Impact of temperature and humidity variability on cloud cover assessed using aircraft data
Impact of temperature and humidity variability on cloud cover assessed using aircraft data
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使用飞机数据评估温度和湿度变化对云量的影响
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发表时间：
2003
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通讯作者：
A. Tompkins
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作者：
A. Tompkins
对北美大平原上空液态水云中进行的飞机热力学测量进行分析，以评估温度和湿度波动对云层的相对重要性。研究发现，温度变化的影响大约是湿度变化的一半。因此，虽然模型中湿度变化的表示是首要任务，但温度扰动的参数化也是可取的。如果温度和湿度波动相关，那么这个目标会更容易，因为这样就可以进行诊断参数化。对数据的检查表明，大多数事件都表现出负相关性，这与之前的观察结果一致。此外，由于两个原因，在温度和湿度扰动不相关的情况下，云量误差明显更小。首先，去相关可能是温度波动耗散时间更快的结果，这也降低了它们的幅度，从而降低了重要性。其次，结果表明，当温度和湿度之间不存在相关性时，云量误差会显着抵消。假设垂直绝热运动是水平波动的唯一来源，对温度/湿度相关性进行了简单的估计，并且发现在某些情况下它与数据产生了紧密的拟合，大多数情况下当温度波动的幅度较大时，如预测的那样。总之，分析表明，温度波动的简单诊断参数化可能会有所帮助，而且确实是可能的。版权所有© 2003 英国皇家气象学会。
Aircraft thermodynamic measurements taken in liquid‐water clouds over the North American Great Plains are analysed to assess the relative importance of temperature and humidity fluctuations for cloud cover. It is found that the effect of temperature variability is roughly half that of humidity. Thus, although the representation of humidity variability in models is the primary task, the parametrization of temperature perturbations is also desirable. This aim would be easier if temperature and humidity fluctuations were correlated, since a diagnostic parametrization may then be possible. Examination of the data reveals that the majority of events show a negative correlation, in agreement with previous observations. Moreover, the cloud cover error is significantly smaller in the cases where temperature and humidity perturbations are uncorrelated for two reasons. Firstly, the de‐correlation is likely to be the result of a faster dissipation time‐scale for temperature fluctuations, which also reduces their magnitude and thus importance. Secondly, it is shown that there is a significant cancellation of cloud cover errors when no correlation between temperature and humidity exists. A simple estimate of the temperature/humidity correlation is made assuming vertical adiabatic motion is the only source of horizontal fluctuations, and it is found to produce a close fit to the data in some instances, mostly when the magnitude of the temperature fluctuations is larger, as predicted. In summary, the analysis implies that a simple diagnostic parametrization for temperature fluctuations may be helpful and indeed possible. Copyright © 2003 Royal Meteorological Society.