Abstract
Abstract. Commercially available Doppler lidars have now been proven to be efficient tools for studying winds and turbulence in the planetary boundary layer. However, in many cases low signal-to-noise ratio is still a limiting factor for utilising measurements by these devices. Here, we present a novel post-processing algorithm for Halo Stream Line Doppler lidars, which enables an improvement in sensitivity of a factor of 5 or more. This algorithm is based on improving the accuracy of the instrumental noise floor and it enables longer integration times or averaging of high temporal resolution data to be used to obtain signals down to −32 dB. While this algorithm does not affect the measured radial velocity, it improves the accuracy of radial velocity uncertainty estimates and consequently the accuracy of retrieved turbulent properties. Field measurements using three different Halo Doppler lidars deployed in Finland, Greece and South Africa demonstrate how the new post-processing algorithm increases data availability for turbulent retrievals in the planetary boundary layer, improves detection of high-altitude cirrus clouds and enables the observation of elevated aerosol layers.
Highlights
Turbulent mixing in the planetary boundary layer (PBL) is one of the most important processes for air quality, weather and climate (e.g. Garratt, 1994; Baklanov et al, 2011; Ryan, 2016)
The benefit is that aerosol backscatter profiles can be obtained routinely with high temporal resolution (e.g. Emeis et al, 2008), but as this method is not a direct measure of turbulent mixing, it is prone to erroneous interpretation, especially during morning and evening transition periods of the convective PBL (Schween et al, 2014)
Note that A0 and Abkg are not saved by the firmware, which means that the high and low mode in Stream Line XR lidars cannot be identified in the SNR0 time series
Summary
Turbulent mixing in the planetary boundary layer (PBL) is one of the most important processes for air quality, weather and climate (e.g. Garratt, 1994; Baklanov et al, 2011; Ryan, 2016). The post-processing algorithm by Manninen et al (2016) has the added benefit of improving the accuracy of the signal-to-noise ratio (SNR), which leads to more accurate uncertainty estimates of the measured radial velocity (Rye and Hardesty, 1993; Pearson et al, 2009) This is especially important for the retrieval of turbulent properties under weak signal conditions, as uncertainty in instrumental noise level propagates into turbulent properties and wind retrievals (O’Connor et al, 2010; Vakkari et al, 2015; Newsom et al, 2017). Case studies from different environments in Finland, Greece and South Africa are presented to demonstrate how the new post-processing algorithm increases data availability for turbulent retrievals in the PBL, improves detection of high-altitude cirrus clouds and enables observation of elevated aerosol layers 2 to 4 km above ground level. 46, Stream Line 53, Stream Line Pro 146, Stream Line XR 1.5 μm 15 kHz (46 and 53) or 10 kHz (146) 20 m s−1 50 MHz 0.038 m s−1 10 30 m 9600 m (46 and 53) or 12 000 m (146) 0.2 μs 8 cm 33 μrad monostatic optic-fibre coupled
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
