Articles | Volume 17, issue 10
https://doi.org/10.5194/gmd-17-4433-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-4433-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
China Meteorological Administration Meteorological Observation Centre, Beijing, 100081, China
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), 27570 Bremerhaven, Germany
Lars Nerger
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), 27570 Bremerhaven, Germany
Related authors
No articles found.
Frauke Bunsen, Judith Hauck, Sinhué Torres-Valdés, and Lars Nerger
Ocean Sci., 21, 437–471, https://doi.org/10.5194/os-21-437-2025, https://doi.org/10.5194/os-21-437-2025, 2025
Short summary
Short summary
Computer models are often used to estimate the ocean's CO2 uptake due to a lack of direct observations. Because such idealized models do not match precisely with the real world, we combine real-world observations of ocean temperature and salinity with a model and study the effect on the modeled air–sea CO2 flux (2010–2020). The corrections of temperature and salinity have their largest effect on regional CO2 fluxes in the Southern Ocean in winter and a small effect on the global CO2 uptake.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Short summary
To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Yumeng Chen, Lars Nerger, and Amos S. Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-1078, https://doi.org/10.5194/egusphere-2024-1078, 2024
Short summary
Short summary
In this paper, we present pyPDAF, a Python interface to the parallel data assimilation framework (PDAF) allowing for coupling with Python-based models. We demonstrate the capability and efficiency of pyPDAF under a coupled data assimilation setup.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
Short summary
Short summary
We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, https://doi.org/10.5194/tc-17-2509-2023, 2023
Short summary
Short summary
Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study uses ensemble-based data assimilation in a stand-alone sea ice model to investigate the impacts of assimilating three different kinds of sea ice observation, including the novel assimilation of sea ice thickness distribution. We show that assimilating ice thickness distribution has a positive impact on thickness and volume estimates within the ice pack, especially for very thick ice.
Hao-Cheng Yu, Yinglong Joseph Zhang, Lars Nerger, Carsten Lemmen, Jason C. S. Yu, Tzu-Yin Chou, Chi-Hao Chu, and Chuen-Teyr Terng
EGUsphere, https://doi.org/10.5194/egusphere-2022-114, https://doi.org/10.5194/egusphere-2022-114, 2022
Preprint archived
Short summary
Short summary
We develop a new data assimilative approach by combining two parallel frameworks: PDAF and ESMF. This allows maximum flexibility and easy implementation of data assimilation for fully coupled earth system model applications. It is also validated by using a simple benchmark and applied to a realistic case simulation around Taiwan. The real case test shows significant improvement for temperature, velocity and surface elevation before, during and after typhoon events.
Cited articles
Anderson, J. L., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn R., and Arellano, A.: The Data Assimilation Research Testbed: A Community Facility, B. Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009.
Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017.
Barker, D., Huang, X.-Y., Liu, Z., Auligné, T., Zhang, X., Rugg, S., Ajjaji, R., Bourgeois, A., Bray, J., Chen, Y., Demirtas, M., Guo, Y.-R., Henderson, T., Huang, W., Lin, H.-C., Michalakes, J., Rizvi, S., and Zhang, X.: The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA, B. Am. Meteorol. Soc., 93, 831–843, https://doi.org/10.1175/BAMS-D-11-00167.1, 2012.
Brusdal, K., Brankart, J. M., Halberstadt, G., Evensen, G., Brasseur, P., van Leeuwen, P. J., Dombrowsky, E., and Verron, J.: A demonstration of ensemble-based assimilation methods with a layered ogcm from the perspective of operational ocean forecasting system, J. Marine Syst., 40–41, 253–289, https://doi.org/10.1016/S0924-7963(03)00021-6, 2003.
Chandra, R., Dagum, L., Kohr, D., Menon, R., Maydan, D., and McDonald, J.: Parallel programming in OpenMP, Morgan Kaufmann, ISBN 9781558606718, 2001
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis-error statistics in observation space, Q. J. Roy. Meteor. Soc., 131, 3385–3396, https://doi.org/10.1256/qj.05.108, 2005.
Feng, C. and Pu, Z.: The impacts of assimilating Aeolus horizontal line-of-sight winds on numerical predictions of Hurricane Ida (2021) and a mesoscale convective system over the Atlantic Ocean, Atmos. Meas. Tech., 16, 2691–2708, https://doi.org/10.5194/amt-16-2691-2023, 2023.
Gaspari, G. and Cohn, S. E.: Construction of Correlation Functions in Two and Three Dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, https://doi.org/10.1002/qj.49712555417, 1999.
Goodliff, M., Bruening, T., Schwichtenberg, F., Li, X., Lindenthal, A., Lorkowski, I., and Nerger, L.: Temperature assimilation into a coastal ocean-biogeochemical model: Assessment of weakly- and strongly-coupled data assimilation, Ocean Dynam., 69, 1217–1237, https://doi.org/10.1007/s10236-019-01299-7, 2019.
Greybush, S. J., Kalnay, E., Miyoshi, T., Ide, K., and Hunt, B. R.: Balance and Ensemble Kalman Filter Localization Techniques, Mon. Weather Rev., 139, 511–522, https://doi.org/10.1175/2010MWR3328.1, 2011.
Gropp, W., Lusk, E., and Skjellum, A.: Using MPI: Portable Parallel Programming with the Message-Passing Interface, The MIT Press, Cambridge, Massachusetts, ISBN 9780262571043, 1994.
Holbach, H. M., Bousquet, O., Bucci, L., Chang, P., Cione, J., Ditchek, S., Doyle, J., Duvel, J-P., Elston, J., Goni, G., Hon, K. K., Ito, K., Jelenak, Z., Lei, X., Lumpkin, R., McMahon, C. R., Reason, C., Sanabia, E., Shay, L. K., Sippel, J. A., Sushko, A. Tang, J., Tsuboki, K., Yamada, H., Zawislak, J., and Zhang, J. A.: Recent Advancements in Aircraft and In Situ Observations of Tropical Cyclones, Tropical Cyclone Research and Review, 12, 81–99, https://doi.org/10.1016/j.tcrr.2023.06.001, 2023.
Huang, B., Wang, X., Kleist, D. T., and Lei, T. A.: Simultaneous Multiscale Data Assimilation Using Scale-Dependent Localization in GSI-Based Hybrid 4DEnVar for NCEP FV3-Based GFS, Mon. Weather Rev., 2, 149, https://doi.org/10.1175/MWR-D-20-0166.1, 2021.
Hunt, B. R., Kostelich, E. J. and Szunyogh, I.: Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter, Physica D, 230, 112–126, https://doi.org/10.1016/j.physd.2006.11.008, 2007.
Karspeck, A. R., Danabasoglu, G., Anderson, J., Karol, S., Karol, S., Collins, N., Vertenstein, M., Raeder, K., Hoar, T., Neale, R., Edwards, J., and Craig, A.: A global coupled ensemble data assimilation system using the community earth system model and the data assimilation research testbed, Q. J. Roy. Meteor. Soc., 144, 2304–2430, https://doi.org/10.1002/qj.3308, 2018.
Kleist, D. T., Parrish, D. F., Derber, J. C., Treadon, R., Wu, W.-S., and Lord, S.: Introduction of the GSI into the NCEP Global Data Assimilation System, Mon. Weather Rev., 24, 1691–1705, https://doi.org/10.1175/2009WAF2222201.1, 2009.
Kurzrock, F., Nguyen, H., Sauer, J., Chane Ming, F., Cros, S., Smith Jr., W. L., Minnis, P., Palikonda, R., Jones, T. A., Lallemand, C., Linguet, L., and Lajoie, G.: Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment, Geosci. Model Dev., 12, 3939–3954, https://doi.org/10.5194/gmd-12-3939-2019, 2019.
Lawrence, B. N., Rezny, M., Budich, R., Bauer, P., Behrens, J., Carter, M., Deconinck, W., Ford, R., Maynard, C., Mullerworth, S., Osuna, C., Porter, A., Serradell, K., Valcke, S., Wedi, N., and Wilson, S.: Crossing the chasm: how to develop weather and climate models for next generation computers?, Geosci. Model Dev., 11, 1799–1821, https://doi.org/10.5194/gmd-11-1799-2018, 2018.
