One year of observations from a network of five 915-MHz boundary-layer radar wind profilers equipped with radio acoustic sounding systems located in California’s Central Valley are used to investigate the annual variability of convective boundary-layer depth and its correlation to meteorological parameters and conditions. Results from the analysis show that at four of the sites, the boundary-layer height reaches its maximum in the late-spring months then surprisingly decreases during the summer months, with mean July depths almost identical to those for December. The temporal decrease in boundary-layer depth, as well as its spatial variation, is found to be consistent with the nocturnal low-level lapse rate observed at each site. Multiple forcing mechanisms that could explain the unexpected seasonal behaviour of boundary-layer depth are investigated, including solar radiation, precipitation, boundary-layer mesoscale convergence, low-level cold-air advection, local surface characteristics and irrigation patterns and synoptic-scale subsidence. Variations in solar radiation, precipitation and synoptic-scale subsidence do not explain the shallow summertime convective boundary-layer depths observed. Topographically forced cold-air advection and local land-use characteristics can help explain the shallow CBL depths at the four sites, while topographically forced low-level convergence helps maintain larger CBL depths at the fifth site near the southern end of the valley.

A previous study showed success in determining the convective boundary layer depth with radar wind-profiling radars using fuzzy logic methods, and improvements to the earlier work are discussed. The improved method uses the Vaisala multipeak picking (MPP) procedure to identify the atmospheric signal in radar spectra in place of a fuzzy logic peak picking procedure that was previously used. The method then applies fuzzy logic techniques to calculate the depth of the convective boundary layer. The planetary boundary layer depth algorithm is improved with respect to the one used in the previous study in that it adds information obtained from the small-scale turbulence (vertical profiles of the spectral width of the vertical velocity), while also still using vertical profiles of the radar-derived refractive index structure parameter C2n and the variance of vertical velocity. Modifications to the fuzzy logic rules (especially to those using vertical velocity data) that improve the algorithm’s accuracy in cloudy boundary layers are incorporated. In addition, a reliability threshold value to the fuzzy logic–derived score is applied to eliminate PBL depth data values with low score values. These low score values correspond to periods when the PBL structure does not match the conceptual model of the convective PBL built into the algorithm. Also, as a final step, an optional temporal continuity test on boundary layer depth has been developed that helps improve the algorithm’s skill. A comparison with independent boundary layer depth estimations made “by eye” by meteorologists at two radar wind-profiler sites, significantly different in their characteristics, shows that the new improved method gives significantly more accurate estimates of the boundary layer depth than does the previous method, and also much better estimates than the simpler “standard” method of selecting the peak of C2n. The new method produces an absolute error of the mixing-depth estimates comparable to the vertical range resolution of the profilers.

Accurate three-dimensional information of wind flow fields can be an important tool in not only visualizing complex flow but also understanding the underlying physical processes and improving flow modeling. However, a thorough analysis of the measurement uncertainties is required to properly interpret results. The XPIA (eXperimental Planetary boundary layer Instrumentation Assessment) field campaign conducted at the Boulder Atmospheric Observatory (BAO) in Erie, CO, from 2 March to 31 May 2015 brought together a large suite of in situ and remote sensing measurement platforms to evaluate complex flow measurement strategies. In this paper, measurement uncertainties for different single and multi-Doppler strategies using simple scan geometries (conical, vertical plane and staring) are investigated. The tradeoffs (such as time–space resolution vs. spatial coverage) among the different measurement techniques are evaluated using co-located measurements made near the BAO tower. Sensitivity of the single-/multi-Doppler measurement uncertainties to averaging period are investigated using the sonic anemometers installed on the BAO tower as the standard reference. Finally, the radiometer measurements are used to partition the measurement periods as a function of atmospheric stability to determine their effect on measurement uncertainty. It was found that with an increase in spatial coverage and measurement complexity, the uncertainty in the wind measurement also increased. For multi-Doppler techniques, the increase in uncertainty for temporally uncoordinated measurements is possibly due to requiring additional assumptions of stationarity along with horizontal homogeneity and less representative line-of-sight velocity statistics. It was also found that wind speed measurement uncertainty was lower during stable conditions compared to unstable conditions.

