References

AMT

Atmospheric Measurement Techniques

AMT

Atmos. Meas. Tech.

1867-8548

Copernicus Publications

Göttingen, Germany

10.5194/amt-7-2695-2014

The performance of Aeolus in heterogeneous atmospheric conditions using high-resolution radiosonde data

Sun

X. J.

¹ Zhang

R. W.

¹ Marseille

G. J.

² Stoffelen

https://orcid.org/0000-0002-4018-4073

² Donovan

³ Liu

¹ Zhao

College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing, China

Weather Research Department of the Royal Netherlands Meteorological Institute, De Bilt, the Netherlands

Regional Climate Department of the Royal Netherlands Meteorological Institute, De Bilt, the Netherlands

26 08 2014

7 8 2695 2717

2014

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://amt.copernicus.org/articles/7/2695/2014/amt-7-2695-2014.html

The full text article is available as a PDF file from https://amt.copernicus.org/articles/7/2695/2014/amt-7-2695-2014.pdf

The European Space Agency Aeolus mission aims to measure wind profiles from space. A major challenge is to retrieve high quality winds in heterogeneous atmospheric conditions, i.e. where both the atmospheric dynamics and optical properties vary strongly within the sampling volume. In preparation for launch we aim to quantify the expected error of retrieved winds from atmospheric heterogeneity, particularly in the vertical, and develop algorithms for wind error correction, as part of the level-2B processor (L2Bp). We demonstrate that high-resolution data from radiosondes provide valuable input to establish a database of collocated wind and atmospheric optics at 10 m vertical resolution to simulate atmospheric conditions along Aeolus' lines of sight. The database is used to simulate errors of Aeolus winds retrieved from the Mie and Rayleigh channel signals. The non-uniform distribution of molecules in the measurement bin introduces height assignment errors in Rayleigh channel winds up to 2.5% of the measurement bin size in the stratosphere which translates to 0.5 m s−1 bias for typical atmospheric conditions, if not corrected. The presence of cloud or aerosol layers in the measurement bin yields biases in Mie channel winds which cannot be easily corrected and mostly exceed the mission requirement of 0.4 m s−1. The collocated Rayleigh channel wind solution is generally preferred because of smaller biases, in particular for transparent cloud and aerosol layers with one-way transmission above 0.8. The results show that Aeolus L2Bp, under development, can be improved by the estimation of atmosphere optical properties to correct for height assignment errors and to identify wind solutions potentially detrimental when used in Numerical Weather Prediction.

References 1

Ackermann, J.: The extinction-to-backscatter ratio of tropospheric aerosol: a numerical study, J. Atmos. Ocean. Tech., 15, 1043–1050, 1998.

Alduchov, O. A. and Eskridge, E. E.: Improved Magnus form approximation of saturation vapor pressure, J. Appl. Meteorol., 35, 601–609, 1996.

Ansmann, A., Ingmann, P., Le Rille, O., Lajas, D., and Wanginger, U.: Particle backscatter and extinction profiling with the spaceborne HSR Doppler wind lidar ALADIN, Proc. of 23rd Int. Laser Radar Conf. (ILRC), Nara, Japan, 1015–1018, 2006.

Chernykh, I. V. and Eskridge, R. E.: Determination of clouds amount and level from radiosonde soundings, J. Appl. Meteorol., 35, 1362–1369, 1996.

ESA: ADM-Aeolus science report, avialable at: <a href="http://esamultimedia.esa.int/docs/SP-1311_ADM-Aeolus FINAL low-res.pdf">http://esamultimedia.esa.int/docs/SP-1311_ADM-Aeolus FINAL low-res.pdf</a> (last access: 10 February 2014), 2008.

Evans, B. T. N.: Sensitivity of the backscatter/extinction ratio to changes in aerosol properties: implications for lidar, Appl. Optics, 27, 3299–3305, 1988.

Fernald, F. G.: Analysis of atmospheric lidar observations: some comments, Appl. Optics, 23, 652–653, 1984.

Gasso, S., Hegg, D. A., Covert, D. S., Collins, D., Noone, K. J., Oström, E., Schmid, B., Russel, P. B., Livingston, J. M., Durkee, P. A., and Josson, H.: Influence of humidity on the aerosol scattering coefficient and its effect on the upwelling radiance during ACE-2, Tellus B, 52, 546–567, 2000.

Groß, S., Tesche, M., Freudenthaler, V., Toledano, C., Wiegner, M., Ansmann, A., Althausen, D., and Seefeldner, M.: Characterization of Saharan dust, marine aerosols and mixtures of biomass-burning aerosols and dust by means of multi-wavelength depolarization and Raman lidar measurements during SAMUM 2, Tellus B, 63, 706–724, 2011.

