Articles | Volume 25, issue 2
Nonlin. Processes Geophys., 25, 429–439, 2018
https://doi.org/10.5194/npg-25-429-2018

Special issue: Numerical modeling, predictability and data assimilation in...

Nonlin. Processes Geophys., 25, 429–439, 2018
https://doi.org/10.5194/npg-25-429-2018

Research article 21 Jun 2018

Research article | 21 Jun 2018

Sensitivity analysis with respect to observations in variational data assimilation for parameter estimation

Victor Shutyaev et al.

Related authors

Toward the assimilation of images
F.-X. Le Dimet, I. Souopgui, O. Titaud, V. Shutyaev, and M. Y. Hussaini
Nonlin. Processes Geophys., 22, 15–32, https://doi.org/10.5194/npg-22-15-2015,https://doi.org/10.5194/npg-22-15-2015, 2015

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
Fast hybrid tempered ensemble transform filter formulation for Bayesian elliptical problems via Sinkhorn approximation
Sangeetika Ruchi, Svetlana Dubinkina, and Jana de Wiljes
Nonlin. Processes Geophys., 28, 23–41, https://doi.org/10.5194/npg-28-23-2021,https://doi.org/10.5194/npg-28-23-2021, 2021
Short summary
A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective
Olivier Pannekoucke, Richard Ménard, Mohammad El Aabaribaoune, and Matthieu Plu
Nonlin. Processes Geophys., 28, 1–22, https://doi.org/10.5194/npg-28-1-2021,https://doi.org/10.5194/npg-28-1-2021, 2021
Short summary
A method for predicting the uncompleted climate transition process
Pengcheng Yan, Guolin Feng, Wei Hou, and Ping Yang
Nonlin. Processes Geophys., 27, 489–500, https://doi.org/10.5194/npg-27-489-2020,https://doi.org/10.5194/npg-27-489-2020, 2020
Short summary
Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst
Reinhold Hess
Nonlin. Processes Geophys., 27, 473–487, https://doi.org/10.5194/npg-27-473-2020,https://doi.org/10.5194/npg-27-473-2020, 2020
Short summary
Training a convolutional neural network to conserve mass in data assimilation
Yvonne Ruckstuhl, Tijana Janjić, and Stephan Rasp
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2020-38,https://doi.org/10.5194/npg-2020-38, 2020
Revised manuscript accepted for NPG
Short summary

Cited articles

Agoshkov, V. I., Parmuzin, E. I., and Shutyaev, V. P.: Numerical algorithm of variational assimilation of the ocean surface temperature data, Comp. Math. Math. Phys., 48, 1371–1391, 2008. a, b, c, d, e, f
Agoshkov, V. I., Parmuzin, E. I., Zalesny, V. B., Shutyaev, V. P., Zakharova, N. B., and Gusev, A. V.: Variational assimilation of observation data in the mathematical model of the Baltic Sea dynamics, Russ. J. Numer. Anal. Math. Modelling, 30, 203–212, 2015. a
Agoshkov, V. I. and Sheloput, T. O.: The study and numerical solution of some inverse problems in simulation of hydrophysical fields in water areas with “liquid” boundaries, Russ. J. Numer. Anal. Math. Modelling, 32, 147–164, 2017. a
Alifanov, O. M., Artyukhin, E. A., and Rumyantsev, S. V.: Extreme Methods for Solving Ill-posed Problems with Applications to Inverse Heat Transfer Problems, Begell House Publishers, Danbury, USA, 1996. a
Baker, N. L. and Daley, R.: Observation and background adjoint sensitivity in the adaptive observation-targeting problem, Q. J. Roy. Meteorol. Soc., 126, 1431–1454, 2000. a
Download
Short summary
The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find unknown parameters of the model. The observation data, and hence the optimal solution, may contain uncertainties. A response function is considered as a functional of the optimal solution after assimilation. The sensitivity of the response function to the observation data is studied. The results are relevant for monitoring and prediction of sea and ocean states.