EAR70、CLDAS和ERA Interim表层土壤温度在中国地区的评估

本文利用2010—2015年2400多国家气象站逐小时观测数据对覆盖中国的EAR70、CLDAS和ERA Interim 3种表层土壤温度进行了评估和对比。结果表明：空间上CLDAS表层土壤温度精度最高（平均误差为-0.5 ℃，均方根误差为3.0 ℃，相关系数为0.96），受益于CLDAS高精度的陆面初始场，EAR70平均误差得到了改善；时间上ERA Interim再分析表层土壤温度在6:00和夏、秋季精度会明显下降，再分析表层土壤温度在数值较高时段表现出冷偏差，原因是模拟的土壤温度数值上升速度慢，对应的参数化方案有待改进。再分析表层土壤温度在东北地区冬季存在冷偏差，可能和积雪覆盖有关，陆面参数化方案也有待提高。在地形复杂的青藏高原地区，融合地面观测的CLDAS提高了大气驱动的质量进而改进了土壤的模拟。ERA Interim分辨率较粗不适合在青藏高原或者沿海地区使用，结合了CLDAS的EAR70在青藏高原精度提高。土壤表层温度的精度随着高精度的土壤状态初始场进入模式中时间延长会显著下降。因此，CLDAS的实时同化方式，能够有效提高在分析数据的精度。     

一个简单海气耦合模式预报性能评估

对改进初始强迫风场后的Zebiak-Cane海气耦合模式预报性能进行了全面评估。结果表明：1）耦合模式在20世纪90年代预报能力小于80年代；提前0～5个月的耦合模式预报能力小于同期持续预报能力，之后则相反；耦合模式对Nino3区指数预报能力最强。2）在1997／1998年El Nino事件期间，耦合模式对东太平洋SSTA场预报能力大于其对中西太平洋SSTA场的预报能力，且提前0～2个月之后的耦合模式对东太平洋SSTA场预报能力远远大于持续预报。     

基于高分辨率风场的海洋近惯性能通量计算——时空特征及其影响因素

基于高分辨率CFSR(climate forecast system reanalysis)风场资料、气候态海洋混合层厚度资料和卫星高度计海面高度异常资料,本文估计了大气风场向全球海洋混合层的近惯性能通量和近惯性能量输入功率,并探究了混合层厚度、风场时间分辨率、经验衰减系数和中尺度涡旋涡度对近惯性能通量和能量输入功率的影响。浮标实测风场和流速表明,本文所用的风场和阻尼平板模型可用于估计风场向全球海洋的近惯性能通量。本文计算得到的大气向全球海洋输入近惯性能量的功率为0.56TW(1TW=10~(12)W),其中北半球贡献0.22TW,南半球贡献0.34TW。在时间上,风场的近惯性能通量呈现各个半球冬季最强、夏季最弱的特征,这和西风带风场的季节变化有关。在空间上,近惯性能通量的高值海域为南、北半球西风带海洋,尤其是南大洋。混合层厚度和风场空间不均匀性使得西风带近惯性能通量呈现纬向变化,即海盆西部强于海盆东部。风场时间分辨率对近惯性能通量的估计至关重要,低时间分辨率风场对近惯性能通量的低估达到13%—30%。阻尼平板模型中的经验衰减系数对近惯性能通量估计的影响不超过5%。中尺度涡旋涡度仅改变近惯性能通量的空间分布,而对全球近惯性能量输入功率的影响可以忽略。     

