Accurately predicting moisture and stability in the Antarctic planetary boundary layer (PBL) is essential for low-cloud forecasts, especially when Antarctic forecasters often use relative humidity as a proxy for cloud cover. These forecasters typically rely on the Antarctic Mesoscale Prediction System (AMPS) Polar Weather Research and Forecasting (Polar WRF) Model for high-resolution forecasts. To complement the PBL observations from the 30-m Alexander Tall Tower! (ATT) on the Ross Ice Shelf as discussed in a recent paper by Wille and coworkers, a field campaign was conducted at the ATT site from 13 to 26 January 2014 using Small Unmanned Meteorological Observer (SUMO) aerial systems to collect PBL data. The 3-km-resolution AMPS forecast output is combined with the global European Centre for Medium-Range Weather Forecasts interim reanalysis (ERAI), SUMO flights, and ATT data to describe atmospheric conditions on the Ross Ice Shelf. The SUMO comparison showed that AMPS had an average 2–3 m s−1 high wind speed bias from the near surface to 600 m, which led to excessive mechanical mixing and reduced stability in the PBL. As discussed in previous Polar WRF studies, the Mellor–Yamada–Janjić PBL scheme is likely responsible for the high wind speed bias. The SUMO comparison also showed a near-surface 10–15-percentage-point dry relative humidity bias in AMPS that increased to a 25–30-percentage-point deficit from 200 to 400 m above the surface. A large dry bias at these critical heights for aircraft operations implies poor AMPS low-cloud forecasts. The ERAI showed that the katabatic flow from the Transantarctic Mountains is unrealistically dry in AMPS.
The interaction of synoptic and mesoscale circulations with the steep topography surrounding the Ross Ice Shelf, Antarctica, greatly influences the wind patterns in the region of the Ross Ice Shelf. The topography provides forcing for features such as katabatic winds, barrier winds, and barrier wind corner jets. The combination of topographic forcing and synoptic and mesoscale forcing from cyclones that traverse the Ross Ice Shelf sector create a region of strong but varying winds. This paper identifies the dominant surface wind patterns over the Ross Ice Shelf using output from the Weather Research and Forecasting Model run within the Antarctic Mesoscale Prediction System and the method of self-organizing maps (SOM). The dataset has 15-km grid spacing and is the first study to identify the dominant surface wind patterns using data at this resolution. The analysis shows that the Ross Ice Shelf airstream, a dominant stream of air flowing northward from the interior of the continent over the western and/or central Ross Ice Shelf to the Ross Sea, is present over the Ross Ice Shelf approximately 34% of the time, the Ross Ice Shelf airstream varies in both its strength and position over the Ross Ice Shelf, and barrier wind corner jets are present in the region to the northwest of the Prince Olav Mountains approximately 14% of the time and approximately 41% of the time when the Ross Ice Shelf airstream is present.
Typical model evaluation strategies evaluate models over large periods of time (months, seasons, years, etc.) or for single case studies such as severe storms or other events of interest. The weather-pattern-based model evaluation technique described in this paper uses self-organizing maps to create a synoptic climatology of the weather patterns present over a region of interest, the Ross Ice Shelf for this analysis. Using the synoptic climatology, the performance of the model, the Weather Research and Forecasting Model run within the Antarctic Mesoscale Prediction System, is evaluated for each of the objectively identified weather patterns. The evaluation process involves classifying each model forecast as matching one of the weather patterns from the climatology. Subsequently, statistics such as model bias, root-mean-square error, and correlation are calculated for each weather pattern. This allows for the determination of model errors as a function of weather pattern and can highlight if certain errors occur under some weather regimes and not others. The results presented in this paper highlight the potential benefits of this new weather-pattern-based model evaluation technique.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.