AbstractTyphoon rainfall predictions provide critical information that can be used for flood control and advanced disaster prevention preparations. However, total rainfall “nowcasts” (i.e., several days ahead) are not available in Taiwan when typhoons are distant. In this paper, we propose a long-distance total rainfall forecast (LTRF) model and present a real-time forecasting process that can use the LTRF model to determine the formation and possible approach of typhoons in the future. The LTRF model was formulated using two designed climate scenarios. Scenario 1 considered El Niño–Southern Oscillation (ENSO) effects, whereas Scenario 2 did not. Various raw sensor data, comprising climatological characteristics, sea surface temperature, satellite brightness temperatures, and total rainfall, were collected; moreover, attributes of the ENSO indices, including the Southern Oscillation Index and Niño 3.4 sea surface temperature anomaly, were reviewed. The scenario models were constructed using the C4.5 and Random Forests tree-based algorithms. Typhoon events occurring during 2001–2013 and 2014–2015 (specifically, Typhoons Matmo and Fung-Wong in 2014 and Soudelor and Dujuan in 2015) were examined for training and testing purposes, respectively. The Hualien Weather Station in Taiwan was selected as a study site, and the forecasting horizon was set at 6 hours. Finally, the model simulations, observations, and Central Weather Bureau (Taiwan) nowcasts were compared. The simulation results showed that the proposed LTRF model, when ENSO effects were accounted for, can efficiently forecast total typhoon rainfall when typhoons are distant from Taiwan.
Journal of Atmospheric and Oceanic Technology – American Meteorological Society
Published: Aug 18, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera