一种新的集合预报权重平均方法

提出了一种新的考虑权重的集合预报成员平均方法。使用气候等概率区间来对集合成员进行分组, 并根据气候等概率区间的大小及其中的成员数, 对集合成员的权重进行调整, 得到了一种改进的集合平均预报结果。检验表明, 它可以进一步提高集合平均预报的效果。相对于提高模式分辨率或发展庞大的集合预报系统, 这种方法的效果是显著的。     

2006年6—8月T213与ECMWF模式中期预报性能检验

6—8月是我国高温干旱、暴雨洪涝等气象灾害最为严重的季节。该季节天气系统变化快，活动剧烈，天气预报难度大，最能考验数值预报模式的预报性能。为更好地应用T213模式中期预报产品，对2006年6—8月T213模式中期96小时数值预报产品进行了统计和天气学检验，并与ECMWF模式同类产品进行了对比分析。结果表明，T213和ECMWF模式对中高纬度大型环流的调整、副高及850hPa温度等均有较好的预报能力，对台风路径也都有一定的预报能力，但T213模式台风移速预报过快，登陆偏早，而ECMWF模式预报相反。     

Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning

The evaluation of surface water resources is a necessary input to solving water management problems. Neural network models have been trained to predict monthly runoff for the Tirso basin, located in Sardinia (Italy) at the S. Chiara section. Monthly time series data were available for 69 years and are characterized by non-stationarity and seasonal irregularity, which is typical of a Mediterranean weather regime. This paper investigates the effects of data preprocessing on model performance using continuous and discrete wavelet transforms and data partitioning. The results showed that networks trained with pre-processed data performed better than networks trained on undecomposed, noisy raw signals. In particular, the best results were obtained using the data partitioning technique.     

中部六省城市体系规模序列研究

随着我国"中部崛起"战略的实施和中部地区城市化进程的加快,整合中部地区的城市体系,优化城市体系规模结构有着重要的意义.在对城市体系规模理论综述的基础上,对中部六省城市体系演变进行了分析.认为:中部六省具备了成为一个独立城市体系的基础条件,且内部具有很强的关联性;规模级城市呈首位式分布明显,而市区非农业人口在10万以上中部地区城市组成的体系符合位序规模分布;城市体系规模结构趋向合理,城市人口不断向少数高位次城市集中的趋势明显.根据中部六省城市体系未来发展趋势,采用了灰色预测方法对2004～2010年城市体系规模维数和首位城市规模进行了预测,分析了"十一五"期间中部六省城市体系规模序列演化趋势,进而提出城市体系规模结构的优化设想.     

黄沙坪铅锌矿南部54#成矿预测区铅锌资源前景分析

湖南黄沙坪铅锌矿是我国重要的铅锌原料生产基地,也是湖南省最大的铅锌开采矿山和铅锌原料生产基地.由于矿山长期开采,铅锌资源逐渐减少,近几年矿山已处于资源危机状态,生产组织与计划安排已呈现紧张局面.因此,寻找和发现新的铅锌资源已迫在眉睫.在分析前人资料的同时,通过分析研究区内铅锌矿控矿地质条件、成矿规律及成矿预测靶区等,推断黄沙坪铅锌矿南部54#成矿预测区存在新的与成矿有关的隐伏花岗斑岩或花斑岩类岩体,有望发现新的成矿中心,找到新的铅锌成矿带,并有望取得矿区找矿理论、找矿方向和找矿实际效果上的根本性突破.     

我国岩溶塌陷研究综述

随经济的不断发展,对岩溶区资源的开发日益增强,由此引发的岩溶塌陷问题也日趋频繁、严重,已成为岩溶区可持续发展的一大障碍,有效减轻其造成的危害势在必行。文章根据近年来国内外岩溶塌陷的研究现状及发展,概述了岩溶塌陷的三大形成条件:一定发育程度的岩溶、上覆盖层、岩溶地下水系统;以及中国岩溶塌陷的时空分布规律,分别举例例证了它的几种成因机制,如重力致塌、潜蚀致塌等。由于其突发性及危害性,有必要对岩溶塌陷的预测防治进行深入研究,做到防灾减灾。基于此,总结了中国岩溶塌陷的监测预报和安全评价等方面的新方法和措施等。针对塌陷的形成条件、塌陷现象或其危情的存在状况及其危害性,归纳了有针对性的防治措施。     