Estimation of mean and extreme waves in the East China Seas

Accurately estimating the mean and extreme wave statistics and better understanding their directional and seasonal variations are of great importance in the planning and designing of ocean and coastal engineering works. Due to the lack of long-term wave measurement data, the analysis of extreme waves is often based on the numerical wave hind-casting results. In this study, the wave climate in the East China Seas (including the Bohai Sea, the Yellow Sea and the East China Sea) for the past 35 years (1979–2013) is hind-casted using a third generation wave model – WAMC4 (Cycle 4 version of WAM model). Two sets of reanalysis wind data from NCEP (National Centers for Environmental Prediction, USA) and ECMWF (European Centre for Medium-range Weather Forecasts) are used to drive the wave model to generate the long-term wave climate. The hind-casted waves are then analysed to study the mean and extreme wave statistics in the study area. The results show that the mean wave heights decrease from south to north and from sea to land in general. The extreme wave heights with return periods of 50 and 100 years in the summer and autumn seasons are significantly higher than those in the other two seasons, mainly due to the effect of typhoon events. The mean wave heights in the winter season have the highest values, mainly due to the effect of winter monsoon winds. The comparison of extreme wave statistics from both wind fields with the field measurements at several nearshore wave observation stations shows that the extreme waves generated by the ECMWF winds are better than those generated by the NCEP winds. The comparison also shows the extreme waves in deep waters are better reproduced than those in shallow waters, which is partly attributed to the limitations of the wave model used. The results presented in this paper provide useful insight into the wave climate in the area of the East China Seas, as well as the effect of wind data resolution on the simulation of long-term waves.     

中国近海以及邻近海域海洋再分析产品(CORA)中季节内变化的评估

A regional reanalysis product—China Ocean Reanalysis(CORA)—has been developed for the China's seas and the adjacent areas. In this study, the intraseasonal variabilities(ISVs) in CORA are assessed by comparing with observations and two other reanalysis products(ECCO2 and SODA). CORA shows a better performance in capturing the intraseasonal sea surface temperatures(SSTs) and the intraseasonal sea surface heights(SSHs) than ECCO2 and SODA do, probably due to its high resolution, stronger response to the intraseasonal forcing in the atmosphere(especially the Madden-Julian Oscillation), and more available regional data for assimilation. But at the subsurface, the ISVs in CORA are likely to be weaker than reality, which is probably attributed to rare observational data for assimilation and weak diapycnal eddy diffusivity in the CORA model. According to the comparison results, CORA is a good choice for the study related to variabilities at the surface, but cares have to be taken for the study focusing on the subsurface processes.     

Intercomparison of CRA-Interim Precipitation Products with ERA5 and JRA-55

By using the hourly data from surface meteorological stations in China, the 3-hour precipitation data from CRA-Interim (Chinese Reanalysis-Interim), ERA5 (ECMWF Reanalysis 5) and JRA-55 (Japanese Reanalysis-55) are compared, both on the spatial-temporal distributions and on bias with observation precipitation in China. The results show that: (1) The three sets of reanalysis datasets can all reflect the basic spatial distribution characteristics of annual average precipitation in China. The simulation of topographic forced precipitation in complex terrain by CRA-interim is more detailed, while CRA-interim has larger negative bias in central and East China, and larger positive bias in southwest China. (2) In terms of seasonal precipitation, the three sets of reanalysis datasets overestimate the precipitation in the heavy rainfall zone of spring and summer, especially in southwest China. CRA interim’s location of the rain belt in the First Rainy Season in South China is west by south, the summer precipitation has positive bias in southwest and South China. (3) All of the reanalysis datasets can basically reflect the distribution difference of inter-annual variation of drought and flood, but the overall the CRA-Interim generally shows negative bias, while the ERA5 and JRA-55 exhibit positive bias. (4) For the diurnal variation of precipitation in summer, all the reanalysis datasets perform better in simulating the daytime precipitation than in the night, and bias of CRA-interim is less in southeast and northeast than elsewhere. (5) ERA5 generally performs the best on the evaluation of quantitative precipitation forecast, the JRA-55 is the next, followed by the CRA-Interim. CRA-Interim has higher missing rate and lower threat score for heavy rains; however, at the level of downpour, the CRA-Interim performs slightly better.     

