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利用Langley法并结合直接辐射观测数据,选取符合条件的晴空天气对太阳光度计进行定标[22-23]。图1所示为2015年各个波段18天定标值分布情况(其他年份做相同处理),图中横坐标表示定标日期,纵坐标表示定标值,不同的符号表示不同波段。由图可知,2015年全年晴好天气的仪器定标值具有很大差异。定标值在400~1 020 nm的可见光到近红外光波段变化波动较大,340、380 nm波段变化波动较小,数据比较集中,说明仪器的定标值对可见和近红外波段的影响较大。同样的结论从不同波段定标值的平均值和标准差(图2)也可以看出,在观测的7个波段中,400~1 020 nm波段的标准差明显大于340 nm和380 nm。
图 1 每日各波段定标值分布情况
Figure 1. Distributional characteristics of calibration values of each wavelength and every day
综上所述,若只选用符合条件的某一天作定标,会给反演结果带来较大误差。而目前常用的方法是选用几天晴好天气的平均值作定标,但该方法不能排除粗大误差的影响。
文中在选取晴好天气进行定标的基础上,利用期望平均法与拟合平均法对定标值进行筛选,从而获得较为精确和稳定的仪器定标值。图3为期望平均法与拟合平均法对每个波段(以500、675 nm波段为例,其他波段图略)定标值的筛选、检验,其中虚线所在的范围为拟合平均法得到的最佳定标范围,实线所在的区域为期望平均法的最佳定标范围,红色方形为初始所有定标值的平均值。图4为期望平均法与拟合平均法筛选后得到的定标值平均值与标准差,由图可知,各方法所得的平均值差别不大,但经过数据筛选后每个波段的标准差明显变小,说明定标值的离散程度明显减小,尤其在400~1 020 nm波段。如表1所示,期望平均法使各波段定标值标准差减小了39.5%~86.8%,而期望平均法使标准差减小了68.1%~86.8%,说明再筛选处理对这些波段的定标值具有很大影响。但总体看来,在每个波段期望平均法比拟合平均法的标准差更小,说明期望平均法的筛选效果比拟合平均法更好。因此文中最终选取的仪器各波段定标值为期望平均法上下区间内的平均值,在此基础上获得了西安地区2015~2018年气溶胶光学厚度和波长指数的分布特征。
表 1 期望平均法与拟合平均法的各波段定标值标准差(减小比例)情况
Table 1. Standard deviation and its improved percentage of calibration values derived from the fitting and expectation method at each wavelength
Method 340 nm 380 nm 400 nm 500 nm 675 nm 870 nm 1 020 nm Fitting method 0.016(40.7) 0.026(39.5) 0.129(43.7) 0.195(50.5) 0.167(55.9) 0.073(65.4) 0.026(86.8) Expectation method 0.008(70.3) 0.009(79.1) 0.038(83.4) 0.092(76.6) 0.121(68.1) 0.055(73.9) 0.026(86.8) -
表2为太阳光度计2015~2018年每月的有效观测天数和有效观测样本数。由表可知,9~11月太阳光度计的有效观测天数均少于10天,且有效样本数均小于50,说明9~11月的结果代表性较差,而其他月份的结果具有较好的代表性。经过统计,西安地区气溶胶光学厚度日变化特征(λ = 500 nm),大致分为五种类型:平稳型、上升型、下降型、凹型和凸型(图5)。平稳型表示全天AOD变化较稳定,此种情况大多发生在天气稳定、气溶胶浓度和分布均较稳定的情况,如图6所示,西安市内此种天气出现频率最低(3.55%);凸型出现的频率最高(34.25%),其主要特征为AOD在午间大,早晚小,可能原因是午间前后太阳辐射和人类活动的增加导致AOD增大;凹型出现的频率为27.95%,表现为AOD早晚较大,午间较小,多发生于早晚湿度较高的天气;而上升型和下降型出现的频率相当,分别为16.93%和17.32%。上升型表明AOD在清晨较小随后逐渐升高,一般与污染物的逐渐积累有关。下降型,即AOD呈下降趋势,清晨较高随后逐渐降低,一般发生在清晨存在逆温层和霾层、随着气温升高逆温层和霾层逐渐消散的天气。
表 2 POM-02 每月有效观测天数和观测样本数(2015年2月~2018年3月)
Table 2. Effective days and samples of every month from POM-02 (Feb. 2015 – Mar. 2018)
Month 1 2 3 4 5 6 7 8 9 10 11 12 Effective days 18 57 21 25 41 49 57 18 3 8 4 13 Effective samples 217 1 322 614 839 949 900 1 403 630 44 86 12 166 图 5 气溶胶光学厚度(500 nm)的日变化类型 。(a) 平稳型, (b) 上升型, (c) 下降型, (d) 凹型, (e) 凸型
