= Publications and Presentations =

[[PageOutline(2,Publications and Presentations)]]

== Presentations ==

PDAF has been presented at different conferences. Here are slides from some of these presentations:

[http://pdaf.awi.de/files/Nerger_PDAF_ISDAonline2022-01.pdf PDAF - Community Software for Ensemble-based Data Assimilation],[[BR]]
International Symposium on Data Assimilation - Online (ISDA-Online), January 14, 2022.

[http://pdaf.awi.de/files/EGU2019-SC1.1_PDAF_web.pdf Data Assimilation in the Geosciences - Practical Data Assimilation with the Parallel Data Assimilation Framework],[[BR]]
Short Course at the EGU General Assembly 2019, Vienna, Austria, April 8-12, 2019.

[http://pdaf.awi.de/files/Nerger_Tutorial_PDAF_OSM2018.pdf Ensemble Data Assimilation with the Parallel Data Assimilation Framework],[[BR]]    Tutorial Session at Ocean Sciences Meeting 2018, Portland, OR, USA, February 12-16, 2018. 

[http://pdaf.awi.de/files/Nerger_EnsDA_OSM2016.pdf An introduction to Ensemble Data Assimilation],[[BR]]    Tutorial Session at Ocean Sciences Meeting 2016, New Orleans, USA, February 21-26, 2016. 


== Publications about PDAF ==

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, [https://doi.org/10.5194/gmd-13-4305-2020 doi:10.5194/gmd-13-4305-2020]

Nerger, L., Hiller, W. (2013). Software for Ensemble-based Data Assimilation Systems - Implementation Strategies and Scalability. Computers and Geosciences, 55, 110-118. [http://dx.doi.org/10.1016/j.cageo.2012.03.026 doi:10.1016/j.cageo.2012.03.026]

Nerger, L., Hiller, W., Schröter, J.(2005). PDAF - The Parallel Data Assimilation Framework: Experiences with Kalman Filtering, Use of high performance computing in meteorology : proceedings of the Eleventh ECMWF Workshop on the Use of High Performance Computing in Meteorology, Reading, UK, 25 - 29 October 2004 / Eds.: Walter Zwieflhofer; George Mozdzynski, Singapore: World Scientific, 63-83. [http://doi.org/10.1142/9789812701831_0006 doi:10.1142/9789812701831_0006] [http://hdl.handle.net/10013/epic.22580 preprint]

== Publications involving PDAF ==

(This list is likely incomplete. If you see a paper missing, please write to us at pdaf _at_ awi _dot_ de)

=== Accepted ===

Düsterhus, A., S. Brune (2023) The effect of initialisation on 20 year multi-decadal climate predictions. Climate Dynamcics, online [https://doi.org/10.1007/s00382-023-06941-1 doi:10.1007/s00382-023-06941-1]

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, 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. [https://sesmo.org/article/view/18613 doi:10.18174/sesmo.18613]

=== 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, 6, 0025 [https://doi.org/10.34133/olar.0025 doi:10.34133/olar.0025]

Cook, S., F. Gillet-Chaulet, J. Fuerst. Robust reconstruction of glacier beds using transient 2D assimilation with Stokes. Journal of Glaciology. 69, 1393-1402, [https://doi.org/10.1017/jog.2023.26 doi:10.1017/jog.2023.26]

Koul, V., S. Brune, A. Akimova, A. Düsterhus, P. Pieper, L. Hövel, A. Parekh, C. Schrum, J. Baehr (2023) Geophysical Research Letters, 50, e2023GL103975 [httpd://doi.org/10.1029/2023GL103975 doi:10.1029/2023GL103975]

Fan, H., L. F. Borchert, S. Brune, V. Koul, J. Baehr (2023) North Atlantic subpolar gyre provides downstream ocean predictability. npj Climate and Atmospheric Science, 6, 145 [https://doi.org/10.1038/s41612-023-00469-1 doi:10.1038/s41612-023-00469-1]

Sievers, I., T. A. S. Rasmussen, L. Stenseng (2023) Assimilating !CryoSat-2 freeboard to improve Arctic sea ice thickness estimates. The Cryosphere, 17, 3721-3738, [https://doi.org/10.5194/tc-17-3721-2023 10.5194/tc-17-3721-2023]

Brunet, A., Dahmen, N., Katsavrias, C., Santolík, O., Bernoux, G., Pierrard, V., et al. (2023). Improving the electron radiation belt nowcast and forecast using the !SafeSpace data assimilation modeling pipeline. Space Weather, 21, e2022SW003377 [https://doi.org/10.1029/2022SW003377 doi:10.1029/2022SW003377]

