Short-term forecast of the total and spectral solar irradiance

Among several heliophysical and geophysical quantities, the accurate evolution of the solar irradiance is fundamental to forecast the evolution of the neutral and ionized components of the Earth's atmosphere.We developed an artificial neural network model to compute the evolution of the solar irradiance in near-real time. The model is based on the assumption that that great part of the solar irradiance variability is due to the evolution of the structure of the solar magnetic field. We employ a Layer-Recurrent Network (LRN) to model the complex relationships between the evolution of the bipolar magnetic structures (input) and the solar irradiance (output). The evolution of the bipolar magnetic structures is obtained from near-real time solar disk magnetograms and intensity images. The magnetic structures are identify and classified according to the area of the solar disk covered. We constrained the model by comparing the output of the model and observations of the solar irradiance made by instruments onboard of SORCE spacecraft. Here we focus on two regions of the spectra that are covered by SORCE instruments. The generalization of the network is tested by dividing the data sets on two groups: the training set; and, the validation set. We have found that the model error is wavelength dependent. While the model error for 24-hour forecast in the band from 115 to 180 nm is lower than 5%, the model error can reach 20% in the band from 180 to 310 nm. The performance of the network reduces progressively with the increase of the forecast period, which limits significantly the maximum forecast period that we can achieve with the discussed architecture. The model proposed allows us to predict the total and spectral solar irradiance up to three days in advance.