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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.

The Polar Weather Research and Forecasting Model (Polar WRF), a polar-optimized version of the WRF Model, is developed and made available to the community by Ohio State University’s Polar Meteorology Group (PMG) as a code supplement to the WRF release from the National Center for Atmospheric Research (NCAR). While annual NCAR official releases contain polar modifications, the PMG provides very recent updates to users. PMG supplement versions up to WRF version 3.4 include modified Noah land surface model sea ice representation, allowing the specification of variable sea ice thickness and snow depth over sea ice rather than the default 3-m thickness and 0.05-m snow depth. Starting with WRF V3.5, these options are implemented by NCAR into the standard WRF release. Gridded distributions of Arctic ice thickness and snow depth over sea ice have recently become available. Their impacts are tested with PMG’s WRF V3.5-based Polar WRF in two case studies. First, 20-km-resolution model results for January 1998 are compared with observations during the Surface Heat Budget of the Arctic Ocean project. Polar WRF using analyzed thickness and snow depth fields appears to simulate January 1998 slightly better than WRF without polar settings selected. Sensitivity tests show that the simulated impacts of realistic variability in sea ice thickness and snow depth on near-surface temperature is several degrees. The 40-km resolution simulations of a second case study covering Europe and the Arctic Ocean demonstrate remote impacts of Arctic sea ice thickness on midlatitude synoptic meteorology that develop within 2 weeks during a winter 2012 blocking event.

This study initiates the application of the maturing Weather Research and Forecasting (WRF) model to the polar regions in the context of the real-time Antarctic Mesoscale Prediction System (AMPS). The behavior of the Advanced Research WRF (ARW) in a high-latitude setting and its ability to capture a significant Antarctic weather event are investigated. Also, in a suite of sensitivity tests, the impacts of the assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric motion vectors on ARW Antarctic forecasts are explored. The simulation results are analyzed and the statistical significance of error differences is assessed. It is found that with the proper consideration of MODIS data the ARW can accurately simulate a major Antarctic event, the May 2004 McMurdo windstorm. The ARW simulations illuminate an episode of high-momentum flow responding to the complex orography of the vital Ross Island region. While the model captures the synoptic setting and basic trajectory of the cyclone driving the event, there are differences on the mesoscale in the evolution of the low pressure system that significantly affect the forecast results. In general, both the ARW and AMPS’s fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) tend to underforecast the wind magnitudes, reflecting their stalling and filling of the system near Ross Island. It is seen, however, that both targeted data assimilation and grid resolution enhancement can yield improvement in the forecast of the key parameter of wind speed. It is found that the assimilation of MODIS observations can significantly improve the forecast for a high-impact Antarctic weather event. However, the application to the retrievals of a filter accounting for instrument channel, observation height, and surface type is necessary. The results indicate benefits to initial conditions and high-resolution, polar, mesoscale forecasts from the careful assimilation of nontraditional satellite observations over Antarctica and the Southern Ocean.

In response to the need for improved weather prediction capabilities in support of the U.S. Antarctic Program’s field operations, the Antarctic Mesoscale Prediction System (AMPS) was implemented in October 2000. AMPS employs the Polar MM5, a version of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model optimized for use over ice sheets. The modeling system consists of several domains ranging in horizontal resolution from 90 km covering a large part of the Southern Hemisphere to 3.3 km over the complex terrain surrounding McMurdo, the hub of U.S. operations. The performance of the 30-km AMPS domain versus observations from manned and automatic weather stations is statistically evaluated for a 2-yr period from September 2001 through August 2003. The simulated 12–36-h surface pressure and near-surface temperature at most sites have correlations of r > 0.95 and r > 0.75, respectively, and small biases. Surface wind speeds reflect the complex topography and generally have correlations between 0.5 and 0.6, and positive biases of 1–2 m s−1. In the free atmosphere, r > 0.95 (geopotential height), r > 0.9 (temperature), and r > 0.8 (wind speed) at most sites. Over the annual cycle, there is little interseasonal variation in skill. Over the length of the forecast, a gradual decrease in skill is observed from hours 0–72. One exception is the surface pressure, which improves slightly in the first few hours, due in part to the model adjusting from surface pressure biases that are caused by the initialization technique over the high, cold terrain. The impact of the higher-resolution model domains over the McMurdo region is also evaluated. It is shown that the 3.3-km domain is more sensitive to spatial and temporal changes in the winds than the 10-km domain, which represents an overall improvement in forecast skill, especially on the windward side of the island where the Williams Field and Pegasus runways are situated, and in the lee of Ross Island, an important area of mesoscale cyclogenesis (although the correlation coefficients in these regions are still relatively low).