江西省山地风场风速订正方法研究

利用气象站、探空及NASA再分析资料,对江西省4县山地风场的12座测风塔风速进行订正研究。研究结果表明:测风塔与气象站风速数据相关性较低,相关系数一般远小于0.45;测风塔与探空资料的风速相关系数可达到0.6以上,最高可达到0.8;NASA再分析资料可以作为江西山地风场风速订正参证数据,其与测风塔风速数据相关性较高,相关系数可达到0.54～0.77,大多数测风塔相关系数可达0.7左右。海拔高度小于1000 m的测风塔与NASA 50 m风速的相关系数明显高于其与NASA 850 hPa风速的相关系数,高度为1000—1200 m的测风塔与NASA 50 m风速和与NASA 850 hPa风速的相关系数相差不明显,高度大于1200 m的测风塔与NASA 850 hPa风速的相关系数明显大于其与NASA 50 m风速的相关系数。比值法订正效果略好于线性回归法的,订正后的风功率密度总体偏大。     

江淮流域极端降水时空变化特征:站点观测和再分析的对比

利用淮河流域1979—2011年260个站点观测、ERA-Interim和NCEP/DOE再分析资料的日降水量数据,选用8个极端降水指数,从空间分布、发展趋势、时间变化等方面对比分析了我国江淮流域极端降水的变化规律,研究了再分析数据的适用性,结果表明:1)持续湿润指数(CWD)、强降水日数(R10mm,R20mm)以及百分位指数(R95p,R99p)具有一致的北少南多的分布特征,而持续干燥指数(CDD)为北多南少,且强度指数(Rx1day,Rx5day)和百分位指数在浙江沿海均有极大值存在。2)大部分地区的强降水日数呈减少趋势,仅在江淮周边地区有弱上升趋势。3)区域平均的降水强度指数具有上升的趋势变化,逐月变化具有先增长后减少的结构特征,5—6月的增长量最大,峰值出现在7月,在夏末、冬季有较明显的随年代增加的趋势,在秋季则随年代减少。4)再分析资料ERA-Interim和NCEP/DOE对不同指数的再现能力有所不同,ERA-Interim对强降水日数(R10mm)、CDD、百分位指数的空间分布以及CDD的变化趋势再现能力较好,与强度指数和百分位指数年际变化的相关性较高,但对CWD变化趋势分布特点的再现能力较弱;NCEP/DOE更善于再现较强降水日数(R20mm)的空间分布以及强度指数和百分位指数的线性变化趋势。5)两种再分析资料能合理地再现强降水日数(R10mm,R20mm)和CDD年际变化特征和强度指数的季节变化特征。     

用NCEP／NCAR再分析辐射资料估算月平均地表反照率

本文利用１９７９年￣１９９５年１７年平均的ＮＣＥＰ／ＮＣＡＲ（Ｎａｔｉｏｎａｌ Ｃｅｎｔｅｒ ｆｏｒ Ｅｎｖｉｒｏｎｍｅｎ－ｔａｌ Ｐｒｅｄｉｃｔｉｏｎ／Ｎａｔｉｏｎａｌ Ｃｅｎｔｅｒ ｆｏｒ Ａｔｍｏｓｐｈｅｒｉｃ Ｒｅｓｅａｒｃｈ,美国国家环境预报中心／美国国家大气研究中心）再分析辐射资料估算了全球月平均地表反照率．从所得结果的时空分布来看,用ＮＣＥＰ／ＮＣＡＲ辐射资料得到的全球地表反照率基本     

ERA-Interim气温数据在中国区域的适用性评估

运用中国756个观测站点的逐月平均气温数据,对比分析了ERA-Interim再分析资料的误差。结果发现:ERA-Interim再分析资料能够很好地反映观测值的年际变化,相关性达到0.955~0.995。ERA-Interim在580个站点的冷偏差或暖偏差小于1℃,占站点总数的76.7%,可信度较高。64个站点的冷偏差或暖偏差大于5℃,可信度较低。ERA-Interim在东部地区的暖偏差多于西部地区,冷偏差的高值主要集中在西部地区的高海拔站点。海拔低于200 m的站点偏差最小,适用性好,多数海拔3 000 m以上的站点呈现较大冷偏差,适用性较差。通过回归分析发现,观测站点与ERA-Interim格点的高度差是导致误差的主要原因,因此通过高程校正能够有效降低误差,提高ERA-Interim适用性。