  The Japan Meteorological Agency operates gridded temperature guidance to
predict two-dimensional snowfall amounts and precipitation types, e.g., rain
and snow, because surface temperature is one of the key elements to predict
them. Operational temperature guidance is based on the Kalman filter, which
uses temperature observation and numerical weather prediction (NWP) outputs
only around observation sites. Correcting a temperature field when NWP models
incorrectly predict a front's location or when observed temperatures are
extremely cold or hot has been challenging. In this study, an
encoder-decoder-based convolutional neural network has been proposed to predict
gridded temperatures at the surface around the Kanto region in Japan.
Verification results showed that the proposed model greatly improves the
operational guidance and can correct NWP model biases, such as a positional
error of fronts and extreme temperatures.
