Abstract. Doppler lidars provide two measured parameters, radial velocity and signal-to-noise ratio, from which winds and turbulent properties are routinely derived. Attenuated backscatter, which gives quantitative information on aerosols, clouds, and precipitation in the atmosphere, can be used in conjunction with the winds and turbulent properties to create a sophisticated classification of the state of the atmospheric boundary layer. Calculating attenuated backscatter from the signal-to-noise ratio requires accurate knowledge of the telescope focus function, which is usually unavailable. Inaccurate assumptions of the telescope focus function can significantly deform attenuated backscatter profiles, even if the instrument is focused at infinity. Here, we present a methodology for deriving the telescope focus function using a co-located ceilometer for pulsed heterodyne Doppler lidars. The method was tested with Halo Photonics StreamLine and StreamLine XR Doppler lidars but should also be applicable to other pulsed heterodyne Doppler lidar systems. The method derives two parameters of the telescope focus function, the effective beam diameter and the effective focal length of the telescope. Additionally, the method provides uncertainty estimates for the retrieved attenuated backscatter profile arising from uncertainties in deriving the telescope function, together with standard measurement uncertainties from the signal-to-noise ratio. The method is best suited for locations where the absolute difference in aerosol extinction at the ceilometer and Doppler lidar wavelengths is small.

Abstract. We use Doppler lidar wind profiles from six locations around the globe to evaluate the wind profile forecasts in the boundary layer generated by the operational global Integrated Forecast System (IFS) from the European Centre for Medium-range Weather Forecasts (ECMWF). The six locations selected cover a variety of surfaces with different characteristics (rural, marine, mountainous urban, and coastal urban). We first validated the Doppler lidar observations at four locations by comparison with co-located radiosonde profiles to ensure that the Doppler lidar observations were of sufficient quality. The two observation types agree well, with the mean absolute error (MAE) in wind speed almost always less than 1 m s−1. Large deviations in the wind direction were usually only seen for low wind speeds and are due to the wind direction uncertainty increasing rapidly as the wind speed tends to zero. Time–height composites of the wind evaluation with 1 h resolution were generated, and evaluation of the model winds showed that the IFS model performs best over marine and coastal locations, where the mean absolute wind vector error was usually less than 3 m s−1 at all heights within the boundary layer. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. For example, in Granada, which is near a high mountain range, the IFS model failed to capture a commonly occurring mountain breeze, which is highly dependent on the sub-grid-size terrain features that are not resolved by the model. The uncertainty in the wind forecasts increased with forecast lead time, but no increase in the bias was seen. At one location, we conditionally performed the wind evaluation based on the presence or absence of a low-level jet diagnosed from the Doppler lidar observations. The model was able to reproduce the presence of the low-level jet, but the wind speed maximum was about 2 m s−1 lower than observed. This is attributed to the effective vertical resolution of the model being too coarse to create the strong gradients in wind speed observed. Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.

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