A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale

Research output: Contribution to a Journal (Peer & Non Peer) › Article › peer-review

3 Citations (Scopus)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

@article{0a825ab5e29e4936a056cf92c27f90fb,

title = "A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale",

author = "S. Annadate and S. Falasca and R. Cesari and U. Giostra and M. Maione and J. Arduini",

year = "2024",

doi = "10.3390/atmos15010051",

language = "English",

journal = "Atmosphere",

issn = "2073-4433",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

}

TY - JOUR

T1 - A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale

AU - Annadate, S.

AU - Falasca, S.

AU - Cesari, R.

AU - Giostra, U.

AU - Maione, M.

AU - Arduini, J.

PY - 2024

Y1 - 2024

UR - https://www.scopus.com/inward/record.url?eid=2-s2.0-85183314787&partnerID=MN8TOARS

U2 - 10.3390/atmos15010051

DO - 10.3390/atmos15010051

M3 - Article

SN - 2073-4433

JO - Atmosphere

JF - Atmosphere

ER -