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It is well established that most operational numerical weather prediction (NWP) models consistently over-predict irradiance. While more accurate than imagery-based or statistical techniques, their applicability for day-ahead solar forecasting is limited. Overall, error is dependent on the expected meteorological conditions. For regions with dynamic cloud systems, forecast accuracy is low. Specifically, the North American Model (NAM) predicts insufficient cloud cover along the California coast, especially during summer months. Since this region represents significant potential for distributed photovoltaic generation, accurate solar forecasts are critical. To improve forecast accuracy, a high-resolution, direct-cloud-assimilating NWP based on the Weather and Research Forecasting model (WRF-CLDDA) was developed and implemented at the University of California, San Diego (UCSD). Using satellite imagery, clouds were directly assimilated in the initial conditions. Furthermore, model resolution and parameters were chosen specifically to facilitate the formation and persistence of the low-altitude clouds common to the California coast. Compared to the UCSD pyranometer network, intra-day WRF-CLDDA forecasts were 17.4% less biased than the NAM and relative mean absolute error (rMAE) was 4.1% lower. For day-ahead forecasts, WRF-CLDDA accuracy did not diminish; relative mean bias error was only 1.6% and rMAE 18.2% (5.5% smaller than the NAM). Spatially, the largest improvements occurred for the morning hours along coastal regions when cloud cover is expected. Additionally, the ability of WRF-CLDDA to resolve intra-hour variability was assessed. Though the horizontal (1.3 km) and temporal (5 min.) resolutions were fine, ramp rates for time scales of less than 30 min. were not accurately characterized. Thus, it was concluded that the cloud sizes resolved by WRF-CLDDA were approximately five times as large as its horizontal discretization.

A novel progressive damage and failure model for fiber reinforced laminated composites is presented in this work. The model uses the thermodynamically based Schapery Theory (ST) to model progressive microdamage in the matrix phase. Matrix failure is not governed with a matrix failure criterion, but rather matrix failure occurs naturally through the evolution of microdamage. A maximum strain criterion is used to dictate tensile failure in the fiber direction, while compressive failure is automatically accounted for by allowing local fiber rotations and tracking the evolution of rotation. The results of this model are compared to a previously developed model that used ST at the lamina level to calculate matrix microdamage, but used the Generalized Method of Cells to resolve the lamina level strains into constituent level stresses and strains and determines constituent failure by evaluating failure criteria at the micro, fiber/matrix level. Results for global load versus displacement and local strain from both models are compared to experimental data for notched laminates loaded in uniaxial tension. The results show remarkable agreement qualitatively, and in many cases the quantitative agreement is good. Accurate damage contours and failure paths are predicted.

No abstract

This is a pre-print version of an article published in Climate Dynamics.