In this study, an Observing System Simulation Experiment was used to examine how the assimilation of temperature, water vapor, and wind profiles from a potential array of ground-based remote sensing boundary layer profiling instruments impacts the accuracy of atmospheric analyses when using an ensemble Kalman filter data assimilation system. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). The case study tracked the evolution of several extratropical weather systems that occurred across the contiguous United States during 7-8 January 2008. Overall, the results demonstrate that using networks of high-quality temperature, wind, and moisture profile observations of the lower troposphere has the potential to improve the accuracy of wintertime atmospheric analyses over land. The impact of each profiling system was greatest in the lower and middle troposphere on the variables observed or retrieved by that instrument; however, some minor improvements also occurred in the unobserved variables and in the upper troposphere, particularly when RAM observations were assimilated. The best analysis overall was achieved when DWL wind profiles and temperature and moisture observations from the RAM, AERI, or MWR were assimilated simultaneously, which illustrates that both mass and momentum observations are necessary to improve the analysis accuracy.

In-cloud ice mass accretion on wind turbines is a common challenge that is faced by energy companies operating in cold climates. On-shore wind farms in Scandinavia are often located in regions near patches of forest, the heterogeneity length scales of which are often less than the resolution of many numerical weather prediction (NWP) models. The representation of these forests—including the cloud water response to surface roughness and albedo effects that are related to them—must therefore be parameterized in NWP models used as meteorological input in ice prediction systems, resulting in an uncertainty that is poorly understood and, to the present date, not quantified. The sensitivity of ice accretion forecasts to the subgrid representation of forests is examined in this study. A single column version of the HARMONIE-AROME three-dimensional (3D) NWP model is used to determine the sensitivity of the forecast of ice accretion on wind turbines to the subgrid forest fraction. Single column simulations of a variety of icing cases at a location in northern Sweden were examined in order to investigate the impact of vegetation cover on ice accretion in varying levels of solar insolation and wind magnitudes. In mid-winter cases, the wind speed response to surface roughness was the primary driver of the vegetation effect on ice accretion. In autumn cases, the cloud water response to surface albedo effects plays a secondary role in the impact of in-cloud ice accretion, with the wind response to surface roughness remaining the primary driver for the surface vegetation impact on icing. Two different surface boundary layer (SBL) forest canopy subgrid parameterizations were tested in this study that feature different methods for calculating near-surface profiles of wind, temperature, and moisture, with the ice mass accretion again following the wind response to surface vegetation between both of these schemes.

To characterize the effects of subgrid surface heterogeneity, the blending height concept has been developed as a coupling strategy for surface parameterization schemes used in numerical weather prediction (NWP) models. Previous modelling studies have tested this concept using stationary conditions with one-dimensional strips of surface roughness. Here, Large Eddy Simulations (LES) are used to examine the response of the blending height and effective surface roughness to tiled land cover heterogeneity, or a two-dimensional chessboard pattern of alternating high and low vegetation given a diurnal cycle of solar irradiance in subarctic conditions. In each experiment, the length scale of the roughness elements is increased while the total domain fraction of each vegetation type is kept constant. The effective surface roughness was found to decrease with increasing length scale of surface cover heterogeneity, which is shown to have a significant impact on estimated wind turbine power calculated from logarithmic wind profiles. In stable conditions, the blending height in cases with large heterogeneity length scales was found to exist well above the surface layer. As the behavior of the blending height has implications for coupled models, a simple model for the blending height as a function of heterogeneity length scale is introduced.

The flow over arbitrary roughness changes is investigated, revisiting the analysis of Belcher et al. (Q J R Meteorol Soc 116:611–635, 1990) regarding surface-roughness heterogeneity. The proposed theory is restricted to steady neutral boundary layers over flat regions with changes of roughness sufficiently slow and mild to inhibit the growth of nonlinear terms. The approach is based on a triple-deck decomposition of the flow above the roughness, although only the first two layers are interactive at leading order. Two experimental datasets (one with a smooth-to-rough and the other with a rough-to-smooth transition) are used to validate the theory. The latter is further compared against two large-eddy simulations featuring chessboard patterns of alternating surface roughness with relatively short and long length scales, respectively. All the comparisons show that the proposed theory is able to reasonably assess the wind-field perturbation due to the roughness heterogeneity, supporting the use of the model to quickly assess the effect of roughness changes in the flow field.

In-cloud ice mass accretion on wind turbines is a common challenge faced by energy companies operating in cold climates. On-shore wind farms in Scandinavia are often located in regions near patches of forest, the heterogeneity length scales of which are often less than the resolution of many numerical weather prediction (NWP) models. The representation of these forests--including the cloud water response to surface roughness and albedo effects related to them--must therefore be parameterized in NWP models used as meteorological input in ice prediction systems, resulting in an uncertainty that is poorly understood and to present date not quantified. The sensitivity of ice accretion forecasts to the subgrid representation of forests is examined in this study. A single column version of the HARMONIE-AROME 3D NWP model is used to determine the sensitivity of the forecast of ice accretion on wind turbines to the subgrid forest fraction. Single column simulations of a variety of icing cases at a location in northern Sweden were examined in order to investigate the impact of vegetation cover on ice accretion in varying levels of solar insulation and wind magnitudes. In mid-winter cases, the wind speed response to surface roughness was the primary driver of the vegetation effect on ice accretion. In early season cases, the cloud water response to surface albedo effects plays a secondary role in the impact of in-cloud ice accretion, with the wind response to surface roughness remaining the primary driver for the surface vegetation impact on icing. Two different surface boundary layer (SBL) forest canopy subgrid parameterizations were tested in this study that feature different methods for calculating near-surface profiles of wind, temperature, and moisture, with the ice mass accretion again following the wind response to surface vegetation between both of these schemes.