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Suitability of WRF for air quality simulations: comparison of two dynamical cores Oriol Jorba 1, Thomas Loridan 2, Pedro Jiménez-Guerrero 1, José M. Baldasano.

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Presentation on theme: "Suitability of WRF for air quality simulations: comparison of two dynamical cores Oriol Jorba 1, Thomas Loridan 2, Pedro Jiménez-Guerrero 1, José M. Baldasano."— Presentation transcript:

1 Suitability of WRF for air quality simulations: comparison of two dynamical cores Oriol Jorba 1, Thomas Loridan 2, Pedro Jiménez-Guerrero 1, José M. Baldasano 1,3 Corresponding author: 1 Barcelona Supercomputing Center, Earth Sciences Department, Barcelona 2 Kings College London, Department of Geography, London 3 Environmental Modelling Laboratory, Technical University of Catalonia, Barcelona EGU 2008, Vienna, April 16th, 2008

2 Evaluation of the two dynamical cores of the WRF modelling system: WRF-NMM WRF-ARW 12 km horizontal resolution for a European domain Annual simulation for 2004 – surface and boundary layer Temperature Moisture Wind speed Wind direction Objective To analyse the skills of meteorological mesoscale models as drivers of air quality modelling systems

3 Sistema CALIOPE WRF-ARW current driver of CALIOPE air quality modelling system

4 Common air pollution episodes Strong anthropogenic PM episode over western Europe - wintertime Strong anthropogenic O 3 episode over Europe - summertime Common characteristics: high pressure, low winds High stability, cold temperatureHigh mixing, warm temperature 2 important episodes of air quality pollution during 2004: February 2004: PM and primary pollutants episode over Europe July-August 2004: O 3 season

5 WRF v2.2.1 WRF-NMM Time step: 30 s NX,NY,NZ: 360, 564, 38 Dx, Dy: 0.078º, º (~12km) PTOP: 50 hPa Hydrostatic WRF-ARW Time step: 72 s NX, NY, NZ: 481, 401, 38 Dx, Dy: 12 km PTOP: 50 hPa Hydrostatic IC-BC: 6h 1-degree FNL analysis 366*36 h simulation -12 hours of cold-start 2004 annual simulation: Model configuration Microphysics: Ferrier LW, SW radiation: GFDL Surface-layer: Janjic scheme Land-surface: NMM LSM Boundary layer: Mellor-Yamada-Janjic Cumulus scheme: Betts-Miller-Janjic Microphysics: WSM 3-class LW, SW radiation: RRTM, Dudhia scheme Surface-layer: Monin-Obukhov scheme Land-surface: Noah LSM Boundary layer: YSU scheme Cumulus scheme: Kain-Fritsch

6 Evaluation against 2 datasets 931 surface meteorological stations from the NCEP ADP Global Surface Observations archive (includes METAR, SYNOP, AWOS and ASOS) 61 meteorological soundings from European network of radiosoundings

7 Classical Statistics

8 Surface evaluation: 931 surface meteorological stations from METAR and SYNOP network

9 Performance of the models: general overview ARW Temp MAE (C) NMM Temp MAE (C) ARW SPD MAE (m/s) NMM SPD MAE (m/s) We have identified problems with NMM simulation for May month related to technical aspects. For the following evaluation, the period 29/4/2004 to 26/5/2004 is not taken into account.

10 Monthly evolution of mean surface errors Monthly evolution of mean surface errors (MAE, MBE) Temperature and moisture: underprediction of ARW/ overprediction of NMM. Similar absolute errors. Increased underestimation of ARW during spring Wind speed overestimation – similar errors. Lower wind speed error during summertime didi d iobs + - (ºC) (deg) (ºC) (m/s)

11 2m Temperature Monthly errors – January to March ARW NMM Good performance over continental flat terrain Major problems - complex terrain and coastal areas with complex topography (Mediterranean sea). Jan 2 m Temp MAE (º C) Mar 2 m Temp MAE (º C) Feb 2 m Temp MAE (º C) Lower MAE – NMM

12 2m Temperature Monthly errors – July to September ARW NMM Stagnant situation over the Mediterranean and low baric gradients over Europe Summertime - increased errors, but generally below 2.5ºC Lower errors with NMM in western and central Europe Jul 2 m Temp MAE (º C) Sep 2 m Temp MAE (º C) Aug 2 m Temp MAE (º C)

13 2m TemperatureMonthly errors – October to December 2m Temperature Monthly errors – October to December ARW NMM Coastal areas: Good performance during the whole year in northwestern European coasts. Major problems in Mediterranean coasts – systematic error. Complex terrain: Higher errors during the whole year Need high resolution for a better representation of orography (mountains-valleys) Continental flat terrain: Overall best performance – larger errors related to meteorological conditions Oct 2 m Temp MAE (º C) Dec 2 m Temp MAE (º C) Nov 2 m Temp MAE (º C)

14 Systematic errors of the models – annual mean RMSE, RMSEs 2m Temperature ARW NMM Lower errors over northern Europe – larger errors over southern Europe. Complex topographic areas and Mediterranean coasts – large errors. From the generally 1.5 to 2.5ºC total RMSE, 0.5 to 1.5ºC corresponds to systematic error. Higher systematic RMSE of ARW in western and central Europe.

