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Laura M. Hinkelman

Abstract

The representation of the long-term radiative energy budgets in NASA’s MERRA and MERRA-2 reanalyses has been evaluated, emphasizing changes associated with the reanalysis system update. Data from the CERES EBAF Edition 2.8 satellite product over 2001–15 were used as a reference. For both MERRA and MERRA-2, the climatological global means of most TOA radiative flux terms agree to within ~3 W m−2 of EBAF. However, MERRA-2’s all-sky reflected shortwave flux is ~7 W m−2 higher than either MERRA or EBAF’s, resulting in a net TOA flux imbalance of −4 W m−2. At the surface, all-sky downward longwave fluxes are problematic for both reanalyses, while high clear-sky downward shortwave fluxes indicate that their atmospheres are too transmissive. Although MERRA-2’s individual all-sky flux terms agree better with EBAF, its net flux agreement is worse (−8.3 vs −3.3 W m−2 for MERRA) because MERRA benefits from cancellation of errors. Analysis by region and surface type gives mixed outcomes. The results consistently indicate that clouds are overrepresented over the tropical oceans in both reanalyses, particularly MERRA-2, and somewhat underrepresented in marine stratocumulus areas. MERRA-2 also exhibits signs of excess cloudiness in the Southern Ocean. Notable discrepancies occur in the polar regions, where the effects of snow and ice cover are important. In most cases, MERRA-2 better represents variability and trends in the global mean radiative fluxes over the period of analysis. Overall, the performance of MERRA-2 relative to MERRA is mixed; there is still room for improvement in the radiative fluxes in this family of reanalysis products.

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Laura M. Hinkelman, Bjorn Stevens, and K. Franklin Evans

Abstract

Causes of anisotropy in fair-weather cumulus cloud fields were investigated using quantitative measures of anisotropy and a large-eddy simulation (LES) model. Case six of the Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study Working Group 1 was used as the standard model scenario. This case represents radiatively forced development of cumulus clouds over the southern Great Plains. Cloud formation under a variety of environmental conditions was simulated and the degree of anisotropy in the output fields was calculated as a function of spatial scale. Wind shear was found to be the single greatest factor in the development of both vertically tilted and horizontally stretched cloud structures. Other factors included mean wind speed, initial water vapor mixing ratio, and the magnitude of the surface forcing.

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Laura M. Hinkelman, K. Franklin Evans, Eugene E. Clothiaux, Thomas P. Ackerman, and Paul W. Stackhouse Jr.

Abstract

Cumulus clouds can become tilted or elongated in the presence of wind shear. Nevertheless, most studies of the interaction of cumulus clouds and radiation have assumed these clouds to be isotropic. This paper describes an investigation of the effect of fair-weather cumulus cloud field anisotropy on domain-averaged solar fluxes and atmospheric heating rate profiles. A stochastic field generation algorithm was used to produce 20 three-dimensional liquid water content fields based on the statistical properties of cloud scenes from a large eddy simulation. Progressively greater degrees of xz plane tilting and horizontal stretching were imposed on each of these scenes, so that an ensemble of scenes was produced for each level of distortion. The resulting scenes were used as input to a three-dimensional Monte Carlo radiative transfer model. Domain-averaged transmission, reflection, and absorption of broadband solar radiation were computed for each scene along with the average heating rate profile. Both tilt and horizontal stretching were found to significantly affect calculated fluxes, with the amount and sign of flux differences depending strongly on sun position relative to cloud distortion geometry. The mechanisms by which anisotropy interacts with solar fluxes were investigated by comparisons to independent pixel approximation and tilted independent pixel approximation computations for the same scenes. Cumulus anisotropy was found to most strongly impact solar radiative transfer by changing the effective cloud fraction (i.e., the cloud fraction with respect to the solar beam direction).

