AUTHOR=Wang Tianyi , Chu Cuijiao , Sun Xuguang , Li Tim TITLE=Improving Real-Time Forecast of Intraseasonal Variabilities of Indian Summer Monsoon Precipitation in an Empirical Scheme JOURNAL=Frontiers in Earth Science VOLUME=8 YEAR=2020 URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2020.577311 DOI=10.3389/feart.2020.577311 ISSN=2296-6463 ABSTRACT=

In contrast to the historical forecast test which is temporally successive with a near-steady forecast skill, the real-time forecast made at any one moment produces a forecast time-series whose skill rapidly decreases as the forecast lead time increases; thus, only the leading section of the latter is adopted in practical applications. As compared with the intraseasonal filtered historical forecast, the real-time extended-range forecast has a lower skill in the absence of filtering. In addition, it is difficult to estimate the intraseasonal phases near the end of the real-time forecast time-series due to missed information afterward. The current work developed a simple but useful method to improve the real-time forecast skill from the above two aspects for an empirical extended-range forecast scheme. The scheme is devoted to predict the intraseasonal variabilities of Indian summer monsoon precipitation, in which the boreal summer intraseasonal oscillation acts as the precursor. The intraseasonal signals in the previous observations, the better forecast skills of shorter lead times, the implicit information regarding the intraseasonal phases in the forecast of longer lead times, and the data-adaptive intraseasonal filter are adopted in the improving method, so as to extract intraseasonal signals as much as possible from the currently available information at each forecast moment. A practical comparison shows that the forecast skills of the real-time forecast improved by this method are close to or even better than the intraseasonal filtered historical forecast. Even at the longest acceptable forecast lead time that the forecast after which is considered to be worthless, it helps extract useful information regarding the intraseasonal phases.