Gerdener, H., J. Kusche, K. Schulze, P. Doell, A. Klos (2023). The global land water storage data set release 2 (GLWS2.0) derived via assimilating GRACE and GRACE-FO data into a global hydrological model. Journal of Geodesy, 97, 73 [https://doi.org/10.1007/s00190-023-01763-9 doi:10.1007/s00190-023-01763-9]

Folch, A., et al. (2023) The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase. Future Generation Computer Systems, 147, 47-61 [https://doi.org/10.1016/j.future.2023.04.006 doi:10.1016/j.future.2023.04.006]

Brandhorst, N., I. Neuweiler. (2023) Impact of parameter updates on soil moisture assimilation in a 3D heterogeneous hillslope model. Hydrology and Earth System Sciences (HESS), 27, 1301-1323 [https://doi.org/10.5194/hess-27-1301-2023 doi:10.5194/hess-27-1301-2023]

Pohlmann, H., S. Brune, K. Fröhlich, J. H. Jungclaus, C. Sgoff , J. Baehr. (2023) Impact of ocean data assimilation on climate predictions with ICON-ESM. Climate Dynamics, 61, 357–373 [https://doi.org/10.1007/s00382-022-06558-w]

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 [https://doi.org/10.5194/tc-17-2509-2023 10.5194/tc-17-2509-2023]

Fang, L., W. Kurtz, C.P. Hung, H. Vereecken, H.-J. Hendricks Franssen (2023)
Water table depth assimilation in integrated terrestrial system models at the larger catchment scale. Frontiers in Water 5, 1150999 [https://doi.org/10.3389/frwa.2023.1150999 doi:10.3389/frwa.2023.1150999]

Li, Y., Z. Cong, D. Yang. (2023) Remotely Sensed Soil Moisture Assimilation in the Distributed Hydrological Model Based on the Error Subspace Transform Kalman Filter. Remote Sensing, 15, 1852. [https://doi.org/10.3390/rs15071852 doi:10.3390/rs15071852]

=== 2022 ===

Tian, Z., X Liang, J. Zhang, H. Bi, F. Zhao, C. Li (2022) Thermodynamical and Dynamical Impacts of an Intense Cyclone on Arctic Sea Ice, Journal of Geophysical Research Oceans, 127, e2022JC018436. [https://doi.org/10.1029/2022JC018436 doi:10.1029/2022JC018436]

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, [https://doi.org/10.1029/2022MS003176 doi:10.1029/2022MS003176]

Krieger, D., S. Brune, P. Pieper, R. Weisse, J. Baehr. (2022) Skillful decadal prediction of German Bight storm activity. Nat. Hazards Earth Syst. Sci, 22, 3993-4009. [https://doi.org/10.5194/nhess-22-3993-2022 doi:10.5194/nhess-22-3993-2022]

Corbin, A, J. Kusche. (2022) Improving the estimation of thermospheric neutral density via two-step assimilation of in situ neutral density into a numerical model. Earth, Planets and Space, 74, 183. [https://doi.org/10.1186/s40623-022-01733-z doi:10.1186/s40623-022-01733-z]

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 in Marine Science, 9, 984236. [https://doi.org/10.3389/fmars.2022.984236 doi:10.3389/fmars.2022.984236]

Hövel, L., S. Brune, J. Baehr. (2022) Decadal prediction of Marine HEatwaves in MPI_ESM. Geophysical Research Letters, 49, e2022GL099347. [https://doi.org/10.1029/2022GL099347 doi:10.1029/2022GL099347]

Friedemann, S., Raffin, B. (2022) An elastic framework for ensemble-based large-scale data assimilation. International Journal of High Performance Computing Applications. [https://doi.org/10.1177/10943420221110507 doi:10.1177/10943420221110507]

Hung, C. P., B. Schalge, G. Baroni, H. Vereecken, H.-J. Hendricks-Franssen (2022) Assimilation of Groundwater Level and Soil Moisture Data in an Integrated Land Surface-Subsurface Model for Southwestern Germany. Water Resources Research, 58, e2021WR031549, [https://doi.org/10.1029/2021WR031549 doi:10.1029/2021WR031549]

Wang, H., T. Yang, Z. Wang, J. Li, W. Chai, G. Tang, L. Kong, X. Chen (2022) An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application. Geoscientific Model Development, 15, 3555-3585 [https://doi.org/10.5194/gmd-15-3555-2022 doi:10.5194/gmd-15-3555-2022]

