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>Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors
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Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors
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机译:人造卫星主动和被动传感器的落雪检测阈值
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摘要
Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the surface of 0.25 g / cubic m and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Sensitivity analyses were performed to better ascertain the relationships between multifrequency microwave and millimeter-wave sensor observations and the falling snow/underlying field of view. In addition, thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types were studied. Results will be presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz.
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机译:包括雨雪在内的降水是地球能源和水文循环的关键部分。降水在局部影响潜热剖面,而全球环流模式将降水和能量从赤道分布到两极。对于水文循环,在冬季,降雪是北纬地区的主要因素。降雪是积雪堆积的来源,积雪为世界上许多社区提供了淡水资源。此外,降雪会在严重的降雪事件中造成交通中断,从而影响社会。为了收集有关整个全球降水周期的信息,必须收集液态和冷冻降水。尽管正在取得进展,但估计从太空降雪的挑战仍然存在。这些挑战包括相对于陆地表面无源传感器的背景(表面,水蒸气)特征而言,飘落的雪花特征较弱,雪花的球形和非球形形状,其粒径分布(PSD)以及如何假设的未知性。未知因素会影响观测到的亮度温度或雷达反射率,近地表降雪量和总立柱降雪量之间的差异以及要验证的有限的地面真实性。尽管仍然存在这些挑战,但了解它们对预期取回结果的影响是了解落雪取回估计的重要关键。由于来自太空的降雪是来自太空的下一个降水测量挑战,因此必须确定信息以指导这些当前和未来任务的检索算法开发。此信息包括各种传感器通道配置的检测阈值,降雪事件系统特征,雪花粒子假设和表面类型。例如,是否可以检测到湖面降雪系统的云顶低(约2.5 km),冰表面水含量(IWC)为0.25 g /立方米,并检测到枝晶状雪花?如果知道了这些信息,我们就可以将检索工作集中在可检测到的风暴上,并将进展集中在可实现的结果上。在此,重点是确定陆地和湖泊表面各种雪况下落雪的检测阈值。这些结果依赖于模拟的降雪天气研究(WRF)模拟,因为模拟提供了所有信息,可以根据空间和地面真相确定测量结果。进行了敏感性分析,以更好地确定多频微波和毫米波传感器观测值与落雪/下层视场之间的关系。此外,还研究了各种传感器通道配置,雪事件系统特征,雪花粒子假设和表面类型的检测阈值。将针对Ku,Ka和W波段的有源雷达以及10至183 GHz的无源辐射计信道呈现结果。
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