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I. Hoteit
,
D.-T. Pham
,
M. E. Gharamti
, and
X. Luo

Abstract

The stochastic ensemble Kalman filter (EnKF) updates its ensemble members with observations perturbed with noise sampled from the distribution of the observational errors. This was shown to introduce noise into the system and may become pronounced when the ensemble size is smaller than the rank of the observational error covariance, which is often the case in real oceanic and atmospheric data assimilation applications. This work introduces an efficient serial scheme to mitigate the impact of observations’ perturbations sampling in the analysis step of the EnKF, which should provide more accurate ensemble estimates of the analysis error covariance matrices. The new scheme is simple to implement within the serial EnKF algorithm, requiring only the approximation of the EnKF sample forecast error covariance matrix by a matrix with one rank less. The new EnKF scheme is implemented and tested with the Lorenz-96 model. Results from numerical experiments are conducted to compare its performance with the EnKF and two standard deterministic EnKFs. This study shows that the new scheme enhances the behavior of the EnKF and may lead to better performance than the deterministic EnKFs even when implemented with relatively small ensembles.

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M. U. Altaf
,
T. Butler
,
T. Mayo
,
X. Luo
,
C. Dawson
,
A. W. Heemink
, and
I. Hoteit

Abstract

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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M. U. Altaf
,
T. Butler
,
X. Luo
,
C. Dawson
,
T. Mayo
, and
I. Hoteit

Abstract

This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H filter. By design, an H filter is more robust than the common Kalman filter in the sense that the estimation error in the H filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H -based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.

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T. Butler
,
M. U. Altaf
,
C. Dawson
,
I. Hoteit
,
X. Luo
, and
T. Mayo

Abstract

Accurate, real-time forecasting of coastal inundation due to hurricanes and tropical storms is a challenging computational problem requiring high-fidelity forward models of currents and water levels driven by hurricane-force winds. Despite best efforts in computational modeling there will always be uncertainty in storm surge forecasts. In recent years, there has been significant instrumentation located along the coastal United States for the purpose of collecting data—specifically wind, water levels, and wave heights—during these extreme events. This type of data, if available in real time, could be used in a data assimilation framework to improve hurricane storm surge forecasts. In this paper a data assimilation methodology for storm surge forecasting based on the use of ensemble Kalman filters and the advanced circulation (ADCIRC) storm surge model is described. The singular evolutive interpolated Kalman (SEIK) filter has been shown to be effective at producing accurate results for ocean models using small ensemble sizes initialized by an empirical orthogonal function analysis. The SEIK filter is applied to the ADCIRC model to improve storm surge forecasting, particularly in capturing maximum water levels (high water marks) and the timing of the surge. Two test cases of data obtained from hindcast studies of Hurricanes Ike and Katrina are presented. It is shown that a modified SEIK filter with an inflation factor improves the accuracy of coarse-resolution forecasts of storm surge resulting from hurricanes. Furthermore, the SEIK filter requires only modest computational resources to obtain more accurate forecasts of storm surge in a constrained time window where forecasters must interact with emergency responders.

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M. W. Qian
,
A. Longhetto
,
C. Cassardo
,
C. Giraud
,
Z. X. Hong
,
W. D. Luo
, and
Y. J. Zhao

Abstract

A simple and physically consistent expression for the turbulent heat flux equation in the convective atmospheric boundary layer (CABL) has been suggested by . In their equation, valid under quasi-steady states and horizontal homogeneity, the countergradient term resulted from the third-moment transport effect rather than from the buoyancy production term. In this paper, experimental observation data from the World Laboratory Applied Research Project on Drought and Desertification (WL-ARPDD94 Experiment), carried out in a flat region of the greater Beijing area, China, have been utilized with the purpose of checking the validity of the Holtslag and Moeng equation. The result of this experimental check proved to be more than satisfactory through most of the CABL.

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