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Most of the short term forecast approaches use [[numerical weather prediction]] models (NWP) that provide an important estimation of the development of weather variables. The models used included the [[Global Forecast System]] (GFS) or data provided by the European Center for Medium Range Weather Forecasting ([[ECMWF]]). These two models are considered the state of the art of global forecast models, which provide meteorological forecasts all over the world.
In order to increase spatial and temporal resolution of these models, other models have been developed which are generally called mesoscale models. Among others, [[HIRLAM]], [[Weather Research and Forecasting Model|WRF]] or [[MM5 (weather model)|MM5]]. Since these NWP models are highly complex and difficult to run on local computers, these variables are usually considered as exogeneous inputs to [[solar irradiance]] models and ingested form the respective data provider. Best forecasting results are achieved with [[data assimilation]].
Some researchers argue for the use of post-processing techniques, once the models’ output is obtained, in order to obtain a [[Probability|probabilistic]] point of view of the accuracy of the output. This is usually done with ensemble techniques that mix different outputs of different models perturbed in strategic meteorological values and finally provide a better estimate of those variables and a degree of uncertainty, like in the model proposed by Bacher et al. (2009).
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