Journal Article

FZJ-2021-04161

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png The assessment of potential observability for joint chemical states and emissions in atmospheric modelings

Wu, X. (Corresponding author)FZJ* ; Elbern, H.FZJ* ; Jacob, B.

2022
Springer New York, NY

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Please use a persistent id in citations: http://hdl.handle.net/2128/31207  doi:10.1007/s00477-021-02113-x

Abstract: In predictive geophysical model systems, uncertain initial values and model parameters jointly influence the temporal evolution of the system. This renders initial-value-only optimization by traditional data assimilation methods as insufficient. However, blindly extending the optimization parameter set jeopardizes the validity of the resulting analysis because of the increase of the ill-posedness of the inversion task. Hence, it becomes important to assess the potential observability of measurement networks for model state and parameters in atmospheric modelings in advance of the optimization. In this paper, we novelly establish the dynamic model of emission rates and extend the transport-diffusion model extended by emission rates. Considering the Kalman smoother as underlying assimilation technique, we develop a quantitative assessment method to evaluate the potential observability and the sensitivity of observation networks to initial values and emission rates jointly. This benefits us to determine the optimizable parameters to observation configurations before the data assimilation procedure and make the optimization more efficiently. For high-dimensional models in practical applications, we derive an ensemble based version of the approach and give several elementary experiments for illustrations.

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Contributing Institute(s):

1. Troposphäre (IEK-8)

Research Program(s):

1. 2111 - Air Quality (POF4-211) (POF4-211)

Appears in the scientific report 2022

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Engineering, Computing and Technology ; DEAL Springer ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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