• Baker, W. E., and Coauthors, 2014: Lidar-measured wind profiles: The missing link in the global observing system. Bull. Amer. Meteor. Soc., 95, 543564, https://doi.org/10.1175/BAMS-D-12-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bauer, P., A. Thorpe, and G. Brunet, 2015: The quiet revolution of numerical weather prediction. Nature, 525, 4755, https://doi.org/10.1038/nature14956.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Belmonte Rivas, M., and A. Stoffelen, 2019: Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT. Ocean Sci ., 15, 831852, https://doi.org/10.5194/os-15-831-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bony, S., and Coauthors, 2015: Clouds, circulation and climate sensitivity. Nat. Geosci., 8, 261268, https://doi.org/10.1038/ngeo2398.

  • Clarizia, M. P., C. P. Gommenginger, S. T. Gleason, M. A. Srokosz, C. Galdi, and M. Di Bisceglie, 2009: Analysis of GNSS-R delay-Doppler maps from the UK-DMC satellite over the ocean. Geophys. Res. Lett., 36, L02608, https://doi.org/10.1029/2008GL036292.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CNES, 1988: BEST—Tropical system energy budget. CNES Rep., 58 pp.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Haan, S., and A. Stoffelen, 2012: Assimilation of high-resolution Mode-S wind and temperature observations in a regional NWP model for nowcasting applications. Wea. Forecasting, 27, 918937, https://doi.org/10.1175/WAF-D-11-00088.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • English, S., and Coauthors, 2013: Impact of satellite data. ECMWF Tech. Memo. 711, 48 pp., www.ecmwf.int/sites/default/files/elibrary/2013/9301-impact-satellite-data.pdf.

  • ESA, 2015: ESA’s Living Planet Programme: Scientific achievements and future challenges. ESA Rep. SP-1329/2, 70 pp., esamultimedia.esa.int/multimedia/publications/SP-1329_2/.

  • Figa-Saldaña, J., K. Scipal, D. Long, M. A. Bourassa, W. Wagner, and A. Stoffelen, 2017: Foreword to the special issue on “new challenges and opportunities in scatterometry.” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 20832085, https://doi.org/10.1109/JSTARS.2017.2694898.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fisher, D., C. A. Poulsen, G. E. Thomas, and J.-P. Muller, 2016: Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: Evaluation and application to AATSR. Atmos. Meas. Tech., 9, 909928, https://doi.org/10.5194/amt-9-909-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flamant, P., J. Cuesta, M. Denneulin, A. Dabas, and D. And Huber, 2008: ADM-Aeolus retrieval algorithms for aerosol and cloud products. Tellus, 60A, 273286, https://doi.org/10.1111/j.1600-0870.2007.00287.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Folger, K., and M. Weissmann, 2014: Height correction of atmospheric motion vectors using satellite lidar observations from CALIPSO. J. Appl. Meteor. Climatol., 53, 18091819, https://doi.org/10.1175/JAMC-D-13-0337.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geer, A. J., F. Boardo, N. Bormann, and S. English, 2014: All-sky assimilation of microwave humidity sounders. ECMWF Tech. Memo. 741, 59 pp., www.ecmwf.int/sites/default/files/elibrary/2014/9507-all-sky-assimilation-microwave-humidity-sounders.pdf.

  • Horányi, A., C. Cardinali, M. Rennie and L. Isaksen, 2015: The assimilation of horizontal line-of-sight wind information into the ECMWF data assimilation and forecasting system. Part I: The assessment of wind impact. Quart. J. Roy. Meteor. Soc., 141, 12231232, https://doi.org/10.1002/qj.2430.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houchi, K., A. Stoffelen, G. J. Marseille, and J. de Kloe, 2010: Comparison of wind and wind shear climatologies derived from high-resolution radiosonde and ECMWF model. J. Geophys. Res., 115, D22123, https://doi.org/10.1029/2009JD013196.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Illingworth, A., and Coauthors, 2018: WIVERN: A new satellite concept to provide global in-cloud winds, precipitation and cloud properties. Bull. Amer. Meteor. Soc., 99, 16691687, https://doi.org/10.1175/BAMS-D-16-0047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, G. P., J. Vogelzang, and A. Stoffelen, 2015: Upscale and downscale energy transfer over the tropical Pacific revealed by scatterometer winds. J. Geophys. Res. Oceans, 120, 346361, https://doi.org/10.1002/2014JC009993.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, G. P., M. Portabella, W. Lin, and A. Stoffelen, 2017: Correlating extremes in wind and stress divergence with extremes in rain over the tropical Atlantic, version 1.0. Ocean and Sea Ice Satellite Application Facility Rep. OSI_AVS_15_02, 35 pp., www.osi-saf.org/sites/default/files/dynamic/page_with_files/file/OSI_AVS15_02_Correlating_extremes_wind_and_rain_Tropical_Atlantic_Gregory_King.pdf.

