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Dispersion modelling in complex terrain: Sensitivity studies with the CALMET model

Dispersion modelling in complex terrain: Sensitivity studies with the CALMET model. Author : Matic Ivančič Milan Vidmar Electric Power Research Institute Ljubljana, 15.12.2011. CONTENTES. Introduction - air pollution modeling CALMET model Three different wind field initializations Results.

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Dispersion modelling in complex terrain: Sensitivity studies with the CALMET model

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  1. Dispersion modelling in complex terrain: Sensitivity studies with the CALMET model Author: Matic Ivančič Milan Vidmar Electric PowerResearchInstitute Ljubljana, 15.12.2011

  2. CONTENTES • Introduction - air pollution modeling • CALMET model • Three different wind field initializations • Results

  3. INTRODUCTION • Air pollution modeling: • Advection with wind • 3D wind fields • Dispersion in cross-section • Stability of atmosphere • ALADIN - operative meteorological forecast in Slovenia

  4. INTRODUCTION • ALADIN – CALMET – CALPUFF are online coupled • Two-days prediction are available on web: www.okolje.info

  5. CALMET • Diagnostic wind model (DFW) • 3 main parts: • Initial field (first guess) • First step • Second step

  6. First step • Kinematic effects of terrain • Slope flows • Blocking effects • 3D divergence minimization

  7. Second step • Interpolation • Smoothing • O’Brien adjustment of vertical velocities • 2D divergence minimization

  8. Divergence minimization • Varitional technique (Sherman 1978) • Minimization of following functional: • Strong condition: • New functional:

  9. Divergence minimization • Solution with EL equations : • numerical solution forλ:

  10. TEST AREA • Šoštanj basin • Biggest thermal power plant in Slovenia • 9 meteorological and air quality stations

  11. 9 surface stations – only 6 stations has representative wind data

  12. Radio-sounding measurements in Zagreb

  13. ALADIN points

  14. 3 DIFFERENT WIND FIELD INITIALIZATIONS A Data from surface stations and upper air data from radio-sounding B Meteorological analysis from ALADIN C Combination of data from surface stations and meteorological analysis from ALADIN

  15. Experiment A • First guess: from measurements • First step: • Terrain effects • 3D divergence minimization • Second step: • Again interpolation of measurements

  16. ExperimentB • First guess is prepared with data from model ALADIN • First step • Second step

  17. ExperimentC • First guess: from model ALADIN • First step: • Terrain effects • 3D divergence minimization • Second step: • Import measurements with objective analysis procedure

  18. RESULTS – stability calculation

  19. Wind rose – station YA

  20. Wind rose – station YC

  21. Wind rose – station YD

  22. Wind rose – station YE

  23. Wind rose – station YF

  24. Wind rose – station YG

  25. CALPUFF results • Dispersion calculation of SO2

  26. CONCLUSION • It is hard to judge which model initialization gives better results • Radio-sounding measurements are far away • ALADIN analysis have better space but time interpolation required • ALADIN predictions may improve results – hourly data • Case B: for areas without meteorological measurements and for predictions • Case C: maximum number of inputs

  27. Acknowledgements • Author would like to thank the Environmental Agency of the Republic of Slovenia for providing all fields from ALADIN model

  28. THANK YOU FORYOUR ATTENTION

  29. LITERATURE • Sherman, C. A., 1978: A mass-consistent model for wind fields over complex terrain. J. Appl. Meteor., 17, 312–319. • Scire J. S., F. R. Robe, M. E. Fernau, R. J. Yamartino, 2000: A User’s Guide for the CALMET Meteorological Model, Version 5, Earth Tech, Inc, Concord. • Scire J. S., D. G. Strimaitis, R. J. Yamartino, 2000: A User’s Guide for the CALPUFF Dispersion Model, Vesrion 5, Earth Tech, Inc, Concord.

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