RS92和CFH对青藏高原大气湿度廓线观测比较分析

A comparative analysis of atmospheric humidity profiles observed by RS92 radiosonde and CFH in the Qing-zang plateau

  • 摘要: 为了解青藏高原大气湿度廓线准确测值分布,文中对在西藏拉萨(白天)和林芝(夜间)应用维萨拉 RS92气象探空仪(简称:RS92)和与iMet气象探空仪(简称:iMet)所搭配的霜点湿度计(CFH)在线观测的湿度廓线进行比较分析。结果表明,相比于测量性能最优的RS92气压(p)和气温(T)数据,iMet气压对CFH的水汽体积混合比( \chi _\mathrmV )影响在对流层未超过100 μL/L(约2%)、10 km高度以上平均未超过1%;而iMet因T测值偏高使得拉萨和林芝的(液面)相对湿度(RH)在16—18 km高度分别偏低3.2%和4.1%;iMet的T值偏低又使得CFH在6—8 km云层高度的RH测值偏高约10%且RH过饱和次数增加近50%。以RS92的pT和CFH的露(霜)点温度测值离线计算的大气湿度(RH和 \chi _\mathrmV )廓线为准,在林芝10 km高度以下,RS92经湿度传感器响应时间滞后订正的RH和 \chi _\mathrmV 分别平均偏低0.4±2.8个百分点((0.7±6.3)%)和172±332 μL/L((1.8±5.2)%);在拉萨未经订正RS92的RH和 \chi _\mathrmV 则分别平均偏低4±7.4个百分点((5.3±10.4)%)和539±866 μL/L((2.7±15.6)%);RS92的 \chi _V 值在林芝和拉萨在10—16 km高度分别偏低13±21 μL/L((16±25)%)和19±88 μL/L((5±33)%);16 km高度以上RS92测值出现伪增湿的现象,不能反映真实大气湿度廓线垂直精细结构变化。研究建议,大气湿度廓线观测应选pT测量准确的气象探空仪为平台且应避免受太阳辐射加热的影响;10 km高度以下的RS92湿度数据精确度与CFH测值相当,10—16 km的RS92湿度数据订正或未经订正均显著低于CFH测值而不适于做长期趋势分析。

     

    Abstract: The atmospheric humidity profiles simultaneously observed by the Vaisala RS92 radiosonde (RS92) and the Cryogenic Frost-point Hygrometer (CFH) online operated by the iMet radiosonde (iMet) in Lhasa (daytime) and Nyingchi (nighttime), Xizang autonomous region, are comparatively analyzed for better understanding of accurate vertical distribution of water vapor over the Qing-zang plateau. With highly accurate atmospheric pressure ( p ) and temperature ( T ) measurements, RS92 indicates that CFH online water vapor volume mixing ratio ( \chi _\mathrmV ) is lower than 100 μL/L (about 2%) in the troposphere and generally is less than 1% above 10 km, while the CFH online relative humidity (RH, on the liquid surface) is lower by 3.2% in Lhasa and 4.1% in Nyingchi at the altitudes of 16—18 km. When iMet T is used, CFH online RH is about 10% higher and times of water vapor supersaturation about 50% higher in clouds at 6—8 km height. Using the humidity profiles of RH and \chi _\mathrmV recalculated offline based on RS92 T and CFH dew (frost) point temperature as references, the RS92 humidity data are evaluated. In Nyingchi, the corrected RS92 RH and \chi _\mathrmV with the consideration of the radiosonde humidity sensor time lagging and solar heating effect are respectively 0.4±2.8 percentage point ((0.7±6.3)%) and 172±332 μL/L((1.8±5.2)%) lower than their references below 10 km height; in Lhasa, the uncorrected RS92 RH and \chi _\mathrmV are respectively 4±7.4 percentage point ((−5.3±10.4)%) and 539±866 μL/L ((−2.7±15.6)%) lower, the corrected and uncorrected \mathrm\chi _\mathrmV values in Nyingchi and Lhasa are 13±21 μL/L((16±25)%) and 19±88 μL/L((5±33)%) lower respectively from 10 to 16 km height, while spurious moistening occurs above 16 km. These results indicate that the fine vertical structure of the humidity profile can not be displayed in the corrcted RS92 RH and \chi _\mathrmV data. It is suggested that the atmospheric humidity profile observation should be conducted through the radiosonde platform with high accuracy of p and T measurements and the influence of solar heating effect on the radiosonde sensors should be avoided. RS92 humidity profile below 10 km is sufficiently accurate as that of CFH, however, either the corrected or uncorrected RS92 humidity data from 10 to 16 km are significantly lower and should not be used for long-term trend analysis.

     

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