天气发生器MulGETS和k-NN对区域历史气象场特征重现能力的比较  

Reproducibility Evaluation of Multi-Site Stochastic Weather Generators: a Comparison between a Typical Parametric Model and a Non-Parametric Model

作　　者：周凌峰 孟耀斌 逯超 伍甘霖 张东妮 宋昊政 吴丹 ZHOU Ling-feng;MENG Yao-bin;LU Chao;WU Gan-lin;ZHANG Dong-ni;SONG Hao-zheng;WU Dan(Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education/Academy of Disaster Reduction and Emergency Management/Ministry of Emergency Management & Ministry of Education/Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;Hunan Liuyang Hydrology and Water Resources Bureau, Liuyang 410300)

机构地区：[1]北京师范大学环境演变与自然灾害教育部重点实验室/北京师范大学应急管理部/教育部减灾与应急管理研究院/北京师范大学地理科学学部,北京100875 [2]湖南浏阳市水文水资源局,浏阳410300

出　　处：《中国农业气象》2019年第6期341-349,共9页Chinese Journal of Agrometeorology

基　　金：北京师范大学地表过程与资源生态国家重点实验室方向性项目(2017-FX-07)

摘　　要：传统单站点天气发生器未考虑不同站点气象变量间的空间相关性,导致其在区域影响评价中的应用受到限制,而多站点天气发生器可以克服单站点天气发生器的缺点,近年来得到迅速发展。评估和验证多站点天气发生器对区域历史气象场特征的重现能力是开展影响评价的前提和基础。为此,本研究选取MulGETS(参数型)和k-NN(非参数型)发生器为代表模型,利用湘江流域12个气象站点1981-2010年日序列降水量、最高气温、最低气温资料,通过均值、标准差、偏度、极值、空间相关系数、空间连接度和自相关系数等指标的对比,评估了MulGETS和k-NN模型的优缺点及适用性。结果表明:MulGETS和k-NN模型均较好地再现了原气象场的均值、标准差和偏度,k-NN表现稍好于MulGETS。同时k-NN相比MulGETS在保持气象要素空间相关性上具有优势,特别是降水量的空间间歇性。由于算法本身的限制,k-NN无法模拟出超出历史数据范围的极值,而MulGETS具备一定的极值模拟能力。此外,MulGETS和k-NN在重现原始日尺度降水量的自相关性上均存在不足。总体来看,两个模型各具优势和不足,MulGETS更适于极端气象事件模拟,而k-NN可以更好地体现原始气象场的空间差异,实际使用时应根据不同的研究目的选择合适的模型。Many impact models(e.g., hydrological and agricultural models) require simulations of weather variables reflecting the spatial and temporal dependence of observed meteorological fields. New techniques are recently available to generate weather variables simultaneously at multiple locations. This paper presents a comparison of two types of multi-site stochastic weather generators(MulGETS model and k-NN model) for simulation of precipitation and temperature at a network of 12 stations in Xiang River Basin, China. These two models were evaluated for their ability to reproduce the statistical features of the historical meteorological field. The results showed that both MulGETS and k-NN model were successful in reproducing the mean, standard deviation, and skewness of the weather variables, while the performance of k-NN was generally superior to that of MulGETS. The k-NN model was found to perform satisfactorily in preserving the spatial structure of the weather variable, especially the spatial intermittence. Only MulGETS model could generate extreme values out of the historical range. New technology is needed because both MulGETS and k-NN model have the limitation in representing temporal dependence of weather sequence, especially the autocorrelation of daily precipitation.

关 键 词：多站点 天气发生器 模型比较 湘江流域 

分 类 号：P4[天文地球—大气科学及气象学]