Probabilistic forecasts of the onset of the North Australian wet season

• Published in: Monthly weather review 2007-10, Vol.135 (10), p.3506-3520
• Main Authors: LO, Fiona, WHEELER, Matthew C, MEINKE, Holger, DONALD, Alexis
• Format: Article
• Language: English
• Subjects: Agricultural industry; Agricultural production; Anomalies; Climatological means; Climatology; Daily rainfall; darwin; Datasets; duration; Earth, ocean, space; El Nino; Exact sciences and technology; External geophysics; Interannual variability; Marine; Meteorology; Precipitation; prediction; Probability; Probability theory; quality; Rain; Rainfall; Rainy season; Regression analysis; Research methodology; Sea surface; Sea surface temperature; Seasons; Southern Oscillation; Southern Oscillation Index; Statistical analysis; Surface temperature; systems; variability; Wet season
• ISSN: 0027-0644; 1520-0493
• DOI: 10.1175/mwr3473.1

The amount and timing of early wet-season rainfall are important for the management of many agricultural industries in north Australia. With this in mind, a wet-season onset date is defined based on the accumulation of rainfall to a predefined threshold, starting from 1 September, for each square of a 1° gridded analysis of daily rainfall across the region. Consistent with earlier studies, the interannual variability of the onset dates is shown to be well related to the immediately preceding July–August Southern Oscillation index (SOI). Based on this relationship, a forecast method using logistic regression is developed to predict the probability that onset will occur later than the climatological mean date. This method is expanded to also predict the probabilities that onset will be later than any of a range of threshold dates around the climatological mean. When assessed using cross-validated hindcasts, the skill of the predictions exceeds that of climatological forecasts in the majority of locations in north Australia, especially in the Top End region, Cape York, and central Queensland. At times of strong anomalies in the July–August SOI, the forecasts are reliably emphatic. Furthermore, predictions using tropical Pacific sea surface temperatures (SSTs) as the predictor are also tested. While short-lead (July–August predictor) forecasts are more skillful using the SOI, long-lead (May–June predictor) forecasts are more skillful using Pacific SSTs, indicative of the longer-term memory present in the ocean.