Ben Ali, M.Y., O. Léon, D. Donjat, H. Bézard, E. Laroche, V. Mons, F. Champagnat (2022) Data assimilation for aerothermal mean flow reconstruction using aero-optical observations: a synthetic investigation, 56th 3AF International Conference on Applied Aerodynamics 28 — 30 March 2022, Toulouse – France [https://www.researchgate.net/publication/359619309]

Nerger, L. (2022) Data assimilation for nonlinear systems with a hybrid nonlinear-Kalman ensemble transform filter. Q. J. Meteorol. Soc., 148, 620-640 [https://doi.org/10.1002/qj.4221 doi:10.1002/qj.4221]

Schachtschneider, R., J. Saynisch-Wagner, V. Klemann, M. Bagge, M. Thomas (2022) An approach for constraining mantle viscosities through assimilation of palaeo sea level data into a glacial isostatic adjustment model. Nonlinear Processes in Geophysics 29, 53-75 [https://doi.org/10.5194/npg-29-53-2022 doi:10.5194/npg-29-53-2022]

Mingari, L., A. Folch, A. T. Prata, F. Pardini, G. Macedonio, and A. Costa (2022) Data assimilation of volcanic aerosol observations using FALL3D+PDAF. Atmospheric Chemistry and Physics 21, 1773-1792 [https://doi.org/10.5194/acp-22-1773-2022 10.5194/acp-22-1773-2022]

Miesner AK, S. Brune, P. Pieper, V. Koul, J. Baehr and C. Schrum (2022) Exploring the Potential of Forecasting Fish Distributions in the North East Atlantic With a Dynamic Earth System Model, Exemplified by the Suitable Spawning Habitat of Blue Whiting. Frontiers in Marine Science 8:777427. [https://doi.org/10.3389/fmars.2021.777427 doi:10.3389/fmars.2021.777427] (unfortuntely the authors missed to cite PDAF, but the used model system (Brune et al., 2015, 2020) applied PDAF)

Koul V., S. Brune, J. Baehr, C. Schrum (2022) Impact of Decadal Trends in the Surface Climate of the North Atlantic Subpolar Gyre on the Marine Environment of the Barents Sea, Frontiers in Marine Science 8:778335. [https://doi.org/10.3389/fmars.2021.778335 doi:10.3389/fmars.2021.778335] (unfortuntely the authors missed to cite PDAF, but the used model system (Brune et al., 2015, 2000) applied PDAF)

Strebel, L., H. R. Bogena, H Vereecken, H.-J. Hendricks Franssen (2022) Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: description and applications, Geoscientific Model Development 15, 395-411 [https://doi.org/10.5194/gmd-15-395-2022 doi:10.5194/gmd-15-395-2022]

Friedemann, S. (2022) Ensemble-based data assimilation for large scale simulations (Assimilation de données par ensemble pour les simulations à grandes échelles), PhD Thesis, Université Grenoble Alpes, France, [https://www.theses.fr/2022GRALM020 link]

=== 2021 ===

Farinotti D, Brinkerhoff DJ, Fürst JJ, Gantayat P, Gillet-Chaulet F, Huss M, Leclercq PW, Maurer H, Morlighem M, Pandit A, Rabatel A, Ramsankaran
RAAJ, Reerink TJ, Robo E, Rouges E, Tamre E, van Pelt WJ J, Werder MA, Azam MF, Li H and Andreassen LM. (2021) Results from the Ice Thickness
Models Intercomparison eXperiment Phase 2 (ITMIX2), Frontiers in  Earth Science 8, 571923. [https://doi.org/10.3389/feart.2020.571923 doi:10.3389/feart.2020.571923]

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

Shu, Q., F. Qiao, J. Liu, Z. Song, Z. Chen, J. Zhao, X. Yin, Y. Song. (2021) Arctic sea ice concentration and thickness data assimilation in
the FIO-ESM climate forecast system, Acta Oceanol. Sin., 40, 65–75 [https://doi.org/10.1007/s13131-021-1768-4 doi:10.1007/s13131-021-1768-4]

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 [https://doi.org/10.1017/jog.2021.57 doi:10.1017/jog.2021.57] 

von Schuckmann K. et al (2021) Copernicus Marine Service Ocean State Report, Issue 5. J. Oper. Oce. 14:sup1, 1-185 [https://doi.org/10.1080/1755876X.2021.1946240 doi:10.1080/1755876X.2021.1946240]

