We present a method using Doppler lidar data for identifying the main sources of turbulent mixing within the atmospheric boundary layer. The method identifies the presence of turbulence and then assigns a turbulent source by combining several lidar quantities: attenuated backscatter coefficient, vertical velocity skewness, dissipation rate of turbulent kinetic energy, and vector wind shear. Both buoyancy-driven and shear-driven situations are identified, and the method operates in both clear-sky and cloud-topped conditions, with some reservations in precipitation. To capture the full seasonal cycle, the classification method was applied to more than 1 year of data from two sites, Hyytiälä, Finland, and Jülich, Germany. Analysis showed seasonal variation in the diurnal cycle at both sites; a clear diurnal cycle was observed in spring, summer, and autumn seasons, but due to their respective latitudes, a weaker cycle in winter at Jülich, and almost non-existent at Hyytiälä. Additionally, there are significant contributions from sources other than convective mixing, with cloud-driven mixing being observed even within the first 500 m above ground. Also evident is the considerable amount of nocturnal mixing within the lowest 500 m at both sites, especially during the winter. The presence of a low-level jet was often detected when sources of nocturnal mixing were diagnosed as wind shear. The classification scheme and the climatology extracted from the classification provide insight into the processes responsible for mixing within the atmospheric boundary layer, how variable in space and time these can be, and how they vary with location.
Low-level-jet (LLJ) periods are investigated by exploiting a long-term record of ground-based remote sensing Doppler wind lidar measurements supported by tower observations and surface flux measurements at the Jülich Observatory for Cloud Evolution (JOYCE), a midlatitude site in western Germany. LLJs were found 13% of the time during continuous observations over more than 4 yr. The climatological behavior of the LLJs shows a prevailing nighttime appearance of the jets, with a median height of 375 m and a median wind speed of 8.8 m s−1 at the jet nose. Significant turbulence below the jet nose only occurs for high bulk wind shear, which is an important parameter for describing the turbulent characteristics of the jets. The numerous LLJs (16% of all jets) in the range of wind-turbine rotor heights below 200 m demonstrate the importance of LLJs and the associated intermittent turbulence for wind-energy applications. Also, a decrease in surface fluxes and an accumulation of carbon dioxide are observed if LLJs are present. A comprehensive analysis of an LLJ case shows the influence of the surrounding topography, dominated by an open pit mine and a 200-m-high hill, on the wind observed at JOYCE. High-resolution large-eddy simulations that complement the observations show that the spatial distribution of the wind field exhibits variations connected with the orographic flow depending on the wind direction, causing high variability in the long-term measurements of the vertical velocity.
In this article, liquid water cloud microphysical properties are retrieved by a combination of microwave and infrared ground‐based observations. Clouds containing liquid water are frequently occurring in most climate regimes and play a significant role in terms of interaction with radiation. Small perturbations in the amount of liquid water contained in the cloud can cause large variations in the radiative fluxes. This effect is enhanced for thin clouds (liquid water path, LWP <100 g/m2), which makes accurate retrieval information of the cloud properties crucial. Due to large relative errors in retrieving low LWP values from observations in the microwave domain and a high sensitivity for infrared methods when the LWP is low, a synergistic retrieval based on a neural network approach is built to estimate both LWP and cloud effective radius (reff). These statistical retrievals can be applied without high computational demand but imply constraints like prior information on cloud phase and cloud layering. The neural network retrievals are able to retrieve LWP and reff for thin clouds with a mean relative error of 9% and 17%, respectively. This is demonstrated using synthetic observations of a microwave radiometer (MWR) and a spectrally highly resolved infrared interferometer. The accuracy and robustness of the synergistic retrievals is confirmed by a low bias in a radiative closure study for the downwelling shortwave flux, even for marginally invalid scenes. Also, broadband infrared radiance observations, in combination with the MWR, have the potential to retrieve LWP with a higher accuracy than a MWR‐only retrieval.
