The Energy Exascale Earth System Model (E3SM) is a new coupled Earth system model sponsored by the U.S Department of Energy. Here we present E3SM global simulations using active ocean and sea ice that are driven by the Coordinated Ocean‐ice Reference Experiments II (CORE‐II) interannual atmospheric forcing data set. The E3SM ocean and sea ice components are MPAS‐Ocean and MPAS‐Seaice, which use the Model for Prediction Across Scales (MPAS) framework and run on unstructured horizontal meshes. For this study, grid cells vary from 30 to 60 km for the low‐resolution mesh and 6 to 18 km at high resolution. The vertical grid is a structured z‐star coordinate and uses 60 and 80 layers for low and high resolution, respectively. The lower‐resolution simulation was run for five CORE cycles (310 years) with little drift in sea surface temperature (SST) or heat content. The meridional heat transport (MHT) is within observational range, while the meridional overturning circulation at 26.5°N is low compared to observations. The largest temperature biases occur in the Labrador Sea and western boundary currents (WBCs), and the mixed layer is deeper than observations at northern high latitudes in the winter months. In the Antarctic, maximum mixed layer depths (MLD) compare well with observations, but the spatial MLD pattern is shifted relative to observations. Sea ice extent, volume, and concentration agree well with observations. At high resolution, the sea surface height compares well with satellite observations in mean and variability.

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As traffic simulation software becomes more effective for realistically simulating and analyzing traffic dynamics and vehicle interactions on the mesoscopic and microscopic level, the management, dissemination, and collaborative visualization of traffic simulation results produced by individual transportation planners presents a significant challenge. Existing online content management systems have a very limited capability in allowing users to query specific traffic simulation scenarios and geospatially visualize simulation results through shareable and interactive web interfaces. This paper presents a web-based application for promoting the archiving, sharing, and visualization of large-scale traffic simulation outputs. The application is developed to enhance cyber-physical controls, communications, and public education for collaborative transportation planning. Unique features of the web application include: (a) allowing users to upload their new traffic simulation scenarios (parameters and outputs), as well as search existing scenarios using easily accessible interfaces; (b) optimizing simulation output files with heterogeneous data formats and projected coordinate systems for web-based storage and management using a scalable and searchable data/metadata standard; (c) standardizing user-uploaded simulation outputs using web interfaces and data processing libraries with parallel computing capacity; and (d) providing shareable web visual interfaces for visualizing the traffic flow and signal information stored in simulation outputs (e.g., regional traffic patterns and individual vehicle interactions) and visually comparing multiple simulation outputs both spatially and temporally. The paper presents the conceptual design and implementation of this application, and demonstrates the application’s performance for sharing, comparing, and visualizing simulation outputs from VISSIM and SUMO, two commonly used traffic simulation software programs.

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In this paper, we present a suite of visualization techniques for sensor-based transportation system data at different scales to facilitate the exploration of interconnected traffic dynamics at intersections and highways. These techniques are designed for analyzing multivariate traffic data from radar-based highway sensors and camera-based intersection sensors recording turn movements and vehicle speed, in the Chattanooga Metropolitan Area, with the capability of (a) revealing multiscale mobility patterns using different levels of data aggregation (e.g., individual sensor for microscale, multiple sensors along a corridor for mesoscale, and a larger number of sensors across the region for macroscale visualization) at different intervals (e.g., 5-min intervals, time of day, full day, and day-of-the-week), and (b) exploring the spatial variation of multiple traffic-related variables (e.g., volumes, speeds, turn movements, and traffic light colors) provided by the sensors. We close with a case study to demonstrate the effectiveness of our multiscale and multivariate visualization techniques. At microscale, we focused on intersection data from a shopping district around Shallowford Road in East Chattanooga. For mesoscale visualization, we studied the Shallowford Road corridor and an adjacent stretch of I-75. At macroscale, we included highway data from the Chattanooga Metropolitan Area. All visualizations were integrated into a web-based situational awareness tool to promote user access and interaction. At a minimum, each visualization provides the option for selecting dates for real-time (depending on sensor availability) and historical data, and additional information on hovering, though most provide more detailed information, including different views of the selected data, or interactive highlights.

This paper presents a scalable object detection workflow for detecting objects, such as settlements, from remotely sensed (RS) imagery. We have successfully deployed this workflow on Titan supercomputer and utilized it for the task of mapping human settlement at a country scale.The performance of various stages in the workflow was analyzed before making it operational.The workflow implemented various strategies to address issues such as suboptimal resource utilization and long-tail effects due to unbalanced image workload, data loss due to runtime failures, and maximum wall-time constraints imposed by Titan's job scheduling policy. A mean shift clustering-based static load balancing strategy was implemented, which partitions the image load such that each partition contained similar-sized images. Furthermore, a checkpoint-restart strategy was added in the workflow as a fault-tolerance mechanism to prevent the data losses due to unforeseen runtime failures. The performance of the above-mentioned strategies was observed in various scenarios, such as node failure, exceeding wall time, and successful completion. Using this workflow, we have processed an RS data set that has a spatial resolution of 0.31 m and is comprised of 685 675 km 2 of area of the Republic of Zambia in under six hours using 5426 nodes of the Titan supercomputer. KEYWORDS convolutional neural network, deep learning, fault tolerance, HPC, human settlement mapping, load balancing 1 INTRODUCTION The recent development in sensor electronics is enabling remote sensing (RS) satellites to capture data at a submeter scale. This advancement in RS technology offers new opportunities to understand Earth's features at a finer spatial scale and motivates the development of novel approaches for complex problems such as mapping human settlements. However, as an effect of the development in sensor technology, a rapid growth in the cumulative volume of Earth observation (EO) archives has been observed during the last few years. 1 A typical very-high-resolution RS imagery contains billions of pixels, and for a country-scale mapping, thousands of such images need to be processed, which is computationally demanding. This computational load can pose problems during time-critical events, such as with flooding and wild fire natural disasters, when real-time/near-real-time mapping of human settlement in the affected area is necessary. However, high-performance computing (HPC)-driven approaches for processing such huge volume of data can mitigate this problem.

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