Multimedia Content Analysis with Dynamic Data Driven Applications Systems (DDDAS)

Blasch, Erik P.; Aved, Alex J.; Bhattacharyya, Shuvra S.

doi:10.1007/978-3-030-74568-4_28

Cited by 2 publications

(2 citation statements)

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“…Here, we propose a novel dynamic data‐driven downscaling approach (Blasch et al., 2018) that overcomes these issues. While the methodology is extensible to many meteorological fields (e.g., near‐surface wind and heat stress), this study focuses on rainfall extremes in the mid‐latitudes, a fundamental driver of inland flooding.…”

Section: Introductionmentioning

confidence: 99%

Statistical‐Physical Adversarial Learning From Data and Models for Downscaling Rainfall Extremes

Saha,

Ravela

2024

J Adv Model Earth Syst

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Quantifying the risk from extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Climate models capturing different scenarios are often the starting point for physical risk. However, accurate risk assessment for mitigation and adaptation often demands a level of detail they typically cannot resolve. Here, we develop a dynamic data‐driven downscaling (super‐resolution) method that incorporates physics and statistics in a generative framework to learn the fine‐scale spatial details of rainfall. Our approach transforms coarse‐resolution (0.25°) climate model outputs into high‐resolution (0.01°) rainfall fields while efficaciously quantifying the hazard and its uncertainty. The downscaled rainfall fields closely match observed spatial fields and their distributions. Contrary to conventional thinking, our results suggest that coupling simple statistics and physics to learning improves the efficacy of downscaling midlatitude rainfall extremes from climate models.

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Section: Introductionmentioning

confidence: 99%

Statistical‐Physical Adversarial Learning From Data and Models for Downscaling Rainfall Extremes

Saha,

Ravela

2024

J Adv Model Earth Syst

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“…The traffic simulation system may dynamically integrate the real-time traffic status data throughout the simulation by merging the real-time traffic status data with the traffic simulation system. The real-time traffic state may be studied and forecasted in conjunction with the simulation system by altering the state of the traffic simulation system by the real-time traffic state's development trend [4].…”

mentioning

confidence: 99%

A DDDAS-Based Impact Area Simulation Study of Highway Abnormalities

Zubair

ALI

Anam

2022

Proceedings of MOL2NET'22, Conference on Molecular, Biomedical &Amp; Computational Sciences and Engineering, 8th Ed. - MOL2NET:

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Roadway irregularities including vehicle failures, traffic accidents, and more may cause traffic bottlenecks.Targeted traffic management and control depend on precise evaluation of uncommon occurrences' influence range and increasing tendency. It enhances roadway service and operation. Expressway abnormal incidence estimation uses traffic flow theory. Traffic parameter detection accuracy cannot satisfy model input, making engineering application difficult. Emulation analysis was utilized to assess highway traffic flow space-time correlation and vehicle detector features, and VISSIM simulation system was used to calibrate traffic flow parameters and rectify driving behavior parameters. The authors developed a Dynamic-Data-Driven Application Systems (DDDAS)-based highway exceptional event impact area emulation analysis approach after a particle

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Multimedia Content Analysis with Dynamic Data Driven Applications Systems (DDDAS)

Cited by 2 publications

References 58 publications

Statistical‐Physical Adversarial Learning From Data and Models for Downscaling Rainfall Extremes

Statistical‐Physical Adversarial Learning From Data and Models for Downscaling Rainfall Extremes

A DDDAS-Based Impact Area Simulation Study of Highway Abnormalities

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