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  • 1.
    Amorim, Jorge Humberto
    et al.
    SMHI, Research Department, Air quality.
    Asker, Christian
    SMHI, Research Department, Air quality.
    Belusic, Danijel
    SMHI, Research Department, Climate research - Rossby Centre.
    Carvalho, Ana
    SMHI, Research Department, Air quality.
    Engardt, Magnuz
    SMHI, Research Department, Air quality.
    Gidhagen, Lars
    SMHI, Research Department, Air quality.
    Hundecha, Yeshewatesfa
    SMHI, Research Department, Hydrology.
    Körnich, Heiner
    SMHI, Research Department, Meteorology.
    Lind, Petter
    SMHI, Research Department, Climate research - Rossby Centre.
    Olsson, Esbjörn
    SMHI, Research Department, Meteorology.
    Olsson, Jonas
    SMHI, Research Department, Hydrology.
    Segersson, David
    SMHI, Research Department, Air quality.
    Strombäck, Lena
    SMHI, Research Department, Hydrology.
    Joe, Paul
    Baklanov, Alexander
    Integrated Urban Services for European cities: the Stockholm case2018In: WMO Bulletin, ISSN 0042-9767, Vol. 67, no 2, p. 33-40Article in journal (Refereed)
    Download full text (pdf)
    fulltext
  • 2. Bolin, Karl
    et al.
    Almgren, Martin
    Olsson, Esbjörn
    SMHI, Research Department, Meteorology.
    Karasalo, Ilkka
    Long term estimations of low frequency noise levels over water from an off-shore wind farm2014In: Journal of the Acoustical Society of America, ISSN 0001-4966, E-ISSN 1520-8524, Vol. 135, no 3, p. 1106-1114Article in journal (Refereed)
    Abstract [en]

    This article focuses on computations of low frequency sound propagation from an off-shore wind farm. Two different methods for sound propagation calculations are combined with meteorological data for every 3 hours in the year 2010 to examine the varying noise levels at a reception point at 13 km distance. It is shown that sound propagation conditions play a vital role in the noise impact from the off-shore wind farm and ordinary assessment methods can become inaccurate at longer propagation distances over water. Therefore, this paper suggests that methodologies to calculate noise immission with realistic sound speed profiles need to be combined with meteorological data over extended time periods to evaluate the impact of low frequency noise from modern off-shore wind farms. (C) 2014 Acoustical Society of America.

  • 3.
    Gidhagen, Lars
    et al.
    SMHI, Research Department, Air quality.
    Olsson, Jonas
    SMHI, Research Department, Hydrology.
    Amorim, Jorge Humberto
    SMHI, Research Department, Air quality.
    Asker, Christian
    SMHI, Research Department, Air quality.
    Belušić, Danijel
    SMHI, Research Department, Climate research - Rossby Centre.
    Carvalho, Ana Cristina
    SMHI, Research Department, Air quality.
    Engardt, Magnuz
    Hundecha, Yeshewatesfa
    SMHI, Research Department, Hydrology.
    Körnich, Heiner
    SMHI, Research Department, Meteorology.
    Lind, Petter
    SMHI, Research Department, Climate research - Rossby Centre.
    Lindstedt, David
    SMHI, Research Department, Climate research - Rossby Centre.
    Olsson, Esbjörn
    SMHI, Research Department, Meteorology.
    Rosberg, Jörgen
    SMHI, Research Department, Hydrology.
    Segersson, David
    SMHI, Research Department, Air quality.
    Strombäck, Lena
    SMHI, Research Department, Hydrology.
    Towards climate services for European cities: Lessons learnt from the Copernicus project Urban SIS2020In: Urban Climate, ISSN 2212-0955, E-ISSN 2212-0955, Vol. 31, article id 100549Article in journal (Refereed)
    Download full text (pdf)
    fulltext
  • 4.
    Landelius, Tomas
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Dahlgren, Per
    SMHI, Research Department, Meteorology.
    Gollvik, Stefan
    SMHI, Research Department, Meteorology.
    Jansson, A.
    Olsson, Esbjörn
    SMHI, Research Department, Meteorology.
    A high-resolution regional reanalysis for Europe. Part 2: 2D analysis of surface temperature, precipitation and wind2016In: Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, E-ISSN 1477-870X, Vol. 142, no 698, p. 2132-2142Article in journal (Refereed)
  • 5. Molinder, Jennie
    et al.
    Körnich, Heiner
    SMHI, Research Department, Meteorology.
    Olsson, Esbjörn
    SMHI, Research Department, Meteorology.
    Bergstrom, Hans
    Sjoblom, Anna
    Probabilistic forecasting of wind power production losses in cold climates: a case study2018In: Wind Energy Science, ISSN 2213-3968, E-ISSN 2366-7443, Vol. 3, no 2, p. 667-680Article in journal (Refereed)
    Download full text (pdf)
    fulltext
  • 6. Molinder, Jennie
    et al.
    Körnich, Heiner
    SMHI, Research Department, Meteorology.
    Olsson, Esbjörn
    SMHI, Research Department, Meteorology.
    Hessling, Peter
    The Use of Uncertainty Quantification for the Empirical Modeling of Wind Turbine Icing2019In: Journal of Applied Meteorology and Climatology, ISSN 1558-8424, E-ISSN 1558-8432, Vol. 58, no 9, p. 2019-2032Article in journal (Refereed)
    Abstract [en]

