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降雪含水比研究进展 总被引：3，自引：2，他引：1
降雪深度预报与定量降水预报(QPF)一样是冬季天气预报最重要的业务之一,而降雪含水比是降雪深度预报中所必须的重要参数,国外一般多将其称为snow-to-liquid ratio(SLR)。本文回顾了过去几十年来国内外在SLR的变化特征、影响因素等方面的主要研究进展,并对其预报技术和方法进行了总结和比较。研究表明:SLR具有明显的时间变化,并且存在季节和空间分布差异;大气温度和相对湿度是影响SLR的两个最重要气象因子,气压、垂直运动等气象因素,以及地表风、积雪自重、太阳辐射和积雪融化也会不同程度地影响SLR;随着预报技术的发展,SLR的预报方法可概括为气候学的、统计学的和基于物理基础的三类预报方法,气候学方法过于简单化,统计学方法的应用提高了SLR的预报能力,但仍无法摆脱统计方法自身的缺陷,比较而言,基于数值模式的瞬时预报更符合未来雪深预报业务的精细化发展趋势;目前,国内降雪深度观测资料较少、观测频率较低,有效开展地面降雪和探空加密观测,解决观测资料不足是今后SLR研究中亟待解决的问题;基于数值天气预报业务模式,探索气象因子对SLR的影响机理,建立适合我国冬季天气预报业务需求的降雪预报系统是未来的发展方向。 相似文献
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冬季降水相态及其转变时间的精细化客观预报对提高气象预报和服务质量具有重要的现实意义。利用京津冀地区国家级自动气象站观测资料及网格化快速更新精细集成产品,统计分析了京津冀地区复杂地形下各类降水相态温度和湿球温度平均气候概率的分布差异及不同降水相态时网格化快速更新精细集成产品中可能影响降水相态判断的特征信息。然后将地面观测天气现象资料、复杂地形下降水相态气候特征及高分辨率模式输出产品作为特征向量,分别基于梯度提升（XGBoost）、支持向量机（SVM）、深度神经网络（DNN）3种机器学习方法建立了降水相态的高分辨率客观分类模型,并对同样条件下3种机器学习方法对雨、雨夹雪和雪3种京津冀主要降水相态的预报效果进行了对比检验,进一步提升了雨夹雪复杂降水相态的客观分类预报技巧。 相似文献
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采用面雨量模糊评分方法，对三峡库区流域面雨量预报中3种客观预报(相似预报、T213降水预报、MM5模式预报)和面雨量综合集成预报结果进行综合评定。检验结果表明，3种预报模式对流域面雨量预报水平相差不大，冬半年的预报评分高于夏半年。在业务中采用动态权重系数法对3种预报方法预报结果进行集成，其集成预报的评定质量高于每种单独预报质量。在流域面雨量预报质量检验中采用了模糊评分法，该方法能够较为客观地反映预报和实况之间的差距，也可以用于降水定量预报评定中。 相似文献
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Weather forecasts by any forecast system are verified using either distributions-oriented or measures-oriented forecast verification measures. Both the forecast verification schemes represent different aspects of the forecast quality, and advantages of them can be utilized to get better insight and to identify particular strengths (deficiencies) in the forecast performance of any forecast system. Keeping this in view, multi-faced verification (binary and continuous) of quantitative precipitation forecasts for consecutive 3 days by a Regional Meso-scale Weather Simulation Model (MM5 Model) has been carried out to get complete insight into its performance. The MM5 model forecasts at 10-km resolution for 792 days of six winters (winter 2003/2004 to winter 2008/2009) are compared with the observational data of six stations in the complex topography of Northwest Himalaya (NWH) in India. The model forecasts are verified using binary categorical forecast verification measures such as Probability of Detection, False Alarm Rate, Miss Rate, Correct Non-occurrence, Critical Success Index and Percent correct, and continuous forecast verification measures such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). BIAS is computed to know over-forecast/under-forecast tendency of a precipitation day (PT day) by the MM5 model. MAE (RMSE) of the MM5 model is computed separately for all days, PT days and no precipitation days (NPT days). MAE (RMSE) of PT days is found to be relatively larger as compared to NPT days and all days. These findings indicate that MAE (RMSE) computed separately for all days, PT days and NPT days provides better insight into the performance of the MM5 model. Results also suggest that the MM5 model shows reasonably good performance for binary forecasts (PT days/NPT days) for day 1 (0–24 h), day 2 (24–48 h) and day 3 (48–72 h). However, large errors are seen in predicting the observed precipitation amount of PT days over NWH. 相似文献
