Temporal co-registration for TROPOMI cloud clearing

Genkova, I.; Robaidek, J.; Roebling, R.; Sneep, Maarten; Veefkind, Pepijn

doi:10.5194/amt-5-595-2012

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…We separately evaluate the effect of instrumental and algorithmic improvements in the TropOMAER retrieval algorithm by direct comparison of the satellite product to groundbased globally distributed (over land) level 2 version 3 measurements of AOD (Giles et al, 2019) by the Aerosol Robotic Network (AERONET;Holben et al, 1998). Measurements of AOD at 380 nm are available at most AERONET sites, allowing for a direct comparison to OMI and TROPOMI 388 nm retrievals.…”

Section: Evaluation Of Retrieved Aerosol Optical Depth and Single-scamentioning

confidence: 99%

TROPOMI aerosol products: evaluation and observations of synoptic-scale carbonaceous aerosol plumes during 2018–2020

et al. 2020

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Abstract. TROPOspheric Monitoring Instrument (TROPOMI) near-ultraviolet (near-UV) radiances are used as input to an inversion algorithm that simultaneously retrieves aerosol optical depth (AOD), single-scattering albedo (SSA), and the qualitative UV aerosol index (UVAI). We first present the TROPOMI aerosol algorithm (TropOMAER), an adaptation of the currently operational OMI near-UV (OMAERUV and OMACA) inversion schemes that takes advantage of TROPOMI's unprecedented fine spatial resolution at UV wavelengths and the availability of ancillary aerosol-related information to derive aerosol loading in cloud-free and above-cloud aerosols scenes. TROPOMI-retrieved AOD and SSA products are evaluated by direct comparison to sun-photometer measurements. A parallel evaluation analysis of OMAERUV and TropOMAER aerosol products is carried out to separately identify the effect of improved instrument capabilities and algorithm upgrades. Results show TropOMAER improved levels of agreement with respect to those obtained with the heritage coarser-resolution sensor. OMI and TROPOMI aerosol products are also intercompared at regional daily and monthly temporal scales, as well as globally at monthly and seasonal scales. We then use TropOMAER aerosol retrieval results to discuss the US Northwest and British Columbia 2018 wildfire season, the 2019 biomass burning season in the Amazon Basin, and the unprecedented January 2020 fire season in Australia that injected huge amounts of carbonaceous aerosols in the stratosphere.

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Section: Evaluation Of Retrieved Aerosol Optical Depth and Single-scamentioning

confidence: 99%

TROPOMI aerosol products: evaluation and observations of synoptic-scale carbonaceous aerosol plumes during 2018–2020

et al. 2020

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The role of cloud contamination, aerosol layer height and aerosol model in the assessment of the OMI near-UV retrievals over the ocean

Gassó¹,

Torres²

2016

Atmos. Meas. Tech.

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Retrievals of aerosol optical depth (AOD) at 388 nm over the ocean from the Ozone Monitoring Instrument (OMI) two-channel near-UV algorithm (OMAERUV) have been compared with independent AOD measurements. The analysis was carried out over the open ocean (OMI and MODerate-resolution Imaging Spectrometer (MODIS) AOD comparisons) and over coastal and island sites (OMI and AERONET, the AErosol RObotic NETwork). Additionally, a research version of the retrieval algorithm (using MODIS and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) information as constraints) was utilized to evaluate the sensitivity of the retrieval to different assumed aerosol properties.Overall, the comparison resulted in differences (OMI minus independent measurements) within the expected levels of uncertainty for the OMI AOD retrievals (0.1 for AOD < 0.3, 30 % for AOD > 0.3). Using examples from case studies with outliers, the reasons that led to the observed differences were examined with specific purpose to determine whether they are related to instrument limitations (i.e., pixel size, calibration) or algorithm assumptions (such as aerosol shape, aerosol height).The analysis confirms that OMAERUV does an adequate job at rejecting cloudy scenes within the instrument's capabilities. There is a residual cloud contamination in OMI pixels with quality flag 0 (the best conditions for aerosol retrieval according to the algorithm), resulting in a bias towards high AODs in OMAERUV. This bias is more pronounced at low concentrations of absorbing aerosols (AOD 388 nm ∼ < 0.5). For higher aerosol loadings, the bias remains within OMI's AOD uncertainties.In pixels where OMAERUV assigned a dust aerosol model, a fraction of them (< 20 %) had retrieved AODs significantly lower than AERONET and MODIS AODs. In a case study, a detailed examination of the aerosol height from CALIOP and the AODs from MODIS, along with sensitivity tests, was carried out by varying the different assumed parameters in the retrieval (imaginary index of refraction, size distribution, aerosol height, particle shape). It was found that the spherical shape assumption for dust in the current retrieval is the main cause of the underestimate. In addition, it is demonstrated in an example how an incorrect assumption of the aerosol height can lead to an underestimate. Nevertheless, this is not as significant as the effect of particle shape. These findings will be incorporated in a future version of the retrieval algorithm.

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Development of a neural network model for cloud fraction detection using NASA-Aura OMI VIS radiance measurements

Saponaro¹,

Kolmonen²,

Karhunen³

et al. 2013

Preprint

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The discrimination of cloudy pixels is required in almost any estimate of a parameter retrieved from a satellite image in the ultraviolet (UV), visual (VIS) or infra-red (IR) parts of the electromagnetic spectrum. Also, the distincion of clouds within satellite imagery and the distribution of their micro-physical properties is essential to the understanding of radiative transfer through the atmosphere. <br><br> This paper reports the development of neural network algorithms for cloud detection for the NASA-Aura Ozone Monitoring Instrument (OMI). We present and discuss the results obtained by training mathematical neural networks with simultaneous application to OMI and Aqua-MODerate Resolution Imaging Spectrometer (MODIS) data. The neural network delivers cloud fraction estimates in a fast and automated way. The developed neural network approach performs generally well in the training. Highly reflective surfaces, such as ice, snow, sun glint and desert, or atmospheric dust mislead the neural network to a wrong predicted cloud fraction

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Temporal co-registration for TROPOMI cloud clearing

Cited by 3 publications

References 11 publications

TROPOMI aerosol products: evaluation and observations of synoptic-scale carbonaceous aerosol plumes during 2018–2020

TROPOMI aerosol products: evaluation and observations of synoptic-scale carbonaceous aerosol plumes during 2018–2020

The role of cloud contamination, aerosol layer height and aerosol model in the assessment of the OMI near-UV retrievals over the ocean

Development of a neural network model for cloud fraction detection using NASA-Aura OMI VIS radiance measurements

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