Publications on Data Assimilation

2016

  • Yang, Q., Losch, M., Losa, S. N., Jung T., Nerger, L. (2016) Taking into account atmospheric uncertainty improves sequential assimilation of SMOS sea ice thickness data in an ice-ocean model. Journal of Atmospheric and Oceanic Technology, 33, 397-407, doi:10.1175/JTECH-D-15-0176.1
  • Yang, Q., Losch, M., Losa, S. N., Jung T., Nerger, L., Lavergne, T. (2016) Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation. The Cryosphere, 10, 761-774, doi:10.5194/tc-10-761-2016
  • Tödter, J., Kirchgessner, P., Nerger, L., Ahrens, B. (2016) Assessment of a nonlinear ensemble transform filter for high-dimensional data assimilation. Monthly Weather Review, 144, 409-427, doi:10.1175/MWR-D-15-0073.1

2015

  • Brune, S., Nerger, L., Baehr, J. (2015) Assimilation of oceanic observations in a global coupled Earth system model with the SEIK filter, Ocean Modelling, 96, 254-264, doi:10.1016/j.ocemod.2015.09.011
  • Yang, Q., Losa, S. N., Losch, M., Jung, T., Nerger, L. (2015) The role of atmospheric uncertainty in Arctic summer sea ice data assimilation and prediction. Quarterly Journal of the Royal Meteorological Society, 141, 2314-2323, doi:10.1002/qj.2523.
  • Nerger, L. (2015) On serial observation processing in localized ensemble Kalman filters. Monthly Weather Review, 143, 1554-1567, doi:10.1175/MWR-D-14-00182.1
  • Yang, Q., Losa, S. N., Losch, M., Liu, J., Zhang, Z., Nerger, L., Yang, H. (2015) Assimilating summer sea ice concentration into a coupled ice-ocean model using a local SEIK filter. Annals of Glaciology, 56(69) 38-44, doi:10.3189/2015AoG69A740

2014

  • Kirchgessner, P., Nerger, L., Bunse-Gerstner, A. (2014) On the choice of an optimal localization radius in ensemble Kalman filter methods. Monthly Weather Review, 142, 2165-2175, doi:10.1175/MWR-D-13-00246.1
  • Losa, S.N., Danilov, S., Schröter, J., Janjic, T., Nerger, L., Janssen, F. (2014). Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Part 2. Sensitivity of the forecast's skill to the prior model error statistics. Journal of Marine Systems, 120, 259-270, doi:10.1016/j.jmarsys.2013.06.011.
  • Nerger, L., Schulte, S., Bunse-Gerstner, A. (2014) On the influence of model nonlinearity and localization on ensemble Kalman smoothing, Quarterly Journal of the Royal Meteorological Society, 140, 2249-2259, doi:10.1002/qj.2293
  • Yang, Q., Losa, S. N., Losch, M., Tian-Kunze, X., Nerger, L., Liu, J., Kaleschke, L., Zhang, Z. (2014) Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter. JGR-Oceans, 119, 6680-6692, doi:10.1002/2014JC009963

2013

  • Fournier, A., Nerger, L., Aubert, J. (2013), An ensemble Kalman filter for the time-dependent analysis of the geomagnetic field. Geochemistry, Geophysics, Geosystems, 14, 4035-4043, doi:10.1002/ggge.20252
  • Nerger, L., Hiller, W. (2013). Software for Ensemble-based Data Assimilation Systems - Implementation Strategies and Scalability. Computers and Geosciences, 55, 110-118, doi:10.1016/j.cageo.2012.03.026.

2012

  • Losa, S. N., Danilov, S., Schröter, J., Nerger, L., Massmann, S., Janssen, F. (2012). Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data. Journal of Marine Systems, 105-108, pp. 152-162, doi:10.1016/j.jmarsys.2012.07.008.
  • Nerger, L., Janjić, T., Schröter, J., Hiller, W. (2012b). A unification of  ensemble square root Kalman filters. Monthly Weather Review, 140, 2335-2345, doi:10.1175/MWR-D-11-00102.1
  • Nerger, L., Janjić, T., Schröter, J., Hiller, W. (2012a). A regulated localization scheme for ensemble-based Kalman filters. Quarterly Journal of the Royal Meteorological Society, 138, 802-812, doi:10.1002/qj.945.

2011

  • Janjić, T., Nerger, L., Albertella, A., Schröter, J., Skachko S. (2011). On domain localization in ensemble based Kalman filter algorithms. Monthly Weather Review, 139, 2046-2060, doi:10.1175/2011MWR3552.1.

2008

  • Nerger, L., Gregg, W. W.(2008). Improving Assimilation of SeaWiFS Data by the Application of Bias Correction with a Local SEIK Filter, Journal of Marine Systems, 73, 87-102,  doi:10.1016/j.jmarsys.2007.09.007.

2007

  • Nerger, L., Gregg, W. W.(2007). Assimilation of SeaWiFS data into a global ocean-biogeochemical model using a local SEIK Filter, Journal of Marine Systems, 68, 237-254,  doi:10.1016/j.jmarsys.2006.11.009.
  • Nerger, L., Danilov, S., Kivman, G., Hiller, W., Schröter, J.(2007). Data assimilation with the Ensemble Kalman Filter and the SEIK filter applied to a finite element model of the North Atlantic, Journal of Marine Systems, 65, 288-298,  doi:10.1016/j.jmarsys.2005.06.009.

2006

  • Nerger, L., Danilov, S., Hiller, W., Schröter, J.(2006). Using sea-level data to constrain a finite-element primitive-equation ocean model with a local SEIK filter, Ocean Dynamics, 56, 634-649,  doi:10.1007/s10236-006-0083-0.

2005

  • Nerger, L., Hiller, W., Schröter, J.(2005). A Comparison of Error Subspace Kalman Filters, Tellus A: Dynamic Meteorology and Oceanography, 57A(5), 715-735,  doi:10.1111/j.1600-0870.2005.00141.x.
  • L. Nerger, W. Hiller, and J. Schröter (2005). PDAF - The Parallel Data Assimilation Framework: Experiences with Kalman Filtering, in Use of High Performance Computing in Meteorology - Proceedings of the 11th ECMWF Workshop / Eds. W. Zwieflhofer, G. Mozdzynski. Singapore: World Scientific, pp. 63-83.

2004