Improving Subseasonal Forecasting with Machine Learning
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Improving our ability to forecast the weather and climate is of interest to all sectors of the economy and to government agencies from the local to the national level. Weather forecasts zero to ten days ahead and climate forecasts seasons to decades ahead are currently used operationally in decision-making, and the accuracy and reliability of these forecasts has improved consistently in recent decades (Troccoli, 2010). However, many critical applications – including water allocation, wildfire management, and drought and flood mitigation – require subseasonal forecasts with lead times in between these two extremes (Merryfield et al., 2020; White et al., 2017).