  • Lean, K., and N. Bohrmann, 2019: Investigation of low-level AMV height assignment. Proc. Joint Satellite Conf., Boston, MA, AMS–EUMETSAT–NOAA, 13B.3.

  • Lin, W., M. Portabella, A. Stoffelen, J. Vogelzang, and A. Verhoef, 2016: On mesoscale analysis and ASCAT ambiguity removal. Quart. J. Roy. Meteor. Soc., 142, 17451756, https://doi.org/10.1002/qj.2770.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lux, O., Ch. Lemmerz, F. Weiler, U. Marksteiner, B. Witschas, S. Rahm, A. Schäfler, and O. Reitebuch, 2018a: Airborne wind lidar observations over the North Atlantic in 2016 for the pre-launch validation of the satellite mission Aeolus. Atmos. Meas. Tech., 11, 32973322, https://doi.org/10.5194/amt-11-3297-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lux, O., and Coauthors, 2018b: WindVal II final report: Wind validation II for Aeolus. DLR Rep. FR.DLR.WindVal_II.020318, V1.0, 270 pp.

  • Marseille, G. J., and A. Stoffelen, 2017: Toward scatterometer winds assimilation in the mesoscale HARMONIE model. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 23832393, https://doi.org/10.1109/JSTARS.2016.2640339.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marseille, G. J., A. Stoffelen, and J. Barkmeijer, 2008a: A cycled sensitivity observing system experiment on simulated Doppler wind lidar data during the 1999 Christmas storm “Martin.” Tellus, 60A, 249260, https://doi.org/10.1111/j.1600-0870.2007.00290.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marseille, G. J., A. Stoffelen, and J. Barkmeijer, 2008b: Impact assessment of prospective space-borne Doppler wind lidar observation scenarios. Tellus, 60A, 234248, https://doi.org/10.1111/j.1600-0870.2007.00289.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marseille, G. J., A. Stoffelen, H. Schyberg, L. Megner, and H. Kornich, 2013: Vertical and horizontal Aeolus measurement positioning. ESA Rep. AE-FR-VHAMP_v1.0, 211 pp., www.researchgate.net/publication/324363496_VHAMP_-_Vertical_and_Horizontal_Aeolus_Measurement_Positioning_-_Final_Report.

  • Megner, L., D. G. Tan, H. Körnich, L. Isaksen, A. Horányi, A. Stoffelen, and G.-J. Marseille, 2015: Linearity aspects of the ensemble of data assimilations technique. Quart. J. Roy. Meteor. Soc., 141, 426432, https://doi.org/10.1002/qj.2362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muller, J.-P., and Coauthors, 2017: FLIRt: Flow by IR tandem. ESA EE09 Doc., v.1, 82 pp.

