CFSv2对2018年浙江梅雨降水多尺度预测的性能评估

从梅雨预测的业务需求出发,系统开展了CFSv2模式对2018年浙江梅雨期降水预报能力的多时间尺度评估。结果发现3月1日—5月31日的起报结果整体上未能较准确地预测6月浙江大部降水偏少的趋势、仅5月31日的预测结果与实况相符;在延伸期尺度上,CFSv2预测的梅雨期总降水量较实况偏少30%左右;基于相关系数、均方根误差和新定义的综合预报技巧指数等指标分析模式的延伸期预报性能,发现对梅雨期总降水量、逐日区域平均降水量和逐日全省各站降水量的预报技巧有限,对浙江梅雨区的预报水平总体高于浙江全省。评估结果表明CFSv2预报产品表现出显著的系统性干偏差;在延伸期尺度上,随着预报时效的缩短,预报效果并非逐步提升、而是客观存在一个最佳预报时效,各起报日也分别对应着不同的最优预报时段,整体而言梅雨降水的延伸期预测可能对初值并不敏感。     

拓宽方式对软土路基工程特性影响的离心模型试验

如何保持新旧路基间的变形协调是拓宽工程中普遍关注的问题,目前尚缺乏对于拓宽方式对软土路基工程特性影响的直接对比分析。本文开展离心模型试验,采用普通填料或气泡轻质土进行放坡或挡墙拓宽,分析了新旧路基变形、地基土中孔压和土压力在路基拓宽后的变化规律。试验结果表明,气泡轻质土显著减小拓宽过程中产生的孔隙水压力增量和附加应力增量。相较于边坡拓宽,挡墙拓宽方式对地基影响更小。轻质土路堤采用挡墙拓宽方式引起的挡墙倾角和墙背土压力均较小。采用Boussinesq公式计算得到的拓宽路堤引起的地基中附加应力分布与实测值基本吻合,且偏于保守。本文的研究成果对指导路基拓宽工程具有借鉴意义。     

不同分辨率再分析资料对浙江省气温刻画能力的对比评估

利用浙江省66个基本气象站1979—2010年的日平均气温数据,系统评估了三套再分析资料R1、R2和CFSR对浙江省气温的刻画能力。结果表明:三套再分析资料的气候平均态与观测均存在一定差异,其中R1、R2的空间分布型与观测较为接近,CFSR与观测差异较大;三套再分析资料均存在系统性冷偏差且这一偏差在32年中稳定存在,其中CFSR的冷偏差更显著,浙南地区是其冷偏差的重要来源。三套资料的均方根误差均存在季节变化:冬季（特别是1月）误差较小而夏季（特别是7-8月）误差较大,R1和R2的季节差异强于CFSR。CFSR对浙江省气温变率的把握能力优于R1和R2,其距平场EOF分解前三模态的空间型态和时间系数与观测更为接近。系统误差订正后,三套再分析资料的可信度得到显著改善,CFSR的改善效果最明显,说明系统性误差是三套再分析资料偏差的重要来源。改善后三套再分析资料的均方根误差和空间相关系数大体相当。CFSR网格点气温插值到观测站点时因海拔差异导致的误差以及CFSR在浙江省的模式地形偏高可能是其有较大冷偏差的重要原因。      

Evaluation of Precipitation in Multi-Generation Reanalyses with Land Observations over Zhejiang Province

Based on various statistical indices, the abilities of multi-generation reanalyses, namely the NCEP/NCAR Reanalysis 1 (R1), the NCEP-DOE Reanalysis 2 (R2) and the NCEP Climate Forecast System Reanalysis (CFSR), to reproduce the spatiotemporal characteristics of precipitation over Zhejiang Province are comprehensively compared. The mean absolute bias percentages for three reanalyses are 20% (R1), 10% (R2) and 37% (CFSR). R2 (R1) gives the best (worst) general depiction of the spatial characteristics of the observed precipitation climatology, whereas a significant wet bias is noticed in CFSR. All reanalyses reasonably reproduce the interannual variability with the correlation coefficients of 0.72 (R1), 0.72 (R2) and 0.84 (CFSR). All reanalyses well represent the first two modes of the observed precipitation through Empirical Orthogonal Function analysis, with CFSR giving the best capture of the principal components. The root-mean-square error (RMSE) is the largest (smallest) in CFSR (R2). The large RMSE of CFSR in summer (especially in June) contributes mostly to its systematic wet bias. After 2001, the wet-bias of CFSR substantially weakens, probably attributed to increasing observations assimilated in the CFSR. On a monthly basis, the percentage of neutral bias cases are similar for all reanalyses, while the ratio of positive (negative) bias cases for CFSR is distinctly larger (smaller) than that of R1 and R2. The proportions of negative bias cases for R1 and R2 begin to increase after 2001 while keeping stable for CFSR. On a daily basis, all reanalyses give good performances of reproducing light rain; however, the reflection of moderate rain and heavier rain by CFSR is better than R1 and R2. Overall, despite being a third-generation reanalysis product, CFSR does not exhibit comprehensive superiorities over R1 and R2 in all aspects on a regional scale.