scholarly journals Ensemble Kalman Filter data assimilation and storm surge experiments of tropical cyclone Nargis

2015 ◽  
Vol 67 (1) ◽  
pp. 25941 ◽  
Author(s):  
Le Duc ◽  
Tohru Kuroda ◽  
Kazuo Saito ◽  
Tadashi Fujita
Keyword(s):  
Kalman Filter ◽  
Tropical Cyclone ◽  
Data Assimilation ◽  
Storm Surge ◽  
Ensemble Kalman Filter ◽  
Cyclone Nargis

scholarly journals Assimilation of High-Resolution Tropical Cyclone Observations with an Ensemble Kalman Filter Using NOAA/AOML/HRD’s HEDAS: Evaluation of the 2008–11 Vortex-Scale Analyses

2013 ◽  
Vol 141 (6) ◽  
pp. 1842-1865 ◽  
Author(s):  
Altuğ Aksoy ◽  
Sim D. Aberson ◽  
Tomislava Vukicevic ◽  
Kathryn J. Sellwood ◽  
Sylvie Lorsolo ◽  
...  
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Tropical Cyclone ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Inner Core ◽  
Weather Research And Forecasting ◽  
Planetary Boundary Layer Parameterization ◽  
Data Assimilation Technique ◽  
Horizontal Grid

Abstract The Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS) is developed to assimilate tropical cyclone inner-core observations for high-resolution vortex initialization. It is based on a serial implementation of the square root ensemble Kalman filter (EnKF). In this study, HWRF is used in an experimental configuration with horizontal grid spacing of 9 (3) km on the outer (inner) domain. HEDAS is applied to 83 cases from years 2008 to 2011. With the exception of two Hurricane Hilary (2011) cases in the eastern North Pacific basin, all cases are observed in the Atlantic basin. Observed storm intensity for these cases ranges from tropical depression to category-4 hurricane. Overall, it is found that high-resolution tropical cyclone observations, when assimilated with an advanced data assimilation technique such as the EnKF, result in analyses of the primary circulation that are realistic in terms of intensity, wavenumber-0 radial structure, as well as wavenumber-1 azimuthal structure. Representing the secondary circulation in the analyses is found to be more challenging with systematic errors in the magnitude and depth of the low-level radial inflow. This is believed to result from a model bias in the experimental HWRF caused by the overdiffusive nature of the planetary boundary layer parameterization utilized. Thermodynamic deviations from the observed structure are believed to be caused by both an imbalance between the number of the kinematic and thermodynamic observations in general and the suboptimal ensemble covariances between kinematic and thermodynamic fields. Future plans are discussed to address these challenges.

scholarly journals Storm-Relative Observations in Tropical Cyclone Data Assimilation with an Ensemble Kalman Filter

2013 ◽  
Vol 141 (2) ◽  
pp. 506-522 ◽  
Author(s):  
Altuğ Aksoy
Keyword(s):  
Spatial Distribution ◽  
Kalman Filter ◽  
Tropical Cyclone ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Doppler Radar ◽  
Positive Impact ◽  
Research Division ◽  
Wind Structure ◽  
Relative Version

Abstract A storm-relative data assimilation method for tropical cyclones is introduced for the ensemble Kalman filter, using the Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS) developed at the Hurricane Research Division of the Atlantic Oceanographic and Meteorological Laboratory at the National Oceanic and Atmospheric Administration. The method entails translating tropical cyclone observations to storm-relative coordinates and requires the assumption of simultaneity of all observations. The observations are then randomly redistributed to assimilation cycles to achieve a more homogeneous spatial distribution. A proof-of-concept study is carried out in an observing system simulation experiment in which airborne Doppler radar radial wind observations are simulated from a higher-resolution (4.5/1.5 km) version of the same model. The results here are compared to the earth-relative version of HEDAS. When storm-relative observations are assimilated using the original HEDAS configuration, improvements are observed in the kinematic representation of the tropical cyclone vortex in analyses. The use of the storm-relative observations with a more homogeneous spatial distribution also reveals that a reduction of the covariance localization horizontal length scale by ½ to ~120 km provides the greatest incremental improvements. Potential positive impact is also seen in the slower cycle-to-cycle error growth. Spatially smoother analyses are obtained in the horizontal, and the evolution of the azimuthally averaged wind structure during short-range forecasts demonstrates better consistency with the nature run.

