摘要
该文详细介绍了空间回归检验方法,并使用2003年我国671站的逐日平均气温、最高气温、最低气温、平均水汽压、平均风速、平均0 cm地温、降水量资料,检验该方法在气象资料质量检验中的适用性。按区号将全国划分为10个区,利用该方法分别对各区7个要素进行了检验试验。结果表明:空间回归检验方法能够有效检验出可疑数据,适用于对单一要素的检验;对降水、风速等空间变化比较大的要素,该方法有比较好的检验效果;应用该方法计算时,在不同地区、不同要素之间存在差异;当固定出错比率时,各区应该选择不同的f值。与一般空间检验方法相同,该方法也与地理环境、周边台站分布有关,并受台站密度的影响。
With the rapid growth of the AWS spatial distribution density, it is more rational to use spatial consistency checks in quality control of meteorological observations of some newly built stations and single element observing stations（i, e. the automatic rainfall stations densely covered all over our country）. For that under these situations traditional historical comparative study and different elements comparing are difficult to do for lacking of data. A new spatial regression checking method used abroad is introduced in detail and applied to spatial checking of some basic surface observing meteorological elements of China for the year of 2003 in order to evaluate the applicability of this approach in China. The method is designed for identification of suspected observing values among neighboring observations. First, some neighboring stations are selected by distance. Second, the root mean square （RMS） errors of the univariate regression equations which are established basing on the examined station observation and neighboring observations are calculated and five reference stations are determined by minimizing root mean square errors. The five reference stations are weighted differentially. Stations with smaller RMS errors get more weighting points. Then, the weighting estimate values and their weighting standard errors of the examined station are computed and used to determine the data range. Data not in this range would be flagged suspected.
The spatial checking tests are conducted on 7 basic meteorological elements including daily mean tempera ture, maximum temperature, minimum temperature, mean vapor pressure, mean wind speed, mean surface temperature and precipitation. The data are obtained from 671 weather stations all over China, and to get more reasonable results the data are divided into 10 districts according to their station designator.
Results show that this method works well in identifying errors of single meteorological element especially to the elements with larger spatial variation such as precipitation and wind speed. It should be noticed that there are some differences when applied this method to different areas and different elements. To get a fixed error rate, the values of f should be selected according to the corresponding districts. Finally, same as other spatial checking methods, geographical environment and distribution of neighboring weather stations should be concerned necessarily as influence factors. The approach performs poorer under the condition of sparse station density.
出处
《应用气象学报》
CSCD
北大核心
2006年第1期37-43,共7页
Journal of Applied Meteorological Science
关键词
空间回归检验方法
气象资料
适用性
spatial regression test
meteorological datasets
applicability
