长江中下游地区暖区暴雨特征分析

利用2007到2013年5-9月间常规和非常规资料以及6 h一次的NCEP 1°×1°再分析资料,将长江中下游地区暖区暴雨按天气形势划分为冷锋前暖区暴雨、暖切变暖区暴雨以及副热带高压边缘暖区暴雨三种类型。统计表明暖区暴雨一般发生在距离切变线(锋线)100~300 km的暖区内。主要结论包括:(1)冷锋型降水强度偏弱且分布均匀,集中在5、6月;暖切变型发生次数最多且强度大,主要发生在6、7月长江中下游地区的偏南部;副热带高压边缘型发生次数最少但强度较大,发生在7、8月。暖区暴雨的发生次数及强度在大别山、皖南山区较为集中。(2)暖区暴雨中短时强降水贡献大。(3)冷锋背景下的暖区暴雨一般产生在锋前低压槽中,暴雨落区与高低空急流耦合有紧密联系;暖切变型以低层暖切变线为主要天气背景,地面常有弱静止锋,暖区对流活动与中尺度急流结构、地形强迫等因素存在较高的相关性;副热带高压边缘暖区暴雨与局地的水汽积累和对流不稳定条件的发展有密切关系。据此建立三类暖区暴雨的概念模型。     

四川烤烟主产区冰雹灾害风险评估

以四川烤烟主产区冰雹灾害为研究对象,将烤烟气候适应性特征和自然灾害系统论相结合,利用冰雹频率、DEM高程数据、下垫面类型以及农业经济数据等资料,构建冰雹灾害风险评估指标体系,从致灾因子危险性、孕灾环境敏感性、承灾体易损性和抗灾能力4个方面进行分析,最终建立四川烤烟主产区冰雹灾害精细化风险评估模型并计算风险分布指数,借此将攀西烟区划分为由高至低的5个风险区域和1个不适宜种植区。结果表明,安宁河源头河谷地区、雅砻江下游流域以及攀西地区南部的河谷低山区和丘陵区冰雹灾害风险处于较高水平。评估结果为区域烤烟种植结构调整及冰雹灾害防御工作提供了科学指导。     

Comparison of prediction performance using statistical postprocessing methods

As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.