Articles | Volume 15, issue 19
https://doi.org/10.5194/gmd-15-7353-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7353-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 Oct 2022
Development and technical paper |
| 05 Oct 2022
| 05 Oct 2022
Repeatable high-resolution statistical downscaling through deep learning
Dánnell Quesada-Chacón
CORRESPONDING AUTHOR
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
Klemens Barfus
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
Christian Bernhofer
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
Related authors
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, and Matthieu Lengaigne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
Short summary
Short summary
This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, and Matthieu Lengaigne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
Short summary
Short summary
This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Stefanie Fischer, Ronald Queck, Christian Bernhofer, and Matthias Mauder
EGUsphere, https://doi.org/10.5194/egusphere-2025-2118, https://doi.org/10.5194/egusphere-2025-2118, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Accurate estimates of interception are important to assess the water availability in ecosystems. We analyzed rainfall interception for a forest site from plot to stand scale. During interception, eddy-covariance measurements of evaporation were systematically underestimated accounting for 24% of precipitation, while modelled interception evaporation accounted for 45%. As a consequence, we developed a hybrid correction approach to fit the evaporation data to both the energy and the water balance.
Ivan Vorobevskii, Jeongha Park, Dongkyun Kim, Klemens Barfus, and Rico Kronenberg
Hydrol. Earth Syst. Sci., 28, 391–416, https://doi.org/10.5194/hess-28-391-2024, https://doi.org/10.5194/hess-28-391-2024, 2024
Short summary
Short summary
High-resolution precipitation data are often a “must” as input for hydrological and hydraulic models (i.e. urban drainage modelling). However, station or climate projection data usually do not provide the required (e.g. sub-hourly) resolution. In the work, we present two new statistical models of different types to disaggregate precipitation from a daily to a 10 min scale. Both models were validated using radar data and then applied to climate models for 10 stations in Germany and South Korea.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
Short summary
Short summary
In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Ivan Vorobevskii, Thi Thanh Luong, Rico Kronenberg, Thomas Grünwald, and Christian Bernhofer
Hydrol. Earth Syst. Sci., 26, 3177–3239, https://doi.org/10.5194/hess-26-3177-2022, https://doi.org/10.5194/hess-26-3177-2022, 2022
Short summary
Short summary
In the study we analysed the uncertainties of the meteorological data and model parameterization for evaporation modelling. We have taken a physically based lumped BROOK90 model and applied it in three different frameworks using global, regional and local datasets. Validating the simulations with eddy-covariance data from five stations in Germany, we found that the accuracy model parameterization plays a bigger role than the quality of the meteorological forcing.
Judith Marie Pöschmann, Dongkyun Kim, Rico Kronenberg, and Christian Bernhofer
Nat. Hazards Earth Syst. Sci., 21, 1195–1207, https://doi.org/10.5194/nhess-21-1195-2021, https://doi.org/10.5194/nhess-21-1195-2021, 2021
Short summary
Short summary
We examined maximum rainfall values for different durations from 16 years of radar-based rainfall records for whole Germany. Unlike existing observations based on rain gauge data no clear linear relationship could be identified. However, by classifying all time series, we could identify three similar groups determined by the temporal structure of rainfall extremes observed in the study period. The study highlights the importance of using long data records and a dense measurement network.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado,
G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I. J., Harp,
A., Irving, G., Isard, M., Jia, Y., Józefowicz, R., Kaiser, L., Kudlur,
