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- Author or Editor: Tanya Otte x
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Abstract
It is common practice to use Newtonian relaxation, or nudging, throughout meteorological model simulations to create “dynamic analyses” that provide the characterization of the meteorological conditions for retrospective air quality model simulations. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorological conditions, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This article, which is the first of two parts, demonstrates the impact of nudging in the meteorological model on retrospective air quality model simulations. Here, meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and using forecasts for a summertime period. The resultant fields are then used to characterize the meteorological conditions for emissions processing and air quality simulations using the Community Multiscale Air Quality (CMAQ) Modeling System. As expected, on average, the near-surface meteorological fields show a significant degradation over time in the forecasts (when nudging is not used), while the dynamic analyses maintain nearly constant statistical scores in time. The use of nudged MM5 fields in CMAQ generally results in better skill scores for daily maximum 1-h ozone mixing ratio simulations. On average, the skill of the daily maximum 1-h ozone simulation deteriorates significantly over time when nonnudged MM5 fields are used in CMAQ. The daily maximum 1-h ozone mixing ratio also degrades over time in the CMAQ simulation that uses MM5 dynamic analyses, although to a much lesser degree, despite no aggregate loss of skill over time in the dynamic analyses themselves. These results affirm the advantage of using nudging in MM5 to create the meteorological characterization for CMAQ for retrospective simulations, and it is shown that MM5-based dynamic analyses are robust at the surface throughout 5.5-day simulations.
Abstract
It is common practice to use Newtonian relaxation, or nudging, throughout meteorological model simulations to create “dynamic analyses” that provide the characterization of the meteorological conditions for retrospective air quality model simulations. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorological conditions, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This article, which is the first of two parts, demonstrates the impact of nudging in the meteorological model on retrospective air quality model simulations. Here, meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and using forecasts for a summertime period. The resultant fields are then used to characterize the meteorological conditions for emissions processing and air quality simulations using the Community Multiscale Air Quality (CMAQ) Modeling System. As expected, on average, the near-surface meteorological fields show a significant degradation over time in the forecasts (when nudging is not used), while the dynamic analyses maintain nearly constant statistical scores in time. The use of nudged MM5 fields in CMAQ generally results in better skill scores for daily maximum 1-h ozone mixing ratio simulations. On average, the skill of the daily maximum 1-h ozone simulation deteriorates significantly over time when nonnudged MM5 fields are used in CMAQ. The daily maximum 1-h ozone mixing ratio also degrades over time in the CMAQ simulation that uses MM5 dynamic analyses, although to a much lesser degree, despite no aggregate loss of skill over time in the dynamic analyses themselves. These results affirm the advantage of using nudging in MM5 to create the meteorological characterization for CMAQ for retrospective simulations, and it is shown that MM5-based dynamic analyses are robust at the surface throughout 5.5-day simulations.
Abstract
For air quality modeling, it is important that the meteorological fields that are derived from meteorological models reflect the best characterization of the atmosphere. It is well known that the accuracy and overall representation of the modeled meteorological fields can be improved for retrospective simulations by creating dynamic analyses in which Newtonian relaxation, or “nudging,” is used throughout the simulation period. This article, the second of two parts, provides additional insight into the value of using nudging-based data assimilation for dynamic analysis in the meteorological fields for air quality modeling. Meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and forecasts for a summertime period. The resultant meteorological fields are then used for emissions processing and air quality simulations using the Community Multiscale Air Quality Modeling System (CMAQ). The predictions of surface and near-surface meteorological fields and ozone are compared with a small network of collocated meteorological and air quality observations. Comparisons of 2-m temperature, 10-m wind speed, and surface shortwave radiation show a significant degradation over time when nudging is not used, whereas the dynamic analyses maintain consistent statistical scores over time for those fields. Using nudging in MM5 to generate dynamic analyses, on average, leads to a CMAQ simulation of hourly ozone with smaller error. Domainwide error patterns in specific meteorological fields do not directly or systematically translate into error patterns in ozone prediction at these sites, regardless of whether nudging is used in MM5, but large broad-scale errors in shortwave radiation prediction by MM5 directly affect ozone prediction by CMAQ at specific sites.
