  Japan Meteorological Agency (JMA) has been operating gridded temperature
guidance for predicting snow amount and precipitation type because those
elements are susceptible to a temperature at the surface. The operational
temperature guidance is based on the Kalman filter technique and uses
temperature observation and NWP outputs only at observation sites; it has been
difficult to correct a temperature field when NWP models did not predict the
location of a front correctly or when the observed temperature was extremely
cold or hot. In the present paper, encoder-decoder-based convolutional neural
networks (CNNs) were employed to predict gridded temperatures at the surface
around the Kanto district. The verification results showed that the proposed
method improves operational guidance significantly and can correct NWP model
biases, including a positional error of fronts and extreme temperatures.
