Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

Zhou, Huifen; Ren, Huiying; Royer, Patrick; Hou, Hongfei; Yu, Xiao‐Ying

doi:10.3390/atmos13010136

Cited by 4 publications

(3 citation statements)

References 57 publications

(61 reference statements)

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“…According to Na-Yemeh et al [16], Oklahoma's OK-First weather DSS helped through generating $1.2 million in self-reported cost savings for 12 months. Research studies have developed user interfaces to support decision-makers with statistical summaries [17] and probabilistic forecasts [18] for rapid responses to weather conditions. Another style of DSS allows the user to reason about the impact of data, especially with the rising complexity in both the number and complexity of data sources [19].…”

Section: Introductionmentioning

confidence: 99%

Smart Installation Weather Warning Decision Support

Tran,

Kreinberg,

Specking

et al. 2024

Systems

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Army installation commanders need timely weather information to make installation closure decisions before or during adverse weather events (e.g., hail, thunderstorms, snow, and floods). We worked with the military installation in Fort Carson, CO, and used their Weather Warning, Watch, and Advisory (WWA) criteria list to establish the foundation for our algorithm. We divided the Colorado Springs area into 2300 grids (2.5 square kilometers areas) and grouped the grids into ten microclimates, geographically and meteorologically unique regions, per pre-defined microclimate regions provided by the Fort Carson Air Force Staff Weather Officers (SWOs). Our algorithm classifies each weather event in the WWA list using the National Weather Service’s and National Digital Forecast Database’s data. Our algorithm assigns each event a criticality level: none, advisory, watch, or warning. The traffic network data highlight the importance of each road segment for travel to and from Fort Carson. The algorithm also uses traffic network data to assign weight to each grid, which enables the aggregation to the region and installation levels. We developed a weather dashboard in ArcGIS Pro to verify our algorithm and visualize the forecasted warnings for the grids and regions that are or may be affected by weather events.

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Section: Introductionmentioning

confidence: 99%

Smart Installation Weather Warning Decision Support

Tran,

Kreinberg,

Specking

et al. 2024

Systems

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“…Moreover, many models grapple with the complexity of analyzing large-scale data and exhibit limited adaptability, being designed for specific scenarios or locations, which restricts their broad applicability. Traditional approaches to data management often face difficulties in handling the real-time nature of meteorological data and the intricate relationships between various environmental factors [34][35][36][37]. Meteorological data is characterized by its dynamic and time-sensitive nature.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Thankappan,

Mary,

Yoon

et al. 2023

CMC

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Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world. Therefore, timely and accurate decision-making is essential for mitigating flood-related damages. The traditional flood prediction techniques often encounter challenges in accuracy, timeliness, complexity in handling dynamic flood patterns and leading to substandard flood management strategies. To address these challenges, there is a need for advanced machine learning models that can effectively analyze Internet of Things (IoT)-generated flood data and provide timely and accurate flood predictions. This paper proposes a novel approach-the Adaptive Momentum and Backpropagation (AM-BP) algorithm-for flood prediction and management in IoT networks. The AM-BP model combines the advantages of an adaptive momentum technique with the backpropagation algorithm to enhance flood prediction accuracy and efficiency. Real-world flood data is used for validation, demonstrating the superior performance of the AM-BP algorithm compared to traditional methods. In addition, multilayer high-end computing architecture (MLCA) is used to handle weather data such as rainfall, river water level, soil moisture, etc. The AM-BP's real-time abilities enable proactive flood management, facilitating timely responses and effective disaster mitigation. Furthermore, the AM-BP algorithm can analyze large and complex datasets, integrating environmental and climatic factors for more accurate flood prediction. The evaluation result shows that the AM-BP algorithm outperforms traditional approaches with an accuracy rate of 96%, 96.4% F1-Measure, 97% Precision, and 95.9% Recall. The proposed AM-BP model presents a promising solution for flood prediction and management in IoT networks, contributing to more resilient and efficient flood control strategies, and ensuring the safety and well-being of communities at risk of flooding.

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“…Identifying the potential sources and affected areas due to emissions of hazardous chemicals plays a critical role in risk assessment because it supports decision-making and protects workers and communities from being incidentally exposed to chemical hazards within the identified areas ( TALA, 2001 ). Real-time volatile organic compound (VOC) measurements, such as those from modern analytical instruments like proton transfer mass spectrometer (PTR-MS), coupled with long-term meteorological data ( Zhou et al, 2022 ) are necessary to understand the pollutant concentrations at the emission point. Atmospheric dispersion models are used to perform trajectory simulations of particles formed as a result of VOCs emitted into the air ( Stockie, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources

Hou

Ren

Royer

et al. 2023

PeerJ

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Background Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals’ emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

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Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

Cited by 4 publications

References 57 publications

Smart Installation Weather Warning Decision Support

Smart Installation Weather Warning Decision Support

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources

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