Air Quality, Atmosphere & Health

, Volume 12, Issue 1, pp 115–125 | Cite as

Weather research and forecasting model simulations over the Pearl River Delta Region

  • D. LopesEmail author
  • J. Ferreira
  • K. I. Hoi
  • A. I. Miranda
  • K. V. Yuen
  • K. M. Mok


Pearl River Delta (PRD), located in south-eastern coast of mainland China, is one of the regions affected by heavy particulate matter (PM) levels. Notwithstanding the potential use of meteorological and air quality modelling to characterize the air pollution problems, little attention has been paid to meteorological model configuration and its impact on air quality modelling applications over the region. Aiming to find the most suitable set of parameterization schemes of the Advanced Research Weather Research and Forecasting (WRF-ARW) model for air quality modelling applications over the PRD region, a performance experiment was performed. Three tests, with different combinations of parameterization schemes, were created and evaluated. For the best configuration modelling setup, meteorological simulations for a winter (i.e. January) and summer (i.e. July) periods are provided. The meteorological model showed a clockwise deviation for the wind direction and tends to overestimate the temperature and wind speed. It is expected that the present work could reduce the meteorological and air quality modelling uncertainty over the PRD region.


Pearl River Delta Parameterization schemes WRF-ARW Air quality modelling 



The authors wish to thank the Macau Meteorological and Geophysical Bureau for supplying the data and the NOAA Air Resources Laboratory (ARL) for the weather data used in this publication. The WRF modelling system was made available by the National Center for Atmospheric Research (NCAR). This work was performed in part at the High Performance Computing Cluster (HPCC) which is supported by Information and Communication Technology Office (ICTO) of the University of Macau.

Funding information

This study was supported by the Science and Technology Development Fund of the Macau SAR government under grant no. 079/2013/A3, the university multi-year research grant MYRG-2014-00038-FST of the research committee of University of Macau, and the university postgraduate studentship.

Supplementary material

11869_2018_636_MOESM1_ESM.docx (486 kb)
ESM 1 (DOCX 485 kb)