To support the forecasting needs of the United States Antarctic Program at McMurdo, Antarctica, a special numerical weather prediction program, the Antarctic Mesoscale Prediction System (AMPS), was established for the 2000–01 field season. AMPS employs the Polar MM5, a version of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) that has physics modifications for polar environments. This study assesses the performance of AMPS in forecasting an event of mesoscale cyclogenesis in the western Ross Sea during 13–17 January 2001. Observations indicate the presence of a complex trough having two primary mesoscale lows that merge to the east of Ross Island shortly after 0700 UTC 15 January. In contrast, AMPS predicts one primary mesoscale low throughout the event, incorrectly placing it until the 1800 UTC 15 January forecast, when the observed system carries a prominent signature in the initialization. The model reproduces the evolution of upper-level conditions in agreement ...

A coupled air‐sea numerical model comprising a mesoscale atmospheric model, a marine circulation model, and a surface wave model is presented. The coupled model is tested through simulations of an event of frontal passage through the Lake Erie region. Experiments investigate the effects of different sea surface roughness parameterizations on the atmospheric simulations. The coupled system’s components are the fifth-generation Pennsylvania State University‐National Center for Atmospheric Research Mesoscale Model (MM5), the Princeton Numerical Ocean Model (POM), and the GLERL‐ Donelan Wave Model (GDM). The finest of the MM5’s three nested grids covers Lake Erie, on which the POM and GDM operate. The MM5 provides surface heat and momentum fluxes to the POM, and the POM returns lake surface temperatures to the MM5. The MM5 provides 10-m winds to the GDM, and the GDM returns sea state information to the MM5. The MM5 uses this sea state information in calculating overwater roughness lengths (z 0’s). Experiments varying the MM5’s roughness parameterization over Lake Erie are performed, resulting in a broad range of z 0’s. It is found that wave model coupling can significantly increase overwater roughnesses in the MM5, leading to increased surface heat and moisture fluxes and to changes in PBL characteristics. The impacts on the atmosphere from marine model coupling can appear far downstream of the coupled zones. The accuracy of the mesoscale atmospheric simulation appears sensitive to the assumptions behind the marine roughness parameterizations used. The results suggest that, for consistent forecast improvement, marine roughness parameterizations should account for wave age. In addition, it is found that accounting for wave movement in an air‐sea coupling scheme can be a significant factor in the calculation of surface stresses and, with them, surface heat fluxes over marine areas. Thus, the approach with which a coupling scheme implements sea-statedependent roughness parameterizations can be as influential as the parameterizations themselves.

This study presents numerical model experiments and spectral investigations involving a mesoscale gravity wave event. Its purposes are to determine the sensitivity of mesoscale gravity wave simulation to model configuration and physics and to evaluate spectrally both an observed and simulated wave episode. The case is the large-amplitude wave event of 15 December 1987 in the central United States, and the model employed is the Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model 5 (MM5). The primary MM5 configuration features a nested domain with 10-km horizontal resolution, 41 σ-level vertical resolution, nonhydrostatic physics, a radiative upper boundary condition, an explicit moist process scheme with ice physics, and the Grell cumulus parameterization. Experiments are performed to investigate the effects on wave simulation of upper boundary condition, hydrostatics, vertical and horizontal resolution, and moist physics. From the sensitivity tests it is found t...

An observational study, employing spectral methods, is first made to establish a background for a modeling effort of the mesoscale gravity-wave event of 15 December 1987. The waves are found to have wavelengths of 100–160 km, phase speeds of approximately 30 m s−1, and lifetimes of over 6 h. Conditions for their maintenance are evaluated, indicating the presence of a wave duct and a supportive role for wave-CISK. Convection, shearing instability, and geostrophic adjustment are all implicated as possible source mechanisms for the observed waves. The case is then simulated with the Pennsylvania State University–National Center for Atmospheric Research MM4 mesoscale forecast model, with the following primary objectives: (i) to test the model's ability to simulate a mesoscale gravity-wave event, (ii) to examine in detail the environments of mesoscale gravity-wave development, and (iii) to investigate the mechanisms of mesoscale gravity-wave generation and maintenance. The full-physics control experim...