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The relationship between polar mesospheric clouds and their background atmosphere as observed by Odin-SMR and Odin-OSIRIS

机译:Odin-SMR和Odin-OSIRIS观测到的极地中层云与背景大气之间的关系

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摘要

In this study the properties of polar mesospheric clouds (PMCs)and the background atmosphere in which they exist are studied usingmeasurements from two instruments, OSIRIS and SMR, on board the Odinsatellite. The data comes from a set of tomographic measurements conducted bythe satellite during 2010 and 2011. The expected ice mass density and cloudfrequency for conditions of thermodynamic equilibrium, calculated using thetemperature and water vapour as measured by SMR, are compared to the ice massdensity and cloud frequency as measured by OSIRIS. We find that assumingthermodynamic equilibrium reproduces the seasonal, latitudinal and verticalvariations in ice mass density and cloud frequency, but with a high bias of afactor of 2 in ice mass density.To investigate this bias, we use a simple ice particle growth model toestimate the time it would take for the observed clouds to sublimatecompletely and the time it takes for these clouds to reform. We find adifference in the median sublimation time (1.8 h) and the reformation time(3.2 h) at peak cloud altitudes (82–84 km). This difference implies thattemperature variations on these timescales have a tendency to reduce the icecontent of the clouds, possibly explaining the high bias of the equilibriummodel.Finally, we detect and are, for the first time, able to positively identifycloud features with horizontal scales of 100 to 300 km extending far belowthe region of supersaturation (  2 km). Using the growth model, we concludethese features cannot be explained by sedimentation alone and suggest thatthese events may be an indication of strong vertical transport.
机译:在这项研究中,使用Odinsatellite卫星上的两种仪器OSIRIS和SMR的测量结果,研究了极地中层云(PMC)的特性及其所处的背景大气。数据来自卫星在2010年和2011年进行的一系列层析成像测量。将热力学平衡条件下的预期冰块密度和云层频率(使用SMR测量的温度和水蒸气计算)与冰块密度和云层频率进行比较由OSIRIS测量。我们发现,假设热力学平衡再现了冰块密度和云层频率的季节性,纬度和垂直变化,但是冰块密度的偏差为2的高偏差。要研究这种偏差,我们使用简单的冰粒生长模型来估算时间观测到的云需要完全升华,而这些云需要花费的时间才能恢复。我们发现在峰值云高度(82-84 km)处的中值升华时间(1.8 h)和重现时间(3.2 h)存在差异。这种差异意味着在这些时间尺度上的温度变化具有减少云层冰含量的趋势,这可能解释了平衡模型的高偏差。最后,我们首次检测到并能够肯定地识别水平尺度为100的云层特征延伸至远低于过饱和区域(> 2 km)的300 km。使用生长模型,我们得出结论,这些特征不能仅靠沉积来解释,并暗示这些事件可能是强烈的垂直运移的指示。

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