Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Eikei University of Hiroshima, 1-5 Nobori-cho, Naka-ku, Hiroshima-shi, Hiroshima 730-0016, Japan
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
I am simply not convinced by this paper, it is very short with one figure and is not up to the standards and depth expected for NPG. Authors need to heavily revise and extend the manuscript to improve presentation and their arguments. Hopefully my comments below are helpful.
The authors argue that short-term (2hr ahead) time series prediction for precipitation at Tokyo station in 1-min sampling can be improved by using data two weeks in the past and some form of analogs method. This is similar to looking for needle in a haystack and I find it very doubtful without additional analysis and presentation. First of all it would be helpful to show time series. Secondly, are there any periodicities in the time series itself by using classical spectral analysis methods? Finally, they should think on how to better present and illustrate their prediction method, perhaps using some toy model data, not simply as a short appendix.