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- Author or Editor: David Richardson x
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Abstract
To better understand the physical processes of the stable boundary layer and to quantify “submeso motions” in moderately complex terrain, exploratory case-study analyses were performed using observational field data supplemented by gridded North American Regional Reanalysis data and Pennsylvania State University real-time Weather Research and Forecasting Model output. Submeso motions are nominally defined as all motions between the largest turbulent scales and the smallest mesoscales. Seven nighttime cases from August and September of 2011 are chosen from a central Pennsylvania [“Rock Springs” (RS)] network of eight ground-based towers and two sound detection and ranging (sodar) systems . The observation network is located near Tussey Ridge, ~15 km southeast of the Allegheny Mountains. The seven cases are classified by the dominant synoptic-flow direction and proximity to terrain to assess the influence of synoptic conditions on the local submeso and mesogamma motions. It is found that synoptic winds with a large crossing angle over nearby Tussey Ridge can generate mesogamma wave motions and larger-magnitude submeso temperature and wind fluctuations in the RS network than do winds from the direction of the more distant Allegheny Mountains. Cases with synoptic winds that are nearly parallel to the topographic contours or are generally weak exhibit the smallest fluctuations. Changes in the magnitude of near-surface submeso temperature and wind fluctuations in response to local indicator variables are also analyzed. The observed submeso wind and temperature fluctuations are generally larger when the low-level wind speed and thermal stratification, respectively, are greater, but the synoptic flow and its relation to the terrain also play an important role.
Abstract
To better understand the physical processes of the stable boundary layer and to quantify “submeso motions” in moderately complex terrain, exploratory case-study analyses were performed using observational field data supplemented by gridded North American Regional Reanalysis data and Pennsylvania State University real-time Weather Research and Forecasting Model output. Submeso motions are nominally defined as all motions between the largest turbulent scales and the smallest mesoscales. Seven nighttime cases from August and September of 2011 are chosen from a central Pennsylvania [“Rock Springs” (RS)] network of eight ground-based towers and two sound detection and ranging (sodar) systems . The observation network is located near Tussey Ridge, ~15 km southeast of the Allegheny Mountains. The seven cases are classified by the dominant synoptic-flow direction and proximity to terrain to assess the influence of synoptic conditions on the local submeso and mesogamma motions. It is found that synoptic winds with a large crossing angle over nearby Tussey Ridge can generate mesogamma wave motions and larger-magnitude submeso temperature and wind fluctuations in the RS network than do winds from the direction of the more distant Allegheny Mountains. Cases with synoptic winds that are nearly parallel to the topographic contours or are generally weak exhibit the smallest fluctuations. Changes in the magnitude of near-surface submeso temperature and wind fluctuations in response to local indicator variables are also analyzed. The observed submeso wind and temperature fluctuations are generally larger when the low-level wind speed and thermal stratification, respectively, are greater, but the synoptic flow and its relation to the terrain also play an important role.
Abstract
The process of moving from an ensemble of global climate model temperature projections to local sea level projections requires several steps. Sea level was estimated in Olympia, Washington (a city that is very concerned with sea level rise because parts of downtown are barely above mean highest high tide), by relating global mean temperature to global sea level; relating global sea level to sea levels at Seattle, Washington; and finally relating Seattle to Olympia. There has long been a realization that accurate assessment of the precision of projections is needed for science-based policy decisions. When a string of statistical and/or deterministic models is connected, the uncertainty of each individual model needs to be accounted for. Here the uncertainty is quantified for each model in the described system and the total uncertainty is assessed in a cascading effect throughout the system. The projected sea level rise over time and its total estimated uncertainty are visualized simultaneously for the years 2000–2100, the increased uncertainty due to each of the component models at a particular projection year is identified, and estimates of the time at which a certain sea level rise will first be reached are made.
Abstract
The process of moving from an ensemble of global climate model temperature projections to local sea level projections requires several steps. Sea level was estimated in Olympia, Washington (a city that is very concerned with sea level rise because parts of downtown are barely above mean highest high tide), by relating global mean temperature to global sea level; relating global sea level to sea levels at Seattle, Washington; and finally relating Seattle to Olympia. There has long been a realization that accurate assessment of the precision of projections is needed for science-based policy decisions. When a string of statistical and/or deterministic models is connected, the uncertainty of each individual model needs to be accounted for. Here the uncertainty is quantified for each model in the described system and the total uncertainty is assessed in a cascading effect throughout the system. The projected sea level rise over time and its total estimated uncertainty are visualized simultaneously for the years 2000–2100, the increased uncertainty due to each of the component models at a particular projection year is identified, and estimates of the time at which a certain sea level rise will first be reached are made.
Abstract
Anthropogenic emissions from urban areas represent 70% of the fossil fuel carbon emitted globally according to carbon emission inventories. The authors present here the first operational system able to monitor in near–real time daily emission estimates, using a mesoscale atmospheric inversion framework over the city of Davos, Switzerland, before, during, and after the World Economic Forum 2012 Meeting (WEF-2012). Two instruments that continuously measured atmospheric mixing ratios of greenhouse gases (GHGs) were deployed at two locations from 23 December 2011 to 3 March 2012: one site was located in the urban area and the other was out of the valley in the surrounding mountains. Carbon dioxide, methane, and carbon monoxide were measured continuously at both sites. The Weather Research and Forecasting mesoscale atmospheric model (WRF), in four-dimensional data assimilation mode, was used to simulate the transport of GHGs over the valley of Davos at 1.3-km resolution. Wintertime emissions prior to the WEF-2012 were about 40% higher than the initial annual inventory estimate, corresponding to the use of heating fuel in the winter. Daily inverse fluxes were highly correlated with the local climate, especially during the severe cold wave that affected most of Europe in early February 2012. During the WEF-2012, emissions dropped by 35% relative to the first month of the deployment, despite similar temperatures and the presence of several thousand participants at the meeting. On the basis of composite diurnal cycles of hourly CO/CO2 ratios, the absence of traffic peaks during the WEF-2012 meeting indicated that change in road emissions is potentially responsible for the observed decrease in the city emissions during the meeting.
Abstract
Anthropogenic emissions from urban areas represent 70% of the fossil fuel carbon emitted globally according to carbon emission inventories. The authors present here the first operational system able to monitor in near–real time daily emission estimates, using a mesoscale atmospheric inversion framework over the city of Davos, Switzerland, before, during, and after the World Economic Forum 2012 Meeting (WEF-2012). Two instruments that continuously measured atmospheric mixing ratios of greenhouse gases (GHGs) were deployed at two locations from 23 December 2011 to 3 March 2012: one site was located in the urban area and the other was out of the valley in the surrounding mountains. Carbon dioxide, methane, and carbon monoxide were measured continuously at both sites. The Weather Research and Forecasting mesoscale atmospheric model (WRF), in four-dimensional data assimilation mode, was used to simulate the transport of GHGs over the valley of Davos at 1.3-km resolution. Wintertime emissions prior to the WEF-2012 were about 40% higher than the initial annual inventory estimate, corresponding to the use of heating fuel in the winter. Daily inverse fluxes were highly correlated with the local climate, especially during the severe cold wave that affected most of Europe in early February 2012. During the WEF-2012, emissions dropped by 35% relative to the first month of the deployment, despite similar temperatures and the presence of several thousand participants at the meeting. On the basis of composite diurnal cycles of hourly CO/CO2 ratios, the absence of traffic peaks during the WEF-2012 meeting indicated that change in road emissions is potentially responsible for the observed decrease in the city emissions during the meeting.
