Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-7031-2022
https://doi.org/10.5194/gmd-15-7031-2022
Model description paper
 | 
16 Sep 2022
Model description paper |  | 16 Sep 2022

Introduction of the DISAMAR radiative transfer model: determining instrument specifications and analysing methods for atmospheric retrieval (version 4.1.5)

Johan F. de Haan, Ping Wang, Maarten Sneep, J. Pepijn Veefkind, and Piet Stammes

Related authors

Evaluation of Aeolus feature mask and particle extinction coefficient profile products using CALIPSO data
Ping Wang, David Patrick Donovan, Gerd-Jan van Zadelhoff, Jos de Kloe, Dorit Huber, and Katja Reissig
Atmos. Meas. Tech., 17, 5935–5955, https://doi.org/10.5194/amt-17-5935-2024,https://doi.org/10.5194/amt-17-5935-2024, 2024
Short summary
The EarthCARE lidar cloud and aerosol profile processor (A-PRO): the A-AER, A-EBD, A-TC, and A-ICE products
David Patrick Donovan, Gerd-Jan van Zadelhoff, and Ping Wang
Atmos. Meas. Tech., 17, 5301–5340, https://doi.org/10.5194/amt-17-5301-2024,https://doi.org/10.5194/amt-17-5301-2024, 2024
Short summary
Tropical tropospheric ozone distribution and trends from in situ and satellite data
Audrey Gaudel, Ilann Bourgeois, Meng Li, Kai-Lan Chang, Jerald Ziemke, Bastien Sauvage, Ryan M. Stauffer, Anne M. Thompson, Debra E. Kollonige, Nadia Smith, Daan Hubert, Arno Keppens, Juan Cuesta, Klaus-Peter Heue, Pepijn Veefkind, Kenneth Aikin, Jeff Peischl, Chelsea R. Thompson, Thomas B. Ryerson, Gregory J. Frost, Brian C. McDonald, and Owen R. Cooper
Atmos. Chem. Phys., 24, 9975–10000, https://doi.org/10.5194/acp-24-9975-2024,https://doi.org/10.5194/acp-24-9975-2024, 2024
Short summary
Current potential of CH4 emission estimates using TROPOMI in the Middle East
Mengyao Liu, Ronald van der A, Michiel van Weele, Lotte Bryan, Henk Eskes, Pepijn Veefkind, Yongxue Liu, Xiaojuan Lin, Jos de Laat, and Jieying Ding
Atmos. Meas. Tech., 17, 5261–5277, https://doi.org/10.5194/amt-17-5261-2024,https://doi.org/10.5194/amt-17-5261-2024, 2024
Short summary
Deep Transfer Learning Method for Seasonal TROPOMI XCH4 Albedo Correction
Alexander C. Bradley, Barbara Dix, Fergus Mackenzie, J. Pepijn Veefkind, and Joost A. de Gouw
EGUsphere, https://doi.org/10.5194/egusphere-2024-2352,https://doi.org/10.5194/egusphere-2024-2352, 2024
Short summary

Related subject area

Atmospheric sciences
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024,https://doi.org/10.5194/gmd-17-8093-2024, 2024
Short summary
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024,https://doi.org/10.5194/gmd-17-8069-2024, 2024
Short summary
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024,https://doi.org/10.5194/gmd-17-7995-2024, 2024
Short summary
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024,https://doi.org/10.5194/gmd-17-7867-2024, 2024
Short summary
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024,https://doi.org/10.5194/gmd-17-7915-2024, 2024
Short summary

Cited articles

Anderson, G. P., Clough, S. A., Kneizys, F. X., Chetwynd, J. H., and Shettle, E. P.: AFGL (Air Force Geophysical Laboratory) atmospheric constituent profiles (0-120 km). Environmental research papers, 1986-05-15, Air Force Geophysics Lab., Hanscom AFB, MA (USA), https://apps.dtic.mil/sti/pdfs/ADA175173.pdf (last access: 2 September 2022), 1986. a
Bai, W., Zhang, P., Zhang, W., Li, J., Ma, G., Qi, C., and Liu, H.: Jacobian matrix for near-infrared remote sensing based on vector radiative transfer model, Science China Earth Sciences, 63, 1353–1365, https://doi.org/10.1007/s11430-019-9586-7, 2020. a
Bass, A. M. and Paur, R. J.: The ultraviolet cross-sections of ozone: I. The measurements II. Results and temperature dependence, in atmospheric ozone, in: Proceedings of the Quadrennial Ozone Symposium, edited by: Zerefos, C. and Ghazi, A., Halkidiki, Greece, 3–7 September 1984, Dordrecht, Reidel, 606–616, https://doi.org/10.1007/978-94-009-5313-0_120, 1985. a
Castellanos, P., Boersma, K. F., Torres, O., and de Haan, J. F.: OMI tropospheric NO2 air mass factors over South America: effects of biomass burning aerosols, Atmos. Meas. Tech., 8, 3831–3849, https://doi.org/10.5194/amt-8-3831-2015, 2015. a
Chance, K. V. and Spurr, R. J. D.: Ring effect studies: Rayleigh scattering, including molecular parameters for rotational Raman scattering, and the Fraunhofer spectrum, Appl. Optics, 36, 5224–5230, 1997. a
Download
Short summary
We present an overview of the DISAMAR radiative transfer code, highlighting the novel semi-analytical derivatives for the doubling–adding formulae and the new DISMAS technique for weak absorbers. DISAMAR includes forward simulations and retrievals for satellite spectral measurements from 270 to 2400 nm to determine instrument specifications for passive remote sensing. It has been used in various Sentinel-4/5P/5 projects and in the TROPOMI aerosol layer height and ozone profile products.