Bruening, T., Li, X, Schwichtenberg, F., Lorkowski, I. (2021) An operational, assimilative model system for hydrodynamic and biogeochemical applicatios for German coastal waters. Hydrographische Nachrichten, 118, 6-15 [https://doi.org/10.23784/HN118-01 doi:10.23784/HN118-01]

Stepanov, V. N., Resnyanskii, Y. D., Strukov, B. S., Zelen'ko A. A. (2021) Evaluating Effects of Observational Data Assimilation in General Ocean Circulation Model by Ensemble Kalman Filtering: Numerical Experiments with Synthetic Observations. Russian Meteorology and Hydrology, 46, 94-105 [https://doi.org/10.3103/S1068373921020047 doi:10.3103/S1068373921020047]

Klos, A., Karegar, M. A., Kusche, J., Springer, A.(2021) Quantifying Noise in Daily GPS Height Time Series: Harmonic Function Versus GRACE-Assimilating Modeling Approaches. IEEE Geoscience and Remote Sensing Letters, 18, 627-631 [https://doi.org/10.1109/LGRS.2020.2983045 doi:10.1109/LGRS.2020.2983045]

Li, Z., Wang Z., Li, Y., Zhang, Y., Zhengm J., Gao, S. (2021) Evaluation of global high-resolution reanalysis products based on the Chinese Global Oceanography Forecasting System. Atmospheric and Oceanic Science Letters, online, 100032, [https://doi.org/10.1016/j.aosl.2021.100032 doi:10.1016/j.aosl.2021.100032]

=== 2020 ===

Friedemann, S., Raffin, B.  An elastic framework for ensemble-based large-scale data assimilation. [Research Report] RR-9377, Inria Grenoble Rhône-Alpes, Université de Grenoble. 2020. hal-03017033v2 [https://hal.archives-ouvertes.fr/hal-03017033v2]

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. Q. J. Royal Metorol. Soc., 146, 4014-4029 [https://doi.org/10.1002/qj.3885 doi:10.1002/qj.3885]

Zheng, Y., Albergel, C., Munier, S., Bonan, B., Calvet, J.-C. (2020) An offline framework for high-dimensional ensemble Kalman filters to reduce the time to solution. Geoscientific Model Development. 13, 3607-3625 [http://doi.org/10.5194/gmd-13-3607-2020 doi:10.5194/gmd-13-3607-2020]

Klos, A., Karegar, M. A., Kusche, J., Springer, A. (2020) Quantifying Noise in Daily GPS Height Time Series: Harmonic Function Versus GRACE-Assimilating Modeling Approaches, IEEE Geoscience and Remote Sensing Letters, [https://doi.org/10.1109/LGRS.2020.2983045 doi:10.1109/LGRS.2020.2983045]

Yang, C.-Y., Liu, J., Xu, S. (2020) Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations, Journal of Advances in Modeling Earth Systems, 12, e2019MS001938 [https://doi.org/10.1029/2019MS001938 doi:10.1029/2019MS001938]

Pardini, F., Corradini, S., Costa, A., Esposti Ongaro, T., Merucci, L., Neri, A., Stelitano, D., de' Michieli Vitturi, M. (2020) Ensemble-based data assimilation of volcanic ash clouds from satellite observations: Application to the 24 December 2018 Mt. Etna explosive eruption. Atmosphere, 11 359 [https://doi.org/10.3390/atmos11040359 doi:10.3390/atmos11040359]

Brune, S., Baehr, J. (2020) Preserving the coupled atmosphere-ocean feedback in initializations of decadal climate predictios. Wiley Interdisciplinary Reviews - Climate Change, e637 [https://doi.org/10.1002/wcc.637 doi:10.1002/wcc.637]

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 [https://doi.org/10.1029/2019MS001937 doi:10.1029/2019MS001937]

Liang, X., Zhao, F., Li, C., Zhang, L., Li, B. (2020) Evaluation of ArcIOPS sea ice forecasting products during the ninth CHINARE-Arctic in summer 2018. Adv. Polar Science, 31, 14-25 [https://doi.org/10.13679/j.advps.2019.0019 doi:10.13679/j.advps.2019.0019]

Naz, B. S., Kollet, S., Hendricks-Franssen, H.-J., Montzka,C. Kurtz, W. (2020) A 3 km spatially and temporally consustent European daily soil moisture reanalysis from 2000 to 2015. Scientific Data 7, 111 [https://doi.org/10.1038/s41597-020-0450-6 doi:10.1038/s41597-020-0450-6]

