Deep learning for downward longwave radiative flux forecasts in the Arctic

Abstract

Downward longwave radiative flux (LWD), a key factor affecting sea ice properties and warming (i.e., Arctic amplification) in the Arctic, has large uncertainties in numerical weather prediction (NWP) model simulations over the Arctic. LWD estimated in the European Centre for Medium-Range Weather Forecasts (ECMWF)’s fifth-generation reanalysis (ERA5) underestimated the LWD observations at Hopen in Svalbard, Norway. Although LWD underestimation in the ERA5 reanalysis with respect to observations was improved in 24 h forecasts using the Polar Weather Research and Forecasting model (PWRF) without and with data assimilation (DA), 24 h LWD forecasts using PWRF continue to underestimate LWD observations. To improve LWD estimation in the Arctic, a deep learning post-processing model that corrects the bias of the LWD simulation was developed using convolutional neural network and ERA5 reanalysis (2016–2019) as training data. By applying the trained deep learning post-processing model to LWD from three independent datasets (i.e., ERA5 reanalysis data in 2020, 24 h forecasts in 2020 using PWRF without and with DA), the time-averaged root mean square errors (RMSEs) of LWD after deep learning post-processing were reduced by 17.62%, 14.98%, and 13.14%, respectively. Therefore, deep learning reduces uncertainties in LWD simulations in the Arctic. The deep learning model trained with ERA5 reanalysis (2016–2019) was able to correct the bias in LWD simulation from the same type of independent data (i.e., ERA5 reanalysis), as well as from different model type (i.e., PWRF forecasts without and with DA). Therefore, when several NWP models simulate the same atmospheric phenomena, a deep learning model trained with data from one NWP model can be applied to data from other NWP models to reduce uncertainties. Additionally, deep learning can further improve forecasts with DA. Therefore, it is expected that the cost required to generate training data will be reduced, and the efficiency of the deep learning model will increase.