• Amodei, M., and J. Stein, 2009: Deterministic and fuzzy verification methods for a hierarchy of numerical models. Meteor. Appl., 16, 191203, https://doi.org/10.1002/met.101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Auligné, T., A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 133, 631642, https://doi.org/10.1002/qj.56.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brousseau, P., Y. Seity, D. Ricard, and J. Léger, 2016: Improvement of the forecast of convective activity from the AROME-France system. Quart. J. Roy. Meteor. Soc., 142, 22312243, https://doi.org/10.1002/qj.2822.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coniglio, M. C., G. S. Romine, D. D. Turner, and R. D. Torn, 2019: Impacts of targeted AERI and Doppler lidar wind retrievals on short-term forecasts of the initiation and early evolution of thunderstorms. Mon. Wea. Rev., 147, 11491170, https://doi.org/10.1175/MWR-D-18-0351.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Courtier, P., C. Freydier, J.-F. Geleyn, F. Rabier, and M. Rochas, 1991: The Arpege project at Meteo France. Seminar on Numerical Methods in Atmospheric Models, Vol. II, Shinfield Park, Reading, ECMWF, 193–232, https://www.ecmwf.int/node/8798.

    • Search Google Scholar
    • Export Citation
  • Desroziers, G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation, background and analysis-error statistics in observation space. Quart. J. Roy. Meteor. Soc., 131, 33853396, https://doi.org/10.1256/qj.05.108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • EUMETSAT, 2017a: IASI L2 PPF v6.3 validation report. EUMETSAT, 45 pp., https://www.eumetsat.int/media/45996.

  • EUMETSAT, 2017b: IASI level 2: Product guide. EUMETSAT, 80 pp., https://www.eumetsat.int/media/45982.

  • EUMETSAT, 2018: IASI L2 PPF v6.4 validation report. EUMETSAT, 59 pp., https://www.eumetsat.int/media/45739.

  • Eyre, J. R., S. J. English, and M. Forsythe, 2019: Assimilation of satellite data in numerical weather prediction. Part I: The early years. Quart. J. Roy. Meteor. Soc., 146, 4968, https://doi.org/10.1002/qj.3654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feltz, M., R. Knuteson, S. Ackerman, and H. Revercomb, 2014: Application of GPS radio occultation to the assessment of temperature profile retrievals from microwave and infrared sounders. Atmos. Meas. Tech., 7, 37513762, https://doi.org/10.5194/amt-7-3751-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guidard, V., N. Fourrié, P. Brousseau, and F. Rabier, 2011: Impact of IASI assimilation at global and convective scales and challenges for the assimilation of cloudy scenes. Quart. J. Roy. Meteor. Soc., 137, 19751987, https://doi.org/10.1002/qj.928.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hartung, D. C., J. A. Otkin, R. A. Petersen, D. D. Turner, and W. F. Feltz, 2011: Assimilation of surface-based boundary layer profiler observations during a cool-season weather event using an observing system simulation experiment. Part II: Forecast assessment. Mon. Wea. Rev., 139, 23272346, https://doi.org/10.1175/2011MWR3623.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hilton, F., and Coauthors, 2012: Hyperspectral Earth Observation from IASI: Four years of accomplishments. Bull. Amer. Meteor. Soc., 93, 347370, https://doi.org/10.1175/BAMS-D-11-00027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, J., N. Yussouf, D. D. Turner, T. A. Jones, and X. Wang, 2019: Impact of ground-based remote sensing boundary layer observations on short-term probabilistic forecasts of a tornadic supercell event. Wea. Forecasting, 34, 14531476, https://doi.org/10.1175/WAF-D-18-0200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Migliorini, S., 2012: On the equivalence between radiance and retrieval assimilation. Mon. Wea. Rev., 140, 258265, https://doi.org/10.1175/MWR-D-10-05047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Otkin, J. A., D. C. Hartung, D. D. Turner, R. A. Petersen, W. F. Feltz, and E. Janzon, 2011: Assimilation of surface-based boundary layer profiler observations during a cool-season weather event using an observing system simulation experiment. Part I: Analysis impact. Mon. Wea. Rev., 139, 23092326, https://doi.org/10.1175/2011MWR3622.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodgers, C., 2000: Inverse Methods for Atmospheric Sounding: Theory and Practice. Series on Atmospheric, Oceanic and Planetary Physics, Vol. 2, World Scientific, 256 pp.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roman, J., R. Knuteson, T. August, T. Hultberg, S. Ackerman, and H. Revercomb, 2016: A global assessment of NASA AIRS v6 and EUMETSAT IASI v6 precipitable water vapor using ground-based GPS SuomiNet stations. J. Geophys. Res. Atmos., 121, 89258948, https://doi.org/10.1002/2016JD024806.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salonen, K., T. August, T. Hulberg, and A. McNallya, 2019: Impact assessment of IASI temperature and humidity retrievals in the ECMWF system. Int. TOVS Study Conf., Saint-Sauver, Québec, Canada, ECMWF/EUMETSAT, https://cimss.ssec.wisc.edu/itwg/itsc/itsc22/posters/7p.01.salonen.pdf.

