Improving NASA's Multiscale Modeling Framework for Tropical Cyclone Climate Study

Shen, Bo-Wen; Nelson, Bron; Cheung, Samson; Tao, Wei‐Kuo

doi:10.1109/mcse.2012.90

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…The multiscale basis functions have subgrid-scale resolutions, ensuring that the fine-scale heterogeneity is correctly accounted for in a systematic manner [11][12][13]. Similarly, in turbulent flows [14,15], climate forecast [16], multiphase systems [17,18], reactive transport [19,20], complex fluids [21] and biological systems [22], multiscale approaches attempt to relate the coarse-scale solutions to the fine-scale local solutions taking into account the fine-scale physics and dynamics. In these multiscale approaches, although the small scale physics and dynamics are being captured and accounted for at larger scales, the fine-grid solutions are extremely costly and hence impractical to solve for.…”

Section: Introductionmentioning

confidence: 99%

MESHFREEFLOWNET: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

Jiang

Esmaeilzadeh

Azizzadenesheli

et al. 2020

SC20: International Conference for High Performance Computing, Networking, Storage and Analysis

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We propose MeshfreeFlowNet, a novel deep learningbased super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs. While being computationally efficient, Mesh-freeFlowNet accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet allows for: (i) the output to be sampled at all spatio-temporal resolutions, (ii) a set of Partial Differential Equation (PDE) constraints to be imposed, and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal domains owing to its fully convolutional encoder.We empirically study the performance of Mesh-freeFlowNet on the task of super-resolution of turbulent flows in the Rayleigh-Bénard convection problem. Across a diverse set of evaluation metrics, we show that Mesh-freeFlowNet significantly outperforms existing baselines. Furthermore, we provide a large scale implementation of MeshfreeFlowNet and show that it efficiently scales across large clusters, achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of less than 4 minutes.We provide an open-source implementation of our method that supports arbitrary combinations of PDE constraints 1 . * Denotes equal contribution.

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Section: Introductionmentioning

confidence: 99%

MESHFREEFLOWNET: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

Jiang

Esmaeilzadeh

Azizzadenesheli

et al. 2020

SC20: International Conference for High Performance Computing, Networking, Storage and Analysis

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“…In GMMF v1.0, radiation was called from the GEOS model with cloud properties provided by the embedded GCE [Tao et al, 2009a]. This version has been evaluated using NASA high-resolution satellite data [Waliser et al, 2009;Chen et al, 2011;Li et al, 2012], improved with the addition of a land surface model [Mohr et al, 2013], and computationally enhanced for high impact weather studies [Shen et al, 2013].…”

Section: Introductionmentioning

confidence: 99%

Performance of the Goddard multiscale modeling framework with Goddard ice microphysical schemes

Chern

Tao

Lang

et al. 2016

J Adv Model Earth Syst

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The multiscale modeling framework (MMF), which replaces traditional cloud parameterizations with cloud-resolving models (CRMs) within a host atmospheric general circulation model (GCM), has become a new approach for climate modeling. The embedded CRMs make it possible to apply CRM-based cloud microphysics directly within a GCM. However, most such schemes have never been tested in a global environment for long-term climate simulation. The benefits of using an MMF to evaluate rigorously and improve microphysics schemes are here demonstrated. Four one-moment microphysical schemes are implemented into the Goddard MMF and their results validated against three CloudSat/CALIPSO cloud ice products and other satellite data. The new four-class (cloud ice, snow, graupel, and frozen drops/hail) ice scheme produces a better overall spatial distribution of cloud ice amount, total cloud fractions, net radiation, and total cloud radiative forcing than earlier three-class ice schemes, with biases within the observational uncertainties. Sensitivity experiments are conducted to examine the impact of recently upgraded microphysical processes on global hydrometeor distributions. Five processes dominate the global distributions of cloud ice and snow amount in long-term simulations: (1) allowing for ice supersaturation in the saturation adjustment, (2) three additional correction terms in the depositional growth of cloud ice to snow, (3) accounting for cloud ice fall speeds, (4) limiting cloud ice particle size, and (5) new size-mapping schemes for snow and graupel. Despite the cloud microphysics improvements, systematic errors associated with subgrid processes, cyclic lateral boundaries in the embedded CRMs, and momentum transport remain and will require future improvement.

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“…[39,[42][43][44]). Many more environments do exist, some not necessarily tied to multiscale models, others specifically developed for multiscale modelling and simulation [5,8,45,46]. So far, many of these frameworks seem to be quite pragmatic.…”

Section: Multiscale Computingmentioning

confidence: 99%

Multiscale modelling and simulation: a position paper

Hoekstra

Chopard

Coveney

2014

Phil. Trans. R. Soc. A.

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One contribution of 13 to a Theme Issue 'Multi-scale systems in fluids and soft matter: approaches, numerics and applications' . We argue that, despite the fact that the field of multiscale modelling and simulation has enjoyed significant success within the past decade, it still holds many open questions that are deemed important but so far have barely been explored. We believe that this is at least in part due to the fact that the field has been mainly developed within disciplinary silos. The principal topics that in our view would benefit from a targeted multidisciplinary research effort are related to reaching consensus as to what exactly one means by 'multiscale modelling', formulating a generic theory or calculus of multiscale modelling, applying such concepts to the urgent question of validation and verification of multiscale models, and the issue of numerical error propagation in multiscale models. Moreover, we believe that this would, in principle, also lay the foundation for more efficient, well-defined and usable multiscale computing environments. We believe that multidisciplinary research to fill in the gaps is timely, highly relevant, and with substantial potential impact on many scientific disciplines.

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Improving NASA's Multiscale Modeling Framework for Tropical Cyclone Climate Study

Cited by 10 publications

References 15 publications

MESHFREEFLOWNET: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

MESHFREEFLOWNET: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

Performance of the Goddard multiscale modeling framework with Goddard ice microphysical schemes

Multiscale modelling and simulation: a position paper

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