Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks

Sharma, Sanjib; Lee, Benjamin Seiyon; Hosseini‐Shakib, Iman; Haran, Murali; Keller, Klaus

doi:10.1029/2022ef003050

Cited by 3 publications

(1 citation statement)

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“…The estimated motion winds obtained from the DMW Algorithm are used as one of the input variables in the Multi‐platform Tropical Cyclone Surface Wind Analysis (MTCSWA) model (Knaff et al, 2011), whose main purpose is to create an algorithm package to operationally generate an estimation of surface wind fields around active tropical cyclones. It has also been seen that, considering uncertainties of parameters usually results in improved projections from these large‐scale scientific models (Sharma et al, 2023). As such, more accurate estimates of motion winds along with their estimated uncertainties obtained from the proposed method, can potentially improve the forecasts obtained from the MTCSWA model.…”

Section: Discussionmentioning

confidence: 99%

Estimating atmospheric motion winds from satellite image data using space‐time drift models

2023

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Geostationary weather satellites collect high‐resolution data comprising a series of images. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data and estimate atmospheric winds by tracking features in the images. However, the wind estimates from the DMW Algorithm are often missing and do not come with uncertainty measures. Also, the DMW Algorithm estimates can only be half‐integers, since the algorithm requires the original and shifted data to be at the same locations, in order to calculate the displacement vector between them. This motivates us to statistically model wind motions as a spatial process drifting in time. Using a covariance function that depends on spatial and temporal lags and a drift parameter to capture the wind speed and wind direction, we estimate the parameters by local maximum likelihood. Our method allows us to compute standard errors of the local estimates, enabling spatial smoothing of the estimates using a Gaussian kernel weighted by the inverses of the estimated variances. We conduct extensive simulation studies to determine the situations where our method performs well. The proposed method is applied to the GOES‐15 brightness temperature data over Colorado and reduces prediction error of brightness temperature compared to the DMW Algorithm.

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Section: Discussionmentioning

confidence: 99%

Estimating atmospheric motion winds from satellite image data using space‐time drift models

2023

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Implementation of a dynamic specific leaf area (SLA) into a land surface model (LSM) incorporated crop-growth model

Li,

Zhan,

et al. 2023

Computers and Electronics in Agriculture

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Rapid prediction of urban flooding at street-scale using physics-informed machine learning-based surrogate modeling

Bhattarai,

Bista,

Talchabhadel

et al. 2024

Total Environment Advances

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Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks

Abstract: It is important to characterize the uncertainties surrounding flood hazards in order to understand the impacts on multi-sector dynamics and to inform the design of risk-management strategies (

Cited by 3 publications

References 135 publications

Estimating atmospheric motion winds from satellite image data using space‐time drift models

Estimating atmospheric motion winds from satellite image data using space‐time drift models

Implementation of a dynamic specific leaf area (SLA) into a land surface model (LSM) incorporated crop-growth model

Rapid prediction of urban flooding at street-scale using physics-informed machine learning-based surrogate modeling

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