Articles | Volume 30, issue 2
https://doi.org/10.5194/npg-30-237-2023
© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jun 2023
Research article |
| 29 Jun 2023
| 29 Jun 2023
Physically constrained covariance inflation from location uncertainty
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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
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Nonlin. Processes Geophys., 30, 515–525, 2023
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Cited articles
Anderson, J. L.: An adaptive covariance inflation error correction algorithm
for ensemble filters, Tellus A, 59, 210–224,
2007. a
Berner, J., Shutts, G., Leutbecher, M., and Palmer, T.: A spectral stochastic
kinetic energy backscatter scheme and its impact on flow-dependent
predictability in the ECMWF ensemble prediction system, J. Atmos. Sci., 66,
603–626, 2009. a
Brzeźniak, Z., Capiński, M., and Flandoli, F.: Stochastic partial
differential equations and turbulence, Math. Models Methods Appl. Sci., 1,
41–59, 1991. a
Buizza, R., Miller, M., and Palmer, T.: Stochastic representation of model
uncertainties in the ECMWF Ensemble Prediction System, Q. J. Roy. Meteor.
Soc., 125, 2887–2908, 1999. a
