World Library  

Add to Book Shelf
Flag as Inappropriate
Email this Book

The Horizontal Resolution of Mipas : Volume 1, Issue 1 (06/10/2008)

By Von Clarmann, T.

Click here to view

Book Id: WPLBN0003979913
Format Type: PDF Article :
File Size: Pages 23
Reproduction Date: 2015

Title: The Horizontal Resolution of Mipas : Volume 1, Issue 1 (06/10/2008)  
Author: Von Clarmann, T.
Volume: Vol. 1, Issue 1
Language: English
Subject: Science, Atmospheric, Measurement
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Ridolfi, M., Höpfner, M., Lambert, J., Clarmann, T. V., & Clercq, C. D. (2008). The Horizontal Resolution of Mipas : Volume 1, Issue 1 (06/10/2008). Retrieved from

Description: Forschungszentrum Karlsruhe, Institut für Meteorologie und Klimaforschung, Karlsruhe, Germany. Limb remote sensing from space provides atmospheric composition measurements at high vertical resolution while the information is smeared in the horizontal domain. The horizontal components of two-dimensional (altitude and along-track coordinate) averaging kernels of a limb retrieval constrained to horizontal homogeneity can be used to estimate the horizontal resolution of limb retrievals. This is useful for comparisons of measured data with modeled data, to construct horizontal observation operators in data assimilation applications or when measurements of different horizontal resolution are intercompared. We present these averaging kernels for retrievals of temperature, H2O, O3, CH4, N2O, HNO3 and NO2 from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) high-resolution limb emission spectra. The horizontal smearing of a MIPAS retrieval in terms of full width at half maximum of the rows of the horizontal averaging kernel matrix varies typically between about 200 and 350 km for most species, altitudes and atmospheric conditions. The range where 95% of the information originates from varies from about 260 to 440 km for these cases. This information spread is smaller than the MIPAS horizontal sampling, i.e. MIPAS data are horizontally undersampled, and the effective horizontal resolution is driven by the sampling rather than the smearing. The point where the majority of the information originates from is displaced from the tangent point towards the satellite by typically less than 10 km for trace gas profiles and about 50 to 100 km for temperature, with a few exceptions for uppermost altitudes. The geolocation of a MIPAS profile is defined as the tangent point of the middle line of sight in a MIPAS limb scan. The majority of the information displacement with respect to this nominal geolocation of the measurement is caused by the satellite movement and the geometrical displacement of the actual tangent point as a function of the elevation angle. In none of the cases investigated, propagation of the horizontal smoothing on the vertical profile shape has been observed.