Li, L., Žagar, N., Raeder, K., and Anderson, J. L.: Comparison of temperature and wind observations in the Tropics in a perfect-model, global EnKF data assimilation system, Q. J. Roy. Meteor. Soc., 149, 1–19, https://doi.org/10.1002/qj.4511, 2023.
Li, Y., Cong, Z., and Yang, D.: Remotely Sensed Soil Moisture Assimilation in the Distributed Hydrological Model Based on the Error Subspace Transform Kalman Filter, Remote Sens., 15, 1852, https://doi.org/10.3390/rs15071852, 2023.
Liu, Y. A., Sun, Z., Chen, M., et al.: Assimilation of atmospheric infrared sounder radiances with WRF-GSI for improving typhoon forecast, Front. Earth Sci., 12, 457–467, https://doi.org/10.1007/s11707-018-0728-6, 2018.
Liu, Z., Ban, J., Hong, J.-S., and Kuo, Y.-H.: Multi-resolution incremental 4D-Var for WRF: Implementation and application at convective scale, Q. J. Roy. Meteor. Soc., 146, 3661–3674, https://doi.org/10.1002/qj.3865, 2020.
Lorenc, A. C.: Analysis methods for numerical weather prediction, Q. J. Roy. Meteor. Soc., 112, 473, 1177–1194, https://doi.org/10.1002/qj.49711247414, 1986.
Mingari, L., Folch, A., Prata, A. T., Pardini, F., Macedonio, G., and Costa, A.: Data assimilation of volcanic aerosol observations using FALL3D+PDAF, Atmos. Chem. Phys., 22, 1773–1792, https://doi.org/10.5194/acp-22-1773-2022, 2022.
Mu, L., Nerger, L., Streffingl, J., Tang, Q., Niraulal, B., Zampieri, L., Loza, S. N., and Goessling, H. F.: Sea-ice forecasts with an upgraded AWI Coupled Prediction System, J. Adv. Model. Earth Sy., 14, e2021MS002631, https://doi.org/10.1029/2022MS003176, 2023.
Nerger, L.: Data assimilation for nonlinear systems with a hybrid nonlinear Kalman ensemble transform filter, Q. J. Roy. Meteor. Soc., 148, 620–640, https://doi.org/10.1002/qj.4221, 2022.
Nerger, L. and Hiller, W.: Software for Ensemble-based Data Assimilation Systems-Implementation Strategies and Scalability, Comput. Geosci., 55, 110–118, https://doi.org/10.1016/j.cageo.2012.03.026, 2013.
Nerger, L., Danilov, S., Hiller, W., and Schröter, J.: Using sea-level data to constrain a finite-element primitive-equation ocean model with a local SEIK filter, Ocean Dynam., 56, 634–649, https://doi.org/10.1007/s10236-006-0083-0, 2006.
Nerger, L., Janjic, T., Schroeter, J., and Hiller, W.: A unification of ensemble square root filters, Mon. Weather Rev., 140, 2335–2345, https://doi.org/10.1175/MWR-D-11-00102.1, 2012.
Nerger, L., Tang, Q., and Mu, L.: Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0), Geosci. Model Dev., 13, 4305–4321, https://doi.org/10.5194/gmd-13-4305-2020, 2020.
OpenMP: OpenMP Application Program Interface Version 3.0, http://www.openmp.org/ (last access: 26 June 2023), 2008.
Pena, I. I.: Improving Satellite-Based Convective Storm Observations: An Operational Policy Based on Static Historical Data, Doctoral dissertation, Stevens Institute of Technology, ISBN 9798379567484, 2023.
Pham, D. T., Verron, J., and Roubaud, M. C.: A singular evolutive extended Kalman filter for data assimilation in oceanography, J. Marine Syst., 16, 323–340, https://doi.org/10.1016/S0924-7963(97)00109-7, 1998.
Raju, A., Parekh, A., Chowdary, J. S., and Gnanaseelan, C.: Impact of satellite-retrieved atmospheric temperature profiles assimilation on Asian summer monsoon 2010 simulation, Theor. Appl. Climatol., 116, 317–326, https://doi.org/10.1007/s00704-013-0956-3, 2014.
