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Andersson, Sandra
Publications (10 of 11) Show all publications
Devasthale, A., Karlsson, K.-G., Andersson, S. & Engström, E. (2023). Difference between WMO Climate Normal and Climatology: Insights from a Satellite-Based Global Cloud and Radiation Climate Data Record. Remote Sensing, 15(23), Article ID 5598.
Open this publication in new window or tab >>Difference between WMO Climate Normal and Climatology: Insights from a Satellite-Based Global Cloud and Radiation Climate Data Record
2023 (English)In: Remote Sensing, E-ISSN 2072-4292, Vol. 15, no 23, article id 5598Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Meteorology; Climate
Identifiers
urn:nbn:se:smhi:diva-6542 (URN)10.3390/rs15235598 (DOI)001116085900001 ()
Available from: 2024-01-09 Created: 2024-01-09 Last updated: 2024-01-09Bibliographically approved
Schimanke, S., Joelsson, M., Andersson, S., Carlund, T., Wern, L., Hellström, S. & Kjellström, E. (2022). Observerad klimatförändring i Sverige 1860–2021.
Open this publication in new window or tab >>Observerad klimatförändring i Sverige 1860–2021
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2022 (Swedish)Report (Other academic)
Abstract [sv]

Historiska observationer av temperatur, vegetationsperiodens längd, nederbörd, snö, globalstrålning och geostrofisk vind i Sverige har analyserats. Längden på de tillgängliga tidsserierna varierar mellan de olika variablerna. Det finns dagliga temperaturobservationer från Uppsala så långt tillbaka som 1722, medan startåret för de globalstrålningsmätningar från åtta svenska stationer som analyserats här är så sent som 1983. Klimatindikatorer som baseras på dessa observationer visar att:• Sveriges årsmedeltemperatur har ökat med 1,9 °C jämfört med perioden 1861–1890. • Sveriges årsnederbörd har ökat sedan 1930 från 600 mm/år till nästan 700 mm/år. • Antalet dagar med snötäcke har minskat sedan 1950. • Globalstrålningen har ökat med cirka 10 % sedan mitten av 1980-talet. • Någon förändring av den geostrofiska vinden kan inte fastslås från 1940.De ovan listade förändringarna syftar alla till årliga genomsnitt för hela Sverige. De är statistiskt signifikanta i de flesta fall. Bilden blir mer tvetydig då genomsnitt för olika landsdelar eller säsonger undersöks. Exempelvis är den ökade årsnederbörden mest ett resultat av ökad nederbörd under vinter och höst, medan det inte finns någon tydlig trend för sommar och vår. Det är också generellt sett svårare att fastslå förändringar i extremvärden. Exempelvis finns ingen signifikant trend vad gäller vinterns största snödjup, trots en tydlig minskning i antalet dagar med snötäcke.

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

Series
Climatology, ISSN 1654-2258
National Category
Meteorology and Atmospheric Sciences
Research subject
Climate
Identifiers
urn:nbn:se:smhi:diva-6362 (URN)
Available from: 2022-11-22 Created: 2022-11-22 Last updated: 2024-09-03Bibliographically approved
Andersson, S., Bärring, L., Landelius, T., Samuelsson, P. & Schimanke, S. (2021). SMHI Gridded Climatology.
Open this publication in new window or tab >>SMHI Gridded Climatology
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2021 (English)Report (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.

Abstract [sv]

