It’s in the patterns
THE best monthly rainfall forecasts come from computer programs able to represent complex relationships between climate data while acquiring knowledge from many examples over time for better pattern detection.
That’s according to CQUniversity researchers Dr John Abbot (pictured left) and Jennifer Marohasy (pictured right), who have considered relationships between lagged values for temperature, atmospheric pressure and rainfall as well as climate data.
They have published their findings in the Atmospheric Research journal.
The authors have compared results from their artificial neural networks (ANNs) analysis of the Inter-decadal Pacific Oscillation index against government-based seasonal rainfallforecasting programs. This index has never before been used for official seasonal forecasts for Queensland.
‘‘Forecasts using the ANN for sites in three geographically distinct regions within Queens- land are shown to be superior ... compared to forecasts from the Predictive Ocean Atmosphere Model for Australia (POAMA), which is the general circulation model used to produce the official season rainfall forecasts,’’ say Abbot and Marohasy.
They say a major limitation of government forecasts is they provide no information about the magnitude of the expected deviation from the median rainfall value within the defined forecast period.