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Carlos Román-Cascón ( carlosromancascon@fis.ucm.es ) Carlos Yagüe Mariano Sastre Gregorio Maqueda

2011-2012 winter RADIATION FOGS at CIBA (Spain): Observations compared to WRF simulations using different PBL parameterizations. Carlos Román-Cascón ( carlosromancascon@fis.ucm.es ) Carlos Yagüe Mariano Sastre Gregorio Maqueda. Universidad Complutense de Madrid.

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Carlos Román-Cascón ( carlosromancascon@fis.ucm.es ) Carlos Yagüe Mariano Sastre Gregorio Maqueda

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  1. 2011-2012 winter RADIATION FOGS at CIBA (Spain): Observations compared to WRF simulations using different PBL parameterizations Carlos Román-Cascón (carlosromancascon@fis.ucm.es) Carlos Yagüe Mariano Sastre Gregorio Maqueda Universidad Complutense de Madrid EMS & ECAC 2012. Łódź, Poland 11th September 2012

  2. CONTENTS • Introduction • Overview • Observations • WRF Model results • Conclusions • Future study 2/19

  3. 1. INTRODUCTION RADIATION FOGS - Effects on daily life – Transport. - Physical processes not well understood. - not well parameterized in NWP models. - no good forecasts of fogs. ROLE OF TURBULENCE OVER FOGS - It acts favoring the development (Welch et al.,1986). - It acts favoring the dissipation (Roach et al.,1976). - Turbulence threshold between development and dissipation (Zhou et al., 2008). MAIN GOALS - To improve the fog prediction and to improve the knowledge about the physical processes affecting the formation/dissipation of fogs. 3/19

  4. 2. OVERVIEW Iberian Peninsula Northern Spanish Plateau Montes Torozos CIBA site 25 km CIBA SITE 800 km2 840m asl 4/19

  5. 3-14 January 2012 (12 days) Synoptic Situation 2. OVERVIEW 500 hPa Geopotential (gpdm) & Sea level pressure (hPa) 5/19

  6. Fog Thickness (approximation) 2. OVERVIEW Fog Thickness (m) Time (day at 00 UTC) 6/19

  7. Fog Thickness & Temperature 3. OBSERVATIONS Fog thickness (m) Observed Temperature at different heights (ºC) 7/19 Time (day at 00 UTC)

  8. Fog Thickness & Friction velocity 3. OBSERVATIONS Fog thickness (m) 0,163 0,263 0,094 0,082 0,056 0,067 0,079 0,100 0,046 0,054 0,057 0,094 Friction velocity (m/s) 8/19 Time (day at 00 UTC)

  9. Fog Thickness & Friction velocity relations 3. OBSERVATIONS Fog thickness (m) Fog thickness (m) 9/19 Friction velocity (m/s)

  10. 4. WRF SIMULATIONS - PBL parameterizations - MYJ - QNSE - MYNN 2.5 - MYNN 2.5 + Gravity settling - Microphysics parameterizations (QNSE fixed) - WSM3 (default) - Lin et al. - Goddard scheme - Land-surface parameterizations (QNSE & Goddard fixed) - Noah LSM (default) - RUC LSM • Horizontal domains - 4 nested domains • Grid - 27, 9, 3, 1 km • Boundary conditions - NCEP, 1º, 6 hours • Vertical resolution 50 levels “eta” • (8 levels< 100 m) • (28 levels< 1 km) • Time step - 90 s • Spin up -36 h (restart run) • SW radiation- Dudhia (1998) • LW radiation - RRTM CIBA 4 km 1 km Average of 17 points centered at CIBA 4 km 10/19

  11. LWC (g/kg) PBL schemes 4. WRF SIMULATIONS MYJ QNSE LWC simulated by WRF (g/kg) MYNN 2.5 MYNN 2.5 GS Time (day at 00 UTC)

  12. Temperature PBL schemes 4. WRF SIMULATIONS 2m Temp. simulated by WRF and obs. (ºC) OBS Time (day at 00 UTC)

  13. LWC (g/kg) MICROPHYSICS schemes 4. WRF SIMULATIONS QNSE fixed! WSM3 (default) LWC simulated by WRF (g/kg) Jin et. al Goddard Time (day at 00 UTC)

  14. Temperature & Mixing Ratio MICROPHYSICS schemes 4. WRF SIMULATIONS QNSE fixed! Temperature (ºC) Mixing ratio (g/kg) Time (day at 00 UTC)

  15. LWC (g/kg) LAND-SURFACE schemes 4. WRF SIMULATIONS QNSE & Goddard microph. fixed! Noah (default) LWC simulated by WRF (g/kg) RUC Time (day at 00 UTC)

  16. LWC (g/kg) LAND-SURFACE schemes 4. WRF SIMULATIONS QNSE & Goddard LSM fixed! Noah (default) LWC simulated by WRF (g/kg) RUC Time (UTC)

  17. 5. CONCLUSIONS • OBSERVATIONS • Certain degree of turbulence to extend the fog in the vertical. • Nocturnal turbulence ~ 0.05 m/s  Great surface thermal inversions  Shallower fogs. • SIMULATIONS • Tendency to overestimate the temperature. • Tendency to “rise up” the fog. • Tendency to dissipate the fog at midday (not able to simulate persistent fogs) • Problems to predict shallow fogs related to high inversions. • QNSE and MYNN2.5 in general better. • Lin et al. & Goddard Microphysics  Improve the fog forecasting for days with difficulties. • RUC Land Surface  Improve more the fog forecasting • Combination of errors  good prediction of fog? • Many different processes working together! • Still many problems simulating fogs, and consequently affecting T2, SW, LW… 17/19

  18. 6. FUTURE STUDY (soon) • - Statistic with more data (bias, RMSE) • Detailed analysis of some concrete day • More data (ceilometer + visibilimeter)  Better comparison with simulations • Interaction between Internal Gravity waves & Fogs Filtered pressure (hPa) Wavelet analysis 35 m Temperature (ºC) 18/19

  19. THANK YOU !! (this is not a radiation fog!!!) Thanks to EMS for the Young Scientist Travel Award (YSTA) 19/19

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