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
A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe
Clara Deser and Adam S. Phillips
Nonlin. Processes Geophys., 30, 63–84,,, 2023
Short summary
Extending ensemble Kalman filter algorithms to assimilate observations with an unknown time offset
Elia Gorokhovsky and Jeffrey L. Anderson
Nonlin. Processes Geophys., 30, 37–47,,, 2023
Short summary
Guidance on how to improve vertical covariance localization based on a 1000-member ensemble
Tobias Necker, David Hinger, Philipp Johannes Griewank, Takemasa Miyoshi, and Martin Weissmann
Nonlin. Processes Geophys., 30, 13–29,,, 2023
Short summary
Weather pattern dynamics over western Europe under climate change: predictability, information entropy and production
Stéphane Vannitsem
Nonlin. Processes Geophys., 30, 1–12,,, 2023
Short summary
Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis
Dikraa Khedhaouiria, Stéphane Bélair, Vincent Fortin, Guy Roy, and Franck Lespinas
Nonlin. Processes Geophys., 29, 329–344,,, 2022
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.