The horizontal resolution of MIPAS

Carlotti, M.: Global–fit approach to the analysis of limb–scanning atmospheric measurements, Appl. Optics, 27, 3250–3254, 1988.; Carlotti, M., Dinelli, B M., Raspollini, P., and Ridolfi, M.: Geo–fit approach to the analysis of limb–scanning satellite measurements, Appl. Optics, 40, 1872–1885, 2001.; Carlotti, M., Brizzi, G., Papandrea, E., Prevedelli, M., Ridolfi, M., Dinelli, B M., and Magnani, L.: GMTR: Two–dimensional geo–fit multitarget retrieval model for Michelson Interferometer for Passive Atmospheric Sounding/ Environmental Satellite observations, Appl. Optics, 45, 716–727, 2006.; Dudhia, A., Jay, V L., and Rodgers, C D.: Microwindow selection for high–spectral–resolution sounders, Appl. Optics, 41, 3665–3673, 2002.; Fischer, H., Birk, M., Blom, C., Carli, B., Carlotti, M., von Clarmann, T., Delbouille, L., Dudhia, A., Ehhalt, D., Endemann, M., Flaud, J M., Gessner, R., Kleinert, A., Koopmann, R., Langen, J., López-Puertas, M., Mosner, P., Nett, H., Oelhaf, H., Perron, G., Remedios, J., Ridolfi, M., Stiller, G., and Zander, R.: MIPAS: an instrument for atmospheric and climate research, Atmos. Chem. Phys., 8, 2151–2188, 2008.; Goldman, A. and Saunders, R S.: Analysis of Atmospheric Infrared Spectra for Altitude Distribution of Atmospheric Trace Constituents – I. Method of Analysis, J. Quant. Spectrosc. Ra., 21, 155–161, 1979.; Ide, K., Courtier, P., Ghil, M., and Lorenc, A C.: Unified notation for data assimilation: Operational, sequential and Variational, J. Meteorol. Soc. Jpn., 75, 181–189, 1997.; Kiefer, M., von Clarmann, T., and Grabowski, U.: State parameter Data Base for MIPAS Data Analysis, Adv. Space Res., 30, 2387–2392, 2002.; Lahoz, W A., Geer, A J., Bekki, S., Bormann, N., Ceccherini, S., Elbern, H., Errera, Q., Eskes, H J., Fonteyn, D., Jackson, D R., Khattatov, B., Marchand, M., Massart, S., Peuch, V.-H., Rharmili, S., Ridolfi, M., Segers, A., Talagrand, O., Thornton, H E., Vik, A F., and von Clarmann, T.: The Assimilation of Envisat data (ASSET) project, Atmos. Chem. Phys., 7, 1773–1796, 2007.; Levenberg, K.: A method for the solution of certain non–linear problems in least squares, Quart. Appl. Math., 2, 164–168, 1944.; Marquardt, D W.: An algorithm for least–squares estimation of nonlinear parameters, J. Soc. Indust. Appl. Math., 11, 431–441, 1963.; McKee, T B., Whitman, R I., and Lambiotte, Jr., J J.: A Technique to Infer Atmospheric Water–Vapor Mixing Ratio from Measured Horizon Radiance Profiles, Tech. Rep. TN D–5252, NASA, Washington, D.C., 1969.; Mill, J D. and Drayson, S R.: A nonlinear technique for inverting limb absorption profiles, Developments in Atmospheric Science, 9, 123–135, 1978.; Phillips, D.: A Technique for the numerical solution of certain integral equations of first kind, J. Ass. Comput. Mat., 9, 84–97, 1962.; Raspollini, P., Belotti, C., Burgess, A., Carli, B., Carlotti, M., Ceccherini, S., Dinelli, B M., Dudhia, A., Flaud, J.-M., Funke, B., Höpfner, M., López-Puertas, M., Payne, V., Piccolo, C., Remedios, J J., Ridolfi, M., and Spang, R.: MIPAS level 2 operational analysis, Atmos. Chem. Phys., 6, 5605–5630, 2006.; Ridolfi, M., Carli, B., Carlotti, M., von Clarmann, T., Dinelli, B., Dudhia, A., Flaud, J.-M., Höpfner, M., Morris, P E., Raspollini, P., Stiller, G., and Wells, R J.: Optimized Forward and Retrieval Scheme for MIPAS Near-Real-Time Data Processing, Appl. Optics, 39, 1323–1340, 2000.; Stiller, G P.: The Karlsruhe Optimized and Precise Radiative Transfer Algorithm (KOPRA), FZKA 6487, Wissenschaftliche Berichte, Forschungszentrum Karlsruhe, 2000.; Ridolfi, M., Blum, U., Carli, B., Catoire, V., Ceccherini, S., Claude, H., De Clercq, C., Fricke, K H., Friedl-Vallon, F., Iarlori, M., Keckhut, P., Kerridge, B., Lambert, J.-C., Meijer, Y J., Mona, L., Oelhaf, H., Pirre, G. P M., Rizi, V., Robert, C., Swart, D., von Clarmann, T., Waterfall, A., and Wetzel, G.: Geophysical validation of temperature retrieved by the ESA processor from MIPAS/ENVISAT atmospheric limb-emission measure


Click To View

Additional Books

  • Sensitivity of Thermal Infrared Sounders... (by )
  • Investigation of the Accuracy for Single... (by )
  • Continuous Measurements of Greenhouse Ga... (by )
  • A Theoretical Study of the Effect of Sub... (by )
  • Cloud Screening and Quality Control Algo... (by )
  • New Dynamic Nnorsy Ozone Profile Climato... (by )
  • Comparison of Profile Total Ozone from S... (by )
  • Airborne Lidar Reflectance Measurements ... (by )
  • 1D-var Retrieval of Daytime Total Column... (by )
  • Measuring So2 Ship Emissions with an Ult... (by )
  • A Review of the Ozone Hole from 2008 to ... (by )
  • Cirrus Crystal Fall Velocity Estimates U... (by )
Scroll Left
Scroll Right


Copyright © World Library Foundation. All rights reserved. eBooks from Hawaii eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.