    • Search Google Scholar
    • Export Citation
  • Saunders, R., and Coauthors, 2018: An update on the RTTOV fast radiative transfer model (currently at version 12). Geosci. Model Dev., 11, 27172737, https://doi.org/10.5194/gmd-11-2717-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seity, Y., P. Brousseau, S. Malardel, G. Hello, P. Bénard, F. Bouttier, C. Lac, and V. Masson, 2011: The AROME-France convective-scale operational model. Mon. Wea. Rev., 139, 976991, https://doi.org/10.1175/2010MWR3425.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wattrelot, E., O. Caumont, and J.-F. Mahfouf, 2014: Operational implementation of the 1D+3D-Var assimilation method of radar reflectivity data in the AROME model. Mon. Wea. Rev., 142, 18521873, https://doi.org/10.1175/MWR-D-13-00230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 552 297 0
Full Text Views 154 95 24
PDF Downloads 172 94 19

Preliminary Assessment of MetOp-Based Temperature and Humidity Statistical Retrievals within the 3D-Var AROME-France Prediction System

Bruna Barbosa SilveiraaCNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

Search for other papers by Bruna Barbosa Silveira in
Current site
Google Scholar
PubMed
Close
,
Nadia FourriéaCNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

Search for other papers by Nadia Fourrié in
Current site
Google Scholar
PubMed
Close
,
Vincent GuidardaCNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

Search for other papers by Vincent Guidard in
Current site
Google Scholar
PubMed
Close
,
Philippe ChambonaCNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

Search for other papers by Philippe Chambon in
Current site
Google Scholar
PubMed
Close
,
Jean-François MahfoufaCNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

Search for other papers by Jean-François Mahfouf in
Current site
Google Scholar
PubMed
Close
,
Pierre BrousseauaCNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

Search for other papers by Pierre Brousseau in
Current site
Google Scholar
PubMed
Close
,
Patrick MollaCNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

Search for other papers by Patrick Moll in
Current site
Google Scholar
PubMed
Close
,
Thomas AugustbEUMETSAT, Darmstadt, Germany

Search for other papers by Thomas August in
Current site
Google Scholar
PubMed
Close
, and
Tim HultbergbEUMETSAT, Darmstadt, Germany

Search for other papers by Tim Hultberg in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The main objective of the study is to evaluate the feasibility and benefits of assimilating satellite temperature and humidity soundings (aka Level 2 or L2 profiles), instead of radiances, from the EUMETSAT Advanced Retransmission Service (EARS) into the AROME-France data assimilation system. The satellite soundings are operational forecast-independent retrievals that used the infrared sounder IASI in synergy with its companion microwave instruments AMSU-A and MHS on board the MetOp platforms. In this assimilation study, L2 profiles are used as pseudoradiosondes, discarding vertical error correlations and the instrument vertical sensitivity in the observation operator due to the lack of available averaging kernels. Three assimilation experiments were performed, the baseline (including all satellite radiances except those from IASI, AMSU-A, and MHS sounders), the control (with observations from the baseline plus IASI, AMSU-A, and MHS radiances), and the L2 experiment (with observations from the baseline and L2 temperature and humidity profiles). The assimilation runs cover the periods of the winter 2017 and summer 2018. The forecast skills of the three experiments are gauged against independent analyses and observations. Despite that the vertical observation operator is not accounted for in this study, it is found that L2 profile assimilation does not have a negative impact on 1-h temperature and humidity forecasts, especially in the midtroposphere. Their impacts are comparable in magnitude to the radiance ones in the operational AROME framework, except in terms of temperature and wind fields during winter where the impact is more negative than positive. These findings encourage further investigations.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bruna Barbosa Silveira, brunabs.silveira@gmail.com

Abstract

The main objective of the study is to evaluate the feasibility and benefits of assimilating satellite temperature and humidity soundings (aka Level 2 or L2 profiles), instead of radiances, from the EUMETSAT Advanced Retransmission Service (EARS) into the AROME-France data assimilation system. The satellite soundings are operational forecast-independent retrievals that used the infrared sounder IASI in synergy with its companion microwave instruments AMSU-A and MHS on board the MetOp platforms. In this assimilation study, L2 profiles are used as pseudoradiosondes, discarding vertical error correlations and the instrument vertical sensitivity in the observation operator due to the lack of available averaging kernels. Three assimilation experiments were performed, the baseline (including all satellite radiances except those from IASI, AMSU-A, and MHS sounders), the control (with observations from the baseline plus IASI, AMSU-A, and MHS radiances), and the L2 experiment (with observations from the baseline and L2 temperature and humidity profiles). The assimilation runs cover the periods of the winter 2017 and summer 2018. The forecast skills of the three experiments are gauged against independent analyses and observations. Despite that the vertical observation operator is not accounted for in this study, it is found that L2 profile assimilation does not have a negative impact on 1-h temperature and humidity forecasts, especially in the midtroposphere. Their impacts are comparable in magnitude to the radiance ones in the operational AROME framework, except in terms of temperature and wind fields during winter where the impact is more negative than positive. These findings encourage further investigations.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bruna Barbosa Silveira, brunabs.silveira@gmail.com
Save