Masoum, A., L. Nerger, M. Willeit, A. Ganopolski, G. Lohmann (2024) Lessons From Transient Simulations of the Last Deglaciation With CLIMBER-X: GLAC1D Versus PaleoMist, Geophysical Research Letters, 51, e2023GL107310, doi:10.1029/2023GL107310
Bruggemann, J. K. Bolding, L. Nerger, A. Teruzzi, S. Spada, J. Skákala, S. Ciavatta (2024) EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters, Geoscientific Model Development, 17, 5619-5639, doi:10.5194/gmd-17-5619-2024
Shao, C. and Nerger, L. (2024) WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework, Geoscientific Model Development, 17, 4433–4445, doi:10.5194/gmd-17-4433-2024
Tang, Q., H. Delottier, W. Kurtz, L. Nerger, O. S. Schilling, P. Brunner (2024) HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model. Geoscientific Model Development, 17, 3559, doi:10.5194/gmd-17-3559-2024
Masoum, A., Nerger, L., Willeit, M., Ganopolski, A., Lohmann, G. (2024) Paleoclimate data assimilation with CLIMBER-X: An ensemble Kalman filter for the last deglaciation, PLoS ONE, 19(4), e0300138, doi:10.1371/journal.pone.0300138
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
