无参考序列条件下地面气温观测资料城市化偏差订正方法：以北京站为例

本文以北京气象站长期地面气温观测资料为例，发展了一种无早期参考序列条件下城镇站城市化偏差评价和订正方法.首先对北京站逐月平均最高、最低气温资料进行质量控制和均一化处理，并借助卫星遥感亮度温度资料遴选附近乡村站;然后利用最近3年逐时地面气温资料，计算观象台和5个乡村站各月平均地面气温的差值，把其作为北京站1915-2012年期间的城市化累积影响;最后假设城市化累积影响在整个研究时期呈匀速增长，采用线性订正法对地面月平均气温序列中的城市化偏差进行订正.分析表明：资料均一化处理后，北京站近百年最高、最低和平均气温多年平均值有所下降，气温日较差则有所提高，但三种年平均气温序列增温趋势和年平均气温日较差序列下降趋势有所增强.北京站年平均最低气温、平均气温和气温日较差序列中均存在较大的城市化累积影响，其中最低气温和平均气温四个季节均为正值，冬季最大，秋季次之，夏季最小，四季和年平均气温日较差均为较大的负值.订正城市化偏差后，最低气温和平均气温增加速率均明显下降，年平均气温日较差下降趋势则明显变弱.

Correcting urban bias for surface air temperature series of Beijing Station over time period 1915-2012

In this paper, a new method of evaluating and correcting urban bias of surface air temperature is developed in case of no reference climate series, taking Beijing Station as an example. After quality control of the data, the discontinuities resulting from relocations are examined and adjusted using a statistical procedure in combination with the station's history information. The 5 rural stations around the urban station are selected by using the data of remote sensing to determine the relative locations of the Automatic Weather Stations (AWS) in the spatial distribution of surface brightness temperature. Applying surface air temperature data of AWS and Beijing Station for the last 3 years, the accumulated urbanization effect and its contribution are calculated. With an assumption that the accumulated urbanization effect increases at a constant rate, a linear correction method is used to correct the urban bias for the surface air temperature series of Beijing Station over 1915-2012. Analysis of the homogenized data of Beijing Station indicates that the increasing trend in annual mean temperature and the decreasing trend in annual mean diurnal temperature range (DTR) get larger than those of the original data. The accumulated urbanization effects for mean minimum, average temperature and DTR of the homogenized series are all large positive values, in spite of the small negative value for annual mean maximum. We correct the urban bias for the time series of Beijing Station. After that, the increasing rate of the annual mean minimum and average temperature, and the decreasing rate of annual mean DTR, are all brought down significantly.