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- Author or Editor: Teddie L. Keller x
- Journal of Applied Meteorology and Climatology x
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Abstract
The forecast skill of upper-level turbulence diagnostics is evaluated using available turbulence observations [viz., pilot reports (PIREPs)] over East Asia. The six years (2003–08) of PIREPs used in this study include null, light, and moderate-or-greater intensity categories. The turbulence diagnostics used are a subset of indices in the Graphical Turbulence Guidance (GTG) system. To investigate the optimal performance of the component GTG diagnostics and GTG combinations over East Asia, various statistical evaluations and sensitivity tests are performed. To examine the dependency of the GTG system on the operational numerical weather prediction (NWP) model, the GTG system is applied to both the Regional Data Assimilation and Prediction System (RDAPS) analysis data and Global Forecasting System (GFS) analysis and forecast data with 30-km and 0.3125° (T382) horizontal grid spacings. The dependency of the temporal variation in the PIREP and GFS data and the forecast lead time of the GFS-based GTG combination are also investigated. It is found that the forecasting performance of the GTG system varies with year and season according to the annual and seasonal variations in the large-scale atmospheric conditions over the East Asia region. The wintertime GTG skill is the highest, because most GTG component diagnostics are related to jet streams and upper-level fronts. The GTG skill improves as the number of PIREP samples and the vertical resolution of the underlying NWP analysis data increase, and the GTG performance decreases as the forecast lead time increases from 0 to 12 h.
Abstract
The forecast skill of upper-level turbulence diagnostics is evaluated using available turbulence observations [viz., pilot reports (PIREPs)] over East Asia. The six years (2003–08) of PIREPs used in this study include null, light, and moderate-or-greater intensity categories. The turbulence diagnostics used are a subset of indices in the Graphical Turbulence Guidance (GTG) system. To investigate the optimal performance of the component GTG diagnostics and GTG combinations over East Asia, various statistical evaluations and sensitivity tests are performed. To examine the dependency of the GTG system on the operational numerical weather prediction (NWP) model, the GTG system is applied to both the Regional Data Assimilation and Prediction System (RDAPS) analysis data and Global Forecasting System (GFS) analysis and forecast data with 30-km and 0.3125° (T382) horizontal grid spacings. The dependency of the temporal variation in the PIREP and GFS data and the forecast lead time of the GFS-based GTG combination are also investigated. It is found that the forecasting performance of the GTG system varies with year and season according to the annual and seasonal variations in the large-scale atmospheric conditions over the East Asia region. The wintertime GTG skill is the highest, because most GTG component diagnostics are related to jet streams and upper-level fronts. The GTG skill improves as the number of PIREP samples and the vertical resolution of the underlying NWP analysis data increase, and the GTG performance decreases as the forecast lead time increases from 0 to 12 h.
Abstract
Takeoff and landing maneuvers can be particularly hazardous at airports surrounded by complex terrain. To address this situation, the Federal Aviation Administration has developed a precipitous terrain classification as a way to impose more restrictive terrain clearances in the vicinity of complex terrain and to mitigate possible altimeter errors and pilot control problems experienced while executing instrument approach procedures. The current precipitous point value (PPV) algorithm relies on the terrain characteristics within a local area of 2 n mi (3.7 km) in radius and is therefore static in time. In this work, we investigate the role of meteorological effects leading to potential aviation hazards over complex terrain, namely, turbulence, altimeter-setting errors, and density-altitude deviations. To that end, we combine observations with high-resolution numerical weather forecasts within a 2° × 2° region over the Rocky Mountains in Colorado containing three airports that are surrounded by precipitous terrain. Both available turbulence reports and model’s turbulence forecasts show little correlation with the PPV algorithm for the region analyzed, indicating that the static terrain characteristics cannot generally be used to reliably capture hazardous low-level turbulence events. Altimeter-setting errors and density-altitude effects are also found to be only very weakly correlated with the PPV algorithm. Altimeter-setting errors contribute to hazardous conditions mainly during cold seasons, driven by synoptic weather systems, whereas density-altitude effects are on the contrary predominantly present during the spring and summer months and follow a very well-marked diurnal evolution modulated by surface radiative effects. These findings demonstrate the effectiveness of high-resolution weather forecast information in determining aviation-relevant hazardous conditions over complex terrain.
