AMT Atmospheric Measurement Techniques AMT Atmos. Meas. Tech. 1867-8548 Copernicus Publications Göttingen, Germany 10.5194/amt-6-359-2013 Quantification of uncertainties of water vapour column retrievals using future instruments Diedrich H. https://orcid.org/0000-0001-7763-2549 1 Preusker R. 1 Lindstrot R. 1 Fischer J. 1 Institut für Weltraumwissenschaften, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany 14 02 2013 6 2 359 370 Copyright: © 2013 H. Diedrich et al. 2013 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/6/359/2013/amt-6-359-2013.html The full text article is available as a PDF file from https://amt.copernicus.org/articles/6/359/2013/amt-6-359-2013.pdf

This study presents a quantification of uncertainties of total column water vapour retrievals based on simulated near-infrared measurements of upcoming instruments. The concepts of three scheduled spectrometers were taken into account: OLCI (Ocean and Land Color Instrument) on Sentinel-3, METimage on an EPS-SG (EUMETSAT Polar System – Second Generation) satellite and FCI (Flexible Combined Imager) on MTG (Meteosat Third Generation). Optimal estimation theory was used to estimate the error of a hypothetical total water vapour column retrieval for 27 different atmospheric cases. The errors range from 100% in very dry cases to 2% in humid cases with a very high surface albedo. Generally, the absolute uncertainties increase with higher water vapour column content due to H<sub>2</sub>O-saturation and decrease with a brighter surface albedo. Uncertainties increase with higher aerosol optical thickness, apart from very dark cases. Overall, the METimage channel setting enables the most accurate retrievals. The retrieval using the MTG-FCI build-up has the highest uncertainties apart from very bright cases. <br><br> On the one hand, a retrieval using two absorption channels increases the accuracy, in some cases by one order of magnitude, in comparison to a retrieval using just one absorption channel. On the other hand, a retrieval using three absorption channels has no significant advantage over a two-absorption channel retrieval. <br><br> Furthermore, the optimal position of the absorption channels was determined using the concept of the "information content". For a single channel retrieval, a channel at 900 or 915 nm has the highest mean information content over all cases. The second absorption channel is ideally weakly correlated with the first one, and therefore positioned at 935 nm, in a region with stronger water vapour absorption.

 References Albert,&nbsp;P., Bennartz,&nbsp;R., and Fischer,&nbsp;J.: Remote sensing of atmospheric water vapor from backscattered sunlight in cloudy atmospheres, J.&nbsp;Atmos. Ocean. Tech., 18, 865–874, <a href="http://dx.doi.org/10.1175/1520-0426(2001)018<0865:RSOAWV>2.0.CO;2">https://doi.org/10.1175/1520-0426(2001)018<0865:RSOAWV>2.0.CO;2</a>, 2001. Albert,&nbsp;P., Bennartz,&nbsp;R., Preusker,&nbsp;R., Leinweber,&nbsp;R., and Fischer,&nbsp;J.: Remote sensing of atmospheric water vapor using the moderate resolution imaging spectroradiometer, J.&nbsp;Atmos. Ocean. Tech., 22, 309–314, <a href="http://dx.doi.org/10.1175/JTECH1708.1">https://doi.org/10.1175/JTECH1708.1</a>, 2005. Bartsch,&nbsp;B., Bakan,&nbsp;S., and Fischer,&nbsp;J.: Passive remote sensing of the atmospheric water vapour content above land surfaces, Adv. Space Res., 18, 25–28, <a href="http://dx.doi.org/10.1016/0273-1177(95)00285-5">https://doi.org/10.1016/0273-1177(95)00285-5</a>, 1996. Bennartz,&nbsp;R. and Fischer,&nbsp;J.: A&nbsp;modified k-distribution approach applied to narrow band water vapour and oxygen absorption estimates in the near infrared, J.&nbsp;Quant. Spectrosc. Ra., 66, 539–553, <a href="http://dx.doi.org/10.1016/S0022-4073(99)00184-3">https://doi.org/10.1016/S0022-4073(99)00184-3</a>, 2000. Carrer, D., Roujean, J.-L., and Meurey, C.: Comparing Operational MSG/SEVIRI Land Surface Albedo Products From Land SAF With Ground Measurements and MODIS, IEEE T. Geosci. Remote, 48, 1714–1728, <a href="http://dx.doi.org/10.1109/TGRS.2009.2034530">https://doi.org/10.1109/TGRS.2009.2034530</a>, 2010. Fischer,&nbsp;J. and Grassl,&nbsp;H.: Radiative transfer in an atmosphere-ocean system: an azimuthally dependent matrix-operator approach, Appl. Optics, 23, 1032–1039, <a href="http://dx.doi.org/10.1364/AO.23.001032">https://doi.org/10.1364/AO.23.001032</a>, 1984. Fischer,&nbsp;J. and Leinweber,&nbsp;R.