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Linear depolarization measurement capabilities were added to the CANDAC Rayleigh–Mie–Raman lidar (CRL) at Eureka, Nunavut, in the Canadian High Arctic in 2010. This upgrade enables measurements of the phases (liquid versus ice) of cold and mixed-phase clouds throughout the year, including during polar night. Depolarization measurements were calibrated according to existing methods using parallel- and perpendicular-polarized profiles as discussed in ). We present a new technique that uses the polarization-independent Rayleigh elastic channel in combination with one of the new polarization-dependent channels, and we show that for a lidar with low signal in one of the polarization-dependent channels this method is superior to the traditional method. The optimal procedure for CRL is to determine the depolarization parameter using the traditional method at low resolution (from parallel and perpendicular signals) and then to use this value to calibrate the high-resolution new measurements (from parallel and polarization-independent Rayleigh elastic signals). Due to its use of two high-signal-rate channels, the new method has lower statistical uncertainty and thus gives depolarization parameter values at higher spatial–temporal resolution by up to a factor of 20 for CRL. This method is easily adaptable to other lidar systems which are considering adding depolarization capability to existing hardware.

The Canadian Network for the Detection of Atmospheric Change (CANDAC) Rayleigh–Mie–Raman lidar (CRL) at Eureka, Nunavut, has measured tropospheric clouds, aerosols, and water vapour since 2007. In remote and meteorologically significant locations, such as the Canadian High Arctic, the ability to add new measurement capability to an existing well-tested facility is extremely valuable. In 2010, linear depolarization 532 nm measurement hardware was installed in the lidar's receiver. To minimize disruption in the existing lidar channels and to preserve their existing characterization so far as is possible, the depolarization hardware was placed near the end of the receiver cascade. The upstream optics already in place were not optimized for preserving the polarization of received light. Calibrations and Mueller matrix calculations are used to determine and mitigate the contribution of these upstream optics on the depolarization measurements. The results show that with appropriate calibration, indications of cloud particle phase (ice vs. water) through the use of the depolarization parameter are now possible to a precision of ±0.05 absolute uncertainty ( ≤ 10 % relative uncertainty) within clouds at time and altitude resolutions of 5 min and 37.5 m respectively, with higher precision and higher resolution possible in select cases. The uncertainty is somewhat larger outside of clouds at the same altitude, typically with absolute uncertainty ≤ 0.1. Monitoring changes in Arctic cloud composition, including particle phase, is essential for an improved understanding of the changing climate locally and globally.

A new microwave satellite water vapour retrieval for the polar winter atmosphere is presented. The retrieval builds on the work of Miao et al. (2001) and Melsheimer and Heygster (2008), employing auxiliary information for atmospheric conditions and numerical optimization. It was tested using simulated and actual measurements from the Microwave Humidity Sounder (MHS) satellite instruments. Ground truth was provided by the G-band vapour radiometer (GVR) at Barrow, Alaska. For water vapour columns less than 6 kg m−2, comparisons between the retrieval and GVR result in a root mean square (RMS) deviation of 0.39 kg m−2 and a systematic bias of 0.08 kg m−2. These results are compared with RMS deviations and biases at Barrow for the retrieval of Melsheimer and Heygster (2008), the AIRS and MIRS satellite data products, and the ERA-Interim, NCEP, JRA-55, and ASR reanalyses. When applied to MHS measurements, the new retrieval produces a smaller RMS deviation and bias than for the earlier retrieval and satellite data products. The RMS deviations for the new retrieval were comparable to those for the ERA-Interim, JRA-55, and ASR reanalyses; however, the MHS retrievals have much finer horizontal resolution (15 km at nadir) and reveal more structure. The new retrieval can be used to obtain pan-Arctic maps of water vapour columns of unprecedented quality. It may also be applied to measurements from the Special Sensor Microwave/Temperature 2 (SSM/T2), Advanced Microwave Sounding Unit B (AMSU-B), Special Sensor Microwave Imager/Sounder (SSMIS), Advanced Technology Microwave Sounder (ATMS), and Chinese MicroWave Humidity Sounder (MWHS) instruments.

We present recent progress on nighttime retrievals of aerosol and cloud optical properties over the PEARL (Polar Environmental Atmospheric Research Laboratory) station at Eureka (Nunavut, Canada) in the High Arctic (80° N, 86° W). In the spring of 2011 and 2012, a star photometer was employed to acquire aerosol optical depth (AOD) data, while vertical aerosol and cloud backscatter profiles were measured using the CANDAC Raman Lidar (CRL). We used a simple backscatter coefficient threshold (βthr) to distinguish aerosols from clouds and, assuming that aerosols were largely fine mode (FM)/sub-micron, to distinguish FM aerosols from coarse mode (CM)/super-micron cloud or crystal particles. Using prescribed lidar ratios, we computed FM and CM AODs that were compared with analogous AODs estimated from spectral star photometry. We found (βthr dependent) coherences between the lidar and star photometer for both FM events and CM cloud and crystal events with averaged, FM absolute differences being R2 values were between 0.2 and 0.8. A βthr sensitivity study demonstrated that zero crossing absolute differences and R2 peaks were in comparable regions of the βthr range (or physical reasons were given for their disparity). The utility of spectral vs. temporal cloud screening of star photometer AODs was also illustrated. In general our results are critical to building confidence in the physical fidelity of derived, weak amplitude, star photometry AODs and, in turn, towards the development of AOD climatologies and validation databases for polar winter models and satellite sensors.