Guerrero-Rascado, J. L., Costa, M. J., Bortoli, D., Silva, A. M., Lyamani, H., and Alados-Arboledas, L.: Infrared lidar overlap function: an experimental determination, Optics Express, 18, 20350–20359, 2010.

Houchi, K.: On high resolution wind, shear and cloud vertical structures – preparation of the Aeolus space mission, PhD. Thesis, University of Technology, Eindhoven, the Netherlands, 2013.

Houchi, K., Stoffelen, A., Marseille, G. J., and de Kloe, J.: Comparison of wind and wind shear climatologies derived from high-resolution radiosondes and the ECMWF model, J. Geophys. Res., 115, D22123, <a href="http://dx.doi.org/10.1029/2009JD013196">https://doi.org/10.1029/2009JD013196</a>, 2010.

Klett, J. F.: Lidar inversion with variable backscatter/extinction ratios, Appl. Optics, 24, 1638–1643, 1985.

Lazarus, S. M., Krueger, S. K., and Mace, G. G.: A cloud climatology of the Southern Great Plains ARM CART, J. Climate, 13, 1762–1775, 2000.

Liu, Z., Sugimoto, N., and Murayama, T.: Extinction-to-backscatter ratio of Asian dust observed with high-spectral-resolution lidar and Raman lidar, Appl. Optics, 41, 2760–2766, 2002.

Liu, Z., Omar, A. H., Hu, Y. X., Vaughan, M. A., and Winker, D. M.: CALIOP algorithm theoretical basis document, part 3: Scene classification algorithms, available at: <a href="http://www-calipso.larc.nasa.gov/resources/pdfs/PC-SCI-202_Part3v1.0.pdf">http://www-calipso.larc.nasa.gov/resources/pdfs/PC-SCI-202_Part3v1.0.pdf</a> (last access: 10 February 2014), 2005.

Mace, G. G., Marchand, R., Zhang, Q., and Stephens, G. L.: Global hydrometeor occurrence as observed by CloudSat: Initial observations from summer 2006, Geophys. Res. Lett., 34, L09808, <a href="http://dx.doi.org/10.1029/2006GL029017">https://doi.org/10.1029/2006GL029017</a>, 2007.

Marseille, G. J. and Stoffelen, A.: Simulation of wind profiles from a space-borne Doppler wind lidar, Q. J. Roy. Meteorol. Soc., 129, 3079–3098, 2003.

Marseille, G. J., Stoffelen, A., and Barkmeijer, J.: Sensitivity Observing System Experiment (SOSE) – a new effective NWP-based tool in designing the global observing system, Tellus A, 60, 216–233, 2008.

Marseille, G. J., Stoffelen, A., Schyberg, H., Körnich, H., and Megner, L.: VAMP – vertical Aeolus measurement positioning, ESA study final report, Contract 20940/07/NL/JA, ESA, Noordwijk, the Netherlands, 2010.

Marseille, G. J., Houchi, K., de Kloe, J., and Stoffelen, A.: The definition of an atmospheric database for Aeolus, Atmos. Meas. Tech., 4, 67–88, <a href="http://dx.doi.org/10.5194/amt-4-67-2011">https://doi.org/10.5194/amt-4-67-2011</a>, 2011.

Marseille, G. J., Stoffelen, A., Schyberg, H., Körnich, H., and Megner, L.: VHAMP – vertical and horizontal Aeolus measurement positioning, ESA study final report, CCN2 to Contract 20940/07/NL/JA, ESA, Noordwijk, the Netherlands, 2013.

Müller, D., Ansmann, A., Mattis, I., Tesche, M., Wandinger, U., Althausen, D., and Pisani, G.: Aerosol-type-dependent lidar ratios observed with Raman lidar, J. Geophys. Res., 112, D16202, <a href="http://dx.doi.org/10.1029/2006JD008292">https://doi.org/10.1029/2006JD008292</a>, 2007.

Onasch, T. B., Siefert, T. L., Brooks, S. D., Prenni, A. J., Murray, B., Wilson, M. A., and Tolbert, M. A.: Infrared spectroscopic study of the deliquescence and efflorescence of ammonium sulfate aerosol as a function of temperature, J. Geophys. Res., 104, 21317–21326, 1999.

Paffrath, U., Lemmerz, C., Reitebuch, O., Witschas, B., Nikolaus, I., and Freudenthaler, V.: The airborne demonstrator for the direct-detection Doppler wind Lidar ALADIN on ADM-Aeolus, Part II: Simulations and Rayleigh receiver radiometric performance, J. Atmos. Ocean. Tech., 26, 2516–2530, 2009.

