Articles | Volume 27, issue 3
https://doi.org/10.5194/npg-27-411-2020
© Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.
Special issue:
Research article
| 
31 Aug 2020
Research article |
| 31 Aug 2020
| 31 Aug 2020
Beyond univariate calibration: verifying spatial structure in ensembles of forecast fields
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Related subject area
Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
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Revised manuscript accepted for NPG
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Cited articles
Anderson, J. L.:
A method for producing and evaluating probabilistic forecasts from ensemble model integrations,
J. Climate,
9, 1518–1530, https://doi.org/10.1175/1520-0442(1996)009<1518:AMFPAE>2.0.CO;2, 1996. a, b
Apanasovich, T. V., Genton, M. G., and Sun, Y.:
A valid Matérn class of cross-covariance functions for multivariate random fields with any number of components,
J. Am. Stat. Assoc.,
107, 180–193, https://doi.org/10.1080/01621459.2011.643197, 2012. a
Bellier, J., Scheuerer, M., and Hamill, T. M.:
Precipitation downscaling with Gibbs sampling: An improved method for producing realistic, weather-dependent and anisotropic fields,
J. Hydrometeorol.,
https://doi.org/10.1175/JHM-D-20-0069.1, online first, 2020. a
Buizza, R., Bidlot, J.-R., Wedi, N., Fuentes, M., Hamrud, M., Holt, G., and Vitart, F.:
The new ECMWF VAREPS (variable resolution ensemble prediction system),
Q. J. Roy. Meteor. Soc.,
133, 681–695, https://doi.org/10.1002/qj.75, 2007. a
Buschow, S. and Friederichs, P.: Using wavelets to verify the scale structure of precipitation forecasts, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 13–30, https://doi.org/10.5194/ascmo-6-13-2020, 2020. a
