Ozone profiles from clear sky thermal infrared measurements of the Infrared Atmospheric Sounding Interferometer: A retrieval approach accounting for thin cirrus

Wassmann, A.; Landgraf, Jochen; Aben, I.

doi:10.1029/2011jd016066

Cited by 5 publications

(5 citation statements)

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“…Wassmann et al (2011). For a non-scattering atmosphere, the down-welling radiation is reflected by the Earth's surface assuming isotropic Lambertian reflection.…”

Section: Forward Modelmentioning

confidence: 99%

Methane profiles from GOSAT thermal infrared spectra

Lange

Landgraf

2018

Atmos. Meas. Tech.

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Abstract. This paper discusses the retrieval of atmospheric methane profiles from the thermal infrared band of the Japanese Greenhouse Gases Observing Satellite (GOSAT) between 1210 and 1310 cm −1 , using the RemoTeC analysis software. Approximately one degree of information on the vertical methane distribution is inferred from the measurements, with the main sensitivity at about 9 km altitude but little sensitivity to methane in the lower troposphere. For verification, we compare the GOSAT-TIR methane profile retrieval results with profiles from model fields provided by the Monitoring Atmospheric Composition and Climate (MACC) project, scaled to the total column measurements of the Total Carbon Column Observing Network (TCCON) at ground-based measurement sites. Without any radiometric corrections of GOSAT observations, differences between both data sets can be as large as 10 %. To mitigate these differences, we developed a correction scheme using a principal component analysis of spectral fit residuals and airborne observations of methane during the HIAPER pole-to-pole observations (HIPPO) campaign II and III. When the correction scheme is applied, the bias in the methane profile can be reduced to less than 2 % over the whole altitude range with respect to MACC model methane fields. Furthermore, we show that, with this correction, the retrievals result in smooth methane fields over land and ocean crossings and no differences can be discerned between daytime and nighttime measurements. Finally, a cloud filter is developed for the nighttime and ocean measurements. This filter is rooted in the GOSAT-TIR (thermal infrared) measurements and its performance, in terms of biases, is consistent with the cloud filter based on the GOSAT-SWIR (shortwave infrared) measurements. The TIR filter shows a higher acceptance rate of observations than the SWIR filter, at the cost of a higher uncertainty in the retrieved methane profiles.

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“…Wassmann et al (2011). For a non-scattering atmosphere, the down-welling radiation is reflected by the Earth's surface assuming isotropic Lambertian reflection.…”

Section: Forward Modelmentioning

confidence: 99%

Methane profiles from GOSAT thermal infrared spectra

Lange

Landgraf

2018

Atmos. Meas. Tech.

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“…It is also very important to get the ozone information above the clouds to help understand the chemical and dynamical processes in the upper troposphere and lower stratosphere (Pittman et al 2009). Ozone weighting function (OWF), which reflects the sensitivity of the radiance measurement to the ozone in the atmosphere at a given pressure level, is used for ozone profile retrieval and ozone information content analyses (Wei et al 2010;Hasekamp andLandgraf 2001, 2002;Wassmann et al 2011;Cheng et al 1990). The ozone data in cloudy regions can be retrieved considering the fact that the OWF peak is above all but the highest clouds; how does cloud height impact the ozone information content?…”

Section: Introductionmentioning

confidence: 99%

Impact of Terrain Altitude and Cloud Height on Ozone Remote Sensing from Satellite

Bai

et al. 2014

Journal of Atmospheric and Oceanic Technology

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Terrain and cloud height heavily impact ozone information despite ozone being concentrated in the stratosphere. The ozone weighting function (OWF) provides important information towards understanding the capabilities and limitations of a given channel. The factors that impact the OWF can be analyzed using radiative transfer theory and modeling. At the 9.6-mm infrared spectral region, both the OWF values and peaks are related to the surface temperature, terrain altitude, and cloud height. Warmer surface temperatures, lower terrain altitude, or lower cloud levels will give larger weighting function values, and the peak of the weighting function slightly increases with the increase in surface temperature, terrain altitude, or cloud height. For longer UV bands such as 306 and 318 nm, OWF shows smaller values for higher terrains, while showing larger values when clouds are present. However, in the shorter UV bands such as 274 and 288 nm, OWF has almost no relationship with the surface and clouds. Therefore, with satellite-based infrared ozone remote sensing, high terrain and cloud presence will reduce ozone sensitivity and information content. In addition, for UV bands, the effect is spectrally dependent: lower terrain altitude and the presence of clouds will increase the zone information content in the longer UV band, but they have no effect in the short UV band. A simulation of an ozone retrieval in the infrared band shows that higher terrain results in lower precision for colder emitting surface temperatures and less ozone absorption signal.

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“…The goal of cloud masking is to flag a sensor's instantaneous field of view (IFOV) as either "cloud free" or "cloud contaminated." The decision is based on a radiance level that is assigned to each IFOV, which determines future processing: cloud-free IFOVs are used for, e.g., land use classification [Friedl et al, 2002;Wilson et al, 2014], aerosol parameter retrieval [Levy et al, 2013;Martins et al, 2002;Remer et al, 2005], or atmospheric chemistry parameterization [Wassmann et al, 2011;Xiong et al, 2013], while cloud-filled IFOVs can be analyzed for, e.g., the optical and radiative properties of clouds Zeng et al, 2012].…”

Section: Introductionmentioning

confidence: 99%

Impact of Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask interpretation on cloud amount estimation

Kotarba

2015

JGR Atmospheres

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Cloud masks serve as a basis for estimates of cloud amount, which is an essential parameter for studying the Earth's radiation budget. The most commonly used cloud mask is a simple thematic classification, which includes qualitative information on the presence of clouds in the satellite's instantaneous field of view (IFOV). Cloud mask classes have to be “translated” into a quantitative measure, in order to be used for cloud amount calculations. The assignment of cloud fractions to cloud mask classes is a subjective process and increases uncertainty in cloud amount estimates. We evaluated this degree of uncertainty using the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product. Together with the operational MODIS cloud mask interpretation, we investigated two extreme alternatives: “rigorous” (only “confident cloudy” IFOVs were 100% cloudy) and “tolerant” (only “confident clear” IFOVs were 0% cloudy). Results showed that the range of uncertainty was 14.3% in Europe and controlled by the frequency of small convective clouds. Comparison with surface‐based observations suggests that the rigorous interpretation of the cloud mask is more accurate than that used operationally for MODIS level 3 product generation. The rigorous approach resulted in the smallest bias (−0.7%), the smallest root‐mean‐square error (4.6%), the small standard deviation (6%), and the strongest correlation (0.935). These results suggest that for climatological applications the rigorous scenario should be considered as a more accurate “best guess” over land.

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Ozone profiles from clear sky thermal infrared measurements of the Infrared Atmospheric Sounding Interferometer: A retrieval approach accounting for thin cirrus

Cited by 5 publications

References 40 publications

Methane profiles from GOSAT thermal infrared spectra

Methane profiles from GOSAT thermal infrared spectra

Impact of Terrain Altitude and Cloud Height on Ozone Remote Sensing from Satellite

Impact of Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask interpretation on cloud amount estimation

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