Optimal Network Design Applied to Monitoring and Forecasting Surface Temperature in Antarctica

Abstract

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.

Trends and space–time patterns of near‐surface temperatures on Maxwell Bay, King George Island, Antarctica

“…In addition, several automatic weather stations were installed in the 1980s (Turner et al, 2020). However, the observational coverage of surface meteorological data is inadequate because meteorological stations are concentrated in coastal areas; this data uncertainty hinders true measurements of climate variability and change in Antarctica (Bozkurt et al, 2020; Hakim et al, 2020; Turner et al, 2020).…”

Assessing observation network design predictions for monitoring Antarctic surface temperature

“…An objective method for the optimal siting of stations for monitoring surface air temperature in Antarctica has been proposed and demonstrated by Hakim et al . (2020) (H20). Preferred regions were clearly identified for siting observations, either in designing a theoretical network from scratch or for augmenting existing networks for improved monitoring of continental surface temperatures.…”

“…An ensemble observational network design method has been proposed and applied to the monitoring of surface air temperature by Hakim et al . (2020, H20 hereafter). The method uses the concept of ensemble sensitivity (Ancell and Hakim, 2007; Torn and Hakim, 2008) to determine optimal sensor placement, by using ensembles composed of samples from model simulations (here surface air temperature) and finding the location where an observation reduces the variance (i.e., uncertainty) in a chosen metric the most.…”

Estimating the benefit of Doppler wind lidars for short‐term low‐level wind ensemble forecasts

“…(2019), and Hakim et al . (2020), but extends preceding approaches by incorporating analysis localization while letting the impact of observations on the forecast propagate freely over time. A detailed comparison of the new approach with preceding ones is presented in a companion article by Griewank et al .…”

“…The formulation of the Kalman gain matrix given in Equation ( 13) can process all available observations at once. Therefore, an iterative procedure, such as that used by Hakim et al (2020), is not required.…”