Conceptual and Scaling Evaluation of Vehicle Traffic Thermal Effects on Snow/Ice-Covered Roads*

The potential thermal effects of traffic on road surface thermal energy balance under frost/snow cover conditions have been largely ignored in meteorological evaluations of road ice deposit conditions. Preliminary exploration of these effects, particularly for heavy traffic scenarios with calm wind conditions and an ambient temperature of 0°C, is provided in this study using a conceptual model. Observational data were used to constrain the model, and parameterizations were employed to estimate the various heat transfer processes involved. The results indicate that, for heavy traffic situations, as well as for stopped traffic at intersections, the traffic thermal flux contribution at the surface is noticeable in a wide range of possible frost/snow-covered road conditions. The sensitivity to variation in traffic density, speed, and the emissivity of vehicle radiative surfaces, among others, is evaluated. Simple quantification of these traffic thermal effects, which might be considered in operational meteorological model forecasting of icy road conditions, is offered

Dynamic Grid Adaptation Using the MPDATA Scheme

A dynamic grid adaptation (DGA) scheme is developed using various combinations of the multidimensional positive definite advection transport algorithm (MPDATA) to show the applicability of DGA with the MPDATA scheme to solve advection problems. A one-dimensional model is used to show the effects of varying the number of grid points and the parameters that control the grid redistribution scheme, to determine a stability criteria for the scheme and to investigate the effect of several MPDATA options. A two-dimensional model is used to show the applicability of the scheme in multiple dimensions and to illustrate the effects of DGA in combination with MPDATA options. Diffusion errors are reduced by more than 90% using DGA when compared to static, uniformly spaced grid computations. Phase errors are reduced using certain MPDATA options by more than 25%

Multi-scale waves in sound-proof global simulations with EULAG

EULAG is a computational model for simulating flows across a wide range of scales and physical scenarios. A standard option employs an anelastic approximation to capture nonhydrostatic effects and simultaneously filter sound waves from the solution. In this study, we examine a localized gravity wave packet generated by instabilities in Held-Suarez climates. Although still simplified versus the Earth’s atmosphere, a rich set of planetary wave instabilities and ensuing radiated gravity waves can arise. Wave packets are observed that have lifetimes ≤ 2 days, are negligibly impacted by Coriolis force, and do not show the rotational effects of differential jet advection typical of inertia-gravity waves. Linear modal analysis shows that wavelength, period, and phase speed fit the dispersion equation to within a mean difference of ∼ 4%, suggesting an excellent fit. However, the group velocities match poorly even though a propagation of uncertainty analysis indicates that they should be predicted as well as the phase velocities. Theoretical arguments suggest the discrepancy is due to nonlinearity — a strong southerly flow leads to a critical surface forming to the southwest of the wave packet that prevents the expected propagation

Improving deep neural network design with new text data representations

Abstract Using traditional machine learning approaches, there is no single feature engineering solution for all text mining and learning tasks. Thus, researchers must determine and implement the best feature engineering approach for each text classification task; however, deep learning allows us to skip this step by extracting and learning high-level features automatically from low-level text representations. Convolutional neural networks, a popular type of neural network for deep learning, have been shown to be effective at performing feature extraction and classification for many domains including text. Recently, it was demonstrated that convolutional neural networks can be used to train classifiers from character-level representations of text. This approach achieved superior performance compared to classifiers trained on word-level text representations, likely due to the use of character-level representations preserving more information from the data. Training neural networks from character level data requires a large volume of instances; however, the large volume of training data and model complexity makes training these networks a slow and computationally expensive task. In this paper, we propose a new method of creating character-level representations of text to reduce the computational costs associated with training a deep convolutional neural network. We demonstrate that our method of character embedding greatly reduces training time and memory use, while significantly improving classification performance. Additionally, we show that our proposed embedding can be used with padded convolutional layers to enable the use of current convolutional network architectures, while still facilitating faster training and higher performance than the previous approach for learning from character-level text

Simulations of Gravity Wave Induced Turbulence Using 512 PE Cray T3E

A 3D nonhydrostatic, Navier-Stokes solver has been employed to simulate gravity wave induced turbulence at mesopause altitudes. This paper extends our earlier 2D study reported in the literature to three spatial dimensions while maintaining fine resolution required to capture essential physics of the wave breaking. The calculations were performed on the 512 processor Cray T3E machine at the National Energy Research Scientific Computing Center (NERSC) in Berkeley. The physical results of this study clearly demonstrate advantages of highly parallel technologies. We briefly outline the physical outcome of the study, as well as compare the relative model performance across several machines using both MPI and Shmem communication software

Scientific Final Report: COLLABORATIVE RESEARCH: CONTINUOUS DYNAMIC GRID ADAPTATION IN A GLOBAL ATMOSPHERIC MODEL: APPLICATION AND REFINEMENT

