Ensemble Adjustment Kalman Filter Data Assimilation for a Global Atmospheric Model

2015 ◽  
pp. 284-298 ◽  
Author(s):  
Tarkeshwar Singh ◽  
Rashmi Mittal ◽  
H. C. Upadhyaya
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Atmospheric Model ◽  
Global Atmospheric Model ◽  
Ensemble Adjustment Kalman Filter

Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter

2015 ◽  
Vol 59 (3) ◽  
pp. 484-494 ◽  
Author(s):  
Hui Chen ◽  
XunQiang Yin ◽  
Ying Bao ◽  
FangLi Qiao
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Satellite Data ◽  
Ensemble Adjustment Kalman Filter

scholarly journals Assimilation of GPSRO Bending Angle Profiles into the Brazilian Global Atmospheric Model

2019 ◽  
Vol 11 (3) ◽  
pp. 256 ◽  
Author(s):  
Ivette Banos ◽  
Luiz Sapucci ◽  
Lidia Cucurull ◽  
Carlos Bastarz ◽  
Bruna Silveira
Keyword(s):  
Data Assimilation ◽  
Weather Forecast ◽  
Atmospheric Model ◽  
Meridional Wind ◽  
Point Of View ◽  
Bending Angle ◽  
Global Atmospheric Model ◽  
Angle Data ◽  
Meteorological Systems ◽  
Bending Angles

The Global Positioning System (GPS) Radio Occultation (RO) technique allows valuable information to be obtained about the state of the atmosphere through vertical profiles obtained at various processing levels. From the point of view of data assimilation, there is a consensus that less processed data are preferable because of their lowest addition of uncertainties in the process. In the GPSRO context, bending angle data are better to assimilate than refractivity or atmospheric profiles; however, these data have not been properly explored by data assimilation at the CPTEC (acronym in Portuguese for Center for Weather Forecast and Climate Studies). In this study, the benefits and possible deficiencies of the CPTEC modeling system for this data source are investigated. Three numerical experiments were conducted, assimilating bending angles and refractivity profiles in the Gridpoint Statistical Interpolation (GSI) system coupled with the Brazilian Global Atmospheric Model (BAM). The results highlighted the need for further studies to explore the representation of meteorological systems at the higher levels of the BAM model. Nevertheless, more benefits were achieved using bending angle data compared with the results obtained assimilating refractivity profiles. The highest gain was in the data usage exploring 73.4% of the potential of the RO technique when bending angles are assimilated. Additionally, gains of 3.5% and 2.5% were found in the root mean square error values in the zonal and meridional wind components and geopotencial height at 250 hPa, respectively.

Data assimilation and driver estimation for the Global Ionosphere–Thermosphere Model using the Ensemble Adjustment Kalman Filter

2013 ◽  
Vol 104 ◽  
pp. 126-136 ◽  
Author(s):  
Alexey V. Morozov ◽  
Aaron J. Ridley ◽  
Dennis S. Bernstein ◽  
Nancy Collins ◽  
Timothy J. Hoar ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Adjustment Kalman Filter

scholarly journals Regional and basin scale applications of ensemble adjustment Kalman filter and 4D-Var ocean data assimilation systems

2020 ◽  
Vol 189 ◽  
pp. 102450
Author(s):  
Andrew Moore ◽  
Javier Zavala-Garay ◽  
Hernan G. Arango ◽  
Christopher A. Edwards ◽  
Jeffrey Anderson ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ocean Data Assimilation ◽  
Ensemble Adjustment Kalman Filter ◽  
Basin Scale

Application of the Ensemble Kalman Filter to a One-Dimensional Linear Advection Model

2015 ◽  
Vol 804 ◽  
pp. 287-290
Author(s):  
Somsiri Payakkarak ◽  
Dusadee Sukawat
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Ensemble Member ◽  
Observation Data ◽  
State Estimate ◽  
One Dimensional ◽  
Weather Forecasts

Data assimilation is used in numerical weather prediction to improve weather forecasts by incorporating observation data into the model forecast. The Ensemble Kalman Filter (EnKF) is a method of data assimilation which updates an ensemble of states to provide a state estimate and associated error at each step. The atmospheric model that is used in this research is a one-dimensional linear advection model. This model describes the motion of a scalar field as it is advected by a known speed field. The result shows that by selecting appropriate initial ensemble, model noise and measurement perturbations, it is possible to achieve a significant improvement in the EnKF results. The accuracy of the EnKF increases when the number of ensemble member grows. That is, the larger ensemble sizes perform better than those of smaller sizes.

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