Facility Southern Great Plains Research Articles

Optimizing cloud motion estimation on the edge with phase correlation and optical flow

Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

The Impact of Surface Heterogeneities and Land‐Atmosphere Interactions on Shallow Clouds Over ARM SGP Site

AbstractContinental shallow clouds are an important component of the land‐atmosphere coupled climate system because of their role in modulating energy and water budgets. The parameterization of these clouds in climate models presents a significant challenge. We evaluate the potential impact of subgrid‐scale (for climate models) land‐atmosphere interactions on shallow clouds using nested large‐eddy simulations (LESs). We compare LESs that allow for land‐atmosphere interactions to ones that artificially suppress all or part of them by smoothing out surface heterogeneities in surface heat, moisture and radiation fluxes within the domain. The LES domains are coupled to an interactive land surface model and nested inside a mesoscale domain. Three summertime shallow convection cases over the Department of Energy Atmospheric Radiation Measurement Facility's Southern Great Plains site are examined. A consistent increase in cloud water content is observed in all cases when surface heterogeneities and the land‐atmosphere interactions they induce in the LES domain are removed. By comparing experiments where only surface flux heterogeneities induced by cloud shadows are removed to ones where all surface flux heterogeneities are removed, we find that cloud shading‐induced surface heterogeneities can have a larger impact on shallow convection than static land surface heterogeneities in our cases. Furthermore, the impact of cloud shading on cloud water content and cloud size is found to vary significantly with the solar incidence angle. Our results suggest that the impact of cloud shadows needs to be considered when parameterizing land‐atmosphere interactions in the presence of shallow clouds for regional or global climate models.

RACORO continental boundary layer cloud investigations: 2. Large‐eddy simulations of cumulus clouds and evaluation with in situ and ground‐based observations

AbstractA 60 h case study of continental boundary layer cumulus clouds is examined using two large‐eddy simulation (LES) models. The case is based on observations obtained during the RACORO Campaign (Routine Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations) at the ARM Climate Research Facility's Southern Great Plains site. The LES models are driven by continuous large‐scale and surface forcings and are constrained by multimodal and temporally varying aerosol number size distribution profiles derived from aircraft observations. We compare simulated cloud macrophysical and microphysical properties with ground‐based remote sensing and aircraft observations. The LES simulations capture the observed transitions of the evolving cumulus‐topped boundary layers during the three daytime periods and generally reproduce variations of droplet number concentration with liquid water content (LWC), corresponding to the gradient between the cloud centers and cloud edges at given heights. The observed LWC values fall within the range of simulated values; the observed droplet number concentrations are commonly higher than simulated, but differences remain on par with potential estimation errors in the aircraft measurements. Sensitivity studies examine the influences of bin microphysics versus bulk microphysics, aerosol advection, supersaturation treatment, and aerosol hygroscopicity. Simulated macrophysical cloud properties are found to be insensitive in this nonprecipitating case, but microphysical properties are especially sensitive to bulk microphysics supersaturation treatment and aerosol hygroscopicity.

Retrievals of cloud optical depth and effective radius from Thin-Cloud Rotating Shadowband Radiometer measurements

[1] A Thin-Cloud Rotating Shadowband Radiometer (TCRSR) was developed and deployed in a field test at the Atmospheric Radiation Measurement Climate Research Facility's Southern Great Plains site. The TCRSR measures the forward-scattering lobe of the direct solar beam (i.e., the solar aureole) through an optically thin cloud (optical depth < 8). We applied the retrieval algorithm of Min and Duan (2005) to the TCRSR measurements of the solar aureole to derive simultaneously the cloud optical depth (COD) and cloud drop effective radius (DER), subsequently inferring the cloud liquid-water path (LWP). After careful calibration and preprocessing, our results indicate that the TCRSR is able to retrieve simultaneously these three properties for optically thin water clouds. Colocated instruments, such as the MultiFilter Rotating Shadowband Radiometer (MFRSR), atmospheric emitted radiance interferometer (AERI), and Microwave Radiometer (MWR), are used to evaluate our retrieval results. The relative difference between retrieved CODs from the TCRSR and those from the MFRSR is less than 5%. The distribution of retrieved LWPs from the TCRSR is similar to those from the MWR and AERI. The differences between the TCRSR-based retrieved DERs and those from the AERI are apparent in some time periods, and the uncertainties of the DER retrievals are discussed in detail in this article.