15 Systematic errors of the models – annual mean RMSE, RMSEs 2m Dewpoint temperature ARW NMM Similar errors of both models ranging between 1.5-2ºC Lower errors – northern European coast Higher errors – Alpine region. Homogeneous error distribution over flat continental terrain areas. Lower systematic RMSE in NMM (0 to 1ºC)

16 Systematic errors of the models – annual mean RMSE, RMSEs 10m Wind speed ARW NMM No clear spatial pattern of the annual error for wind speed. RMSE ranges from 1 to 3 m/s Similar absolute and systematic errors between ARW and NMM. Systematic error ranges from 0.5 to 1.5 m/s, accounting for the half part of total error.

17 Monthly MAE – Wind speed (10 m) Major problems in complex terrain regions, and coastal regions with near mountain ranges (Mediterranean, Scandinavia, north UK) ARW NMM Feb July

18 Different performance in northern and southern Europe Major skills over the wetter continental Europe

19 Vertical structure evaluation: 61 meteorological soundings from European network of radiosoundings

20 MAE errors ranging around 2.5ºC for ARW and 1.5ºC for NMM during the whole Larger underestimation of ARW. Larger diurnal errors in ARW but similar for NMM. Both models presents an underestimation of temperature in lower layers that is increased in upper levels of the ABL. NMM presents a regular MAE from 0 to 2000 m, while ARW tends to have higher errors in upper levels. Temperature profile m evaluation (MAE, MBE, RMSEs)

21 Mixing ratio vertical structure evaluation (MAE, MBE, RMSEs) Moisture errors higher under dryer conditions. NMM has slightly larger errors during summertime Both models presents an homogeneous overestimation of moisture for winter and spring and a slight underestimation in summer. Day-night conditions over-underestimation. Overestimation to underestimation from 0 to 2000 m for NMM Major differences during summertime

22 Wind speed Vertical structure evaluation (MAE, MBE, RMSEs) Similar pattern of both models – general overestimation of wind speed. Daytime larger errors under wintertime conditions of ARW. Nighttime same performance. Larger overestimation at surface levels (0-500 m).

23 Summary of results and impacts over an air quality modelling system 2004 Annual evaluation of the two dynamical cores of WRF modelling system over Europe: WRF-NMM WRF-ARW Results have highlighted: Coastal areas: Good performance during the whole year in northwestern European coasts. Major problems in Mediterranean coasts – systematic error. Complex terrain: Higher errors during the whole year for temperature, but reasonable good wind results. Need high resolution for a better representation of topography (mountains-valleys) Continental flat terrain: Overall best performance – larger errors related to meteorological conditions Larger errors over southern Europe and under high pressure or stagnant conditions situation Systematic errors higher with ARW, more work for improvement than NMM

24 Summary of results and impacts over an air quality modelling system (cont.) Normal range of mean absolute errors: Temperature: 1º to 3 ºC – ARW underestimation : NMM overestimation Dewpoint: 1º to 2.5ºC – ARW underestimation : NMM overestimation Wind speed: 1 to 5 m/s – ARW and NMM overestimation Wind direction: 30 to 70 degrees Impacts of accurate meteorological fields for air quality modelling: From Pérez et al. (2006): increase in 3ºC of temperature leads to an increase of g m -3 of ozone concentration in background areas. Moisture errors impact in the formation of secondary organic aerosols and in the aqueous chemistry by modifying the OH radical concentrations. Overestimation of wind speed will produce an increase mixing of pollutants within the boundary layer and a major advective effect, the result will be a simulation of lower background pollutant concentrations over the affected areas. The direction will strongly impact with the geographical accuracy and the levels of the polluted air mass.

25 CONTACT THANK YOU FOR YOUR ATTENTION Suitability of WRF model for air quality simulations: comparison of two dynamical cores on a yearly basis Acknowledgments This work was funded by the project CALIOPE project 441/2006/ and A357/200/ of the Spanish Ministry of the Environment. The FNL analysis and verification data are provided by the NCARs Data Support Section. WRF- ARW and WRF-NMM simulations were performed with the MareNostrum supercomputer held by the Barcelona Supercomputing Center.


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