Articles | Volume 21, issue 12
Nat. Hazards Earth Syst. Sci., 21, 3679–3691, 2021
https://doi.org/10.5194/nhess-21-3679-2021

Special issue: Recent advances in drought and water scarcity monitoring,...

Nat. Hazards Earth Syst. Sci., 21, 3679–3691, 2021
https://doi.org/10.5194/nhess-21-3679-2021
Research article
03 Dec 2021
Research article | 03 Dec 2021

Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations

Elizaveta Felsche and Ralf Ludwig

Related authors

Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble
Nicola Maher, Sebastian Milinski, and Ralf Ludwig
Earth Syst. Dynam., 12, 401–418, https://doi.org/10.5194/esd-12-401-2021,https://doi.org/10.5194/esd-12-401-2021, 2021
Ten-year return levels of sub-daily extreme precipitation over Europe
Benjamin Poschlod, Ralf Ludwig, and Jana Sillmann
Earth Syst. Sci. Data, 13, 983–1003, https://doi.org/10.5194/essd-13-983-2021,https://doi.org/10.5194/essd-13-983-2021, 2021
Short summary
Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe
Fabian von Trentini, Emma E. Aalbers, Erich M. Fischer, and Ralf Ludwig
Earth Syst. Dynam., 11, 1013–1031, https://doi.org/10.5194/esd-11-1013-2020,https://doi.org/10.5194/esd-11-1013-2020, 2020
Short summary
Anthropogenic climate change versus internal climate variability: impacts on snow cover in the Swiss Alps
Fabian Willibald, Sven Kotlarski, Adrienne Grêt-Regamey, and Ralf Ludwig
The Cryosphere, 14, 2909–2924, https://doi.org/10.5194/tc-14-2909-2020,https://doi.org/10.5194/tc-14-2909-2020, 2020
Short summary
Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for central Europe
Andrea Böhnisch, Ralf Ludwig, and Martin Leduc
Earth Syst. Dynam., 11, 617–640, https://doi.org/10.5194/esd-11-617-2020,https://doi.org/10.5194/esd-11-617-2020, 2020
Short summary

Related subject area

Atmospheric, Meteorological and Climatological Hazards
Hotspots for warm and dry summers in Romania
Viorica Nagavciuc, Patrick Scholz, and Monica Ionita
Nat. Hazards Earth Syst. Sci., 22, 1347–1369, https://doi.org/10.5194/nhess-22-1347-2022,https://doi.org/10.5194/nhess-22-1347-2022, 2022
Short summary
Development of a forecast-oriented kilometre-resolution ocean–atmosphere coupled system for western Europe and sensitivity study for a severe weather situation
Joris Pianezze, Jonathan Beuvier, Cindy Lebeaupin Brossier, Guillaume Samson, Ghislain Faure, and Gilles Garric
Nat. Hazards Earth Syst. Sci., 22, 1301–1324, https://doi.org/10.5194/nhess-22-1301-2022,https://doi.org/10.5194/nhess-22-1301-2022, 2022
Short summary
Tropical cyclone storm surge probabilities for the east coast of the United States: a cyclone-based perspective
Katherine L. Towey, James F. Booth, Alejandra Rodriguez Enriquez, and Thomas Wahl
Nat. Hazards Earth Syst. Sci., 22, 1287–1300, https://doi.org/10.5194/nhess-22-1287-2022,https://doi.org/10.5194/nhess-22-1287-2022, 2022
Short summary
Hydrometeorological analysis of the 12 and 13 September 2019 widespread flash flooding in eastern Spain
Arnau Amengual
Nat. Hazards Earth Syst. Sci., 22, 1159–1179, https://doi.org/10.5194/nhess-22-1159-2022,https://doi.org/10.5194/nhess-22-1159-2022, 2022
Short summary
Monitoring the daily evolution and extent of snow drought
Benjamin J. Hatchett, Alan M. Rhoades, and Daniel J. McEvoy
Nat. Hazards Earth Syst. Sci., 22, 869–890, https://doi.org/10.5194/nhess-22-869-2022,https://doi.org/10.5194/nhess-22-869-2022, 2022
Short summary

Cited articles

Barnston, A. G. and Livezey, R. E.: Classification, Seasonality and Persistence of Low-Frequency Atmospheric Circulation Patterns, Mon. Weather Rev., 115, 1083–1126, https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2, 1987. a
Belayneh, A., Adamowski, J., Khalil, B., and Quilty, J.: Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction, Atmos. Res., 172–173, 37–47, https://doi.org/10.1016/j.atmosres.2015.12.017, 2016. a, b
Biesiada, J. and Duch, W.: Feature Selection for High-Dimensional Data – A Pearson Redundancy Based Filter, in: Computer Recognition Systems 2, edited by: Kurzynski, M., Puchala, E., Wozniak, M., and Zolnierek, A., Springer, Berlin, Heidelberg, 242–249, 2007. a
Bishop, C. M.: Pattern Recognition and Machine Learning (Information Science and Statistics), 1st edn., Springer, Berlin, Heidelberg, 2007. a
Bonaccorso, B., Cancelliere, A., and Rossi, G.: Probabilistic forecasting of drought class transitions in Sicily (Italy) using Standardized Precipitation Index and North Atlantic Oscillation Index, J. Hydrol., 526, 136–150, https://doi.org/10.1016/j.jhydrol.2015.01.070, 2015. a, b, c, d
Download
Short summary
This study applies artificial neural networks to predict drought occurrence in Munich and Lisbon, with a lead time of 1 month. An analysis of the variables that have the highest impact on the prediction is performed. The study shows that the North Atlantic Oscillation index and air pressure 1 month before the event have the highest importance for the prediction. Moreover, it shows that seasonality strongly influences the goodness of prediction for the Lisbon domain.
Altmetrics
Final-revised paper
Preprint