On the improved stability of the version 7 MIPAS ozone record

Laeng, Alexandra; Eckert, E.; Clarmann, T. von; Kiefer, Michael; Hubert, D.; Stiller, G. P.; Glatthor, N.; López‐Puertas, M.; Funke, B.; Grabowski, U.; Plieninger, J.; Kellmann, S.; Linden, A.; Lossow, Stefan; Babenhauserheide, Arne; Froidevaux, L.; Walker, Kaley A.

doi:10.5194/amt-11-4693-2018

Cited by 10 publications

(16 citation statements)

References 26 publications

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“…To date, these drifts have not been evaluated as part of the routine error analysis of the Level-2 product, but they are assessed by careful comparison with other instruments. While it is not easily possible to get absolute drift estimates from this, at least the relative drifts between instruments can be estimated (e.g., Eckert et al, 2014;Laeng et al, 2018;Hubert et al, 2012;Rahpoe et al, 2015;DeLand et al, 2012). It is important to note that relative drifts between instruments may have causes beyond time-dependent calibration changes (e.g., a drift in tangent height registra-tion as shown in Livesey et al, 2018, or Kramarova et al, 2018.…”

Section: Driftsmentioning

confidence: 99%

Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature

et al. 2020

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Abstract. Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. Reported characterization data should be intercomparable between different instruments, empirically validatable, grid-independent, usable without detailed knowledge of the instrument or retrieval technique, traceable and still have reasonable data volume. The latter may force one to work with representative rather than individual characterization data. Many errors derive from approximations and simplifications used in real-world retrieval schemes, which are reviewed in this paper, along with related error estimation schemes. The main sources of uncertainty are measurement noise, calibration errors, simplifications and idealizations in the radiative transfer model and retrieval scheme, auxiliary data errors, and uncertainties in atmospheric or instrumental parameters. Some of these errors affect the result in a random way, while others chiefly cause a bias or are of mixed character. Beyond this, it is of utmost importance to know the influence of any constraint and prior information on the solution. While different instruments or retrieval schemes may require different error estimation schemes, we provide a list of recommendations which should help to unify retrieval error reporting.

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Section: Driftsmentioning

confidence: 99%

Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature

et al. 2020

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“…The general processing scheme is described by von Clarmann et al (2003bClarmann et al ( , 2009b. We study covariances for MIPAS O 3 and hydrogen cyanide (HCN) profiles (Laeng et al, 2018 andGlatthor et al, 2015, respectively).…”

Section: Covariancesmentioning

confidence: 99%

The application of mean averaging kernels to mean trace gas distributions

Clarmann

Glatthor

2019

Atmos. Meas. Tech.

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Abstract. To avoid unnecessary data traffic it is sometimes desirable to apply mean averaging kernels to mean profiles of atmospheric state variables. Unfortunately, application of averaging kernels and averaging are not commutative in cases when averaging kernels and state variables are correlated. That is to say, the application of individual averaging kernels to individual profiles and subsequent averaging will, in general, lead to different results than averaging of the original profiles prior to the application of the mean averaging kernels, unless profiles and averaging kernels are fully independent. The resulting error, however, can be corrected by subtraction of the covariance between the averaging kernel and the vertical profile. Thus, it is recommended to calculate the covariance profile along with the mean profile and the mean averaging kernel.

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“…Its adequacy has been proven by the fact that it removes the major fraction of the drifts found in previous data versions. For ozone, it has been shown that application of this new correction scheme improves the long-term stability (Laeng et al, 2018). However, temperature validation has revealed that the version 7 non-linearity correction led to too small values, especially at the beginning of the mission (Hubert et al, 2016).…”

Section: Change In Level-1b Gain Calibrationmentioning

confidence: 99%

“…These temperature retrieval errors further propagated noticeably into retrievals of trace species, e.g. ozone (Laeng et al, 2018).…”

Section: A Priori Temperature and Trace Gas Distributionsmentioning

confidence: 99%

IMK/IAA MIPAS temperature retrieval version 8: nominal measurements

Kiefer¹,

Clarmann²,

Funke³

et al. 2020

Preprint

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Abstract. A new global set of atmospheric temperature profiles is retrieved from recalibrated radiance spectra recorded with the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). Changes with respect to previous data versions include a new radiometric calibration considering the time-dependency of the detector non-linearity, and a more robust frequency calibration scheme. Temperature is retrieved using a smoothing constraint, while tangent altitude pointing information is constrained using optimal estimation. ECMWF ERA-Interim is used as temperature a priori below 43 km. Above, a priori data is based on data from the Whole Atmosphere Community Climate Model Version 4 (WACCM4). Bias-corrected fields from specified dynamics runs, sampled at the MIPAS times and locations, are used, blended with ERA-Interim between 43 and 53 km. Horizontal variability of temperature is considered by scaling an a priori 3D temperature field in the orbit plane in a way that the horizontal structure is provided by the a priori while the vertical structure comes from the measurements. Additional microwindows with better sensitivity at higher altitudes are used. The background continuum is jointly fitted with the target parameters up to 58 km altitude. The radiance offset correction is strongly regularized towards an empirically determined vertical offset profile. In order to avoid the propagation of uncertainties of O3 and H2O a priori assumptions, the abundances of these species are retrieved jointly with temperature. The retrieval is based on HITRAN 2016 spectroscopic data, with a few amendments. Temperature-adjusted climatologies of vibrational populations of CO2 states emitting in the 15 micron region are used in the radiative transfer modelling in order to account for non-local thermodynamic equilibrium. Numerical integration in the radiative transfer model is now performed at higher accuracy. The random component of the temperature uncertainty typically varies between 0.4 and 0.8 K, with occasional excursions up to 1.3 K above 60 km altitude. The leading sources of the random component of the temperature error are measurement noise, gain calibration uncertainty, spectral shift, and uncertain CO2 mixing ratios. The systematic error is caused by uncertainties in spectroscopic data and line shape uncertainties. It ranges from 0.2 K at 24 km altitude for northern midlatitude nighttime conditions to 2.3 K at 12 km for tropical nighttime conditions. The estimated total uncertainty amounts to values between 0.5 K at 24 km and northern polar winter conditions to 2.3 K at 12 km and northern midlatitude day conditions. The vertical resolution varies around 3 km for altitudes below 50 km. The long-term drift encountered in the previous temperature product has been largely reduced. The consistency between high spectral resolution results from 2002–2004 and the reduced spectral resolution results from 2005–2012 has been largely improved. As expected, most pronounced temperature differences between version 8 and previous data versions are found in elevated stratopause situations. The fact that the phase of temperature waves seen by MIPAS is not locked to the wave phase found in ECMWF analyses demonstrates that our retrieval provides independent information and does not merely reproduce the prior information.

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On the improved stability of the version 7 MIPAS ozone record

Cited by 10 publications

References 26 publications

Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature

Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature

The application of mean averaging kernels to mean trace gas distributions

IMK/IAA MIPAS temperature retrieval version 8: nominal measurements

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