  1. Balzarini A, Angelini F, Ferrero L, Moscatelli M, Perrone MG, Pirovano G, Riva GM, Sangiorgi G, Toppetti AM, Gobbi GP, Bolzacchini E (2014) Sensitivity analysis of PBL schemes by comparing WRF model and experimental data. Geosci Model Dev Discuss 7:6133–6171. CrossRefGoogle Scholar
  2. Borge R, Alexandrov V, José del Vas J, Lumbreras J, Rodríguez E (2008) A comprehensive sensitivity analysis of the WRF model for air quality applications over the Iberian Peninsula. Atmos Environ 42:8560–8574. CrossRefGoogle Scholar
  3. Carvalheiro L (2014) Prevention and detection of fires. PhD thesis, University of AveiroGoogle Scholar
  4. Chan CK, Yao X (2008) Air pollution in mega cities in China. Atmos Environ 42:1–42. CrossRefGoogle Scholar
  5. Chen F, Dudhia J (2001) Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part II: preliminary model validation. Mon Weather Rev 129:587–604.<0587:CAALSH>2.0.CO;2 CrossRefGoogle Scholar
  6. Cheng FY, Chin SC, Liu TH (2012) The role of boundary layer schemes in meteorological and air quality simulations of the Taiwan area. Atmos Environ 54:714–727. CrossRefGoogle Scholar
  7. Cheng F-Y, Hsu Y-C, Lin P-L, Lin T-H (2013) Investigation of the effects of different land use and land cover patterns on mesoscale meteorological simulations in the Taiwan area. J Appl Meteorol Climatol 52(3):570–587Google Scholar
  8. Chou M-D, Suarez MJ (1994) An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech MemoGoogle Scholar
  9. Darmenova K, Sokolik IN, Shao Y, Marticorena B, Bergametti G (2009) Development of a physically based dust emission module within the weather research and forecasting (WRF) model: assessment of dust emission parameterizations and input parameters for source regions in central and east asia. J Geophys Res 114:1–28. CrossRefGoogle Scholar
  10. Di Z, Duan Q, Gong W et al (2014) Assessing WRF model parameter sensitivity: a case study with 5 day summer precipitation forecasting in the Greater Beijing Area. Geophys Res Lett 42:579–587.
  11. DSPA (Direcção dos Serviços de Protecção Ambiental) (2016) Report on the State of the Environment of Macao - 2015Google Scholar
  12. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107CrossRefGoogle Scholar
  13. Dyer AJ, Hicks BB (1970) Flux-gradient relationships in the constant flux layer. Q J R Meteorol Soc 96:715–721. CrossRefGoogle Scholar
  14. Emery C, Tai E, Yarwood G (2001) Enhanced meteorological modeling and performance evaluation for two Texas ozone episodes. Env Int Corp 235Google Scholar
  15. Fan Q, Lan J, Liu Y, Wang X, Chan P, Hong Y, Feng Y, Liu Y, Zeng Y, Liang G (2015) Process analysis of regional aerosol pollution during spring in the Pearl River Delta region, China. Atmos Environ 122:829–838. CrossRefGoogle Scholar
  16. Ferreira AP, Castanheira JM, Rocha A, Ferreira J (2008) Sensitivity study of the surface predictions in Portugal by WRF regarding the variation of the physical parameterizations. In: XXX Jornadas Científicas de la Associación Meteorológica Española. ZaragozaGoogle Scholar
  17. Heinke K, Sokhi RS (2008) Overview of tools and methods for meteorological and air pollution mesoscale model evaluation and user training. Join Report of COST Action 728, 25–26Google Scholar
  18. Hoi KI, Mok KM, Yuen KV, Pun MH (2013) Investigation of fine particulate pollution in a coastal city with a mobile monitoring platform. Glob Nest J 15:178–187CrossRefGoogle Scholar
  19. Hong S, Lim J (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151Google Scholar
  20. Hong S, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341. CrossRefGoogle Scholar
  21. Hong S-Y, Lim K-SS, Lee Y-H, Ha JC, Kim HW, Ham SJ, Dudhia J (2010) Evaluation of the WRF double-moment 6-class microphysics scheme for precipitating convection. Adv Meteorol 2010:1–10. CrossRefGoogle Scholar
  22. Hu J, Wu Y, Wang Z, Li Z, Zhou Y, Wang H, Bao X, Hao J (2012) Real-world fuel efficiency and exhaust emissions of light-duty diesel vehicles and their correlation with road conditions. J Environ Sci Health A Tox Hazard Subst Environ Eng 24:865–874. Google Scholar
  23. Janjic Z, Black T, Pyle M, et al (2014) Weather research & forecasting NMM version 3 modeling system user’s guideGoogle Scholar
  24. Kim J, Lee C, Belorid M, Zhao P (2011) A study of sensitivity of WRF simulation to microphysics parameterizations, slope option and analysis nudging in Haean Basin, South Korea BayceerUni-BayreuthDe 77–84Google Scholar
  25. Koo YS, Kim ST, Cho JS, Jang YK (2012) Performance evaluation of the updated air quality forecasting system for Seoul predicting PM10. Atmos Environ 58:56–69. CrossRefGoogle Scholar
  26. Kumar R, Naja M, Pfister GG, Barth MC, Brasseur GP (2012) Simulations over South Asia using the Weather Research and Forecasting model with Chemistry (WRF-Chem): set-up and meteorological evaluation. Geosci Model Dev 5:321–343. CrossRefGoogle Scholar
  27. Kwok RHF, Fung JCH, Lau AKH, Fu JS (2010) Numerical study on seasonal variations of gaseous pollutants and particulate matters in Hong Kong and Pearl River Delta Region. J Geophys Res 115:D16308. CrossRefGoogle Scholar
  28. Li M, Ma Y, Hu Z, Ishikawa H, Oku Y (2009) Snow distribution over the Namco lake area of the Tibetan Plateau. Hydrol Earth Syst Sci Discuss 6:843–857. CrossRefGoogle Scholar
  29. Li Q, Guo Y, Song J-Y, Song Y, Ma J, Wang HJ (2018) Impact of long-term exposure to local PM10 on children’s blood pressure: a Chinese national cross-sectional study. Air Qual Atmos Health 11:705–713. CrossRefGoogle Scholar
  30. Liao Z, Sun J, Liu J et al (2018) Long-term trends in ambient particulate matter, chemical composition, and associated health risk and mortality burden in Hong Kong (1995-2016). Air Qual Atmos Health:773–783.
  31. Lim K-SS, Hong S-Y (2010) Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon Weather Rev 138:1587–1612. CrossRefGoogle Scholar
  32. Lopes D, Hoi KI, Mok KM et al (2016) Air quality in the main cities of the Pearl River Delta Region. Glob NEST J 18:794–802CrossRefGoogle Scholar
  33. Maussion F, Scherer D, Finkelnburg R, Richters J, Yang W, Yao T (2011) WRF simulation of a precipitation event over the Tibetan Plateau, China - an assessment using remote sensing and ground observations. Hydrol Earth Syst Sci 15:1795–1817. CrossRefGoogle Scholar
  34. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102:16663–16682. CrossRefGoogle Scholar
  35. Mohan M, Bhati S (2011) Analysis of WRF model performance over subtropical region of Delhi, India. Adv Meteorol 2011:1–13. CrossRefGoogle Scholar
  36. Mok KM, Hoi KI (2005) Effects of meteorological conditions on PM10 concentrations - a study in Macau. Environ Monit Assess 102:201–223. CrossRefGoogle Scholar
  37. NOAA (2015) NOAA - National Oceanic and AtmosphericGoogle Scholar
  38. Park S-Y, Lee H-W, Lee S-H, Kim D-H (2010) Impact of wind profiler data assimilation on wind field assessment over coastal areas. Asian J Atmos Environ 4:198–210. CrossRefGoogle Scholar
  39. Paulson CA (1970) The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J Appl Meteorol 9:857–861.<0857:TMROWS>2.0.CO;2 CrossRefGoogle Scholar
  40. Rogers E, Black T, Ferrier B, et al (2001) Changes to the NCEP Meso eta analysis and forecast system: increase in resolution, new cloud microphysics, modified precipitation assimilation, modified 3DVAR analysisGoogle Scholar
  41. Shrivastava R, Dash SK, Oza RB, Hegde MN (2015) Evaluation of parameterization schemes in the Weather Research and Forecasting (WRF) model: a case study for the Kaiga nuclear power plant site. Ann Nucl Energy 75:693–702. CrossRefGoogle Scholar
  42. Skamarock WC (2005) Why is there more than one dynamical core in WRF? A technical perspective. Colorado, BoulderGoogle Scholar
  43. Skamarock WC, Klemp JB, Dudhi J, et al (2008) A description of the advanced research WRF version 3Google Scholar
  44. SMG (2015) Macau Meteorological and Geophysical BureauGoogle Scholar
  45. Wang XY, Wang KC (2014) Estimation of atmospheric mixing layer height from radiosonde data. Atmos Meas Tech 7:1701–1709. CrossRefGoogle Scholar
  46. Webb EK (1970) Profile relationships: the log-linear range, and extension to strong stability. Q J R Meteorol Soc 96:67–90. CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Civil & Environmental EngineeringUniversity of MacauTaipaChina
  2. 2.Department of Environment and Planning & CESAMUniversity of AveiroAveiroPortugal

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