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  • 1.
    Karlsson, Karl-Göran
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Devasthale, Abhay
    SMHI, Research Department, Atmospheric remote sensing.
    Inter-Comparison and Evaluation of the Four Longest Satellite-Derived Cloud Climate Data Records: CLARA-A2, ESA Cloud CCI V3, ISCCP-HGM, and PATMOS-x2018In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 10, no 10, article id 1567Article in journal (Refereed)
  • 2.
    Karlsson, Karl-Göran
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Håkansson, Nina
    SMHI, Research Department, Atmospheric remote sensing.
    Mittaz, Jonathan P. D.
    Hanschmann, Timo
    Devasthale, Abhay
    SMHI, Research Department, Atmospheric remote sensing.
    Impact of AVHRR Channel 3b Noise on Climate Data Records: Filtering Method Applied to the CM SAF CLARA-A2 Data Record2017In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 9, no 6, article id 568Article in journal (Refereed)
  • 3.
    Karlsson, Karl-Göran
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Johansson, Erik
    SMHI, Research Department, Atmospheric remote sensing.
    Multi-Sensor Calibration Studies of AVHRR-Heritage Channel Radiances Using the Simultaneous Nadir Observation Approach2014In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 6, no 3, p. 1845-1862Article in journal (Refereed)
    Abstract [en]

    The European Space Agency project for studies of cloud properties in the Climate Change Initiative programme (ESA-CLOUD-CCI) aims at compiling the longest possible time series of cloud products from one single multispectral sensor-The five-channel Advanced Very High Resolution Radiometer (AVHRR) instrument. A particular aspect here is to include corresponding products based on other existing (Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Along-Track Scanning Radiometer (AATSR), MEdium Resolution Imaging Spectrometer (MERIS), Visible and Infrared Radiometer Suite (VIIRS)) and future Sea and Land Surface Temperature Radiometer (SLSTR) sensors measuring in similar (AVHRR-heritage) spectral channels. Initial inter-comparisons of the involved AVHRR-heritage channel radiances over a short demonstration period (2007-2009) were performed. Using Aqua-MODIS as reference, AVHRR (NOAA-18), AATSR, and MERIS channel radiances were evaluated using the simultaneous nadir (SNO) approach. Results show generally agreeing radiances within approximately 3% for channels at 0.6 mu m and 0.8 mu m. Larger deviations (+5%) were found for the corresponding AATSR channel at 0.6 mu m. Excessive deviations but with opposite sign were also indicated for AATSR 1.6 mu m and MERIS 0.8 mu m radiances. Observed differences may largely be attributed to residual temporal and spatial matching differences while excessive AATSR and MERIS deviations are likely partly attributed to incomplete compensation for spectrally varying surface and atmospheric conditions. However, very good agreement was found for all infrared channels among all the studied sensors. Here, deviations were generally less than 0.2% for the measured brightness temperatures with the exception of some remaining non-linear deviations at extreme low and high temperatures.

  • 4. Pareeth, Sajid
    et al.
    Delucchi, Luca
    Metz, Markus
    Rocchini, Duccio
    Devasthale, Abhay
    SMHI, Research Department, Atmospheric remote sensing.
    Raspaud, Martin
    SMHI, Core Services.
    Adrian, Rita
    Salmaso, Nico
    Neteler, Markus
    New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data2016In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, no 3Article in journal (Refereed)
    Abstract [en]

    Analyzing temporal series of satellite data for regional scale studies demand high accuracy in calibration and precise geo-rectification at higher spatial resolution. The Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration (NOAA) series of satellites provide daily observations for the last 30 years at a nominal resolution of 1.1 km at nadir. However, complexities due to on-board malfunctions and orbital drifts with the earlier missions hinder the usage of these images at their original resolution. In this study, we developed a new method using multiple open source tools which can read level 1B radiances, apply solar and thermal calibration to the channels, remove bow-tie effects on wider zenith angles, correct for clock drifts on earlier images and perform precise geo-rectification by automated generation and filtering of ground control points using a feature matching technique. The entire workflow is reproducible and extendable to any other geographical location. We developed a time series of brightness temperature maps from AVHRR local area coverage images covering the sub alpine lakes of Northern Italy at 1 km resolution (1986-2014; 28 years). For the validation of derived brightness temperatures, we extracted Lake Surface Water Temperature (LSWT) for Lake Garda in Northern Italy and performed inter-platform (NOAA-x vs. NOAA-y) and cross-platform (NOAA-x vs. MODIS/ATSR/AATSR) comparisons. The MAE calculated over available same day observations between the pairs-NOAA-12/14, NOAA-17/18 and NOAA-18/19 are 1.18 K, 0.67 K, 0.35 K, respectively. Similarly, for cross-platform pairs, the MAE varied between 0.5 to 1.5 K. The validation of LSWT from various NOAA instruments with in-situ data shows high accuracy with mean R-2 and RMSE of 0.97 and 0.91 K respectively.

