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Manajit Sengupta, Eugene E. Clothiaux, and Thomas P. Ackerman

Abstract

A 4-yr climatology (1997–2000) of warm boundary layer cloud properties is developed for the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site. Parameters in the climatology include cloud liquid water path, cloud-base height, and surface solar flux. These parameters are retrieved from measurements produced by a dual-channel microwave radiometer, a millimeter-wave cloud radar, a micropulse lidar, a Belfort ceilometer, shortwave radiometers, and atmospheric temperature profiles amalgamated from multiple sources, including radiosondes. While no significant interannual differences are observed in the datasets, there are diurnal variations with nighttime liquid water paths consistently higher than daytime values. The summer months of June, July, and August have the lowest liquid water paths and the highest cloud-base heights. Model outputs of cloud liquid water paths from the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the Eta Model for 104 model output location time series (MOLTS) stations in the environs of the SGP central facility are compared to observations. The ECMWF and MOLTS median liquid water paths are greater than 3 times the observed values. The MOLTS data show lower liquid water paths in summer, which is consistent with observations, while the ECMWF data exhibit the opposite tendency. A parameterization of normalized cloud forcing that requires only cloud liquid water path and solar zenith angle is developed from the observations. The parameterization, which has a correlation coefficient of 0.81 with the observations, provides estimates of surface solar flux that are comparable to values obtained from explicit radiative transfer calculations based on plane-parallel theory. This parameterization is used to estimate the impact on the surface solar flux of differences in the liquid water paths between models and observations. Overall, there is a low bias of 50% in modeled normalized cloud forcing resulting from the excess liquid water paths in the two models. Splitting the liquid water path into two components, cloud thickness and liquid water content, shows that the higher liquid water paths in the model outputs are primarily a result of higher liquid water contents, although cloud thickness may a play a role, especially for the ECMWF model results.

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Manajit Sengupta, Eugene E. Clothiaux, Thomas P. Ackerman, Seiji Kato, and Qilong Min

Abstract

A 1-yr observational study of overcast boundary layer stratus at the U.S. Department of Energy Atmospheric Radiation Measurement Program Southern Great Plains site illustrates that surface radiation has a higher sensitivity to cloud liquid water path variations when compared to cloud drop effective radius variations. The mean, median, and standard deviation of observed cloud liquid water path and cloud drop effective radius are 0.120, 0.101, 0.108 mm and 7.38, 7.13, 2.39 μm, respectively. Liquid water path variations can therefore cause 3 times the variation in optical depth as effective radius—a direct consequence of the comparative variability displayed by the statistics of the two parameters. Radiative transfer calculations demonstrate that, over and above the impact of higher liquid water path variability on optical depth, normalized cloud forcing is 2 times as sensitive to liquid water path variations as it is to effective radius variations. Consequently, radiative transfer calculations of surface flux using observed liquid water paths and a fixed effective radius of 7.5 μm have a 79% correlation with observed values. This higher sensitivity of solar flux to liquid water path is a result of the regimes of natural occurrence of cloud liquid water paths and cloud drop effective radii.

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Steven D. Miller, John M. Forsythe, Philip T. Partain, John M. Haynes, Richard L. Bankert, Manajit Sengupta, Cristian Mitrescu, Jeffrey D. Hawkins, and Thomas H. Vonder Haar

Abstract

The launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat’s nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or “curtain,” of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e.g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3D structure for the topmost cloud layer. The technique was developed on multiyear CloudSat data and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) swath data from the NASA Aqua satellite. Data-exclusion experiments along the CloudSat ground track show improved predictive skill over both climatology and type-independent nearest-neighbor estimates. More important, the statistical methods, which employ a dynamic range-dependent weighting scheme, were also found to outperform type-dependent near-neighbor estimates. Application to the 3D cloud rendering of a tropical cyclone is demonstrated.

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Isidora Jankov, Lewis D. Grasso, Manajit Sengupta, Paul J. Neiman, Dusanka Zupanski, Milija Zupanski, Daniel Lindsey, Donald W. Hillger, Daniel L. Birkenheuer, Renate Brummer, and Huiling Yuan

Abstract

The main purpose of the present study is to assess the value of synthetic satellite imagery as a tool for model evaluation performance in addition to more traditional approaches. For this purpose, synthetic GOES-10 imagery at 10.7 μm was produced using output from the Advanced Research Weather Research and Forecasting (ARW-WRF) numerical model. Use of synthetic imagery is a unique method to indirectly evaluate the performance of various microphysical schemes available within the ARW-WRF. In the present study, a simulation of an atmospheric river event that occurred on 30 December 2005 was used. The simulations were performed using the ARW-WRF numerical model with five different microphysical schemes [Lin, WRF single-moment 6 class (WSM6), Thompson, Schultz, and double-moment Morrison]. Synthetic imagery was created and scenes from the simulations were statistically compared with observations from the 10.7-μm band of the GOES-10 imager using a histogram-based technique. The results suggest that synthetic satellite imagery is useful in model performance evaluations as a complementary metric to those used traditionally. For example, accumulated precipitation analyses and other commonly used fields in model evaluations suggested a good agreement among solutions from various microphysical schemes, while the synthetic imagery analysis pointed toward notable differences in simulations of clouds among the microphysical schemes.

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Sue Ellen Haupt, Branko Kosović, Tara Jensen, Jeffrey K. Lazo, Jared A. Lee, Pedro A. Jiménez, James Cowie, Gerry Wiener, Tyler C. McCandless, Matthew Rogers, Steven Miller, Manajit Sengupta, Yu Xie, Laura Hinkelman, Paul Kalb, and John Heiser

Abstract

As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results.

Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, which forms the basis of the system beyond about 6 h. For short-range (0–6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short- to midterm irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed.

This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

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