  • Nieman, S. J., J. Schmetz, and W. P. Menzel, 1993: A comparison of several techniques to assign heights to cloud tracers. J. Appl. Meteor., 32, 15591568, https://doi.org/10.1175/1520-0450(1993)032<1559:ACOSTT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peubey, C., and A. P. McNally, 2009: Characterization of the impact of geostationary clear sky radiances on wind analyses in a 4D-Var context. Quart. J. Roy. Meteor. Soc., 135, 18631876, https://doi.org/10.1002/qj.500.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Polvani, L. M., L. Sun, A. H. Butler, J. H. Richter, and C. Deser, 2017: Distinguishing stratospheric sudden warmings from ENSO as key drivers of wintertime climate variability over the North Atlantic and Eurasia. J. Climate, 30, 19591969, https://doi.org/10.1175/JCLI-D-16-0277.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Portabella, M., A. Stoffelen, W. Lin, A. Turiel, A. Verhoef, J. Verspeek, and J. Ballabrera-Poy, 2012: Rain effects on ASCAT-retrieved winds: Toward an improved quality control. IEEE Trans. Geosci. Remote Sens., 50, 24952506, https://doi.org/10.1109/TGRS.2012.2185933.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reitebuch, O., C. Lemmerz, E. Nagel, U. Paffrath, Y. Durand, M. Endemann, F. Fabre, and M. Chaloupy, 2009: The airborne demonstrator for the direct-detection Doppler wind lidar ALADIN on ADM-Aeolus: I. Instrument design and comparison to satellite instrument. J. Atmos. Oceanic Technol., 26, 25012515, https://doi.org/10.1175/2009JTECHA1309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reitebuch, O., C. Lemmerz, U. Lux, B. Marksteiner, R. Witschas, and I. I. I. Neely, 2017: WindVal—Joint DLR-ESA-NASA wind validation for Aeolus. ESA Final Rep. 4000114053/15/NL/FF/gp, 185 pp.

  • Salonen, K., and N. Bormann, 2016: Atmospheric motion vector observations in the ECMWF system: Fifth year report. EUMETSAT/ECMWF Fellowship Programme Rep. 41, 36 pp., www.ecmwf.int/en/elibrary/16339-atmospheric-motion-vector-observations-ecmwf-system-fifth-year-report.

  • Salonen, K., and A. McNally, 2017: Impact of hyperspectral IR radiances on wind analyses. 21st International TOVS Study Conf ., Darmstadt, Germany, EUMETSAT, http://cimss.ssec.wisc.edu/itwg/itsc/itsc21/program/1december/1045_9.05_HyIR_KS_v3.pdf.

    • Search Google Scholar
    • Export Citation
  • Sato, Y., and L. P. Riishojgaard, 2016: Sixth WMO Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction. WMO Rep., 26 pp., www.wmo.int/pages/prog/www/WIGOS-WIS/reports/WMO-NWP-6_2016_Shanghai_Final-Report.pdf.

    • Search Google Scholar
    • Export Citation
  • Schäfler, A., and Coauthors, 2018: The North Atlantic Waveguide and Downstream Impact Experiment. Bull. Amer. Meteor. Soc., 99, 16071637, https://doi.org/10.1175/BAMS-D-17-0003.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., and A. Hollingsworth, 2002: Some aspects of the improvement in skill of numerical weather prediction. Quart. J. Roy. Meteor. Soc., 128, 647677, https://doi.org/10.1256/003590002321042135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sherwood, S. C., J.-H. Chae, P. Minnis, and M. McGill, 2004: Underestimation of deep convective cloud tops by thermal imagery. Geophys. Res. Lett., 31, L11102, https://doi.org/10.1029/2004GL019699.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoffelen, A., and Coauthors, 2005: The Atmospheric Dynamics Mission for global wind field measurement. Bull. Amer. Meteor. Soc., 86, 7388, https://doi.org/10.1175/BAMS-86-1-73.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoffelen, A., G. J. Marseille, F. Bouttier, D. Vasiljevic, S. de Haan, and C. Cardinali, 2006: ADM-Aeolus Doppler wind lidar observing system simulation experiment. Quart. J. Roy. Meteor. Soc., 132, 19271947, https://doi.org/10.1256/qj.05.83.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoffelen, A., K. Atkinson, and A. Regan, 2014: Geometric cloud motion winds in a convoy of satellites. 12th Int. Winds Workshop, Copenhagen, Denmark, EUMETSAT, indico.nbi.ku.dk/event/614/sessions/1016/attachments/1144/1628/IWW_Convoy_2014.pdf.

    • Search Google Scholar
    • Export Citation
  • Winker, D. M., and Coauthors, 2010: The CALIPSO mission: A global 3D view of aerosols and clouds. Bull. Amer. Meteor. Soc., 91, 12111230, https://doi.org/10.1175/2010BAMS3009.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • WMO, 2009: Vision for WIGOS in 2025. WMO Rep., 6 pp., www.wmo.int/pages/prog/sat/documents/SAT-GEN_ST-11-Vision-for-GOS-in-2025.pdf.

  • WMO, 2017: Rolling requirements review and statement of guidance RRR and GCOS. WMO, www.wmo.int/pages/prog/sat/RRR-and-SOG.html.