  • 5. Pareeth, Sajid
    et al.
    Delucchi, Luca
    Metz, Markus
    Rocchini, Duccio
    Devasthale, Abhay
    SMHI, Research Department, Atmospheric remote sensing.
    Raspaud, Martin
    SMHI, Core Services.
    Adrian, Rita
    Salmaso, Nico
    Neteler, Markus
    New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data2016In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, no 3, p. NIL_481-NIL_508Article in journal (Refereed)
    Abstract [en]

    Analyzing temporal series of satellite data for regional scale studies demand high accuracy in calibration and precise geo-rectification at higher spatial resolution. The Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration (NOAA) series of satellites provide daily observations for the last 30 years at a nominal resolution of 1.1 km at nadir. However, complexities due to on-board malfunctions and orbital drifts with the earlier missions hinder the usage of these images at their original resolution. In this study, we developed a new method using multiple open source tools which can read level 1B radiances, apply solar and thermal calibration to the channels, remove bow-tie effects on wider zenith angles, correct for clock drifts on earlier images and perform precise geo-rectification by automated generation and filtering of ground control points using a feature matching technique. The entire workflow is reproducible and extendable to any other geographical location. We developed a time series of brightness temperature maps from AVHRR local area coverage images covering the sub alpine lakes of Northern Italy at 1 km resolution (1986-2014; 28 years). For the validation of derived brightness temperatures, we extracted Lake Surface Water Temperature (LSWT) for Lake Garda in Northern Italy and performed inter-platform (NOAA-x vs. NOAA-y) and cross-platform (NOAA-x vs. MODIS/ATSR/AATSR) comparisons. The MAE calculated over available same day observations between the pairs-NOAA-12/14, NOAA-17/18 and NOAA-18/19 are 1.18 K, 0.67 K, 0.35 K, respectively. Similarly, for cross-platform pairs, the MAE varied between 0.5 to 1.5 K. The validation of LSWT from various NOAA instruments with in-situ data shows high accuracy with mean R-2 and RMSE of 0.97 and 0.91 K respectively.

  • 6.
    Pimentel, Rafael
    et al.
    SMHI, Research Department, Hydrology.
    Herrero, Javier
    Polo, Maria Jose
    Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery2017In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 9, no 10, article id 995Article in journal (Refereed)
  • 7. Riihela, Aku
    et al.
    Carlund, Thomas
    SMHI, Core Services.
    Trentmann, Joerg
    Mueller, Richard
    Lindfors, Anders V.
    Validation of CM SAF Surface Solar Radiation Datasets over Finland and Sweden2015In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 7, no 6, p. 6663-6682Article in journal (Refereed)
    Abstract [en]

    Accurate determination of the amount of incoming solar radiation at Earth's surface is important for both climate studies and solar power applications. Satellite-based datasets of solar radiation offer wide spatial and temporal coverage, but careful validation of their quality is a necessary prerequisite for reliable utilization. Here we study the retrieval quality of one polar-orbiting satellite-based dataset (CLARA-A1) and one geostationary satellite-based dataset (SARAH), using in situ observations of solar radiation from the Finnish and Swedish meteorological measurement networks as reference. Our focus is on determining dataset quality over high latitudes as well as evaluating daily mean retrievals, both of which are aspects that have drawn little focus in previous studies. We find that both datasets are generally capable of retrieving the levels and seasonal cycles of solar radiation in Finland and Sweden well, with some limitations. SARAH exhibits a slight negative bias and increased retrieval uncertainty near the coverage edge, but in turn offers better precision (less scatter) in the daily mean retrievals owing to the high sampling rate of geostationary imaging.

  • 8.
    Scheirer, Ronald
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Dybbroe, Adam
    SMHI, Core Services.
    Raspaud, Martin
    SMHI, Core Services.
    A General Approach to Enhance Short Wave Satellite Imagery by Removing Background Atmospheric Effects2018In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 10, no 4, article id 560Article in journal (Refereed)
1 - 8 of 8
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