Articles | Volume 16, issue 16
https://doi.org/10.5194/amt-16-3901-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-3901-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The impact of Aeolus winds on near-surface wind forecasts over tropical ocean and high-latitude regions
Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
Charlotte Bay Hasager
Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
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Cited articles
Andersson, E., Dabas, A., Endemann, M., Ingmann, P., Källén, E., Offiler, D., and Stoffelen, A.: ADM-Aeolus Science Report, SP-1311, ESA, 121 pp., https://esamultimedia.esa.int/multimedia/publications/SP-1311/SP-1311.pdf
(last access: 2 September 2022), 2008.
Banyard, T. P., Wright, C. J., Hindley, N. P., Halloran, G., Krisch, I.,
Kaifler, B., and Hoffmann, L.: Atmospheric Gravity Waves in Aeolus Wind
Lidar Observations, Geophys. Res. Lett., 48, e2021GL092756,
https://doi.org/10.1029/2021GL092756, 2021.
Belova, E., Kirkwood, S., Voelger, P., Chatterjee, S., Satheesan, K., Hagelin, S., Lindskog, M., and Körnich, H.: Validation of Aeolus winds using ground-based radars in Antarctica and in northern Sweden, Atmos. Meas. Tech., 14, 5415–5428, https://doi.org/10.5194/amt-14-5415-2021, 2021.
Bidlot, J.-R., Holmes, D. J., Wittmann, P. A., Lalbeharry, R., and Chen, H.
S.: Intercomparison of the Performance of Operational Ocean Wave Forecasting
Systems with Buoy Data, Weather Forecast., 17, 287–310,
https://doi.org/10.1175/1520-0434(2002)017<0287:IOTPOO>2.0.CO;2, 2002.
Bromwich, D. H., Monaghan, A. J., Manning, K. W., and Powers, J. G.:
Real-Time Forecasting for the Antarctic: An Evaluation of the Antarctic
Mesoscale Prediction System (AMPS), Mon. Weather Rev., 133, 579–603,
https://doi.org/10.1175/MWR-2881.1, 2005.
Charlton, A. J., O'neill, A., Lahoz, W. A., and Massacand, A. C.: Sensitivity
of tropospheric forecasts to stratospheric initial conditions, Q. J. Roy.
Meteor. Soc., 130, 1771–1792, https://doi.org/10.1256/qj.03.167, 2004.
Christiansen, B.: Downward propagation of zonal mean zonal wind anomalies
from the stratosphere to the troposphere: Model and reanalysis, J. Geophys.
Res., 106, 27307–27322, https://doi.org/10.1029/2000JD000214, 2001.
Caires, S. and Sterl, A.: Validation of ocean wind and wave data using
triple collocation, J. Geophys. Res., 108, 3098,
https://doi.org/10.1029/2002JC001491, 2003.
Cress, A., Martin, A., Born, M., and Weismann, M.: Impact of Aeolus HLOS
winds in the global NWP System of DWD, Towards an operational Doppler Wind
Lidar Programme, Darmstadt, Germany, 8–9 September 2022, EUMETSAT, https://www.eventsforce.net/eumetsat/frontend/reg/tAgendaWebsite.csp?pageID=15588&ef_sel_menu=247&eventID=38&mode= (last access: 1 November 2022), 2022.
Data Innovation and Science Cluster (DISC): Summary of Quality of Aeolus
Data Products from 1st Reprocessing Campaign covering June to December 2019,
ESA,
https://earth.esa.int/eogateway/documents/20142/0/Aeolus-Summary-Reprocessing-1-DISC.pdf
(last access: 2 November 2022), 2020.
ECMWF: Fact sheet: Earth system data assimilation, ECMWF,
https://www.ecmwf.int/sites/default/files/medialibrary/2020-05/ecmwf-fact-sheet-data-assimilation.pdf
(last access: 10 November 2022), 2020.
ECMWF Research Department: ECMWF Research Experiments (RD), ECMWF [data set],
https://www.ecmwf.int/en/forecasts/dataset/ecmwf-research-experiments, last access: 28 July 2022.
Forsythe, M. and Halloran, G.: Impact of Aeolus Doppler Wind Lidar at the UK
Met Office, Towards an operational Doppler Wind Lidar Programme, Darmstadt,
Germany, 8–9 September 2022, EUMETSAT,
https://www.eventsforce.net/eumetsat/frontend/reg/tAgendaWebsite.csp?pageID=15588&ef_sel_menu=247&eventID=38&mode= (last access: 1 November 2022), 2022.
