Assessing observation network design predictions for monitoring Antarctic surface temperature. (10th January 2022)

• Record Type: Journal Article
• Authors: Tardif, Robert; Hakim, Gregory J.; Bumbaco, Karin A.; Lazzara, Matthew A.; Manning, Kevin W.; Mikolajczyk, David E.; Powers, Jordan G.
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 148:Number 743(2022)
• Page Start: 727
• Page End: 746
• Publication Date: 2022-01-10
• Subjects: Antarctica -- data assimilation -- network design -- optimization -- surface observations -- validation; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.4226
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract: 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 : The skill of a method for optimal network design in predicting the impact of observations is tested with observing‐system experiments (OSEs) performed using a cost‐efficient offline data assimilation framework. The method is applied to surface air temperature observations from a network of frequently reporting stations in Antarctica (a), and observation impact is defined based on information content integrated over the continent. We find that predictions of observation impact verify well against the error reduction obtained from assimilating station observations (b), and that optimal networks outperform randomly‐chosen networks (c).