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An exceptionally heavy snowfall in Northeast china: large-scale circulation anomalies and hindcast of the NCAR WRF model 总被引：4，自引：0，他引：4
In Northeast China (NEC), snowfalls usually occur during winter and early spring, from mid-October to late March, and strong snowfalls rarely occur in middle spring. During 12?C13 April 2010, an exceptionally strong snowfall occurred in NEC, with 26.8?mm of accumulated water-equivalent snow over Harbin, the capital of the most eastern province in NEC. In this study, the major features of the snowfall and associated large-scale circulation and the predictability of the snowfall are analyzed using both observations and models. The Siberia High intensified and shifted southeastward from 10?days before the snowfall, resulting in intensifying the low-pressure system over NEC and strengthening the East Asian Trough during 12?C13 April. Therefore, large convergence of water vapor and strong rising motion appeared over eastern NEC, resulting in heavy snowfall. Hindcast experiments were carried out using the NCAR Weather Research and Forecasting (WRF) model in a two-way nesting approach, forced by NCEP Global Forecast System data sets. Many observed features including the large-scale and regional circulation anomalies and snowfall amount can be reproduced reasonably well, suggesting the feasibility of the WRF model in forecasting extreme weather events over NEC. A quantitative analysis also shows that the nested NEC domain simulation is even better than mother domain simulation in simulating the snowfall amount and spatial distribution, and that both simulations are more skillful than the NCEP Global Forecast System output. The forecast result from the nested forecast system is very promising for an operational purpose. 相似文献
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利用张家口市崇礼1960-2014年逐日降水和天气现象资料,分析了雪季与冬奥会赛期(2月4-20日)的降雪特征。结果显示：崇礼最早降雪初日为10月13日,最迟降雪终日为4月30日,初、终雪日多年平均为11月2日和4月6日;雪季长度最长和最短分别为190 d和123 d,多年平均为156 d。雪季间最长连续无降雪时段多出现在12月末到1月下旬。冬奥会赛期前的11月上旬降雪日数少,但降雪量较大;此后各旬降雪日数、雪量差异不大。2月4-20日间平均4~5 d出现一次降雪,且主要为中小雪,出现大雪的概率极低。这些结果为冬奥会赛场充分利用降雪资源、制定赛事计划和赛事期间的气象条件预测及预报等保障提供参考依据。 相似文献
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随着数值预报技术的飞速发展, 模式定量降水预报已成为天气预报业务工作中的主要参考依据。本文对目前在国家气象中心应用的3个业务运行模式T213L31, HLAFS0.25, 华北中尺度模式MM5和德国模式及日本模式的降水预报产品进行了季节空间分布、区域时间序列演变及统计检验, 试图从空间、时间及统计方面对降水预报产品的预报性能进行综合评估。检验结果表明:目前的数值预报模式对短期时效内定量降水预报均具有一定的空间预报能力, 但强降水中心位置有一定的偏差; 从时间序列演变检验来看, 模式对区域强降水过程的发展趋势具有较强的预报能力, 但降水量预报与实况有一定的差距; 从累加统计评分检验结果来看, 模式短期时效的预报性能差别不大, 全球模式在小中雨预报方面有一定优势, 其中日本模式的综合预报性能最好, 大雨以上量级的预报则是国内的模式有一定的优势, 其中华北中尺度MM5模式, T213L31模式各有所长, 但均存在预报量和预报区偏大问题。 相似文献
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根据1995-2005年冬季邯郸市气象台现有的28个月日本数值预报产品资料,对≥5 mm的降水预报日数和全市16个观测站降水实况,按预报评分量级进行分县检验和天气系统分型检验,总结出产生大雪以上降水的天气类型,并确立大雪和暴雪预报指标。 相似文献
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Abstract Most water balance studies in the High Arctic indicate that the weather stations underestimate annual precipitation, but the magnitude of such error is unknown. Based on up to seven years of field measurements, this study provides a comparison of snowfall at weather stations with the winter snow accumulation in their nearby drainage basins. Snowfall is the major form of precipitation in the polar region for nine months every year. Without vegetation, snowdrift is controlled by the local terrain. By establishing the snow characteristics for different