基于高分辨数值模式预报、数字高程模型和区域自动站逐时气温观测资料，以兼具平原、丘陵、盆地、山地、海岛和海洋等复杂地形的浙江省为研究区域，建立了适用于该地区的综合分析方法，并将该方法与反距离加权插值法、普通克里格插值法和考虑高程的反距离加权插值法进行比较。结果表明：综合分析法能给出最优的格点化温度分析场，考虑高程的反距离加权法其次，最后是反距离加权法和普通克里格法。考虑高程的反距离加权插值法和综合分析法对高海拔无测站区域气温的重建有较为明显的优势。综合分析法获取的温度分析场在山地、海岛和海洋等资料稀少或缺测的复杂地形区域仍旧能保持合理的空间分布状态，对复杂地形资料稀少或缺测地区的插补能力有突出优势。温度资料格点化精度随站点数增加而提高，且当站点数达到一定数值后，精度趋于稳定；综合分析法在站点数达到1000个（站点平均间距约16 km）后就趋于稳定，而其余三种方法在站点数增加到1600个（站点平均间距约13 km）后趋于稳定。
Generating accurate and fine gridded meteorological data becomes one of the final goals in meteorological modernization, which is the key fact in doing researches in meteorology, hydrology, and ecology sciences. How to convert the information from local observations into the whole region is getting more and more attention from scientists in meteorology, hydrology, geography, ecology and so on. In practical application, interpolation method is usually used to achieve the grid data of the problem. To obtain accurate and fine gridded meteorology information, a new interpolation method named Comprehensive Analysis method (IN) is used to reproduce the gridded 2 m temperature dataset based on the high resolution numerical prediction, digital elevation model, and surface observation data over a complex terrain region. This method divides the model prediction error into the error of modeling the weather system and the error of describing the topography, and then uses the observation data to revise these two parts separately. Four steps are used in the revising:Firstly, the numerical model predicting temperature is used as the first guess field in the reanalysis grid system; Secondly, the first guess value and the corresponding error are obtained on the observation site; Thirdly, this error is interpolated to the reanalysis grid system; Finally, the gridded error is added into the first guess field to obtain the final temperature field. The inter-comparison is mode among the Inverse Distance Weighting (IW), Ordinary Kriging (KR), Gradient Inverse Distance Weighting (IG) and IN based on the hourly observed scattered station 2 m temperature data over Zhejiang province and its surround area. The results show that the IN method produces the best result, follows the IG, IW, and KR. The IG and IN have greater abilities on reproduce the temperature over high altitude area than the IW and KR. The IN can produce a normal and reasonable temperature field over the no data area, which shows a stronger ability on no data interpolation. The interpolation accuracy increases with the number of sites increase, and no improvements appear when the number of sites reaches 1000 in IN and 1600 in other three methods.
[1]Clark I.1982.Practical Geostatistics [M].London:Elseivier Applied Science, 129.
[2]Dubois G.1999.Spatial interpolation comparison 97:Foreword and introduction[J].Journal Geographic Information and Decision Analysis, 2(2):1-10.
[3]Haiden T, Kann A, Wittmann C, et al.2011.The integrated nowcasting through comprehensive analysis (INCA) system and its validation over the Eastern Alpine region[J].Wea Forecasting, 26(2):166-183.
[4]Kann A, Pistotnik G, Bica B.2012.INCA-CE:A Central European initiative in nowcasting severe weather and its applications[J].Adv Sci Res, 8(1):67-75.
[5]Luo Z, Wahba G, Johnson D R.1998.Spatial-temporal analysis of temperature using smoothing spline ANOVA [J].J Climate, 11(1):18-28.
[6]Price D T, Mckenny D W, Nalde I A, et al.2000.A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data[J].Agricultural and Forest meteorology, 101(2):81-94.
[7]Robeson S M, Janis M J.1998.Comparison of temporal and unresolved spatial variability in multiyear time-averages of air temperature[J].Climate Research, 10(1):15-26.
[8]Running S W, Nemani R R, Hungerford R D.1987.Extrapolation of synoptic meteorological data in mountainous terrain and its use for simulating forest evapotranspiration and photosynthesis[J].Canadian Journal of Forest Research, 17(6):472-483.
[9]Sadahiro Y.1999.Accuracy of areal interpolation:A comparison of alternative methods[J].Journal of Geographical Systems, 1(4):323-346.
[10]Thornton P E, Running S W, White M A.1997.Generating surfaces of daily meteorological variables over large regions of complex terrain[J].J Hydrol, 190(3):214-251.
[11]Wackemagel H.1995.Multivariate geostatistics:An introduction with applications[M].Berlin:Berlin Heidelberg Springer-Verlag.
[12]Zimmerman D, Pavlik C, Ruggles A, et al.1999.An experimental comparison of ordinary and universal kriging and inverse distance weighting[J].Mathematical Geology, 31(4):375-390.
[13]Feng Jinming, Zhao Tianbao, Zhang Yingjuan.2004.Intercomparison of spatial interpolation based on observed precipitation data[J].Climatic Environ Res, 9(2):261-277.<br/>冯锦明, 赵天保, 张英娟.2004.基于台站降水资料对不同空间内插方法的比较[J].气候与环境研究, 9(2):261-277.
[14]Hou Jingru.1995.Practical geostatistics[M].Beijing:Geological Press, 31-50.<br/>侯景儒.1995.实用地质统计学[M].北京:地质出版社, 31-50.
[15]Kong Yunfeng, Tong Wenwei.2008.Spatial exploration and interpolation of the surface precipitation data[J].Geographical Research, 27(5), 1097-1108.<br/>孔云峰, 仝文伟.2008.降雨量地面观测数据空间探索与插值方法探讨[J].地理研究, 27(5):1097-1108.
[16]Li Xin, Cheng Guodong, Lu Lin.2000.Comparison of spatial interpolation methods [J].Adv Earth Sci, 15(3):260-265.<br/>李新, 程国栋, 卢玲.2000.空间内插方法比较[J].地球科学进展, 15(3):260-265.
[17]Lin Zhonghui, Mo Xingguo, Li Hongxuan, et al.2002.Comparison of three spatial interpolation methods for climate variables in China [J].Acta Geographica Sinica, 57(1):47-56.<br/>林忠辉, 莫兴国, 李宏轩, 等.2002.中国陆地区域气象要素的空间插值[J].地理学报, 57(1):47-56.
[18]Qiu Jinjing, Chen Feng, Dong Meiyin, et al.2014.Evaluation the Performance of the Rapid Refresh Data Assimilation and Weather Forecast System [J].Zhejiang Meteor, 35(4):1-6.<br/>邱金晶, 陈锋, 董美莹, 等.2014.快速更新同化预报系统预报性能的检验与分析[J].浙江气象, 35(4):1-6.
[19]Yang Yin, Zhu Keyun, Zhang Jie, et al.2015.Study on doppler radar data assimilation over complex terrain[J].Plateau Meteor, 34(5):1495-1501.DOI:10.7522/j.issn.1000-0534.2014.00059.<br/>杨银, 朱克云, 张杰, 等.2015.复杂地形下多普勒雷达资料同化的研究[J].高原气象, 34(5):1495-1501.
[20]Zhao Yufei, Zhu Jiang.2015.Assessing quality of grid daily precipitation datasets in China in recent 50 years[J].Plateau Meteor, 34(2):50-58.DOI:10.7522/j.issn.1000-0534.2013.00141.<br/>赵煜飞, 朱江.2015.近50年中国降水格点日值数据集精度及评估[J].高原气象, 34(1):50-58.
