Validation of the Cloud_CCI (Cloud Climate Change Initiative) cloud products in the Arctic
Validation of the Cloud_CCI (Cloud Climate Change Initiative) cloud products in the Arctic
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DOI：
10.5194/amt-16-2903-2023
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发表时间：
2023-06
影响因子：
3.8
通讯作者：
K. S. Vinjamuri;M. Vountas;L. Lelli;M. Stengel;M. Shupe;K. Ebell;J. Burrows
中科院分区：
文献类型：
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作者：
K. S. Vinjamuri;M. Vountas;L. Lelli;M. Stengel;M. Shupe;K. Ebell;J. Burrows
抽象的。云在北极辐射预算中的作用还没有得到很好的理解。地面和机载测量为检验和提高我们的理解提供了宝贵的数据。然而，地面测量本质上是稀疏的，而机载观测是时间和空间上的快照。卫星传感器的被动遥感测量提供了很高的空间覆盖率和不断演变的时间序列，可能有几十年的长度。然而，由于雪和冰在紫外线和可见光光谱区域的亮度，以及与地表的亮度温度对比较小，用被动卫星遥感传感器探测北极上空的云是具有挑战性的。因此，需要对由此产生的云数据产品的质量进行定量评估。在这项研究中，我们验证了从极轨NOAA-19卫星的高级甚高分辨率辐射计(AVHRR)子午线(PM)数据中提取的云数据产品，并将它们与阳光充足的月份从地面仪器获得的数据进行了比较。欧洲航天局(欧空局)云气候变化倡议(Cloud_CCI)项目的AVHRR云数据产品使用可见光和红外波段的观测结果来确定云的特性。本次调查选择了四个高纬度站点的地面测量数据：HyytiäLä(61.84∘N，24.29∘E)、阿拉斯加北坡(美国国家安全局；71.32∘N，156.61∘W)、NY奥勒苏德(纽约；78.92∘N，11.93∘E)和顶峰(72.59∘N，38.42∘W)。液态水路径(LWP)地面数据由微波辐射计反演，云顶高度(Cth)由激光雷达综合测量确定。已使用美国国家航天局的数据评估了卫星产品的质量，即云面罩和云光学深度，而在HyytiäLä、美国国家航空航天局、纽约和顶峰对LWP和CTH进行了调查。液态水云的Cloud_CCI COD结果与美国国家航空航天局的辐射计资料比冰云的结果更符合。对于液态水云，Cloud_CCI COD被低估了大约3个光学厚度(OD)单位。当包括冰云时，低估值增加到约5个OD单位。≈7 g m−2高估了云_CCI LWPä，≈16 g m−2高估了NSA，≈24 g m−2高估了NY-ä≤_CCI LWP。峰会期间，CCI LWP高估了≤20 g m−2，而低估了>；20 g m−2。总体而言，CCI LWP反演结果在地面仪器不确定范围内。为了了解多层云对CTH反演的影响，比较了单层云和所有类型(单层+多层)的统计数据。对于Cth反演，Cloud_CCI产品高估了单层云的Cth。当包括多层云(即所有类型)时，观测到的Cth高估变成了大约360-420m的低估。与其他三个站点相比，顶峰站的Cth结果显示出最大的偏差。为了了解卫星和地面数据之间的尺度依赖差异，采用了Bland-Altman方法。该方法不能为所有选定的云参数确定任何与比例相关的差异，但在顶峰站上进行的反演除外。总而言之，所调查的Cloud_CCI云数据产品与在四个高纬度站点进行的地面测量结果吻合得相当好。
Abstract. The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. However, the ground-based measurements are intrinsically sparse, and the airborne observations are snapshots in time and space. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions and because of the small brightness temperature contrast to the surface. Consequently, the quality of the resulting cloud data products needs to be assessed quantitatively. In this study, we validate the cloud data products retrieved from the Advanced Very High Resolution Radiometer (AVHRR) post meridiem (PM) data from the polar-orbiting NOAA-19 satellite and compare them with those derived from the ground-based instruments during the sunlit months. The AVHRR cloud data products by the European Space Agency (ESA) Cloud Climate Change Initiative (Cloud_CCI) project uses the observations in the visible and IR bands to determine cloud properties. The ground-based measurements from four high-latitude sites have been selected for this investigation: Hyytiälä (61.84∘ N, 24.29∘ E), North Slope of Alaska (NSA; 71.32∘ N, 156.61∘ W), Ny-Ålesund (Ny-Å; 78.92∘ N, 11.93∘ E), and Summit (72.59∘ N, 38.42∘ W). The liquid water path (LWP) ground-based data are retrieved from microwave radiometers, while the cloud top height (CTH) has been determined from the integrated lidar–radar measurements. The quality of the satellite products, cloud mask and cloud optical depth (COD), has been assessed using data from NSA, whereas LWP and CTH have been investigated over Hyytiälä, NSA, Ny-Å, and Summit. The Cloud_CCI COD results for liquid water clouds are in better agreement with the NSA radiometer data than those for ice clouds. For liquid water clouds, the Cloud_CCI COD is underestimated roughly by 3 optical depth (OD) units. When ice clouds are included, the underestimation increases to about 5 OD units. The Cloud_CCI LWP is overestimated over Hyytiälä by ≈7 g m−2, over NSA by ≈16 g m−2, and over Ny-Å by ≈24 g m−2. Over Summit, CCI LWP is overestimated for values ≤20 g m−2 and underestimated for values >20 g m−2. Overall the results of the CCI LWP retrievals are within the ground-based instrument uncertainties. To understand the effects of multi-layer clouds on the CTH retrievals, the statistics are compared between the single-layer clouds and all types (single-layer + multi-layer). For CTH retrievals, the Cloud_CCI product overestimates the CTH for single-layer clouds. When the multi-layer clouds are included (i.e., all types), the observed CTH overestimation becomes an underestimation of about 360–420 m. The CTH results over Summit station showed the highest biases compared to the other three sites. To understand the scale-dependent differences between the satellite and ground-based data, the Bland–Altman method is applied. This method does not identify any scale-dependent differences for all the selected cloud parameters except for the retrievals over the Summit station. In summary, the Cloud_CCI cloud data products investigated agree reasonably well with those retrieved from ground-based measurements made at the four high-latitude sites.