Abstract
Takeoff and landing maneuvers can be particularly hazardous at airports surrounded by complex terrain. To address this situation, the Federal Aviation Administration has developed a precipitous terrain classification as a way to impose more restrictive terrain clearances in the vicinity of complex terrain and to mitigate possible altimeter errors and pilot control problems experienced while executing instrument approach procedures. The current precipitous point value (PPV) algorithm relies on the terrain characteristics within a local area of 2 n mi (3.7 km) in radius and is therefore static in time. In this work, we investigate the role of meteorological effects leading to potential aviation hazards over complex terrain, namely, turbulence, altimeter-setting errors, and density-altitude deviations. To that end, we combine observations with high-resolution numerical weather forecasts within a 2° × 2° region over the Rocky Mountains in Colorado containing three airports that are surrounded by precipitous terrain. Both available turbulence reports and model’s turbulence forecasts show little correlation with the PPV algorithm for the region analyzed, indicating that the static terrain characteristics cannot generally be used to reliably capture hazardous low-level turbulence events. Altimeter-setting errors and density-altitude effects are also found to be only very weakly correlated with the PPV algorithm. Altimeter-setting errors contribute to hazardous conditions mainly during cold seasons, driven by synoptic weather systems, whereas density-altitude effects are on the contrary predominantly present during the spring and summer months and follow a very well-marked diurnal evolution modulated by surface radiative effects. These findings demonstrate the effectiveness of high-resolution weather forecast information in determining aviation-relevant hazardous conditions over complex terrain.
Abstract
At 1818 mountain standard time 20 December 2008, a Boeing 737 jetliner encountered significant crosswinds while accelerating for takeoff at the Denver International Airport (DIA), ran off the side of the runway, and burst into flames. Passengers and crew were able to evacuate quickly, and, although there were injuries, there were no fatalities. Winds around the time of the accident were predominantly from the west, with substantial spatial and temporal speed variability across the airport property. Embedded in this mostly westerly flow were intermittent gusts that created strong crosswinds for the north–south runways. According to the report from the National Transportation Safety Board, it was one of these strong gusts that initiated the events that led to the runway excursion and subsequent crash of the aircraft. Numerous aircraft reported significant mountain-wave activity and turbulence over Colorado on the day of the accident. To determine whether wave activity may have contributed to the strong, intermittent gustiness at DIA, a high-resolution multinested numerical simulation was performed using the Clark–Hall model, with a horizontal grid spacing of 250 m in the inner domain. Results from this simulation suggest that the surface gustiness at DIA was associated with undulations in a train of lee waves in a midtropospheric stable layer above the airport, creating regions of higher-velocity air descending toward the surface. In contrast, a simulation with horizontal grid spacing that was similar to that of a state-of-the-art operational forecast model (3 km) did not predict strong winds at DIA.
Abstract
At 1818 mountain standard time 20 December 2008, a Boeing 737 jetliner encountered significant crosswinds while accelerating for takeoff at the Denver International Airport (DIA), ran off the side of the runway, and burst into flames. Passengers and crew were able to evacuate quickly, and, although there were injuries, there were no fatalities. Winds around the time of the accident were predominantly from the west, with substantial spatial and temporal speed variability across the airport property. Embedded in this mostly westerly flow were intermittent gusts that created strong crosswinds for the north–south runways. According to the report from the National Transportation Safety Board, it was one of these strong gusts that initiated the events that led to the runway excursion and subsequent crash of the aircraft. Numerous aircraft reported significant mountain-wave activity and turbulence over Colorado on the day of the accident. To determine whether wave activity may have contributed to the strong, intermittent gustiness at DIA, a high-resolution multinested numerical simulation was performed using the Clark–Hall model, with a horizontal grid spacing of 250 m in the inner domain. Results from this simulation suggest that the surface gustiness at DIA was associated with undulations in a train of lee waves in a midtropospheric stable layer above the airport, creating regions of higher-velocity air descending toward the surface. In contrast, a simulation with horizontal grid spacing that was similar to that of a state-of-the-art operational forecast model (3 km) did not predict strong winds at DIA.
