Retrieval of the boundary layer height from active and passive remote sensors. Comparison with a NWP model
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In this study, we used boundary layer heights derived from lidar in Romania to validate the Weather Research Forecast (WRF) model improved by ARIA Technologies SA in the framework of ROMAIR LIFE project. Lidar retrievals were also compared to the retrievals from meteorological data, both modeled (Global Data Assimilation System; GDAS) and measured (microwave radiometry). Both the gradient and the wavelet covariance methods were used to compute the boundary layer height (BLH) from the range corrected lidar signal, and their equivalence was shown.
The analysis was performed on 102 datasets, spread over all seasons and 3 years (2009–2011). A good agreement was found for the remote sensors (lidar and microwave radiometer) which are co-located and measure simultaneously. The correlation of the measured boundary layer height and the modelled one was 0.66 for the entire dataset, and 0.73 when considering daytime data, i.e., for a well defined boundary layer. A systematic underestimation of the boundary layer height by the WRF during non-convective periods (nocturne, stable atmosphere) was found.
Key wordslidar microwave radiometer WRF boundary layer
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- Freudenthaler, V., M. Esselborn, M. Wiegner, B. Heese, M. Tesche, A. Ansmann, D. Müller, D. Althausen, M. Wirth, A. Fix, G. Ehret, P. Knippertz, C. Toledano, J. Gasteiger, M. Garhammer, and M. Seefeldner (2009), Depolarization ratio profiling at several wavelengths in pure Saharan dust during SAMUM 2006, Tellus B 61,1, 165–179, DOI: 10.1111/j.1600-0889.2008.00396.x.CrossRefGoogle Scholar
- Honoré, C., L. Rouïl, R. Vautard, M. Beekmann, B. Bessagnet, A. Dufour, C. Elichegaray, J.-M. Flaud, L. Malherbe, F. Meleux, L. Menut, D. Martin, A. Peuch, V.-H. Peuch, and N. Poisson (2008), Predictability of European air quality: Assessment of three years of operational forecasts and analyses by the PREV’AIR system, J. Geophys. Res. 113,D4, D04301, DOI: 10.1029/2007JD 008761.CrossRefGoogle Scholar
- Pappalardo, G. (2010), EARLINET: the European Lidar network for aerosol study at continental scale, EARSeL Newslett. 82, 13–18.Google Scholar
- Radu, C., L. Belegante, C. Talianu, and D. Nicolae (2010), Optimization of the multiwavelength Raman lidar during EARLI09 campaign, J. Optoelectron. Adv. Mat. 12,1, 165–168.Google Scholar
- Sorbjan, Z. (1989), Structure of the Atmospheric Boundary Layer, Prentice Hall, Englewood Cliffs, 317 pp.Google Scholar
- Talianu, C., D. Nicolae, J. Ciuciu, M. Ciobanu, and V. Babin (2006), Planetary boundary layer height detection from LIDAR measurements, J. Optoelectron. Adv. Mat. 8,1, 243–246.Google Scholar
- Wu, W.S. (2005), Background error for NCEP’s GSI analysis in regional mode. In: Proc. Fourth WMO Int. Symp. on Assimilation of Observations in Meteorology and Oceanography, Prague, Czech Republic, 3A.27.Google Scholar
- Wyant, M.C., R. Wood, C.S. Bretherton, C.R. Mechoso, J. Bacmeister, M.A. Balmaseda, B. Barrett, F. Codron, P. Earnshaw, J. Fast, C. Hannay, J.W. Kaiser, H. Kitagawa, S.A. Klein, M. Köhler, J. Manganello, H.-L. Pan, F. Sun, S. Wang, and Y. Wang (2010), The PreVOCA experiment: modeling the lower troposphere in the Southeast Pacific, Atmos. Chem. Phys. 10, 4757–4774, DOI: 10.5194/acp-10-4757-2010.CrossRefGoogle Scholar