H 2 O retrieval from S5 NIR K. Weigel, M. Reuter, S. Noël, H. Bovensmann, and J. P. Burrows University of Bremen, Institute of Environmental Physics

Outline 1)Water vapour retrieval and initial assessment 2)End-to-end tests, scenarios, and gain 3)Wavelength range 4)Vertical sensitivity of the column retrieval 5)Retrieval over ocean 6)Error estimation 1) Intrumental errors 2) Model and scene dependent errors 7)Summary

1) Water vapour retrieval and initial assessment

2) Scenarios and end-to-end tests Comparison to reference water vapour: Reference Scenarios

2) Scenarios and end-to-end tests Basic scenarios: April scenarios from Butz et al., 2010 Above: reference water vapour and albedo Comparison to reference: Right: Histogram for representation errors of the corrected water vapour 4783 scenarios, converged: 4702 (98%), filtered: 4018 (84%)

2) Gain matrix Gain matrix for oxygen corrected water vapour columns delivered Gain for US standard water vapour, albedo=0.02, SZA=0° should be representative/ larger than most scenarios

3) Wavelength range Used wavelength range for basic scenarios: nm, planned nm All scenarios converged for nm, mean difference to reference larger than for basic scenarios, both wavelength ranges work Larger ones (e.g nm) tested on small data sets are rather worse.

3) Wavelength range Used wavelength range for basic scenarios: nm, planned nm All scenarios converged for nm, mean difference to reference larger than for basic scenarios, both wavelength ranges work Larger ones (e.g nm) tested on small data sets are rather worse.

4) Vertical sensitivity Basic scenarios compared with scenarios, where the H2O is doubled in the lowest altitude 4783 scenarios, converged: 4696, filtered: 4014 Large errors caused by strong change of profile shape

4) Vertical sensitivity Basic scenarios compared with scenarios, where the H2O is doubled in the lowest altitude 4783 scenarios, converged: 4696, filtered: 4014 Large errors caused by strong change of profile shape Difference to reference depends on depth of the lowest layer

4) Vertical sensitivity Scene dependent differences between profile and column retrieval O2 correction factor more important for profile retrieval

5) Retrieval over ocean Interpolated scenarios over ocean, right unfiltered, left filtered data. Albedo 0.01 for most, fixed aerosol phase function and ratio between scattering (85%) and absorbing (15%) aerosols, few scenarios pass filter (9%) Differences larger than expected from retrieval of SCIAMACHY measurements and scenarios based on them (bottom)

6.1) Overview over instrumental errors Histogram for instrumental errors (without RSRA and intra-band co-registration) Different realisations of ISRF asymmetry shown Noise, ISRF width, ARA additive and multiplicative calculated from comparison to basic scenarios Others calculated with the gain of basic scenarios Estimated intra-band co- registration random error for different directions. O2 band disturbed: 3.71%, H2O band disturbed: 5.02%, average 4.36%

Calculated from basic scenarios Random 0.23%, systematic: -0.13% (for +3% intensity) 6.1) ARA multiplicative

Calculated from basic scenarios Random 0.07%, systematic: 0.08% (for 750nm for the high latitude dark scenario), ARA multiplicative and additive systematic errors have opposite signs 6.1) ARA additive

Calculated from basic scenarios Random 0.19%, systematic: -0.16% 6.1) ISRF width

Calculated from gain of the basic scenarios All ISRF errors have only small effects Random 0.33%, systematic: 0.11% from worst case (pattern -3) 6.1) ISRF asymmetry

RSRA error calculated from sine (upper left), cosine (upper right) and the linear combination of both (lower panel, to simulate all possible phase shifts) which leads to the largest absolute error of different periods Calculated with the gain for the tropical dark scenario provided to ESA (cyan) and as average over the result with the gain matrices of all basic scenarios (black, histogram) Long periods (> 0.53) can mimic the H2O and O2 spectral structure and cause large errors The errors could be smaller in the retrieval depending on the filter and the albedo polynomials Estimated RSRA error: random 13.4%, systematic -11.5% (period 7.83, max. lin comb. average over scenarios ) 6.1) RSRA

Assuming that 0.1SSD for the whole NIR band can lead to an intensity change of up to over the given wavelength range Based on mixing the intensity of each basic scenario with one of a random one with a weighting factor depending on the wavelength increasing from 0 to to disturb rather the H2O (left panel) end of the wavelength range and reverse to disturb rather the O2 end (right panel). Estimated intra-band co-registration random error for different directions. O2 band disturbed: 3.71%, H2O band disturbed: 5.02%, average 4.36% No systematic error assumed, could be smaller due to filter and albedo polynomials 6.1) Intra-band co-registration

Calculated from gain of the basic scenarios Small effects, symmetric Random 0.17%, systematic: +/-0.06% 6.1) Scene inhomogeneity

6.1) Overview over instrumental errors Spectral calibration error probably not relevant due to shift and squeeze retrieval RSRA and intra-band co-registration dominate the instrumental errors, because they can be similar to the spectral features used for the water vapour retrieval Estimation of intra- band co-registration uncertain It would be better, if ESRA = 0.5* user requirement (= 5%) would apply instead of RSRA

6.2) Overview over model and scene dependent errors Histogram for model and scene dependent errors (without spectroscopic error) All errors calculated from comparison to basic scenarios Spectroscopic error: difference between SCIAMACHY and SMMI and ECMWF smaller than 10% (where probably the representation and residual cloud error dominates), therefore the 5-10% error of the HITRAN 2012 line strength probably leads to much smaller cross section errors

6.2) Temperature Calculated from basic scenarios Estimated residual cloud error: random 0.09% for +2K (from mean error because systematic disturbance)

6.2) Pressure Calculated from basic scenarios Estimated residual cloud error: random 0.67% for 1% (from mean error because systematic disturbance)

6.2) Surface height Calculated from basic scenarios Estimated residual cloud error: random 0.87% for 100m (from mean error because systematic disturbance)

6.2) Fluorescence Calculated from basic scenarios, fluorescence from L. Guanter, different solar spectrum Estimated residual cloud error: random 0.11%, systematic 0.04%

6.2) Thick clouds Comparison of corrected water vapour with reference (left) and result of basic scenarios (right) Estimated residual cloud error: random 4.20%, systematic: -3.34% Based on basic scenarios, thick clouds instead of cirrus clouds Cloud fraction modelled by weighted mean of clouded and basic scenario Used filter detects many, but not all problematic clouds (about 39% pass the filter), better filter could improve the result

6.2) Overview over model and scene dependent errors Total random error calculated as root sum square, systematic error as sum of the absolute values Representation error and residual cloud error dominant error sources Spectroscopic error probably smaller Residual cloud error could be improved with stricter cloud filter

7) Summary Only instrument concept A allows water vapour retrieval from NIR Would allow to extend GOME/SCIAMACHY/GOME2 time series with the same wavelength range Retrieval over ocean possible but simulations worse than expected from SCIAMACHY measurements Model and scene dependent errors: representation error and residual cloud error dominating, spectroscopic error probably smaller All other model and scene dependent errors are small compared to the user requirement Instrumental errors: RSRA dominating and could not meet user requirement dependent on its spectral shape Apart from RSRA and Intra-band co-registration other instrumental errors small compared to the user requirement User requirement (10%) could be met with ESRA 5%: total error 8.75% random, 9.87% systematic but not with “worst case” spectral shape of RSRA (15.20% random, systematic)

3) Vertical sensitivity Weighting functions for water vapour (tropical dark scenario)