Status: this preprint was under review for the journal NPG but the revision was not accepted.
Fortnight conditioning of historical data to improve short-term precipitation predictions
Yoshito Hirataand Yoshinori Yamada
Abstract. The effects of changes in weather variables, including precipitation dependence on the days-of-the-week, have known applications in weather predictions. However, the use of these effects to improve weather forecasting has not been determined. Here we investigate if conditioning past data somehow by considering the days-of-the-week helps us to obtain the better short-term time series prediction for precipitation. Especially, we demonstrate that short-term time series prediction of precipitation up to 2 h ahead can be improved using the data points of the days whose differences from the current day are multiples of 14. For short-term predictions, we employ infinite-dimensional delay coordinates (Hirata et al., Sci. Rep. 5, 15736, 2015) to reconstruct the underlying dynamics. Although the results demonstrate that the two-week periodicity seems to exist in the weather at Tokyo, and thus some anthropogenic activities could influence weather, the mechanism of the influence remains unclear.
How to cite. Hirata, Y. and Yamada, Y.: Fortnight conditioning of historical data to improve short-term precipitation predictions, Nonlin. Processes Geophys. Discuss. [preprint], https://doi.org/10.5194/npg-2022-9, 2022.
Received: 17 Feb 2022 – Discussion started: 08 Mar 2022