Articles | Volume 15, issue 9
https://doi.org/10.5194/amt-15-2719-2022
https://doi.org/10.5194/amt-15-2719-2022
Research article
 | 
06 May 2022
Research article |  | 06 May 2022

Exploiting Aeolus level-2b winds to better characterize atmospheric motion vector bias and uncertainty

Katherine E. Lukens, Kayo Ide, Kevin Garrett, Hui Liu, David Santek, Brett Hoover, and Ross N. Hoffman

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Cited articles

Abdalla, S., de Kloe, J., Flament, T., Krisch, I., Marksteiner, U., Reitebuch, O., Rennie, M., Weiler, F., and Witschas, B.: Verification report of first Reprocessing campaign for FM-B covering the time period 2019-06 to 2019-12. Tech. rep., Aeolus Data Innovation Science Cluster DISC, Version 1.0, REF: AED-TN-ECMWF-GEN-040, internal document available for registered Aeolus Cal/Val teams, summary of this document available at: https://earth.esa.int/eogateway/documents/20142/0/Aeolus-Summary-Reprocessing-1-DISC.pdf (last access: 4 January 2022), 2020. 
Alekseev, G., Kuzmina, S., Bobylev, L., Urazgildeeva, A., and Gnatiuk, N.: Impact of atmospheric heat and moisture transport on the Arctic warming, Int. J. Climatol., 39, 3582–3592, https://doi.org/10.1002/joc.6040, 2018. 
Bedka, K. M., Velden, C. S. Petersen, R. A. Feltz, W. F., and Mecikalski, J. R.: Comparisons of Satellite-Derived Atmospheric Motion Vectors, Rawinsondes, and NOAA Wind Profiler Observations, J. Appl. Meteorol. Clim., 48, 1542–1561, https://doi.org/10.1175/2009JAMC1867.1, 2009. 
Berger, H., Langland, R., Velden, C. S., Reynolds, C. A., and Pauley, P. M.: Impact of enhanced satellite-derived atmospheric motion vector observations on numerical tropical cyclone track forecasts in the western North Pacific during TPARC/TCS-08, J. Appl. Meteorol. Clim., 50, 2309–2318, https://doi.org/10.1175/JAMC-D-11-019.1, 2011. 
Bormann, N., Kelly, G., and Thépaut, J.-N.: Characterising and correcting speed biases in atmospheric motion vectors within the ECMWF system, in: Sixth Int. Winds Workshop, 7–10 May 2002, Madison, WI, USA, EUMETSAT, 113–120, http://cimss.ssec.wisc.edu/iwwg/iww6/session3/bormann_1_bias.pdf (last access: 18 April 2021), 2002. 
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Short summary
Winds that are crucial to weather forecasting derived from two different techniques – tracking satellite images (AMVs) and direct measurement of molecular and aerosol motions by Doppler lidar (Aeolus satellite winds) – are compared. We find that AMVs and Aeolus winds are highly correlated. Aeolus Mie-cloudy winds have great potential value as a comparison standard for AMVs. Larger differences are found in the Southern Hemisphere related to higher wind speed and higher vertical variation in wind.