At NCAR our scientists have been working to push the boundaries of NWP capabilities to provide much-improved forecasts of solar irradiance and energy generation. An enhanced version of the WRF model developed at NCAR, called WRF-Solar™, provides hour-ahead solar irradiance forecasts that are competitive with various persistence forecasting techniques, with forecasts out to the day-ahead range for energy trading and unit commitment. A nowcasting version of WRF-Solar provides 15-min irrandiance forecasts up to six hours ahead. WRF-Solar is the first NWP model specifically designed to meet the growing demand for specialized numerical forecast products for solar power applications.
Sketch representing the physical processes that WRF-Solar™ improves. The different components of the radiation are indicated.
Research was conducted on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system, which were then blended, converted to power, analog ensemble applied to for the SunCast™ Solar Power Forecasting System. The shortest range forecasts are based on observations in the field. TSICast operates on the shortest time scale, with a latency of only a few minutes and forecasts that currently extend to approximately15 min. This project facilitated research in improving hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast uses pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employs satellite imagery and uses that information to discern clouds and their motion, allowing these systems to project the clouds, and the resulting blockage of irradiance in time. CIRACast was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team advanced cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CiraCast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC. A second satellite-based system, MADCast, assimilates data from multiple satellite imagers and profilers to assimilate a three-dimensional picture of the cloud into the dynamic core of WRF. This allows advection of the clouds via the WRF dynamics directly. During 2015, MADCast provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs extending to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts extending to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-Solar™ physics to develop and dissipate clouds. MAE improvements for MAD-WRF forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components provide improvement over Smart Persistence individually, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast.
The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We found that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%.