M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D. G., Olah,
C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker,
P. A., Vanhoucke, V., Vasudevan, V., Viégas, F. B., Vinyals, O.,
Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow:
Large-Scale Machine Learning on Heterogeneous Systems, tensorflow.org [code],
https://www.tensorflow.org/ (last access: 12 December 2021), 2015. a
Alahmari, S. S., Goldgof, D. B., Mouton, P. R., and Hall, L. O.: Challenges
for the Repeatability of Deep Learning Models, IEEE Access, 8,
211860–211868, https://doi.org/10.1109/ACCESS.2020.3039833, 2020. a, b, c, d
Allaire, J. J., Ushey, K., Tang, Y., and Eddelbuettel, D.: Reticulate: R
Interface to Python, GitHub [code], https://github.com/rstudio/reticulate (last access: 12 December 2021),
2017. a
Association for Computing Machinery (ACM): Artifact Review and Badging
Version 2.0, ACM,
https://www.acm.org/publications/policies/artifact-review-badging,
2021. a
Bastian, O., Syrbe, R. U., Slavik, J., Moravec, J., Louda, J., Kochan, B.,
Kochan, N., Stutzriemer, S., and Berens, A.: Ecosystem services of
characteristic biotope types in the Ore Mountains (Germany/Czech Republic),
International Journal of Biodiversity Science, Ecosystem Services and
Management, 13, 51–71, https://doi.org/10.1080/21513732.2016.1248865, 2017. a
Bush, R., Dutton, A., Evans, M., Loft, R., and Schmidt, G. A.: Perspectives on
Data Reproducibility and Replicability in Paleoclimate and Climate Science,
Harvard Data Science Review, 2, https://doi.org/10.1162/99608f92.00cd8f85, 2020. a, b
Cannon, A. J.: Probabilistic multisite precipitation downscaling by an
expanded Bernoulli-Gamma density network, J. Hydrometeorol., 9,
1284–1300, https://doi.org/10.1175/2008JHM960.1, 2008. a
Cavazos, T. and Hewitson, B.: Performance of NCEP–NCAR reanalysis variables in
statistical downscaling of daily precipitation, Clim. Res., 28,
95–107, 2005. a
Chollet, F. et al.: Keras, GitHub [code], https://github.com/fchollet/keras (last access: 12 December 2021),
2015. a
CORDEX: CORDEX – ESGF data availability overview, [data set]
http://is-enes-data.github.io/CORDEX_status.html, last
access: 13 November 2021. a
Deutsch, C. V.: Correcting for negative weights in ordinary kriging,
Comput. Geosci., 22, 765–773, https://doi.org/10.1016/0098-3004(96)00005-2,
1996. a
Deutsch, C. V. and Journel, A. G.: GSLIB: Geostatistical Software Library and
User's Guide, second edn., Oxford University Press, ISBN 9780195100150, 1998. a
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S., Collins, W.,
Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P.,
Guilyardi, É., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.:
Evaluation of climate models, in: Climate Change 2013 – The Physical
Science Basis: Working Group I Contribution to the Fifth Assessment Report of
the Intergovernmental Panel on Climate Change, Cambridge
University Press, 741–866, https://doi.org/10.1017/CBO9781107415324.020, 2013. a
Goodman, S. N., Fanelli, D., and Ioannidis, J. P.: What does research
reproducibility mean?, Sci. Transl. Med., 8, 96–102,
https://doi.org/10.1126/SCITRANSLMED.AAF5027, 2016. a, b, c, d
Gutiérrez, J. M., Maraun, D., Widmann, M., Huth, R., Hertig, E.,
Benestad, R., Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S., San
Martín, D., Herrera, S., Bedia, J., Casanueva, A., Manzanas, R.,
Iturbide, M., Vrac, M., Dubrovsky, M., Ribalaygua, J., Pórtoles, J.,
Räty, O., Räisänen, J., Hingray, B., Raynaud, D., Casado,
M. J., Ramos, P., Zerenner, T., Turco, M., Bosshard, T.,
Štěpánek, P., Bartholy, J., Pongracz, R., Keller, D. E.,
Fischer, A. M., Cardoso, R. M., Soares, P. M. M., Czernecki, B., and
Pagé, C.: An intercomparison of a large ensemble of statistical
downscaling methods over Europe: Results from the VALUE perfect predictor
cross-validation experiment, Int. J. Climatol., 39,
3750–3785, https://doi.org/10.1002/joc.5462, 2019. a, b, c
Hallett, J.: Climate change 2001: The scientific basis. Edited by J. T.
Houghton, Y. Ding, D. J. Griggs, N. Noguer, P. J. van der Linden, D. Xiaosu, K.
Maskell and C. A. Johnson. Contribution of Working Group I to the Third
Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge
University Press, Cambridge. 2001. 881 pp. ISBN 0521 01495 6., Q.
