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Abstract
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 that wave development and maintenance are insensitive to the upper boundary condition, that wave simulation is insensitive to hydrostatic/nonhydrostatic differences at 10-km horizontal resolution, and that wave production and structure are insensitive to vertical resolution. With respect to horizontal resolution, an expanded wave scale spectrum and shorter minimum wavelengths appear as grid size is decreased. With respect to moist physics, latent heating is found to be necessary for model wave development, and model wave production and strength are, to an extent, sensitive to the moist process package employed. Elevated convection is the model wave forcing mechanism, and, at a given grid size, wave response varies with the degree to which such convection is explicit.
Statistical analyses consisting of high-pass filtering and power spectrum analysis of observed and model surface pressure data are performed. The filtering analyses indicate more realistic simulated wave activity as horizontal resolution is increased. The spectral analyses uncover bimodal distributions in both the observed and model spectra and show that the model, in general, does not overproduce mesoscale gravity wave energy. With respect to the model alone, the spectral analyses reveal that as grid size is decreased the significant spectral frequencies shift upward while both the average power in the mesoscale wave band and spatial variability of the power in such band decrease.
Abstract
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 that wave development and maintenance are insensitive to the upper boundary condition, that wave simulation is insensitive to hydrostatic/nonhydrostatic differences at 10-km horizontal resolution, and that wave production and structure are insensitive to vertical resolution. With respect to horizontal resolution, an expanded wave scale spectrum and shorter minimum wavelengths appear as grid size is decreased. With respect to moist physics, latent heating is found to be necessary for model wave development, and model wave production and strength are, to an extent, sensitive to the moist process package employed. Elevated convection is the model wave forcing mechanism, and, at a given grid size, wave response varies with the degree to which such convection is explicit.
Statistical analyses consisting of high-pass filtering and power spectrum analysis of observed and model surface pressure data are performed. The filtering analyses indicate more realistic simulated wave activity as horizontal resolution is increased. The spectral analyses uncover bimodal distributions in both the observed and model spectra and show that the model, in general, does not overproduce mesoscale gravity wave energy. With respect to the model alone, the spectral analyses reveal that as grid size is decreased the significant spectral frequencies shift upward while both the average power in the mesoscale wave band and spatial variability of the power in such band decrease.
Abstract
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.
Abstract
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.
Abstract
A modeling investigation explores the impacts of the assimilation of satellite-retrieved soundings on forecast error in the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Simulations of the period of the U.S. Air Force’s Contrail Experiment (18–29 September 1995) vary the initialization method and datasets assimilated, the performance of first-guess reanalysis, the imposition of quality control (QC) on the satellite data, and the frequency of the model update cycle. MM5 experiments employing four-dimensional data assimilation (FDDA) are compared with a control experiment without FDDA. In the former, combinations of conventional surface and radiosonde observations and retrieved temperature and moisture soundings from the Defense Meteorological Satellite Program (DMSP) and Television and Infrared Observation Satellite Operational Vertical Sounder (TOVS) satellite instruments are assimilated. Forecast error statistics for the experiments are computed and analyzed. It is found that for retrieved temperatures the DMSP and TOVS sounding datasets used have similar, reasonable accuracy, but for retrieved dewpoints they display significant, and more differing, errors. Overall, the TOVS retrievals obtained are of poorer quality than are the DMSP retrievals. Sensitivity tests reveal that imposing a QC filter on the satellite data prior to assimilation does improve the resultant MM5 simulations. With such QC, it is found that assimilating DMSP and TOVS soundings with the methods used can significantly improve the forecasts of both temperature and moisture variables in the MM5. Model performance, however, can still reflect the relative quality of the satellite retrievals assimilated, with the lower-error DMSP data yielding better simulations than do the TOVS data. Tests exploring the reanalysis of first-guess fields obtained from FDDA show that it does benefit the short-term (0–12 h) forecast but that significant gains diminish thereafter.
Abstract
A modeling investigation explores the impacts of the assimilation of satellite-retrieved soundings on forecast error in the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Simulations of the period of the U.S. Air Force’s Contrail Experiment (18–29 September 1995) vary the initialization method and datasets assimilated, the performance of first-guess reanalysis, the imposition of quality control (QC) on the satellite data, and the frequency of the model update cycle. MM5 experiments employing four-dimensional data assimilation (FDDA) are compared with a control experiment without FDDA. In the former, combinations of conventional surface and radiosonde observations and retrieved temperature and moisture soundings from the Defense Meteorological Satellite Program (DMSP) and Television and Infrared Observation Satellite Operational Vertical Sounder (TOVS) satellite instruments are assimilated. Forecast error statistics for the experiments are computed and analyzed. It is found that for retrieved temperatures the DMSP and TOVS sounding datasets used have similar, reasonable accuracy, but for retrieved dewpoints they display significant, and more differing, errors. Overall, the TOVS retrievals obtained are of poorer quality than are the DMSP retrievals. Sensitivity tests reveal that imposing a QC filter on the satellite data prior to assimilation does improve the resultant MM5 simulations. With such QC, it is found that assimilating DMSP and TOVS soundings with the methods used can significantly improve the forecasts of both temperature and moisture variables in the MM5. Model performance, however, can still reflect the relative quality of the satellite retrievals assimilated, with the lower-error DMSP data yielding better simulations than do the TOVS data. Tests exploring the reanalysis of first-guess fields obtained from FDDA show that it does benefit the short-term (0–12 h) forecast but that significant gains diminish thereafter.
