The Equivalence Between Certain Statistical Prediction Methods and Linearized Dynamical Methods

Harris, D. Lee

doi:10.1175/1520-0493(1962)090<0331:tebcsp>2.0.co;2

Cited by 14 publications

(12 citation statements)

References 5 publications

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“…dynamic numerical methods and data-driven methods. Harris (1962) expounded on the difference between the two approaches; the former integrates the governing shallow water equations explaining the underlying physical processes that induce storm surges, whereas the latter quantifies the relationship between predictors (the number of which can vary) and predictands using statistical methods and/or machine learning. Numerical models require high quality bathymetric and topographic data to simulate storm surges, and are computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Global Storm Surges

2020

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In many areas, storm surges caused by tropical or extratropical cyclones are the main contributors to critical extreme sea level events. Storm surges can be simulated using numerical models that are based on the underlying physical processes, or by using data-driven models that quantify the relationship between the predictand (storm surge) and relevant predictors (wind speed, mean sea-level pressure, etc.). This study explores the potential of data-driven models to simulate storm surges globally. A multitude of predictors (obtained from remote sensing and climate reanalysis) along with predictands (from tide gage observations and storm surge reanalysis) are utilized to train and validate data-driven models to simulate daily maximum surge for the global coastline. Datadriven models simulate daily maximum surge better in extratropical and subtropical regions [average correlation and root-mean-square error (RMSE) of 0.79 and 7.5 cm, respectively], than in the tropics (average correlation and RMSE of 0.45 and 5.3 cm, respectively). For extreme events, the average correlation decreases to 0.54 (0.33) and RMSE increases to 14.5 (13.1) cm for extratropical (tropical) regions. Models forced with remotely sensed predictors showed a slightly better performance (average correlation of 0.69) than models forced with predictors obtained from reanalysis products (average correlation of 0.68). Results also highlight a significant improvement (i.e., average correlation increases from 0.54 to 0.68; RMSE reduces from 11 to 7 cm) over the Global Tide and Surge Reanalysis (GTSR), derived from the only global hydrodynamic model. For approximately 70% of tide gages, mean sea-level pressure is the most important predictor to model daily maximum surge. Our results highlight the added value of datadriven models in the context of simulating storm surges at the global scale, in addition to existing hydrodynamic numerical models.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Global Storm Surges

2020

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“…He used the SverdrupMunk method as revised by Bretschneider (1951) to compute significant wave heights from equations that relate wave heights to wind data and a regression equation to forecast storm surge from these computed significant wave heights. Another statistical method, based on linearized two-dimensional hydrodynamic equations, was developed by Harris (1962) and consisted of a regression equation that related the surge at a specific location and time to a ''meteorological factor'' selected according to the type of observation and the location of the observation station. Harris and Angelo (1963) tested the model using past data from Buffalo, New York, and Toledo, Ohio.…”

Section: Introductionmentioning

confidence: 99%

“…Another statistical method, based on linearized two-dimensional hydrodynamic equations, was developed by Harris (1962) and consisted of a regression equation that related the surge at a specific location and time to a ''meteorological factor'' selected according to the type of observation and the location of the observation station. Harris and Angelo (1963) tested the model using past data from Buffalo, New York, and Toledo, Ohio. They concluded that the prediction obtained with this approach was equivalent or superior to a prediction based on the direct integration of the hydrodynamic equations and using the same data.…”

Section: Introductionmentioning

confidence: 99%

Statistical Prediction of the Storm Surge Associated with Cool-Weather Storms at the Battery, New York

Salmun

Molod

Wisniewska

et al. 2011

Journal of Applied Meteorology and Climatology

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The winter and early spring weather in the New York City metropolitan region is highly influenced by extratropical storm systems, and the storm surge associated with these systems is one of the main factors contributing to inundation of coastal areas. This study demonstrates the predictive capability of an established statistical relationship between the ''storm maximum'' storm surge associated with an extratropical storm system and the ''average maximum'' significant wave height during that storm. Data from publicly available retrospective forecasts of sea level pressure and wave heights, along with a regression equation for storm surge, were used to predict the storm-maximum storm surge for 41 storms in the New York metropolitan region during the period from February 2005 to December 2008. The statistical storm-surge estimates were compared with the surge values predicted by NOAA's extratropical storm-surge model and NOAA's operational surge forecast, which includes an error correction, and with water gauge observations taken at the Battery, located at the southern tip of Manhattan Island, New York. The mean difference between the statistical surge prediction and the observed values is shown to be smaller than the difference between NOAA's deterministic surge prediction and the observed surge at the 95% significance level and to be statistically indistinguishable from the difference between NOAA's operational surge forecast and the observed values of surge. These statistical estimates can be used as part of a system for predicting coastal flooding.

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“…On the other hand, the meteorological data are highly redundant. A few terms in equation (5) carefully selected, may be expected to have almost as much information as the complete set. A rather small number of predictors may be sufficient for determining the wind stress and frictional coefficients.…”

Section: October-december 1963mentioning

confidence: 99%

“…Harris [5] re-examined the problem of the empirical prediction of storm surges. By taking into account the limitations of the meteorological data that can be made available, he showed that it is possible to derive a regression equation that contains all of the information that can be put into a numerical solution of the linearized hydrodynamic equation.…”

Section: Introductionmentioning

confidence: 99%

A Regression Model for Storm Surge Prediction

Harris¹,

Angelo²

1963

Mon. Wea. Rev.

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The practical difficulties arising in the solution of the hydrodynamic equations for storm surges by iiumcrical methods are reviewed. It is concluded that some of these can be avoided by means of a statistical approach if sufficient records of past surges are available. A statistical approach, based on dynamic principles, is presented and the recent literature of similar studies is reviewed. The agreement between the surge values compnted by the statistical method and the observed values is considered good. The test verified the statistical approach but did not lead to an operational prediction q&xrn, because of recent changes in the observational practices at some of the weather stations bordering Lake Erie. One somewlmt unexpected result was a finding that a prediction scheme based on the assumption of wind stress proportional to wind speed is not significantly inferior to one based on the assumption of a quadratic mind stress law. A test is made of this statistical method by applying it to recorded storm surges on Lake Erie.

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The Equivalence Between Certain Statistical Prediction Methods and Linearized Dynamical Methods

Cited by 14 publications

References 5 publications

Data-Driven Modeling of Global Storm Surges

Data-Driven Modeling of Global Storm Surges

Statistical Prediction of the Storm Surge Associated with Cool-Weather Storms at the Battery, New York

A Regression Model for Storm Surge Prediction

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