Publications and Presentations


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

Data Assimilation in the Geosciences - Practical Data Assimilation with the Parallel Data Assimilation Framework,
Short Course at the EGU General Assembly 2019, Vienna, Austria, April 8-12, 2019.

Ensemble Data Assimilation with the Parallel Data Assimilation Framework,
Tutorial Session at Ocean Sciences Meeting 2018, Portland, OR, USA, February 12-16, 2018.

An introduction to Ensemble Data Assimilation,
Tutorial Session at Ocean Sciences Meeting 2016, New Orleans, USA, February 21-26, 2016.

Building Ensemble-Based Data Assimilation Systems for High-Dimensional Models,
47th International Liege Colloquium, Liege, Belgium, May 4-8, 2015.

The Parallel Data Assimilation Framework PDAF for scalable sequential data assimilation,
Workshop on Programming Environments for Data Assimilation: Software and Applications, Deltares, Delft, Netherlands, January 31, 2011.

Scalable sequential data assimilation with the Parallel Data Assimilation Framework PDAF,
2010 Ocean Sciences Meeting, Portland, Oregon, February 20-26, 2010.

Sequential data assimilation on high-performance computers with the Parallel Data Assimilation Framework,
13th ECMWF Workshop on High Performance Computing in Meteorology, Reading, UK, November 3-7, 2008.

PDAF - The Parallel Data Assimilation Framework: Experiences with Kalman Filtering,
11th ECMWF Workshop on Use of High Performance Computing in Meteorology, Reading, UK, October 25-29, 2004.

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, 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. 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. doi:10.1142/9789812701831_0006 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)


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, 10.1016/j.aosl.2021.100032


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 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 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, 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 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 doi:10.3390/atmos11040359

Brune, S., Baehr, J. (2020) Preserving the coupled atmosphere-ocean feedback in initializations of decadal climate predictios. Wilet Interdisciplinary Reviews - Climate Change, e637 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 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 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 doi:10.1038/s41597-020-0450-6 Brune, S., Baehr, J. (2020) Preserving th ecoupled atmosphere-ocean feedback in initialization of decadal climate predictions. WIREs Clim Change 2020:e637 doi:10.1002/wcc.637

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

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


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 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. 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, 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. 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. 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. 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 dacadal climate predictions: a comparison of different methods. JAMES 11, 149-172. 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, 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, 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, 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, doi:10.1029/2018JC014329


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

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 (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

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

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

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

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

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 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


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

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

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

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

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, 2017 doi:10.1080/16000870.2017.1327766


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

Korthe, S., J. Tödter, B. Ahrens. (2016) Strategies for soil initialization of regional decadal climate predictions. Meteorologisch Zeitschrift, 25 (2016) 775-794

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 Confer- ence. 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 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 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 doi:10.1175/MWR-D-15-0073.1


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

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 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), 39-44, doi:10.3189/2015AoG69A740


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, 129, 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. Journal of Geophysical Research-Oceans, 119, 6680-6692, doi:10.1002/2014JC009963


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


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 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 doi:10.5194/os-8-65-2012.

Saynisch, J., Thomas, M. (2012). Ensemble Kalman‐Filtering of Earth rotation observations with aglobal ocean model. Journal of Geodynamics, 62, 24‐29 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. 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. 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. doi:10.1016/j.jmarsys.2012.07.008


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.


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, doi:10.1029/2008JC005067.


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, 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, doi:10.5194/os-4-307-2008.


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(1/4), 288-298., doi:10.1016/j.jmarsys.2005.06.009.


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., doi:10.1007/s10236-006-0083-0.


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, doi:10.1111/j.1600-0870.2005.00141.x.


Nerger, L.(2004). Parallel Filter Algorithms for Data Assimilation in Oceanography, PhD Thesis, University of Bremen, 2004 (Reports on Polar and Marine Research, 487, 174 pp.)

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