J. Roy. Meteor. Soc., 128, 1038–1039,
https://doi.org/10.1002/qj.200212858119, 2002. a
Harder, P., Jones, W., Lguensat, R., Bouabid, S., Fulton, J.,
Quesada-Chacón, D., Marcolongo, A., Stefanović, S., Rao, Y.,
Manshausen, P., and Watson-Parris, D.: NightVision: Generating Nighttime
Satellite Imagery from Infra-Red Observations, arXiv [preprint],
https://doi.org/10.48550/arXiv.2011.07017, 13 November 2020. a
He, X., Chaney, N. W., Schleiss, M., and Sheffield, J.: Spatial downscaling of
precipitation using adaptable random forests, Water Resour. Res., 52,
8217–8237, https://doi.org/10.1002/2016WR019034, 2016. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee,
D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M.,
Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E.,
Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti,
G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut,
J. N.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Höhlein, K., Kern, M., Hewson, T., and Westermann, R.: A comparative
study of convolutional neural network models for wind field downscaling,
Meteorol. Appl., 27, e1961, https://doi.org/10.1002/met.1961, 2020. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working
Group I to the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 2391 pp.,
2021. a
Iturbide, M., Bedia, J., Herrera, S., Baño-Medina, J., Fernández,
J., Frías, M. D., Manzanas, R., San-Martín, D., Cimadevilla, E.,
Cofiño, A. S., and Gutiérrez, J. M.: The R-based climate4R open
framework for reproducible climate data access and post-processing,
Environ. Modell. Softw., 111, 42–54,
https://doi.org/10.1016/j.envsoft.2018.09.009, 2019. a
Jézéquel, F., Lamotte, J. L., and Saïd, I.: Estimation of
numerical reproducibility on CPU and GPU, Proceedings of the 2015 Federated
Conference on Computer Science and Information Systems, FedCSIS 2015, 13–16 September 2015, Łódź, Poland, 5,
675–680, https://doi.org/10.15439/2015F29, 2015. a, b, c
Joint Committee for Guides in Metrology: International Vocabulary of
Metrology – Basic and General Concepts and Associated Terms (VIM), 3rd
edn., Joint Committee for Guides in Metrology (JCGM), 1–127,
https://www.nist.gov/system/files/documents/pml/div688/grp40/International-Vocabulary-of-Metrology.pdf,
2006. a
Kingma, D. P. and Ba, J. L.: Adam: A method for stochastic optimization, 3rd
International Conference on Learning Representations, ICLR 2015 – Conference
Track Proceedings, San Diego, CA, USA, 7–9 May 2015, 1–15, https://doi.org/10.48550/ARXIV.1412.6980, 2015. a
Kronenberg, R. and Bernhofer, C.: A method to adapt radar-derived
precipitation fields for climatological applications, Meteorol.
Appl., 22, 636–649, https://doi.org/10.1002/met.1498, 2015. a, b
Kurtzer, G. M., Sochat, V., and Bauer, M. W.: Singularity: Scientific
containers for mobility of compute, PLoS ONE, 12, 1–20,
https://doi.org/10.1371/journal.pone.0177459, 2017. a
Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T.: Visualizing the
loss landscape of neural nets, in: Advances in Neural Information Processing
Systems, edited by: Bengio, S., Wallach, H., Larochelle, H., Grauman, K.,
Cesa-Bianchi, N., and Garnett, R., vol. 31, Curran Associates, Inc.,
https://proceedings.neurips.cc/paper/2018/file/a41b3bb3e6b050b6c9067c67f663b915-Paper.pdf (last access: 13 November 2021),
2018. a
Manzanas, R., Frías, M. D., Cofiño, A. S., and Gutiérrez,
J. M.: Validation of 40 year multimodel seasonal precipitation forecasts:
The role of ENS on the global skill, J. Geophys. Res., 119,
1708–1719, https://doi.org/10.1002/2013JD020680, 2014. a
Maraun, D. and Widmann, M.: Statistical downscaling and bias correction for
