Publications on Data Assimilation

2024

  • Shao, C. and L. Nerger (2024). The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case. Remote Sensing 16, 430, ​doi:10.3390/rs16020430

2023

  • Nerger, L., Y. Sun, S. Vliegen (2023). Improving ocean ecosystem predictions by coupled data assimilation of physical and biogeochemical observations. in Proceedings of the 10th EuroGOOS International Conference. "European Operational Oceanography for the ocean we want - Addressing the UN Ocean Decade challenges". 3-5 October 2023, Galway, Ireland. Eparkhina, D., Nolan, J.E. (Eds.), hdl:10793/1883
  • 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