Data assimilation with the weighted ensemble Kalman filter

2010 ◽  
Author(s):  
Nicolas Papadakis ◽  
Etienne Mémin ◽  
Anne Cuzol ◽  
Nicolas Gengembre
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter

Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea

2016 ◽  
Vol 66 (8) ◽  
pp. 955-971 ◽  
Author(s):  
Stéphanie Ponsar ◽  
Patrick Luyten ◽  
Valérie Dulière
Keyword(s):  
Kalman Filter ◽  
Numerical Model ◽  
Data Assimilation ◽  
North Sea ◽  
Ensemble Kalman Filter ◽  
The North ◽  
The North Sea

An ensemble Kalman filter data assimilation system for the martian atmosphere: Implementation and simulation experiments

Icarus ◽  
2010 ◽  
Vol 209 (2) ◽  
pp. 470-481 ◽  
Author(s):  
Matthew J. Hoffman ◽  
Steven J. Greybush ◽  
R. John Wilson ◽  
Gyorgyi Gyarmati ◽  
Ross N. Hoffman ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Martian Atmosphere ◽  
Simulation Experiments ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Non-linear wave data assimilation with an ANN-type wind-wave model and Ensemble Kalman Filter (EnKF)

2010 ◽  
Vol 34 (8) ◽  
pp. 1984-1999 ◽  
Author(s):  
Ahmadreza Zamani ◽  
Ahmadreza Azimian ◽  
Arnold Heemink ◽  
Dimitri Solomatine
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Wind Wave ◽  
Wave Model ◽  
Linear Wave ◽  
Wave Data ◽  
Non Linear

scholarly journals Data Assimilation of the High-Resolution Sea Surface Temperature Obtained from the Aqua-Terra Satellites (MODIS-SST) Using an Ensemble Kalman Filter

2013 ◽  
Vol 5 (6) ◽  
pp. 3123-3139 ◽  
Author(s):  
Yasumasa Miyazawa ◽  
Hiroshi Murakami ◽  
Toru Miyama ◽  
Sergey Varlamov ◽  
Xinyu Guo ◽  
...  
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ensemble Kalman Filter ◽  
Sea Surface

Evaluation of Surface Analyses and Forecasts with a Multiscale Ensemble Kalman Filter in Regions of Complex Terrain

2011 ◽  
Vol 139 (6) ◽  
pp. 2008-2024 ◽  
Author(s):  
Brian C. Ancell ◽  
Clifford F. Mass ◽  
Gregory J. Hakim
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Complex Terrain ◽  
Error Variance ◽  
Small Scale ◽  
Observation Error ◽  
Grid Spacing ◽  
Background Error

Abstract Previous research suggests that an ensemble Kalman filter (EnKF) data assimilation and modeling system can produce accurate atmospheric analyses and forecasts at 30–50-km grid spacing. This study examines the ability of a mesoscale EnKF system using multiscale (36/12 km) Weather Research and Forecasting (WRF) model simulations to produce high-resolution, accurate, regional surface analyses, and 6-h forecasts. This study takes place over the complex terrain of the Pacific Northwest, where the small-scale features of the near-surface flow field make the region particularly attractive for testing an EnKF and its flow-dependent background error covariances. A variety of EnKF experiments are performed over a 5-week period to test the impact of decreasing the grid spacing from 36 to 12 km and to evaluate new approaches for dealing with representativeness error, lack of surface background variance, and low-level bias. All verification in this study is performed with independent, unassimilated observations. Significant surface analysis and 6-h forecast improvements are found when EnKF grid spacing is reduced from 36 to 12 km. Forecast improvements appear to be a consequence of increased resolution during model integration, whereas analysis improvements also benefit from high-resolution ensemble covariances during data assimilation. On the 12-km domain, additional analysis improvements are found by reducing observation error variance in order to address representativeness error. Removing model surface biases prior to assimilation significantly enhances the analysis. Inflating surface wind and temperature background error variance has large impacts on analyses, but only produces small improvements in analysis RMS errors. Both surface and upper-air 6-h forecasts are nearly unchanged in the 12-km experiments. Last, 12-km WRF EnKF surface analyses and 6-h forecasts are shown to generally outperform those of the Global Forecast System (GFS), North American Model (NAM), and the Rapid Update Cycle (RUC) by about 10%–30%, although these improvements do not extend above the surface. Based on these results, future improvements in multiscale EnKF are suggested.

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