Publications on Data Assimilation

2023

  • Min, C., Q. Yang, H. Luo, D. Chen, T. Krumpen, N. Mamnun, X. Liu, and L. Nerger (2023). Improving Arctic sea-ice thickness estimates with the assimilation of CryoSat-2 summer observations. Ocean-Land-Atmosphere Research, 2, 0025, ​doi:10.34133/olar.0025
  • Luo, H., Q. Yang, M. Mazloff, L. Nerger, and D. Chen (2023) The impacts of optimizing model-dependent parameters on the Antarctic sea ice data assimilation. Geophysical Research Letters, 50, e2023GL105690, ​doi:10.1029/2023GL105690
  • Mamnun, N., C. Völker, S. Krumscheid, M. Vrekoussis, L. Nerger (2023). Global sensitivity analysis of a one-dimensional ocean biogeochemical model. Socio-Environmental Systems Modelling, 5, 18613, doi:10.18174/sesmo.18613
  • Williams, N., N. Byrne, D. Feltham, P. J. van Leeuwen, R. Bannister, D. Schroeder, A. Ridout, L. Nerger (2023) The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system. The Cryosphere, 17, 2509–2532, ​doi:10.5194/tc-17-2509-2023

2022

  • Mu, L., L. Nerger, J. Streffing, Q. Tang, B. Niraula, L. Zampieri, S. N. Loza, H. F. Goessling. (2022) Sea-ice forecasts with an upgraded AWI Coupled Prediction System, Journal of Advances in Modeling Earth Systems, 14, e2022MS003176, doi:10.1029/2022MS003176
  • Mamnun, N., C. Voelker, M. Vrekoussis, L. Nerger (2022) Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model. Frontiers Marine Science, 9, 984236. ​doi:10.3389/fmars.2022.984236
  • Nerger, L. (2022) Data assimilation for nonlinear systems with a hybrid nonlinear-Kalman ensemble transform filter, Quarterly Journal of the Royal Meteorological Society, 148, 620-640, ​doi:10.1002/qj.4221

2021

  • Q. Tang, L. Mu, H. F. Goessling, T. Semmler, L. Nerger (2021) Stroungly coupled data assimilation of ocean observations into an ocean-atmosphere model, Geophys. Res. Lett., 48, e2021GL094941, doi:10.1029/2021GL094941

  • Luo, H., Q. Yang, L. Mu, X. Tian-Kunze, L. Nerger, M. Mazloff, L. Kaleschke, D. Chen. (2021) DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations, Journal of Glaciology, 67, 1235-1240 ​doi:10.1017/jog.2021.57

2020

  • Tang, Q., Mu, L., Sidorenko, D., Goessling, H., Semmler, T., Nerger, L. (2020) Improving the ocean and atmosphere in a coupled ocean‐atmosphere model by assimilating satellite sea surface temperature and subsurface profile data. Quarterly Journal of the Royal Meteorological Society, 146, 4014-4029, ​doi:10.1002/qj.3885
  • Nerger, L., Tang, Q., Mu, L. (2020). Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM. Geoscientific Model Development, 13, 4305-4321, ​doi:10.5194/gmd-13-4305-2020
  • Mu, L., Nerger, L., Tang, Q., Losa, S. N., Sidorenko, D., Wang, Q., Semmler, T., Zampieri, L., Losch, M., Goessling, H. F. (2020) Towards a data assimilation system for seamless sea ice prediction based o the AWI climate model. Journal of Advances in Modeling Earth Systems, 12, e2019MS001937, doi:10.1029/2019MS001937
  • Pradhan, H.K., Voelker, C., Losa, S.N., Bracher, A., Nerger, L. (2020) Global assimilation of ocean-color data of phytoplankton functional types: Impact of different datasets. J. Geophys. Res. Oceans, 125, e2019JC015586, doi:10.1029/2019JC015586