    A novel uncertainty quantification method is used to evaluate the impact of uncertainties of parameters within the icing model in the modeling chain for icing-related wind power production loss forecasts. As a first step, uncertain parameters in the icing model were identified from the literature and personal communications. These parameters are the median volume diameter of the hydrometeors, the sticking efficiency for snow and graupel, the Nusselt number, the shedding factor, and the wind erosion factor. The sensitivity of these parameters on icing-related wind power production losses is examined. An icing model ensemble representing the estimated parameter uncertainties is designed using so-called deterministic sampling and is run for two periods over a total of 29 weeks. Deterministic sampling allows an exact representation of the uncertainty and, in future applications, further calibration of these parameters. Also, the number of required ensemble members is reduced drastically relative to the commonly used random-sampling method, thus enabling faster delivery and a more flexible system. The results from random and deterministic sampling are compared and agree very well, confirming the usefulness of deterministic sampling. The ensemble mean of the nine-member icing model ensemble generated with deterministic sampling is shown to improve the forecast skill relative to one single forecast for the winter periods. In addition, the ensemble spread provides valuable information as compared with a single forecast in terms of forecasting uncertainty. However, addressing uncertainties in the icing model alone underestimates the forecast uncertainty, thus stressing the need for a fully probabilistic approach in the modeling chain for wind power forecasts in a cold climate.

    Download full text (pdf)
    fulltext
  • 7. Olofsson, B
    et al.
    Olsson, Esbjörn
    SMHI, Research Department, Meteorology.
    Andersson, S
    Martensson, T
    Martensson, E
    A new algorithm to estimate aircraft icing in the HIRLAM model2003In: Meteorological Applications, ISSN 1350-4827, E-ISSN 1469-8080, Vol. 10, no 2, p. 111-114Article in journal (Refereed)
    Abstract [en]

    A new index to estimate aircraft icing in clouds from operational meteorological models has been developed by Swedish meteorologists. Although rather simple it takes into account, directly or indirectly, all the principal meteorological variables for icing. The index has been evaluated during three winter seasons and is now operational in the Swedish HIRLAM model. A graphical representation of the index is presented.

  • 8. Rydblom, Staffan
    et al.
    Thornberg, Benny
    Olsson, Esbjörn
    SMHI, Research Department, Meteorology.
    Field Study of LWC and MVD Using the Droplet Imaging Instrument2019In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 68, no 2, p. 614-622Article in journal (Refereed)
1 - 8 of 8
CiteExportLink to result list
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  • de-DE
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  • fi-FI
  • nn-NO
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