Sensitivity of mesoscale dust simulation to WRF_Chem boundary layer scheme (Case study: March 14th 2012)
,
Abstract
Introduction: Recently, local dust events increased in Khuzestan province. Therefore, knowledge on its properties can have a crucial role in future prediction and planning.
Materials and methods: This study investigated the effect of different boundary layer schemes for dust simulation by WRF_Chem model on March 14th 2012 in Khuzestan province. To validate the model, observation data such as horizontal visibility, 10-m wind speed and PM10 were provided.
Results: The results indicated that the MYN scheme has the highest correlation between model outputs and observation for 10-m wind speed, PM10 and horizontal visibility. Due to the highest correlation of the 10 m wind speed, horizontal visibility, PM10 respectively with 0.83, -0.76 and 0.76 values and the highest consistency with the day-night variation of PM10, MYN scheme can be selected as the most suitable scheme. At the second level, UW scheme seems to be an appropriate option. In MYN and UW schemes, the maximum wind speed in 925 hPa level was estimated 24 m/s at 03 UTC, March 14th which caused an increase in the 10 m wind speed at 06 and 09UTC. Therefore, the dust emitted from the surface to the air. Although the results of MYJ scheme showed proper correlation and temporal variation with observed, but as it determined PM10 concentration with high difference, it can’t be considered as a suitable scheme for simulation dust concentration.
Conclusion: Although the PM10 concentration obtained by WRF_Chem showed difference with the observation for all the selected boundary layer schemes, MYN scheme gives the most appropriate result.
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21. Bao JW, Michelson SA, Persson PO, Djalalova IV, Wilczak JM. Observed and WRF-simulated low-level
winds in a high-ozone episode during the Central California Ozone Study. Journal of Applied Meteorology and Climatology. 2008 Sep;47(9):2372-94.
22. Cheng FY, Chin SC, Liu TH. The role of boundary layer schemes in meteorological and air quality simulations
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23. Gilliam RC, Godowitch JM, Rao ST. Improving the horizontal transport in the lower troposphere with four dimensional data assimilation. Atmospheric environment. 2012 Jun 1;53:186-201.
24. Hu XM, Doughty DC, Sanchez KJ, Joseph E, Fuentes JD. Ozone variability in the atmospheric boundary layer in Maryland and its implications for vertical transport model. Atmospheric Environment. 2012 Jan 1;46:354-64
25. Storm B, Dudhia J, Basu S, Swift A, Giammanco I. Evaluation of the weather research and forecasting model on forecasting low‐level jets: Implications for wind energy. Wind Energy: An International Journal for
Progress and Applications in Wind Power Conversion Technology. 2009 Jan;12(1):81-90.
26. García‐Díez M, Fernández J, Fita L, Yagüe C. Seasonal dependence of WRF model biases and sensitivity to PBL
schemes over Europe. Quarterly Journal of the Royal Meteorological Society. 2013 Jan 1;139(671):501-14.
27. Bossioli E, Tombrou M, Dandou A, Athanasopoulou E, Varotsos KV. The role of planetary boundary-layer parameterizations in the air quality of an urban area with complex topography. Boundary-layer meteorology.
2009 Apr 1;131(1):53-72.
28. Hu XM, Nielsen-Gammon JW, Zhang F. Evaluation of three planetary boundary layer schemes in the WRF
model. Journal of Applied Meteorology and Climatology. 2010 Sep;49(9):1831-44.
29. Troen IB, Mahrt L. A simple model of the atmospheric boundary layer; sensitivity to surface evaporation.
Boundary-Layer Meteorology. 1986 Oct 1;37(1-2):129- 48.
30. Stull RB. Mean boundary layer characteristics. InAn Introduction to Boundary Layer Meteorology 1988 (pp. 1-27). Springer, Dordrecht.
31. Stensrud DJ. Parameterization schemes: keys to understanding numerical weather prediction models. Cambridge
University Press; 2009 Dec 3.
32. Stull RB. Static stability—An update. Bulletin of the American Meteorological Society. 1991 Oct;72(10):1521-30.
33. Misenis C, Hu XM, Krishnan S, Zhang Y, Fast J. Sensitivity of WRF/Chem predictions to meteorological
schemes. In86th Annual Conference/14th Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA, Atlanta, GA, USA 2006 Jan (Vol.27).
34. Xie B, Fung JC, Chan A, Lau A. Evaluation of nonlocal and local planetary boundary layer schemes in the WRF
model. Journal of Geophysical Research: Atmospheres. 2012 Jun 27;117(D12).
