鄂西电线积冰微物理观测研究

对2009年2月25日—3月4日湖北恩施雷达站的一次电线积冰过程,使用自动气象站、能见度仪、雾滴谱仪、雨滴谱仪等实测资料进行深入研究,分析了多种气象要素的演变,发现积冰期比无积冰期平均气温低5.6℃。对积冰期间3种天气状况下雾滴谱和降水粒子谱的特征进行了分析,结果表明:雾天和雨天的雾滴谱为单峰型,雨夹雪天为双峰型;雨天和雨夹雪天的降水粒子谱都为单峰型;雾滴平均数密度从大到小依次是:雨天(223.5 cm-3)、雨夹雪天(181.3 cm-3)、雾天(138.7 cm-3);雨天雨滴平均数密度为335.6 cm-3;雨夹雪天降水粒子平均数密度为1 502.1 cm-3。另外,实测数据表明积冰增长率从大到小依次是:雨夹雪天、雨天、雾天。     

不同陆面方案对沪宁高速公路团雾的模拟

利用沪宁高速公路实时监测的气象数据分析了2007年11月24日发生在沪宁高速公路镇江段团雾过程的气象要素变化。通过WRF模式耦合三种不同陆面方案对此次过程进行了数值模拟,旨在检验WRF模式耦合陆面方案对镇江段团雾的模拟能力。结果表明:(1)WRF模式模拟出的团雾天气过程对陆面方案的选择比较敏感,耦合了不同陆面参数化方案后的试验结果更接近实况。(2)水汽参量模拟结果中,SLAB方案比NOAH方案和RUC方案效果好些,NOAH方案与RUC方案差异不大。(3)在地面感热通量变化率模拟上,三者有些区别;在长波辐射变化率模拟上,NOAH方案较优越。(4)在涡度场高值区模拟上,NOAH方案效果比SLAB、RUC方案更好。     

Analysis of Fog Probability from a Combination of Satellite and Ground Observation Data

The Cloud Type product, developed by the Satellite Application Facility to support to nowcasting and very short-range forecasting (SAFNWC) of EUMETSAT and based on Météosat-8/SEVIRI, identifies cloud categories, and especially low and very low clouds which are first estimates of areas where fog is likely to occur. This cloud type is combined with precipitation information from radar data and with hourly diagnostic analyses of 2-metre relative humidity and 10-metre wind to elaborate an hourly analysis of fog probability. This analysis provides four levels of fog probability with a 3-kilometre horizontal resolution: No risk, low-level risk, medium-level risk and high-level risk. An evaluation of such fog probability analyses versus a one-year set of French hourly SYNOP reports shows encouraging results (potential of detection = 0.73 for low and medium and high-level risks), even if false alarm ratios remain high. Most of the non-detections occur at twilight and are due to satellite non-detections. Eventually, we show case studies that clearly illustrate the high potential of the method.     

A One-dimensional Ensemble Forecast and Assimilation System for Fog Prediction

A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.     

Fog and rain water chemistry at Mt. Fuji: A case study during the September 2002 campaign

Measurements of fog and rain water chemistry at the summit of Mt. Fuji, the highest peak in Japan, as well as at Tarobo, the ESE slope of Mt. Fuji in September 2002. The pH of fog and rain water sampled at Mt. Fuji varied over a range of 4.0–6.8. Acidic fogs (pH < 5.0) were observed at the summit when the air mass came from the industrial regions on the Asian continent. The ratio of [SO₄²⁻]/[NO₃⁻] in the fog water was lower at Tarobo than at the summit. High concentrations of Na⁺ and Cl⁻ were determined in the rain water sampled at the summit, possibly because of the long-range transport of sea-salt particles raised by a typhoon through the middle troposphere. The vertical transport of sea-salt particles would influence the cloud microphysical properties in the middle troposphere. Significant loss of Mg²⁺ was seen in the rain water at the summit. The concentrations of peroxides in the fog and rain water were relatively large (10–105 μM). The potential capacity for SO₂ oxidation seems to be strong from summer to early autumn at Mt. Fuji. The fog water peroxide concentrations displayed diurnal variability. The peroxide concentrations in the nighttime were significantly higher than those in the daytime.     

