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  • 1.
    Andersson, Sandra
    et al.
    SMHI, Core Services.
    Bärring, Lars
    SMHI, Research Department, Climate research - Rossby Centre.
    Landelius, Tomas
    SMHI, Research Department, Atmospheric remote sensing.
    Samuelsson, Patrick
    SMHI, Research Department, Climate research - Rossby Centre.
    Schimanke, Semjon
    SMHI, Research Department, Oceanography.
    SMHI Gridded Climatology2021Report (Other academic)
    Abstract [en]

    A gridded dataset (SMHI Gridded Climatology - SMHIGridClim) has been produced forthe years 1961 - 2018 over an area covering the Nordic countries on a grid with 2.5 kmhorizontal resolution. The variables considered are the two meter temperature and twometer relative humidity on 1, 3 or 6 hour resolution, varying over the time periodcovered, the daily minimum and maximum temperatures, the daily precipitation and thedaily snow depth. The gridding was done using optimal interpolation with the gridppopen source software from the Norwegian Meteorological Institute.Observations for the analysis are provided by the Swedish, Finish and Norwegianmeteorological institutes, and the ECMWF. The ECA&D observation data set (e.g. usedfor the gridded E-OBS dataset) was considered for inclusion but was left out because ofcomplications with time stamps and accumulation periods varying between countries andperiods. Quality check of the observations was performed using the open source softwareTITAN, also developed at the Norwegian Meteorological Institute.The first guess to the optimal interpolation was given by statistically downscaledforecasts from the UERRA-HARMONIE reanalysis at 11 km horizontal resolution. Thedownscaling was done to fit the output from the operational MEPS NWP system at 2.5km with a daily and yearly variation in the downscaling parameters.The quality of the SMHIGridClim dataset, in terms of annual mean RMSE, was shown tobe similar to that of gridded datasets covering the other Nordic countries; “seNorge”from Norway and the dataset “FMI_ClimGrid” from Finland.

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    RMK_118 SMHI Gridded Climatology
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    RMK_118 SMHI Gridded Climatology_appendixA_observations
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    RMK_118 SMHI Gridded Climatology_appendixB_errors
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    RMK_118 SMHI Gridded Climatology_appendixC_scripts
  • 2. Campana, P. E.
    et al.
    Zhang, J.
    Yao, T.
    Andersson, Sandra
    SMHI, Core Services.
    Landelius, Tomas
    SMHI, Research Department, Atmospheric remote sensing.
    Melton, F.
    Yan, J.
    Managing agricultural drought in Sweden using a novel spatially-explicit model from the perspective of water-food-energy nexus2018In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 197, p. 1382-1393Article in journal (Refereed)
  • 3. Campana, Pietro Elia
    et al.
    Landelius, Tomas
    SMHI, Research Department, Atmospheric remote sensing.
    Andersson, Sandra
    SMHI, Core Services.
    Lundstrom, Lukas
    Nordlander, Eva
    He, Tao
    Zhang, Jie
    Stridh, Bengt
    Yan, Jinyue
    A gridded optimization model for photovoltaic applications2020In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 202, p. 465-484Article in journal (Refereed)
  • 4.
    Devasthale, Abhay
    et al.
    SMHI, Research Department, Meteorology.
    Karlsson, Karl-Göran
    SMHI, Research Department, Meteorology.
    Andersson, Sandra
    SMHI, Samhällsplanering.
    Engström, Erik
    SMHI, Samhällsplanering.
    Difference between WMO Climate Normal and Climatology: Insights from a Satellite-Based Global Cloud and Radiation Climate Data Record2023In: Remote Sensing, E-ISSN 2072-4292, Vol. 15, no 23, article id 5598Article in journal (Refereed)
    Download full text (pdf)
    Difference between WMO Climate Normal and Climatology: Insights from a Satellite-Based Global Cloud and Radiation Climate Data Record
  • 5.
    Landelius, Tomas
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Andersson, Sandra
    SMHI, Core Services.
    Abrahamsson, Roger
    MODELLING AND FORECASTING PV PRODUCTION IN THE ABSENCE OF BEHIND-THE-METER MEASUREMENTS2018Conference paper (Refereed)
  • 6.
    Landelius, Tomas
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Andersson, Sandra
    SMHI, Core Services.
    Abrahamsson, Roger
    Modelling and forecasting PV production in the absence of behind-the-meter measurements2019In: Progress in Photovoltaics, ISSN 1062-7995, E-ISSN 1099-159X, Vol. 27, no 11, p. 990-998Article in journal (Refereed)
    Download full text (pdf)
    fulltext
  • 7.
    Landelius, Tomas
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Andersson, Sandra
    SMHI, Core Services.
    Abrahamsson, Roger
    System imbalance from solar energy trading2018In: 8th Solar International Workshop on Integration of Solar into Power Systems, 2018Conference paper (Refereed)
  • 8.
    Landelius, Tomas
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Andersson, Sandra
    SMHI, Core Services.
    Carlund, Thomas
    SMHI, Core Services.
    Josefsson, Weine
    SMHI, Core Services. SMHI, Research Department, Atmospheric remote sensing.
    Karteringen av solstrålningen i Sverige2018In: Polarfront, no 168, p. 31-40Article in journal (Other academic)
  • 9.
    Schimanke, Semjon
    et al.
    SMHI, Research Department, Oceanography.
    Joelsson, Magnus
    SMHI, Core Services. SMHI, Samhällsplanering.
    Andersson, Sandra
    SMHI, Core Services.
    Carlund, Thomas
    SMHI, Core Services.
    Wern, Lennart
    SMHI, Core Services.
    Hellström, Sverker
    SMHI, Core Services.
    Kjellström, Erik
    SMHI, Research Department, Climate research - Rossby Centre.
    Observerad klimatförändring i Sverige 1860–20212022Report (Other academic)
    Abstract [en]

