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Dataset card Data Studio Files
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Claudio Heinrich-Mertsching, Thordis L. Thorarinsdottir, Peter Guttorp, and Max Schneider. Validation of
point process predictions with proper scoring rules. October 2021.
Hans Hersbach. Decomposition of the continuous ranked probability score for ensemble prediction systems.
Weather and Forecasting, 15(5):559–570, October 2000. ISSN 1520-0434. https://doi.org/10.1175/
1520-0434(2000)015<0559:dotcrp>2.0.co;2.
Hajo Holzmann and Matthias Eulert. The role of the information set for forecasting—with applications
to risk management. The Annals of Applied Statistics, 8(1), March 2014. ISSN 1932-6157. https:
//doi.org/10.1214/13-aoas709.
Weiming Hu, Mohammadvaghef Ghazvinian, William E. Chapman, Agniv Sengupta, Fred Martin Ralph,
and Luca Delle Monache. Deep learning forecast uncertainty for precipitation over the western united
states. Monthly Weather Review, 151(6):1367–1385, June 2023. ISSN 1520-0493. https://doi.org/10.
1175/mwr-d-22-0268.1.
Aapo Hyvärinen. Estimation of non-normalized statistical models by score matching. Journal of Machine
Learning Research, 6(24):695–709, 2005. URL http://jmlr.org/papers/v6/hyvarinen05a.html.
Ian T. Jolliffe and Cristina Primo. Evaluating rank histograms using decompositions of the chi-square test
statistic. Monthly Weather Review, 136(6):2133–2139, June 2008. ISSN 0027-0644. https://doi.org/
10.1175/2007mwr2219.1.
Alexander Jordan, Fabian Krüger, and Sebastian Lerch. Evaluating probabilistic forecasts with scoringrules.
Journal of Statistical Software, 90(12), 2019. ISSN 1548-7660. https://doi.org/10.18637/jss.v090.
i12.
Thomas H. Jordan, Yun-Tai Chen, Paolo Gasparini, Raul Madariaga, Ian Main, Warner Marzocchi, Geras
simos Papadopoulos, Gennady Sobolev, Koshun Yamaoka, and Jochen Zschau. Operational earthquake
forecasting. state of knowledge and guidelines for utilization. Annals of Geophysics, 54(4), August 2011.
ISSN 2037-416X. https://doi.org/10.4401/ag-5350.
Victor Richmond Jose. A characterization for the spherical scoring rule. Theory and Decision, 66(3):263–281,
July 2007. ISSN 1573-7187. https://doi.org/10.1007/s11238-007-9067-x.
Ryan Keisler. Forecasting global weather with graph neural networks. February 2022. https://doi.org/
10.48550/ARXIV.2202.07575.
S. Kullback and R. A. Leibler. On information and sufficiency. The Annals of Mathematical Statistics, 22
(1):79–86, March 1951. ISSN 0003-4851. https://doi.org/10.1214/aoms/1177729694.
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet,
Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George
Holland, Oriol Vinyals, Jacklynn Stott, Alexander Pritzel, Shakir Mohamed, and Peter Battaglia. Graph
cast: Learning skillful medium-range global weather forecasting. December 2022. https://doi.org/10.
48550/ARXIV.2212.12794.
Sebastian Lerch and Kai L. Polsterer. Convolutional autoencoders for spatially-informed ensemble post
processing. In ICLR 2022- AI for Earth and Space Science Workshop, 2022.
32
Sebastian Lerch and Thordis L. Thorarinsdottir. Comparison of non-homogeneous regression models for
probabilistic wind speed forecasting. Tellus A: Dynamic Meteorology and Oceanography, 65(1):21206,
December 2013. ISSN 1600-0870. https://doi.org/10.3402/tellusa.v65i0.21206.
Sebastian Lerch, Thordis L. Thorarinsdottir, Francesco Ravazzolo, and Tilmann Gneiting. Forecaster’s
dilemma: Extreme events and forecast evaluation. Statistical Science, 32(1), February 2017. ISSN 0883
4237. https://doi.org/10.1214/16-sts588.
Georges Matheron. Principles of geostatistics. Economic Geology, 58(8):1246–1266, December 1963. ISSN
0361-0128. https://doi.org/10.2113/gsecongeo.58.8.1246.
James E. Matheson and Robert L. Winkler. Scoring rules for continuous probability distributions. Manage
ment Science, 22, 1976. https://doi.org/10.2307/2629907.
Xiaochun Meng, James W. Taylor, Souhaib Ben Taieb, and Siran Li. Scores for multivariate distributions and
level sets. Operations Research, July 2023. ISSN 1526-5463. https://doi.org/10.1287/opre.2020.0365.