&nbsp;P.&nbsp;R.: Retrieval of total water vapour content from MERIS measurements algorithm theoretical basis document ATBD 2.4, Free University Berlin, Institute for Space Science, Berlin, 2010. Gao,&nbsp;B.-C., Goetz,&nbsp;A.&nbsp;F.&nbsp;H., Westwater,&nbsp;E.&nbsp;R., Conel,&nbsp;J.&nbsp;E., and Green,&nbsp;R.&nbsp;O.: Possible near-IR channels for remote sensing precipitable water vapor from geostationary satellite platforms, J.&nbsp;Appl. Meteorol., 32, 1791–1801, <a href="http://dx.doi.org/10.1175/1520-0450(1993)032<1791:PNICFR>2.0.CO;2">https://doi.org/10.1175/1520-0450(1993)032<1791:PNICFR>2.0.CO;2</a>, 1993. Grant,&nbsp;I.&nbsp;P. and Hunt,&nbsp;G.&nbsp;E.: Discrete space theory of radiative transfer,&nbsp;I. Fundamentals, P.&nbsp;Roy. Soc. Lond.&nbsp;A Mat., 313, 183–197, 1969{a}. Grant,&nbsp;I.&nbsp;P. and Hunt,&nbsp;G.&nbsp;E.: Discrete space theory of radiative transfer, II. Stability and non- negativity, P.&nbsp;Roy. Soc. Lond.&nbsp;A Mat., 313, 199–216, 1969{b}. Hollstein,&nbsp;A. and Fischer,&nbsp;J.: Radiative transfer solutions for coupled atmosphere ocean systems using the matrix operator technique, J.&nbsp;Quant. Spectrosc. Ra., 113, 536–548, <a href="http://dx.doi.org/10.1016/j.jqsrt.2012.01.010">https://doi.org/10.1016/j.jqsrt.2012.01.010</a>, 2012. Hess, M., Koepke, P., and Schult, I.: Optical Properties of Aerosols and Clouds: The Software Package OPAC, B. Am. Meteorol. Soc., 79, 831–844, <a href="http://dx.doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2">https://doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2</a>, 1998. Lindstrot,&nbsp;R., Preusker,&nbsp;R., Diedrich,&nbsp;H., Doppler,&nbsp;L., Bennartz,&nbsp;R., and Fischer,&nbsp;J.: 1D-Var retrieval of daytime total columnar water vapour from MERIS measurements, Atmos. Meas. Tech., 5, 631–646, <a href="http://dx.doi.org/10.5194/amt-5-631-2012">https://doi.org/10.5194/amt-5-631-2012</a>, 2012. No{ël}, S., Buchwitz, M., and Burrows, J.&nbsp;P.: Retrieval of total water vapour column amounts from GOME/ERS-2 data, Adv. Space Res., 29, 1697–1702, 2002. Plass,&nbsp;G.&nbsp;N., Kattawar,&nbsp;G.&nbsp;W., and Catchings,&nbsp;F.&nbsp;E.: Matrix operator theory of radiative transfer, 1: Rayleigh scattering, Appl. Optics, 12, 314–329, <a href="http://dx.doi.org/10.1364/AO.12.000314">https://doi.org/10.1364/AO.12.000314</a>, 1973. Rodgers,&nbsp;C.: Inverse Methods for Atmospheric Sounding: Theory and Practice, World Scientific Pub Co., Oxford, 2000. Rothman,&nbsp;L.&nbsp;S., Rinsland,&nbsp;C.&nbsp;P., Goldman,&nbsp;A., Massie,&nbsp;S.&nbsp;T., Edwards,&nbsp;D.&nbsp;P., Flaud,&nbsp;J.-M., Perrin,&nbsp;A., Camy-Peyret,&nbsp;C., Dana,&nbsp;V., Mandin,&nbsp;J.-Y., Schroeder,&nbsp;J., McCann,&nbsp;A., Gamache,&nbsp;R.&nbsp;R., Wattson,&nbsp;R.&nbsp;B., Yoshino,&nbsp;K., Chance,&nbsp;K., Jucks,&nbsp;K., Brown,&nbsp;L.&nbsp;R., Nemtchinov,&nbsp;V., and Varanasi,&nbsp;P.: Reprint of: The HITRAN molecular spectroscopic database and HAWKS (HITRAN Atmospheric Workstation): 1996 edition, J.&nbsp;Quant. Spectrosc. Ra., 111, 1568–1613, <a href="http://dx.doi.org/10.1016/j.jqsrt.2010.04.019">https://doi.org/10.1016/j.jqsrt.2010.04.019</a>, 2010. Schluessel,&nbsp;P. and Emery,&nbsp;W.&nbsp;J.: Atmospheric water vapour over oceans from SSM/I measurements, Int.&nbsp;J. Remote Sens., 11, 753–766, <a href="http://dx.doi.org/10.1080/01431169008955055">https://doi.org/10.1080/01431169008955055</a>, 1990. Shannon,&nbsp;C.&nbsp;E. and Weaver,&nbsp;W.: The Mathematical Theory of Communication, 1949. IPCC: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., 996&nbsp;pp., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007. Susskind, J., Barnet, C. D., and Blaisdell, J. M.: Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds, IEEE T. Geosci. Remote, 41, 390–409, 2003. Twomey,&nbsp;S., Jacobowitz,&nbsp;H., and Howell,&nbsp;H.&nbsp;B.: Matrix methods for multiple-scattering problems, J.&nbsp;Atmos. Sci., 23, 289–298, 1966. Wiscombe,&nbsp;W.&nbsp;J.: Improved Mie scattering algorithms, Appl. Optics, 19, 1505–1509, https://doi.org/\href{http://dx.doi.org/10.1364/AO.19.001505}, 1980.