    Historical Swedish observations of temperature, length of vegetation period, precipitation, snow, global radiation, and geostrophic wind have been analysed. The length of available time series varies among these variables. Whereas there are temperature observations for Uppsala ranging back to 1722 continuous measurements of global radiation at eight Swedish stations start only in 1983. Climate indicators based on these observations show that: • The annual mean temperature for Sweden has increased by 1.9 °C compared to the period 1861• The amount of annual precipitation increased since 1930 from about 600 mm/year to almost 700 mm/year. • The number of days with snow cover has reduced since 1950. • The global radiation increased with circa 10 % since the mid-1980’s. • The geostrophic wind has no clear change pattern since 1940. The listed changes are annual averages for Sweden. These are robust and statistically significant in most cases. The picture is getting more diverse when investigating smaller regions or different seasons instead of annual means. For instance, the increase of precipitation is mainly related to enhanced precipitation during autumn and winter whereas there are no obvious trends in spring and summer. Moreover, changes in extremes are generally harder to identify. For instance, despite the clear negative trend in the number of days with snow cover there is no significant trend for the maximum snow depth. –1890.Denna

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    Observerad klimatförändring i Sverige 1860–2021
  • 10. Schmalwieser, Alois W.
    et al.
    Grobner, Julian
    Blumthaler, Mario
    Klotz, Barbara
    De Backer, Hugo
    Bolsee, David
    Werner, Rolf
    Tomsic, Davor
    Metelka, Ladislav
    Eriksen, Paul
    Jepsen, Nis
    Aun, Margit
    Heikkila, Anu
    Duprat, Thierry
    Sandmann, Henner
    Weiss, Tilman
    Bais, Alkis
    Toth, Zoltan
    Siani, Anna-Maria
    Vaccaro, Luisa
    Diemoz, Henri
    Grifoni, Daniele
    Zipoli, Gaetano
    Lorenzetto, Giuseppe
    Petkov, Boyan H.
    di Sarra, Alcide Giorgio
    Massen, Francis
    Yousif, Charles
    Aculinin, Alexandr A.
    den Outer, Peter
    Svendby, Tove
    Dahlback, Arne
    Johnsen, Bjorn
    Biszczuk-Jakubowska, Julita
    Krzyscin, Janusz
    Henriques, Diamantino
    Chubarova, Natalia
    Kolarz, Predrag
    Mijatovic, Zoran
    Groselj, Drago
    Pribullova, Anna
    Moreta Gonzales, Juan Ramon
    Bilbao, Julia
    Vilaplana Guerrero, Jose Manuel
    Serrano, Antonio
    Andersson, Sandra
    SMHI, Core Services.
    Vuilleumier, Laurent
    Webb, Ann
    O'Hagan, John
    UV Index monitoring in Europe2017In: Photochemical and Photobiological Sciences, ISSN 1474-905X, E-ISSN 1474-9092, Vol. 16, no 9, p. 1349-1370Article in journal (Refereed)
  • 11. van Noord, Michiel
    et al.
    Landelius, Tomas
    SMHI, Research Department, Atmospheric remote sensing.
    Andersson, Sandra
    SMHI, Core Services.
    Snow-Induced PV Loss Modeling Using Production-Data Inferred PV System Models2021In: Energies, E-ISSN 1996-1073, Vol. 14, no 6, article id 1574Article in journal (Refereed)
    Abstract [en]

    Snow-induced photovoltaic (PV)-energy losses (snow losses) in snowy and cold locations vary up to 100% monthly and 34% annually, according to literature. Levels that illustrate the need for snow loss estimation using validated models. However, to our knowledge, all these models build on limited numbers of sites and winter seasons, and with limited climate diversity. To overcome this limitation in underlying statistics, we investigate the estimation of snow losses using a PV system's yield data together with freely available gridded weather datasets. To develop and illustrate this approach, 263 sites in northern Sweden are studied over multiple winters. Firstly, snow-free production is approximated by identifying snow-free days and using corresponding data to infer tilt and azimuth angles and a snow-free performance model incorporating shading effects, etc. This performance model approximates snow-free monthly yields with an average hourly standard deviation of 6.9%, indicating decent agreement. Secondly, snow losses are calculated as the difference between measured and modeled yield, showing annual snow losses up to 20% and means of 1.5-6.2% for winters with data for at least 89 sites. Thirdly, two existing snow loss estimation models are compared to our calculated snow losses, with the best match showing a correlation of 0.73 and less than 1% bias for annual snow losses. Based on these results, we argue that our approach enables studying snow losses for high numbers of PV systems and winter seasons using existing datasets.

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    Snow-Induced PV Loss Modeling Using Production-Data Inferred PV System Models
1 - 11 of 11
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