1D-Var retrieval of daytime total columnar water vapour from MERIS measurements

Lindstrot, R.; Preusker, R.; Diedrich, Hannes; Doppler, Lionel; Bennartz, Ralf; Fischer, Jürgen

doi:10.5194/amt-5-631-2012

Cited by 47 publications

(61 citation statements)

References 35 publications

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“…Thus, for the bright targets the majority of the at-sensor radiance consists of photons that are not scattered. In contrast, most of the photons for the dark targets are scattered [27,66]. This contribution amplifies under high scattering conditions resulting in performance degradation.…”

Section: Discussionmentioning

confidence: 96%

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

et al. 2018

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Abstract:A key parameter for atmospheric correction (AC) is Aerosol Optical Depth (AOD), which is often estimated from sensor radiance (L rs,t (λ)). Noise, the dependency on surface type, viewing and illumination geometry cause uncertainty in AOD inference. We propose a method that determines pre-estimates of surface reflectance (ρ t,pre ) where effects associated with L rs,t (λ) are less influential. The method identifies pixels comprising pure materials from ρ t,pre . AOD values at the pure pixels are iteratively estimated using l 2 -norm optimization. Using the adjacency range function, the AOD is estimated at each pixel. We applied the method on Hyperspectral Mapper and Airborne Prism Experiment instruments for experiments on synthetic data and on real data. To simulate real imaging conditions, noise was added to the data. The estimation error of the AOD is minimized to 0.06-0.08 with a signal-to-reconstruction-error equal to 35 dB. We compared the proposed method with a dense dark vegetation (DDV)-based state-of-the-art method. This reference method, resulted in a larger variability in AOD estimates resulting in low signal-to-reconstruction-error between 5-10 dB. For per-pixel estimation of AOD, the performance of the reference method further degraded. We conclude that the proposed method is more precise than the DDV methods and can be extended to other AC parameters.

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Section: Discussionmentioning

confidence: 96%

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

et al. 2018

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“…The TCWV retrieval itself is described in detail in Lindstrot et al (2012). The algorithm is divided into two individual steps, starting with a determination of the surface reflectance in MERIS band 14 at 885 nm (window channel) and 15 at 900 nm (absorption channel) and a subsequent iterative optimization of TCWV by minimizing the cost function, represented by the absolute difference between the modelled (see below) and observed ratios of MERIS bands 15 and 14.…”

Section: Meris Water Vapour Retrievalmentioning

confidence: 99%

“…In contrast, the retrieval uncertainties introduced by the temperature profile and surface pressure uncertainties were found to be of secondary order. For a detailed description of the uncertainty calculation, please see Lindstrot et al (2012).…”

Section: Meris Water Vapour Retrievalmentioning

confidence: 99%

“…Over the dark ocean, this technique is not appropriate for the retrieval of TCWV. Spaceborne sensors, such as ESA's Medium Resolution Imaging Spectrometer (MERIS) or NASA's Moderate Resolution Imaging Spectroradiometer (MODIS), provide the needed measurements in the near infrared and have been successfully deployed for the retrieval of TCWV (Bennartz and Fischer, 2001;Gao and Kaufman, 2003;Albert et al, 2005;Lindstrot et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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A global climatology of total columnar water vapour from SSM/I and MERIS

Lindstrot

Stengel

Schröder

et al. 2014

Earth Syst. Sci. Data

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Abstract.A global time series of total columnar water vapour from combined data of the Medium Resolution Imaging Spectrometer (MERIS) onboard ESA's Environmental Satellite (ENVISAT) and the Special Sensor Microwave/Imager (SSM/I) onboard the satellite series of the US Defense Meteorological Satellite Program (DMSP) is presented. The unique data set, generated in the framework of the ESA Data User Element (DUE) GlobVapour project, combines atmospheric water vapour observations over land and ocean, derived from measurements in the near-infrared and the microwave range, respectively. Daily composites and monthly means of total columnar water vapour are available as global maps on rectangular latitude-longitude grids with a spatial resolution of 0.05 • × 0.05 • over land and 0.5 • × 0.5 • over ocean for the years 2003 to 2008. The data are stored in NetCDF files and is fully compliant with the NetCDF Climate Forecast convention. Through the combination of high-quality microwave observations and near-infrared observations over ocean and land surfaces, respectively, the data set provides global coverage. The combination of both products is carried out such that the individual properties of the microwave and near-infrared products, in particular their uncertainties, are not modified by the merging process and are therefore well defined. Due to the global coverage and the provided uncertainty estimates this data set is potentially of high value for climate research. The SSM/I-MERIS TCWV data set is freely available via the GlobVapour project web page (www.globvapour.info) with associated doi:10.5676/DFE/WV_COMB/FP.In this paper, the details of the data set generation, i.e. the satellite data used, the retrieval techniques and merging approaches, are presented. The derived level 3 products are compared to global radiosonde data from the GCOS upper air network (GUAN), showing a high agreement with a root-mean-square deviation of roughly 4.4 kg m −2 and a small wet bias well below 1 kg m −2 . Furthermore, the data set is shown to be free of seasonal biases. The consistency of the MERIS and SSM/I retrievals is demonstrated by applying the MERIS retrieval to sun glint areas over ocean.

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“…Consequently, many forward simulation modules employed in retrieval algorithms in the shortwave region are based on pre-calculated look-up tables of the desired radiative quantity, which is tabulated as a function of the measurement geometry and any influencing geophysical parameter (see e.g. Gao and Kaufman, 2003;Schneising et al, 2009;Lindstrot et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

On the efficient treatment of temperature profiles for the estimation of atmospheric transmittance under scattering conditions

Lindstrot

Preusker

2012

Atmos. Meas. Tech.

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Abstract. The vertical temperature profile of the atmosphere has an influence on the width and intensity of gaseous absorption lines. In the visible and near infrared part of the spectrum, this poses a problem for the fast forward simulation of the radiative transfer, needed in algorithms for the retrieval of any atmospheric or surface-related parameter from satellite measurements. We show that the main part of the global variability of temperature profiles can be described by their first 2 to 6 eigenvectors, depending on the accuracy requirement, by performing a Principal Component Analysis (PCA) on a global set of temperature profiles from the Global Forecast System (GFS). Furthermore, we demonstrate the possibility to approximate the atmospheric transmittance in the O 2 A band for any temperature profile with almost perfect accuracy by a linear combination of the transmittances attributed to each of the significant temperature eigenvectors. For the retrieval of surface pressure from O 2 A band measurements, this reduces the global root mean square error from > 30 hPa to better than 1 hPa by strongly reducing the regional bias of surface pressure, retrieved on the assumption of an average temperature profile. The technique can be applied under scattering conditions to eliminate temperatureinduced errors in, e.g., simulated radiances. In principal, the method can be useful for any problem including gaseous absorption or emission with a significant influence of the temperature profile, such as the retrieval of total water vapour content or sea surface temperature.

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1D-Var retrieval of daytime total columnar water vapour from MERIS measurements

Cited by 47 publications

References 35 publications

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

A global climatology of total columnar water vapour from SSM/I and MERIS

On the efficient treatment of temperature profiles for the estimation of atmospheric transmittance under scattering conditions

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