COMPARATIVE EVALUATION OF THE ABILITY OF REANALYSES WITH DIFFERENT RESOLUTION TO PORTRAY TEMPERATURE IN ZHEJIANG PROVINCE
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摘要: 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。Abstract: The present study comprehensively evaluates the ability of three reanalyses, namely R1, R2 and Climate Forecast System Reanalysis (CFSR), to reproduce the temperature characteristics of Zhejiang Province using observed daily temperature data from 66 basic stations over the period 1979—2010. The result shows that the climatological normals calculated from all three reanalyses differ from the observed values, with the spatial characteristics presented by R1 and R2 being more consistent with the observation and the CFSR data deviating significantly from the observation. A systematic cold bias appears persistently throughout 1979—2010 in all the three reanalyses, with the CFSR having the largest cold bias, which mainly originates from the southern area of Zhejiang. The root-mean-square error (RMSE) of all the three reanalyses exhibits seasonal variability. The RMSE in winter (especially in January) is the smallest and the RMSE in summer (especially in July and August) is the largest for all three reanalyses. Besides, the seasonal variability of R1 and R2 is stronger than that of the CFSR. The Empirical Orthogonal Function (EOF) analysis reveals that the spatial pattern and temporal coefficient of the first three modes of the temperature anomaly of the CFSR agree with observation better than those of R1 and R2. Therefore, the CSFR can better capture the variability of temperature in Zhejiang. Correction of the systematic cold bias greatly enhances the credibility of the three reanalyses, especially for the CFSR, indicating that systematic error is a major contributor to the discrepancy between the three reanalyses and the observation. After bias correction, the RMSE and spatial correlation coefficient of the three reanalyses have comparable magnitude to each other respectively. The error resulted from elevation difference between the CFSR grid points and observational stations during the interpolation, as well as the possible high bias of the model terrain of the CFSR in Zhejiang may be important factors in producing relatively large cold bias.
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Key words:
- data error /
- mean air temperature /
- CFSR /
- R1 /
- R2
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图 5 1979—2010年R1（a、d）、R2（b、e）、CFSR（c、f）春季、夏季、秋季、冬季气温RMSE时间序列（a、b、c）（d、e、f）和32年平均1—12月气温RMSE（黑色实线）及其上下限分布（灰色误差线）
图 8 修正系统误差后R1（a）、R2（b）、CFSR（c）1979—2010年32年平均气温RMSE的空间分布；R1、R2、CFSR 1979—2010年逐年气温RMSE时间序列（d）；R1、R2、CFSR 1979—2010年逐年年平均气温与站点观测值之间的SCC时间序列（e）
表 1 浙江省66个国家基本站信息
站号 站名 海拔高度/m 58443 长兴 25.00 58446 安吉 72.20 58448 临安 117.60 58449 富阳 47.90 58450 湖州 4.10 58451 嘉善 2.60 58452 嘉兴 4.80 58453 绍兴 7.90 58454 德清 102.00 58455 海宁 5.30 58456 桐乡 6.00 58457 杭州 43.20 58458 海盐 4.80 58459 萧山 96.50 58464 平湖 4.00 58467 慈溪 5.70 58468 余姚 38.00 58472 嵊泗 79.60 58477 定海 35.70 58537 开化 153.60 58542 桐庐 44.80 58543 淳安 172.20 58544 建德 87.20 58546 浦江 85.20 58547 龙游 66.20 58548 兰溪 48.30 58549 金华 64.70 58550 诸暨 39.10 58553 上虞 6.40 58555 新昌 115.10 58556 嵊州 104.30 58557 义乌 90.00 58558 东阳 91.90 58559 天台 107.60 58562 鄞州 6.20 58563 北仑 5.00 58565 奉化 41.50 58567 宁海 25.00 58569 石浦 129.20 58570 普陀 85.20 58631 常山 137.00 58632 江山 126.30 58633 衢州 82.40 58642 武义 103.80 58643 永康 102.90 58644 遂昌 238.60 58646 丽水 59.70 58647 龙泉 222.10 58652 仙居 83.00 58654 缙云 179.10 58656 乐清 60.80 58657 青田 57.80 58658 永嘉 34.30 58659 温州 28.30 58660 临海 6.60 58664 温岭 35.30 58665 洪家 4.60 58666 大陈岛 86.20 58667 玉环 95.90 58742 云和 159.60 58745 庆元 400.80 58746 泰顺 538.90 58750 文成 105.40 58751 平阳 254.00 58752 瑞安 39.70 58760 洞头 68.60 
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表 2 R1、R2、CFSR与站点观测EOF分解前三模态时间系数之间的相关系数
再分析资料 PC1 PC2 PC3 R1 0.92 0.57 0.43 R2 0.92 0.78 0.49 CFSR 0.93 0.86 0.42
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