A quick reminder, summarizing the reading from the previous page.
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.
Numerical Weather Prediction [1] 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.
Local dedicated NWP models have been developed as a collaboration among NOAA and NCAR. The approach is termed WRF (pronounced "worf") [4]. 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 [4] (Accessed Oct. 20, 2013)
Links
[1] https://www.ncei.noaa.gov/products/weather-climate-models/numerical-weather-prediction
[2] https://rapidrefresh.noaa.gov/
[3] https://www.ncei.noaa.gov/products/weather-climate-models/north-american-mesoscale#:~:text=The%20North%20American%20Mesoscale%20Forecast,continent%20at%20various%20horizontal%20resolutions.
[4] https://www.mmm.ucar.edu/wrf-model-general
[5] http://asrc.albany.edu/people/faculty/perez/directory/RA.html
[6] https://cer.ucsd.edu/research/renewable-energy.html
[7] http://coimbra.ucsd.edu/