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. Journal of Geophysical Research Oceans, 125, e2019JC015586 [https://doi.org/10.1029/2019JC015586 doi:10.1029/2019JC015586]

Sanchez, S., Wicht, J., Bärenzung, J. (2020) Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations. Earth, Planets and Space, Vol. 72, 157 [https://doi.org/10.1186/s40623-020-01279-y]

Gillet-Chaulet, F. (2020) Assimilation of surface observations in a transient marine ice sheet model using an ensemble Kalman filter. The Cryosphere, 14, 811-832 [https://doi.org/10.5194/tc-14-811-2020 doi:10.5194/tc-14-811-2020]

=== 2019 ===

Gebler, S., Kurtz, W., Pauwels, V. R. N., Kollet, S. J., Vereecken, H., Hendricks-Franssen, H.-J. (2019) Assimilation of high-resolution soil moisutre data into an integrated terrestrial model for a small-scale head-water catchment. Water Resources Research, 55, 10358-10385 [https://doi.org/10.1029/2018WR024658 doi:10.1029/2018WR024658]

Min, C., Mu, L., Yang, Q., Ricker, R., Shi, Q., Han, B., Wu, R., and Liu, J. (2019) Sea ice export through the Fram Strait derived from a combined model and satellite data set. The Cryosphere. 13, 3209–3224. [https://doi.org/10.5194/tc-13-3209-2019 doi:10.5194/tc-13-3209-2019]

Springer, A., Karegar, M. A., Kusche, J., Keune, J., Kurtz, W., Kollet, S. (2019) Evidence of daily hydrological loading in GPS time series over Europe. Journal of Geodesy. 93, 2145-2153, [https://doi.org/10.1007/s00190-019-01295-1 doi:10.1007/s00190-019-01295-1]

Mu L., Liang X., Yang Q., Liu J., Zheng F. (2019). Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong’s first trans-Arctic Passage in summer 2017. Journal of Glaciology 1–9. [https://doi.org/10.1017/jog.2019.55 doi:10.1017/jog.2019.55]

Sanchez, S., Wicht, J., Bärenzung, J., Holschneider, M. (2019) Sequential assimilation of geomagnetic observations: perspectives for the reconstruction and prediction of core dynamics. Geophysical Journal Internations 217, 1434–1450. [https://doi.org/10.1093/gji/ggz090 doi:10.1093/gji/ggz090]

Naz, B. S., Kurtz, W., Montzka, C., Sharples, W., Goergen, K., Keune, J., Gao, H., Springer, A., Hendricks Franssen, H.-J., Kollet, S. (2019). Improving soil moisture and runoff simulations at 3\,km over Europe using land surface data assimilation. Hydrology and Earth System Sciences. 23 , 277-301. [https://doi.org/10.5194/hess-23-277-2019 doi:10.5194/hess-23-277-2019]

Polkova, I., Brune, S., Kadow, C., Romanova, V., Gollan, G., Baehr, J., Glowienka-Hense, R., Greatbatchm R. J., Hense, A., Koehl, A., Kroeger, J., Mueller, W. A., Pankatz, K., Stammer, D.(2019) Initialization and ensemble generation for decadal climate predictions: a comparison of different methods. JAMES 11, 149-172. [https://doi.org/10.1029/2018ms001439 doi:10.1029/2018ms001439]

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, [http://doi.org/10.1007/s10236-019-01299-7 doi:10.1007/s10236-019-01299-7]

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, [https://doi.org/10.1029/2019JC015073 doi:10.1029/2019JC015073]

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, [https://doi.org/10.1007/s00190-018-1151-1 doi:10.1007/s00190-018-1151-1]

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, [https://doi.org/10.1029/2018JC014329 doi:10.1029/2018JC014329]

Springer, A. (2019) A water storage reanalysis over the European continent: assimilation of GRACE data into a high-resolution hydrological model and validation, PhD Thesis, University of Bonn, Germany [https://nbn-resolving.org/urn:nbn:de:hbz:5n-53930 link]

Kirchgessner, P. (2019) An investigation of linear and nonlinear data assimilation methods in the presence of model error, PhD Thesis, University of Bremen, Germany [https://suche.suub.uni-bremen.de/peid=B172795531&LAN=EN&CID=&index=L&Hitnr=27&dtyp=h&rtyp=a&Exemplar=1 link]

