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124726 |
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|a dc
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|a Emanuel, Kerry Andrew
|e author
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|a Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
|e contributor
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|a Lorenz Center
|q (Massachusetts Institute of Technology)
|e contributor
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|a What is the predictability limit of midlatitude weather?
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|b American Meteorological Society,
|c 2020-04-17T17:51:45Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/124726
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|a © 2019 American Meteorological Society. Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.
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|a NSF (grant no. AGS-1305798)
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|a NSF (grant no. 1712290)
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|a ONR (grant no. N000140910526)
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|a en
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|a Article
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|t 10.1175/JAS-D-18-0269.1
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|t Journal of the atmospheric sciences
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