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, the observational data of various places are learned to the self-organizing map as an input, and the pattern is classified.
Next, next day's observational data is studied to the self-organizing map as an input, and next day's weather is forecasted.
And,the a margin of error of the forecasted weather and the actual weather is evaluated.
As a result, the rain hitting ratio resulted in about 63.7%,
the error margin of highest temperature resulted in about 2.82
C,
and of lowest temperature resulted in about 2.05C.