Impact of inherent meteorology uncertainty on air quality model predictions

Robert C. Gilliam1, Christian Hogrefe1, James M. Godowitch1, Sergey L. Napelenok1, Rohit Mathur1, S. Trivikrama Rao1,2
1Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
2Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina, USA

Tóm tắt

AbstractIt is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is important to understand how uncertainties in these inputs affect the simulated concentrations. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. Most studies explore this uncertainty by running different meteorological models or the same model with different physics options and in some cases combinations of different meteorological and air quality models. While these have been shown to be useful techniques in some cases, we present a technique that leverages the initial condition perturbations of a weather forecast ensemble, namely, the Short‐Range Ensemble Forecast system to drive the four‐dimensional data assimilation in the Weather Research and Forecasting (WRF)‐Community Multiscale Air Quality (CMAQ) model with a key focus being the response of ozone chemistry and transport. Results confirm that a sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, can cause a relatively large range of ozone‐mixing ratios. Pollutant transport can be altered by hundreds of kilometers over several days. Ozone‐mixing ratios of the ensemble can vary as much as 10–20 ppb or 20–30% in areas that typically have higher pollution levels.

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Tài liệu tham khảo

10.5194/gmd-7-2817-2014

10.5194/gmd-6-883-2013

Astitha M. S. T.Rao J.YangandH.Luo(2015) Inherent uncertainty in the prediction of ozone and particulate matter for NE US Presentation Fourteenth Annual Community Analysis and Modeling System Conf. Chapel Hill NC University of North Carolina Institute for the Environment. [Available athttps://www.cmascenter.org/conference/2015/agenda.cfm#tooltip1722.]

10.5194/acp-14-317-2014

10.1175/1520-0434(1994)009<0265:TNNMEM>2.0.CO;2

10.1115/1.2128636

Carvalho A. C. L.Menut R.Vautard andJ.Nicolau(2008) Air quality ensemble forecast coupling ARPEGE and CHIMERE over western Europe NATO Science for Peace and Security Series C: Environmental Security Air Pollution Modeling and Its Application XIX 367 375.

10.1016/S1352-2310(00)00141-2

Dabberdt W. F. et al. (2003) Meteorological research needs for improved air quality forecasting: Report of the 11th Prospectus Development Team of the U.S. Weather Research Program Tech. Rep. Natl. Cent. for Atmos. Res. Boulder Colo.

10.1007/BF00208992

10.1016/j.atmosenv.2009.11.007

Du J. andM. S.Tracton(2001) Implementation of a real‐time short‐range ensemble forecasting system at NCEP: An update Preprints 9th Conference on Mesoscale Processes Ft. Lauderdale Florida Am. Meteorol. Soc. 355‐356.

Du J. G.DiMego M. S.Tracton andB.Zhou(2003) NCEP short‐range ensemble forecasting (SREF) system: multi‐IC multi‐model and multi‐physics approach Research Activities in Atmospheric and Oceanic Modelling edited by J. Cote Report 33 CAS/JSC Working Group Numerical Experimentation (WGNE) WMO/TD‐No. 1161 5.09‐5.10.

Du J. G.Dimego Z.Toth D.Jovic B.Zhou J.Zhu J.Wang andH.Juang(2009) Recent upgrade of NCEP short‐range ensemble forecast (SREF) system presented at the 23rd Conference on Weather Analysis and Forecasting/19th Conference on Numerical Weather Prediction June.

Du J. G.DiMego B.Zhou D.Jovic B.Ferrier B.Yang andS.Benjamin(2014) NCEP regional ensembles: Evolving toward hourly‐updated convection‐allowing scale and storm‐scale predictions within a unified regional modeling system 22nd Conf. on Numerical Weather Prediction and 26th Conf. on Weather Analysis and Forecasting Atlanta GA Am. Meteorol. Soc. Feb. 1–6 2014 Pap. J1.4.

Flemming J. V.‐H.Peuch R.Engelen andJ. W.Kaiser(2013) A European global‐to‐regional air pollution forecasting system environmental manager pp. 6–10 Nov.