Garrett, K., Liu, H., Ide, K., Hoffman, R. N., and Lukens, K. E.: Optimization and impact assessment of Aeolus HLOS wind assimilation in
NOAA's global forecast system, Q. J. Roy. Meteor. Soc., 148, 2703–2716, https://doi.org/10.1002/qj.4331, 2022.
Hagelin, S., Azad, R., Lindskog, M., Schyberg, H., and Körnich, H.: Evaluating the use of Aeolus satellite observations in the regional numerical weather prediction (NWP) model Harmonie–Arome, Atmos. Meas. Tech., 14, 5925–5938, https://doi.org/10.5194/amt-14-5925-2021, 2021.
Iwai, H., Aoki, M., Oshiro, M., and Ishii, S.: Validation of Aeolus Level 2B wind products using wind profilers, ground-based Doppler wind lidars, and radiosondes in Japan, Atmos. Meas. Tech., 14, 7255–7275, https://doi.org/10.5194/amt-14-7255-2021, 2021.
King, G. P., Portabella, M., Lin, W., and Stoffelen, A.: Correlating Extremes in Wind Divergence with Extremes in Rain over the Tropical Atlantic, Remote Sensing, 14, 1147, https://doi.org/10.3390/rs14051147, 2022.
Kodera, K., Yamazaki, K., Chiba, M., and Shibata, K.: Downward propagation
of upper stratospheric mean zonal wind perturbation to the troposphere,
Geophys. Res. Lett., 17, 1263–1266, https://doi.org/10.1029/GL017i009p01263, 1990.
Laroche, S. and St-James, J.: Impact of the Aeolus Level-2B horizontal
line-of-sight winds in the Environment and Climate Change Canada global
forecast system, Q. J. Roy. Meteor. Soc., 148, 2047–2062, https://doi.org/10.1002/qj.4300, 2022.
Met Office: Beaufort wind force scale, Met Office, https://www.metoffice.gov.uk/weather/guides/coast-and-sea/beaufort-scale,
last access: 24 February 2023.
Mile, M., Azad, R., and Marseille, G.-J.: Assimilation of Aeolus Rayleigh-Clear Winds Using a Footprint Operator in AROME-Arctic Mesoscale Model, Geophys. Res. Lett., 49, e2021GL097615, https://doi.org/10.1029/2021GL097615, 2022.
National Centers for Environmental Information (NCEI): Integrated Surface Database (ISD), NCEI, National Oceanic and Atmospheric Administration,
https://www.ncei.noaa.gov/products/land-based-station/integrated-surface-database#:~:text=Global%20Climate%20Station%20Summaries%20Summaries%20are%20simple%20indicators,or%20longer%20time%20periods%20or%20for%20customized%20periods, last access: 11 August 2022.
Pacific Marine Environmental Laboratory (PMEL): Global Tropical Moored Buoy
Array, National Oceanic and Atmospheric Administration, PMEL, National Oceanic and Atmospheric Administration,
https://www.pmel.noaa.gov/tao/drupal/disdel/, last access: 4 August 2022.
Parrington, M., Rennie, M., Inness, A., and Duncan, D.: Monitoring the
atmospheric impacts of the Hunga-Tonga eruption, ECMWF,
https://www.ecmwf.int/en/newsletter/171/news/monitoring-atmospheric-impacts-hunga-tonga-eruption (last access: 2 November 2022), 2022.
Pourret, V., Šavli, M., Mahfouf, J., Raspaud, D., Doerenbecher, A.,
Bénichou, H., and Payan, C.: Operational assimilation of Aeolus winds in
the Météo-France global NWP model ARPEGE, Q. J. Roy. Meteor. Soc., 148, 2652–2671, https://doi.org/10.1002/qj.4329, 2022.
Reitebuch, O., Krisch, I., Lemmerz, C., Lux, O., Marksteiner, U.,
Masoumzadeh, N., Weiler, F., Witschas, B., Filomarino, V. C., Meringer, M.,
Schmidt, K., Huber, D., Nikolaus, I., Fabre, F., Vaughan, M., Reissig, K.,
Dabas, A., Flament, T., Lacour, A., Mahfouf, J.-F., Seck, I., Trapon, D.,
Abdalla, S., Isaksen, L., Rennie, M., Benedetti, A., McLean, W., Henry, K.,
Donovan, D., de Kloe, J., Marseille, G.-J., Stoffelen, A., Wang, P., van
Zadelhoff, G.-J., Perron, G., Jupin-Langlois, S., Pijnacker-Hordijk, B.,
Veneziani, M., Bucci, S., Gostinicchi, G., Di Ciolo, L., Bley, S., Geiss,
A., Kanitz, T., Straume, A.-G., Wernham, D., Krisna, T., von Bismarck, J.,
Colangeli, G., Trivigno, V., Romanazzo, M., Aprile, S., and Parrinello, T.:
Contributions from the DISC to accomplish the Aeolus mission objectives,
Aeolus 3rd Anniversary Conference, Taormina, Italy, 23–27 March 2022,
https://elib.dlr.de/186034/ (last access: 20 October 2022), 2022.
