Downscaling and extrapolating dynamic seasonal marine forecasts for coastal ocean users. (April 2016) - Vanhatalo, Jarno; Hobday, Alistair J.; Little, L. Richard; Spillman, Claire M.
Abstract: Marine weather and climate forecasts are essential in planning strategies and activities on a range of temporal and spatial scales. However, seasonal dynamical forecast models, that provide forecasts in monthly scale, often have low offshore resolution and limited information for inshore coastal areas. Hence, there is increasing demand for methods capable of fine scale seasonal forecasts covering coastal waters. Here, we have developed a method to combine observational data with dynamical forecasts from POAMA (Predictive Ocean Atmosphere Model for Australia; Australian Bureau of Meteorology) in order to produce seasonal downscaled, corrected forecasts, extrapolated to include inshore regions that POAMA does not cover. We demonstrate the method in forecasting the monthly sea surface temperature anomalies in the Great Australian Bight (GAB) region. The resolution of POAMA in the GAB is approximately 2° × 1° (lon. × lat.) and the resolution of our downscaled forecast is approximately 1° × 0.25°. We use data and model hindcasts for the period 1994–2010 for forecast validation. The predictive performance of our statistical downscaling model improves on the original POAMA forecast. Additionally, this statistical downscaling model extrapolates forecasts to coastal regions not covered by POAMA and its forecasts are probabilistic which allows straightforward assessment of uncertainty in downscaling and prediction. A range of marine users will benefit from access to downscaled and nearshore forecasts at seasonal timescales. Highlights: We combine data with POAMA forecasts to produce downscaled and corrected forecast. The downscaled forecasts cover the inshore regions not resolved by POAMA forecasts. We assess the method with monthly mean sea surface temperature anomaly forecasts. The method generates calibrated forecasts at a finer resolution than POAMA. The method improves the skill compared to raw POAMA forecasts.