terrain types, total basin snow storage can be obtained by areally weighting the snow cover for various terrain units in the basin. Such a method was successfully employed to compute total winter snowfall in the drainage basins near Resolute, Eureka and Mould Bay. Results show that the basins had 130 to 300per cent more snow than the weather stations recorded. Using revised snowfall values that are reinforced by Koerner's snow core measurements from ice‐caps, it is hoped that a more realistic precipitation map can be provided for the High Arctic. 相似文献
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论文以逐日气温和降水量数据、NCEP/NCAR再分析资料以及预报场资料为基础,将表征冬季低温冷害的冷湿指数作为预报量,先利用随机森林方法进行冬季逐日冷湿极端天气定性判别预报分析,再进一步以粒子群算法为基础的模糊神经网络集成个体生成技术方法,建立一种新的非线性智能计算定量集成预报模型（PSO-FNN）,进行了广西冷湿极端天气定量预报模型的预报建模研究。结果表明,论文提出的这种以不同的智能计算方法构建的定性、定量综合预报分析方法,比较符合极端天气小概率事件的预报特点,其中随机森林算法构建的定性预报模型,对广西冷湿极端天气事件的预报TS评分（Threat Score）为0.77,空报率为0.23,漏报率为0,ETS评分（Equitable Threat Score）为0.41,TSS评分（True Skill Statistic）为0.53。而采用粒子群—模糊神经网络方法构建的极端冷湿指数定量集成预报模型比其他线性和非线性预报模型具有更好的预报精度。其中PSO-FNN集成预报模型在预报建模样本和独立预报样本个例相同的情况下,比回归方法的预报平均绝对误差下降了25%以上,比一般的普通模糊神经网络预报平均绝对误差下降了14.37%。主要原因是因为PSO-FNN集成预报模型通过改进集成个体的预报能力和增强集成个体的种群差异性,提高了集成预报模型的预报精度。因此,该智能计算集成预报模型的泛化能力显著提高,预报结果稳定可靠,为冷湿极端天气客观预报提供了新的预报工具和预报建模方法。 相似文献
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Global climate models predict that terrestrial northern high-latitude snow conditions will change substantially over the twenty-first century. Results from a Community Climate System Model simulation of twentieth and twenty-first (SRES A1B scenario) century climate show increased winter snowfall (+10–40%), altered maximum snow depth (?5 ± 6 cm), and a shortened snow-season (?14 ± 7 days in spring, +20 ± 9 days in autumn). By conducting a series of prescribed snow experiments with the Community Land Model, we isolate how trends in snowfall, snow depth, and snow-season length affect soil temperature trends. Increasing snowfall, by countering the snowpack-shallowing influence of warmer winters and shorter snow seasons, is effectively a soil warming agent, accounting for 10–30% of total soil warming at 1 m depth and ~16% of the simulated twenty-first century decline in near-surface permafrost extent. A shortening snow season enhances soil warming due to increased solar absorption whereas a shallowing snowpack mitigates soil warming due to weaker winter insulation from cold atmospheric air. Snowpack deepening has comparatively less impact due to saturation of snow insulative capacity at deeper snow depths. Snow depth and snow-season length trends tend to be positively related, but their effects on soil temperature are opposing. Consequently, on the century timescale the net change in snow state can either amplify or mitigate soil warming. Snow state changes explain less than 25% of total soil temperature change by 2100. However, for the latter half of twentieth century, snow state variations account for as much as 50–100% of total soil temperature variations. 相似文献
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利用1960～2003年新疆阿勒泰地区7测站及塔城地区北部5测站当年11月至次年1月,44a气温、降水资料,研究了北疆北部冬季气候变化特征,解释了2000年该地区冬季特大雪灾极端气候事件出现的必然性,最后探讨了该地区冬季降水的预测问题;并得出一点很有意义的结论:在气候增暖、增湿背景下,特大雪灾也是可以预报的。 相似文献
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吴彦 《沙漠与绿洲气象(新疆气象)》2013,7(5):66-70
