Research Facility Southern Great Plains Research Articles

Fast All-Sky Radiation Model for Solar Applications (FARMS): A Brief Overview of Mechanisms, Performance, and Applications: Preprint

Solar radiation can be computed using radiative transfer models, such as the Rapid Radiation Transfer Model (RRTM) and its general circulation model applications, and used for various energy applications. Due to the complexity of computing radiation fields in aerosol and cloudy atmospheres, simulating solar radiation can be extremely time-consuming, but many approximations--e.g., the two-stream approach and the delta-M truncation scheme--can be utilized. To provide a new fast option for computing solar radiation, we developed the Fast All-sky Radiation Model for Solar applications (FARMS) by parameterizing the simulated diffuse horizontal irradiance and direct normal irradiance for cloudy conditions from the RRTM runs using a 16-stream discrete ordinates radiative transfer method. The solar irradiance at the surface was simulated by combining the cloud irradiance parameterizations with a fast clear-sky model, REST2. To understand the accuracy and efficiency of the newly developed fast model, we analyzed FARMS runs using cloud optical and microphysical properties retrieved using GOES data from 2009-2012. The global horizontal irradiance for cloudy conditions was simulated using FARMS and RRTM for global circulation modeling with a two-stream approximation and compared to measurements taken from the U.S. Department of Energy's Atmospheric Radiation Measurement Climate Research Facility Southern Great Plains site. Ourmore » results indicate that the accuracy of FARMS is comparable to or better than the two-stream approach; however, FARMS is approximately 400 times more efficient because it does not explicitly solve the radiative transfer equation for each individual cloud condition. Radiative transfer model runs are computationally expensive, but this model is promising for broad applications in solar resource assessment and forecasting. It is currently being used in the National Solar Radiation Database, which is publicly available from the National Renewable Energy Laboratory at http://nsrdb.nrel.gov.« less

Mechanisms Affecting the Transition from Shallow to Deep Convection over Land: Inferences from Observations of the Diurnal Cycle Collected at the ARM Southern Great Plains Site

AbstractSummertime observations for 11 yr from the Atmospheric Radiation Measurement (ARM) Climate Research Facility Southern Great Plains (SGP) site are used to investigate mechanisms controlling the transition from shallow to deep convection over land. It is found that a more humid environment immediately above the boundary layer is present before the start of late afternoon heavy precipitation events. The higher moisture content is brought by wind from the south. Greater boundary layer inhomogeneity in moist static energy, temperature, moisture, and horizontal wind before precipitation begins is correlated to larger rain rates at the initial stage of precipitation. In an examination of afternoon rain statistics, higher relative humidity above the boundary layer is correlated to an earlier onset and longer duration of afternoon precipitation events, whereas greater boundary layer inhomogeneity and atmospheric instability in the 2–4-km layer above the surface are positively correlated to the total rain amount and the maximum rain rate. Although other interpretations may be possible, these observations are consistent with theories for the transition from shallow to deep convection that emphasize the role of a moist lower free troposphere and boundary layer inhomogeneity.

Assessment of variability in continental low stratiform clouds based on observations of radar reflectivity

The variability of overcast low stratiform clouds observed over the ARM Climate Research Facility Southern Great Plains (ACRF SGP) site is analyzed, and an approach to characterizing subgrid variability based on assumed statistical distributions is evaluated. The analysis is based on a vast (>1000 hours) radar reflectivity database collected by the Millimeter‐Wave Cloud Radar at ACRF SGP site. The radar data are classified into two low cloud categories and stratified by scale and the presence of precipitation. Cloud variability is analyzed by studying statistical distributions for the first two moments of the probability distribution functions (PDF) of radar reflectivity. Results indicate that variability for a broadly defined low‐altitude stratiform cloud type exhibits on average 40% greater standard deviation than canonical boundary layer clouds topped by an inversion. Cloud variability also dramatically depends on microphysical processes (as manifested in radar reflectivity) and increases by 2–5 times within a typical reflectivity range. Finally, variability is a strong function of scale and almost doubles in the 20–100 min temporal scale range. Formulations of subgrid variability, based on PDFs of reflectivity, are evaluated for the two cloud types and two scales of 10 and 30 km, taken to be representative of mesoscale and NWP model grid sizes. The results show that for these cloud types and scales the PDF of reflectivity can be reasonably well approximated by a truncated Gaussian function, specified by mean and standard deviation with the latter parameterized as a linear function of the mean.