Direct inversion method for the retrieval of ozone number density profiles from observations of solar radiation scattering by the atmospheric limb
| dc.contributor.author | Fussen, D. | |
| dc.contributor.author | Baker, N. | |
| dc.contributor.author | Berthelot, A. | |
| dc.contributor.author | Dekemper, E. | |
| dc.contributor.author | Gramme, P. | |
| dc.contributor.author | Mateshvili, N. | |
| dc.contributor.author | Rose, K. | |
| dc.contributor.author | Sotiriadis, S. | |
| dc.date | 2025 | |
| dc.date.accessioned | 2025-03-23T12:05:42Z | |
| dc.date.available | 2025-03-23T12:05:42Z | |
| dc.identifier.uri | https://orfeo.belnet.be/handle/internal/13996 | |
| dc.description | Atmospheric sounding from a space instrument usually leads to solving some inverse problem to retrieve a vertical number density profile of a particular constituent like ozone. The paper starts to consider the total number of calls to the forward model that are necessary to iteratively process a large ensemble of observations. For a comparable computational effort, it can be useful to generate a large ensemble of synthetic cases and the associated principal components for both state vector and measurement vector spaces. Then, a direct inverse mapping is obtained by a nonlinear regression through an artificial neural network. The inversion operator is accurate and robust to noise. A test bench is to apply this direct method to the OMPS-LP limb data and to compare the performances with two other published retrieval algorithms. The inter-comparison turns out to be statistically meaningful for a full month of data. Measurement errors are estimated by a Monte-Carlo approach, and averaging kernels are computed with two different methods. | |
| dc.language | eng | |
| dc.title | Direct inversion method for the retrieval of ozone number density profiles from observations of solar radiation scattering by the atmospheric limb | |
| dc.type | Article | |
| dc.subject.frascati | Physical sciences | |
| dc.audience | Scientific | |
| dc.subject.free | ||
| dc.subject.free | Direct inversion | |
| dc.subject.free | Neural network | |
| dc.subject.free | Remote sensing | |
| dc.source.title | Journal of Quantitative Spectroscopy and Radiative Transfer | |
| dc.source.volume | 339 | |
| dc.source.page | A109426 | |
| Orfeo.peerreviewed | Yes | |
| dc.identifier.doi | 10.1016/j.jqsrt.2025.109426 | |
| dc.identifier.url |