Poore, K. D., Wang, J. H., and Rossow, W. B.: Cloud layer thicknesses from a Combination of surface and upper-air obversions, J. Climate, 8, 550–568, 1995.

Reitebuch, O., Lemmerz, C., Nagel, E., Paffrath, U., Durand, Y., Endemann, M., Fabre, F., and Chaloupy, M.: The airborne demonstrator for the direct-detection Doppler wind lidar ALADIN on ADM-Aeolus, Part I: Instrument design and comparison to satellite instrument, J. Atmos. Ocean. Tech., 26, 2501–2515, 2009.

Rogers, R. M., McCann, K., and Hoff, R. M.: Quantifying the effect of humidity on aerosol scattering with a Raman lidar, 14th Joint Conference on the Applications of Air Pollution Meteorology with the Air and Waste Management Assoc., Atlanta, Georgia, 2006.

Schillinger, M., Morancais, D., Fabre, F., and Culoma, A. J.: ALADIN: the LIDAR instrument for the AEOLUS mission, Proc. SPIE, 4881, 40–51, 2003.

Spinhirne, J. D., Chudamani, S., Cabanaugh, J. F., and Bufton, J. L.: Aerosol and cloud backscatter at 1.06, 1.54, and 0.53 μm by airborne hard-target-calibrated Nd:YAG/methane Raman lidar, Appl. Optics, 36, 3475–3489, 1997.

Stoffelen, A., Pailleux, J., Källén, E., Vaughan, J. M., Isaksen, L., Flamant, P., Wergen, W., Andersson, E., Schyberg, H., Culoma, A., Meynart, M., Endemann, M., and Ingmann, P.: The Atmospheric dynamics mission for global wind measurement, B. Am. Meteorol. Soc., 86, 73–87, 2005.

Stoffelen, A., Marseille, G. J., Bouttier, F., Vasiljevic, D., de Haan, S., and Cardinali, C.: ADM-Aeolus doppler wind lidar observing system simulation experiment, Q. J. Roy. Meteorol. Soc., 132, 1927–1947, <a href="http://dx.doi.org/10.1256/qj.05.83">https://doi.org/10.1256/qj.05.83</a>, 2006.

Tan, D. G. H., Andersson, E., Fisher, M., and Isaksen, L.: Observing system impact assessment using a data assimilation ensemble technique: Application to the ADM-Aeolus wind profiling mission, Q. J. Roy. Meteorol. Soc., 133, 381–390, 2007.

Tan, D. G. H., Andersson, E., de Kloe, J., Marseille, G. J., Stoffelen, A., Poli, P., Denneulin, M. L., Dabas, A., Huber, D., Reitebuch, O., Flamant, P., Rille, O. L., and Nett, H.: The ADM-Aeolus wind retrieval algorithms, Tellus A, 60, 191–205, 2008.

Vaisala: Vaisala RS92 Number One in WMO Intercomparison, available at: <a href="http://www.vaisala.com/Vaisala20News20Documents/Vaisala%20News

Vaughan, J. M.: Scattering in the atmosphere, in: Scattering and Inverse Scattering in Pure and Applied Science, edited by: Pike, E. R. and Sabatier, P. C., Academic Press, San Diego, 2002.

Vaughan, J. M., Geddes, N. J., Flamant, P. H., and Flesia, C.: Establishment of a backscatter coefficient and atmospheric database, ESA contract 12510/97/NL/RE, ESA, Noordwijk, the Netherlands, p. 110, 1998.

Vogelzang, J., Stoffelen, A., Verhoef, A., and Figa-Saldana, J.: On the quality of high-resolution scatterometer winds, J. Geophys. Res., 116, C10033, <a href="http://dx.doi.org/10.1029/2010JC006640">https://doi.org/10.1029/2010JC006640</a>, 2011.

Wang, J. H. and Rossow, W. B.: Determination of cloud vertical structure from upper-air observations, J. Appl. Meteorol., 34, 2243–2258, 1995.

Winker, D. M.: The CALIPSO Mission and Initial Observations of Aerosols and Clouds from CALIOP, Proc. SPIE, 6409, 1–3, 2006.

Zhang, J. Q., Chen, H. B., Li, Z. Q., Fan, X. H., Peng, L., Yu, Y., and Cribb, M.: Analysis of cloud layer structure in Shouxian, China using RS92 radiosonde aided by 95 GHz cloud radar, J. Geophys. Res., 115, D00K30, <a href="http://dx.doi.org/10.1029/2010JD014030">https://doi.org/10.1029/2010JD014030</a>, 2010.