Evaluating the Antarctic Observational Network with the Antarctic Mesoscale Prediction System (AMPS)

Abstract

Station siting for environmental observing networks is usually made subjectively, which suggests that the monitoring goals for the network may not be met optimally or cost effectively. In Antarctica, where harsh weather conditions make it difficult to install and maintain stations, practical considerations have largely guided the development of the staffed and automated weather station network. The current network coverage in Antarctica is evaluated as a precursor to optimal network design. The Antarctic Mesoscale Prediction System (AMPS) 0000 UTC analysis is used for 4 years (2008-12) with 15-km horizontal grid spacing, and results show that AMPS reproduces the daily correlations in surface temperature and pressure observed between weather stations across the continent. Temperature correlation length scales are greater in East Antarctica than in West Antarctica (including the Antarctic Peninsula), implying that more stations per unit area are needed to sample weather in West Antarctica compared to East Antarctica. There is variability in the temperature correlation length scales within these regions, emphasizing the need for objective studies such as this one for determining the impact of current and new stations. Further analysis shows that large regions are not well sampled by the current network, particularly on daily time scales. Observations are particularly limited in West Antarctica. Combined with the shorter temperature correlation length scales, this implies that West Antarctica is a compelling location for implementing an objective, optimal network design approach.

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

“…Furthermore, most relevant to the applicability of network design, the pointwise statistics of the AMPS output and observations have been shown to be comparable. For example, as a precursor to this study, Bumbaco et al (2014) found good agreement for point correlations between station observations and AMPS output (both the analyses and forecasts) for 2-m temperature and surface pressure, lending confidence that AMPS output can be used for optimal network design. Here, we use 2-m air temperature field output from the 15-km AMPS grid over the period 31 September 2008-1 October 2012.…”

“…While the network is important for furthering these goals, both existing and potential new stations may also be valuable for the analysis of specific phenomena, forecasting for a specific area of operations, or various other monitoring purposes. We consider the locations of weather stations that are assimilated into WRF as defined in Bumbaco et al (2014). Namely, we use the locations of the weather stations that reported temperature (i) on at least 90% of the days (defined as ''CD90'' stations for 90% ''complete data'') and (ii) on at least 75% of the days (defined as ''CD75'' stations for 75% ''complete data'') over the 2008-12 period.…”

“…Namely, we use the locations of the weather stations that reported temperature (i) on at least 90% of the days (defined as ''CD90'' stations for 90% ''complete data'') and (ii) on at least 75% of the days (defined as ''CD75'' stations for 75% ''complete data'') over the 2008-12 period. The 18 (45) stations in the CD90 (CD75) subset are assumed to represent the existing temperature network over the continent (Bumbaco et al 2014).…”

“…The ensemble sensitivity approach does not transform covariance matrices, and uses the sensitivity of state variables to a generic metric function (which can involve nonstate variables) to find optimal measurements. The ensemble sensitivity approach has been tested in theoretical modeling for time-averaged observations (Huntley and Hakim 2010), used for an example climate observing network for the Pacific Northwest (Mauger et al 2013), shown as an example for continent-averaged surface temperature monitoring in Antarctica (Bumbaco et al 2014), and applied to find salinity and sea surface paleoclimate estimates from corals (Comboul et al 2015).…”

Impact of Assimilation of Radiosonde and UAV Observations from the Southern Ocean in the Polar WRF Model

“…Dee et al, 2011;Murphy et al, 2014;Driemel et al, 2016). These observations could also play a critical role in modern NWP systems, especially at high latitudes where in-situ observations are rare (e.g., Bumbaco et al, 2014). However, benefits from assimilation of radiosonde and wind profiler data have been detected also for short-range forecasts for Central Europe (Federico, 2013), and studies on temperature and humidity retrievals from satellite and ground-based microwave radiometers and their assimilation into NWP system have also been made (e.g., Knupp et al, 2009;Guedj et al, 2010;Caumont et al, 2016).…”