Parallel-in-Time Multi-Level Integration of the Shallow-Water Equations on the Rotating Sphere

The modeling of atmospheric processes in the context of weather and climate simulations is an important and computationally expensive challenge. The temporal integration of the underlying PDEs requires a very large number of time steps, even when the terms accounting for the propagation of fast atmospheric waves are treated implicitly. Therefore, the use of parallel-in-time integration schemes to reduce the time-to-solution is of increasing interest, particularly in the numerical weather forecasting field. We present a multi-level parallel-in-time integration method combining the Parallel Full Approximation Scheme in Space and Time (PFASST) with a spatial discretization based on Spherical Harmonics (SH). The iterative algorithm computes multiple time steps concurrently by interweaving parallel high-order fine corrections and serial corrections performed on a coarsened problem. To do that, we design a methodology relying on the spectral basis of the SH to coarsen and interpolate the problem in space. The methods are evaluated on the shallow-water equations on the sphere using a set of tests commonly used in the atmospheric flow community. We assess the convergence of PFASST-SH upon refinement in time. We also investigate the impact of the coarsening strategy on the accuracy of the scheme, and specifically on its ability to capture the high-frequency modes accumulating in the solution. Finally, we study the computational cost of PFASST-SH to demonstrate that our scheme resolves the main features of the solution multiple times faster than the serial schemes

Surrogate Model for Geological CO2 Storage and Its Use in MCMC-based History Matching

Deep-learning-based surrogate models show great promise for use in geological carbon storage operations. In this work we target an important application - the history matching of storage systems characterized by a high degree of (prior) geological uncertainty. Toward this goal, we extend the recently introduced recurrent R-U-Net surrogate model to treat geomodel realizations drawn from a wide range of geological scenarios. These scenarios are defined by a set of metaparameters, which include the mean and standard deviation of log-permeability, permeability anisotropy ratio, horizontal correlation length, etc. An infinite number of realizations can be generated for each set of metaparameters, so the range of prior uncertainty is large. The surrogate model is trained with flow simulation results, generated using the open-source simulator GEOS, for 2000 random realizations. The flow problems involve four wells, each injecting 1 Mt CO2/year, for 30 years. The trained surrogate model is shown to provide accurate predictions for new realizations over the full range of geological scenarios, with median relative error of 1.3% in pressure and 4.5% in saturation. The surrogate model is incorporated into a Markov chain Monte Carlo history matching workflow, where the goal is to generate history matched realizations and posterior estimates of the metaparameters. We show that, using observed data from monitoring wells in synthetic `true' models, geological uncertainty is reduced substantially. This leads to posterior 3D pressure and saturation fields that display much closer agreement with the true-model responses than do prior predictions

The Benefits of the Ka-Band as Evidenced from the SARAL/AltiKa Altimetric Mission: Scientific Applications

The India-France SARAL/AltiKa mission is the first Ka-band altimetric mission dedicated primarily to oceanography. The mission objectives were firstly the observation of the oceanic mesoscales but also global and regional sea level monitoring, including the coastal zone, data assimilation, and operational oceanography. SARAL/AltiKa proved also to be a great opportunity for inland waters applications, for observing ice sheet or icebergs, as well as for geodetic investigations. The mission ended its nominal phase after three years in orbit and began a new phase (drifting orbit) in July 2016. The objective of this paper is to highlight some of the most remarkable achievements of the SARAL/AltiKa mission in terms of scientific applications. Compared to the standard Ku-band altimetry measurements, the Ka-band provides substantial improvements in terms of spatial resolution and data accuracy. We show here that this leads to remarkable advances in terms of observation of the mesoscale and coastal ocean, waves, river water levels, ice sheets, icebergs, fine scale bathymetry features as well as for the many related applications

Predictors and Clinical Impact of Late Ventricular Arrhythmias in Patients With Continuous-Flow Left Ventricular Assist Devices

International audienceObjectives - This study aimed to evaluate the incidence, clinical impact, and predictors of late ventricular arrhythmias (VAs) in left ventricular assist device (LVAD) recipients aiming to clarify implantable cardioverter-defibrillator (ICD) indications. Background - The arrhythmic risk and need for ICD in patients implanted with an LVAD are not very well known. Methods - This observational study was conducted in 19 centers between 2006 and 2016. Late VAs were defined as sustained ventricular tachycardia or fibrillation occurring >30 days post-LVAD implantation, without acute reversible cause and requiring appropriate ICD therapy, external electrical shock, or medical therapy. Results - Among 659 LVAD recipients, 494 (median 58.9 years of age; mean left ventricular ejection fraction 20.7 ± 7.4%; 73.1% HeartMate II, 18.6% HeartWare, 8.3% Jarvik 2000) were discharged alive from hospital and included in the final analysis. Late VAs occurred in 133 (26.9%) patients. Multivariable analysis identified 6 independent predictors of late VAs: VAs before LVAD implantation, atrial fibrillation before LVAD implantation, idiopathic etiology of the cardiomyopathy, heart failure duration >12 months, early VAs (<30 days post-LVAD), and no angiotensin-converting enzyme inhibitors during follow-up. The "VT-LVAD score" was created, identifying 4 risk groups: low (score 0 to 1), intermediate (score 2 to 4), high (score 5 to 6), and very high (score 7 to 10). The rates of VAs at 1 year were 0.0%, 8.0%, 31.0% and 55.0%, respectively. Conclusions - Late VAs are common after LVAD implantation. The VT-LVAD score may help to identify patients at risk of late VAs and guide ICD indications in previously nonimplanted patients. (Determination of Risk Factors of Ventricular Arrhythmias [VAs] after implantation of continuous flow left ventricular assist device with continuous flow left ventricular assist device [CF-LVAD] [ASSIST-ICD]; NCT02873169)