长江山区航道雾的时空分布特征分析

利用长江航道局布设在长江航道沿线重庆段39个人工雾情信号台近3 a的雾情观测资料，详细分析了重庆段航道雾的时空分布特征，并基于以上雾情及整个长江山区航道（宜宾到宜昌）逐里程的经度、纬度、水道宽度、河面弯曲度、河道变化剧烈度、河道支流岔道等6类地理信息因子，采用神经网络方法模拟了长江山区航道雾情综合指数的地域精细化分布。统计结果显示：长江山区航道雾总体上呈现冬多夏少的季节特点，但也有冬少夏多或四季比较平均的情况存在；大多数雾情形成于0～8时，结束于8～12时，其中大雾开始早、结束晚、持续时间长；重庆段航道雾空间分布差异较大，涪陵的蔺市到丰都段、万州的黄花城分布最多，年均大雾30～50次，最少的安坪至夔峡段年均不到5次。模拟结果表明，利用地理信息因子及神经网络法基本可以模拟出重庆段航道雾情分布状况，此方法推广应用，可获取整个长江山区航道雾情的精细化分布，输出结果与航道雾情资料分析及实地考察调研结果在分布趋势上比较接近，但由于试验存在局部误差收敛及因子选择局限性问题，因此模拟结果还不能完全代表航道雾情的实际分布状况，模拟试验还需更多资料及影响因子加入

ANALYSIS OF TEMPORAL AND SPATIAL DISTRIBUTION OF FOG OVER THE YANGTZE RIVER MOUNTAINOUS WATERWAY

In this paper, the three years observational data for fog of 39 fog signal stations laid in the Chongqing section of the Yangtze River mountainous waterway are used to investigate the temporal and spatial distribution of fog over the Yangtze River mountainous waterway. Meantime, a neural network is used to simulate the regional fine distribution of fog composite index over the Yangtze River mountainous waterway according to the whole fog and six kinds of geographic information fators such as longitude, latitude, channel width, river bend, river change intensity, river tributaries fork over the whole Yangtze River mountainous waterway (from Yibin to Yichang). The results show that fog of the Yangtze River mountainous waterway increased in winter and decreased in summer overall. Most fog formed from 0 to 8 am and ended from 8 to 12 am, while heavy fog exhibited forming early, ending late and keeping longer time. There were much difference in spatial distribution of fog over the Chongqing section. About 30-50 heavy fogs were occurred in Lanshi to Fengdu section of Peiling and Huanghua section of Wanzhou every year, while less than 5 from Anping to Kuixia section. The simulation results show that we can simulate the distribution of fog over the Chongqing section on the whole by making use of geographic information factors and the neural network. Besides, we can obtain the fine distribution of fog over the whole Yangtze River mountainous waterway when using the neural network further, and the simulation results keep close to the observational results and the field survey in distribution trend. But the simulation results can not represent the actual distribution of fog over the Yangtze River mountainous waterway completely because of local error convergence and limitations of factor selection, thus the simulation test needs more data and impact factors.〖JP〗〖HJ1〗〖HJ〗〖HQ〗