1. An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties
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Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, Vinciane Unger, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), This research has been supported by the Agence Nationale de la Recherche (grant no. AAPG-2018-CE01-004.)., The instrumental data used in this study are part of the SOFOG-3D experiment. The SOFOG-3D field campaign was supported by METEO-FRANCE and ANR through grant AAPG-2018-CE01-004. Data are managed by the French National Centre for Atmospheric Data and Services (AERIS).The MWR network deployment was carried out thanks to support by IfU GmbH, the University of Cologne, the Met Office, Laboratoire d’Aérologie, MeteoSwiss, ONERA, and Radiometer Physics GmbH. MWR data have been made available and were quality-controlled and processed in the frame of CPEXLAB (Cloud and Precipitation Exploration LABoratory, http://www.cpex-lab.de/, last access: 8 September 2022), a competence centre within the Geoverbund ABC/J by acting support of Ulrich Löhnert, Rainer Haseneder-Lind and Arthur Kremer from the University of Cologne. This article is based upon work from COST action CA18235 PROBE, supported by COST (European Cooperation in Science and Technology, https://www.cost.eu/, 8 September 2022). Thibaut Montmerle and Yann Michel are thanked for their support on the use of the AROME EDA to compute background error covariance matrices. The authors also thank the two anonymous reviewers., Author contributions. AB and PM made developments to the 1DVar algorithm. AB performed the analysis documented in the paper, which was supervised by PM and OC. JD and SJ provided the radar data, which they also used to perform verification checks, and they also provided relevant assistance. FB provided the in situ data from the SOFOG-3D field campaign. YS provided the configuration to generate AROME forecasts. VU provided treatment for the microwave radiometer data., Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), SPACE - LATMOS, Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), and Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)
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[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph] ,Atmospheric Science ,[SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology - Abstract
The 1D-Var retrieval framework described hereused the NWPSAF 1D package. This can be downloaded fromthe NWPSAF website (https://nwp-saf.eumetsat.int/site/software/1d-var/download/, last access: 22 August 2022, Pavelin and Collard, 2009). The radiative transfer model RTTOV-gb v1.0 is available to licensed users free of charge. RTTOV-gb may be obtainedby registering (https://www.nwpsaf.eu/site/register/, last access: 22August 2022) and then selecting RTTOV-gb in your software preferences. Developments and updates of the RTTOV-gb code willbe published on the MWRnet website (http://cetemps.aquila.infn.it/rttovgb/rttovgb_updates.html, last access: 8 September 2022). Foraccess to the radar simulator and modified 1D-Var code, please contact pauline.martinet@meteo.fr; International audience; A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution Numerical Weather Prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D/4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. It is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than that found from the retrievals on the dataset of real observations. It was found that retrieval of LWC can be obtained on idealised conditions with an uncertainty of less than 0.04 gm −3. With real data, as expected, retrievals with a good correlation (0.7) to in-situ measurements, but with a higher uncertainty than the synthetic dataset, of around 0.06 gm −3 , was found. This was reduced to 0.05 gm −3 when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the for- ward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational back- ground profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging re- sults for future improvement of the AROME model initial state during fog conditions.
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- 2022