Rakesh, V., Singh, R., and Joshi, P. C.: Intercomparison of the performance of MM5/WRF with and without satellite data assimilation in short-range forecast applications over the Indian region, Meteorol. Atmos. Phys., 105, 133–155, https://doi.org/10.1007/s00703-009-0038-3, 2009.
Risanto, C. B., Castro, C. L., Arellano, A. F., Moker, J. M., and Adams, D. K.: The Impact of Assimilating GPS Precipitable Water Vapor in Convective-Permitting WRF-ARW on North American Monsoon Precipitation Forecasts over Northwest Mexico, Mon. Weather Rev., 149, 3013–3035, https://doi.org/10.1175/MWR-D-20-0394.1, 2021.
Shao, C.: shchl1/WRF-PDAF-development: v1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.8367112, 2023a.
Shao, C.: shchl1/GMD-DATA: Data for GMD-WRF-PDAF_V1.0 (v1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.10083810, 2023b.
Shao, C. and Nerger, L.: The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case, Remote Sens., 16, 430, https://doi.org/10.3390/rs16020430, 2024.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z. Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D., and Huang, X.: A Description of the Advanced Research WRF Model Version 4.3 (No. NCAR/TN-556+STR), https://doi.org/10.5065/1dfh-6p97, 2021.
Song, L. Shen, F., Shao, C., Shu, A., and Zhu, L.: Impacts of 3DEnVar-Based FY-3D MWHS-2 Radiance Assimilation on Numerical Simulations of Landfalling Typhoon Ampil (2018), Remote Sens., 14, 6037, https://doi.org/10.3390/rs14236037, 2022.
Sun, J., Jiang, Y., Zhang, S., Zhang, W., Lu, L., Liu, G., Chen, Y., Xing, X., Lin, X., and Wu, L.: An online ensemble coupled data assimilation capability for the Community Earth System Model: system design and evaluation, Geosci. Model Dev., 15, 4805–4830, https://doi.org/10.5194/gmd-15-4805-2022, 2022.
Tödter, J. and Ahrens, B.: A second-order exact ensemble square root filter for nonlinear data assimilation, Mon. Weather Rev., 143, 1347–1467, https://doi.org/10.1175/MWR-D-14-00108.1, 2015.
Wang, Q., Danilov, S., and Schröter, J.: Finite element ocean circulation model based on triangular prismatic elements with application in studying the effect of topography representation, J. Geophys. Res., 113, C05015, https://doi.org/10.1029/2007JC004482, 2008.
Wang, S. and Qiao, X.: A local data assimilation method (Local DA v1.0) and its application in a simulated typhoon case, Geosci. Model Dev., 15, 8869–8897, https://doi.org/10.5194/gmd-15-8869-2022, 2022.
Xue, M., Droegemeier, K., and Wong, V.: The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification, Meteorol. Atmos. Phys., 75, 161–193, https://doi.org/10.1007/s007030070003, 2000.
Yang, Y., Wang, Y., and Zhu, K.: Assimilation of Chinese Doppler Radar and Lightning Data Using WRF-GSI: A Case Study of Mesoscale Convective System, Adv. Meteorol., 2015, 763919, https://doi.org/10.1155/2015/763919, 2015.
Zhang, S., Harrison, M. J., Rosati, A., and Wittenberg, A.: System Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies, Mon. Weather Rev., 135, 3541–3564, https://doi.org/10.1175/MWR3466.1, 2007.
Zheng, Y., Albergel, C., Munier, S., Bonan, B., and Calvet, J.-C.: An offline framework for high-dimensional ensemble Kalman filters to reduce the time to solution, Geosci. Model Dev., 13, 3607–3625, https://doi.org/10.5194/gmd-13-3607-2020, 2020.
Zupanski, D., Zhang, S. Q., Zupanski, M., Hou, A. Y., and Cheung, S. H.: A Prototype WRF-Based Ensemble Data Assimilation System for Dynamically Downscaling Satellite Precipitation Observations, J. Hydrometeorol., 12, 118–134, https://doi.org/10.1175/2010JHM1271.1, 2011.
Short summary
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation...