Reduced-cost construction of Jacobian matrices for high-resolution inversions of satellite observations of atmospheric composition
-
Published:2021-08-12
Issue:8
Volume:14
Page:5521-5534
-
ISSN:1867-8548
-
Container-title:Atmospheric Measurement Techniques
-
language:en
-
Short-container-title:Atmos. Meas. Tech.
Author:
Nesser HannahORCID, Jacob Daniel J., Maasakkers Joannes D.ORCID, Scarpelli Tia R., Sulprizio Melissa P., Zhang YuzhongORCID, Rycroft Chris H.
Abstract
Abstract. Global high-resolution observations of atmospheric composition from satellites can greatly improve our understanding of surface emissions through inverse analyses. Variational inverse methods can optimize
surface emissions at any resolution but do not readily quantify the error
and information content of the posterior solution. The information content
of satellite data may be much lower than its coverage would suggest because
of failed retrievals, instrument noise, and error correlations that
propagate through the inversion. Analytical solution of the inverse problem
provides closed-form characterization of posterior error statistics and
information content but requires the construction of the Jacobian matrix
that relates emissions to atmospheric concentrations. Building the Jacobian
matrix is computationally expensive at high resolution because it involves
perturbing each emission element, typically individual grid cells, in the
atmospheric transport model used as the forward model for the inversion. We
propose and analyze two methods, reduced dimension and reduced rank, to
construct the Jacobian matrix at greatly decreased computational cost while retaining information content. Both methods are two-step iterative
procedures that begin from an initial native-resolution estimate of the
Jacobian matrix constructed at no computational cost by assuming that
atmospheric concentrations are most sensitive to local emissions. The
reduced-dimension method uses this estimate to construct a Jacobian matrix
on a multiscale grid that maintains a high resolution in areas with high
information content and aggregates grid cells elsewhere. The reduced-rank
method constructs the Jacobian matrix at native resolution by perturbing the leading patterns of information content given by the initial estimate. We demonstrate both methods in an analytical Bayesian inversion of Greenhouse Gases Observing Satellite (GOSAT) methane data with augmented information content over North America in July 2009. We show that both methods reproduce the results of the native-resolution inversion while achieving a factor of 4 improvement in computational performance. The reduced-dimension method produces an exact solution at a lower spatial resolution, while the reduced-rank method solves the inversion at native resolution in areas of high information content and defaults to the prior estimate elsewhere.
Funder
National Science Foundation Earth Sciences Division
Publisher
Copernicus GmbH
Subject
Atmospheric Science
Reference42 articles.
1. Alexe, M., Bergamaschi, P., Segers, A., Detmers, R., Butz, A., Hasekamp, O., Guerlet, S., Parker, R., Boesch, H., Frankenberg, C., Scheepmaker, R. A., Dlugokencky, E., Sweeney, C., Wofsy, S. C., and Kort, E. A.: Inverse modelling of CH4 emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY, Atmos. Chem. Phys., 15, 113–133, https://doi.org/10.5194/acp-15-113-2015, 2015. 2. Bergamaschi, P., Frankenberg, C., Meirink, J. F., Krol, M., Villani, M. G.,
Houweling, S., Dentener, F., Dlugokencky, E. J., Miller, J. B., Gatti, L.
V., Engel, A., and Levin, I.: Inverse modeling of global and regional CH4
emissions using SCIAMACHY satellite retrievals, J. Geophys. Res.-Atmos.,
114, D22301, https://doi.org/10.1029/2009JD012287, 2009. 3. Bergamaschi, P., Houweling, S., Segers, A., Krol, M., Frankenberg, C.,
Scheepmaker, R. A., Dlugokencky, E., Wofsy, S. C., Kort, E. A., Sweeney, C.,
Schuck, T., Brenninkmeijer, C., Chen, H., Beck, V., and Gerbig, C.:
Atmospheric CH<span class="inline-formula"><sub>4</sub></span> in the first decade of the 21st century: Inverse modeling
analysis using SCIAMACHY satellite retrievals and NOAA surface measurements,
J. Geophys. Res.-Atmos., 118, 7350–7369, https://doi.org/10.1002/jgrd.50480, 2013. 4. Bloom, A. A., Bowman, K. W., Lee, M., Turner, A. J., Schroeder, R., Worden, J. R., Weidner, R., McDonald, K. C., and Jacob, D. J.: A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0), Geosci. Model Dev., 10, 2141–2156, https://doi.org/10.5194/gmd-10-2141-2017, 2017. 5. Bocquet, M. and Wu, L.: Bayesian design of control space for optimal
assimilation of observations. Part II: Asymptotic solutions, Q. J. Roy.
Meteor. Soc., 137, 1357–1368, https://doi.org/10.1002/qj.841, 2011.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|