Merging multiple precipitation sources for flash flood forecasting

We investigated the effectiveness of combining gauge observations and satellite-derived precipitation on flood forecasting. Two data merging processes were proposed: the first one assumes that the individual precipitation measurement is non-bias, while the second process assumes that each precipitation source is biased and both weighting factor and bias parameters are to be calculated. Best weighting factors as well as the bias parameters were calculated by minimizing the error of hourly runoff prediction over Wu-Tu watershed in Taiwan. To simulate the hydrologic response from various sources of rainfall sequences, in our experiment, a recurrent neural network (RNN) model was used.

The results demonstrate that the merged method used in this study can efficiently combine the information from both rainfall sources to improve the accuracy of flood forecasting during typhoon periods. The contribution of satellite-based rainfall, being represented by the weighting factor, to the merging product, however, is highly related to the effectiveness of ground-based rainfall observation provided gauged. As the number of gauge observations in the basin is increased, the effectiveness of satellite-based observation to the merged rainfall is reduced. This is because the gauge measurements provide sufficient information for flood forecasting; as a result the improvements added on satellite-based rainfall are limited. This study provides a potential advantage for extending satellite-derived precipitation to those watersheds where gauge observations are limited.     

Probabilistic Visibility Forecasting Using Neural Networks

Statistical methods are widely applied in visibility forecasting. In this article, further improvements are explored by extending the standard probabilistic neural network approach. The first approach is to use several models to obtain an averaged output, instead of just selecting the overall best one, while the second approach is to use deterministic neural networks to make input variables for the probabilistic neural network. These approaches are extensively tested at two sites and seen to improve upon the standard approach, although the improvements for one of the sites were not found to be of statistical significance.     

Fog Research: A Review of Past Achievements and Future Perspectives

The scientific community that includes meteorologists, physical scientists, engineers, medical doctors, biologists, and environmentalists has shown interest in a better understanding of fog for years because of its effects on, directly or indirectly, the daily life of human beings. The total economic losses associated with the impact of the presence of fog on aviation, marine and land transportation can be comparable to those of tornadoes or, in some cases, winter storms and hurricanes. The number of articles including the word ``fog' in Journals of American Meteorological Society alone was found to be about 4700, indicating that there is substantial interest in this subject. In spite of this extensive body of work, our ability to accurately forecast/nowcast fog remains limited due to our incomplete understanding of the fog processes over various time and space scales. Fog processes involve droplet microphysics, aerosol chemistry, radiation, turbulence, large/small-scale dynamics, and surface conditions (e.g., partaining to the presence of ice, snow, liquid, plants, and various types of soil). This review paper summarizes past achievements related to the understanding of fog formation, development and decay, and in this respect, the analysis of observations and the development of forecasting models and remote sensing methods are discussed in detail. Finally, future perspectives for fog-related research are highlighted.     

The dependence of UK extreme sea levels and storm surges on the North Atlantic Oscillation

The role of the North Atlantic Oscillation (NAO) in effecting changes in winter extreme high and low waters and storm surges in UK waters has been investigated with the use of a depth-averaged tide+surge numerical model. Spatial patterns of correlation of extreme high and low waters (extreme still water sea levels) with the NAO index are similar to those of median or mean sea level studied previously. Explanations for the similarities, and for differences where they occur, are proposed. Spatial patterns of correlations of extreme high and low and median surge with the NAO index are similar to the corresponding extreme sea-level patterns. Suggestions are made as to which properties of surges (frequency, duration, magnitude) are linked most closely to NAO variability. Several climate models suggest higher (more positive) average values of NAO index during the next 100 years. However, the impact on the UK coastline in terms of increased flood risk should be low (aside from other consequences of climate change such as a global sea-level rise) if the existing relationships between extreme high waters and NAO index are maintained.