Figure 2.

Schematic of SurfUVNet. (a) The underlying encoder–decoder neural network architecture showing the flow of data from the encoder to the decoder via the central connection denoted by S_t. LSTM and Dense indicates the Long Short-Term Memory and fully connected neural network layers, respectively. UV data from days prior to the forecast date are fed into the encoder part while UV data from the same date of previous year are fed into the decoder part. The model forecasts next-day UV radiation at 10-min resolution. (b) The auto-recursive mode for long-term UV forecasting. To forecast UV radiation for the next N days, SurfUVNet first forecast next-day’s UV radiation profile and then uses the prediction as input to forecast UV radiation profile for the day after. This process is repeated until the forecasts for the next N days are generated.