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Yingtao Ma, Rachel T. Pinker, Margaret M. Wonsick, Chuan Li, and Laura M. Hinkelman

Abstract

Snow-covered mountain ranges are a major source of water supply for runoff and groundwater recharge. Snowmelt supplies as much as 75% of the surface water in basins of the western United States. Net radiative fluxes make up about 80% of the energy balance over snow-covered surfaces. Because of the large extent of snow cover and the scarcity of ground observations, use of remotely sensed data is an attractive option for estimating radiative fluxes. Most of the available methods have been applied to low-spatial-resolution satellite observations that do not capture the spatial variability of snow cover, clouds, or aerosols, all of which need to be accounted for to achieve accurate estimates of surface radiative fluxes. The objective of this study is to use high-spatial-resolution observations that are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive surface shortwave (0.2–4.0 μm) downward radiative fluxes in complex terrain, with attention on the effect of topography (e.g., shadowing or limited sky view) on the amount of radiation received. The developed method has been applied to several typical melt seasons (January–July during 2003, 2004, 2005, and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability of shortwave fluxes. Issues of scale in both the satellite and ground observations are also addressed to illuminate difficulties in the validation process of satellite-derived quantities. It is planned to apply the findings from this study to test improvements in estimation of snow water equivalent.

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Laura M. Hinkelman, Karl E. Lapo, Nicoleta C. Cristea, and Jessica D. Lundquist

Abstract

The benefit of using solar and longwave surface irradiance data from NASA’s Clouds and the Earth’s Radiant Energy System (CERES) synoptic (SYN) satellite product in simulations of snowmelt has been examined. The accuracy of the SYN downwelling solar and longwave irradiances was first assessed by comparison to measurements at NOAA’s Surface Radiation Network (SURFRAD) reference stations and to remote mountain observations. Typical shortwave (longwave) biases had magnitudes less than 30 (10) W m−2, with most standard deviations below 140 (30) W m−2. The performance of a range of snow models of varying complexity when using SYN irradiances as forcing data was then evaluated. Simulated snow water equivalent and runoff from cases using SYN data fell in the range of those from simulations forced with irradiances from well-maintained surface observation sites as well as empirical methods that have been shown to perform well in mountainous terrain. The SYN irradiances are therefore judged to be suitable for use in snowmelt modeling. It is also noted that the SYN upwelling shortwave irradiances, and hence albedos derived from them, are frequently not representative of individual monitoring stations because of the high spatial variability of snow cover and other surface properties in mountainous regions. In addition, adjusting the SYN downwelling longwave irradiances to reflect the exact elevation of the point of interest relative to the mean altitude of the satellite grid box is recommended.

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Kajsa M. Parding, Beate G. Liepert, Laura M. Hinkelman, Thomas P. Ackerman, Knut-Frode Dagestad, and Jan Asle Olseth

Abstract

Observations have revealed strong variability of shortwave (SW) irradiance at Earth’s surface on decadal time scales, referred to as global dimming and brightening. Previous studies have attributed the dimming and brightening to changes in clouds and atmospheric aerosols. This study assesses the influence of atmospheric circulation on clouds and SW irradiance to separate the influence of “natural” SW variability from direct and, to some extent, indirect aerosol effects. The focus is on SW irradiance in northern Europe in summer and spring because there is little high-latitude SW irradiance during winter. As a measure of large-scale circulation the Grosswetterlagen (GWL) dataset, a daily classification of synoptic weather patterns, is used. Empirical models of normalized SW irradiance are constructed based on the GWL, relating the synoptic weather patterns to the local radiative climate. In summer, a temporary SW peak in the 1970s and subsequent dimming is linked to variations in the synoptic patterns over Scandinavia, possibly related to a northward shift in the North Atlantic storm track. In spring, a decrease of anticyclonic and increase of cyclonic weather patterns over northern Europe contributes to the dimming from the 1960s to 1990. At many sites, there is also a residual SW irradiance trend not explained by the GWL model: a weak nonsignificant residual dimming from the 1950s or 1960s to around 1990, followed by a statistically significant residual brightening. It is concluded that factors other than the large-scale circulation (e.g., decreasing aerosol emissions) also play an important role in northern Europe.

Open access
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.

Open access