Figure 5. Diurnal variation types of AOD(500 nm). (a) Flat type, (b) ascending type, (c) descending type, (d) concave type, (e) convex type
图7表示2015~2018年气溶胶光学厚度和波长指数的月、季节均值和频率分布图。由图可知,气溶胶光学厚度随波长的增加而减小。气溶胶光学厚度峰值主要集中在10月到次年3月(11月除外)。500 nm波段AOD高值区主要出现在3、10月和12月,其值分别为0.76±0.29、0.86±0.4和0.75±0.47,低值区出现在4月和9月,其值分别为0.49±0.25和0.35±0.13。图7(b)表示500 nm波段气溶胶光学厚度的季节变化,分别为:0.60±0.36,0.59±0.33,0.62±0.40,0.68±0.36,表明气溶胶光学厚度冬秋季较高,春夏季较低,符合西安地区的季节变化特性。西安地区冬季为集中供暖期,采暖燃煤造成空气中的气溶胶粒子增多。此外,冬季常出现的逆温现象导致污染物不易扩散,从而形成冬季AOD的高值现象。而春季气溶胶光学厚度较大的主要原因是春季为沙尘天气多发期,受地形和盛行风向的影响,沙尘气溶胶的输送使得西安地区气溶胶光学厚度增大。图7(d)和(e)分别表示Ångström波长指数的月变化和季节变化,由图可知,Ångström波长指数在7、9月最高,分别为1.16±0.26和1.15±0.12;4、5月最低,为0.77±0.34和0.79±0.31。各季节的平均值分别为0.81±0.32,1.06±0.33,1.03±0.16,0.88±0.33,表明Ångström波长指数在夏季最高,春季最低,说明西安地区春季以粒径较大的沙尘气溶胶为主导,而夏、秋季以人为源排放和生物质燃烧等共同产生的细粒子为主导。
图 7 气溶胶光学厚度月均值(a)、季节均值(b)、频率(c)和波长指数月均值(d)、季节均值(e)、频率(f)的分布特征
Figure 7. Monthly (a) and seasonal mean values(b) , and frequencies (c) of AOD; Monthly (d) and seasonal mean values(e) , and frequencies (f) of AE
观测期间气溶胶光学厚度与Ångström波长指数的频率分布如图7(c)和(f)所示。西安市AOD值主要集中在0.2~0.8,占样本总体的68.59%。其中AOD出现频率最高区间为0.2~0.4,占总样本的28.21%,次高区间为0.4~0.6,占总样本的21.79%。而AOD在极端清洁值区间0~0.2的出现频率仅为6.09%,在>1.0区间出现的频率为15.38%,说明西安市清洁天气出现概率较低,污染较严重天气出现概率较高。Ångström波长指数主要集中在0.6~1.5之间,占比为81.41%,表明该地区细颗粒气溶胶占主导。波长指数出现频率最高区间为0.9~1.2,占总样本的41.03%,次高区间为1.2~1.5,频率为20.83%。在>1.5区间出现的频率为1.28%,0~0.3出现的概率为4.81%。综合观测资料分析可得,西安地区气溶胶主控粒子平均半径较小,以人为气溶胶为主导。
Study on calibration method of sky radiometer and aerosol optical properties in Xi'an region
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摘要: 利用西安理工大学2015~2018年的太阳光度计观测资料,在传统Langley法定标的基础上,利用期望平均法和拟合平均法获得了更为稳定的仪器定标系数,分析了西安地区气溶胶光学厚度和Ångström波长指数的变化特征。研究结果表明:(1)仅用Langley法对仪器进行定标带来的误差较大,引入期望平均法与拟合平均法后,得到的仪器定标值更合理,有效解决了Langley法定标值波动较大的问题;(2)西安地区气溶胶光学厚度日变化呈现5种特征:平稳型、上升型、下降型、凹型和凸型,其中平缓型出现频率最低(3.55%),凸型出现频率最高(34.25%);(3) 500 nm气溶胶光学厚度季节均值为0.60±0.36,0.59±0.33,0.62±0.40,0.68±0.36,呈春夏低、秋冬高的季节变化趋势。Ångström波长指数季节均值在夏季最大(1.06±0.33),春季最小(0.81±0.32)。
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关键词:
- 太阳光度计定标 /
- 气溶胶光学厚度 /
- Ångström波长指数
