Web-service framework for environmental models

Hossain, Moinul; Wu, Rui; Painumkal, Jose T.; Kettouch, Mohamed Salah; Luca, Cristina; Dascalu, Sergiu M.; Harris, Frederick C.

doi:10.1109/itecha.2017.8101919

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…The number of Docker Workers can be predefined or automatically updated based upon user needs. More details on the scalability framework design and validation are introduced in our previous papers (Hossain et al, 2017;Wu et al, 2018). Scientists can also inspect and download previous model runs (input and output files) that are discoverable through the VWP interface.…”

Section: Standalone Models With Http Interfacesmentioning

confidence: 99%

“…An interactive data visualization interface is available, shown in components C and F of Figure 3, for a user to visualize and input parameters in a 2D and 3D visualization environments. Figure 9 is a screenshot of the vegetation parameter visualization overlain on a Google Map (Hossain et al, 2017), provided within the PRMS web interface. Figure 10 illustrates the visualization of model parameters combined with additional topographical data within the 3D Unity visualization environment.…”

Section: Usage Scenariomentioning

confidence: 99%

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Efficient Model-Data Integration for Flexible Modeling, Parameter Analysis and Visualization, and Data Management

Gregory

Chen

et al. 2020

Front. Water

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Due to the complexity and heterogeneity inherent to the hydrologic cycle, the modeling of physical water processes has historically and inevitably been characterized by a broad spectrum of disciplines including data management, visualization, and statistical analyses. This is further complicated by the sub-disciplines within the water science community, where specific aspects of water processes are modeled independently with simplification and model boundary integration receiving little attention. This can hinder current and future research efforts to understand, explore, and advance water science. We developed the Virtual Watershed Platform to improve understanding of hydrologic processes and more generally streamline model-data integration and data integration with tools for data visualization, analysis, and management. Currently, four models have been developed as components and integrated into the overall platform, demonstrating data prepossessing (e.g., sub gridding), data interaction, model execution, and visualization capabilities. The developed data management technologies provide a suite of capabilities, enabling diverse computation capabilities, data storage capacity, connectivity, and accessibility. The developed Virtual Watershed Platform explored the use of virtual reality and 3D visualization for scientific experimentation and learning, provided web services for the transfer of data between models and centralized data storage, enabled the statistical distribution of hydrometeorological model input, and coupled models using multiple methods, both to each other and to a distributed data management and visualization system.

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Section: Standalone Models With Http Interfacesmentioning

confidence: 99%

Section: Usage Scenariomentioning

confidence: 99%

Efficient Model-Data Integration for Flexible Modeling, Parameter Analysis and Visualization, and Data Management

Gregory

Chen

et al. 2020

Front. Water

Self Cite

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“…To make cloud computing accessible to scientists, several high-level, black-box services have been proposed to hide the technical details of cloud platforms (Tsaftaris, 2014;Hossain et al, 2017;Li et al, 2017; Section 2.3 of Netto et al, 2017). However, they tend to make strict assumptions on the workflow and lack customizability and generalizability.…”

Section: Science-oriented Documentationmentioning

confidence: 99%

Enabling Immediate Access to Earth Science Models through Cloud Computing: Application to the GEOS-Chem Model

Zhuang

Jacob

Gaya

et al. 2019

Bulletin of the American Meteorological Society

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Cloud computing platforms can provide fast and easy access to complex Earth science models and large datasets. This article presents a mature capability for running the GEOS-Chem global 3D model of atmospheric chemistry on the Amazon Web Services (AWS) cloud. GEOS-Chem users at any experience level can get immediate access to the latest, standard version of the model in a preconfigured software environment with all needed meteorological and other input data, and they can analyze model output data easily within the cloud using Python tools in Jupyter notebooks. Users with no prior knowledge of cloud computing are provided with easy-to-follow, step-by-step instructions. They can learn how to complete a demo project in less than one hour, and from there they can configure and submit their own simulations. The cloud is particularly attractive for beginning and occasional users who otherwise may need to spend substantial time configuring a local computing environment. Heavy users with their own local clusters can also benefit from the cloud to access the latest standard model and datasets, share simulation configurations and results, benchmark local simulations, and respond to surges in computing demand. Software containers allow GEOS-Chem and its software environment to be moved smoothly between cloud platforms and local clusters, so that the exact same simulation can be reproduced everywhere. Because the software requirements and workflows tend to be similar across Earth science models, the work presented here provides general guidance for porting models to cloud computing platforms in a user-accessible way.

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“…2017; Hossain et al. 2017; Metzger et al. 2017; Ramamurthy 2017; Ramamurthy and Fisher 2017; Signell and Pothina 2019; van den Oord et al.…”

Section: Introductionmentioning

confidence: 99%

An Open‐Source Python Library for Varying Model Parameters and Automating Concurrent Simulations of the National Water Model

Raney

Maghami

Feng

et al. 2021

J American Water Resour Assoc

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The National Water Model (NWM), a configuration of the Weather Research and Forecasting Hydrological model, operates as the United States’ hydrological model. The NWM predicts streamflow at more than 2.7 million river reaches; and is a subject of growing attention in the hydrological modeling community. Large‐scale computationally distributed models such as the NWM, often require technical knowledge of, and access to, cluster‐based computing environments for model compilation and simulation. User‐friendly tools capable of setting up and running such models to adjust and explore their parameter space generally do not exist. Here we present the Dockerized Job Scheduler (DJS) a Python library that takes a service approach to modeling. The library is capable of (1) generating varied parameter sets and (2) orchestrating concurrent NWM simulations via Docker. DJS is designed to automate the deployment of varied parameter simulations and lower the model usage entrance barrier. In this paper, we use a case study to demonstrate its installation and usage.

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Web-service framework for environmental models

Cited by 5 publications

References 6 publications

Efficient Model-Data Integration for Flexible Modeling, Parameter Analysis and Visualization, and Data Management

Efficient Model-Data Integration for Flexible Modeling, Parameter Analysis and Visualization, and Data Management

Enabling Immediate Access to Earth Science Models through Cloud Computing: Application to the GEOS-Chem Model

An Open‐Source Python Library for Varying Model Parameters and Automating Concurrent Simulations of the National Water Model

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