Technical Note: Functional sliced inverse regression to infer temperature, water vapour and ozone from IASI data

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Abstract

A retrieval algorithm that uses a statistical strategy based on dimension reduction is proposed. The methodology and details of the implementation of the new algorithm are presented and discussed. The algorithm has been applied to high resolution spectra measured by the Infrared Atmospheric Sounding Interferometer instrument to retrieve atmosphericprofiles of temperature, water vapour and ozone. The performance of the inversion strategy has been assessed by comparing the retrieved profiles to the ones obtained by collocating in space and time profiles from the European Centre for Medium-Range Weather Forecasts analysis. © 2009 Author(s).

Figures

  • Fig. 1. Root mean square error of the retrievals as a function of the number of scores, for various atmospheric layers and parameters.
  • Fig. 2. Temperature root mean square error of the retrievals on the Chevallier dataset as obtained for FSIR and EOF.
  • Fig. 3. Water vapour percentage root mean square error of the retrievals on the Chevallier dataset as obtained for FSIR and EOF.
  • Figures 2–4 exemplify the expected root mean square error for temperature, water vapour and ozone, respectively. The figures also allow us to compare the forecast skill of FSIR with that of the EOF regression. For the temperature the two regression schemes are almost equivalent in terms of expected root mean square error. For water vapour FSIR is superior to EOF, the same as for ozone.
  • Fig. 5. Temperature root mean square difference (IASI retrieval – ECMWF) for 3 choices of the number of EDR (PC) scores for FSIR (left plot) and EOF (right plot).
  • Fig. 6. Water Vapour root mean square difference (IASI retrieval – ECMWF) for 3 choices of the number of EDR (PC) scores for FSIR (left plot) and EOF (right plot).
  • Fig. 7. Ozone root mean square difference (IASI retrieval – ECMWF) for 3 choices of the number of EDR (PC) scores for FSIR (left plot) and EOF (right plot).
  • Fig. 9. (a) – Mean retrieved water vapour profile obtained by averaging over the 603 IASI soundings and comparison with the ECMWF corresponding mean profile. (b) Percentage root mean square difference (IASI retrieval – ECMWF) as obtained for EOF and FSIR methodologies.

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CITATION STYLE

APA

Amato, U., Antoniadis, A., De Feis, I., Masiello, G., Matricardi, M., & Serio, C. (2009). Technical Note: Functional sliced inverse regression to infer temperature, water vapour and ozone from IASI data. Atmospheric Chemistry and Physics, 9(14), 5321–5330. https://doi.org/10.5194/acp-9-5321-2009

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