=== 2018 ===

Tuomi, Laura; Jun She et al. (2018): Overview of CMEMS BAL MFC Service and Developments. Proceedings of the Eigth EuroGOOS International Conference, 3–5 October 2017, Bergen, pp. 261–268

Janjic, T., N. Bormann, M. Bocquet, J. A. Carton, S. E. Cohn, S. L. Dance, S. N. Losa, N. K. Nichols, R. Potthast, J. A. Waller, P. Weston (2018) On the representation error in data assimilation. Q. J. Royal Metorol. Soc., 144, 1257-1278 [https://doi.org/10.1002/qj.3130 doi:10.1002/qj.3130] (PDAF is not cited, but the experiments for the Baltic sea are performed using PDAF)

Shrestha, P., Kurtz, W., Vogel, G., Schulz, J.-P., Sulis, M., Hendricks Franssen, H.-J., Kollet, S., Simmern, C. (2018). Connection between root
zone soil moisture and surface energy flux partitioning using modeling, observations, and data assimilation for a temperate grassland site in Germany.
Journal of Geophysical Research: Biogeosciences, 123 (2018) 2839-2862 [https://doi.org/10.1029/2016JG003753 doi:10.1029/2016JG003753]

Liu, Y, W. Fu (2018). Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea. Ocean Science 14(2018) 525-541 [https://doi.org/10.5194/os-14-525-2018 doi:10.5194/os-14-525-2018] (The paper doesn't mention PDAF, but the author's confirmed that PDAF with offline-coupling to NEMO was used)

Bocher, M., A. Fournier, N. Coltice (2018). Ensemble Kalman filter for the reconstruction of the Earth's mantle circulation. Nonlin. Proc. Geophys., 25 (2018) 99-123 [https://doi.org/10.5194/npg-25-99-2018 doi:10.5194/npg-25-99-2018]

Zhang, H., W. Kurtz, S. Kollet, H. Vereecken, H.-J. Hendricks Franssen (2018). Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model. Advances in Water Resources, 111 (2018) 224-238 [http://doi.org/10.1016/j.advwatres.2017.11.003]

Brune, S., Düsterhus, A., Pohlmann, H., Müller, W. A., Baehr, J. (2018). Time dependency of the prediction skill for the North Atlantic subpolar gyre in initialized decadal hindcasts. Climate Dynamics, 51 (2018) 1947–1970 [https://doi.org/10.1007/s00382-017-3991-4]

Sharples, W., I. Zhukov, M. Geimer, K. Goergen, S. Luehrs, T. Breuer, B. Naz, K. Kulkarni, S. Brdar, S. Kollet (2018). A run control framework to streamline profiling, porting, andtuning simulation runs and provenance trackingof geoscientific applications. Geosci. Model Dev. 11 (2018) 2875 [https://doi.org/10.5194/gmd-11-2875-2018]

Bogena, H. R., C. Montzka, J.A. Huisman, A. Graf, M. Schmidt, M. Stockinger, C. von Hebel, H.J. Hendricks-Franssen, J. van der Kruk, W. Tappe, A. Lücke, R. Baatz, R. Bol, J. Groha, T. Pütz, J. Jakobi, R. Kunkel, J. Sorg and H. Vereecken. (2018). The TERENO-Rur Hydrological Observatory: A Multiscale Multi-Compartment Research Platform for the Advancement of Hydrological Science. Vadose Zone Journal, 17, UNSP 180055 [https://doi.org/10.2136/vzj2018.03.0055]

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, 7763-7780 [https://doi.org/10.1029/2018JC014316 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 [https://doi.org/10.1080/16000870.2018.1445364 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 [https://doi.org/10.1002/qj.3225 doi:10.1002/qj.3225]

=== 2017 ===

Zhang, Y., Hou, J., Gu, J., Huang, C., Li, X. (2017). SWAT-based hydrological data assimilation system (SWAT-HDAS): Description and case application to river basin-scale hydrological predictions. Journal of Advances in Modeling Earth Systems, 9 (2017) 2863–2882 [https://doi.org/10.1002/2017MS001144]

Chen, Z., J. Liu, M. Song, Q. Yang, S. Xu (2017). Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System. J. Climate, 30 (2017) 8429-8446 [https://doi.org/10.1175/JCLI-D-17-0093.1]

Irrgang, C., J. Saynisch, M. Thomas (2017). Utilizing oceanic electromagnetic induction to constrain an ocean general circulation model: A data assimilation twin experiment. Journal of Advances in Modeling Earth Systems, 9 (2017) 1703-1720 [https://doi.org/10.1002/2017MS000951]