35. Alapaty K, Raman S, Niyogi DS. Uncertainty in the specification of surface characteristics: A study of prediction errors in the boundary layer. Boundary-Layer Meteorology. 1997 Mar 1;82(3):475-502.
36. Banks RF, Tiana-Alsina J, Baldasano JM, Rocadenbosch F, apayannis A, Solomos S, Tzanis CG. Sensitivity
of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, and radiosondes during the HygrA-CD campaign. Atmospheric Research. 2016 Jul 1;176:185- 201.
37. Hu XM, Klein PM, Xue M. Evaluation of the updated YSU planetary boundary layer scheme within WRF for wind resource and air quality assessments. Journal of Geophysical Research: Atmospheres. 2013 Sep 27;118(18):10-490.
38. Jankov I, Gallus Jr WA, Segal M, Shaw B, Koch SE. The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall. Weather and forecasting. 2005 Dec;20(6):1048-60.
39. Jankov I, Schultz PJ, Anderson CJ, Koch SE. The impact of different physical parameterizations and
their interactions on cold season QPF in the American River basin. Journal of Hydrometeorology. 2007Oct;8(5):1141-51.
40. Li X, Pu Z. Sensitivity of numerical simulation of early rapid intensification of Hurricane Emily (2005) to cloud microphysical and planetary boundary layer parameterizations. Monthly Weather Review. 2008 Dec;136(12):4819-38.
41. Gan CM, Wu Y, Madhavan BL, Gross B, Moshary F. Application of active optical sensors to probe the vertical
structure of the urban boundary layer and assess anomalies in air quality model PM2.5 forecasts. Atmospheric environment. 2011 Dec 1;45(37):6613-21.
42. Zhang Y, Bocquet M, Mallet V, Seigneur C, Baklanov A. Real-time air quality forecasting, part I: History,
techniques, and current status. Atmospheric Environment. 2012 Dec 1;60:632-55.
43. Misenis C, Zhang Y. An examination of sensitivity of WRF/Chem predictions to physical parameterizations,
horizontal grid spacing, and nesting options. Atmospheric Research. 2010 Aug 1;97(3):315-34.
44. Werner M, Kryza M, Ojrzynska H, Skjoth CA, Walaszek K, Dore AJ. Application of WRF-Chem to forecasting
PM10 concentration over Poland. International Journal
of Environment and Pollution. 2015;58(4):280-92.
45. Rizza U, Barnaba F, Miglietta MM, Mangia C, Di Liberto L, Dionisi D, Costabile F, Grasso F, Gobbi GP. WRF-Chem model simulations of a dust outbreak over the central Mediterranean and comparison with multisensor desert dust observations. Atmospheric Chemistry and Physics. 2017;17(1):93.
46. WRF-Chem Tutorial, Feb. 6, 2017
2. Marsham JH, Parker DJ, Grams CM, Johnson BT, Grey WM, Ross AN. Observations of mesoscale and boundary-
layer scale circulations affecting dust transport and uplift over the Sahara. Atmospheric Chemistry and Physics. 2008;8(23):6979-93.
3. Cavazos-Guerra C, Todd MC. Model simulations of complex dust emissions over the Sahara during the West African monsoon onset. Advances in Meteorology. 2012;2012.
4. Engelstaedter S, Tegen I, Washington R. North African dust emissions and transport. Earth-Science Reviews.
2006 Nov 1;79(1-2):73-100.
5. Chaboureau JP, Tulet P, Mari C. Diurnal cycle of dust and cirrus over West Africa as seen from Meteosat Second
Generation satellite and a regional forecast model. Geophysical research letters. 2007 Jan;34(2).
6. Warner TT, Sheu RS. Multiscale local forcing of the Arabian Desert daytime boundary layer, and implications for the dispersion of surface-released contaminants. Journal of Applied Meteorology. 2000 May;39(5):686-707.
7. Chen F, Dudhia J. Coupling an advanced land surface– hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Monthly Weather Review. 2001 Apr;129(4):569-85.
8. Chen S, Huang J, Zhao C, Qian Y, Leung LR, Yang B. Modeling the transport and radiative forcing of Taklimakan dust over the Tibetan Plateau: A case study in the summer of 2006. Journal of Geophysical Research: Atmospheres. 2013 Jan 16;118(2):797-812.
9. Lu H, Shao Y. A new model for dust emission by saltation bombardment. Journal of Geophysical Research:
Atmospheres. 1999 Jul 27;104(D14):16827-42.
