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  • 1. Bennartz, Ralf
    et al.
    Hoschen, Heidrun
    Picard, Bruno
    Schroder, Marc
    Stengel, Martin
    Sus, Oliver
    Bojkov, Bojan
    Casadio, Stefano
    Diedrich, Hannes
    Eliasson, Salomon
    SMHI, Research Department, Atmospheric remote sensing.
    Fell, Frank
    Fischer, Jurgen
    Hollmann, Rainer
    Preusker, Rene
    Willén, Ulrika
    SMHI, Research Department, Climate research - Rossby Centre.
    An intercalibrated dataset of total column water vapour and wet tropospheric correction based on MWR on board ERS-1, ERS-2, and Envisat2017In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 10, no 4, p. 1387-1402Article in journal (Refereed)
  • 2.
    Carlund, Thomas
    et al.
    SMHI, Core Services.
    Kouremeti, Natalia
    Kazadzis, Stelios
    Grobner, Julian
    Aerosol optical depth determination in the UV using a four-channel precision filter radiometer2017In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 10, no 3, p. 905-923Article in journal (Refereed)
  • 3.
    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)
  • 4.
    Karlsson, Karl-Göran
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Håkansson, Nina
    SMHI, Research Department, Atmospheric remote sensing.
    Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information: demonstration of results based on the CM SAF CLARA-A2 climate data record2018In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 11, no 1, p. 633-649Article in journal (Refereed)
    Abstract [en]

    The sensitivity in detecting thin clouds of the cloud screening method being used in the CM SAF cloud, albedo and surface radiation data set from AVHRR data (CLARA-A2) cloud climate data record (CDR) has been evaluated using cloud information from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite. The sensitivity, including its global variation, has been studied based on collocations of Advanced Very High Resolution Radiometer (AVHRR) and CALIOP measurements over a 10-year period (2006-2015). The cloud detection sensitivity has been defined as the minimum cloud optical thickness for which 50% of clouds could be detected, with the global average sensitivity estimated to be 0.225. After using this value to reduce the CALIOP cloud mask (i.e. clouds with optical thickness below this threshold were interpreted as cloud-free cases), cloudiness results were found to be basically unbiased over most of the globe except over the polar regions where a considerable underestimation of cloudiness could be seen during the polar winter. The overall probability of detecting clouds in the polar winter could be as low as 50% over the highest and coldest parts of Greenland and Antarctica, showing that a large fraction of optically thick clouds also remains undetected here. The study included an in-depth analysis of the probability of detecting a cloud as a function of the vertically integrated cloud optical thickness as well as of the cloud's geographical position. Best results were achieved over oceanic surfaces at mid-to high latitudes where at least 50% of all clouds with an optical thickness down to a value of 0.075 were detected. Corresponding cloud detection sensitivities over land surfaces outside of the polar regions were generally larger than 0.2 with maximum values of approximately 0.5 over the Sahara and the Arabian Peninsula. For polar land surfaces the values were close to 1 or higher with maximum values of 4.5 for the parts with the highest altitudes over Greenland and Antarctica. It is suggested to quantify the detection performance of other CDRs in terms of a sensitivity threshold of cloud optical thickness, which can be estimated using active lidar observations. Validation results are proposed to be used in Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulation Package (COSP) simulators for cloud detection characterization of various cloud CDRs from passive imagery.

  • 5.
    Norin, Lars
    SMHI, Research Department, Atmospheric remote sensing.
    A quantitative analysis of the impact of wind turbines on operational Doppler weather radar data2015In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 8, no 2, p. 593-609Article in journal (Refereed)
    Abstract [en]

