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Twenty-six months of continuous ceilometer data are used to estimate the convective mixed-layer height for 710 days by identifying backscatter gradients associated with the entrainment zone. To accomplish this, a semi-automatic procedure is developed that removes all non-applicable data before applying a mixed-layer height algorithm to the backscatter profiles. Two different algorithms for estimating the mixed-layer height are assessed: the minimum-gradient method and the ideal-profile method. The latter of these two algorithms is found to be more robust. Comparisons of mixed-layer height values estimated from the ceilometer agree with previous observations with slightly higher estimates in the mornings and evenings. For clear days with no cumulus cloud formation, the seasonal cycle in mixed-layer heights peaks in late June to early July. Daily maximum values are suppressed by the site's coastal location, remaining below 800 m for all but a few days. The mean daily maximum mixed-layer height increases by 384 m for days with boundary-layer clouds. The mean summer diurnal trend is found not to differ greatly from that in spring on clear days, while days with boundary-layer clouds have higher spring values than in summer. Net surface heat flux and synoptic stability likely have the largest influence on the mixed-layer heights. Additionally, large intra-monthly variability suggests a strong influence from regional dynamics.

Surface wind speed is a key climatic variable of interest in many applications, including assessments of storm-related infrastructure damage and feasibility studies of wind power generation. In this work and a companion paper (van der Kamp et al. 2011), the relationship between local surface wind and large-scale climate variables was studied using multiple regression analysis. The analysis was performed using monthly mean station data from British Columbia, Canada and large-scale climate variables (predictors) from the NCEP-2 reanalysis over the period 1979-2006. Two regression-based methodologies were compared. The first relates the annual cycle of station wind speed to that of the large-scale predictors at the closest grid box to the station. It is shown that the relatively high correlation coefficients obtained with this method are attributable to the dominant influence of region-wide seasonality, and thus contain minimal information about local wind behaviour at the stations. The second method uses interannually varying data for individual months, aggregated into seasons, and is demonstrated to contain intrinsically local information about the surface winds. The dependence of local wind speed upon large-scale predictors over a much larger region surrounding the station was also explored, resulting in 2D maps of spatial correlations. The cross-validated explained variance using the interannual method was highest in autumn and winter, ranging from 30 to 70% at about a dozen stations in the region. Reasons for the limited predictive skill of the regressions and directions for future progress are reviewed.

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