10. Tegen I. Modeling the mineral dust aerosol cycle in the climate system. Quaternary Science Reviews. 2003 Sep
1;22(18-19):1821-34.
11. Shao Y, Ishizuka M, Mikami M, Leys JF. Parameterization of size‐resolved dust emission and validation with
measurements. Journal of Geophysical Research: Atmospheres. 2011 Apr 27;116(D8).
12. Allen CJ, Washington R. The low‐level jet dust emission mechanism in the central Sahara: Observations from Bordj‐Badji Mokhtar during the June 2011 Fennec Intensive Observation Period. Journal of Geophysical Research: Atmospheres. 2014 Mar 27;119(6):2990-3015.
13. Marticorena B, Bergametti G. Modeling the atmospheric dust cycle: 1. Design of a soil‐derived dust
emission scheme. Journal of Geophysical Research: Atmospheres. 1995 Aug 20;100(D8):16415-30.
14. Pokharel A, Kaplan M. Dust Climatology of the NASA Dryden Flight Research Center (DFRC) in Lancaster, California, USA. Climate. 2017 Mar;5(1):15.
15. Washington R, Todd MC. Atmospheric controls on mineral dust emission from the Bodélé Depression, Chad: The role of the low level jet. Geophysical Research Letters. 2005 Sep;32(17).
16. Daum PH, Kleinman LI, Springston SR, Nunnermacker LJ, Lee YN, Weinstein‐Lloyd J, Zheng J, Berkowitz CM. A comparative study of O3 formation in the Houston urban and industrial plumes during the 2000 Texas Air Quality Study. Journal of Geophysical Research: Atmospheres. 2003 Dec 16;108(D23).
17. Banta RM, Senff CJ, Nielsen-Gammon J, Darby LS, Ryerson TB, Alvarez RJ, Sandberg SP, Williams EJ, Trainer M. A bad air day in Houston. Bulletin of the American Meteorological Society. 2005 May;86(5):657-70.
18. Zhang F, Bei N, Nielsen‐Gammon JW, Li G, Zhang R, Stuart A, Aksoy A. Impacts of meteorological uncertainties
on ozone pollution predictability estimated through meteorological and photochemical ensemble forecasts. Journal of Geophysical Research: Atmospheres. 2007 Feb 27;112(D4).
19. Storm B, Basu S. The WRF model forecast-derived low-level wind shear climatology over the United States Great Plains. Energies. 2010;3(2):258-76.
20. Carvalho D, Rocha A, Gómez-Gesteira M, Santos C. A sensitivity study of the WRF model in wind simulation
for an area of high wind energy. Environmental Modelling & Software. 2012 Jul 1;33:23-34.
21. Bao JW, Michelson SA, Persson PO, Djalalova IV, Wilczak JM. Observed and WRF-simulated low-level
winds in a high-ozone episode during the Central California Ozone Study. Journal of Applied Meteorology and Climatology. 2008 Sep;47(9):2372-94.
22. Cheng FY, Chin SC, Liu TH. The role of boundary layer schemes in meteorological and air quality simulations
of the Taiwan area. Atmospheric environment. 2012 Jul 1;54:714-27.
23. Gilliam RC, Godowitch JM, Rao ST. Improving the horizontal transport in the lower troposphere with four dimensional data assimilation. Atmospheric environment. 2012 Jun 1;53:186-201.
24. Hu XM, Doughty DC, Sanchez KJ, Joseph E, Fuentes JD. Ozone variability in the atmospheric boundary layer in Maryland and its implications for vertical transport model. Atmospheric Environment. 2012 Jan 1;46:354-64
25. Storm B, Dudhia J, Basu S, Swift A, Giammanco I. Evaluation of the weather research and forecasting model on forecasting low‐level jets: Implications for wind energy. Wind Energy: An International Journal for
Progress and Applications in Wind Power Conversion Technology. 2009 Jan;12(1):81-90.
26. García‐Díez M, Fernández J, Fita L, Yagüe C. Seasonal dependence of WRF model biases and sensitivity to PBL
schemes over Europe. Quarterly Journal of the Royal Meteorological Society. 2013 Jan 1;139(671):501-14.
27. Bossioli E, Tombrou M, Dandou A, Athanasopoulou E, Varotsos KV. The role of planetary boundary-layer parameterizations in the air quality of an urban area with complex topography. Boundary-layer meteorology.
2009 Apr 1;131(1):53-72.
28. Hu XM, Nielsen-Gammon JW, Zhang F. Evaluation of three planetary boundary layer schemes in the WRF
model. Journal of Applied Meteorology and Climatology. 2010 Sep;49(9):1831-44.
