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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.
    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.

  • 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 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.

  • 5. Hamann, U.
    et al.
    Walther, A.
    Baum, B.
    Bennartz, R.
    Bugliaro, L.
    Derrien, M.
    Francis, P. N.
    Heidinger, A.
    Joro, S.
    Kniffka, A.
    Le Gleau, H.
    Lockhoff, M.
    Lutz, H. -J
    Meirink, J. F.
    Minnis, P.
    Palikonda, R.
    Roebeling, R.
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Platnick, S.
    Watts, P.
    Wind, G.
    Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms2014In: ATMOSPHERIC MEASUREMENT TECHNIQUES, ISSN 1867-1381, Vol. 7, no 9, p. 2839-2867Article in journal (Refereed)
    Abstract [en]

    The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) - a crucial parameter to estimate the thermal cloud radiative forcing - can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 km lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The average CTHs derived by the SEVIRI algorithms are closer to the CPR measurements than to CALIOP measurements. The biases between SEVIRI and CPR retrievals range from -0.8 km to 0.6 km. The correlation coefficients of CPR and SEVIRI observations vary between 0.82 and 0.89. To discuss the origin of the CTH deviation, we investigate three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference data sets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SEVIRI algorithms yield CTHs that are lower than CALIOP and similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to the uncertainties and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed.

  • 6.
    Håkansson, Nina
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Adok, Claudia
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Scheirer, Ronald
    SMHI, Research Department, Atmospheric remote sensing.
    Hörnquist, Sara
    SMHI, Research Department, Atmospheric remote sensing.
    Neural network cloud top pressure and height for MODIS2018In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 11, no 5, p. 3177-3196Article in journal (Refereed)
  • 7. 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.

  • 8. Kuenzer, Claudia
    et al.
    Zhang, Jianzhong
    Tetzlaff, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    van Dijk, Paul
    Voigt, Stefan
    Mehl, Harald
    Wagner, Wolfgang
    Uncontrolled coal fires and their environmental impacts: Investigating two arid mining regions in north-central China2007In: Applied Geography, ISSN 0143-6228, E-ISSN 1873-7730, Vol. 27, no 1, p. 42-62Article in journal (Refereed)
    Abstract [en]

    Uncontrolled coal fires occur worldwide and pose a great threat to the environment. This paper introduces the problem of coal fires referring to two coalfields in north-central China. These areas were regularly investigated during numerous fieldwork campaigns between 2002 and 2005. Emphasis is put on the environmental impacts of the fires, such as atmospheric influences, land subsidence, landscape degradation, as well as the danger for water resources and human health. New approaches for coal fire research are undertaken in numerous national and multi-lateral projects. Research disciplines, addressing the problem of coal fires, include geography, geology, geo-physics, mining-engineering, and remote sensing. In combination, they lead the direction towards a holistic approach to detect, monitor, quantify, and finally extinguish the coal fires. (c) 2006 Elsevier Ltd. All rights reserved.

  • 9. Pfreundschuh, Simon
    et al.
    Eriksson, Patrick
    Duncan, David
    Rydberg, Bengt
    Håkansson, Nina
    SMHI, Research Department, Atmospheric remote sensing.
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems2018In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 11, no 8, p. 4627-4643Article in journal (Refereed)
  • 10. Roebeling, Rob
    et al.
    Baum, Bryan
    Bennartz, Ralf
    Hamann, Ulrich
    Heidinger, Andrew
    Meirink, Jan Fokke
    Stengel, Martin
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Walther, Andi
    Watts, Phil
    Summary of the Fourth Cloud Retrieval Evaluation Workshop2015In: Bulletin of The American Meteorological Society - (BAMS), ISSN 0003-0007, E-ISSN 1520-0477, Vol. 96, no 4, p. ES71-ES74Article in journal (Refereed)
  • 11. Roebeling, Rob
    et al.
    Baum, Bryan
    Bennartz, Ralf
    Hamann, Ulrich
    Heidinger, Andy
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Walther, Andi
    EVALUATING AND IMPROVING CLOUD PARAMETER RETRIEVALS2013In: Bulletin of The American Meteorological Society - (BAMS), ISSN 0003-0007, E-ISSN 1520-0477, Vol. 94, no 4, p. ES41-ES44Article in journal (Other academic)
  • 12. Schulz, J.
    et al.
    Albert, P.
    Behr, H. -D
    Caprion, D.
    Deneke, H.
    Dewitte, S.
    Durr, B.
    Fuchs, P.
    Gratzki, A.
    Hechler, P.
    Hollmann, R.
    Sheldon, Johnston, Marston
    SMHI, Research Department, Atmospheric remote sensing.
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    Manninen, T.
    Mueller, R.
    Reuter, M.
    Riihela, A.
    Roebeling, R.
    Selbach, N.
    Tetzlaff, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Thomas, W.
    Werscheck, M.
    Wolters, E.
    Zelenka, A.
    Operational climate monitoring from space: the EUMETSAT Satellite Application Facility on Climate Monitoring (CM-SAF)2009In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 9, no 5, p. 1687-1709Article in journal (Refereed)
    Abstract [en]

    The Satellite Application Facility on Climate Monitoring (CM-SAF) aims at the provision of satellite-derived geophysical parameter data sets suitable for climate monitoring. CM-SAF provides climatologies for Essential Climate Variables (ECV), as required by the Global Climate Observing System implementation plan in support of the UNFCCC. Several cloud parameters, surface albedo, radiation fluxes at the top of the atmosphere and at the surface as well as atmospheric temperature and humidity products form a sound basis for climate monitoring of the atmosphere. The products are categorized in monitoring data sets obtained in near real time and data sets based on carefully intercalibrated radiances. The CM-SAF products are derived from several instruments on-board operational satellites in geostationary and polar orbit as the Meteosat and NOAA satellites, respectively. The existing data sets will be continued using data from the instruments on-board the new joint NOAA/EUMETSAT Meteorological Operational Polar satellite. The products have mostly been validated against several ground-based data sets both in situ and remotely sensed. The accomplished accuracy for products derived in near real time is sufficient to monitor variability on diurnal and seasonal scales. The demands on accuracy increase the longer the considered time scale is. Thus, interannual variability or trends can only be assessed if the sensor data are corrected for jumps created by instrument changes on successive satellites and more subtle effects like instrument and orbit drift and also changes to the spectral response function of an instrument. Thus, a central goal of the recently started Continuous Development and Operations Phase of the CM-SAF (2007-2012) is to further improve all CM-SAF data products to a quality level that allows for studies of interannual variability.

  • 13. Sporre, Moa K.
    et al.
    O'Connor, Ewan J.
    Håkansson, Nina
    SMHI, Research Department, Atmospheric remote sensing.
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Swietlicki, Erik
    Petaja, Tuukka
    Comparison of MODIS and VIIRS cloud properties with ARM ground-based observations over Finland2016In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 9, no 7, p. 3193-3203Article in journal (Refereed)
  • 14. Wu, Dong L.
    et al.
    Baum, Bryan A.
    Choi, Yong-Sang
    Foster, Michael J.
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    Heidinger, Andrew
    Poulslsen, Caroline
    Pavolonis, Michael
    Riedi, Jerome
    Roebeling, Robert
    Sherwood, Steven
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Watts, Philip
    TOWARD GLOBAL HARMONIZATION OF DERIVED CLOUD PRODUCTS2017In: Bulletin of The American Meteorological Society - (BAMS), ISSN 0003-0007, E-ISSN 1520-0477, Vol. 98, no 2, p. ES49-ES52Article in journal (Refereed)
  • 15. Zhang, Jianzhong
    et al.
    Kuenzer, Claudia
    Tetzlaff, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Oertel, Dieter
    Zhukov, Boris
    Wagner, Wolfgang
    Thermal characteristics of coal fires 2: Results of measurements on simulated coal fires2007In: Journal of Applied Geophysics, ISSN 0926-9851, E-ISSN 1879-1859, Vol. 63, no 3-4, p. 135-147Article in journal (Refereed)
    Abstract [en]

    In this paper we present thermal characteristics of coal fires as measured during simulated fires under an experimental setting in Germany in July 2002. It is thus a continuation of the previously published paper "Thermal surface characteristics of coal fire 1: Results of in-situ measurement", in which we presented temperature measurements of real subsurface coal fires in China [Zhang, J., Kuenzer, C., accepted for publication. Thermal Surface Characteristics of Coal Fires 1: Results of in-situ measurements. Accepted for publication at Journal of Applied Geophysics.]. The focus is on simulated coal fires, which are less complex in nature than fires under natural conditions. In the present study we simulated all the influences usually occurring under natural conditions in a controllable manner (uniform background material of known thermal properties, known ventilation pathways, homogeneous coal substrate), creating two artificial outdoor coal fires under simplified settings. One surface coal fire and one subsurface coal fire were observed over the course of 2 days. The set up of the fires allowed for measurements not always feasible under "real" in-situ conditions: thus compared to the in-situ investigations presented in paper one we could retrieve numerous temperature measurements inside of the fires. Single temperature measurements, diurnal profiles and airborne thermal surveying present the typical temperature patterns of a small surface-and a subsurface fire under undisturbed conditions (easily accessible terrain, 24 hour measurements period, homogeneous materials). We found that the outside air temperature does not influence the fire's surface temperature (up to 900 degrees C), while fire centre temperatures of up to 1200 degrees C strongly correlate with surface temperatures of the fire. The fires could heat their surrounding up to a distance of 4.5 m. However, thermal anomalies on the background surface only persist as long as the fire is burning and disappear very fast if the heat source is removed. Furthermore, heat outside of the fires is transported mainly by convection and not by radiation. In spatial thermal line scanner data the diurnal thermal patterns of the coal fire are clearly represented. Our experiments during that data collection also visualize the thermal anomaly differences between covered (underground) and uncovered (surface) coal fires. The latter could not be observed in-situ in a real coal fire area. Subsurface coal fires express a much weaker signal than open surface fires and contrast only by few degrees against the background. In airborne thermal imaging scanner data the fires are also well represented. Here we could show that the mid-infrared domain (3.8 mu m) is more suitable to pick up very hot anomalies, compared to the common thermal (8.8 mu m) domain. Our results help to understand coal fires and their thermal patterns as well as the limitations occurring during their analysis. We believe that the results presented here can practicably help for the planning of coal fire thermal mapping campaigns - including remote sensing methods and the thermal data can be included into numerical coal fire modelling as initial or boundary conditions. (c) 2007 Elsevier B.V. All rights reserved.

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