Publications and Presentations

Presentations

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

​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., 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.

Publications involving PDAF - by the PDAF developers

Accepted

Mu, L., Yang, Q., Losch, M., Losa, S.N., Ricker, R., Nerger, L., Liang, X. (2017) Improving sea ice thickness estimates by assimilating CryoSat-2 and SMOS sea ice thickness data simultaneously. Quarterly Journal of the Royal Meteorological Society. accepted ​doi:10.1002/qj.3225

2017

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

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 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

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. 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

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, 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

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

2012

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

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 (2008) 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(1/4), 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(5/6), 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 series A-Dynamic Meteorology and Oceanography, 57A(5), 715-735, ​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 (​Reports on Polar and Marine Research, 487, 174 pp.)

Publications by PDAF users

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

2018

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

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

2017

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

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 ​http://dx.doi.org/10.5194/gmd-9-1341-2016

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 ​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

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, ​doi:10.1029/2008JC005067.

2008

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.