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NPG | Articles | Volume 25, issue 1
Nonlin. Processes Geophys., 25, 129–143, 2018
https://doi.org/10.5194/npg-25-129-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Nonlin. Processes Geophys., 25, 129–143, 2018
https://doi.org/10.5194/npg-25-129-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 01 Mar 2018

Research article | 01 Mar 2018

Accelerating assimilation development for new observing systems using EFSO

Guo-Yuan Lien et al.

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Cited articles

Bauer, P., Ohring, G., Kummerow, C., and Auligne, T.: Assimilating satellite observations of clouds and precipitation into NWP models, B. Am. Meteor. Soc., 92, ES25–ES28, https://doi.org/10.1175/2011BAMS3182.1, 2011. 
Cardinali, C.: Monitoring the observation impact on the short-range forecast, Q. J. Roy. Meteor. Soc., 135, 239–250, https://doi.org/10.1002/qj.366, 2009. 
Ehrendorfer, M., Errico, R. M., and Raeder, K. D.: Singular-vector perturbation growth in a primitive equation model with moist physics, J. Atmos. Sci., 56, 1627–1648, https://doi.org/10.1175/1520-0469(1999)056>1627:SVPGIA<2.0.CO;21999. 
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Geer, A. J.: Significance of changes in medium-range forecast scores, Tellus A, 68, 30229, https://doi.org/10.3402/tellusa.v68.30229, 2016. 
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Short summary
The ensemble forecast sensitivity to observation (EFSO) method can efficiently clarify under what conditions observations are beneficial or detrimental for assimilation. Based on EFSO, an offline assimilation method is proposed to accelerate the development of data selection strategies for new observing systems. The usefulness of this method is demonstrated with the assimilation of global satellite precipitation data.
The ensemble forecast sensitivity to observation (EFSO) method can efficiently clarify under...
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