Smith, D.K.E. orcid.org/0000-0003-0818-672X, Reka, S. orcid.org/0000-0002-6194-5871, Dorling, S.R. orcid.org/0000-0001-9087-2547 et al. (7 more authors) (2024) Forecasts of fog events in northern India dramatically improve when weather prediction models include irrigation effects. Communications Earth & Environment, 5. 141. ISSN 2662-4435
Abstract
Dense wintertime fog regularly impacts Delhi, severely affecting road and rail transport, aviation and human health. Recent decades have seen an unexplained increase in fog events over northern India, coincident with a steep rise in wintertime irrigation associated with the introduction of double-cropping. Accurate fog forecasting is challenging due to a high sensitivity to numerous processes across many scales, and uncertainties in representing some of these in state-of-the-art numerical weather prediction models. Here we show fog event simulations over northern India with and without irrigation, revealing that irrigation counteracts a common model dry bias, dramatically improving the simulation of fog. Evaluation against satellite products and surface measurements reveals a better spatial extent and temporal evolution of the simulated fog events. Increased use of irrigation over northern India in winter provides a plausible explanation for the observed upward trend in fog events, highlighting the critical need for optimisation of irrigation practices.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2024. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst for Climate & Atmos Science (ICAS) (Leeds) |
Funding Information: | Funder Grant number Met Office No External Reference Met Office P107104 |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Mar 2024 15:55 |
Last Modified: | 21 Mar 2024 15:55 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1038/s43247-024-01314-w |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:210651 |