This project had goals of advancing the performance capabilities of the numerical general circulation model EULAG and using it to produce a fully operational atmospheric global climate model (AGCM) that can employ either static or dynamic grid stretching for targeted phenomena. The resulting AGCM combined EULAG's advanced dynamics core with the 'physics' of the NCAR Community Atmospheric Model (CAM). Effort discussed below shows how we improved model performance and tested both EULAG and the coupled CAM-EULAG in several ways to demonstrate the grid stretching and ability to simulate very well a wide range of scales, that is, multi-scale capability. We leveraged our effort through interaction with an international EULAG community that has collectively developed new features and applications of EULAG, which we exploited for our own work summarized here. Overall, the work contributed to over 40 peer-reviewed publications and over 70 conference/workshop/seminar presentations, many of them invited

COLLABORATIVE RESEARCH: CONTINUOUS DYNAMIC GRID ADAPTATION IN A GLOBAL ATMOSPHERIC MODEL: APPLICATION AND REFINEMENT

This project had goals of advancing the performance capabilities of the numerical general circulation model EULAG and using it to produce a fully operational atmospheric global climate model (AGCM) that can employ either static or dynamic grid stretching for targeted phenomena. The resulting AGCM combined EULAG's advanced dynamics core with the "physics" of the NCAR Community Atmospheric Model (CAM). Effort discussed below shows how we improved model performance and tested both EULAG and the coupled CAM-EULAG in several ways to demonstrate the grid stretching and ability to simulate very well a wide range of scales, that is, multi-scale capability. We leveraged our effort through interaction with an international EULAG community that has collectively developed new features and applications of EULAG, which we exploited for our own work summarized here. Overall, the work contributed to over 40 peer-reviewed publications and over 70 conference/workshop/seminar presentations, many of them invited. 3a. EULAG Advances EULAG is a non-hydrostatic, parallel computational model for all-scale geophysical flows. EULAG's name derives from its two computational options: EULerian (flux form) or semi-LAGrangian (advective form). The model combines nonoscillatory forward-in-time (NFT) numerical algorithms with a robust elliptic Krylov solver. A signature feature of EULAG is that it is formulated in generalized time-dependent curvilinear coordinates. In particular, this enables grid adaptivity. In total, these features give EULAG novel advantages over many existing dynamical cores. For EULAG itself, numerical advances included refining boundary conditions and filters for optimizing model performance in polar regions. We also added flexibility to the model's underlying formulation, allowing it to work with the pseudo-compressible equation set of Durran in addition to EULAG's standard anelastic formulation. Work in collaboration with others also extended the demonstrated range of validity of soundproof models, showing that they are more broadly applicable than some had previously thought. Substantial testing of EULAG included application and extension of the Jablonowski-Williamson baroclinic wave test - an archetype of planetary weather - and further analysis of multi-scale interactions arising from collapse of temperature fronts in both the baroclinic wave test and simulations of the Held-Suarez idealized climate. These analyses revealed properties of atmospheric gravity waves not seen in previous work and further demonstrated the ability of EULAG to simulate realistic behavior over several orders of magnitude of length scales. Additional collaborative work enhanced capability for modeling atmospheric flows with adaptive moving meshes and demonstrated the ability of EULAG to move into petascale computing. 3b. CAM-EULAG Advances We have developed CAM-EULAG in collaboration with former project postdoc, now University of Cape Town Assistant Professor, Babatunde Abiodun. Initial study documented good model performance in aqua-planet simulations. In particular, we showed that the grid adaptivity (stretching) implemented in CAM-EULAG allows higher resolution in selected regions without causing anomalous behavior such as spurious wave reflection. We then used the stretched-grid version to analyze simulated extreme precipitation events in West Africa, comparing the precipitation and event environment with observed behavior. The model simulates fairly well the spatial scale and the interannual and intraseasonal variability of the extreme events, although its extreme precipitation intensity is weaker than observed. In addition, both observations and the simulations show possible forcing of extreme events by African easterly waves. 3c. Other Contributions Through our collaborations, we have made contributions to a wide range of outcomes. For research focused on terrestrial behavior, these have included (1) upwind schemes for gas dynamics, (2) a nonlinear perspective on the dynamics of the Madden-Julian Oscillation, (3) numerical realism of thermal convection, (4) extreme precipitation processes, (5) unstructured meshes, (6) porous-media analogies for simulating flow in urban environments, (7) Monge-Ampere enhancements of semi-Lagrangian methods and (8) further extensions of the generality of EULAG applications to geophysical problems. The generality of EULAG for multi-scale flow was also demonstrated through solar applications. Simulations of solar magnetohydrodynamics showed that EULAG produces the solar cycle fairly well. Although the model's solar cycle is significantly longer than observed, it shows remarkable agreement with observations in features of spatial evolution. These efforts in environments that are extreme from a geophysical perspective have helped to establish the robustness of EULAG as an all-scale code for geophysical applications