Seasonal forecast results for restoration case studies

Retrospective analysis of restoration case studies to identify the potential benefit of seasonal weather forecasting . For each case study, the dataset contains: (1) the weather issues experienced; (2) the forecast date (set as one month prior to the planting or seeding date to allow time for adapting decisions); (2) the relevant variables available in the Predictive Ocean Atmosphere Model for Australia, version M24 (POAMA-2) which predicts the weather issue; (3) Anomaly and tercile probability forecasts for applicable case study locations for the relevant weather variable (i.e. daily precipitation [rainfall], daily minimum surface temperature [Tmin] and/or daily maximum surface temperature [Tmax]) for four lead times (first fortnight [w2_1], second fortnight [w2_2], first calendar month [m_1] and first season [m3_1]; (4) forecast skill - case studies that experienced the weather issues within the acceptable forecast period of 4 months; and (5) forecast accuracy - if the anomaly or tercile probability forecasts correctly predicted the weather issue within the timeframe reported. The proportion of case studies that had correct forecasts for both anomalies and terciles were calculated. This was repeated for secondary weather issues. Case study names have been removed.