Search from over 60,000 research works

Advanced Search

Forecasting annual maximum water level for the Negro River at Manaus using dynamical seasonal predictions

[thumbnail of Open access]
Preview
1-s2.0-S2405880723000031-main.pdf - Published Version (7MB) | Preview
Available under license: Creative Commons Attribution
[thumbnail of Chevuturi et al 2023 Climate Services.pdf]
Chevuturi et al 2023 Climate Services.pdf - Accepted Version (12MB)
Restricted to Repository staff only
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Chevuturi, A. orcid id iconORCID: https://orcid.org/0000-0003-2815-7221, Klingaman, N. P. orcid id iconORCID: https://orcid.org/0000-0002-2927-9303, Woolnough, S. J. orcid id iconORCID: https://orcid.org/0000-0003-0500-8514, Rudroff, C. M., Coelho, C. A.S. and Schongart, J. (2023) Forecasting annual maximum water level for the Negro River at Manaus using dynamical seasonal predictions. Climate Services, 30. 100342. ISSN Elsevier doi: 10.1016/j.cliser.2023.100342

Abstract/Summary

Early and skilful prediction of the Negro River maximum water levels at Manaus is critical for effective mitigation measures to safeguard lives and livelihoods. Using dynamical seasonal prediction hindcasts, from six prediction centres, we investigate extending the lead time of previously developed statistical models, which issue forecasts in March for Manaus. The original statistical forecast models used observed rainfall as the major predictor. We advance the capability to issue skilful forecasts earlier, in February. We develop ensemble forecasts by combining predictor data from observations and seasonal hindcasts. We compare those forecasts against the original statistical forecast models and forecasts using the observed climatology or persistence of predictors. The ensemble-mean forecasts, issued in February, using European Centre for Medium-Range Weather Forecasts (ECMWF) hindcast input, perform similarly as the original forecasts issued in March and gain one month of lead time. The ECMWF-based ensemble forecasts skilfully predict the likelihood of water levels exceeding the severe flood level of 29 m. Forecast performance reduces and ensemble spread increases with increasing lead time from February to January. We conclude that forecasts for Manaus maximum water levels can be produced using combined input from observations and real-time ECMWF forecasts.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/110511
Item Type Article
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher Elsevier
Download/View statistics View download statistics for this item

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar