Observation of snowfall with a low-power FM-CW K-band radar (Micro Rain Radar)
Observation of snowfall with a low-power FM-CW K-band radar (Micro Rain Radar)
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使用低功率 FM-CW K 波段雷达（微雨雷达）观测降雪
DOI：
10.1007/s00703-011-0142-z
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发表时间：
2011
影响因子：
2
通讯作者：
C. Simmer
中科院分区：
文献类型：
--
作者：
Kneifel;M. Maahn;G. Peters;C. Simmer
量化降雪强度是一项挑战，特别是在北极条件下，因为风和雪的漂移会恶化从地面测量仪和散射计获得的估计值。使用主动仪器的地面遥感可能是一种解决方案，因为它们可以在漂浮的雪上进行测量，而且不会受到仪器造成的流动扭曲的影响。然而，明显的缺点是，雷达回波依赖于积雪习惯，这可能会导致类似的巨大不确定性。此外，高灵敏度雷达的成本仍然太高，无法在网络和恶劣条件下运行。在本文中，我们比较了低成本、低功耗垂直指向FM-CW雷达(微雨雷达，MRR)和35.5 GHz云雷达(MIRA36)在海拔2650 GHz的阿尔卑斯山站(环境研究站Schnefernerhaus，UFS)6个月的观测期内对干降雪的观测结果。目标是量化MRR相对于云雷达所能实现的目标的潜力和局限性。计算有效反射系数(ZE)或平均多普勒速度(W)等标准雷达变量的业务MRR程序必须针对降雪进行修改，因为MRR最初是为降雨观测而设计的。针对降雪雷达回波弱于可比降雨量的特点，分析了降雪雷达在接近探测门限时的雷达回波特性，提出了一种基于晴空观测的雷达回波噪声量化方法。通过将得到的MRR-Zee值转换为35.5千兆赫当量Zvalues，可以获得低于1dBz的剩余差值，并且接近噪声阈值的值略高。由于MIRA36的高灵敏度，可以观察到MRR从真实信号到噪声的转变，这与独立的晴空噪声估计很好地吻合。两部雷达的平均多普勒速度差都在0.3ms−1以下。最后利用MIRA36的Z值分布估计了利用该雷达反演降雪和积雪的不确定性。在超视场，当比较积雪强度时，MRR漏掉的低降雪率可以忽略不计，这主要是由0.1mmh−1的强度引起的。MRR高估了总积雪量约7%。这一误差比不确定的Ze-降雪率关系造成的误差小得多，后者将对MIRA36的估计产生类似程度的影响。
Quantifying snowfall intensity especially under arctic conditions is a challenge because wind and snow drift deteriorate estimates obtained from both ground-based gauges and disdrometers. Ground-based remote sensing with active instruments might be a solution because they can measure well above drifting snow and do not suffer from flow distortions by the instrument. Clear disadvantages are, however, the dependency of e.g. radar returns on snow habit which might lead to similar large uncertainties. Moreover, high sensitivity radars are still far too costly to operate in a network and under harsh conditions. In this paper we compare returns from a low-cost, low-power vertically pointing FM-CW radar (Micro Rain Radar, MRR) operating at 24.1 GHz with returns from a 35.5 GHz cloud radar (MIRA36) for dry snowfall during a 6-month observation period at an Alpine station (Environmental Research Station Schneefernerhaus, UFS) at 2,650 m height above sea level. The goal was to quantify the potential and limitations of the MRR in relation to what is achievable by a cloud radar. The operational MRR procedures to derive standard radar variables like effective reflectivity factor (Ze) or the mean Doppler velocity (W) had to be modified for snowfall since the MRR was originally designed for rain observations. Since the radar returns from snowfall are weaker than from comparable rainfall, the behavior of the MRR close to its detection threshold has been analyzed and a method is proposed to quantify the noise level of the MRR based on clear sky observations. By converting the resulting MRR-Zeinto 35.5 GHz equivalentZevalues, a remaining difference below 1 dBz with slightly higher values close to the noise threshold could be obtained. Due to the much higher sensitivity of MIRA36, the transition of the MRR from the true signal to noise can be observed, which agrees well with the independent clear sky noise estimate. The mean Doppler velocity differences between both radars are below 0.3 ms−1. The distribution ofZevalues from MIRA36 are finally used to estimate the uncertainty of retrieved snowfall and snow accumulation with the MRR. At UFS low snowfall rates missed by the MRR are negligible when comparing snow accumulation, which were mainly caused by intensities between 0.1 and 0.8 mm h−1. The MRR overestimates the total snow accumulation by about 7%. This error is much smaller than the error caused by uncertainZe–snowfall rate relations, which would affect the MIRA36 estimated to a similar degree.
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地面无源微波辐射计测量中的雪散射信号
DOI：
10.1029/2010jd013856
发表时间：
2010
期刊：
影响因子：
--
作者：
Kneifel;U. Löhnert;A. Battaglia;S. Crewell;D. Siebler
通讯作者：
D. Siebler
湖效应雪中的雪粒径光谱。
DOI：
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发表时间：
1990
期刊：
影响因子：
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R. Braham
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R. Braham
DOI：
10.12968/sece.2008.6.1225
发表时间：
2008-06
期刊：
--
影响因子：
--
作者：
Carrie Dunn
通讯作者：
Carrie Dunn
地面无源微波辐射计测量中的雪散射信号
DOI：
10.1029/2010jd013856/abstract
发表时间：
2010
期刊：
Scopus
影响因子：
--
作者：
S. Kneifel;U. Löhnert;S. Crewell;A. Battaglia;D. Siebler
通讯作者：
D. Siebler
DOI：
--
发表时间：
2008
期刊：
影响因子：
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作者：
E. Brandes;K. Ikeda;G. Thompson;M. Schönhuber
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M. Schönhuber