Auto station precipitation data making up using an improved neuro net

Abstract

In the real world, precipitation data of automatic weather stations are easily influenced by direct thunderstrokes, instrument ageing, electromagnetic interference, human operation errors and other factors. When close to the observation time, if the missing automatic station data cannot be corrected in a timely fashion, the whole quality of the station data will be affected. Thus, correct handling of the missing precipitation data to maintain their integrity has important significance. In this paper, we propose a "from coarse to fine" (FCTF) neural network to fill out the missing blanks and experiments show that this method to solve the problem of meteorological data shortage is effective.