  • Žagar, N., E. Andersson, and M. Fisher, 2005: Balanced tropical data assimilation based on a study of equatorial waves in ECMWF short-range forecast errors. Quart. J. Roy. Meteor. Soc., 131, 9871011, https://doi.org/10.1256/qj.04.54.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Žagar, N., A. Stoffelen, G.-J. Marseille, C. Accadia, and P. Schlüssel, 2008: Impact assessment of simulated Doppler wind lidars with a multivariate variational assimilation in the tropics. Mon. Wea. Rev., 136, 24432460, https://doi.org/10.1175/2007MWR2335.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 194 194 51
PDF Downloads 220 220 61

Wind Profile Satellite Observation Requirements and Capabilities

View More View Less
  • 1 Royal Netherlands Meteorological Institute (KNMI), de Bilt, Netherlands
  • 2 European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
  • 3 European Organisation for the Exploitation of Meteorological Satellites, Darmstadt, Germany
  • 4 Centre National de Recherches Météorologiques, Météo France, Toulouse, France
  • 5 Institut Pierre Simon Laplace, Paris, France
  • 6 Met Office, Exeter, United Kingdom
  • 7 National Oceanic and Atmospheric Administration, Boulder, Colorado
  • 8 European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
  • 9 Department of Meteorology, Stockholm University (MISU), Stockholm, Sweden
  • 10 NASA Science Mission Directorate, Washington, D.C.
  • 11 Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen, Germany
  • 12 European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
  • 13 World Meteorological Organization, Geneva, Switzerland
  • 14 Norwegian Meteorological Institute, Oslo, Norway
  • 15 European Space Research and Technology Centre, Noordwijk, Netherlands
  • 16 Lidar and Optics Associates (OLA), Malvern, United Kingdom
© Get Permissions
Restricted access

Abstract

The Aeolus mission objectives are to improve numerical weather prediction (NWP) and enhance the understanding and modeling of atmospheric dynamics on global and regional scale. Given the first successes of Aeolus in NWP, it is time to look forward to future vertical wind profiling capability to fulfill the rolling requirements in operational meteorology. Requirements for wind profiles and information on vertical wind shear are constantly evolving. The need for high-quality wind and profile information to capture and initialize small-amplitude, fast-evolving, and mesoscale dynamical structures increases, as the resolution of global NWP improved well into the 3D turbulence regime on horizontal scales smaller than 500 km. In addition, advanced requirements to describe the transport and dispersion of atmospheric constituents and better depict the circulation on climate scales are well recognized. Direct wind profile observations over the oceans, tropics, and Southern Hemisphere are not provided by the current global observing system. Looking to the future, most other wind observation techniques rely on cloud or regions of water vapor and are necessarily restricted in coverage. Therefore, after its full demonstration, an operational Aeolus-like follow-on mission obtaining globally distributed wind profiles in clear air by exploiting molecular scattering remains unique.

Deceased

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.

Corresponding author: Ad Stoffelen, ad.stoffelen@knmi.nl

Abstract

The Aeolus mission objectives are to improve numerical weather prediction (NWP) and enhance the understanding and modeling of atmospheric dynamics on global and regional scale. Given the first successes of Aeolus in NWP, it is time to look forward to future vertical wind profiling capability to fulfill the rolling requirements in operational meteorology. Requirements for wind profiles and information on vertical wind shear are constantly evolving. The need for high-quality wind and profile information to capture and initialize small-amplitude, fast-evolving, and mesoscale dynamical structures increases, as the resolution of global NWP improved well into the 3D turbulence regime on horizontal scales smaller than 500 km. In addition, advanced requirements to describe the transport and dispersion of atmospheric constituents and better depict the circulation on climate scales are well recognized. Direct wind profile observations over the oceans, tropics, and Southern Hemisphere are not provided by the current global observing system. Looking to the future, most other wind observation techniques rely on cloud or regions of water vapor and are necessarily restricted in coverage. Therefore, after its full demonstration, an operational Aeolus-like follow-on mission obtaining globally distributed wind profiles in clear air by exploiting molecular scattering remains unique.

Deceased

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.

Corresponding author: Ad Stoffelen, ad.stoffelen@knmi.nl
Save