Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment: an application case in Lima, Peru

We propose the use of variable resolution boundaries based on central Voronoi tessellations (CVTs) to spatially aggregate building exposure models for risk assessment to various natural hazards. Such a framework is especially beneficial when the spatial distribution of the considered hazards presents intensity measures with contrasting footprints and spatial correlations, such as in coastal environments. This work avoids the incorrect assumption that a single intensity value from hazards with low spatial correlation (e.g. tsunami) can be considered to be representative within large-sized geo-cells for physical vulnerability assessment, without, at the same time, increasing the complexity of the overall model. We present decoupled earthquake and tsunami scenario-based risk estimates for the residential building stock of Lima (Peru). We observe that earthquake loss models for far-field subduction sources are practically insensitive to the exposure resolution. Conversely, tsunami loss models and associated uncertainties depend on the spatial correlations of the hazard intensities as well as on the resolution of the exposure models. We note that for the portfolio located in the coastal area exposed to both perils in Lima, the ground shaking dominates the losses for lower-magnitude earthquakes, whilst tsunamis cause the most damage for larger-magnitude events. For the latter, two sets of existing empirical flow depth fragility models are used, resulting in large differences in the calculated losses. This study, therefore, raises awareness about the uncertainties associated with the selection of fragility models and spatial aggregation entities for exposure modelling and loss mapping.

NIMG-21. VARIABLE RESOLUTION HYPERPOLARIZED [2-13C]PYRUVATE MRI IN HEALTHY VOLUNTEERS AND PATIENTS WITH IDH-MUTANT GLIOMA

INTRODUCTION Mutations in isocitrate dehydrogenase (IDH) have been investigated as a prognostic biomarker in glioma. The presence of the IDH mutation (IDHm) is associated with 2-hydroxyglutarate (2HG) production and inhibition of glutamate synthesis (McBrayer, Cell 2018). Hyperpolarized carbon-13 (HP-13C) MRI enables dynamic measurements of in-vivo metabolism using a [2-13C]pyruvate labeled probe that undergoes conversion to [2-13C]lactate and [5-13C]glutamate. Here, we present HP [2-13C]pyruvate data from healthy volunteers and patients with IDHm diffuse glioma. Due to its intrinsic low signal-to-noise ratio (SNR), we demonstrate the ability of post-processing denoising to improve its utility and aid in detection of metabolic changes associated with IDHm. METHODS Dynamic HP 13C data were acquired following intravenous injection of [2-13C]pyruvate from five healthy volunteers and one patient with IDHm grade III astrocytoma. A novel multi-resolution frequency specific multislice EPI sequence was used to obtain [2-13C]pyruvate, [5-13C]glutamate, and downfield and upfield [2-13C]lactate signals (3s temporal resolution, pyruvate/lactate/glutamate spatial resolutions = 0.75x0.75cm2/ 2.25x2.25cm2/ 2.25x2.25cm2, 5 slices 3cm thick). Following phase correction, patch-based tensor decomposition denoising was applied to metabolite images. Metabolite differences between normal-appearing white matter (NAWM) and T2 lesion were examined for the patient data. RESULTS HP [2-13C]pyruvate imaging is able to simultaneously probe glycolytic ([2-13C]lactate) and oxidative ([5-13C]glutamate) metabolism. Denoised pyruvate/lactate/glutamate signals achieved a 4-9/3-6/3-7 fold increase in SNR. T2 lesion exhibited decreased glutamate-to-pyruvate and glutamate-to-lactate AUC ratios versus contralateral NAWM (p< 0.018, p < 1.5e-5), consistent with IDH mutant status. CONCLUSION We successfully demonstrated the feasibility of applying variable resolution HP [2-13C]pyruvate metabolic imaging to detect IDHm specific metabolism. This technique addresses a major hurdle in HP 13C MRI by improving SNR while permitting robust metabolism quantification. Future studies will optimize methods for acquiring and processing data to evaluate further data acquired from IDHm glioma patients. Supported by NIH T32 CA151022, P01 CA118816, and NICO.

Strength and Challenges of global model MPAS with regional mesh refinement for mid-latitude storm forecasting: a case study

With the rising share of renewable energy sources like wind energy in the energy mix, high-impact weather events like mid-latitude storms increasingly affect energy production, grid stability and safety and reliable forecasting becomes very relevant for e.g. transmission system operators to allow for actions to reduce imbalances. Traditionally, meteorological forecasts are provided by limited-area weather prediction models (LAMs), which can use high enough model resolution to represent the range of atmospheric scales of motions associated with such storm structures. While generally satisfactory, deterioration and insufficient deepening of large-scale storm structures are observed when they are introduced near the lateral boundaries of the LAM due to inadequate spatial and temporal interpolation. Global models with regional mesh refinement capabilities like the Model for Prediction Across Scales (MPAS) have the potential to provide an alternative, while avoiding sharp resolution jumps and lateral boundaries. In this study, MPAS' capabilities of simulating key evaluation metrics like storm intensity, storm location and storm duration are investigated based on a case study and assessed in comparison with buoy measurements, forecast products from the Climate Forecast System (CFSv2) and simulations with the Weather Research and Forecasting (WRF) LAM. Quasi-uniform and variable-resolution MPAS mesh configurations with different model physics settings are designed to analyze the impact of the mesh refinement and model physics on the model performance. MPAS shows good performance in predicting storm intensity based on the local minimum sea level pressure, while time of local minimum sea level pressure (storm duration) was generally estimated too late (too long) in comparison with the buoy measurements in part due to an early west-wards shift of the storm center in MPAS. The variable-resolution configurations showed a combination of an additional south-westwards shift and deviations in the sea level pressure field south-west of the storm center that introduced additional bias to the time of local minimum sea level pressure at some locations. The study highlights the need for a more detailed analysis of applied mesh refinements for particular applications and emphasizes the importance of methods like data assimilation techniques to prevent model drifts.

Examination of Computational Performance and Potential Applications of a Global Numerical Weather Prediction Model MPAS Using KISTI Supercomputer NURION

To predict extreme weather events, we conducted high-resolution global atmosphere modeling and simulation using high-performance computing. Using a new-generation global weather/climate prediction model called MPAS (Model for Prediction Across Scales) with variable resolution, we tested strong scalability on the KISTI (Korea Institute of Science and Technology Information) supercomputer NURION. In addition to assessing computational performance, we simulated three typhoons that occurred in 2019 to analyze the forecast accuracy of MPAS. MPAS results were also applied to force an ADCIRC (The Advanced CIRCulation) + SWAN (Simulating Waves Nearshore) model to predict coastal flooding over southern Korea. The time-integration of MPAS showed excellent scalability up to 4096 cores of NURION KNL (KNight Landing) nodes, but a serious I/O bottleneck issue was still found after trying two additional I/O strategies (i.e., adjusting the stripe count and using a burst buffer). On the other hand, the forecast accuracy of MPAS showed very encouraging results for wind and pressure during typhoons. ADCIRC+SWAN also generated a good estimate of significant wave height for typhoon Mitag. The proposed variable-resolution MPAS model, under an efficient computational environment, could be utilized to predict and understand the highly nonlinear chaotic atmosphere and coastal flooding in typhoons.