Abstract
For air quality modeling, it is important that the meteorological fields that are derived from meteorological models reflect the best characterization of the atmosphere. It is well known that the accuracy and overall representation of the modeled meteorological fields can be improved for retrospective simulations by creating dynamic analyses in which Newtonian relaxation, or “nudging,” is used throughout the simulation period. This article, the second of two parts, provides additional insight into the value of using nudging-based data assimilation for dynamic analysis in the meteorological fields for air quality modeling. Meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and forecasts for a summertime period. The resultant meteorological fields are then used for emissions processing and air quality simulations using the Community Multiscale Air Quality Modeling System (CMAQ). The predictions of surface and near-surface meteorological fields and ozone are compared with a small network of collocated meteorological and air quality observations. Comparisons of 2-m temperature, 10-m wind speed, and surface shortwave radiation show a significant degradation over time when nudging is not used, whereas the dynamic analyses maintain consistent statistical scores over time for those fields. Using nudging in MM5 to generate dynamic analyses, on average, leads to a CMAQ simulation of hourly ozone with smaller error. Domainwide error patterns in specific meteorological fields do not directly or systematically translate into error patterns in ozone prediction at these sites, regardless of whether nudging is used in MM5, but large broad-scale errors in shortwave radiation prediction by MM5 directly affect ozone prediction by CMAQ at specific sites.
Abstract
An urban canopy parameterization (UCP) is implemented into the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to improve meteorological fields in the urban boundary layer for finescale (∼1-km horizontal grid spacing) simulations. The UCP uses the drag-force approach for dynamics and a simple treatment of the urban thermodynamics to account for the effects of the urban environment. The UCP is evaluated using a real-data application for Philadelphia, Pennsylvania. The simulations show that the UCP produces profiles of wind speed, friction velocity, turbulent kinetic energy, and potential temperature that are more consistent with the observations taken in urban areas and data from idealized wind tunnel studies of urban areas than do simulations that use the roughness approach. In addition, comparisons with meteorological measurements show that the UCP simulations are superior to those that use the roughness approach. This improvement of the treatment of the urban areas in the meteorological model could have implications for simulating air chemistry processes at this scale.
Abstract
An urban canopy parameterization (UCP) is implemented into the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to improve meteorological fields in the urban boundary layer for finescale (∼1-km horizontal grid spacing) simulations. The UCP uses the drag-force approach for dynamics and a simple treatment of the urban thermodynamics to account for the effects of the urban environment. The UCP is evaluated using a real-data application for Philadelphia, Pennsylvania. The simulations show that the UCP produces profiles of wind speed, friction velocity, turbulent kinetic energy, and potential temperature that are more consistent with the observations taken in urban areas and data from idealized wind tunnel studies of urban areas than do simulations that use the roughness approach. In addition, comparisons with meteorological measurements show that the UCP simulations are superior to those that use the roughness approach. This improvement of the treatment of the urban areas in the meteorological model could have implications for simulating air chemistry processes at this scale.
Abstract
Previous research has demonstrated the ability to use the Weather Research and Forecasting model (WRF) and contemporary dynamical downscaling methods to refine global climate modeling results to a horizontal grid spacing of 36 km. Environmental managers and urban planners have expressed the need for even finer resolution in projections of surface-level weather to take into account local geophysical and urbanization patterns. In this study, WRF as previously applied at 36-km grid spacing is used with 12-km grid spacing with one-way nesting to simulate the year 2006 over the central and eastern United States. The results at both resolutions are compared with hourly observations of surface air temperature, humidity, and wind speed. The 12- and 36-km simulations are also compared with precipitation data from three separate observation and analysis systems. The results show some additional accuracy with the refinement to 12-km horizontal grid spacing, but only when some form of interior nudging is applied. A positive bias in precipitation found previously in the 36-km results becomes worse in the 12-km simulation, especially without the application of interior nudging. Model sensitivity testing shows that 12-km grid spacing can further improve accuracy for certain meteorological variables when alternate physics options are employed. However, the strong positive bias found for both surface-level water vapor and precipitation suggests that WRF as configured here may have an unbalanced hydrologic cycle that is returning moisture from land and/or water bodies to the atmosphere too quickly.
Abstract
Previous research has demonstrated the ability to use the Weather Research and Forecasting model (WRF) and contemporary dynamical downscaling methods to refine global climate modeling results to a horizontal grid spacing of 36 km. Environmental managers and urban planners have expressed the need for even finer resolution in projections of surface-level weather to take into account local geophysical and urbanization patterns. In this study, WRF as previously applied at 36-km grid spacing is used with 12-km grid spacing with one-way nesting to simulate the year 2006 over the central and eastern United States. The results at both resolutions are compared with hourly observations of surface air temperature, humidity, and wind speed. The 12- and 36-km simulations are also compared with precipitation data from three separate observation and analysis systems. The results show some additional accuracy with the refinement to 12-km horizontal grid spacing, but only when some form of interior nudging is applied. A positive bias in precipitation found previously in the 36-km results becomes worse in the 12-km simulation, especially without the application of interior nudging. Model sensitivity testing shows that 12-km grid spacing can further improve accuracy for certain meteorological variables when alternate physics options are employed. However, the strong positive bias found for both surface-level water vapor and precipitation suggests that WRF as configured here may have an unbalanced hydrologic cycle that is returning moisture from land and/or water bodies to the atmosphere too quickly.