雾的标准化危险性指数计算方法及其应用

在有雾的天气下经常发生几百辆车连续追尾碰撞的恶性交通事故,因此为了做好交通安全工作有必要开展雾的风险分析研究。在考虑雾的出现频率、持续时间和能见度3个参数的基础上,通过改进雾的危险性指数（FHI）方法,得出了适合我国气象标准的雾的标准化危险性指数(NFHI)的计算方法。为了使危险性指数适应雾的数值预报产品的应用,进一步改进了雾的标准化危险性指数的形式,提出了适合数值预测模式的雾的标准化危险性指数。最后结合京津塘高速公路沿线5个国家气象站36 a的雾的观测数据,利用NFI方法计算得出了4个季节的雾的危险性等级,并使用数值预报结果计算了不同地点的雾的标准化危险性指数。利用该指数计算方法可以通过比较不同路径危险性的大小进行最佳路径的选择,为交通安全提供科学参考。     

汕尾市雾霾天气的能见度多时间尺度特征分析

根据汕尾气象台1961～2003年共43 a的常规气象观测资料,对汕尾市雾霾天气的能见度多时间尺度特征进行了分析.结果表明,汕尾市雾霾天气能见度的时次变化都是在08时次出现最多,在08～14时次逐渐减少,在14时次出现最少;霾天气逐月变化不明显,轻雾和雾主要出现在冬春季节,以春季为多;霾在80年代后出现次数明显增多,而雾则在70年代以后出现次数多,但雾天气年代变化不明显,主要原因可能是城市污染的逐年加重,引起雾霾天气次数明显增多.     

1951—2006年汕头雾变化的气候特征及影响因素分析

利用1951—2006年汕头气候资料,分析大雾天气的气候变化特征。结果表明:汕头年雾日总体呈明显下降趋势。20世纪90年代以前雾日相对偏多,90年代以后雾日明显偏少;大雾的逐月变化呈1峰1谷的特征,峰值出现在3月,谷值出现在8月;下半夜至翌日上午较易出现大雾。起雾时间为04:00—07:00,其中07:00最易起雾,雾消时间为04:00—12:00,09:00—11:00雾最易消散;雾日时静风概率为52%,风速小于等于3 m/s的概率超过95%,不利于近地层空气的水平交换;雾日多伴有逆温层存在且逆温层具有底高较低、厚度较厚、强度较强的特点,不利于近地层空气的上下交换,因而雾日空气质量较差。     

2009年11月江苏南部一次大雾天气诊断分析

利用地面观测和探空资料及NCEP 1^。×1^。再分析资料,分析了2009年11月25-27日江苏省南部大雾的成因。结果表明：逆温层的高度及强度与雾的浓度关系密切,弱冷暖平流有利于产生雾,但是温度平流在近地面一定高度迅速逆转使得温度层结由不稳定转为稳定更利于浓雾产生。边界层在低层辐合上升和高层辐散下沉的界面中形成逆温层,是产生浓雾的重要因素。对大雾天气进行诊断分析,有利于更加准确的对大雾天气进行数值预报,减轻此类灾害性天气的危害。     

北京一次持续性雾霾过程的阶段性特征及影响因子分析

利用北京地区高时间分辨率观测资料对2009年11月3—8日一次持续性雾霾天气过程中的气象因素和气溶胶演变特征进行了分析。结果表明,该次雾霾过程具有明显的阶段性特征,前期以霾为主,中期发展为雾霾交替,后期随着相对湿度减小再次转换为霾并最终消散。边界层逆温是低能见度过程形成的必要条件,但并不最终决定雾霾低能见度强度。相对湿度和PM_2.5浓度是决定能见度大小的两个关键影响因子,对能见度的影响体现出阶段性特征。大部分时段PM_2.5浓度是影响能见度的主要因子,当能见度小于1 km时,能见度变化更多受相对湿度影响。不同的情景计算表明,控制PM_2.5浓度对于改善本次过程的能见度有重要作用。