Articles | Volume 30, issue 1
https://doi.org/10.5194/npg-30-85-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.Rain process models and convergence to point processes
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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
Superstatistical analysis of sea surface currents in the Gulf of Trieste, measured by high-frequency radar, and its relation to wind regimes using the maximum-entropy principle
Physically constrained covariance inflation from location uncertainty
A waveform skewness index for measuring time series nonlinearity and its applications to the ENSO–Indian monsoon relationship
Empirical evidence of a fluctuation theorem for the wind mechanical power input into the ocean
Nonlin. Processes Geophys., 30, 515–525,
2023Nonlin. Processes Geophys., 30, 237–251,
2023Nonlin. Processes Geophys., 29, 1–15,
2022Nonlin. Processes Geophys., 28, 371–378,
2021Cited articles
Ahmed, F. and Neelin, J. D.: Explaining scales and statistics of tropical
precipitation clusters with a stochastic model, J. Atmos.
Sci., 76, 3063–3087, 2019. a
Albano, G., Giorno, V., Nobile, A. G., and Ricciardi, L. M.: Modeling
refractoriness for stochastically driven single neurons, Scientiae
Mathematicae Japonicae, 67, 173–190, 2008. a
Bhat, V. N.: Renewal approximations of the switched Poisson processes and their
applications to queueing systems, J. Oper. Res.
Soc., 45, 345–353, 1994. a