  Forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF;
EC for short) can provide a basis for the establishment of maritime-disaster
warning systems, but they contain some systematic biases.The fifth-generation
EC atmospheric reanalysis (ERA5) data have high accuracy, but are delayed by
about 5 days. To overcome this issue, a spatiotemporal deep-learning method
could be used for nonlinear mapping between EC and ERA5 data, which would
improve the quality of EC wind forecast data in real time. In this study, we
developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit
(MT-DETrajGRU) model, which uses an improved double-encoder forecaster
architecture to model the spatiotemporal sequence of the U and V components of
the wind field; we designed a multi-task learning loss function to correct wind
speed and wind direction simultaneously using only one model. The study area
was the western North Pacific (WNP), and real-time rolling bias corrections
were made for 10-day wind-field forecasts released by the EC between December
2020 and November 2021, divided into four seasons. Compared with the original
EC forecasts, after correction using the MT-DETrajGRU model the wind speed and
wind direction biases in the four seasons were reduced by 8-11% and 9-14%,
respectively. In addition, the proposed method modelled the data uniformly
under different weather conditions. The correction performance under normal and
typhoon conditions was comparable, indicating that the data-driven mode
constructed here is robust and generalizable.
