A hybrid nudging-ensemble Kalman filter approach to data assimilation in WRF/DART

2012 ◽  
Vol 138 (669) ◽  
pp. 2066-2078 ◽  
Author(s):  
Lili Lei ◽  
David R. Stauffer ◽  
Aijun Deng
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Filter Approach

scholarly journals Electron radiation belt data assimilation with an ensemble Kalman filter relying on the Salammbô code

2012 ◽  
Vol 30 (6) ◽  
pp. 929-943 ◽  
Author(s):  
S. A. Bourdarie ◽  
V. F. Maget
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Physical Model ◽  
Ensemble Kalman Filter ◽  
Radiation Belt ◽  
High Time ◽  
Electron Radiation ◽  
Time Period ◽  
Filter Approach ◽  
D Model

Abstract. In this study we implement a data assimilation tool using a 3-D radiation belt model and an ensemble Kalman filter approach. High time and space reanalysis of the electron radiation belt fluxes is obtained over the time period 5 October to 25 October 1990 by combining sparse observations with the Salammbô 3-D model in an optimal way. The convergence of the ensemble Kalman filter is analyzed carefully. The risk of using a biased physical model is discussed and relative consequences are highlighted. Finally, a validation against CRRES data and major improvements compared to pure physics based model are presented.

Using Precipitation Observations in a Mesoscale Short-Range Ensemble Analysis and Forecasting System

2008 ◽  
Vol 23 (3) ◽  
pp. 357-372 ◽  
Author(s):  
Tadashi Fujita ◽  
David J. Stensrud ◽  
David C. Dowell
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Grid Point ◽  
Precipitation Data ◽  
Simple Method ◽  
Forecasting System ◽  
Filter Approach ◽  
Water Vapor Mixing Ratio ◽  
Surface Observations

Abstract A simple method to assimilate precipitation data from a synthesis of radar and gauge data is developed to operate alongside an ensemble Kalman filter that assimilates hourly surface observations. The mesoscale ensemble forecast system consists of 25 members with 30-km grid spacing and incorporates variability in both initial and boundary conditions and model physical process schemes. The precipitation assimilation method only incorporates information on when and where rainfall is observed. Model temperature and water vapor mixing ratio profiles at each grid point are modified if rainfall is observed but not predicted, or if rainfall is predicted but not observed. These modifications act to either increase or decrease, respectively, the likelihood that precipitation develops at that grid point. Two cases are examined in which this technique is applied to assimilate precipitation data every 15 min from 1200 to 1800 UTC, while hourly surface observations are also assimilated at the same time using the more sophisticated ensemble Kalman filter approach. Results show that the simple method for assimilating precipitation data helps the model develop precipitation where it is observed, resulting in the precipitation area being reproduced more accurately than in the run without precipitation-data assimilation, while not negatively influencing the positive results from the surface data assimilation. Improvement is also seen in the reliability of precipitation probabilities for a 1 mm h−1 threshold after the assimilation period, indicating that assimilating precipitation data may provide improved forecasts of the mesoscale environment for a few hours.

scholarly journals A multimodel comparison of stratospheric ozone data assimilation based on an ensemble Kalman filter approach

2013 ◽  
Vol 118 (9) ◽  
pp. 3848-3868 ◽  
Author(s):  
T. Nakamura ◽  
H. Akiyoshi ◽  
M. Deushi ◽  
K. Miyazaki ◽  
C. Kobayashi ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Stratospheric Ozone ◽  
Ozone Data ◽  
Filter Approach

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
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