Abstract: On the base of Langley method, a more reliable instrument calibration coefficient was obtained after the expected average and fitting average method were used, and the spatiotemporal characteristics of aerosol optical depth(AOD) and Ångström wavelength exponent were analyzed using observations of sky radiometer at Xi'an University of Technology from 2015 to 2018. Results revealed that: (1) Only the Langley method is used to calibrate the instrument. The error is large. After the introduction of the expected average method and the fitted average method, the instrument calibration value obtained is more reasonable, effectively solving the problem of large fluctuations in the legal value of the Langley legal calibration value; (2) the diurnal variation of aerosol optical depth has 5 types: flat type, ascending type, descending type, convex type, and concave type, respectively. The frequency of flat type is lowest, 3.55%, and concave type is highest, 34.25%; (3) the seasonal variation of aerosol optical depth at 550 nm wavelength showed spring and summer are lower, and autumn and winter are higher, with the average values: 0.60±0.36, 0.59±0.33, 0.62±0.40, 0.68±0.36, respectively. Moreover, Ångström wavelength exponent have a highest value in summer (1.06±0.33), and have a lowest value in spring (0.81±0.32).espectively. Moreover, Ångström wavelength exponent have a highest value in summer (1.06±0.33), and have a lowest value in spring (0.81±0.32).al variation of aerosol optical depth at 550 nm wavelength showed spring and summer are lower, and autumn and winter are higher, with the average values: 0.60±0.36, 0.59±0.33, 0.62±0.40, 0.68±0.36, respectively. Moreover, Ångström wavelength exponent have a highest value in summer (1.06±0.33), and have a lowest value in spring (0.81±0.32). -
表 1 期望平均法与拟合平均法的各波段定标值标准差(减小比例)情况
Table 1. Standard deviation and its improved percentage of calibration values derived from the fitting and expectation method at each wavelength
Method 340 nm 380 nm 400 nm 500 nm 675 nm 870 nm 1 020 nm Fitting method 0.016(40.7) 0.026(39.5) 0.129(43.7) 0.195(50.5) 0.167(55.9) 0.073(65.4) 0.026(86.8) Expectation method 0.008(70.3) 0.009(79.1) 0.038(83.4) 0.092(76.6) 0.121(68.1) 0.055(73.9) 0.026(86.8) 表 2 POM-02 每月有效观测天数和观测样本数(2015年2月~2018年3月)
Table 2. Effective days and samples of every month from POM-02 (Feb. 2015 – Mar. 2018)
Month 1 2 3 4 5 6 7 8 9 10 11 12 Effective days 18 57 21 25 41 49 57 18 3 8 4 13 Effective samples 217 1 322 614 839 949 900 1 403 630 44 86 12 166 -
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