2019

  • Goodliff, M., Bruening, T., Schwichtenberg, F., Li, X., Lindenthal, A., Lorkowski, I., Nerger, L. (2019) Temperature assimilation into a coastal ocean-biogeochemical model: Assessment of weakly and strongly-coupled data assimilation, Oce. Dyn., 69, 1217-1237, doi:10.1007/s10236-019-01299-7
  • van Leeuwen, P. J., Künsch, H. R., Nerger, L., Potthast, R., and Reich, S. (2019) Particle filters for high-dimensional geoscience applications: a review. Quarterly Journal of the Royal Meteorological Society, 145, 2335-2365, doi:10.1002/qj.3551 (Preprint arXiv:1807.10434)
  • Liang, X., Losch, M., Nerger, L., Mu, L., Yang, Q., Liu, C. (2019) Using sea surface temperature observations to constrain upper ocean properties in an Arctic sea ice-ocean data assimilation system. J. Geophys. Res. Oceans, 124, 4723-4743, doi:10.1029/2019JC015073
  • Pradhan, H.K., Voelker, C., Losa, S.N., Bracher, A., Nerger, L. (2019) Assimilation of global total chlorophyll OC-CCI data and its impact on individual phytoplankton fields. J. Geophys. Res. Oceans, 124, 470-490, doi:10.1029/2018JC014329
  • Liu, J., Chan, Z., Hu, Y., Zhang, Y., Ding, Y., Cheng, Y., Cheng, X., Yang, Q., Nerger, L., Spreen, G., Horton, R., Inoue, R., Yang, C., Li, M., Song, M. (2019) Towards reliable Arctic sea ice prediction using multivariate data assimilation. Science Bulletin, 64, 63-72, doi:10.1016/j.scib.2018.11.018
  • Androsov, A., Nerger, L., Schnur, R., Schröter, J., Albertella, A., Rummel, R., Savcenko, R., Bosch, W., Skachko, S., Danilov, S. (2019) On the assimilation of absolute geodetic dynamic topography in a global ocean model: impact on the deep ocean state. Journal of Geodesy, 93, 141-157, doi:10.1007/s00190-018-1151-1

2018

  • Mu, L., Losch, M., Yang, Q., Ricker, R., Losa, S., Nerger, L., and Zhang, J. (2018) Arctic-wide sea-ice thickness estimates from combining satellite remote sensing data and a dynamic ice-ocean model with data assimilation during the CryoSat-2 period. J. Geophys. Res. Oceans, 123, 7764-7780, doi:10.1029/2018JC014316
  • Vetra-Carvalho, S., van Leeuwen, P. J., Nerger, L., Barth, A., Altaf, M. U., Brasseur, P., Kirchgessner, P., Beckers, J.-M. (2018) State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems. Tellus A, 70:1, 1445364, doi:10.1080/16000870.2018.1445364
  • Mu, L., Yang, Q., Losch, M., Losa, S.N., Ricker, R., Nerger, L., Liang, X. (2018) Improving sea ice thickness estimates by assimilating CryoSat-2 and SMOS sea ice thickness data simultaneously. Quarterly Journal of the Royal Meteorological Society. 144, 529-538, doi:10.1002/qj.3225

2017

  • Barth, A., Yan, Y., Nerger, L., Beckers, J.-M. (2017) The 47th Liege Colloquium: marine environmental monitoring, modelling and prediction, Ocean Dynamics, 67, 1367-1368, doi:10.1007/s10236-017-1091-y
  • Liang, X., Yang, Q., Nerger, L., Losa, S. N., Zhao, B., Zheng, F., Zhang, L., Wu, L. (2017) Assimilating Copernicus SST data into a pan-Arctic ice-ocean coupled model with a local SEIK filter. Journal of Atmospheric and Oceanic Technology, 34, 1985-1999, doi:10.1175/JTECH-D-16-0166.1
  • Kirchgessner, P., Tödter, J., Ahrens, B., Nerger, L. (2017) The smoother extension of the nonlinear ensemble transform filter. Tellus A, 69, 1327766, doi:10.1080/16000870.2017.1327766

2016

  • Nerger, L., Losa, S. N., Brüning, T., Janssen F. (2016) The HBM-PDAF assimilation system for operational forecasts in the North and Baltic Seas, in Operational Oceanography for Sustainable Blue Growth. Proceedings of the Seventh EuroGOOS International Conference. 28-30 October 2014, Lisbon, Portugal / Eds. E. Buch, Y. Antoniou, D. Eparkhina, G. Nolan. ISBN 978-2-9601883-1-8
  • 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. doi:10.1142/9789812701831_0006

2004