climate research, Cambridge University Press, https://doi.org/10.1017/9781107588783, 2018. a, b, c
Maraun, D., Widmann, M., Gutiérrez, J. M., Kotlarski, S., Chandler,
R. E., Hertig, E., Wibig, J., Huth, R., and Wilcke, R. A. I.: Earth's Future
VALUE: A framework to validate downscaling approaches for climate change
studies, Earth's Future, 3, 1–14, https://doi.org/10.1002/2014EF000259, 2014. a, b, c
Mühr, B., Kubisch, S., Marx, A., and Wisotzky, C.: CEDIM Forensic
Disaster Analysis “Dürre & Hitzewelle Sommer 2018 (Deutschland)”,
2018, 1–19,
https://www.researchgate.net/publication/327156086_CEDIM_Forensic_Disaster_Analysis_Durre_Hitzewelle_Sommer_2018_Deutschland_Report_No_1,
2018. a
Pang, B., Yue, J., Zhao, G., and Xu, Z.: Statistical Downscaling of
Temperature with the Random Forest Model, Adv. Meteorol., 2017,
7265178, https://doi.org/10.1155/2017/7265178, 2017. a
Pastén-Zapata, E., Jones, J. M., Moggridge, H., and Widmann, M.:
Evaluation of the performance of Euro-CORDEX Regional Climate Models for
assessing hydrological climate change impacts in Great Britain: A comparison
of different spatial resolutions and quantile mapping bias correction
methods, J. Hydrol., 584, 124653,
https://doi.org/10.1016/j.jhydrol.2020.124653, 2020. a
Plesser, H. E.: Reproducibility vs. Replicability: A brief history of a
confused terminology, Front. Neuroinform., 11, 1–4,
https://doi.org/10.3389/fninf.2017.00076, 2018. a
Pour, S. H., Shahid, S., and Chung, E. S.: A Hybrid Model for Statistical
Downscaling of Daily Rainfall, Procedia Engineer., 154, 1424–1430,
https://doi.org/10.1016/j.proeng.2016.07.514, 2016. a
Quesada-Chacón, D.: Singularity container for “Repeatable high-resolution
statistical downscaling through deep learning”, Zenodo [code],
https://doi.org/10.5281/zenodo.5809705, 2021a. a, b
Quesada-Chacón, D.: Predictors and predictand for “Repeatable high-resolution
statistical downscaling through deep learning”, Zenodo [data set],
https://doi.org/10.5281/zenodo.5809553, 2021b. a
Quesada-Chacón, D.: dquesadacr/Rep_SDDL: Submission to GMD, Zenodo [code],
https://doi.org/10.5281/zenodo.5856118, 2022a. a, b
Quesada-Chacón, D.: Rendered description of the source code of “Repeatable
high-resolution statistical downscaling through deep learning”, GitHub [code],
https://github.com/dquesadacr/Rep_SDDL, last access: 11 July
2022b. a
Quesada-Chacón, D., Barfus, K., and Bernhofer, C.: Climate change
projections and extremes for Costa Rica using tailored predictors from CORDEX
model output through statistical downscaling with artificial neural
networks, Int. J. Climatol., 41, 211–232,
https://doi.org/10.1002/joc.6616, 2020. a
ReKIS: Regionales Klimainformationssystem Sachsen, Sachsen-Anhalt, Thüringen,
https://rekis.hydro.tu-dresden.de (last access: 11 July 2022),
2021. a
Riach, D.: TensorFlow Determinism (slides),
https://bit.ly/dl-determinism-slides-v3 (last access: 11 July
2022), 2021. a
Richter, D.: Ergebnisse methodischer Untersuchungen zur Korrektur des
systematischen Messfehlers des Hellmann-Niederschlagsmessers, Berichte des
Deutschen Wetterdienstes 194, Offenbach am Main, 93 pp., ISBN 978-3-88148-309-4, 1995. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for
Biomedical Image Segmentation, arXiv [preprint],
https://doi.org/10.48550/arXiv.1505.04597, 18 May 2015. a, b
Rougier, N. P., Hinsen, K., Alexandre, F., Arildsen, T., Barba, L. A.,
Benureau, F. C., Brown, C. T., DeBuy, P., Caglayan, O., Davison, A. P.,
Delsuc, M. A., Detorakis, G., Diem, A. K., Drix, D., Enel, P., Girard, B.,
Guest, O., Hall, M. G., Henriques, R. N., Hinaut, X., Jaron, K. S., Khamassi,
M., Klein, A., Manninen, T., Marchesi, P., McGlinn, D., Metzner, C., Petchey,
O., Plesser, H. E., Poisot, T., Ram, K., Ram, Y., Roesch, E., Rossant, C.,