Allan H. Murphy and Robert L. Winkler. A general framework for forecast verification. Monthly Weather
Review, 115(7):1330–1338, July 1987. ISSN 1520-0493. https://doi.org/10.1175/1520-0493(1987)
115<1330:agfffv>2.0.co;2.
Jakub Nowotarski and Rafał Weron. Recent advances in electricity price forecasting: A review of probabilistic
forecasting. Renewable and Sustainable Energy Reviews, 81:1548–1568, January 2018. ISSN 1364-0321.
ERROR: type should be string, got " https://doi.org/10.1016/j.rser.2017.05.234."
Lorenzo Pacchiardi, Rilwan Adewoyin, Peter Dueben, and Ritabrata Dutta. Probabilistic forecasting with
generative networks via scoring rule minimization. Journal of Machine Learning Research, 25(45):1–64,
2024. URL https://jmlr.org/papers/v25/23-0038.html.
T. N. Palmer. Towards the probabilistic earth-system simulator: a vision for the future of climate and
weather prediction. Quarterly Journal of the Royal Meteorological Society, 138(665):841–861, April 2012.
ISSN 1477-870X. https://doi.org/10.1002/qj.1923.
Matthew Parry, A. Philip Dawid, and Steffen Lauritzen. Proper local scoring rules. The Annals of Statistics,
40(1), February 2012. ISSN 0090-5364. https://doi.org/10.1214/12-aos971.
Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza
Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, Pedram Hassanzadeh, Karthik
Kashinath, and Animashree Anandkumar. Fourcastnet: A global data-driven high-resolution weather
model using adaptive fourier neural operators. February 2022.
P. Pinson and R. Girard. Evaluating the quality of scenarios of short-term wind power generation. Applied
Energy, 96:12–20, aug 2012. https://doi.org/10.1016/j.apenergy.2011.11.004.
Pierre Pinson. Wind energy: Forecasting challenges for its operational management. Statistical Science, 28
(4), November 2013. ISSN 0883-4237. https://doi.org/10.1214/13-sts445.
Pierre Pinson and Julija Tastu. Discrimination ability of the energy score. DTU Compute- Technical Report,
2013.
Sabine Radanovics, Jean-Philippe Vidal, and Eric Sauquet. Spatial verification of ensemble precipitation:
An ensemble version of sal. Weather and Forecasting, 33(4):1001–1020, July 2018. ISSN 1520-0434.
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Stephan Rasp and Sebastian Lerch. Neural networks for postprocessing ensemble weather forecasts.
Monthly Weather Review, 146(11):3885–3900, October 2018. ISSN 1520-0493. https://doi.org/10.
1175/mwr-d-18-0187.1.
33
Stephan Rasp, Stephan Hoyer, Alexander Merose, Ian Langmore, Peter Battaglia, Tyler Russel, Alvaro
Sanchez-Gonzalez, Vivian Yang, Rob Carver, Shreya Agrawal, Matthew Chantry, Zied Ben Bouallègue,
Peter Dueben, Carla Bromberg, Jared Sisk, Luke Barrington, Aaron Bell, and Fei Sha. Weatherbench 2:
A benchmark for the next generation of data-driven global weather models. 2024. https://doi.org/10.
48550/ARXIV.2308.15560.
Pauline Rivoire, Olivia Martius, Philippe Naveau, and Alexandre Tuel. Assessment of subseasonal-to-seasonal
(s2s) ensemble extreme precipitation forecast skill over europe. Natural Hazards and Earth System Sciences,
23(8):2857–2871, August 2023. ISSN 1684-9981. https://doi.org/10.5194/nhess-23-2857-2023.
Nigel M. Roberts and Humphrey W. Lean. Scale-selective verification of rainfall accumulations from high
resolution forecasts of convective events. Monthly Weather Review, 136(1):78–97, January 2008. ISSN
0027-0644. https://doi.org/10.1175/2007mwr2123.1.
Mark S. Roulston and Leonard A. Smith. Evaluating probabilistic forecasts using information theory.
Monthly Weather Review, 130(6):1653–1660, June 2002. ISSN 1520-0493. https://doi.org/10.1175/
1520-0493(2002)130<1653:epfuit>2.0.co;2.
Michael Scheuerer and Thomas M. Hamill. Variogram-based proper scoring rules for probabilistic forecasts
of multivariate quantities∗. Monthly Weather Review, 143(4):1321–1334, 2015. https://doi.org/10.
1175/mwr-d-14-00269.1.
Danijel Schorlemmer, Maximilian J. Werner, Warner Marzocchi, Thomas H. Jordan, Yosihiko Ogata,