Papers by Jesse Dorrestijn
We report on the development of a methodology for stochastic parameterization of moist convection... more We report on the development of a methodology for stochastic parameterization of moist convection in General Circulation Models (GCMs), using the data-driven approach proposed by [1]. We use data from convection-resolving Large-eddy simulation (LES) to estimate stochastic processes that represent convection. These stochastic processes take the form of Markov chains that are conditioned on the resolved scale state of the atmosphere. They mimic, in a computationally inexpensive manner, the convective behavior observed in the LES. Therefore, our model can be seen as an emulator of LES. We explore cases of shallow and deep convection.
This work is funded by the program Feedbacks in the Climate System of the Nederlandse Organisatie... more This work is funded by the program Feedbacks in the Climate System of the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO). The usage of supercomputer facilities is sponsored by the National Computing Facilities Foundation (NCF) with financial support of NWO.
The paper presents a circular area Monte Carlo approach to assess scale invariance properties and... more The paper presents a circular area Monte Carlo approach to assess scale invariance properties and scale breaks from AIRS measurements. Overall the paper is very well written but the statistics and correlations showed here are not always convincing. This is a promising technique but it needs to be apply to more data and to acknowledge the poor correlations observed in section 4.5 (and more data will help with correlations). Also, why sometime the authors use /alpha and sometime β? β Is generally more known, especially when its concerns the well-known -5/3 value. There is no such reference for /alpha. I suggest using β through the whole manuscript for consistency reasons.
Atmospheric Measurement Techniques
Http Dx Doi Org 10 1175 Jas D 14 0110 1, Feb 1, 2015
Journal of the Atmospheric Sciences, 2016
Conditional Markov chain (CMC) models have proven to be promising building blocks for stochastic ... more Conditional Markov chain (CMC) models have proven to be promising building blocks for stochastic convection parameterizations. In this paper, it is demonstrated how two different CMC models can be used as mass flux closures in convection parameterizations. More specifically, the CMC models provide a stochastic estimate of the convective area fraction that is directly proportional to the cloud-base mass flux. Since, in one of the models, the number of CMCs decreases with increasing resolution, this approach makes convection parameterizations scale aware and introduces stochastic fluctuations that increase with resolution in a realistic way. Both CMC models are implemented in a GCM of intermediate complexity. It is shown that with the CMC models, trained with observational data, it is possible to improve both the subgrid-scale variability and the autocorrelation function of the cloud-base mass flux as well as the distribution of the daily accumulated precipitation in the tropics. Hovm...
Theoretical and Computational Fluid Dynamics, 2012
Journal of the Atmospheric Sciences, 2015
Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data def... more Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data defining the large-scale dynamic and thermodynamic state of the atmosphere around Darwin to develop a multicloud model based on a stochastic method using conditional Markov chains. The authors assign the radar data to clear sky, moderate congestus, strong congestus, deep convective, or stratiform clouds and estimate transition probabilities used by Markov chains that switch between the cloud types and yield cloud-type area fractions. Cross-correlation analysis shows that the mean vertical velocity is an important indicator of deep convection. Further, it is shown that, if conditioned on the mean vertical velocity, the Markov chains produce fractions comparable to the observations. The stochastic nature of the approach turns out to be essential for the correct production of area fractions. The stochastic multicloud model can easily be coupled to existing moist convection parameterization sch...
Journal of the Atmospheric Sciences, 2015
Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data def... more Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data defining the large-scale dynamic and thermodynamic state of the atmosphere around Darwin to develop a multicloud model based on a stochastic method using conditional Markov chains. The authors assign the radar data to clear sky, moderate congestus, strong congestus, deep convective, or stratiform clouds and estimate transition probabilities used by Markov chains that switch between the cloud types and yield cloud-type area fractions. Cross-correlation analysis shows that the mean vertical velocity is an important indicator of deep convection. Further, it is shown that, if conditioned on the mean vertical velocity, the Markov chains produce fractions comparable to the observations. The stochastic nature of the approach turns out to be essential for the correct production of area fractions. The stochastic multicloud model can easily be coupled to existing moist convection parameterization sch...
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Papers by Jesse Dorrestijn