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.
|Journal||Expert Systems with Applications|
|Publication status||Published - 2022 Dec 30|
Bibliographical noteFunding Information:
The authors appreciate the reviewers’ valuable comments. This study was supported by a National Research Foundation of Korea (NRF) grant funded by the South Korean government (Ministry of Science and ICT) (Grant 2021R1A2C1012572) and Yonsei Signature Research Cluster Program of 2022 (2022-22-0003). The authors appreciate the Byrd Polar Research Center at Ohio State University for providing the polar WRF model and the Norwegian Meteorological Institute for providing observation data through the eKlima site.
© 2022 The Author(s)
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence