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  • 1. Bennartz, R
    et al.
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Dybbroe, Adam
    SMHI, Core Services.
    Michelson, Daniel
    SMHI, Core Services.
    Precipitation analysis using the Advanced Microwave Sounding Unit in support of nowcasting applications2002In: Meteorological Applications, ISSN 1350-4827, E-ISSN 1469-8080, Vol. 9, no 2, p. 177-189Article in journal (Refereed)
    Abstract [en]

    We describe a method to remotely sense precipitation and classify its intensity over water, coasts and land surfaces. This method is intended to be used in an operational nowcasting environment. It is based on data obtained from the Advanced Microwave Sounding Unit (AMSU) onboard NOAA-15. Each observation is assigned a probability of belonging to four classes: precipitation-free, risk of precipitation, precipitation between 0.5 and 5 mm/h, and precipitation higher than 5 mm/h. Since the method is designed to work over different surface types, it relies mainly on the scattering signal of precipitation-sized ice particles received at high frequencies. For the calibration and validation of the method we use an eight-month dataset of combined weather radar and AMSU data obtained over the Baltic area. We compare results for the AMSU-B channels at 89 GHz and 150 GHz and find that the high frequency channel at 150 GHz allows for a much better discrimination of different types of precipitation than the 89 GHz channel. While precipitation-free areas, as well as heavily precipitating areas (> 5 mm/h), can be identified to high accuracy, the intermediate classes are more ambiguous. This stems from the ambiguity of the passive microwave observations as well as from the non-perfect matching of the different data sources and sub-optimal radar adjustment. In addition to a statistical assessment of the method's accuracy, we present case studies to demonstrate its capabilities to classify different types of precipitation and to work over highly structured, inhomogeneous surfaces.

  • 2.
    Bennartz, Ralf
    et al.
    SMHI.
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Dybbroe, Adam
    SMHI, Core Services.
    Michelson, Daniel
    SMHI, Research Department, Atmospheric remote sensing.
    Precipitation Analysis from AMSU (Nowcasting SAF)1999Report (Other academic)
  • 3. Diamandi, Andrei
    et al.
    Dybbroe, Adam
    SMHI, Core Services.
    Nowcasting SAF. Validation of AVHRR cloud products2001Report (Other academic)
  • 4.
    Dybbroe, Adam
    et al.
    SMHI, Core Services.
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    NWCSAF AVHRR cloud detection and analysis using dynamic thresholds and radiative transfer modeling. Part I: Algorithm description2005In: Journal of applied meteorology (1988), ISSN 0894-8763, E-ISSN 1520-0450, Vol. 44, no 1, p. 39-54Article in journal (Refereed)
    Abstract [en]

    New methods and software for cloud detection and classification at high and midlatitudes using Advanced Very High Resolution Radiometer (AVHRR) data are developed for use in a wide range of meteorological, climatological, land surface, and oceanic applications within the Satellite Application Facilities (SAFs) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), including the SAF for Nowcasting and Very Short Range Forecasting Applications (NWCSAF) project. The cloud mask employs smoothly varying (dynamic) thresholds that separate fully cloudy or cloud-contaminated fields of view from cloud-free conditions. Thresholds are adapted to the actual state of the atmosphere and surface and the sun-satellite viewing geometry using cloud-free radiative transfer model simulations. Both the cloud masking and the cloud-type classification are done using sequences of grouped threshold tests that employ both spectral and textural features. The cloud-type classification divides the cloudy pixels into 10 different categories: 5 opaque cloud types, 4 semitransparent clouds, and 1 subpixel cloud category. The threshold method is fuzzy in the sense that the distances in feature space to the thresholds are stored and are used to determine whether to stop or to continue testing. They are also used as a quality indicator of the final output. The atmospheric state should preferably be taken from a short-range NWP model, but the algorithms can also run with climatological fields as input.

  • 5.
    Dybbroe, Adam
    et al.
    SMHI, Core Services.
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    NWCSAF AVHRR cloud detection and analysis using dynamic thresholds and radiative transfer modeling. Part II: Tuning and validation2005In: Journal of applied meteorology (1988), ISSN 0894-8763, E-ISSN 1520-0450, Vol. 44, no 1, p. 55-71Article in journal (Refereed)
    Abstract [en]

    Algorithms for cloud detection (cloud mask) and classification (cloud type) at high and midlatitudes using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the current NOAA satellites and future polar Meteorological and Operational Weather Satellites (METOP) of the European Organisation for the Exploitation of Meteorological Satellites have been extensively validated over northern Europe and the adjacent seas. The algorithms have been described in detail in Part I and are based on a multispectral grouped threshold approach, making use of cloud-free radiative transfer model simulations. The thresholds applied in the algorithms have been validated and tuned using a database interactively built up over more than 1 yr of data from NOAA-12, -14, and -15 by experienced nephanalysts. The database contains almost 4000 rectangular (in the image data)-sized targets (typically with sides around 10 pixels), with satellite data collocated in time and space with atmospheric data from a short-range NWP forecast model, land cover characterization, elevation data, and a label identifying the given cloud or surface type as interpreted by the nephanalyst. For independent and objective validation, a large dataset of nearly 3 yr of collocated surface synoptic observation (Synop) reports, AVHRR data, and NWP model output over northern and central Europe have been collected. Furthermore, weather radar data were used to check the consistency of the cloud type. The cloud mask performs best over daytime sea and worst at twilight and night over land. As compared with Synop, the cloud cover is overestimated during night (except for completely overcast situations) and is underestimated at twilight. The algorithms have been compared with the more empirically based Swedish Meteorological and Hydrological Institute (SMHI) Cloud Analysis Model Using Digital AVHRR Data (SCANDIA), operationally run at SMHI since 1989, and results show that performance has improved significantly.

  • 6. Hultgren, Pia
    et al.
    Dybbroe, Adam
    SMHI, Core Services.
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    SCANDIA -its accuracy in classifying LOW CLOUD1999Report (Other academic)
  • 7. Hyvarinen, Otto
    et al.
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    Dybbroe, Adam
    SMHI, Core Services.
    Investigations of NOAA AVHRR/3 1.6 m m imagery for snow, cloud and sunglint discrimination (Nowcasting SAF)1999Report (Other academic)
  • 8. Joro, S
    et al.
    Dybbroe, Adam
    SMHI, Core Services.
    Nowcasting SAF-IOP Validating the AVHRR Cloud Top Temperature and Height product using weather radar data Visiting Scientist report2004Report (Other academic)
  • 9.
    Karlsson, Karl-Göran
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Dybbroe, Adam
    SMHI, Research Department, Atmospheric remote sensing.
    Evaluation of Arctic cloud products from the EUMETSAT Climate Monitoring Satellite Application Facility based on CALIPSO-CALIOP observations2010In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 10, no 4, p. 1789-1807Article in journal (Refereed)
    Abstract [en]

    The performance of the three cloud products cloud fractional cover, cloud type and cloud top height, derived from NOAA AVHRR data and produced by the EUMETSAT Climate Monitoring Satellite Application Facility, has been evaluated in detail over the Arctic region for four months in 2007 using CALIPSO-CALIOP observations. The evaluation was based on 142 selected NOAA/Metop overpasses allowing almost 400 000 individual matchups between AVHRR pixels and CALIOP measurements distributed approximately equally over the studied months (June, July, August and December 2007). Results suggest that estimations of cloud amounts are very accurate during the polar summer season while a substantial loss of detected clouds occurs in the polar winter. Evaluation results for cloud type and cloud top products point at specific problems related to the existence of near isothermal conditions in the lower troposphere in the polar summer and the use of reference vertical temperature profiles from Numerical Weather Prediction model analyses. The latter are currently not detailed enough in describing true conditions relevant on the pixel scale. This concerns especially the description of near-surface temperature inversions which are often too weak leading to large errors in interpreted cloud top heights.

  • 10. Korpela, Aarno
    et al.
    Dybbroe, Adam
    SMHI, Core Services.
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Nowcasting SAF - Retrieving Cloud Top Temperature and Height in Semi-transparent and Fractional Cloudiness using AVHRR2001Report (Other academic)
    Abstract [en]

    Cloud top temperature and height estimates obtained from AVHRR infrared imagery require a correction for semi-transparency when cirrus layers are present. In this work we investigated the possibility of using the 11 μm and12 μm window channel brightness temperatures for the correction. We developed software which implements a method based on the work of Inoue (1985) and Derrien et al. (1988). In this method the cloud top temperature is derived for each small image segment by fitting a curve to . a twodimensional histogram of the segment, formed by using the brightness temperatureT ( 11 μm) and the brightness temperature diff erence T ( 11 μm) - T(l2μm). By extrapolating the model fit of the distribution to the opaque limit, a temperature estimate can be assigned to the semi-transparent cloud pixels, thereby replacing the measured brightness temperature which observes the combined background radiation and cloud emission. In this work, in addition to implementing data processing with the histogram based correction, we also developed a graphical user interface for testing the method, in order to provide a tool for the overall evaluation of the product.

  • 11.
    Raspaud, Martin
    et al.
    SMHI, Core Services.
    Hoese, David
    Dybbroe, Adam
    SMHI, Core Services.
    Lahtinen, Panu
    Devasthale, Abhay
    SMHI, Research Department, Atmospheric remote sensing.
    Itkin, Mikhail
    Hamann, Ulrich
    Rasmussen, Lars Orum
    Nielsen, Esben Stigard
    Leppelt, Thomas
    Maul, Alexander
    Kliche, Christian
    Thorsteinsson, Hrobjartur
    PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data2018In: Bulletin of The American Meteorological Society - (BAMS), ISSN 0003-0007, E-ISSN 1520-0477, Vol. 99, no 7, p. 1329-1336Article in journal (Refereed)
  • 12.
    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 - 12 of 12
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  • nn-NO
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