Advances in methodology and non-Gaussian approaches
Session 4 Oral Presentation Files
4.1 M. Bonavita: Nonlinear effects in the ECMWF 4D-Var
4.2 G. Inverarity: Improving the MOGREPS global ensemble using 4D-ensemble variational data assimilation
4.3 C. Thomas: Development in Cloud Analyses within NGGPS-FV3GFS
4.4 P.J. van Leeuwen: The accuracy of efficient particle filters
4.5 A Walter: On Localised Particle Filters for the Global Weather Prediction Model ICON
4.6 F.R. Pinheiro: Nonlinear data assimilation using synchronisation in a particle filter
4.7 L. Nerger: A Hybrid Kalman-Nonlinear Ensemble Transform Filter
4.8 J. Anderson: Exploiting Nonlinear Relations between Observations and State Variables in Sequential Ensemble Filters
4.9 S. Fletcher: Mixed Gaussian-Lognormal based Variational Data Assimilation
4.10 T. Janjic: Preservation of physical properties with Ensemble-type Kalman Filter Algorithms
4.11 C. Bishop: Episodic, non-linear and non-Gaussian: ensemble data assimilation for bounded semi-positive definite variables like clouds
4.12 D. Hotta: Toward improved LETKF assimilation of non-local and dense observation by direct covariance localization in model space
4.13 E. Holm: Developments in ECMWF humidity background errors
Downloads
- o4.10_janjic (2 MByte)
- o4.11_bishop (4 MByte)
- o4.12_hotta (3 MByte)
- o4.13_holm (3 MByte)
- o4.1_bonavita (2 MByte)
- o4.2_inverarity (1 MByte)
- o4.3_thomas (3 MByte)
- o4.4_vanleeuwen (730 KByte)
- o4.5_anne_walter (4 MByte)
- o4.6_pinheiro (28 MByte)
- o4.7_nerger (708 KByte)
- o4.8_anderson (3 MByte)
- o4.9_fletcher (2 MByte)