scholarly journals Revisiting the Precipitous Terrain Classification from a Meteorological Perspective

Author(s):  
Domingo Muñoz-Esparza ◽  
Hyeyum Hailey Shin ◽  
Teddie L. Keller ◽  
Kyoko Ikeda ◽  
Robert D. Sharman ◽  
...  

AbstractTakeoff and landing maneuvers can be particularly hazardous at airports surrounded by complex terrain. To address this, 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 NM, 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, while 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.

2016 ◽  
Vol 29 (3) ◽  
pp. 305-346 ◽  
Author(s):  
James Bergman

ArgumentThe history of meteorology has focused a great deal on the “scaling up” of knowledge infrastructures through the development of national and global observation networks. This article argues that such efforts to scale up were paralleled by efforts to define a place for local knowledge. By examining efforts of the Blue Hill Meteorological Observatory, near Boston, Massachusetts, to issuelocalweather forecasts that competed with the centralized forecasts of the U.S. Signal Service, this article finds that Blue Hill, as a user of the Signal Service's observation network, developed a new understanding of local knowledge by combining local observations of the weather with the synoptic maps afforded by the nationwide telegraph network of the U.S. Signal Service. Blue Hill used these forecasts not only as a service, but also as evidence of the superiority of its model of local forecasting over the Signal Service's model, and in the process opened up larger questions about the value of a weather forecast and the value of different kinds of knowledge in meteorology.


Regular grids with even steps of the spatial coordinates in the whole computational domain are the most convenient for implementing numerical methods for integration of equations of weather forecasts. However, computing a local numerical weather forecast based on the global general circulation models of the atmosphere will need enormous increase in computation time exceeding reasonable limits. Moreover, as some regional weather details are well localized it is reasonable to apply high-resolution grids locally. In this chapter, we study how to use the high-resolution grids in the numerical methods for solving regional and mesoscale weather forecast problems.


2019 ◽  
Vol 58 (2) ◽  
pp. 291-315 ◽  
Author(s):  
Pedro Odon ◽  
Gregory West ◽  
Roland Stull

AbstractThis study evaluates how well reanalyses represent daily and multiday accumulated precipitation (hereinafter daily PCP) over British Columbia, Canada (Part I evaluated 2-m temperature). Reanalyses are compared with observations from 66 meteorological stations distributed over the complex terrain of British Columbia, separated into climate regions by k-means clustering. Systematic error, two-sample χ2 statistic, and frequency of daily PCP occurrence are evaluated from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim), the Climate Forecast System Reanalysis (CFSR), the Japanese 55-year Reanalysis (JRA-55), and the latest Modern-Era Retrospective Analysis for Research and Applications (version 2; MERRA-2). The 2- and 30-yr return levels of daily PCP are estimated from a generalized extreme value (GEV) distribution fitted by the method of L moments, and their systematic errors are analyzed. JRA-55 and MERRA-2 generally outperform ERA-Interim and CFSR across all metrics. Biases are largely explained by poor reanalysis representation of terrain characteristics such as steepness, exposure, elevation, location of barriers, and wind speed and direction. Statistical stationarity of precipitation intensity and frequency over the 30-yr period is assessed by using confidence intervals and GEV distributions fitted with and without time-dependent parameters. It is determined that stationary distributions are sufficient to represent the climate of daily PCP for this region and time period.


2016 ◽  
Vol 31 (3) ◽  
pp. 757-773 ◽  
Author(s):  
Corrado Camera ◽  
Adriana Bruggeman ◽  
Panos Hadjinicolaou ◽  
Silas Michaelides ◽  
Manfred A. Lange

2019 ◽  
Vol 100 (4) ◽  
pp. 605-619 ◽  
Author(s):  
A. J. Illingworth ◽  
D. Cimini ◽  
A. Haefele ◽  
M. Haeffelin ◽  
M. Hervo ◽  
...  

Abstract To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.


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