Baatz, D., W. Kurtz, H.J. Hendricks Franssen, H. Vereecken, S.J. Kollet (2017). Catchment tomography - An approach for spatial parameter estimation. Advances in Water Resources 107 (2017) 147–159 [https://doi.org/10.1016/j.advwatres.2017.06.006]

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 [https://doi.org/10.1175/JTECH-D-16-0166.1 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, 2017 [http://dx.doi.org/10.1080/16000870.2017.1327766 doi:10.1080/16000870.2017.1327766]

Gebler, S. (2017) Inverse conditioning of a high resolution integrated terrestrial model at the hillslope scale: the role of input data quality and model structural errors, PhD Thesis, RWTH Aachen, Germany [http://nbn-resolving.org/resolver?verb=redirect&identifier=urn:nbn:de:0001-2019020503 link]

Irrang, C. (2107) Characterization of oceanic signatures in the Earth’s magnetic field in view of their applicability as ocean model constraints, PhD Thesis, Free University of Berlin, Germany [http://nbn-resolving.de/urn:nbn:de:kobv:188-fudissthesis000000104991-1 link]

=== 2016 ===

Kurtz, W., G. He, S. J. Kollet, R. M. Maxwell, H. Vereecken, H.-J. Hendrics Franssen (2016). TerrSysMP–PDAF (version 1.0): a modular high-performance data
assimilation framework for an integrated land surface–subsurface model. Geoscientific Model Development, 9, 1341-1360, 2016 [https://doi.org/10.5194/gmd-9-1341-2016]

Korthe, S., J. Tödter, B. Ahrens. (2016) Strategies for soil initialization of regional decadal climate predictions. Meteorologisch Zeitschrift, 25 (2016) 775-794 [https://doi.org/10.1127/metz/2016/0729]

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., 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, 2016 [http://dx.doi.org/10.5194/tc-10-761-2016 doi:10.5194/tc-10-761-2016])

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 [http://dx.doi.org/10.1175/JTECH-D-15-0176.1 doi:10.1175/JTECH-D-15-0176.1]

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 [http://dx.doi.org/10.1175/MWR-D-15-0073.1 doi:10.1175/MWR-D-15-0073.1]

Bocher, M. (2106) Reconstitution de la convection du manteau terrestre par assimilation de données séquentielle (Reconstruction of Mantle Circulation Using Sequential Data Assimilation), PhD Thesis,  l’École Normale Supérieure de Lyon, France [https://www.theses.fr/2016LYSEN045 link]

=== 2015 ===

Saynisch, J., I. Bergmann-Wolf, M. Thomas (2015). Assimilating of GRACE-derived oceanic mass distributations with a global ocean circulation model. Journal of Geodesy, 89, 121-139 [https://doi.org/10.1007/s00190-014-0766-0]

Brune, S., Nerger, L., Baehr, J. (2015) Assimilation of oceanic observations in a global coupled Earth system
model with the SEIK filter Ocean Modelling. Ocean Modelling, 96, 254-264 [http://dx.doi.org/10.1016/j.ocemod.2015.09.011 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 [http://dx.doi.org/10.1002/qj.2523 doi:10.1002/qj.2523]

Nerger, L. (2015) On serial observation processing in localized ensemble Kalman filters. Monthly Weather Review, 143, 1554-1567 [http://dx.doi.org/10.1175/MWR-D-14-00182.1 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), 39-44, [http://dx.doi.org/10.3189/2015AoG69A740 doi:10.3189/2015AoG69A740]

Yang, Q. (2015) Assimilating Arctic sea ice observations into a coupled ice-ocean model with a local SEIK filter and different uncertainty estimates, PhD Thesis, University of Bremen, Germany [http://nbn-resolving.de/urn:nbn:de:gbv:46-00104541-11 link]

Toedter, J. (2015) Derivation and characterization of a new filter for nonlinear high-dimensional data assimilation, PhD Thesis, Goethe University Frankfurt am Main, Germany [https://ubffm.hds.hebis.de/Record/HEB361102682 link]


=== 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, [http://dx.doi.org/10.1175/MWR-D-13-00246.1 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, 129, 259-270  [http://dx.doi.org/10.1016/j.jmarsys.2013.06.011 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, [http://dx.doi.org/10.1002/qj.2293 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. Journal of Geophysical Research-Oceans, 119, 6680-6692, [http://dx.doi.org/10.1002/2014JC009963 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 [http://dx.doi.org/10.1002/ggge.20252 doi:10.1002/ggge.20252]

=== 2012 ===

Janjić, T., Schröter, J., Savcenko, R., Bosch, W., Albertella, A., Rummel, R., Klatt, O. (2012). Impact of combining GRACE and GOCE gravity data on ocean circulation estimates. Ocean Science, 8, 65-79 [http://dx.doi.org/doi:10.5194/os-8-65-2012 doi:10.5194/os-8-65-2012].

