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
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
Beyond univariate calibration: verifying spatial structure in ensembles of forecast fields
Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory
Nonlin. Processes Geophys., 29, 1–15,
2022Nonlin. Processes Geophys., 28, 371–378,
2021Nonlin. Processes Geophys., 27, 411–427,
2020Nonlin. Processes Geophys., 27, 239–252,
2020Cited 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