Assessing observation network design predictions for monitoring Antarctic surface temperature
Assessing observation network design predictions for monitoring Antarctic surface temperature
复制标题
DOI：
10.1002/qj.4226
复制
发表时间：
2021-12
影响因子：
8.9
通讯作者：
R. Tardif;G. Hakim;K. Bumbaco;M. Lazzara;Kevin W. Manning;David E. Mikolajczyk;Jordan G. Powers
中科院分区：
文献类型：
--
作者：
R. Tardif;G. Hakim;K. Bumbaco;M. Lazzara;Kevin W. Manning;David E. Mikolajczyk;Jordan G. Powers
观测网络最好能为气候和天气预报应用提供足够的监测参数采样。这对任何网络来说都是一个挑战，但在南极大陆的恶劣环境中尤其困难。我们评估了一个网络设计方法，提供客观的信息站选址的最佳采样变量，这里采取的是地面空气温度。该方法使用集合灵敏度的概念来预测位置，减少最总的集合方差，即不确定性，整个大陆。该方法被应用到一个网络的频繁报告站，并使用同化站观测结果进行验证。一个具有成本效益的“离线”数据同化框架用于允许对大样本实验进行测试，包括大量随机选择的网络作为零假设。网络设计预测同意以及观测到的误差减少同化。东南极高原的台站在监测地面气温方面的重要作用在网络设计和数据同化结果中显而易见，其次是西南极洲和罗斯冰架区域的台站。南极沿海和半岛台站提供的信息量最小。验证结果也是强大的协方差本地化，集成方法的一个重要因素。最优网络在所有情况下都优于随机选择的网络，最高可达近50%，具体取决于网络的大小和协方差定位距离。
Networks of observations ideally provide adequate sampling of parameters to be monitored for climate and weather forecasting applications. This is a challenge for any network, but is particularly difficult in the harsh environment of the Antarctic continent. We evaluate a network design method providing objective information on station siting for optimal sampling of a variable, here taken to be surface air temperature. The method uses the concept of ensemble sensitivity to predict locations reducing the most total ensemble variance, that is, uncertainty, across the continent. The method is applied to a network of frequently‐reporting stations, and validation is performed using results from assimilating station observations. A cost‐efficient “offline” data assimilation framework is used to allow testing over a large sample of experiments, including a large number of randomly chosen networks that serve as a null hypothesis. Network design predictions agree well with observed error reductions from assimilation. The important role of stations on the East Antarctic Plateau in monitoring surface air temperature is evident in network design and data assimilation results, followed by stations in West Antarctica and the Ross Ice Shelf region. Antarctic coastal and Peninsula stations are found to provide the smallest information content integrated over the continent. Validation results are also robust to covariance localization, an essential factor for ensemble methods. Optimal networks outperform randomly chosen‐networks in all cases, by up to nearly 50%, depending on the size of the network and the covariance localization distance.