Potential and Shortcomings of Numerical Weather Prediction Models in Providing Meteorological Data for Urban Air Pollution Forecasting

The last decade progress in numericalweather prediction (NWP) modelling and studies of urbanatmospheric processes for providing meteorological data forurban air pollution forecasting is analysed on examples ofseveral European meteorological centres. Modern nested NWP models are utilising land-use databasesdown to 1 km resolution or finer, and are approaching thenecessary horizontal and vertical resolution suitable forcity scale. The recent scientific developments in the fieldof urban atmospheric physics and the growing availabilityof high-resolution urban surface characteristics datapromise further improvements of the capability of NWPmodels for this aim. A strategy to improve NWP data forthe urban air pollution forecasting is suggested.     

生态环境与洪旱灾害

作者将自然运动与人类活动、生态环境与气候结合起来研究，求索洪旱灾害之谜，着重探讨了其中多种生态环境因素。由大灾后的反思，找出灾害加剧的人为原因是：人中激增、生态危机、水利失修等，提出必须加强防灾减灾的对策研究。     

北京沙尘天气与源地气象条件的关系

本介绍了影响北京地区沙尘天气的沙尘源地,沙尘暴发生的条件和传输路径,分析了沙尘暴源地的气候要素特征及其对北京地区沙尘天气的影响,说明了北京沙尘天气发生和加剧的原因,影响北京地区沙尘天气的境外源地主要位于哈萨克斯坦,俄罗斯以及蒙古国境内,境内源地主要位于内蒙古和新疆,以及甘肃和青海的部分地区,沙尘天气发生必须具备三个条件：沙源,大风,气流辐合（垂直对流）,有沙源不一定起沙,但无沙源一定不起沙,沙尘暴源地的气候特征主要表现为冬季寒冷,夏季炎热,全年降水稀水,影响北京的沙尘传输路径,最主要的有两条,即西路传输和北路传输,北京沙尘天气与沙尘暴源地的春季降水比较结果表明,北京地区沙尘暴和浮尘天气发生次数与沙尘源区春季大气降水量有比较显的负相关关系,北京扬沙天气的发生与沙源区冬春季降水量相关关系不显,说明北京扬沙天气起因与源区降水没有明显的关系,北京扬沙天气主要受本地的自然条件和人为活动的影响。     

大兴安岭林区地下火形成火环境研究

地下火作为森林中一种难以控制的燃烧现象,其形成机理极为复杂。我国大兴安岭林区是森林地下火的多发地区。对该地区2002年发生的地下火研究表明：丰富的近土壤层和地下可燃物是森林地下火发生的物质条件,气象条件促进了森林地下火的发生,特别是在遇到降水少、长期干旱、地面温度增加、相对湿度降低和可燃物干燥的情况下,就很容易引起地下火灾。地下火有地理和时间分布特征。地表火主要发生在原始森林区域,如针叶林、阔叶林或针阔混交林,都有可能发生地下火。地下火一般燃烧速度慢,持续时间长,燃烧充分,具有隐蔽性强、燃烧不连续、方向易变等特点,地下火在所有火灾中对森林危害最大,特别是对落叶松、樟子松、云杉等的破坏更为严重。     

呼和浩特强对流天气的数值模拟分析

利用三维积云数值模式模拟了呼和浩特地区强对流天气的发展过程，并结合实时雷达回波资料进行了对比分析，指出模式输出的部分物理量与理论值、雷达回波或地面天气及降雹（水）实况符合得较好。从对模式输出的几个物理量的统计分析，得出利用三维积云数值模式预测强冰雹过程的概念模型。结果表明，三维积云数值模式能用于模拟对流云特别是强对流云的发展过程，可在人工影响天气业务工作中应用并不断发展。     

SPATIAL VARIATION OF PRECIPITATION ON THE TEXAS HIGH PLAINS1

ABSTRACT: Space autocorrelation techniques have been used to reveal the nature and spatial distribution of precipitation in the Texas High Plains. Correlation in precipitation amounts varies with both distance and direction, dropping off rapidly with distance, particularly during the warm season. The analyses can be used to estimate storm characteristics in conjunction with a wide variety of problems dealing with irrigation, crop yields, drainage and water supply, and evaluation of artificial weather modification efforts.     

EFFECTS OF THE URBAN ENVIRONMENT ON HEAVY RAINFALL DISTRIBUTION1

ABSTRACT: A network of 225 recording raingages was operated over an area of 5200 km² in the St. Louis region during 1971-1975, in conjunction with an extensive investigation of urban effects on precipitation. Study of urban-induced effects on the frequency of heavy rainstorms has revealed a pronounced increase in the occurrence of storms producing 25 mm (1 inch) or more of rain. The increase is greatest in an area that is frequently in the path of storms passing across two urban-industrial regions. Analyses of raincells (rain intensity centers) within heavy convective storms shows a pronounced increase in water yield from cells exposed to potential urban effects, compared with those exposed only to the surrounding rural environment. Naturally-occurring heavy cells tend to undergo the greatest enhancement from urban exposure. Other analyses indicate an above-average frequency of excessive rain rates for periods of five minutes to two hours downwind of the urban-industrial complex. It is concluded that urban-induced intensification of short-duration rainstorms is sufficient to merit inclusion in the design and operation of urban-area hydrologic systems that control the flow of surplus storm water.     

Specific features of seedage in Norway spruce (Picea abies karst.) in the northern Moscow region

Climatic characteristics of the northern Moscow region and the periodicity of seedage in Norway spruce were analyzed retrospectively. The relationship between the intensity of seedage and weather conditions in the period before seedage was revealed.     

Wet weather impact on trihalomethane formation potential in tributaries to drinking water reservoirs

During rain storm events, land surface runoff and resuspension of bottom sediments cause an increase in Trihalomethane (THM) precursors in rivers. These precursors, when chlorinated at water treatment facilities will lead to the formation of THMs and hence impact drinking water resources. In order to evaluate the wet weather impact on the potential formation of THMs, river samples were collected before, during and after three rain storms ranging from 15.2 to 24.9 mm precipitation. The samples were tested for THM formation potential and other indicators including UV254 absorbance, turbidity and volatile suspended solid (VSS). Average levels of THMs increased from 61 μg/l during dry weather to 131 μg/l during wet weather, and then went back to 81 μg/l after rain ended. Wet weather values of THM are well above the maximum contaminant level (MCL) 80 μg/l, set by EPA for drinking water. THM indicators also exhibited similar trends. Average levels increased from 0.6 to 1.8 abs; 2.6 to 6 ntu; and 7.5 to 15 mg/l respectively for UV254, turbidity and VSS. A positive correlation was observed between THM formation and THM indicators. The t-test of significance (p-value) was less than 0.05 for all indicators, and R values ranged from 0.85 to 0.92 between THMs and the indicators, and 0.72 to 0.9 among indicators themselves.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.