应用MM5和WRF模式对2006年和2007年12月中国东部地区边界层气象要素进行了逐日模拟, 利用地面常规观测资料及南京和安庆逐日探空资料对两个模式模拟的地面及边界层内气象要素进行了客观评估与比较。结果表明: MM5和WRF模式模拟的地面温度和相对湿度均较理想, 风速效果略差; 两个模式模拟的温、湿、风效果均是白天优于夜间; 根据观测与模拟的相关性, 对温度的模拟效果东部优于西部, 相对湿度的模拟效果由东南向西北变差, 风速模拟效果平原优于丘陵和山区。总体上, WRF模式对地面温度和湿度的模拟效果均优于MM5模式。以南京、安庆两站为例的边界层内气象要素模拟效果评估结果表明: MM5和WRF模式模拟的150 m以上边界层内温、风、湿廓线均较可信, 150 m以下的效果略差, 20:00比08:00(北京时)模拟效果好; 总体上, WRF模式对温度和湿度的模拟效果较好, 而MM5模式对风速模拟效果稍好; 两个模式均能再现近地层逆温, 都有高估逆温频率的倾向。
MM5 and WRF were run daily for December of 2006 and December of 2007 and the results at ground level and in PBL were assessed and compared by calculating a set of common used statistics measures using the ground-level observations of the China Meteorology Agency routine meteorological network, and the high resolution sounding data at observatories of Nanjing and Anqing. Generally, the simulated ground level temperature and humidity by both MM5 and WRF were reliable, but the simulated wind speed was a little worse. Both models performed better during daytime than during nighttime. In addition, the validation results showed evident regional distribution, e.g., the results changed worse from east to west for temperature, from southeast to northwest for humidity, from plain area to hill and mountain areas for wind speed. According to correlation coefficient (R) and mean absolute error (MAE), WRF performed better than MM5 for temperature and humidity at the ground level. Taking Nanjing and Anqing for examples, the modeled sounding in PBL at both 08:00 and 20:00 were acceptable, except for the wind speed below 150 m in Nanjing. The results at 20:00 were better than those at 08:00, and improved with increasing height for both models. In general, WRF performed better for temperature and humidity, while MM5 performed better for wind speed. Both models could reproduce the near surface temperature inversion, with overestimated the occurring frequency. For the near surface temperature inversion, MM5 outperformed WRF for the frequency, while WRF outperformed MM5 for the thickness and intensity.
[1]吴彬贵, 张宏升, 汪靖, 等. 一次持续性浓雾天气过程的水汽输送及逆温特征分析[J]. 高原气象, 2009, 28(2): 258-267.
[2]刘熙明, 胡非, 邹海波, 等. 北京地区一次典型大雾天气过程的边界层特征分析[J]. 高原气象, 2010, 29(5): 1174-1182.
[3]程麟生, 彭新东. 黑河地区行星边界层平均结构演变的三维数值模拟[J]. 高原气象, 1992, 11(4): 389-399.
[4]李子华, 石春娥, 曹必铭. 重庆市区冬季晴天局地环流结构[J]. 南京气象学院学报, 1994, 17(2): 232-237.
[5]石春娥, 曹必铭, 李子华, 等. 复杂地形上三维局地环流的模拟研究[J]. 南京气象学院学报, 1996, 19(3): 320-328.
[6]安兴琴, 吕世华. 金塔绿洲大气边界层特征的数值模拟研究[J]. 高原气象, 2004, 23(2): 200-208.
[7]安兴琴, 吕世华, 陈玉春. 河西绿洲效应的数值模拟研究[J]. 高原气象, 2004, 23(2): 208-215.
[8]文小航, 吕世华, 孟宪红, 等. WRF模式对金塔绿洲效应的数值模拟[J]. 高原气象, 2010, 29(5): 1163-1173.
[9]Zhou Binbin, Ferrier B S. Asymptotic analysis of equilibrium in radiation fog[J]. J Appl Meteor Climatol, 2008, 47: 1704-1722.
