Author:
Cheng Ke-Sheng,Yu Gwo‑Hsing,Tai Yuan-Li,Huang Kuo-Chan,Tsai Sheng‑Fu,Wu Dong‑Hong,Lin Yun-Ching,Lee Ching-Teng,Lo Tzu-Ting
Abstract
AbstractA hypothesis testing approach, based on the theorem of probability integral transformation and the Kolmogorov–Smirnov one-sample test, for performance evaluation of probabilistic seasonal rainfall forecasts is proposed in this study. By considering the probability distribution of monthly rainfalls, the approach transforms the tercile forecast probabilities into a forecast distribution and tests whether the observed data truly come from the forecast distribution. The proposed approach provides not only a quantitative measure for performance evaluation but also a cumulative probability plot for insightful interpretations of forecast characteristics such as overconfident, underconfident, mean-overestimated, and mean-underestimated. The approach has been applied for the performance evaluation of probabilistic season rainfall forecasts in northern Taiwan, and it was found that the forecast performance is seasonal dependent. Probabilistic seasonal rainfall forecasts of the Meiyu season are likely to be overconfident and mean-underestimated, while forecasts of the winter-to-spring season are overconfident. A relatively good forecast performance is observed for the summer season.
Funder
National Science and Technology Council
Publisher
Springer Science and Business Media LLC
Reference36 articles.
1. BoM and IFRC (2015) Linking seasonal forecasts with disaster preparedness in the Pacific: from information to action. Bureau of Meteorology, Australia Government and International Federation of Red Cross and Red Crescent Societies. http://www.climatecentre.org/downloads/files/IFRCGeneva/Seasonal%20Rainfall%20Watch%20Case%20Study%20FINAL.PDF Accessed 2 Nov 2023.
2. Bröcker J, Smith LA (2007) Increasing the reliability of reliability diagrams. Weather Forecast 22:651–661. https://doi.org/10.1175/WAF993.1
3. Broecker J (2012) Probability forecast. In: Jolliffe IT, Stephenson DB (eds) Forecast verification: a practitioner’s guide in atmospheric science. John Wiley & Sons Ltd, Hoboken, pp 119–139
4. Chen G, Wang W-C (2022) Short-term precipitation prediction for contiguous United States using deep learning. Geophys Res Lett 49:e2022GL097904. https://doi.org/10.1029/2022GL097904
5. Cheng KS, Chen BY, Lin TW, Nakamura K, Ruangrassamee P, Chikamori H (2024) Rainfall frequency analysis using event-maximum rainfalls—an event-based mixture distribution modeling approach. Weather Clim Extremes 43:100634. https://doi.org/10.1016/j.wace.2023.100634