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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. [ABSTRACT FROM AUTHOR]

As harsh weather conditions in Antarctica make it difficult to support a dense weather observing network there, it is critical to place new weather stations in locations that are optimal for a given monitoring goal. Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica. We define the optimal location as one that maximizes the reduction in total variance of a given spatial field. Using WRF Model forecast output from the Antarctic Mesoscale Prediction System (AMPS), we identify the best locations for observations across the continent by considering two spatial fields: (i) the daily 0000 UTC 2-m temperature analysis field and (ii) the daily 0000 UTC 2-m air temperature 24-h forecast field. We explore the impact of spatial localization on the results, finding that a covariance length scale of 3000 km is appropriate for these metrics. We find optimal locations assuming that no stations exist on the continent (blank slate) and conditional on existing stations (CD90). In the "blank slate" scenario, the Megadunes region emerges as the most important location to both monitor temperature and reduce temperature forecast errors, with the Ronne Coast and the Siple Coast following. Results for the monitoring and forecasting metrics are similar for the CD90 subset as well, indicating that additional stations could benefit multiple performance goals. Considering the CD90 subset, Wilkes Land–Adelie Coast, Ellsworth Land, and Queen Maud Land–Interior are identified as regions to consider installing new stations for optimizing network performance. [ABSTRACT FROM AUTHOR]