@article{PathirajaLeeuwen2022,
  author    = {Pathiraja, Sahani Darschika and Leeuwen, Peter Jan van},
  title     = {Multiplicative Non-Gaussian model error estimation in data assimilation},
  series = {Journal of advances in modeling earth systems : JAMES},
  volume    = {14},
  journal   = {Journal of advances in modeling earth systems : JAMES},
  number    = {4},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {1942-2466},
  doi       = {10.1029/2021MS002564},
  pages     = {23},
  year      = {2022},
  abstract  = {Model uncertainty quantification is an essential component of effective data assimilation. Model errors associated with sub-grid scale processes are often represented through stochastic parameterizations of the unresolved process. Many existing Stochastic Parameterization schemes are only applicable when knowledge of the true sub-grid scale process or full observations of the coarse scale process are available, which is typically not the case in real applications. We present a methodology for estimating the statistics of sub-grid scale processes for the more realistic case that only partial observations of the coarse scale process are available. Model error realizations are estimated over a training period by minimizing their conditional sum of squared deviations given some informative covariates (e.g., state of the system), constrained by available observations and assuming that the observation errors are smaller than the model errors. From these realizations a conditional probability distribution of additive model errors given these covariates is obtained, allowing for complex non-Gaussian error structures. Random draws from this density are then used in actual ensemble data assimilation experiments. We demonstrate the efficacy of the approach through numerical experiments with the multi-scale Lorenz 96 system using both small and large time scale separations between slow (coarse scale) and fast (fine scale) variables. The resulting error estimates and forecasts obtained with this new method are superior to those from two existing methods.},
  language  = {en}
}
@article{MutothyaXuLietal.2021,
  author    = {Mutothya, Nicholas Mwilu and Xu, Yong and Li, Yongge and Metzler, Ralf and Mutua, Nicholas Muthama},
  title     = {First passage dynamics of stochastic motion in heterogeneous media driven by correlated white Gaussian and coloured non-Gaussian noises},
  series = {Journal of physics. Complexity},
  volume    = {2},
  journal   = {Journal of physics. Complexity},
  publisher = {IOP Publishing},
  address   = {Bristol},
  issn      = {2632-072X},
  doi       = {10.1088/2632-072X/ac35b5},
  pages     = {24},
  year      = {2021},
  abstract  = {We study the first passage dynamics for a diffusing particle experiencing a spatially varying diffusion coefficient while driven by correlated additive Gaussian white noise and multiplicative coloured non-Gaussian noise. We consider three functional forms for position dependence of the diffusion coefficient: power-law, exponential, and logarithmic. The coloured non-Gaussian noise is distributed according to Tsallis' q-distribution. Tracks of the non-Markovian systems are numerically simulated by using the fourth-order Runge-Kutta algorithm and the first passage times (FPTs) are recorded. The FPT density is determined along with the mean FPT (MFPT). Effects of the noise intensity and self-correlation of the multiplicative noise, the intensity of the additive noise, the cross-correlation strength, and the non-extensivity parameter on the MFPT are discussed.},
  language  = {en}
}