Correction Method for Numerically Forecast Wind Speed Data of MSM-GPV Using CNN

Abstract

This paper describes a statistical correction model for wind speed data of the Meso-Scale Model Grid Point Value (MSM-GPV), which is one of the numerical weather forecasting systems. In the numerical forecasting system, there are calculation errors caused by both the physical modeling and estimation of initial values. Because numerical forecast data have two-dimensional spatial information, convolution with a convolutional neural network (CNN) is used to grasp and correct the two-dimensional features of errors contained in the forecast data. In the simulations, several MSM-GPV data used for the input data and various correction models are prepared and compared with the results of a fully connected neural network from the viewpoints of the error improvement rate and error distribution.