Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

Altaf, Muhammad Umer; Butler, Troy; Luo, Xiaodong; Dawson, Clint; Mayo, Talea; Hoteit, Ibrahim

doi:10.1175/mwr-d-12-00310.1

Cited by 20 publications

(21 citation statements)

References 49 publications

(42 reference statements)

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“…The simplest approach is to choose a larger value of the bandwidth parameter b. In many situations, appropriate covariance inflation has been shown not only to improve the performances of the filter (e.g., Anderson and Anderson 1999;Anderson 2007;Lermusiaux 2007), but also to enhance its robustness (Luo and Hoteit 2011;Altaf et al 2013).…”

Section: Practical Implementation With Small Ensemblesmentioning

confidence: 99%

“…Note that perturbing the observation with noise generated from N ( Á j 0, b 21 R k11 ) may allow removing the term G k11 from both (28) and (30), thereafter improving the matching of the analysis covariance. This may, however, introduce some noise to the system, which could degrade the filter's performance (Nerger et al 2005;Altaf et al 2014).…”

Section: A Deterministic Resampling Proceduresmentioning

confidence: 99%

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Efficient Kernel-Based Ensemble Gaussian Mixture Filtering

Liu

Ait‐El‐Fquih

Hoteit

2016

Monthly Weather Review

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The Bayesian filtering problem for data assimilation is considered following the kernel-based ensemble Gaussian mixture filtering (EnGMF) approach introduced by Anderson and Anderson. In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution is analyzed. Then the focus is on two aspects: (i) the efficient implementation of EnGMF with (relatively) small ensembles, where a new deterministic resampling strategy is proposed preserving the first two moments of the posterior GM to limit the sampling error; and (ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.

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Section: Practical Implementation With Small Ensemblesmentioning

confidence: 99%

Section: A Deterministic Resampling Proceduresmentioning

confidence: 99%

Efficient Kernel-Based Ensemble Gaussian Mixture Filtering

Liu

Ait‐El‐Fquih

Hoteit

2016

Monthly Weather Review

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“…The finite computational resources and the necessity for forecast information to be disseminated in a timely manner result in uncertainties in model forecasts. One well-known way to reduce the effects of uncertainty on forecasting decisions is to utilize ensemble-based probability forecasts with more ensemble members (Brown et al 2007;Altaf et al 2013) and multimodels (Di Liberto et al 2011) to yield more skillful probability forecasts. A successful multilinear regression (MLR) offers the opportunity to create large real-time ensemble-based forecasting systems for storm surge predictions, and it also allows one to use decades of climate-model output to study how coastal flooding may vary into the future.…”

Section: B Motivationmentioning

confidence: 99%

A Regression-Based Approach for Cool-Season Storm Surge Predictions along the New York–New Jersey Coast

Roberts

Colle

Georgas

et al. 2015

Journal of Applied Meteorology and Climatology

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A multilinear regression (MLR) approach is developed to predict 3-hourly storm surge during the coolseason months (1 October-31 March 31) between 1979 and 2012 using two different atmospheric reanalysis datasets and water-level observations at three stations along the New York-New Jersey coast (Atlantic City, New Jersey; the Battery in New York City; and Montauk Point, New York). The predictors of the MLR are specified to represent prolonged surface wind stress and a surface sea level pressure minimum for a boxed region near each station. The regression underpredicts relatively large ($95th percentile) storm maximum surge heights by 6.0%-38.0%. A bias-correction technique reduces the average mean absolute error by 10%-15% at the various stations for storm maximum surge predictions. Using the same forecast surface winds and pressures from the North American Mesoscale (NAM) model between October and March 2010-14, raw and bias-corrected surge predictions at the Battery are compared with raw output from a numerical hydrodynamic model's [the Stevens Institute of Technology New York Harbor Observing and Prediction System (SIT-NYHOPS)] predictions. The accuracy of surge predictions between the SIT-NYHOPS output and biascorrected MLR model at the Battery are similar for predictions that meet or exceed the 95th percentile of storm maximum surge heights.

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“…Various inflation methods have been proposed and studied in the literature (see, e.g., Altaf et al 2013;Anderson and Anderson 1999;Anderson 2007Bocquet and Sakov 2012;Hamill and Whitaker 2011;Hoteit 2011, 2013;Meng and Zhang 2007;Miyoshi 2011;Whitaker and Hamill 2012;Zhang et al 2004). A numerical comparison of different inflation schemes is beyond the scope of the current work.…”

Section: Two Auxiliary Techniques In the Enkfmentioning

confidence: 99%

“…Specific to storm surge modeling resulting from hurricanes, the SEIK filter was recently applied to the shortrange forecasting problem using the extensively validated ADCIRC model (Butler et al 2012;Altaf et al 2013). This particular problem exhibits unique fast-evolving dynamics.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation

Altaf

Butler

Mayo

et al. 2014

Monthly Weather Review

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This study evaluates and compares the performances of several variants of the popular ensemble Kalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf of Mexico coastline, the authors implement and compare the standard stochastic ensemble Kalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.

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Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

Cited by 20 publications

References 49 publications

Efficient Kernel-Based Ensemble Gaussian Mixture Filtering

Efficient Kernel-Based Ensemble Gaussian Mixture Filtering

A Regression-Based Approach for Cool-Season Storm Surge Predictions along the New York–New Jersey Coast

A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation

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