- J. Kleissl (ed), 2013. Solar Energy Forecasting and Resource Assessment. Elsevier Science, Academic Press.
The reading continues for pp. 177-182 of Chapter 8: "Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation," by Coimbra, Kleissl, and Marquez [found on Canvas]
- Optional: The sections following 8.2 (pp. 182-192) can be scanned for key topics rather than a deep read. 8.3 deals with metrics for evaluating models, which is a deeper statistical investigation than we have time for here. Also, those methods are still evolving in collaboration with meteorologists.
A quick reminder summarizing the reading from the previous page.
- >6 hour time horizon: physics-based models
- 2-6 hour time horizon: combination of methods via Numerical Weather Prediction (NWP) models
- <30 minute time horizon: apply ground-to-sky imager technologies
Events to be evaluated for <2 hours will use statistical approaches such as time series (Autoregressive Integrated Moving Average; ARIMA) or artificial intelligence (e.g., Artificial Neural Networks; ANN).
As a slight correction from the reading assignment, we provide the following standard terminologies in meteorology for forecasting ranges, called lead times. These are not stated clearly in the reading, and they are important enough to have in your vocabulary. In the prior reading assignment, you should notice that the time horizons tied into solar energy models are not yet aligned with the approaches for meteorological forecasting. This is an indication of the relatively new start of forecasting applied to solar energy. We are still learning the common language of meteorology, and hopefully that language will soon converge. Similarly, meteorologists are beginning to adapt to the solar field's language of GHI, DNI, irradiation, etc.
- Medium-range: 3-7 days
- Short-range: 6 hours to 2 days
- Nowcast: 0 - 6 hours
- Hindcast: negative lead time
Numerical Weather Prediction (NWP)
Numerical Weather Prediction uses an assemblage of modeling methods, along with current weather observation data to forecast weather in a future state. Note that the observations tied to the current state of the data are very important to NWP.
- Numerical Weather Prediction summary at NOAA.
- Numerical Weather Prediction summary at UCAR.
- Description of Rapid Refresh from NOAA.
- The North American Mesoscale Forecast (NAM) was mentioned in Table 8.1 of the reading.
Weather Research and Forecasting (WRF)
Local dedicated NWP models have been developed as a collaboration among NOAA and NCAR. The approach is termed WRF (pronounced "worf"). This is an advanced application of NWP, but the skill with which one can forecast will still decay with increasing lead times due to the chaotic atmospheric behavior.
The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed to serve both atmospheric research and operational forecasting needs. It features two dynamical cores, a data assimilation system, and a software architecture allowing for parallel computation and system extensibility. The model serves a wide range of meteorological applications across scales ranging from meters to thousands of kilometers.
-WRF Homepage (Accessed Oct. 20, 2013)
Research Occurring Now!
- Perez Research Group at SUNY-Albany
- UCSD Solar Resource Assessment and Forecasting Laboratory (Kleissl) and Coimbra Energy Group (Coimbra)