Seamless Prediction and Predictability from weather to subseasonal to seasonal timescales using Average Predictability time.

Achieving seamless predictability across various time scales in weather and climate forecasting is a still a challenge. The metrics for prediction do not transition gradually from weather to subseasonal timescales. To get predictions at longer lead time, weekly to monthly averaging in forecasts is applied to minimize the noise. This results in an artificial, abrupt adhoc transition between one lead time to another.

The goal of this study is to take advantage of current understanding of predictability to determine a metric that maximize the signal-to-noise ratio as we move from weather to subseasonal scales. To achieve this, we use Average Predictability time (ATP) analysis (Delsole and Tippet, 2009) as a metric to diagnose predictability on multiple time scales. APT is applied to a large suite of retrospective forecasts using the NASA's Global Modeling and Assimilation Office (GMAO) subseasonal-to-seasonal (S2S) v2 forecasts produced by the Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation model. We demonstrate how APT can be used as a tool to study smooth transitions between consecutive averaging intervals within the framework of retrospective forecasts. Our analysis will highlight the potential shifts in predictability corresponding to different states or regimes, spanning from weather to subseasonal and that from subseasonal to seasonal scales, without the application of time averaging.

Reference:
DelSole, T., and M. K. Tippett, 2009: Average Predictability Time. Part I: Theory. J. Atmos. Sci., 66, 1172–1187, https://doi.org/10.1175/2008JAS2868.1.