[10]Zhou Binbin, Du Jun. Fog prediction from a multi-model mesoscale ensemble prediction system[J]. Wea Forecasting, 2010, 25: 303-322.
[11]Zhou Binbin, Du Jun, Gultepe I, et al. Forecast of low visibility and fog from NCEP: Current status and efforts[J]. Pure Appl Geophys, 2012, 169(5-6): 895-909.
[12]Burrows W R, Toth G. Automated fog and stratus forecasts from the Canadian RDPS operational NWP model[C]. 24<sup>th</sup> Conference on Weather and Forecasting, AMS, Seattle, Washington, USA, January 23-27, 2011.
[13]Muller M D, Schmutz C, Parlow E. A one-dimensional ensemble forecast and assimilation system for fog prediction[J]. Pure Appl Geophys, 2007, 164: 1241-1264.
[14]Shi C, Wang L, Zhang H, et al. Fog simulations based on multi-model system: A feasibility study[J]. Pure Appl Geophys, 2012, 269(5-6): 941-960.
[15]Kim C K, Yum S S. A numerical study of sea-fog formation over cold sea surface using a one-dimensional turbulence model coupled with the weather research and forecasting model[J]. Bound-Layer Meteor, 2012, 143(3): 481-505.
[16]石春娥, 吴照宪, 张浩, 等. MM5与MM5-PAFOG区域雾预报效果评估比较[J]. 高原气象, 2013, 32(5): 1349-1359, doi: 10.7522/j.issn.1000-0534.2012.00126.
[17]石春娥, 邓学良, 杨元建, 等. 2013年1月安徽持续性霾天气成因分析[J]. 气候与环境研究, 2014, 19(2): 227-236.
[18]程兴宏, 徐祥德, 丁国安, 等. MM5/WRF气象场模拟差异对CMAQ空气质量预报效果的影响[J]. 环境科学研究, 2009, 12: 1411-1419.
[19]周昆, 郝元甲, 姚晨, 等. 6种数值模式在安徽区域天气预报中的检验[J]. 气象科学, 2010, 30 (6): 801-805.
[20]刘振鑫, 刘树华, 胡非, 等. MM5和WRF对北京地区低层大气局地环流模拟能力的对比研究[J]. 中国科学: 地球科学, 2012, 42: 301-312.
[21]李耀孙, 石春娥, 杨军, 等. 东部地区冬季模式边界层探空效果评估[J]. 高原气象, 2012, 31(6): 1690-1703.
[22]陈子通, 闫敬华, 苏耀墀. 模式探空的评估分析及其在强对流天气预报中的应用研究[J]. 大气科学, 2006, 30(2): 235-247.
[23]李佳英, 俞小鼎, 王迎春. 用探空资料检验中尺度数值模式对强对流天气的诊断分析能力[J]. 气象, 2006, 32(7): 13-17.
[24]Otte. The impact of nudging in the meteorological model for retrospective air quality simulations. Part I: evaluation against National Observation Networks[J]. J Appl Meteor Climatol, 2008, 47: 1853-1867.
[25]Lu R, Turco R P, Jacobson M Z. An integrated air pollution modeling system for urban and regional scales: 2. Simulation for SCAQS 1987[J]. J Geophys Res, 1997, 102(5): 6081-6098.
[26]Miao J F, Chen D, Wyser K, et al. Evaluation of MM5 mesoscale model at local scale for air quality applications over the Swedish west coast: Influence of PBL and LSM parameterizations[J]. Meteor Atmos Phys, 2008, 99: 77-103.
[27]杨元建, 石涛, 唐为安, 等. 气象台站环境的卫星遥感调查与评估-以安徽代表气象站为例[J]. 遥感技术与应用, 2011, 26(6): 791-797.
[28]李子华, 刘端阳, 杨军, 等. 南京市冬季雾的物理化学特征[J]. 气象学报, 2011, 69(4): 706-718.