Exploring the impact of ocean data assimilation and use of machine learning for improving weather to subseasonal forecasts
Aneesh Subramanian
Department of Atmospheric & Oceanic Sciences, University of Colorado
Tuesday, Aug 17, 2021, 2:00 pm
Abstract
We evaluate the relative merits of different ocean observation systems (moored buoys, Argo, satellite, XBTs and others) by their impact on ocean analyses and subseasonal forecast skill. Several ocean analyses were performed where different ocean observation platforms were withheld from the assimilation in addition to one ocean analysis where all observations were assimilated. We then use these ocean analyses products for initializing a set of subseasonal forecasts to evaluate the impact of different ocean analyses states on the forecast skill. We use the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system for the twenty-year sub-seasonal hindcast experiments. Results from these hindcast experiments will be presented to highlight changes in the ocean analyses states and their impact on the forecast skill from weather to subseasonal timescales.
We also examine Deep Learning (DL) post-processing methods to obtain reliable and accurate probabilistic forecasts from deterministic numerical weather prediction. Using a 34-year reforecast of North American West Coast IVT, the dynamically/statistically derived 0-120 hour probabilistic forecasts for integrated vapor transport (IVT) under atmospheric river (AR) conditions are tested. These predictions are compared to the Global Ensemble Forecast System (GEFS) dynamic model and the GEFS calibrated with a neural network. Results showing that the DL methods generate reliable and skillful probabilistic forecasts will be presented.
Coupled air-sea interaction processes and water vapor transport processes relevant to weather and intraseasonal variability in the earth’s climate system are inadequately represented in regional and global coupled models. These inaccuracies could be related to either poor parameterization of model physics or insufficient model resolution to resolve the critical processes. New efforts in observations, process understanding and translation into weather and climate models are necessary for improvements in simulation and prediction of the intraseasonal variability and associated weather events. We will discuss the merits of different observation platforms and post-processing techniques in this context and also future observation and model improvement pathways.