Source backtracking for dust storm emission inversion using an adjoint method: case study of Northeast China
<p>Emission inversion using data assimilation fundamentally relies on having the correct assumptions about the emission background error covariance. A perfect covariance accounts for the uncertainty based on prior knowledge and is able to explain differences between model simulations and obser...
Main Authors: | J. Jin, A. Segers, H. Liao, A. Heemink, R. Kranenburg, H. X. Lin |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2020-12-01
|
Series: | Atmospheric Chemistry and Physics |
Online Access: | https://acp.copernicus.org/articles/20/15207/2020/acp-20-15207-2020.pdf |
Similar Items
-
Machine learning for observation bias correction with application to dust storm data assimilation
by: J. Jin, et al.
Published: (2019-08-01) -
Adjoint inversion modeling of Asian dust emission using lidar observations
by: K. Yumimoto, et al.
Published: (2008-06-01) -
Position correction in dust storm forecasting using LOTOS-EUROS v2.1: grid-distorted data assimilation v1.0
by: J. Jin, et al.
Published: (2021-09-01) -
SPATIOTEMPORAL MODELLING OF DUST STORM SOURCES EMISSION IN WEST ASIA
by: E. Khodabandehloo, et al.
Published: (2013-09-01) -
Sensitivity of the adjoint method in the inversion of tsunami source parameters
by: C. Pires, et al.
Published: (2003-01-01)