Abstract
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-state-dependent roughness parameterizations can be as influential as the parameterizations themselves.
Abstract
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-state-dependent roughness parameterizations can be as influential as the parameterizations themselves.
Abstract
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 experiment employed a 30-km grid, the Hsie et al. scheme for explicit moist processes, and a modified Arakawa–Schubert cumulus parameterization. From this experiment it is found that the model can successfully simulate mesoscale gravity waves and can capture many aspects of an observed wave event. For this case the model mesoscale gravity waves arose, matured, and decayed in the same regions as those observed and had similar timing and amplitudes. Model wave speeds, however, were 1–1.8 times those observed. The model output showed that although a good wave duct covered the wave activity area, the model waves were maintained and amplified by wave-CISK processes. These waves appeared to be generated by convection of mesoscale extent above a stable duct. This convection moved with the waves and was associated with steering levels.
Model sensitivity experiments showed that (i) the model mesoscale gravity waves do not stern from initial data imbalances, (ii) model mesoscale gravity-wave development does not occur when latent heating is removed, (iii) model mesoscale gravity-wave production is not necessarily limited to the early hours of a simulation, and (iv) model mesoscale gravity waves can be produced using grid sizes up to 45 km. As applied to the actual case, it is concluded from the simulations that both ducting and wave-CISK contributed to the maintenance of the observed waves. Convection is indicated as the primary wave source, although evidence of shearing instability is also found. The model results, however, do not support the idea of generation by geostrophic adjustment.
Abstract
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 experiment employed a 30-km grid, the Hsie et al. scheme for explicit moist processes, and a modified Arakawa–Schubert cumulus parameterization. From this experiment it is found that the model can successfully simulate mesoscale gravity waves and can capture many aspects of an observed wave event. For this case the model mesoscale gravity waves arose, matured, and decayed in the same regions as those observed and had similar timing and amplitudes. Model wave speeds, however, were 1–1.8 times those observed. The model output showed that although a good wave duct covered the wave activity area, the model waves were maintained and amplified by wave-CISK processes. These waves appeared to be generated by convection of mesoscale extent above a stable duct. This convection moved with the waves and was associated with steering levels.
Model sensitivity experiments showed that (i) the model mesoscale gravity waves do not stern from initial data imbalances, (ii) model mesoscale gravity-wave development does not occur when latent heating is removed, (iii) model mesoscale gravity-wave production is not necessarily limited to the early hours of a simulation, and (iv) model mesoscale gravity waves can be produced using grid sizes up to 45 km. As applied to the actual case, it is concluded from the simulations that both ducting and wave-CISK contributed to the maintenance of the observed waves. Convection is indicated as the primary wave source, although evidence of shearing instability is also found. The model results, however, do not support the idea of generation by geostrophic adjustment.
This paper describes a recent undertaking in distributed numerical weather prediction via high data rate networks. The governing project involved the operation of a coupled mesoscale modeling system on widely separated supercomputers, and experiments and demonstrations were performed to explore both long-distance and local-area operation of the system. Connectivity was provided either by NASA's Advanced Communications Technology Satellite (ACTS) or by a terrestrial Asynchronous Transfer Mode (ATM) network. The modeling system was built around the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (PSU–NCAR MM5) and consisted of the MM5, an ocean circulation model, and a surface wave model.
The primary experiments and demonstrations utilized ACTS to link supercomputers at both NCAR in Boulder, Colorado, and at the Ohio Supercomputer Center in Columbus, Ohio. Other experiments and demonstrations utilized an ATM network to link supercomputers running the system at sites in Boulder. This paper focuses on the design and performance of the distributed numerical weather prediction system and its supporting networks. The project has demonstrated the feasibility of remote, distributed supercomputing, the potential for application of distributed computing principles and message-passing software to numerical modeling and coupled model construction, and means for creatively applying state-of-the-art networking technology to support meteorological modeling.
This paper describes a recent undertaking in distributed numerical weather prediction via high data rate networks. The governing project involved the operation of a coupled mesoscale modeling system on widely separated supercomputers, and experiments and demonstrations were performed to explore both long-distance and local-area operation of the system. Connectivity was provided either by NASA's Advanced Communications Technology Satellite (ACTS) or by a terrestrial Asynchronous Transfer Mode (ATM) network. The modeling system was built around the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (PSU–NCAR MM5) and consisted of the MM5, an ocean circulation model, and a surface wave model.
