Land surface VIS/NIR BRDF atlas for RTTOV‐11: model and validation against SEVIRI land SAF albedo product

Vidot, Jérôme; Borbás, Éva

doi:10.1002/qj.2288

Cited by 22 publications

(41 citation statements)

References 44 publications

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“…FASTEM has an empirical formulation for the third and fourth elements of the Stokes vector but has no rigorous capability for handling the full Stokes vector. Reflectance/emissivity atlases are provided over the land for visible and near-infrared wavelengths (Vidot and Borbás, 2014;Vidot et al, 2018), for infrared UWIREMIS (Borbas and Ruston, 2010) and CAMEL (Borbas et al, 2017), and for the microwave TELSEM (Aires et al, 2011) and the CNRM atlas (Karbou et al, 2006(Karbou et al, , 2010, which are all provided as part of the RTTOV package.…”

Section: Input State Vectormentioning

confidence: 99%

“…Users can explicitly provide ice effective diameter or can choose among four parameterizations in terms of ice water content and temperature (Ou and Liou, 1995;Wyser, 1998;Boudala et al, 2002;McFarquhar et al, 2003). The second scheme uses the methodology developed initially for the IR (Vidot et al, 2015) by using a large database of optical properties of ice clouds provided by Baran et al (2014). It consists of 20 662 particle size distributions using different in situ measured temperature (T ) and estimated ice water content (IWC) observations; this simulates an ensemble of different ice cloud particle shapes and is expected to be more realistic than just assuming specific shapes, as was done previously.…”

Section: Cloud and Aerosol Radiance Simulations At Infrared Wavelengthsmentioning

confidence: 99%

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An update on the RTTOV fast radiative transfer model (currently at version 12)

et al. 2018

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Abstract. This paper gives an update of the RTTOV (Radiative Transfer for TOVS) fast radiative transfer model, which is widely used in the satellite retrieval and data assimilation communities. RTTOV is a fast radiative transfer model for simulating top-of-atmosphere radiances from passive visible, infrared and microwave downward-viewing satellite radiometers. In addition to the forward model, it also optionally computes the tangent linear, adjoint and Jacobian matrix providing changes in radiances for profile variable perturbations assuming a linear relationship about a given atmospheric state. This makes it a useful tool for developing physical retrievals from satellite radiances, for direct radiance assimilation in NWP models, for simulating future instruments, and for training or teaching with a graphical user interface. An overview of the RTTOV model is given, highlighting the updates and increased capability of the latest versions, and it gives some examples of its current performance when compared with more accurate line-by-line radiative transfer models and a few selected observations. The improvement over the original version of the model released in 1999 is demonstrated.

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Section: Input State Vectormentioning

confidence: 99%

Section: Cloud and Aerosol Radiance Simulations At Infrared Wavelengthsmentioning

confidence: 99%

An update on the RTTOV fast radiative transfer model (currently at version 12)

et al. 2018

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“…Observations were provided for collocated views (forward and nadir) in all channels, in addition to the land-sea mask, and confidence flags from the L1b data. Numerical weather prediction (NWP) data were provided for those algorithms dependent on radiative transfer simulation, including skin temperature and TCWV from ERA-Interim data [28], and surface emissivity and reflectance from the UVIREMIS and BRDF atlases [29,30]. These are used as inputs into the RTTOV 11 fast radiative transfer model [31] to simulate top of atmosphere reflectance and brightness temperatures.…”

