Level 1b error budget for MIPAS on ENVISAT

Kleinert, Anne; Birk, Manfred; Perron, Gaétan; Wagner, Georg

doi:10.5194/amt-11-5657-2018

Cited by 29 publications

(58 citation statements)

References 25 publications

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“…Spectral decomposition is also often used for the retrieval of a single species. For example, Kramarova et al (2018) retrieve ozone sequentially in different spectral bands. An alternative to spectral decomposition is the simultaneous analysis of the full spectrum (e.g., Serio et al, 2016).…”

Section: Spectral Decompositionmentioning

confidence: 99%

“…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%

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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: Spectral Decompositionmentioning

confidence: 99%

Section: Driftsmentioning

confidence: 99%

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

et al. 2020

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“…However, although the aerosol layers detected in the observations and discussed here can be considered sufficiently extended and homogeneous, we cannot rule out that some of the larger underestimations can also be attributed to broken cloud conditions. Considering the negative offset of about 0.3 to 0.5 km in average of the MIPAS engineering tangent heights (Kleinert et al, 2018) there would be still an average underestimation of 0.7 to 0.5 km. Further, the majority of the compared profiles would still fall within the ranges predicted by the simulations.…”

Section: Top Heightmentioning

confidence: 97%

“…in the tropics MIPAS sampled down to 10 km and in the polar regions down to 7 km. The MIPAS engineering tangent altitudes, which are corrected for refraction, have a negative offset of about 300 to 500 m compared to retrieved altitudes in the UTLS height range (Kleinert et al, 2018). The vertical widths of the field-of-view of MIPAS is about 3 km.…”

Section: Introductionmentioning

confidence: 93%

Aerosol and cloud top height information of Envisat MIPAS measurements

Grießbach¹,

Hoffmann²,

Spang³

et al. 2019

Preprint

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Abstract. Infrared limb emission instruments have a long history in measuring clouds and aerosol. In particular the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument aboard ESA's Envisat provides 10 years of altitude resolved global measurements. Previous studies found systematic over- and underestimations of cloud top heights for cirrus and polar stratospheric clouds. To assess the cloud top height information and to characterize its uncertainty for the MIPAS instrument we performed simulations for ice clouds, volcanic ash, and sulfate aerosol. From the simulation results we found that in addition to the known effects of the field-of-view that can lead to a cloud top height overestimation, and broken cloud conditions that can lead to underestimation, also the cloud extinction plays an important role. While for optically thick clouds the possible cloud top height overestimation for MIPAS reaches up to 1.6 km due to the field-of-view, for optically thin clouds and aerosol the systematic underestimation reaches 5.1 km. For the detection sensitivity and the degree of underestimation of the MIPAS measurements also the cloud layer thickness plays a role. 1 km thick clouds are detectable down to extinctions of 5 x 10-4 km-1 and 6 km thick clouds are detectable down to extinctions of 1 x 10-4 km-1, where the largest underestimations of the cloud top height occur for the optically thinnest clouds with a vertical extent of 6 km. The relation between extinction coefficient, cloud top height estimate, and layer thickness is confirmed by a comparison of MIPAS cloud top heights of the volcanic sulfate aerosol from the Nabro eruption in 2011 with space- and ground-based lidar measurements and twilight measurements between June 2011 and February 2012. In the fresh to two months old plume, where the extinction was between 1 x 10-4 and 7 x 10-4 km-1 and the layer thickness mostly below 4 km, we found for MIPAS an average underestimation of 1.1 km. In the aged plume with extinctions down to 5 x 10-5 km-1 and layer thicknesses of up to 9.5 km the underestimation was higher reaching 7.2 km. The dependency of the cloud top height over- or underestimation on the extinction coefficient can explain seemingly contradictory results of previous studies. In spite of the relatively large uncertainty range of the cloud top height, the comparison of the detection sensitivity towards sulfate aerosol between MIPAS and a suite of widely used UV/VIS limb and IR nadir satellite aerosol measurements shows that MIPAS provides complementary information in terms of detection sensitivity.

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“…Besides measurement noise, calibration uncertainties also contribute to the measurement error (see, e.g., Kleinert et al, 2018).…”

Section: Calibration Uncertaintiesmentioning

confidence: 99%

Estimating and Reporting Uncertainties in Remotely Sensed Atmospheric Composition and Temperature

Clarmann¹,

Degenstein²,

Livesey³

et al. 2019

Preprint

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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 condition of adequacy may force one to work with representative rather than individual characterization data. 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 shall help to unify retrieval error reporting.

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Level 1b error budget for MIPAS on ENVISAT

Cited by 29 publications

References 25 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

Aerosol and cloud top height information of Envisat MIPAS measurements

Estimating and Reporting Uncertainties in Remotely Sensed Atmospheric Composition and Temperature

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