Sunlight warms sea surface temperature (SST) under calm winds, increasing atmospheric surface buoyancy flux, turbulence, and mixed layer (ML) depth in the afternoon. The diurnal range of SST exceeded 1°C for 24% of days in the central tropical Indian Ocean during the Dynamics of the Madden Julian Oscillation experiment in October‐December 2011. Doppler lidar shows enhancement of the strength and height of convective turbulence in the atmospheric ML over warm SST in the afternoon. The turbulent kinetic energy (TKE) dissipation rate of the marine atmospheric ML scales with surface buoyancy flux like previous measurements of convective MLs. The time of enhanced ML TKE dissipation rate is out of phase with the buoyancy flux generated by nocturnal net radiative cooling of the atmosphere. Diurnal atmospheric convective turbulence over the ocean mixes moisture from the ocean to the lifting condensation level and forms afternoon clouds.
Abstract. Finding observational evidence of land surface and atmosphere interactions is crucial for understanding the spatial and temporal evolution of the boundary layer, as well as for model evaluation, and in particular for large-eddy simulation (LES) models. In this study, the influence of a heterogeneous land surface on the spatial distribution of atmospheric water vapor is assessed. Ground-based remote sensing measurements from a scanning microwave radiometer (MWR) are used in a long-term study over 6 years to characterize spatial heterogeneities in integrated water vapor (IWV) during clear-sky conditions at the Jülich ObservatorY for Cloud Evolution (JOYCE). The resulting deviations from the mean of the scans reveal a season- and direction-dependent IWV that is visible throughout the day. Comparisons with a satellite-derived spatial IWV distribution show good agreement for a selection of satellite overpasses during convective situations but no clear seasonal signal. With the help of a land use type classification and information on the topography, the main types of regions with a positive IWV deviation were determined to be agricultural fields and nearby open pit mines. Negative deviations occurred mainly above elevated forests and urban areas. In addition, high-resolution large-eddy simulations (LESs) are used to investigate changes in the water vapor and cloud fields for an altered land use input.
The existence of subsiding shells on the periphery of shallow cumulus clouds has major implications concerning the parameterization of shallow convection, with the mass exchange between the shell and cloudy air representing a significant deviation from the commonly used bulk-plume parameterization. We examine the structure and frequency of subsiding shells in shallow cumulus convection using Doppler lidars at the Atmospheric Radiation Measurement Southern Great Plains facility in the central United States and at the Jülich ObservatorY for Cloud Evolution in western Germany. Doppler lidar indicates that the vertical subsiding shell extent is asymmetric, while shell width is typicallỹ 100 m. Large-eddy simulation can reasonably simulate the observed shell structure using a grid spacing of 10 m and suggests that much of the observed asymmetry is not a result of transient cloud evolution. Plain Language Summary Doppler lidars allow for the inference of vertical air motion. On the edges of the shallow "popcorn" cumulus clouds, regions of sinking air (subsiding shells) are observed. If we wish to understand how these clouds interact with their environment, we must understand the structure of the subsiding shells that envelop them. As a cloud passes over the lidar, the front edge of the cloud is sampled first, and the back edge is sampled later. The back-edge subsiding shell descends farther below cloud base than the front-edge shell. High-resolution models can resolve the observed shell structure and suggest that the differences between the front-and back-edge shells do not arise from the evolution of the cloud during the tens of seconds it takes to pass over the lidar.
Diurnal warming of the ocean surface is expected to generate turbulence, but measurements of the diurnal vertical profile of marine atmospheric turbulence have never been documented. The afternoon warming of the ocean is much weaker than that of land because the ocean mixes and stores heating over meters to tens of meters. Temperature-stratified diurnal warm layers (DWLs), resulting from strong solar absorption and weak winds (Fairall et al., 1996, Price et al., 1986, reviewed in Kawai & Wada 2007), increase the sea surface temperature (SST) in the afternoon. Clear skies result in more solar absorption. Weak winds result in weak turbulent fluxes and ocean mixing, with DWLs possible for winds under 7.6 m s-1 (Thompson et al., 2019). Over tropical oceans, strong nocturnal atmospheric radiative cooling generates strongest precipitation in the early morning. A secondary peak in the early afternoon is due to diurnal warming of SST (
Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s−1 and RMSEs of 2-3 m s−1. Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.
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