AbstractBecause summer rainfall in the middle-lower reaches of the Yangtze River Valley has remarkable inter-annual and decadal variability and because the precursors that modulate the inter-annual rainfall change with the decadal variation of background state, a new model that employs novel statistical idea is needed to yield accurate prediction. In this study, the inter-annual rainfall model (IAM) and the decadal rainfall model (DM) were constructed, respectively. Moving updating of the IAM with the latest data within an optimal length of training period (20 years) can partially offset the effect of decadal change of precursors in IAM. To predict the inter-annual rainfall of 2001–2013 for validation, thirteen regression models were fitted with precursors that change every 4–5 years, from the preceding winter North Atlantic sea surface temperature anomaly (SSTA) dipole to the Mascarene High, followed by the East Asia sea level pressure anomaly (SLPA) dipole and the preceding autumn North Pacific SSTA dipole. The moving updated model demonstrated high skill in predicting inter-annual rainfall with a correlation coefficient of 0.76 and a hit rate of 76.9%. The DM was linked to the April SLPA in the central tropical Pacific, and it maintained good performance in the testing period with a correlation coefficient of 0.77 and a root-mean-square error (RMSE) of 7.7%. Our statistical model exhibited superior capability even compared with the best CFSv2 forecast initiated in early June, as indicated by increased correlation coefficient from 0.62 to 0.75 and reduced RMSE from 12.3% to 10.7%.
Journal of Applied Meteorology and Climatology – American Meteorological Society
Published: Jun 16, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
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