Performance assessment of retrospective meteorological inputs for use in air quality modeling during TexAQS 2006

Abstract To achieve more accurate meteorological inputs than was used in the daily forecast for studying the TexAQS 2006 air quality, retrospective simulations were conducted using objective analysis and 3D/surface analysis nudging with surface and upper observations. Model ozone using the assimilated meteorological fields with improved wind fields shows better agreement with the observation compared to the forecasting results. In the post-frontal conditions, important factors for ozone modeling in terms of wind patterns are the weak easterlies in the morning for bringing in industrial emissions to the city and the subsequent clockwise turning of the wind direction induced by the Coriolis force superimposing the sea breeze, which keeps pollutants in the urban area. Objective analysis and nudging employed in the retrospective simulation minimize the wind bias but are not able to compensate for the general flow pattern biases inherited from large scale inputs. By using an alternative analyses data for initializing the meteorological simulation, the model can re-produce the flow pattern and generate the ozone peak location closer to the reality. The inaccurate simulation of precipitation and cloudiness cause over-prediction of ozone occasionally. Since there are limitations in the meteorological model to simulate precipitation and cloudiness in the fine scale domain (less than 4-km grid), the satellite-based cloud is an alternative way to provide necessary inputs for the retrospective study of air quality.

[1]  H. Pan,et al.  Nonlocal Boundary Layer Vertical Diffusion in a Medium-Range Forecast Model , 1996 .

[2]  Atul K. Jain,et al.  Modeling of global biogenic emissions for key indirect greenhouse gases and their response to atmospheric CO2 increases and changes in land cover and climate , 2005 .

[3]  David R. Stauffer,et al.  Multiscale four-dimensional data assimilation , 1994 .

[4]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[5]  J. Seinfeld,et al.  Overview of the Second Texas Air Quality Study (TexAQS II) and the Gulf of Mexico Atmospheric Composition and Climate Study (GoMACCS) , 2009 .

[6]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

[7]  B. Rappenglück,et al.  An analysis of the vertical structure of the atmosphere and the upper‐level meteorology and their impact on surface ozone levels in Houston, Texas , 2008 .

[8]  Barbara Barletta,et al.  Space‐based formaldehyde measurements as constraints on volatile organic compound emissions in east and south Asia and implications for ozone , 2007 .

[9]  L. Darby Cluster Analysis of Surface Winds in Houston, Texas, and the Impact of Wind Patterns on Ozone , 2005 .

[10]  T. Ho,et al.  Correcting photolysis rates on the basis of satellite observed clouds , 2007 .

[11]  D. Byun,et al.  Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System , 2006 .

[12]  E. Williams,et al.  A BAD AIR DAY IN HOUSTON , 2005 .

[13]  Robert C. Gilliam,et al.  Performance Assessment of New Land Surface and Planetary Boundary Layer Physics in the WRF-ARW , 2010 .

[14]  D. Stauffer,et al.  Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model. Part I: Experiments with Synoptic-Scale Data , 1990 .

[15]  G. Grell,et al.  Meteorological evaluation of a weather‐chemistry forecasting model using observations from the TEXAS AQS 2000 field experiment , 2005 .

[16]  Raul J. Alvarez,et al.  Dependence of daily peak O3 concentrations near Houston, Texas on environmental factors: Wind speed, temperature, and boundary-layer depth , 2011 .

[17]  J. Dudhia Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model , 1989 .

[18]  D. Byun,et al.  Chapter 2.17 Operational evaluation of the Eastern Texas air quality (ETAQ) forecasting system based on MM5/SMOKE/CMAQ , 2007 .

[19]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[20]  R. Draxler An Overview of the HYSPLIT_4 Modelling System for Trajectories, Dispersion, and Deposition , 1998 .

[21]  D. Stauffer,et al.  On Improving 4-km Mesoscale Model Simulations , 2006 .

[22]  D. Byun,et al.  Application of high resolution land use and land cover data for atmospheric modeling in the Houston-Galveston metropolitan area, Part I: Meteorological simulation results , 2008 .

[23]  Tanya L. Otte,et al.  The Impact of Nudging in the Meteorological Model for Retrospective Air Quality Simulations. Part I: Evaluation against National Observation Networks , 2008 .

[24]  Richard T. McNider,et al.  Mesoscale model performance with assimilation of wind profiler data: Sensitivity to assimilation parameters and network configuration , 2007 .

[25]  H. Frumkin,et al.  Ambient Air Pollution and Respiratory Emergency Department Visits , 2005, Epidemiology.

[26]  D. Byun,et al.  Classification of Weather Patterns and Associated Trajectories of High-Ozone Episodes in the Houston–Galveston–Brazoria Area during the 2005/06 TexAQS-II , 2011 .

[27]  David R. Stauffer,et al.  Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model Part II: Effects of Data Assimilation within the Planetary Boundary Layer , 1991 .

[28]  K. Wyat Appel,et al.  Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5 : Sensitivities impacting model performance Part I-Ozone , 2007 .