Rostami, V., Shifman, A., Stachelek, J., Stimberg, M., Stollmeier, F., Vaggi,
F., Viejo, G., Vitay, J., Vostinar, A. E., Yurchak, R., and Zito, T.:
Sustainable computational science: The ReScience Initiative, PeerJ Computer
Science, 3, e142, https://doi.org/10.7717/peerj-cs.142, 2017. a, b
Serifi, A., Günther, T., and Ban, N.: Spatio-Temporal Downscaling of
Climate Data Using Convolutional and Error-Predicting Neural Networks,
Frontiers in Climate, 3, 1–15, https://doi.org/10.3389/fclim.2021.656479, 2021. a
Srivastava, R. K., Greff, K., and Schmidhuber, J.: Highway Networks, arXiv [preprint],
https://doi.org/10.48550/arXiv.1505.00387, 3 May 2015. a
Stoddart, C.: Is there a reproducibility crisis in science?, Nature,
3–5, https://doi.org/10.1038/d41586-019-00067-3, 2016. a
Taylor, K., Stouffer, R., and Meehl, G.: An Overview of CMIP5 and the
Experiment Design, B. Am. Meteorol. Soc., 93,
485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
TOP500: AlphaCentauri – NEC HPC 22S8Ri-4, EPYC 7352 24C 2.3GHz, NVIDIA A100
SXM4 40 GB, Infiniband HDR200,
https://top500.org/system/179942/, last access: 11 July 2022,
2022. a
Tripathi, S., Srinivas, V. V., and Nanjundiah, R. S.: Downscaling of
precipitation for climate change scenarios: A support vector machine
approach, J. Hydrol., 330, 621–640,
https://doi.org/10.1016/j.jhydrol.2006.04.030, 2006. a
Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., and Ganguly,
A. R.: Generating high resolution climate change projections through single
image super-resolution: An abridged version, International Joint Conference
on Artificial Intelligence, Stockholm, 13–19 July 2018, 5389–5393, https://doi.org/10.24963/ijcai.2018/759,
2018. a
von Storch, H., Zorita, E., and Cubasch, U.: Downscaling of Global Climate
Change Estimates to Regional Scales: An Application to Iberian Rainfall in
Wintertime, J. Climate, 6, 1161–1171,
https://doi.org/10.1175/1520-0442(1993)006<1161:DOGCCE>2.0.CO;2, 1993.
a, b
Voosen, P.: Global temperatures in 2020 tied record highs, Science, 371,
334–335, https://doi.org/10.1126/science.371.6527.334, 2021. a
Wackernagel, H.: Multivariate geostatistics: an introduction with
applications, Springer, Berlin, https://doi.org/10.1007/978-3-662-05294-5, 2010. a
Wahl, S., Bollmeyer, C., Crewell, S., Figura, C., Friederichs, P., Hense, A.,
Keller, J. D., and Ohlwein, C.: A novel convective-scale regional reanalysis
COSMO-REA2: Improving the representation of precipitation, Meteorol.
Z., 26, 345–361, https://doi.org/10.1127/metz/2017/0824, 2017. a
Wilby, R. and Wigley, T.: Downscaling general circulation model output: a
review of methods and limitations, Prog. Phys. Geog., 21, 530–548, https://doi.org/10.1177/030913339702100403, 1997. a
WMO: 2021 one of the seven warmest years on record, WMO consolidated data
shows,
https://public.wmo.int/en/media/press-release/2021-one-of-seven-warmest-years-record-wmo-consolidated-data-shows,
last access: 11 July 2022. a
Xu, B., Wang, N., Chen, T., and Li, M.: Empirical Evaluation of Rectified
Activations in Convolutional Network, arXiv [preprint],
https://doi.org/10.48550/arXiv.1505.00853, 5 May 2015. a
Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., and Liang, J.: Unet++: A
nested u-net architecture for medical image segmentation, Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics), vol. 11045, Springer, Cham,
https://doi.org/10.1007/978-3-030-00889-5_1, 2018. a, b
Short summary
We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU-calculated deep learning models using the TensorFlow, climate4R, and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.
We improved the performance of past perfect prognosis statistical downscaling methods while...