Ett klimatologiskt griddat datasett (SMHI Gridded Climatology - SMHIGridClim) hartagits fram för åren 1961 – 2018. Data täcker de nordiska länderna med en horisontellupplösning av 2,5 km. Variablerna som tagits fram är lufttemperatur och relativluftfuktighet vid 2m höjd med en upplösning av1,3 eller 6 timmar beroende av tidsperiod,samt dygnsupplöst min- och maxtemperatur, nederbörd och snödjup. Datasetet ärframtaget med optimal interpolation av stationsdata genom analysverktyget gridpp, somär en öppet tillgänglig programvara från Norska Meteorologiska Institutet.Observationer till analysen har erhållits från de svenska, norska och finskameteorologiska instituten, samt ECMWF. En ansats gjordes också att användaobservationer från datasetet ECA&D från KNMI, men på grund av svårigheter med atttidsstämplarna för data från olika länder inte överensstämde, uteslöts datasetet uranalysen. Kvalitetskontroll av observationerna gjordes med programvaran TITAN, somäven den finns tillgänglig från och utvecklats av Norska Meteorologiska Institutet.Som en första gissning till interpolationen användes statistiskt nerskalade prognosfält(från 11 km till 2,5 km upplösning) från UERRA-HARMONIE. Nerskalningen gjordesmot fält från den operationella numeriska väderprognosmodellen MEPS. Anpassningengjordes med nedskalningsparametrar som varierar över året och dygnet.Kvalitén hos ”SMHIGridClim med avseende på genomsnittligt RMSE är liknande densom tagits fram för griddade data för andra nordiska länderna med varierandeanalysmetoder; “seNorge” från Norge och “FMI_ClimGrid” från Finland.

Series
RMK: Report Meteorology and Climatology, ISSN 0347-2116 ; 118
National Category
Climate Research
Research subject
Climate
Identifiers
urn:nbn:se:smhi:diva-6192 (URN)
Available from: 2021-11-22 Created: 2021-11-22 Last updated: 2021-11-22Bibliographically approved
van Noord, M., Landelius, T. & Andersson, S. (2021). Snow-Induced PV Loss Modeling Using Production-Data Inferred PV System Models. Energies, 14(6), Article ID 1574.
Open this publication in new window or tab >>Snow-Induced PV Loss Modeling Using Production-Data Inferred PV System Models
2021 (English)In: Energies, E-ISSN 1996-1073, Vol. 14, no 6, article id 1574Article in journal (Refereed) Published
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.

National Category
Meteorology and Atmospheric Sciences Climate Research
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-6092 (URN)10.3390/en14061574 (DOI)000634406900001 ()
Available from: 2021-04-27 Created: 2021-04-27 Last updated: 2023-08-28Bibliographically approved
Campana, P. E., Landelius, T., Andersson, S., Lundstrom, L., Nordlander, E., He, T., . . . Yan, J. (2020). A gridded optimization model for photovoltaic applications. Solar Energy, 202, 465-484
Open this publication in new window or tab >>A gridded optimization model for photovoltaic applications
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2020 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 202, p. 465-484Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Meteorology
Identifiers
urn:nbn:se:smhi:diva-5708 (URN)10.1016/j.solener.2020.03.076 (DOI)000528209300040 ()
Available from: 2020-06-02 Created: 2020-06-02 Last updated: 2020-06-02Bibliographically approved
Landelius, T., Andersson, S. & Abrahamsson, R. (2019). Modelling and forecasting PV production in the absence of behind-the-meter measurements. Progress in Photovoltaics, 27(11), 990-998
Open this publication in new window or tab >>Modelling and forecasting PV production in the absence of behind-the-meter measurements
2019 (English)In: Progress in Photovoltaics, ISSN 1062-7995, E-ISSN 1099-159X, Vol. 27, no 11, p. 990-998Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-5459 (URN)10.1002/pip.3117 (DOI)000491221900010 ()
Available from: 2019-11-05 Created: 2019-11-05 Last updated: 2019-11-05Bibliographically approved
Landelius, T., Andersson, S., Carlund, T. & Josefsson, W. (2018). Karteringen av solstrålningen i Sverige. Polarfront (168), 31-40
Open this publication in new window or tab >>Karteringen av solstrålningen i Sverige
2018 (Swedish)In: Polarfront, no 168, p. 31-40Article in journal (Other academic) Published
Place, publisher, year, edition, pages
Svenska Meteorologiska Sällskapet, 2018
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-5157 (URN)
Available from: 2019-02-01 Created: 2019-02-01 Last updated: 2020-05-04Bibliographically approved
Campana, P. E., Zhang, J., Yao, T., Andersson, S., Landelius, T., Melton, F. & