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Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden

机译:来自CloudSat云剖析雷达和瑞典地面气象雷达网络的降雪估算值的比对

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摘要

Accurate snowfall estimates are important for both weather and climate applications. Ground-based weather radars and space-based satellite sensors are often used as viable alternatives to rain gauges to estimate precipitation in this context. In particular, the Cloud Profiling Radar (CPR) on board CloudSat is proving to be a useful tool to map snowfall globally, in part due to its high sensitivity to light precipitation and its ability to provide near-global vertical structure. CloudSat snowfall estimates play a particularly important role in the high-latitude regions as other ground-based observations become sparse and passive satellite sensors suffer from inherent limitations. In this paper, snowfall estimates from two observing systems-Swerad, the Swedish national weather radar network, and CloudSat - are compared. Swerad offers a well-calibrated data set of precipitation rates with high spatial and temporal resolution, at very high latitudes. The measurements are anchored to rain gauges and provide valuable insights into the usefulness of CloudSat CPR's snowfall estimates in the polar regions. In total, 7 : 2 x 10(5) matchups of CloudSat and Swerad observations from 2008 through 2010 were intercompared, covering all but the summer months (June to September). The intercomparison shows encouraging agreement between the two observing systems despite their different sensitivities and user applications. The best agreement is observed when CloudSat passes close to a Swerad station (46-82 km), where the observational conditions for both systems are comparable. Larger disagreements outside this range suggest that both platforms have difficulty with shallow snow but for different reasons. The correlation between Swerad and CloudSat degrades with increasing distance from the nearest Swerad station, as Swerad's sensitivity decreases as a function of distance. Swerad also tends to overshoot low-level precipitating systems further away from the station, leading to an underestimation of snowfall rate and occasionally to missing precipitation altogether. Several statistical metrics-including the probability of detection, false alarm rate, hit rate, and Pierce's skill score - are calculated. The sensitivity of these metrics to the snowfall rate, as well as to the distance from the nearest radar station, are summarised. This highlights the strengths and the limitations of both observing systems at the lower and upper ends of the snowfall distributions as well as the range of uncertainties that can be expected from these systems in high-latitude regions.
机译:准确的降雪估算对于天气和气候应用都很重要。在这种情况下,地面气象雷达和天基卫星传感器经常被用作雨量计的可行替代方案,以估算降水量。特别是,事实证明,CloudSat上的Cloud Profiling Radar(CPR)被证明是一种用于全球降雪的有用工具,部分原因是它对光降水的高度敏感性以及提供近乎全球的垂直结构的能力。 CloudSat降雪估计值在高纬度地区尤其重要,因为其他地面观测变得稀疏,无源卫星传感器也受到固有限制。在本文中,比较了来自两个观测系统-Swadad,瑞典国家气象雷达网络和CloudSat的降雪估计。 Swerad提供了一个在高纬度地区具有高时空分辨率的高度校准的降水率数据集。这些测量值固定在雨量计上,并提供了宝贵的见解,可了解CloudSat CPR降雪估算在极地地区的有用性。总共比较了从2008年到2010年的CloudSat和Swerad观测值的7:2 x 10(5)配对,涵盖了除夏季月份(6月至9月)以外的所有时间。相互比较表明,尽管两个观测系统的灵敏度和用户应用不同,但令人鼓舞。当CloudSat经过Swerad站(46-82 km)附近时,可以观察到最好的协议,这两个系统的观测条件是可比的。超出此范围的较大分歧表明,两个平台在浅雪条件下均存在困难,但原因不同。 Swerad和CloudSat之间的相关性会随着距最近Swerad站点的距离增加而降低,因为Swerad的灵敏度随距离而变。斯韦拉德还倾向于使远离该站的低空降水系统超调,从而导致降雪率低估,有时甚至会导致降水完全消失。计算了几个统计指标,包括检测概率,错误警报率,命中率和Pierce的技能得分。总结了这些指标对降雪率以及与最近的雷达站的距离的敏感性。这突显了降雪分布上下两端的两个观测系统的优势和局限性,以及这些系统在高纬度地区可以预期的不确定性范围。

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