Articles | Volume 17, issue 10
https://doi.org/10.5194/gmd-17-4433-2024
https://doi.org/10.5194/gmd-17-4433-2024
Development and technical paper
 | 
29 May 2024
Development and technical paper |  | 29 May 2024

WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework

Changliang Shao and Lars Nerger

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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 
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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.
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