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

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 Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions

2011 ◽  
Vol 11 (3) ◽  
pp. 7811-7849 ◽  
Author(s):  
X. Tang ◽  
J. Zhu ◽  
Z. F. Wang ◽  
A. Gbaguidi
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Pollution Monitoring ◽  
Ozone Forecast ◽  
Ozone Data ◽  
Surrounding Areas ◽  
The Impact ◽  
Urban Sites

Abstract. We performed ozone data assimilation by simultaneously adjusting the ozone initial conditions, precursor initial conditions and emissions based on the Ensemble Kalman Filter (EnKF) and assessed its impacts on ozone modeling and forecasting in Beijing and nearby regions. A high-resolution regional air quality model and a newly established regional monitoring network covering Beijing and its surrounding areas were employed. At each assimilation cycle, the forecast error covariance was sampled from a set of forecast ensembles that were generated by perturbing ozone precursor initial conditions, emissions, photolysis rates and deposition velocity. A model-error module and a local analysis scheme have been introduced to reduce the impact of filter divergence and spurious correlation that accompanied with EnKF. The results showed significant improvement of 1-hour ozone forecast in Beijing and its surrounding areas through separately adjusting ozone initial conditions, precursor initial conditions and emissions with ozone observations. However, adjustment of precursor initial conditions and emissions had minor effect on the 1-hour ozone forecast in suburban area. The best ozone forecast skill was obtained through jointly adjusting ozone initial conditions, NOx and VOC initial conditions and emissions. The root mean square errors of 1-hour ozone forecast at urban sites and suburban sites decreased by 54% and 59% respectively compared with those in free run. Furthermore, the specific impacts of observations from urban and suburban sites on ozone data assimilation were evaluated by implementing sensitivity experiments. Both urban and suburban sites were found to be very important for the improvement of regional ozone forecast. The importance of observational data at urban sites was particularly highlighted through its role in constraining the uncertainty of precursor initial conditions and emission rates. Further improvement of regional ozone forecast might therefore be expected with more routine regional air pollution monitoring stations.

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
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