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Predicting abundance and variability of ice nucleating particles in precipitation at the high-altitude observatory Jungfraujoch

机译:预测少女峰少女峰降水中冰核颗粒的丰度和变异性

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Nucleation of ice affects the properties of clouds and the formation of precipitation. Quantitative data on how ice nucleating particles (INPs) determine the distribution, occurrence and intensity of precipitation are still scarce. INPs active at ?8?°C (INPssub?8/sub) were observed for 2 years in precipitation samples at the High-Altitude Research Station Jungfraujoch (Switzerland) at 3580?m?a.s.l. Several environmental parameters were scanned for their capability to predict the observed abundance and variability of INPssub?8/sub. Those singularly presenting the best correlations with observed number of INPssub?8/sub (residual fraction of water vapour, wind speed, air temperature, number of particles with diameter larger than 0.5?μm, season, and source region of particles) were implemented as potential predictor variables in statistical multiple linear regression models. These models were calibrated with 84 precipitation samples collected during the first year of observations; their predictive power was successively validated on the set of 15 precipitation samples collected during the second year. The model performing best in calibration and validation explains more than 75?% of the whole variability of INPssub?8/sub in precipitation and indicates that a high abundance of INPssub?8/sub is to be expected whenever high wind speed coincides with air masses having experienced little or no precipitation prior to sampling. Such conditions occur during frontal passages, often accompanied by precipitation. Therefore, the circumstances when INPssub?8/sub could be sufficiently abundant to initiate the ice phase in clouds may frequently coincide with meteorological conditions favourable to the onset of precipitation events.
机译:冰的成核会影响云的性质和降水的形成。关于冰成核颗粒(INPs)如何确定降水的分布,发生和强度的定量数据仍然很少。在海拔3580?m.a.s.l的少女峰高海拔研究站(瑞士)的降水样品中观察到在8°C时有活性的INP(INPs ?8 )持续2年。扫描了几个环境参数,以预测INPs ?8 的丰度和变异性。那些与观察到的INPs ?8 数量(水蒸气的残余分数,风速,空气温度,直径大于0.5?μm的颗粒数量,季节和来源区域)的相关性最好。粒子)在统计多元线性回归模型中作为潜在的预测变量实现。在第一年的观测中,用84个降水样本对这些模型进行了校准。在第二年收集的15个降水样本中,其预测能力得到了连续验证。该模型在校准和验证中表现最佳,解释了降水中INPs ?8 的全部变异的75%以上,表明INPs ?8 的丰度很高。只要高风速与采样前几乎没有或没有降水的气团重合,就可以预期。这种情况发生在额叶通道,通常伴随着降水。因此,INPs ?8 足够丰富以在云中引发冰期的情况可能经常与有利于降水事件发生的气象条件相吻合。

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