Recently, a weather forecast has improved day by day, and accuracy of the forecast has been improving. At the same time, it is necessary to forecast by the super computer which has larger-scale and higher-performance.
This study consider the small-scale weather forecast which can be executed on a personal computer.
And this study confirms effectiveness of the weather forecast by classifing weather pattern by the self-organizing map.
To forecast, this research uses the idea "If there is a similar day in the observational data to the observation day, the next day tends to resemble the next day of observation day."
First, input the observational data of all days to a self-organizing map and teach it. Next, input the observational data of the day before the forecast day to a self-organizing map, and choose the day that labels the neuron in the self-organizing map near the neuron that reacted to the input data the best. Regard the observational data of the next day of the chosen day as predictive data, and evaluate an error margin of them and actual observed data.
As a result, the rain hitting ratio resulted in about 65.0%, the error margin of highest temperature resulted in about 3.38℃, and of lowest temperature resulted in about 2.29℃.