Articles | Volume 15, issue 18
https://doi.org/10.5194/amt-15-5415-2022
https://doi.org/10.5194/amt-15-5415-2022
Research article
 | 
26 Sep 2022
Research article |  | 26 Sep 2022

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, and Vinciane Unger

Related authors

Developments on a 22 GHz microwave radiometer and reprocessing of 13-year time series for water vapour studies
Alistair Bell, Eric Sauvageat, Gunter Stober, Klemens Hocke, and Axel Murk
Atmos. Meas. Tech., 18, 555–567, https://doi.org/10.5194/amt-18-555-2025,https://doi.org/10.5194/amt-18-555-2025, 2025
Short summary
Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy
Pragya Vishwakarma, Julien Delanoë, Susana Jorquera, Pauline Martinet, Frederic Burnet, Alistair Bell, and Jean-Charles Dupont
Atmos. Meas. Tech., 16, 1211–1237, https://doi.org/10.5194/amt-16-1211-2023,https://doi.org/10.5194/amt-16-1211-2023, 2023
Short summary
W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors
Alistair Bell, Pauline Martinet, Olivier Caumont, Benoît Vié, Julien Delanoë, Jean-Charles Dupont, and Mary Borderies
Atmos. Meas. Tech., 14, 4929–4946, https://doi.org/10.5194/amt-14-4929-2021,https://doi.org/10.5194/amt-14-4929-2021, 2021
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025,https://doi.org/10.5194/amt-18-1325-2025, 2025
Short summary
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025,https://doi.org/10.5194/amt-18-1209-2025, 2025
Short summary
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025,https://doi.org/10.5194/amt-18-773-2025, 2025
Short summary
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025,https://doi.org/10.5194/amt-18-487-2025, 2025
Short summary
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025,https://doi.org/10.5194/amt-18-73-2025, 2025
Short summary

Cited articles

Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics, Q. J. Roy. Meteor. Soc., 134, 1971–1996, 2008. a
Bauer, P., Lopez, P., Benedetti, A., Salmond, D., and Moreau, E.: Implementation of 1D + 4D-Var assimilation of precipitation-affected microwave radiances at ECMWF. I:  1D-Var, Q. J. Roy. Meteorol. Soc., 132, 2277–2306, 2006. a
Bell, A., Martinet, P., Caumont, O., Vié, B., Delanoë, J., Dupont, J.-C., and Borderies, M.: W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors, Atmos. Meas. Tech., 14, 4929–4946, https://doi.org/10.5194/amt-14-4929-2021, 2021. a, b, c, d, e, f, g, h, i
Borderies, M., Caumont, O., Augros, C., Bresson, É., Delanoë, J., Ducrocq, V., Fourrié, N., Bastard, T. L., and Nuret, M.: Simulation of W-band radar reflectivity for model validation and data assimilation, Q. J. Roy. Meteorol. Soc., 144, 391–403, 2018. a
Bouttier, F. and Courtier, P.: Data assimilation concepts and methods March 1999, Meteorological training course lecture series, ECMWF, 718, 59, https://www.ecmwf.int/sites/default/files/elibrary/2002/16928-data-assimilation-concepts-and-methods.pdf (last access: 8 September 2022), 2002. a, b
Download
Short summary
Cloud radars and microwave radiometers offer the potential to improve fog forecasts when assimilated into a high-resolution model. As this process can be complex, a retrieval of model variables is sometimes made as a first step. In this work, results from a 1D-Var algorithm for the retrieval of temperature, humidity and cloud liquid water content are presented. The algorithm is applied first to a synthetic dataset and then to a dataset of real measurements from a recent field campaign.
Share