    In many countries wind turbines are rapidly growing in numbers as the demand for energy from renewable sources increases. The continued deployment of wind turbines can, however, be problematic for many radar systems, which are easily disturbed by turbines located in the radar line of sight. Wind turbines situated in the vicinity of Doppler weather radars can lead to erroneous precipitation estimates as well as to inaccurate wind and turbulence measurements. This paper presents a quantitative analysis of the impact of a wind farm, located in southeastern Sweden, on measurements from a nearby Doppler weather radar. The analysis is based on 6 years of operational radar data. In order to evaluate the impact of the wind farm, average values of all three spectral moments (the radar reflectivity factor, absolute radial velocity, and spectrum width) of the nearby Doppler weather radar were calculated, using data before and after the construction of the wind farm. It is shown that all spectral moments, from a large area at and downrange from the wind farm, were impacted by the wind turbines. It was also found that data from radar cells far above the wind farm (near 3 km altitude) were affected by the wind farm. It is shown that this in part can be explained by detection by the radar sidelobes and by scattering off increased levels of dust and turbulence. In a detailed analysis, using data from a single radar cell, frequency distributions of all spectral moments were used to study the competition between the weather signal and wind turbine clutter. It is shown that, when weather echoes give rise to higher reflectivity values than those of the wind farm, the negative impact of the wind turbines is greatly reduced for all spectral moments.

  • 6.
    Norin, Lars
    SMHI, Research Department, Atmospheric remote sensing.
    Wind turbine impact on operational weather radar I/Q data: characterisation and filtering2017In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 10, no 5, p. 1739-1753Article in journal (Refereed)
  • 7.
    Norin, Lars
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Devasthale, Abhay
    SMHI, Research Department, Atmospheric remote sensing.
    L'Ecuyer, T. S.
    Wood, N. B.
    Smalley, M.
    Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden2015In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 8, no 12, p. 5009-5021Article in journal (Refereed)
    Abstract [en]

    Accurate snowfall estimates are important for both weather and climate applications. Ground-based weather radars and space-based satellite sensors are often used as viable alternatives to rain gauges to estimate precipitation in this context. In particular, the Cloud Profiling Radar (CPR) on board CloudSat is proving to be a useful tool to map snowfall globally, in part due to its high sensitivity to light precipitation and its ability to provide near-global vertical structure. CloudSat snowfall estimates play a particularly important role in the high-latitude regions as other ground-based observations become sparse and passive satellite sensors suffer from inherent limitations. In this paper, snowfall estimates from two observing systems-Swerad, the Swedish national weather radar network, and CloudSat - are compared. Swerad offers a well-calibrated data set of precipitation rates with high spatial and temporal resolution, at very high latitudes. The measurements are anchored to rain gauges and provide valuable insights into the usefulness of CloudSat CPR's snowfall estimates in the polar regions. In total, 7 : 2 x 10(5) matchups of CloudSat and Swerad observations from 2008 through 2010 were intercompared, covering all but the summer months (June to September). The intercomparison shows encouraging agreement between the two observing systems despite their different sensitivities and user applications. The best agreement is observed when CloudSat passes close to a Swerad station (46-82 km), where the observational conditions for both systems are comparable. Larger disagreements outside this range suggest that both platforms have difficulty with shallow snow but for different reasons. The correlation between Swerad and CloudSat degrades with increasing distance from the nearest Swerad station, as Swerad's sensitivity decreases as a function of distance. Swerad also tends to overshoot low-level precipitating systems further away from the station, leading to an underestimation of snowfall rate and occasionally to missing precipitation altogether. Several statistical metrics-including the probability of detection, false alarm rate, hit rate, and Pierce's skill score - are calculated. The sensitivity of these metrics to the snowfall rate, as well as to the distance from the nearest radar station, are summarised. This highlights the strengths and the limitations of both observing systems at the lower and upper ends of the snowfall distributions as well as the range of uncertainties that can be expected from these systems in high-latitude regions.

  • 8.
    Norin, Lars
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Devasthale, Abhay
    SMHI, Research Department, Atmospheric remote sensing.
    L'Ecuyer, Tristan S.
    The sensitivity of snowfall to weather states over Sweden2017In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 10, no 9, p. 3249-3263Article in journal (Refereed)
  • 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. 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)
1 - 10 of 10
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