Synergies and complementarities of CloudSat‐CALIPSO snow observations

Synergies and complementarities of CloudSat‐CALIPSO snow observations
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CloudSat-CALIPSO 雪观测的协同作用和互补性

DOI:
10.1029/2012jd018092
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发表时间:
2013
期刊:
Journal of Geophysical Research: Atmospheres
影响因子:
--
通讯作者:
J. Delanoë
J. Delanoë
中科院分区:
--
文献类型:
--
作者:
Alessandro Battaglia;J. Delanoë

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四年(2007-2010年)共定位的94 GHz CloudSat雷达反射率和532 nm CALIPSO云-气溶胶激光雷达与正交偏振(CALIOP)后向散射系数被用来在全球范围内表征降雪云。CALIOP特别适用于混合和过冷液态水(SLW)层的检测。液态层在降雪云中很常见:总体/海上/陆地上49%/57%/33%的降雪剖面存在SLW或混合相层。我们的研究结果的空间和季节依赖性与降雪云更有可能与夏季期间的混合相有关,与雪层顶部温度有关。SLW发生在大多数(>80%)云顶温度高于250 K的降雪云中,并且在雪层顶部温度约为240 K的时间内存在50%。有一个显着的趋势,这样的层出现在接近雪沉淀层的顶部(75%的时间在500米以内)。这两种仪器可协同用于分析仅含冰相的雪,特别是当CALIOP能够穿透平均超过一半的雪层深度时,用于小雪(Z<0 dBZ,S<0.16 mm/h)。这些结果对于加深我们对冰成核和雪生长过程的理解,改进主动和被动雪遥感技术以及规划降雪任务具有深远的影响。
Four years (2007–2010) of colocated 94 GHz CloudSat radar reflectivities and 532 nm CALIPSO Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) backscattering coefficients are used to globally characterize snow‐precipitating clouds. CALIOP is particularly useful for the detection of mixed and supercooled liquid water (SLW) layers. Liquid layers are common in snow precipitating clouds: overall/over sea/over land 49%/57%/33% of the snowy profiles present SLW or mixed‐phase layers. The spatial and seasonal dependencies of our results—with snowing clouds more likely to be associated with mixed phase during summer periods—are related to snow layer top temperatures. SLW occurs within the majority (>80%) of snow‐precipitating clouds with cloud tops warmer than 250 K, and is present 50% of the time when the snow‐layer top temperature is about 240 K. There is a marked tendency for such layers to occur close to the top of the snow‐precipitating layer (75% of the times within 500 m). Both instruments can be synergetically used for profiling ice‐phase‐only snow, especially for light snow (Z<0 dBZ, S<0.16 mm/h) when CALIOP is capable of penetrating, on average, more than half of the snow layer depth. These results have profound impact for deepening our understanding of ice nucleation and snow growth processes, for improving active and passive snow remote sensing techniques, and for planning snow‐precipitation missions.
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影响因子: 3.1
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