利用2002年12月-2006年12月沿天山高速公路因恶劣天气导致交通管制统计记录,结合相应时间段高速公路沿线气象资料,对比分析了高速公路冰雪灾害对交通安全的影响,结果表明：高速公路冰雪灾害的危害程度依次是道路结冰、风吹雪、强降雪。路面结冰主要影响路段在吐乌大高速公路、乌奎高速公路和乌鲁木齐机场高速公路;风吹雪集中出现在达坂城到乌鲁木齐一线;除了吐乌大高速达坂城到吐鲁番段没有因强降雪影响交通的记录外,沿天山其它各段均有发生。归纳了高速公路冰雪灾害气象要素预报指标,建立了预报模型,制定出冰雪灾害的气象指数等级、安全行车预防措施,为交通运输气象服务提供具体可操作的预报产品。 相似文献
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选取青藏高原东部地区1967~2010年61个测站的积雪数据,分析比较了整年和不同季节高原积雪的年代际变化特征及其与降雪和气温的关系,结果表明:除了秋季以外,高原东部积雪表现出“少雪-多雪-少雪“的显著年代际变化特征,80年代末发生的由少到多突变仅在冬季积雪中表现显著,20世纪末发生的由多到少突变在冬春两季积雪中均表现显著;降雪和气温的变化是影响高原东部积雪的重要因素,降雪变化的影响更加显著,尤其是秋季降雪;在冬春季降雪偏多时段,降雪的变化主导着积雪的变化;在冬春季降雪偏少时段,气温变化的影响增大,某些时段会超过降雪,甚至达到主导积雪变化的程度。 相似文献
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利用Micaps常规观测资料、NCEP/NCAR再分析资料、区域站加密观测资料,对遵义市2016年3月8—9日(过程Ⅰ)和2017年2月21—22日(过程Ⅱ)的两次寒潮降雪天气过程进行对比分析。结果表明:(1)500 h Pa横槽转竖和低槽东移是两次寒潮爆发的重要引导系统,过程Ⅰ属于横槽转竖型寒潮天气过程,过程Ⅱ寒潮属小槽发展型。(2)地面冷高压中心强度(冷源)及南下速度是预报寒潮的关键。(3)700 h Pa切变影响与降雪时段对应较好,在今后降雪预报中应作为重要影响系统加以关注。(4)湿层深厚,整层水汽含量高为降雪的显著特征。(5)有无融化层和融化层厚薄应作为降雪预报温度条件的关注重点。 相似文献
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The present study is conducted to verify the short-range forecasts from mesoscale model version5 (MM5)/weather research and forecasting (WRF) model over the Indian region and to examine the impact of assimilation of quick scatterometer (QSCAT) near surface winds, spectral sensor microwave imager (SSM/I) wind speed and total precipitable water (TPW) on the forecasts by these models using their three-dimensional variational (3D-Var) data assimilation scheme for a 1-month period during July 2006. The control (without satellite data assimilation) as well as 3D-Var sensitivity experiments (with assimilating satellite data) using MM5/WRF were made for 48 h starting daily at 0000 UTC July 2006. The control run is analyzed for the intercomparison of MM5/WRF short-range forecasts and is also used as a baseline for assessing the MM5/WRF 3D-Var satellite data sensitivity experiments. As compared to the observation, the MM5 (WRF) control simulations strengthened (weakened) the cross equatorial flow over southern Arabian sea near peninsular India. The forecasts from MM5 and WRF showed a warm and moist bias at lower and upper levels with a cold bias at the middle level, which shows that the convective schemes of these models may be too active during the simulation. The forecast errors in predicted wind, temperature and humidity at different levels are lesser in WRF as compared to MM5, except the temperature prediction at lower level. The rainfall pattern and prediction skill from day 1 and day 2 forecasts by WRF is superior to MM5. The spatial distribution of forecast impact for wind, temperature, and humidity from 1-month assimilation experiments during July 2006 demonstrated that on average, for 24 and 48-h forecasts, the satellite data improved the MM5/WRF initial condition, so that model errors in predicted meteorological fields got reduced. Among the experiments, MM5/WRF wind speed prediction is most benefited from QSCAT surface wind and SSM/I TPW assimilation while temperature and humidity prediction is mostly improved due to latter. The largest improvement in MM5/WRF rainfall prediction is due to the assimilation of SSM/I TPW. The assimilation of SSM/I wind speed alone in MM5/WRF degraded the humidity and rainfall prediction. In summary the assimilation of satellite data showed similar impact on MM5/WRF prediction; largest improvement due to SSM/I TPW and degradation due to SSM/I wind speed. 相似文献