Revisiting Brugge case study using a Hierarchical Ensemble Kalman filter

2009 ◽  
Author(s):  
Brice Vallhs ◽  
Geir Naevdal
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter

The Ensemble Kalman Filter for Groundwater Plume Characterization: A Case Study

Ground Water ◽  
2018 ◽  
Vol 56 (4) ◽  
pp. 571-579 ◽  
Author(s):  
James L. Ross ◽  
Peter F. Andersen
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter

Improving numerical forecast accuracy with ensemble Kalman filter and chaos theory: Case study on Ciliwung river model

2014 ◽  
Vol 512 ◽  
pp. 540-548 ◽  
Author(s):  
Yabin Sun ◽  
Chi Dung Doan ◽  
Anh Tuan Dao ◽  
Jiandong Liu ◽  
Shie-Yui Liong
Keyword(s):  
Kalman Filter ◽  
Chaos Theory ◽  
Ensemble Kalman Filter ◽  
Forecast Accuracy ◽  
Numerical Forecast

scholarly journals Comparison of Ensemble Kalman Filter–Based Forecasts to Traditional Ensemble and Deterministic Forecasts for a Case Study of Banded Snow

2012 ◽  
Vol 27 (1) ◽  
pp. 85-105 ◽  
Author(s):  
Astrid Suarez ◽  
Heather Dawn Reeves ◽  
Dustan Wheatley ◽  
Michael Coniglio
Keyword(s):  
Kalman Filter ◽  
Flow Pattern ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Closed Surface ◽  
Flow Patterns ◽  
Precipitation Forecast ◽  
Ensemble Mean ◽  
Correct Location

Abstract The ensemble Kalman filter (EnKF) technique is compared to other modeling approaches for a case study of banded snow. The forecasts include a 12- and 3-km grid-spaced deterministic forecast (D12 and D3), a 12-km 30-member ensemble (E12), and a 12-km 30-member ensemble with EnKF-based four-dimensional data assimilation (EKF12). In D12 and D3, flow patterns are not ideal for banded snow, but they have similar precipitation accumulations in the correct location. The increased resolution did not improve the quantitative precipitation forecast. The E12 ensemble mean has a flow pattern favorable for banding and precipitation in the approximate correct location, although the magnitudes and probabilities of relevant features are quite low. Six members produced good forecasts of the flow patterns and the precipitation structure. The EKF12 ensemble mean has an ideal flow pattern for banded snow and the mean produces banded precipitation, but relevant features are about 100 km too far north. The EKF12 has a much lower spread than does E12, a consequence of their different initial conditions. Comparison of the initial ensemble means shows that EKF12 has a closed surface low and a region of high low- to midlevel humidity that are not present in E12. These features act in concert to produce a stronger ensemble-mean cyclonic system with heavier precipitation at the time of banding.

scholarly journals Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part IV: Comparison with 3DVAR in a Month-Long Experiment

2008 ◽  
Vol 136 (10) ◽  
pp. 3671-3682 ◽  
Author(s):  
Zhiyong Meng ◽  
Fuqing Zhang
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Single Case ◽  
Regional Scale ◽  
Wrf Model ◽  
Real Data ◽  
Model Errors ◽  
Scale Data

In previous works in this series study, an ensemble Kalman filter (EnKF) was demonstrated to be promising for mesoscale and regional-scale data assimilation in increasingly realistic environments. Parts I and II examined the performance of the EnKF by assimilating simulated observations under both perfect- and imperfect-model assumptions. Part III explored the application of the EnKF to a real-data case study in comparison to a three-dimensional variational data assimilation (3DVAR) method in the Weather Research and Forecasting (WRF) model. The current study extends the single-case real-data experiments over a period of 1 month to examine the long-term performance and comparison of both methods at the regional scales. It is found that the EnKF systematically outperforms 3DVAR for the 1-month period of interest in which both methods assimilate the same standard rawinsonde observations every 12 h over the central United States. Consistent with results from the real-data case study of Part III, the EnKF can benefit from using a multischeme ensemble that partially accounts for model errors in physical parameterizations. The benefit of using a multischeme ensemble (over a single-scheme ensemble) is more pronounced in the thermodynamic variables (including temperature and moisture) than in the wind fields. On average, the EnKF analyses lead to more accurate forecasts than the 3DVAR analyses when they are used to initialize 60 consecutive, deterministic 60-h forecast experiments for the month. Results also show that deterministic forecasts of up to 60 h initiated from the EnKF analyses consistently outperform the WRF forecasts initiated from the National Centers for Environmental Prediction final analysis field of the Global Forecast System.

Sign in / Sign up

Export Citation Format

Share Document