023
图 6 2023年1月14—15日青阳站观测和CMA-MESO 2023年1月14日08:00起报的2 m温度(黑色实线)、小时降水量(柱状图) 及基于冰相水凝物判据和厚度判据的降水相态
Fig. 6 2 m temperature (the black curve), hourly precipitation (the bar) and precipitation phase discriminated by ice-phase criterion and thickness criterion based on CMA-MESO forecast initialized at 0800 BT 14 Jan 2023 and observations at Qingyang Station during 14-15 Jan 2023
图 7 2023年1月14—15日青阳站水凝物含量垂直廓线
Fig. 7 Profiles of hydrometeor content at Qingyang Station during 14-15 Jan 2023
图 8 2023年1月14—15日绥阳站观测和CMA-MESO 2023年1月14日11:00起报的2 m温度(黑色实线)、降水量(柱状图) 及基于冰相水凝物判据得到的降水相态
Fig. 8 2 m temperature (the black curve), hourly precipitation (the bar) and precipitation phase discriminated by ice-phase criterion based on CMA-MESO forecast initialized at 1100 BT 14 Jan 2023 and observations at Suiyang Station during 14-15 Jan 2023
图 9 2023年1月14—15日绥阳站水凝物含量垂直廓线
Fig. 9 Profiles of hydrometeor content at Suiyang Station during 14-15 Jan 2023
表 1 用于确定雨夹雪与雪判别阈值的个例
Table 1 Cases for determining threshold of sleet and snow
序号 雨雪过程时间 用于计算阈值的时刻 CMA-MESO起报时刻 1 2022-11-10—12 2022-11-11T14:00
2022-11-12T05:00
2022-11-12T12:002022-11-11T11:00
2022-11-12T02:00
2022-11-12T11:002 2022-02-16—17 2022-02-16T21:00
2022-02-17T12:00
2022-02-17T22:002022-02-16T20:00
2022-02-17T11:00
2022-02-17T20:003 2022-02-11—13 2022-02-11T03:00
2022-02-12T23:00
2022-02-13T09:002022-02-11T02:00
2022-02-12T20:00
2022-02-13T08:004 2022-01-25—27 2022-01-25T06:00
2022-01-26T04:00
2022-01-27T04:00
2022-01-27T22:002022-01-25T05:00
2022-01-26T02:00
2022-01-27T02:00
2022-01-27T20:005 2022-01-20—24 2022-01-20T21:00
2022-01-21T09:00
2022-01-22T06:00
2022-01-22T15:00
2022-01-23T09:00
2022-01-24T10:002022-01-20T20:00
2022-01-21T08:00
2022-01-22T05:00
2022-01-22T14:00
2022-01-23T08:00
2022-01-24T08:006 2022-01-04—07 2022-01-04T18:00
2022-01-05T10:00
2022-01-06T16:00
2022-01-07T17:002022-01-04T17:00
2022-01-05T08:00
2022-01-06T12:00
2022-01-07T14:007 2021-12-25—27 2021-12-25T09:00
2021-12-26T13:00
2021-12-27T14:002021-12-25T08:00
2021-12-26T11:00
2021-12-27T11:008 2021-11-29—30 2021-11-29T13:00 2021-11-29T11:00 
下载: 导出CSV
表 2 不同阈值确定的雨夹雪和降雪预报平均TS评分
Table 2 Mean threat score for sleet and snow determined by various thresholds
阈值 TS评分 雨夹雪 降雪 1.00 0.118 0.805 0.95 0.118 0.808 0.90 0.118 0.815 0.85 0.119 0.820 0.80 0.115 0.826 0.75 0.114 0.835 0.70 0.115 0.835 0.65 0.113 0.848 0.60 0.114 0.854 0.55 0.113 0.853 0.50 0.112 0.859
下载: 导出CSV
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[1] Changnon S A.Characteristics of ice storms in the United States.J Appl Meteor Climatol, 2003, 42(5):630-639. doi: 10.1175/1520-0450(2003)042<0630:COISIT>2.0.CO;2 [2] 赵琳娜, 马清云, 杨贵名, 等. 2008年初我国低温雨雪冰冻对重点行业的影响及致灾成因分析. 气候与环境研究, 2008, 13(4): 556-566. https://www.cnki.com.cn/Article/CJFDTOTAL-QHYH200804020.htmZhao L N, Ma Q Y, Yang G M, et al. Disasters and its impact of a severe snow storm and freezing rain over Southern China in January 2008. Climatic Environ Res, 2008, 13(4): 556-566. https://www.cnki.com.cn/Article/CJFDTOTAL-QHYH200804020.htm [3] 林佳璐, 李英, 柳龙生. 风暴-低涡影响下青藏高原一次强降水过程. 应用气象学报, 2023, 34(2): 166-178. doi: 10.11898/1001-7313.20230204Lin J L, Li Y, Liu L S. A heavy precipitation process over the Tibetan Plateau under the joint effects of a tropical cyclone and vortex. J Appl Meteor Sci, 2023, 34(2): 166-178. doi: 10.11898/1001-7313.20230204 [4] Stewart R E, King P. Freezing precipitation in winter storms. Mon Wea Rev, 1987, 115(7): 1270-1280. doi: 10.1175/1520-0493(1987)115<1270:FPIWS>2.0.CO;2 [5] Ryzhkov A V, Zrnić D S. Discrimination between rain and snow with a polarimetric radar. J Appl Meteor Climatol, 1998, 37(10): 1228-1240. doi: 10.1175/1520-0450(1998)037<1228:DBRASW>2.0.CO;2 [6] 常祎, 郭学良, 唐洁, 等. 