10.5194/acp-13-7153-2013

10.1175/2009JAMC2126.1

10.1016/j.atmosenv.2006.01.023

10.1016/j.atmosenv.2011.10.064

10.1016/j.atmosenv.2011.04.062

10.5094/APR.2015.034

Grell G. A. J.Dudhia andD.Stauffer(1994) A description of the fifth‐generation Penn State/NCAR mesoscale model (MM5) NCAR Tech. Note 138 pp. TN‐398 + STR National Center for Atmospheric Research Boulder Colo.

10.1175/1520-0450(1987)026<0410:TVOTM>2.0.CO;2

10.1002/2014JD021504

10.1175/1520-0493(1990)118<1429:TSMCPP>2.0.CO;2

10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2

10.1175/1520-0493(1994)122<0003:TNNRSM>2.0.CO;2

10.1016/1352-2310(96)00017-9

10.1016/j.atmosenv.2013.08.017

10.1016/0004-6981(84)90179-3

10.1175/1520-0493(1974)102<0409:TSOMCF>2.0.CO;2

10.1175/1520-0426(1989)006<0759:MDNFMA>2.0.CO;2

Mathur R. et al. (2004) Adaption and application if the Community Multiscale Air Quality (CMAQ) modeling system for real‐time air quality forecasting during the summer of 2004 Models‐3 Conference Chapel Hill NC 18–20 Oct.

10.1007/978-94-007-1359-8_30

Mathur R., 2013, Air Pollution Modeling and its Application XXII, 303

10.1029/2005JD005858

10.1016/S1352-2310(03)00475-8

10.1029/2005JD006310

10.1029/2005JD006917

Monache L. D., 2007, Air Pollution Modeling and Its Application XVII, 399

NARSTO(2000) An assessment of tropospheric ozone pollution—A north american perspective NARSTO Management Office (Envair) Pasco Wash. [Available athttp://narsto.org.]

10.1175/2007JAMC1790.1

Otte T. L. J. E.Pleim andG.Pouliot(2004) PREMAQ: A new pre‐processor to CMAQ for air‐quality forecasting Presented at 2004 Models‐3 Conference Chapel Hill NC 18–20 Oct.

10.1016/S1352-2310(97)00435-4

10.1175/2009JAMC2053.1

10.1175/1520-0450(2003)042<1811:DOALSM>2.0.CO;2

10.5194/acp-13-9695-2013

10.1016/S1352-2310(99)00466-5

10.1175/1520-0450(1995)034<1739:AMFDDA>2.0.CO;2

10.1016/1352-2310(95)00268-5

Skamarock W. C. J. B.Klemp J.Dudhia D. O.Gill D. M.Barker M. G.Duda X.‐Y.Huang W.Wang andJ. G.Powers(2008) A description of the advanced research WRF version 3 NCAR Tech. Note NCAR/TN 475 STR 125 pp. Boulder Colo.

10.1175/1520-0493(1990)118<1250:UOFDDA>2.0.CO;2

10.1175/1520-0450(1994)033<0416:MFDDA>2.0.CO;2

10.1175/1520-0493(1991)119<0734:UOFDDA>2.0.CO;2

10.1175/1520-0493(1999)127<0433:UEFSRF>2.0.CO;2

10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2

TractonM. S. J.Du Z.Toth andH.Juang(1998) Short‐range ensemble forecasting (SREF) at NCEP/EMC Preprints 12th Conf. on Numerical Weather Prediction Phoenix Am. Meteorol. Soc. Pap. 11.4 269‐272.

10.1175/1520-0493(1988)116<2276:TROIID>2.0.CO;2

U.S. EPA(2007) Guidance on the use of models and other analyses for demonstrating attainment of air quality goals for ozone PM2.5 and regional haze EPA ‐454/B‐07‐002 262 pp. [Available athttp://www.epa.gov/ttn/scram/guidance/guide/final‐03‐pm‐rh‐guidance.pdf.]

10.1111/j.1600-0870.2007.00273.x

10.1016/j.atmosenv.2009.12.029

Wong D. C., 2012, WRF‐CMAQ Two‐way Coupled System With Aerosol Feedback: Software Development and Preliminary Results, 299

Yarwood G. S.Rao M.Yocke andG.Whitten(2005) Updates to the carbon bond chemical mechanism: CB05 Final Rep. to the U.S. EPA RT‐0400675. [Available athttp://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf.]