In:
Geophysical Research Letters, American Geophysical Union (AGU), Vol. 50, No. 8 ( 2023-04-28)
Abstract:
We built a deep‐learning surface ozone ensemble forecast system to quantify pollution risks given the range of possible weather outcomes Deep‐learning models accentuating the spatial patterns of weather effectively represented the ozone‐meteorology relationship Weather forecast uncertainties contributed 38%–54% of the ozone forecast errors at 24‐hr lead time in Shenzhen
Type of Medium:
Online Resource
ISSN:
0094-8276
,
1944-8007
DOI:
10.1029/2022GL102611
Language:
English
Publisher:
American Geophysical Union (AGU)
Publication Date:
2023
detail.hit.zdb_id:
2021599-X
detail.hit.zdb_id:
7403-2
SSG:
16,13
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