Section: Cloud Clearing Inter-comparisonmentioning

confidence: 99%

Quantifying Uncertainty in Satellite-Retrieved Land Surface Temperature from Cloud Detection Errors

et al. 2018

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Clouds remain one of the largest sources of uncertainty in remote sensing of surface temperature in the infrared, but this uncertainty has not generally been quantified. We present a new approach to do so, applied here to the Advanced Along-Track Scanning Radiometer (AATSR). We use an ensemble of cloud masks based on independent methodologies to investigate the magnitude of cloud detection uncertainties in area-average Land Surface Temperature (LST) retrieval. We find that at a grid resolution of 625 km 2 (commensurate with a 0.25 • grid size at the tropics), cloud detection uncertainties are positively correlated with cloud-cover fraction in the cell and are larger during the day than at night. Daytime cloud detection uncertainties range between 2.5 K for clear-sky fractions of 10-20% and 1.03 K for clear-sky fractions of 90-100%. Corresponding night-time uncertainties are 1.6 K and 0.38 K, respectively. Cloud detection uncertainty shows a weaker positive correlation with the number of biomes present within a grid cell, used as a measure of heterogeneity in the background against which the cloud detection must operate (e.g., surface temperature, emissivity and reflectance). Uncertainty due to cloud detection errors is strongly dependent on the dominant land cover classification. We find cloud detection uncertainties of a magnitude of 1.95 K over permanent snow and ice, 1.2 K over open forest, 0.9-1 K over bare soils and 0.09 K over mosaic cropland, for a standardised clear-sky fraction of 74.2%. As the uncertainties arising from cloud detection errors are of a significant magnitude for many surface types and spatially heterogeneous where land classification varies rapidly, LST data producers are encouraged to quantify cloud-related uncertainties in gridded products.

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“…As part of the dataset, 126 material types selected by Vidot and Borbás (2014), who generated Bidirectional Reflectance Distribution Function (BRDF) maps for the RTTOV-11 radiative transfer model, are utilized. The selection includes 26 spectra from Chapter V (Plants, Vegetation Communities, Mixtures with Vegetation, and Microorganisms) (referred to here simply as vegetation) and 100 spectra from Chapter S (Soils, Rocks, and Mixtures) (referred to here as soils).…”

Section: (I) Reflectance Spectra Datasetmentioning

confidence: 99%

True Color Imagery Rendering for Himawari-8 with a Color Reproduction Approach Based on the CIE XYZ Color System

Murata

Saitoh

Sumida

2018

Journal of the Meteorological Society of Japan

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The combination of three visible bands of the Advanced Himawari Imager (AHI) aboard Japan Meteorological Agency's (JMA) new-generation Himawari-8 and Himawari-9 geostationary meteorological satellites enables the production of true color imagery. True color is intuitively understandable to human analysts and beneficial for monitoring surface and atmospheric features. It is particularly useful when applied to frequent observations from a geostationary platform. In this article, we report on an application of a color reproduction approach based on the International Commission on Illumination (CIE) 1931 XYZ color system to imagery rendering. This approach allows the consideration of primary color (RGB) differences among satellite and output devices, which in turn cause differences in the colors reproduced. The RGB signals observed by the AHI are converted to XYZ tristimulus values, which are independent of the devices themselves, and then reconverted to RGB signals for output devices via the application of 3 × 3 conversion matrices. This article also covers an objective technique for the evaluation of the accuracy of XYZ values. The evaluation indicated that the combination of AHI native RGB bands is suboptimal for obtaining XYZ values as is, whereas a combination in which the green band is replaced by a pseudo band with a central wavelength of around 0.555 µm is optimal. The pseudo band is generated via regression with existing visible and near-infrared bands as predictor variables. The imagery produced using this approach was termed True Color Reproduction (TCR). This approach is applicable to other satellites that have several bands in the visible to near-infrared spectral range, and it has the potential for development toward the production of standardized sensor-independent true color imagery.

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Land surface VIS/NIR BRDF atlas for RTTOV‐11: model and validation against SEVIRI land SAF albedo product

Cited by 22 publications

References 44 publications

An update on the RTTOV fast radiative transfer model (currently at version 12)

An update on the RTTOV fast radiative transfer model (currently at version 12)

Quantifying Uncertainty in Satellite-Retrieved Land Surface Temperature from Cloud Detection Errors

True Color Imagery Rendering for Himawari-8 with a Color Reproduction Approach Based on the CIE XYZ Color System

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