青藏高原夏季对流云微物理特征和降水形成机制. 应用气象学报, 2021, 32(6): 720-734. doi: 10.11898/1001-7313.20210607Chang Y, Guo X L, Tang J, et al. Microphysical characteristics and precipitation formation mechanisms of convective clouds over the Tibetan Plateau. J Appl Meteor Sci, 2021, 32(6): 720-734. doi: 10.11898/1001-7313.20210607 [7] 郭学良, 付丹红, 郭欣, 等. 我国云降水物理飞机观测研究进展. 应用气象学报, 2021, 32(6): 641-652. doi: 10.11898/1001-7313.20210601Guo X L, Fu D H, Guo X, et al. Advances in aircraft measurements of clouds and precipitation in China. J Appl Meteor Sci, 2021, 32(6): 641-652. doi: 10.11898/1001-7313.20210601 [8] 赵琳娜, 慕秀香, 马翠平, 等. 冬季稳定性降水相态预报研究进展. 应用气象学报, 2021, 32(1): 12-24. doi: 10.11898/1001-7313.20210102Zhao L N, Mu X X, Ma C P, et al. A review on stable precipitation type forecast in winter. J Appl Meteor Sci, 2021, 32(1): 12-24. doi: 10.11898/1001-7313.20210102 [9] Stewart R E, Thériault J M, Henson W. On the characteristics of and processes producing winter precipitation types near 0℃. Bull Amer Meteor Soc, 2015, 96(4): 623-639. doi: 10.1175/BAMS-D-14-00032.1 [10] 许爱华, 乔林, 詹丰兴, 等. 2005年3月一次寒潮天气过程的诊断分析. 气象, 2006, 32(3): 49-55. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200603008.htmXu A H, Qiao L, Zhan F X, et al. Diagnosis of a cold wave weather event in March 2005. Meteor Mon, 2006, 32(3): 49-55. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200603008.htm [11] 郑婧, 许爱华, 许彬. 2008年江西省冻雨和暴雪过程对比分析. 气象与减灾研究, 2008, 31(2): 29-35. https://www.cnki.com.cn/Article/CJFDTOTAL-HXQO200802008.htmZheng J, Xu A H, Xu B. Contrastive analysis of the freezing rain and heavy snow processes in 2008. Meteor Disaster Reduction Res, 2008, 31(2): 29-35. https://www.cnki.com.cn/Article/CJFDTOTAL-HXQO200802008.htm [12] 李江波, 李根娥, 裴雨杰, 等. 一次春季强寒潮的降水相态变化分析. 气象, 2009, 35(7): 87-94. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200907014.htmLi J B, Li G E, Pei Y J, et al. Analysis on the phase transformation of precipitation during a strong cold wave happened in spring. Meteor Mon, 2009, 35(7): 87-94. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200907014.htm [13] 梁红, 马福全, 李大为, 等. "2009.2"沈阳暴雪天气诊断与预报误差分析. 气象与环境学报, 2010, 26(4): 22-27. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX201004005.htmLiang H, Ma F Q, Li D W, et al. Diagnostic analysis of heavy snow in February 2009 and its forecast error in Shenyang. J Meteor Environ, 2010, 26(4): 22-27. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX201004005.htm [14] Birk K, Lenning E, Donofrio K, et al. A revised bourgouin precipitation-type algorithm. Wea Forecasting, 2021, 36(2): 425-438. doi: 10.1175/WAF-D-20-0118.1 [15] Bocchieri J R. A new operational system for forecasting precipitation type. Mon Wea Rev, 1979, 107(6): 637-649. doi: 10.1175/1520-0493(1979)107<0637:ANOSFF>2.0.CO;2 [16] Bourgouin P. A method to determine precipitation types. Wea Forecasting, 2000, 15(5): 583-592. doi: 10.1175/1520-0434(2000)015<0583:AMTDPT>2.0.CO;2 [17] Hux J D, Knappenberger P C, Michaels P J, et al. Development of a discriminant analysis mixed precipitation(DAMP) forecast model for mid-Atlantic winter storms. Wea Forecasting, 2001, 16(2): 248-259. doi: 10.1175/1520-0434(2001)016<0248:DOADAM>2.0.CO;2 [18] Heppner P. Snow versus rain: Looking beyond the "magic" numbers. Wea Forecasting, 1992, 7(4): 683-691. doi: 10.1175/1520-0434(1992)007<0683:SVRLBT>2.0.CO;2 [19] Czys R R, Scott R W, Tang K C, et al. A physically based, nondimensional parameter for discriminating between locations of freezing rain and ice pellets. Wea Forecasting, 1996, 11(4): 591-598. doi: 10.1175/1520-0434(1996)011<0591:APBNPF>2.0.CO;2 [20] 段云霞, 李得勤, 李大为, 等. 