Articles | Volume 25, issue 1
Research article
01 Mar 2018
Research article |  | 01 Mar 2018

Accelerating assimilation development for new observing systems using EFSO

Guo-Yuan Lien, Daisuke Hotta, Eugenia Kalnay, Takemasa Miyoshi, and Tse-Chun Chen

Related authors

Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction
Juan Ruiz, Guo-Yuan Lien, Keiichi Kondo, Shigenori Otsuka, and Takemasa Miyoshi
Nonlin. Processes Geophys., 28, 615–626,,, 2021
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
Robust weather-adaptive post-processing using model output statistics random forests
Thomas Muschinski, Georg J. Mayr, Achim Zeileis, and Thorsten Simon
Nonlin. Processes Geophys., 30, 503–514,,, 2023
Short summary
Evolution of small-scale turbulence at large Richardson numbers
Lev Ostrovsky, Irina Soustova, Yuliya Troitskaya, and Daria Gladskikh
Nonlin. Processes Geophys. Discuss.,,, 2023
Revised manuscript accepted for NPG
Short summary
Comparative study of strongly and weakly coupled data assimilation with a global land–atmosphere coupled model
Kenta Kurosawa, Shunji Kotsuki, and Takemasa Miyoshi
Nonlin. Processes Geophys., 30, 457–479,,, 2023
Short summary
How far can the statistical error estimation problem be closed by collocated data?
Annika Vogel and Richard Ménard
Nonlin. Processes Geophys., 30, 375–398,,, 2023
Short summary
Using orthogonal vectors to improve the ensemble space of the ensemble Kalman filter and its effect on data assimilation and forecasting
Yung-Yun Cheng, Shu-Chih Yang, Zhe-Hui Lin, and Yung-An Lee
Nonlin. Processes Geophys., 30, 289–297,,, 2023
Short summary

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,, 2011. 
Cardinali, C.: Monitoring the observation impact on the short-range forecast, Q. J. Roy. Meteor. Soc., 135, 239–250,, 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,>1627:SVPGIA<2.0.CO;21999. 
Errico, R. M., Bauer, P., and Mahfouf, J.-F.: Issues regarding the assimilation of cloud and precipitation data, J. Atmos. Sci., 64, 3785–3798,, 2007. 
Geer, A. J.: Significance of changes in medium-range forecast scores, Tellus A, 68, 30229,, 2016. 
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.