Articles | Volume 14, issue 6
https://doi.org/10.5194/amt-14-4721-2021
https://doi.org/10.5194/amt-14-4721-2021
Research article
 | 
28 Jun 2021
Research article |  | 28 Jun 2021

Sensitivity of Aeolus HLOS winds to temperature and pressure specification in the L2B processor

Matic Šavli, Vivien Pourret, Christophe Payan, and Jean-François Mahfouf

Related authors

Assimilation of temperature and relative humidity observations from personal weather stations in AROME-France
Alan Demortier, Marc Mandement, Vivien Pourret, and Olivier Caumont
EGUsphere, https://doi.org/10.5194/egusphere-2024-1673,https://doi.org/10.5194/egusphere-2024-1673, 2024
Short summary
Assimilation of surface pressure observations from personal weather stations in AROME-France
Alan Demortier, Marc Mandement, Vivien Pourret, and Olivier Caumont
Nat. Hazards Earth Syst. Sci., 24, 907–927, https://doi.org/10.5194/nhess-24-907-2024,https://doi.org/10.5194/nhess-24-907-2024, 2024
Short summary
Towards the use of conservative thermodynamic variables in data assimilation: a case study using ground-based microwave radiometer measurements
Pascal Marquet, Pauline Martinet, Jean-François Mahfouf, Alina Lavinia Barbu, and Benjamin Ménétrier
Atmos. Meas. Tech., 15, 2021–2035, https://doi.org/10.5194/amt-15-2021-2022,https://doi.org/10.5194/amt-15-2021-2022, 2022
Short summary
Toward a variational assimilation of polarimetric radar observations in a convective-scale numerical weather prediction (NWP) model
Guillaume Thomas, Jean-François Mahfouf, and Thibaut Montmerle
Atmos. Meas. Tech., 13, 2279–2298, https://doi.org/10.5194/amt-13-2279-2020,https://doi.org/10.5194/amt-13-2279-2020, 2020
Short summary
Impact of additional AMDAR data in the AROME-France model during May 2017
Alexis Doerenbecher and Jean-François Mahfouf
Adv. Sci. Res., 16, 215–222, https://doi.org/10.5194/asr-16-215-2019,https://doi.org/10.5194/asr-16-215-2019, 2019
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Validation and Intercomparisons
Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs
Hai Nguyen, Derek Posselt, Igor Yanovsky, Longtao Wu, and Svetla Hristova-Veleva
Atmos. Meas. Tech., 17, 3103–3119, https://doi.org/10.5194/amt-17-3103-2024,https://doi.org/10.5194/amt-17-3103-2024, 2024
Short summary
Rotary-wing drone-induced flow – comparison of simulations with lidar measurements
Liqin Jin, Mauro Ghirardelli, Jakob Mann, Mikael Sjöholm, Stephan Thomas Kral, and Joachim Reuder
Atmos. Meas. Tech., 17, 2721–2737, https://doi.org/10.5194/amt-17-2721-2024,https://doi.org/10.5194/amt-17-2721-2024, 2024
Short summary
Application of Doppler sodar in short-term forecasting of PM10 concentration in the air in Krakow (Poland)
Ewa Agnieszka Krajny, Leszek Ośródka, and Marek Jan Wojtylak
Atmos. Meas. Tech., 17, 2451–2464, https://doi.org/10.5194/amt-17-2451-2024,https://doi.org/10.5194/amt-17-2451-2024, 2024
Short summary
Radiative closure tests of collocated hyperspectral microwave and infrared radiometers
Lei Liu, Natalia Bliankinshtein, Yi Huang, John R. Gyakum, Philip M. Gabriel, Shiqi Xu, and Mengistu Wolde
Atmos. Meas. Tech., 17, 2219–2233, https://doi.org/10.5194/amt-17-2219-2024,https://doi.org/10.5194/amt-17-2219-2024, 2024
Short summary
Effects of clouds and aerosols on downwelling surface solar irradiance nowcasting and short-term forecasting
Kyriakoula Papachristopoulou, Ilias Fountoulakis, Alkiviadis F. Bais, Basil E. Psiloglou, Nikolaos Papadimitriou, Ioannis-Panagiotis Raptis, Andreas Kazantzidis, Charalampos Kontoes, Maria Hatzaki, and Stelios Kazadzis
Atmos. Meas. Tech., 17, 1851–1877, https://doi.org/10.5194/amt-17-1851-2024,https://doi.org/10.5194/amt-17-1851-2024, 2024
Short summary

Cited articles

Courtier, P., Freydier, C., Geleyn, J.-F., Rabier, F., and Rochas, M.: The Arpege Project at Météo-France, in: Proc ECMWF Workshop, Numerical methods in atmospheric modelling, 9–13 September 1991, Shinfield Park, Reading, UK, ECMWF, vol. 2, 193–232, 1991. a
Dabas, A., Denneulin, M. L., Flamant, P., Loth, C., Garnier, A., and Dolfi-Bouteyre, A.: Correcting Winds Measured with a Rayleigh Doppler Lidar from Pressure and Temperature Effects, Tellus A, 60 A, 206–215, https://doi.org/10.1111/j.1600-0870.2007.00284.x, 2008. a, b, c, d, e, f, g
De Kloe, J., Stoffelen, A., Rennie, M., Tand, D., Andersson, E., Dabas, A., Poli, P., and Hubert, D.: ADM-Aeolus Level-2B/2C Processor Input/Output Data Definitions Interface Control Document, Documentation for Level-2B processor version 3.30, available at: https://confluence.ecmwf.int/display/AEOL/L2B+processor+documentation+and+datasets (last access: 17 May 2021), 2020. a
ESA: ADM-Aeolus Mission Requirements Document, Tech. Rep. EOP-SM/2047, ESA, available at: https://esamultimedia.esa.int/docs/EarthObservation/ADM-Aeolus_MRD.pdf (last access: 17 May 2021), 2016. a
ESA: Aeolus Online Dissemination System, available at: https://aeolus-ds.eo.esa.int/oads/access, last access: 27 November 2020. a
Download
Short summary
The ESA's Aeolus satellite wind retrieval is provided through a series of processors. It depends on the temperature and pressure specification, which, however, are not measured by the satellite. The numerical weather predicted values are used instead, but these are erroneous. This article studies the sensitivity of the wind retrieval by introducing errors in temperature and pressure. This has been found to be small for Aeolus but is expected to be more crucial for future missions.