Near infrared nadir retrieval of vertical column densities: Methodology and application to SCIAMACHY

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Abstract

Nadir observations with the shortwave infrared channels of SCIAMACHY on-board the ENVISAT satellite can be used to derive information on atmospheric gases such as CO, CH 4, N 2O, CO 2, and H 2O. For the operational level 1b-2 processing of SCIAMACHY data, a new retrieval code BIRRA (Beer InfraRed Retrieval Algorithm) has been developed. BIRRA performs a nonlinear or separable least squares fit (with bound constraints optional) of the measured radiance, where molecular concentration vertical profiles are scaled to fit the observed data. Here we present the forward modeling (radiative transfer) and inversion (least squares optimization) fundamentals of the code along with the further processing steps required to generate higher level products such as global distributions and time series. Moreover, various aspects of level 1 (observed spectra) and auxiliary input data relevant for successful retrievals are discussed. BIRRA is currently used for operational analysis of carbon monoxide vertical column densities from SCIAMACHY channel 8 observations, and is being prepared for methane retrievals using channel 6 spectra. A set of representative CO retrievals and first CH 4 results are presented to demonstrate BIRRA's capabilities. © 2011 Author(s).

Figures

  • Fig. 1. Jacobians [erg/s/(cm2srcm−1)/ppm] for molecular concentration profile retrieval in channel 8: CO (top), CH4 (mid), and H2O (bottom). Note the scaling by 10 6 of the CO and CH4 Jacobians. The derivatives were calculated using GARLIC for a US standard atmosphere up to 50 km, a Gaussian slit function with γ = 0.2 cm−1, and vertical downlooking observer at 800 km.
  • Fig. 2. Altitude sensitivity of CO (left), CH4 (mid), and H2O in channel 8.
  • Fig. 3. Evolution of pixel mask from 2002 to 2009 (left) and percentage of flagged pixels (right). Good pixels are marked blue and bad pixels red. Several decontaminations rendering the detectors temporarily useless due to high temperatures and resulting noise are visible as horizontal red lines in the left diagram.
  • Fig. 4. Comparison of a retrieval using a constant mask flagging only pixels that are marked as “bad” for at least half of the cases of the year 2004 (left) and a retrieval using a dynamic mask appropriate for each measurement (right). In February (top) the results look similar while the result for October (bottom) is noisier for the constant mask that does not flag all bad pixels. CO VCD in units molec cm−2.
  • Fig. 5. Sensitivity of BIRRA CO VCDs with respect to perturbations of individual pixels. Top left: absorption cross sections of CO (red), CH4 (green dashed), and H2O (blue long dashed) in channel 8 CO fitting window. Other plots: molecular scaling factors as a function of individual pixel perturbations. Note the different range of the color bars.
  • Fig. 6. Comparison of two VCD [molec cm−2] retrievals for February 2004. Left: all good pixels in the retrieval window are used. Right: pixels over lines with strong water vapour interference are excluded. In the latter case, several features like enhanced CO values in South-East Asia and the North-South gradient are more clearly visible.
  • Fig. 7. Sun mean reference (SMR) spectrum in chann l 8 with ice layer (blue) and with clean detector (red), normalised to the sign l in an arbitrary pixel to illustrate the change in the spectral shape.
  • Fig. 8. Influence of the solar spectrum on monthly average CO retrievals. The plots show relative differences of αCO (top), αCH4 (mid), and xCO (bottom) of retrievals with the Kurucz solar spectrum vs. the SCIAMACHY SMR spectrum for two months in 2004: February (left) and July (right).

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APA

Gimeno García, S., Schreier, F., Lichtenberg, G., & Slijkhuis, S. (2011). Near infrared nadir retrieval of vertical column densities: Methodology and application to SCIAMACHY. Atmospheric Measurement Techniques, 4(12), 2633–2657. https://doi.org/10.5194/amt-4-2633-2011

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