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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 6, issue 11
Atmos. Meas. Tech., 6, 2941–2951, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Tropospheric profiling (ISTP9)

Atmos. Meas. Tech., 6, 2941–2951, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Nov 2013

Research article | 01 Nov 2013

Mixing layer height retrievals by multichannel microwave radiometer observations

D. Cimini1,2, F. De Angelis2, J.-C. Dupont3, S. Pal4,5, and M. Haeffelin6 D. Cimini et al.
  • 1IMAA-CNR, Potenza, Italy
  • 2CETEMPS, University of L'Aquila, L'Aquila, Italy
  • 3Institut Pierre-Simon Laplace, Université Versailles Saint Quentin, Guyancourt, France
  • 4Laboratoire de Météorologie Dynamique (LMD), CNRS-Ecole Polytechnique, Palaiseau, France
  • 5Department of Environmental Sciences, University of Virginia, Charlottesville, VA, USA
  • 6Institut Pierre-Simon Laplace, Centre National de la Recherche Scientifique, Ecole Polytechnique, Palaiseau, France

Abstract. The mixing layer height (MLH) is a key parameter for boundary layer studies, including meteorology, air quality, and climate. MLH estimates are inferred from in situ radiosonde measurements or remote sensing observations from instruments like lidar, wind profiling radar, or sodar. Methods used to estimate MLH from radiosonde profiles are also used with atmospheric temperature and humidity profiles retrieved by microwave radiometers (MWR). This paper proposes an alternative approach to estimate MLH from MWR data, based on direct observations (brightness temperatures, Tb) instead of retrieved profiles. To our knowledge, MLH estimates directly from Tb observations have never been attempted before. The method consists of a multivariate linear regression trained with an a priori set of collocated MWR Tb observations (multifrequency and multi-angle) and MLH estimates from a state-of-the-art lidar system. The proposed method was applied to a 7-month data set collected at a typical midlatitude site. Results show that the method is able to follow both the diurnal cycle and the day-to-day variability as suggested by the lidar measurements, and also it can detect low MLH values that are below the full overlap limit (~200 m) of the lidar system used. Statistics of the comparison between MWR- and reference lidar-based MLH retrievals show mean difference within 10 m, root mean square within 340 m, and correlation coefficient higher than 0.77. Monthly mean analysis for daytime MLH from MWR, lidar, and radiosonde shows consistent seasonal variability, peaking at ~1200–1400 m in June and decreasing down to ~600 m in October. Conversely, nighttime monthly mean MLH from all methods are within 300–500 m without any significant seasonal variability. The proposed method provides results that are more consistent with radiosonde estimates than MLH estimates from MWR-retrieved profiles. MLH monthly mean values agree well within 1 standard deviation with the bulk Richardson number method applied at radiosonde profiles at 11:00 and 23:00 UTC. The method described herewith operates continuously and is expected to work with analogous performances for the entire diurnal cycle, except during considerable precipitation, demonstrating new potential for atmospheric observation by ground-based microwave radiometry.

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