Multi-model ensemble (MME) prediction of rainfall using neural networks during monsoon season in India

Abstract

The prediction of Asian summer monsoon rainfall on various space and time scales is still a difficult task. Compared to the mid-latitudes, proportional improvement in the skill in prediction of monsoon rainfall in medium range has been less in recent years. Global models and data assimilation techniques are being further improved for monsoons and the tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and reducing the model uncertainties. As major centres are exchanging the model output in near real time, MME is a viable, inexpensive, way for enhancing the forecast skill. Apart from a simple ensemble mean, the MME predictions of large-scale monsoon precipitation in the medium range was carried out during the 2009 monsoon at NCMRWF/MoES, India. The neural network weights were obtained and a neural network was trained based upon forecast data from four global models for the 2007 and 2008 monsoons in order to develop the multi-model ensemble system. The skill score for country and sub-regions, indicates that a multi-model ensemble forecast has a higher skill than individual model forecasts and also higher skill than the simple ensemble mean in general. Although the skill of the global models falls beyond day 3, a significant improvement could be seen by employing the MME technique up to day 5. © 2011 Royal Meteorological Society.