Janjić, T., Schröter, J., Albertella, A., Bosch, W., Rummel, R., Savcenko, R., Schwabe, J., Scheinert, M. (2012). Assimilation of geodetic dynamic ocean topography using ensemble based Kalman filter. Journal of Geodynamics, 59-60, pp. 92-98 [http://dx.doi.org/doi:10.1016/j.jog.2011.07.001  doi:10.1016/j.jog.2011.07.001].

Saynisch, J., Thomas, M. (2012). Ensemble Kalman‐Filtering of Earth rotation observations with aglobal ocean model. Journal of Geodynamics, 62, 24‐29 [http://dx.doi.org/10.1016/j.jog.2011.10.003 doi:10.1016/j.jog.2011.10.003]

Nerger, L., Janjić, T., Schröter, J., Hiller, W. (2012). A unification of ensemble square root Kalman filters. Monthly Weather Review, 140, 2335-2345. [http://dx.doi.org/doi:10.1175/MWR-D-11-00102.1 doi:10.1175/MWR-D-11-00102.1]

Nerger, L., Janjić, T., Schröter, J., Hiller, W. (2012). A regulated localization scheme for ensemble-based Kalman filters. Quarterly Journal of the Royal Meteorological Society, 138, 802-812. [http://dx.doi.org/doi:10.1002/qj.945 doi:10.1002/qj.945].

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. [http://dx.doi.org/10.1016/j.jmarsys.2012.07.008 doi:10.1016/j.jmarsys.2012.07.008]

=== 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 [http://dx.doi.org/10.1175/2011MWR3552.1 doi:10.1175/2011MWR3552.1].

=== 2009 ===

Rollenhagen, K., Timmermann, R., Janjic, T., Schröter, J., Danilov, S.(2009). Assimilation of sea ice motion in a Finite Element Sea Ice Model, Journal of Geophysical Research, 114, C05007, [http://dx.doi.org/10.1029/2008JC005067 doi:10.1029/2008JC005067].
 
=== 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 (2008) 87-102, [http://dx.doi.org/10.1016/j.jmarsys.2007.09.007 doi:10.1016/j.jmarsys.2007.09.007].

Skachko, S., Danilov, S., Janjic, T., Schröter, J., Sidorenko, D., Savcenko, R., Bosch, W.(2008). Sequential assimilation of multi-mission dynamical topography into a global finite-element ocean model, Ocean Science, 4, 307-318, [http://dx.doi.org/10.5194/os-4-307-2008 doi:10.5194/os-4-307-2008].

Rollenhagen, K. (2008) Data Assimilation in a Regional Finite Element Sea-Ice Model for the Arctic - Application of the Singular Evolutive Interpolated Kalman Filter, PhD Thesis, University of Bremen, Germany [http://nbn-resolving.de/urn:nbn:de:gbv:46-diss000110398 link]
 
 
=== 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, [http://dx.doi.org/10.1016/j.jmarsys.2006.11.009 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(1/4), 288-298., [http://dx.doi.org/10.1016/j.jmarsys.2005.06.009 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(5/6), 634-649., [http://dx.doi.org/10.1007/s10236-006-0083-0 doi:10.1007/s10236-006-0083-0].
 
=== 2005 ===
Nerger, L., Hiller, W., Schröter, J.(2005). A Comparison of Error Subspace Kalman Filters, Tellus series A-Dynamic Meteorology and Oceanography, 57A(5), 715-735, [http://dx.doi.org/10.1111/j.1600-0870.2005.00141.x doi:10.1111/j.1600-0870.2005.00141.x].
 
=== 2004 === 
Nerger, L.(2004). Parallel Filter Algorithms for Data Assimilation in Oceanography, PhD Thesis, University of Bremen, 2004 ([http://www.awi.de/en/infrastructure/library/awi_periodicals/reports_on_polar_and_marine_research/ Reports on Polar and Marine Research], 487, 174 pp.)