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Steven D. Miller, Fang Wang, Ann B. Burgess, S. McKenzie Skiles, Matthew Rogers, and Thomas H. Painter

Abstract

Runoff from mountain snowpack is an important freshwater supply for many parts of the world. The deposition of aeolian dust on snow decreases snow albedo and increases the absorption of solar irradiance. This absorption accelerates melting, impacting the regional hydrological cycle in terms of timing and magnitude of runoff. The Moderate Resolution Imaging Spectroradiometer (MODIS) Dust Radiative Forcing in Snow (MODDRFS) satellite product allows estimation of the instantaneous (at time of satellite overpass) surface radiative forcing caused by dust. While such snapshots are useful, energy balance modeling requires temporally resolved radiative forcing to represent energy fluxes to the snowpack, as modulated primarily by varying cloud cover. Here, the instantaneous MODDRFS estimate is used as a tie point to calculate temporally resolved surface radiative forcing. Dust radiative forcing scenarios were considered for 1) clear-sky conditions and 2) all-sky conditions using satellite-based cloud observations. Comparisons against in situ stations in the Rocky Mountains show that accounting for the temporally resolved all-sky solar irradiance via satellite retrievals yields a more representative time series of dust radiative effects compared to the clear-sky assumption. The modeled impact of dust on enhanced snowmelt was found to be significant, accounting for nearly 50% of the total melt at the more contaminated station sites. The algorithm is applicable to regional basins worldwide, bearing relevance to both climate process research and the operational management of water resources.

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Jared A. Lee, Sue Ellen Haupt, Pedro A. Jiménez, Matthew A. Rogers, Steven D. Miller, and Tyler C. McCandless

Abstract

The Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0–6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRF-Solar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility District (SMUD) in California. Each case day represents a canonical sky-cover regime for the SMUD region and thus represents different modeling challenges. The analysis found that each of the nowcasting models perform better or worse for particular lead times and weather situations. StatCast performs best in clear skies and for 0–1-h forecasts; CIRACast and MADCast perform reasonably well when cloud fields are not rapidly growing or dissipating; and WRF-Solar, when configured with a high-spatial-resolution aerosol climatology and a shallow cumulus parameterization, generally performs well in all situations. Further research is needed to develop an optimal dynamic blending technique that provides a single best forecast to energy utility operators.

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Yoo-Jeong Noh, John M. Forsythe, Steven D. Miller, Curtis J. Seaman, Yue Li, Andrew K. Heidinger, Daniel T. Lindsey, Matthew A. Rogers, and Philip T. Partain

Abstract

Knowledge of cloud-base height (CBH) is important to describe cloud radiative feedbacks in numerical models and is of practical relevance to the aviation community. Whereas satellite remote sensing with passive radiometers traditionally has provided a ready means for estimating cloud-top height (CTH) and cloud water path (CWP), assignment of CBH requires heavy assumptions on the distribution of CWP within the cloud profile. An attempt to retrieve CBH has been included as part of the VIIRS environmental data records, produced operationally as part of the Suomi–National Polar-Orbiting Partnership (SNPP) and the forthcoming Joint Polar Satellite System. Through formal validation studies tied to the program, it was found that the operational CBH algorithm failed to meet performance specifications in many cases. This paper presents a new methodology for retrieving CBH of the uppermost cloud layer, developed through statistical analyses relating cloud geometric thickness (CGT) to CTH and CWP. The semiempirical approach, which relates these parameters via piecewise fitting, enlists A-Train satellite data [CloudSat cloud profiling radar (CPR), CALIPSO/CALIOP, and Aqua MODIS]. CBH is provided as the residual difference between CTH and CGT. By eliminating cloud type–dependent assumptions on CWP distribution, artifacts common to the operational algorithm (which contribute to high errors) are reduced. Special accommodations are made for handling optically thin cirrus and deep convection. An application to SNPP VIIRS is demonstrated, and the results are compared against global CloudSat observations. From the VIIRS–CloudSat daytime matchups (September–October 2013 and January–May 2015), the new algorithm outperforms the operational SNPP VIIRS algorithm, particularly when the retrieved CTH is accurate. Best performance is expected for single-layer liquid-phase clouds.

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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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