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, USA, active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include radar wind profilers, microwave radiometers, atmospheric emitted radiance interferometers, ceilometers, high spectral resolution lidars, Doppler lidars, and collaborative lower-atmospheric mobile profiling systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary layer depth estimations is also investigated. We found that while all instruments are overall able to provide reasonable boundary layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, radar wind profilers perform well during cloud-free conditions, and microwave radiometers and atmospheric emitted radiance interferometers have a very good agreement during all conditions but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from ceilometers and high spectral resolution lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the collaborative lower atmospheric mobile profiling systems can be constricted to a limited height range in low-aerosol conditions.

The feedback effects of sea-spray on the heat and momentum fluxes under equilibrium conditions associated with winds of tropical cyclones are investigated using a one-dimensional coupled sea-spray and atmospheric surface-layer (ASL) model. This model is capable of simulating the microphysical aspects of the evaporation of saline water droplets of various sizes and their dynamic and thermal interaction with the turbulence mixing that is simulated by the Mellor–Yamada 1.5-order closure scheme. Sea-spray droplet generation is described by a state-of-the-art parametrization that predicts the size spectrum of sea-spray droplets for a given surface forcing. The results from a series of simulations indicate the way in which evaporating droplets of various sizes modify the turbulence mixing near the surface, which in turn affects further droplet evaporation. All these results are direct consequences of the effects of sea-spray on the balance of turbulent kinetic energy in the spray-filled surface layer. In particular, the overall impact of sea-spray droplets on the mean wind depends on the wind speed at the level of sea-spray generation. When the wind speed is below 40 m s⁻¹, the droplets are small in size and tend to evaporate substantially and thus cool the spray-filled layer, while for wind speeds above 50 m s⁻¹, the size of the droplets is so large that they do not have enough time to evaporate much before falling back into the sea. The sensible heat carried by the droplets is released to the ambient air, increasing the buoyancy of the surface layer and enhancing the turbulent mixing. The suspension of sea-spray droplets reduces the buoyancy and makes the surface layer more stable, decreasing the friction velocity and the downward turbulent mixing of momentum. The results from the numerical experiments also suggest that, in order not to violate the constant flux assumption critical to the Monin–Obukhov similarity theory, a displacement equal to the mean wave height should be included in the logarithmic profiles of the wind and thermal fields.

To assess current remote-sensing capabilities for wind energy applications, a remote-sensing system evaluation study, called XPIA (eXperimental Planetary boundary layer Instrument Assessment), was held in the spring of 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. Several remote-sensing platforms were evaluated to determine their suitability for the verification and validation processes used to test the accuracy of numerical weather prediction models. The evaluation of these platforms was performed with respect to well-defined reference systems: the BAO's 300 m tower equipped at six levels (50, 100, 150, 200, 250, and 300 m) with 12 sonic anemometers and six temperature (T) and relative humidity (RH) sensors; and approximately 60 radiosonde launches. In this study we first employ these reference measurements to validate temperature profiles retrieved by two co-located microwave radiometers (MWRs) as well as virtual temperature (T_v) measured by co-located wind profiling radars equipped with radio acoustic sounding systems (RASSs). Results indicate a mean absolute error (MAE) in the temperature retrieved by the microwave radiometers below 1.5 K in the lowest 5 km of the atmosphere and a mean absolute error in the virtual temperature measured by the radio acoustic sounding systems below 0.8 K in the layer of the atmosphere covered by these measurements (up to approximately 1.6–2 km). We also investigated the benefit of the vertical velocity correction applied to the speed of sound before computing the virtual temperature by the radio acoustic sounding systems. We find that using this correction frequently increases the RASS error, and that it should not be routinely applied to all data. Water vapor density (WVD) profiles measured by the MWRs were also compared with similar measurements from the soundings, showing the capability of MWRs to follow the vertical profile measured by the sounding and finding a mean absolute error below 0.5 g m⁻³ in the lowest 5 km of the atmosphere. However, the relative humidity profiles measured by the microwave radiometer lack the high-resolution details available from radiosonde profiles. An encouraging and significant finding of this study was that the coefficient of determination between the lapse rate measured by the microwave radiometer and the tower measurements over the tower levels between 50 and 300 m ranged from 0.76 to 0.91, proving that these remote-sensing instruments can provide accurate information on atmospheric stability conditions in the lower boundary layer.