The primary experiments and demonstrations utilized ACTS to link supercomputers at both NCAR in Boulder, Colorado, and at the Ohio Supercomputer Center in Columbus, Ohio. Other experiments and demonstrations utilized an ATM network to link supercomputers running the system at sites in Boulder. This paper focuses on the design and performance of the distributed numerical weather prediction system and its supporting networks. The project has demonstrated the feasibility of remote, distributed supercomputing, the potential for application of distributed computing principles and message-passing software to numerical modeling and coupled model construction, and means for creatively applying state-of-the-art networking technology to support meteorological modeling.
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.
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.
The Antarctic Mesoscale Prediction System (AMPS) is a real-time numerical weather prediction (NWP) system covering Antarctica that has served a remarkable range of groups and activities for a decade. It employs the Weather Research and Forecasting model (WRF) on varying-resolution grids to generate numerical guidance in a variety of tailored products. While its priority mission has been to support the forecasters of the U.S. Antarctic Program, AMPS has evolved to assist a host of scientific and logistical needs for an international user base. The AMPS effort has advanced polar NWP and Antarctic science and looks to continue this into another decade. To inform those with Antarctic scientific and logistical interests and needs, the history, applications, and capabilities of AMPS are discussed.
The Antarctic Mesoscale Prediction System (AMPS) is a real-time numerical weather prediction (NWP) system covering Antarctica that has served a remarkable range of groups and activities for a decade. It employs the Weather Research and Forecasting model (WRF) on varying-resolution grids to generate numerical guidance in a variety of tailored products. While its priority mission has been to support the forecasters of the U.S. Antarctic Program, AMPS has evolved to assist a host of scientific and logistical needs for an international user base. The AMPS effort has advanced polar NWP and Antarctic science and looks to continue this into another decade. To inform those with Antarctic scientific and logistical interests and needs, the history, applications, and capabilities of AMPS are discussed.
Abstract
In response to the need for improved weather prediction capabilities in support of the U.S. Antarctic Program’s Antarctic field operations, the Antarctic Mesoscale Prediction System (AMPS) was implemented in October 2000. AMPS employs a limited-area model, the Polar fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), optimized for use over ice sheets. Twice-daily forecasts from the 3.3-km resolution domain of AMPS are joined together to study the climate of the McMurdo region from June 2002 to May 2003. Annual and seasonal distributions of wind direction and speed, 2-m temperature, mean sea level pressure, precipitation, and cloud fraction are presented. This is the first time a model adapted for polar use and with relatively high resolution is used to study the climate of the rugged McMurdo region, allowing several important climatological features to be investigated with unprecedented detail.
Orographic effects exert an important influence on the near-surface winds. Time-mean vortices occur in the lee of Ross Island, perhaps a factor in the high incidence of mesoscale cyclogenesis noted in this area. The near-surface temperature gradient is oriented northwest to southeast with the warmest temperatures in the northwest near McMurdo and the gradient being steepest in winter. The first-ever detailed precipitation maps of the region are presented. Orographic precipitation maxima occur on the southerly slopes of Ross Island and in the mountains to the southwest. The source of the moisture is primarily from the large synoptic systems passing to the northeast and east of Ross Island. A precipitation-shadow effect appears to be an important influence on the low precipitation amounts observed in the McMurdo Dry Valleys. Total cloud fraction primarily depends on the amount of open water in the Ross Sea; the cloudiest region is to the northeast of Ross Island in the vicinity of the Ross Sea polynya.
Abstract
In response to the need for improved weather prediction capabilities in support of the U.S. Antarctic Program’s Antarctic field operations, the Antarctic Mesoscale Prediction System (AMPS) was implemented in October 2000. AMPS employs a limited-area model, the Polar fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), optimized for use over ice sheets. Twice-daily forecasts from the 3.3-km resolution domain of AMPS are joined together to study the climate of the McMurdo region from June 2002 to May 2003. Annual and seasonal distributions of wind direction and speed, 2-m temperature, mean sea level pressure, precipitation, and cloud fraction are presented. This is the first time a model adapted for polar use and with relatively high resolution is used to study the climate of the rugged McMurdo region, allowing several important climatological features to be investigated with unprecedented detail.
Orographic effects exert an important influence on the near-surface winds. Time-mean vortices occur in the lee of Ross Island, perhaps a factor in the high incidence of mesoscale cyclogenesis noted in this area. The near-surface temperature gradient is oriented northwest to southeast with the warmest temperatures in the northwest near McMurdo and the gradient being steepest in winter. The first-ever detailed precipitation maps of the region are presented. Orographic precipitation maxima occur on the southerly slopes of Ross Island and in the mountains to the southwest. The source of the moisture is primarily from the large synoptic systems passing to the northeast and east of Ross Island. A precipitation-shadow effect appears to be an important influence on the low precipitation amounts observed in the McMurdo Dry Valleys. Total cloud fraction primarily depends on the amount of open water in the Ross Sea; the cloudiest region is to the northeast of Ross Island in the vicinity of the Ross Sea polynya.
Abstract
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).
Abstract
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).