沈阳降水相态特征分析及预报方法. 干旱气象, 2016, 34(1): 51-57;74. https://www.cnki.com.cn/Article/CJFDTOTAL-GSQX201601007.htmDuan Y X, Li D Q, Li D W, et al. Analysis on precipitation phase characteristics and its forecast methods of Shenyang. Arid Meteor, 2016, 34(1): 51-57;74. https://www.cnki.com.cn/Article/CJFDTOTAL-GSQX201601007.htm [21] 徐辉, 宗志平. 一次降水相态转换过程中温度垂直结构特征分析. 高原气象, 2014, 33(5): 1272-1280. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201405011.htmXu H, Zong Z P. Analysis on characteristics of thermal vertical structure evolution during the transition of precipitation type in winter. Plateau Meteor, 2014, 33(5): 1272-1280. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201405011.htm [22] 漆梁波, 张瑛. 中国东部地区冬季降水相态的识别判据研究. 气象, 2012, 38(1): 96-102. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201201012.htmQi L B, Zhang Y. Research on winter precipitation types' discrimination criterion in eastern China. Meteor Mon, 2012, 38(1): 96-102. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201201012.htm [23] Forbes R, Tsonevsky I, Hewson T, et al. Towards predicting high-impact freezing rain events. ECMWF Newsletter, 2014, 141: 15-21. [24] 董全, 胡宁, 宗志平. ECMWF降水相态预报产品(PTYPE)应用和检验. 气象, 2020, 46(9): 1210-1221. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202009008.htmDong Q, Hu N, Zong Z P. Application and verification of the ECMWF precipitation type forecast product(PTYPE). Meteor Mon, 2020, 46(9): 1210-1221. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202009008.htm [25] Gascón E, Hewson T, Haiden T. Improving predictions of precipitation type at the surface: Description and verification of two new products from the ECMWF ensemble. Wea Forecasting, 2018, 33(1): 89-108. [26] 董全, 张峰, 宗志平. 基于ECMWF集合预报产品的降水相态客观预报方法. 应用气象学报, 2020, 31(5): 527-542. doi: 10.11898/1001-7313.20200502Dong Q, Zhang F, Zong Z P. Objective precipitation type forecast based on ECMWF ensemble prediction product. J Appl Meteor Sci, 2020, 31(5): 527-542. doi: 10.11898/1001-7313.20200502 [27] Scheuerer M, Gregory S, Hamill T M, et al. Probabilistic precipitation-type forecasting based on GEFS ensemble forecasts of vertical temperature profiles. Mon Wea Rev, 2017, 145(4): 1401-1412. [28] 彭霞云, 裘薇, 李文娟, 等. 数据挖掘技术用于降水相态判别的尝试. 科技通报, 2018, 34(1): 44-47. https://www.cnki.com.cn/Article/CJFDTOTAL-KJTB201801010.htmPeng X Y, Qiu W, Li W J, et al. Apply the data digging technique on discerning the precipitation type. Bull Sci Technol, 2018, 34(1): 44-47. https://www.cnki.com.cn/Article/CJFDTOTAL-KJTB201801010.htm [29] 陈双, 谌芸, 何立富, 等. 我国中东部平原地区临界气温条件下降水相态判别分析. 气象, 2019, 45(8): 1037-1051. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201908001.htmChen S, Chen Y, He L F, et al. Discrimination analysis of snow and rain occurring under critical temperature conditions in central and eastern China. Meteor Mon, 2019, 45(8): 1037-1051. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201908001.htm [30] 陆虹, 翟盘茂, 覃卫坚, 等. 低温雨雪过程的粒子群-神经网络预报模型. 应用气象学报, 2015, 26(5): 513-524. doi: 10.11898/1001-7313.20150501Lu H, Zhai P M, Qin W J, et al. A particle swarm optimization-neural network ensemble prediction model for persistent freezing rain and snow storm in Southern China. J Appl Meteor Sci, 2015, 26(5): 513-524. doi: 10.11898/1001-7313.20150501 [31] 董全, 黄小玉, 宗志平. 