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Does improved SSTA prediction ensure better seasonal rainfall forecasts?
Author(s) -
Khan Mohammad Zaved Kaiser,
Sharma Ashish,
Mehrotra Rajeshwar,
Schepen Andrew,
Wang Q. J.
Publication year - 2015
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2014wr015997
Subject(s) - environmental science , climatology , sea surface temperature , forecast skill , meteorology , bayesian probability , consensus forecast , computer science , econometrics , mathematics , geography , artificial intelligence , geology
Seasonal rainfall forecasts in Australia are issued based on concurrent sea surface temperature anomalies (SSTAs) using a Bayesian model averaging (BMA) approach. The SSTA fields are derived from the Predictive Ocean‐Atmosphere Model for Australia (POAMA) initialized in the preceding season. This study investigates the merits of the rainfall forecasted using POAMA SSTAs in contrast to that forecasted using a multimodel combination of SSTAs derived using five existing models. In addition, seasonal rainfall forecasts derived from multimodel and POAMA SSTA fields are subsequently combined to obtain a single weighted forecast over Australia. These three forecasts are compared against “idealized” forecasts where observed SSTAs are used instead of those predicted. The results indicate that while seasonal rainfall forecasts derived using multimodel‐based SSTA indices offer improvements in selected seasons over a majority of grid cells in comparison to the case where a single SSTA model is used in two seasons, these improvements are not as significant as the improvements in the SSTA field that drive the rainfall forecasting model. The forecasts derived from the combination of multimodel and POAMA SSTA indices forecasts are found to offer greater improvements over the multimodel or the POAMA forecasts for a majority of grid cells in all seasons. It is also observed that these combined forecasts are touching the upper limits of forecastability, which are reached when observed SSTAs are used to forecast the rainfall. This suggests that further improvements in rainfall forecasting are only possible through the use of an improved forecasting algorithm, and not the driver (SSTA) information used in the current study.