AbstractThe prediction of drought onset and decay in the U.S. Corn Belt region (CBR) on seasonal-to-subseasonal time scales has not been well studied. This study utilizes the subseasonal-to-seasonal prediction archive to assess model errors in large-scale circulation patterns associated with agricultural drought transition periods, targeting models used by the European Centre for Medium-Range Forecasts, National Centers for Environmental Prediction, and Australian Bureau of Meteorology. An analysis of the seasonal cycle of bias for geopotential anomalies at 200 hPa and net radiation at the top of the atmosphere in each model is presented and used to subtract the long-term bias from each model. Model fields are decomposed into three spectral bands—low frequency (periods > 100 days), intraseasonal (periods 20–100 days), and synoptic (periods < 20 days)—to demonstrate each model’s ability to predict patterns associated with agricultural drought transition periods in each band. Results demonstrate that ECMWF and NCEP struggle in predicting the large-scale circulation patterns associated with 20-day agricultural drought and onset transitions, but are more skillful in predicting the patterns associated with 60-day agricultural drought onset and decay events at reforecast hour lead window 360–480 (F360–F480). BoM was not skillful in predicting the circulation patterns associated with either type of drought transition. Results also demonstrate that the errors associated with these events are no worse than historical errors for the target study period.
Monthly Weather Review – American Meteorological Society
Published: Sep 6, 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