Rennie, M. and Isaksen, L.: The NWP impact of Aeolus Level-2B winds at
ECMWF, ECMWF, 265 pp., https://confluence.ecmwf.int/display/AEOL/L2B+team+technical+reports+and+relevant+papers?preview=/46596815/348788257/AED-TN-ECMWF-NWP-025--20230809_v7.0.pdf (last access: 17 August 2023), 2023.
Rennie, M. P., Isaksen, L., Weiler, F., de Kloe, J., Kanitz, T., and Reitebuch, O.: The impact of Aeolus wind retrievals on ECMWF global weather forecasts, Q. J. Roy. Meteor. Soc., 147, 3555–3586, https://doi.org/10.1002/qj.4142, 2021.
Sandu, I., Bechtold, P., Nuijens, L., Beljaars, A., and Brown, A.: On the
causes of systematic forecast biases in near-surface wind direction over the
oceans, ECMWF, 21 pp., https://www.ecmwf.int/sites/default/files/elibrary/2020/19545-
causes-systematic-forecast-biases-near-surface-wind-direction-over-oceans.pdf
(last access: 22 February 2023), 2020.
Stoffelen, A.: Toward the true near-surface wind speed: Error modeling and
calibration using triple collocation, J. Geophys. Res., 103, 7755–7766,
https://doi.org/10.1029/97JC03180, 1998.
Straume-Lindner, A. G., Parrinello, T., Von Bismarck, J., Bley, S., Wernham,
D., Kanitz, T., Alvarez, E., Fischey, P., De Laurentis, M., Fehr, T.,
Ehlers, F., Duc Tran, V., Krisch, I., Reitebuch, O., and Renni, M.: ESA'S
Wind Mission Aeolus - Overview, Status and Outlook, in: 2021 IEEE
International Geoscience and Remote Sensing Symposium IGARSS, IGARSS 2021 -
2021 IEEE International Geoscience and Remote Sensing Symposium, Brussels,
Belgium, 12–16 July 2021, IEEE, 755–758, https://doi.org/10.1109/IGARSS47720.2021.9554007, 2021.
Tripathi, O. P., Baldwin, M., Charlton-Perez, A., Charron, M., Eckermann, S.
D., Gerber, E., Harrison, R. G., Jackson, D. R., Kim, B., Kuroda, Y., Lang,
A., Mahmood, S., Mizuta, R., Roff, G., Sigmond, M., and Son, S.: The
predictability of the extratropical stratosphere on monthly time-scales and
its impact on the skill of tropospheric forecasts, Q. J. Roy. Meteor. Soc.,
141, 987–1003, https://doi.org/10.1002/qj.2432, 2015.
Vogelzang, J. and Stoffelen, A.: Triple collocation, Royal Netherlands
Meteorological Institute, 22 pp.,
https://cdn.knmi.nl/system/data_center_publications/files/000/068/914/original/triplecollocation_nwpsaf_tr_kn_021_v1.0.pdf?1495621500 (last access: 27 January 2022), 2012.
Witschas, B., Lemmerz, C., Geiß, A., Lux, O., Marksteiner, U., Rahm, S., Reitebuch, O., Schäfler, A., and Weiler, F.: Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics, Atmos. Meas. Tech., 15, 7049–7070, https://doi.org/10.5194/amt-15-7049-2022, 2022.
Zuo, H., Hasager, C. B., Karagali, I., Stoffelen, A., Marseille, G.-J., and de Kloe, J.: Evaluation of Aeolus L2B wind product with wind profiling radar measurements and numerical weather prediction model equivalents over Australia, Atmos. Meas. Tech., 15, 4107–4124, https://doi.org/10.5194/amt-15-4107-2022, 2022.
Short summary
Aeolus is a satellite equipped with a Doppler wind lidar to detect global wind profiles. This study evaluates the impact of Aeolus winds on surface wind forecasts over tropical oceans and high-latitude regions based on the ECMWF observing system experiments. We find that Aeolus can slightly improve surface wind forecasts for the region > 60° N, especially from day 5 onwards. For other study regions, the impact of Aeolus is nearly neutral or limited, which requires further investigation.
Aeolus is a satellite equipped with a Doppler wind lidar to detect global wind profiles. This...