29. Troen IB, Mahrt L. A simple model of the atmospheric boundary layer; sensitivity to surface evaporation.
Boundary-Layer Meteorology. 1986 Oct 1;37(1-2):129- 48.
30. Stull RB. Mean boundary layer characteristics. InAn Introduction to Boundary Layer Meteorology 1988 (pp. 1-27). Springer, Dordrecht.
31. Stensrud DJ. Parameterization schemes: keys to understanding numerical weather prediction models. Cambridge
University Press; 2009 Dec 3.
32. Stull RB. Static stability—An update. Bulletin of the American Meteorological Society. 1991 Oct;72(10):1521-30.
33. Misenis C, Hu XM, Krishnan S, Zhang Y, Fast J. Sensitivity of WRF/Chem predictions to meteorological
schemes. In86th Annual Conference/14th Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA, Atlanta, GA, USA 2006 Jan (Vol.27).
34. Xie B, Fung JC, Chan A, Lau A. Evaluation of nonlocal and local planetary boundary layer schemes in the WRF
model. Journal of Geophysical Research: Atmospheres. 2012 Jun 27;117(D12).
35. Alapaty K, Raman S, Niyogi DS. Uncertainty in the specification of surface characteristics: A study of prediction errors in the boundary layer. Boundary-Layer Meteorology. 1997 Mar 1;82(3):475-502.
36. Banks RF, Tiana-Alsina J, Baldasano JM, Rocadenbosch F, apayannis A, Solomos S, Tzanis CG. Sensitivity
of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, and radiosondes during the HygrA-CD campaign. Atmospheric Research. 2016 Jul 1;176:185- 201.
37. Hu XM, Klein PM, Xue M. Evaluation of the updated YSU planetary boundary layer scheme within WRF for wind resource and air quality assessments. Journal of Geophysical Research: Atmospheres. 2013 Sep 27;118(18):10-490.
38. Jankov I, Gallus Jr WA, Segal M, Shaw B, Koch SE. The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall. Weather and forecasting. 2005 Dec;20(6):1048-60.
39. Jankov I, Schultz PJ, Anderson CJ, Koch SE. The impact of different physical parameterizations and
their interactions on cold season QPF in the American River basin. Journal of Hydrometeorology. 2007Oct;8(5):1141-51.
40. Li X, Pu Z. Sensitivity of numerical simulation of early rapid intensification of Hurricane Emily (2005) to cloud microphysical and planetary boundary layer parameterizations. Monthly Weather Review. 2008 Dec;136(12):4819-38.
41. Gan CM, Wu Y, Madhavan BL, Gross B, Moshary F. Application of active optical sensors to probe the vertical
structure of the urban boundary layer and assess anomalies in air quality model PM2.5 forecasts. Atmospheric environment. 2011 Dec 1;45(37):6613-21.
42. Zhang Y, Bocquet M, Mallet V, Seigneur C, Baklanov A. Real-time air quality forecasting, part I: History,
techniques, and current status. Atmospheric Environment. 2012 Dec 1;60:632-55.
43. Misenis C, Zhang Y. An examination of sensitivity of WRF/Chem predictions to physical parameterizations,
horizontal grid spacing, and nesting options. Atmospheric Research. 2010 Aug 1;97(3):315-34.
44. Werner M, Kryza M, Ojrzynska H, Skjoth CA, Walaszek K, Dore AJ. Application of WRF-Chem to forecasting
PM10 concentration over Poland. International Journal
of Environment and Pollution. 2015;58(4):280-92.
45. Rizza U, Barnaba F, Miglietta MM, Mangia C, Di Liberto L, Dionisi D, Costabile F, Grasso F, Gobbi GP. WRF-Chem model simulations of a dust outbreak over the central Mediterranean and comparison with multisensor desert dust observations. Atmospheric Chemistry and Physics. 2017;17(1):93.
46. WRF-Chem Tutorial, Feb. 6, 2017
| Files | ||
| Issue | Vol 4 No 3 (2019): Summer 2019 | |
| Section | Original Research | |
| DOI | https://doi.org/10.18502/japh.v4i3.1547 | |
| Keywords | ||
| Mesoscale dust; WRF_Chem model; Boundary layer scheme (PBL); PM10; 10-m wind speed | ||
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Hassan E, Ghafarian P, Bahrami F, Karimkhani M, Sabori M. Sensitivity of mesoscale dust simulation to WRF_Chem boundary layer scheme (Case study: March 14th 2012). JAPH. 2019;4(3):171-186.
