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Nastos et al. (2013) applied an artificial neural network to forecast precipitation in Athens, Greece.
The system provides images and text related to estimated and forecast precipitation products.
It's got the upcoming forecast, precipitation reports, air pressure, and all sorts of other things that have always and will always confuse the hell out of me.
In particular, the Procrustes methodology is used to generate mean forecast precipitation fields from a set of ensemble forecasts.
In this study, the GMALSS model was used with exogenous forcing variables such as the ENSO to simulate and forecast precipitation in the East River basin, China.
Moreover, Table 1 presents some examples of studies using univariate time series forecasting approaches that do not utilize exogenous predictor variables to forecast precipitation or temperature variables, and streamflow or river discharge variables.
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In general, the seasonal model better forecasted precipitation in spring, autumn, and winter than the global model; but the global model did better in forecasting precipitation in summer.
(3) In both the track precipitation forecasting and cross-validation precipitation forecasting practices, the seasonal model better forecasts precipitation changes in spring, autumn, and winter; however, the global model better forecasts summer precipitation changes.
Forecasting precipitation at the seasonal time scale remains a formidable challenge.
The seasonal model better forecasted precipitation changes in spring, autumn, and winter than did the global model, but did worse than the global model in forecasting precipitation changes in summer.
However, MAPE indicated differently, that is, the seasonal and global models better forecasted precipitation changes in spring and summer than in autumn and winter.
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