人工神经网络法和线性回归法对降水相态的预报效果对比. 气象, 2013, 39(3): 324-332. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201303007.htmDong Q, Huang X Y, Zong Z P. Comparison of artificial neural network and linear regression methods in forecasting precipitation types. Meteor Mon, 2013, 39(3): 324-332. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201303007.htm [32] 黄骄文, 蔡荣辉, 姚蓉, 等. 深度学习网络在降水相态判识和预报中的应用. 气象, 2021, 47(3): 317-326. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202103005.htmHuang J W, Cai R H, Yao R, et al. Application of deep learning method to discrimination and forecasting of precipitation type. Meteor Mon, 2021, 47(3): 317-326. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202103005.htm [33] 孙健, 曹卓, 李恒, 等. 人工智能技术在数值天气预报中的应用. 应用气象学报, 2021, 32(1): 1-11. doi: 10.11898/1001-7313.20210101Sun J, Cao Z, Li H, et al. Application of artificial intelligence technology to numerical weather prediction. J Appl Meteor Sci, 2021, 32(1): 1-11. doi: 10.11898/1001-7313.20210101 [34] 佟华, 张玉涛. GRAPES_MESO模式预报降水相态诊断及应用研究. 大气科学学报, 2019, 42(4): 502-512. https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201904003.htmTong H, Zhang Y T. Diagnosis of precipitation types and its application in the GRAPES-MESO forecasts. Trans Atmos Sci, 2019, 42(4): 502-512. https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201904003.htm [35] ECMWF. Part Ⅳ: Physical Processes//ECMWF. IFS Documentation Cy43R1. ECMWF, 2016: 118-120. [36] 薛纪善, 陈德辉. 数值预报系统GRAPES的科学设计与应用. 北京: 科学出版社, 2008.Xue J S, Chen D H. Scientific Design and Application of Numerical Forecasting System GRAPES. Beijing: Science Press, 2008. [37] 黄丽萍, 邓莲堂, 王瑞春, 等. CMA-MESO关键技术集成及应用. 应用气象学报, 2022, 33(6): 641-654. doi: 10.11898/1001-7313.20220601Huang L P, Deng L T, Wang R C, et al. Key technologies of CMA-MESO and application to operational forecast. J Appl Meteor Sci, 2022, 33(6): 641-654. doi: 10.11898/1001-7313.20220601 [38] 黄丽萍, 陈德辉, 邓莲堂, 等. GRAPES_Meso V4.0主要技术改进和预报效果检验. 应用气象学报, 2017, 28(1): 25-37. doi: 10.11898/1001-7313.20170103Huang L P, Chen D H, Deng L T, et al. Main technical improvements of GRAPES_Meso V4.0 and verification. J Appl Meteor Sci, 2017, 28(1): 25-37. doi: 10.11898/1001-7313.20170103 [39] Rutledge S A, Hobbs P V. The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. Ⅷ: A model for the "seeder-feeder" process in warm-frontal rainbands. J Atmos Sci, 1983, 40(5): 1185-1206. [40] Dudhia J. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci, 1989, 46(20): 3077-3107. [41] Hong S Y, Dudhia J, Chen S H. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Wea Rev, 2004, 132(1): 103-120. [42] Mlawer E J, Taubman S J, Brown P D, et al. Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J Geophys Res Atmos, 1997, 102(D14): 16663-16682. [43] 庄照荣, 陈静, 黄丽萍, 等. 全球和区域分析的混合方案对区域预报的影响试验. 气象, 2018, 44(12): 1509-1517. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201812001.htmZhuang Z R, Chen J, Huang L P, et al. Impact experiments for regional forecast using blending method of global and regional analyses. Meteor Mon, 2018, 44(12): 1509-1517. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201812001.htm [44] Chen F, Dudhia J. Coupling an advanced land surface hydrology model with the Penn State-NCAR MM5 modeling system. Part Ⅰ: Model implementation and sensitivity. Mon Wea Rev, 2001, 129: 569-585. [45] Han J, Pan H L. Sensitivity of hurricane intensity forecast to convective momentum transport parameterization. Mon Wea Rev, 2006, 134(2): 664-674. -
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