Articles | Volume 14, issue 3
https://doi.org/10.5194/amt-14-2167-2021
https://doi.org/10.5194/amt-14-2167-2021
Research article
 | 
18 Mar 2021
Research article |  | 18 Mar 2021

Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents

Anne Martin, Martin Weissmann, Oliver Reitebuch, Michael Rennie, Alexander Geiß, and Alexander Cress

Related authors

Investigation of links between dynamical scenarios and particularly high impact of Aeolus on numerical weather prediction (NWP) forecasts
Anne Martin, Martin Weissmann, and Alexander Cress
Weather Clim. Dynam., 4, 249–264, https://doi.org/10.5194/wcd-4-249-2023,https://doi.org/10.5194/wcd-4-249-2023, 2023
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Validation and Intercomparisons
A data-driven persistence test for robust (probabilistic) quality control of measured environmental time series: constant value episodes
Najmeh Kaffashzadeh
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-310,https://doi.org/10.5194/amt-2022-310, 2023
Revised manuscript accepted for AMT
Short summary
A comparative evaluation of snowflake particle shape estimation techniques used by the Precipitation Imaging Package (PIP), Multi-Angle Snowflake Camera (MASC), and Two-Dimensional Video Disdrometer (2DVD)
Charles Nelson Helms, Stephen Joseph Munchak, Ali Tokay, and Claire Pettersen
Atmos. Meas. Tech., 15, 6545–6561, https://doi.org/10.5194/amt-15-6545-2022,https://doi.org/10.5194/amt-15-6545-2022, 2022
Short summary
Comparison of GRUAN data products for Meisei iMS-100 and Vaisala RS92 radiosondes at Tateno, Japan
Shunsuke Hoshino, Takuji Sugidachi, Kensaku Shimizu, Eriko Kobayashi, Masatomo Fujiwara, and Masami Iwabuchi
Atmos. Meas. Tech., 15, 5917–5948, https://doi.org/10.5194/amt-15-5917-2022,https://doi.org/10.5194/amt-15-5917-2022, 2022
Short summary
Validation of the Aeolus Level-2B wind product over Northern Canada and the Arctic
Chih-Chun Chou, Paul J. Kushner, Stéphane Laroche, Zen Mariani, Peter Rodriguez, Stella Melo, and Christopher G. Fletcher
Atmos. Meas. Tech., 15, 4443–4461, https://doi.org/10.5194/amt-15-4443-2022,https://doi.org/10.5194/amt-15-4443-2022, 2022
Short summary
Boundary-layer height and surface stability at Hyytiälä, Finland, in ERA5 and observations
Victoria Anne Sinclair, Jenna Ritvanen, Gabin Urbancic, Irina Statnaia, Yurii Batrak, Dmitri Moisseev, and Mona Kurppa
Atmos. Meas. Tech., 15, 3075–3103, https://doi.org/10.5194/amt-15-3075-2022,https://doi.org/10.5194/amt-15-3075-2022, 2022
Short summary

Cited articles

Baars, H., Herzog, A., Heese, B., Ohneiser, K., Hanbuch, K., Hofer, J., Yin, Z., Engelmann, R., and Wandinger, U.: Validation of Aeolus wind products above the Atlantic Ocean, Atmos. Meas. Tech., 13, 6007–6024, https://doi.org/10.5194/amt-13-6007-2020, 2020. a
Baker, W. E., Atlas, R., Cardinali, C., Clement, A., Emmitt, G. D., Gentry, B. M., Hardesty, R. M., Källén, E., Kavaya, M. J., Langland, R., Ma, Z., Masutani, M., McCarty, W., Pierce, R. B., Pu, Z., Riishojgaard, L. P., Ryan, J., Tucker, S., Weissmann, M., and Yoe, J. G.: Lidar-Measured Wind Profiles: The Missing Link in the Global Observing System, B. Am. Meteorol. Soc., 95, 543–564, https://doi.org/10.1175/BAMS-D-12-00164.1, 2014. a
Bormann, N., Saarinen, S., Kelly, G., and Thépaut, J.-N.: The Spatial Structure of Observation Errors in Atmospheric Motion Vectors from Geostationary Satellite Data, Q. J. Roy. Meteor. Soc., 131, 706–718, https://doi.org/10.1175/1520-0493(2003)131<0706:TSSOOE>2.0.CO;2, 2003. a
Dabas, A., Denneulin, M., 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, 206–215, https://doi.org/10.1111/j.1600-0870.2007.00284.x, 2008. a
Dirksen, R. J., Sommer, M., Immler, F. J., Hurst, D. F., Kivi, R., and Vömel, H.: Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosonde, Atmos. Meas. Tech., 7, 4463–4490, https://doi.org/10.5194/amt-7-4463-2014, 2014. a
Download
Short summary
This study provides an overview of validation activities to determine the Aeolus HLOS wind errors and to understand the biases by investigating possible dependencies and testing bias correction approaches. To ensure meaningful validation statistics, collocated radiosondes and two different global NWP models, the ECMWF IFS and the ICON model (DWD), are used as reference data. To achieve an estimate for the Aeolus instrumental error the representativeness errors for the comparisons are evaluated.