An empirical method to correct for temperature-dependent variations in theoverlap function of CHM15k ceilometers

Abstract. Imperfections in a lidar's overlap function lead to artefacts in the background, range and overlap-corrected lidar signals. These artefacts can erroneously be interpreted as an aerosol gradient or, in extreme cases, as a cloud base leading to false cloud detection. A correct specification of the overlap function is hence crucial in the use of automatic elastic lidars (ceilometers) for the detection of the planetary boundary layer or of low cloud. In this study, an algorithm is presented to correct such artefacts. It is based on the assumption of a homogeneous boundary layer and a correct specification of the overlap function down to a minimum range, which must be situated within the boundary layer. The strength of the algorithm lies in a sophisticated quality-check scheme which allows the reliable identification of favourable atmospheric conditions. The algorithm was applied to 2 years of data from a CHM15k ceilometer from the company Lufft. Backscatter signals corrected for background, range and overlap were compared using the overlap function provided by the manufacturer and the one corrected with the presented algorithm. Differences between corrected and uncorrected signals reached up to 45 % in the first 300 m above ground. The amplitude of the correction turned out to be temperature dependent and was larger for higher temperatures. A linear model of the correction as a function of the instrument's internal temperature was derived from the experimental data. Case studies and a statistical analysis of the strongest gradient derived from corrected signals reveal that the temperature model is capable of a high-quality correction of overlap artefacts, in particular those due to diurnal variations. The presented correction method has the potential to significantly improve the detection of the boundary layer with gradient-based methods because it removes false candidates and hence simplifies the attribution of the detected gradients to the planetary boundary layer. A particularly significant benefit can be expected for the detection of shallow stable layers typical of night-time situations. The algorithm is completely automatic and does not require any on-site intervention but requires the definition of an adequate instrument-specific configuration. It is therefore suited for use in large ceilometer networks.

[1]  R. Stull.  An Introduction to Boundary Layer Meteorology , 1988 .

[2]  A. Ansmann,et al.  Experimental determination of the lidar overlap profile with Raman lidar. , 2002, Applied optics.

[3]  Ina Mattis,et al.  RAMSES: German Meteorological Service autonomous Raman lidar for water vapor, temperature, aerosol, and cloud measurements. , 2012, Applied optics.

[4]  Daniele Bortoli,et al.  Infrared lidar overlap function: an experimental determination. , 2010, Optics express.

[5]  Gionata Biavati,et al.  Correction scheme for close-range lidar returns. , 2011, Applied optics.

[6]  Y Sasano,et al.  Geometrical form factor in the laser radar equation: an experimental determination. , 1979, Applied optics.

[7]  V. Freudenthaler,et al.  Characterization of the Eyjafjallajökull ash-plume: Potential of lidar remote sensing , 2012 .

[8]  Jeremy Coupland,et al.  Determination of overlap in lidar systems. , 2011, Applied optics.

[9]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[10]  L. Sauvage,et al.  Evaluation of Mixing-Height Retrievals from Automatic Profiling Lidars and Ceilometers in View of Future Integrated Networks in Europe , 2012, Boundary-Layer Meteorology.

[11]  David D. Turner,et al.  Full-Time, Eye-Safe Cloud and Aerosol Lidar Observation at Atmospheric Radiation Measurement Program Sites: Instruments and Data Analysis , 2013 .

[12]  Stefan Emeis,et al.  Measurement and simulation of the 16/17 April 2010 Eyjafjallajökull volcanic ash layer dispersion in the northern Alpine region , 2011 .

[13]  W. Steinbrecht,et al.  The Eyjafjallajökull eruption in April 2010 – detection of volcanic plume using in-situ measurements, ozone sondes and lidar-ceilometer profiles , 2010 .

[14]  H. Kuze,et al.  Field-of-view dependence of lidar signals by use of Newtonian and Cassegrainian telescopes. , 1998, Applied optics.

[15]  Matthias Wiegner,et al.  Aerosol profiling with the Jenoptik ceilometer CHM15kx , 2012 .

[16]  David W. Roberts,et al.  Optimizing lidar dynamic range by engineering the crossover region , 2002, SPIE Defense + Commercial Sensing.

[17]  Tadeusz Stacewicz,et al.  Analytical function for lidar geometrical compression form-factor calculations. , 2005, Applied optics.

[18]  E. Welton,et al.  Micro-Pulse Lidar Signals: Uncertainty Analysis , 2013 .

[19]  Alexander Smirnov,et al.  Ground-Based Lidar Measurements of Aerosols During ACE-2 Instrument Description, Results, and Comparisons with Other Ground-Based and Airborne Measurements , 2000 .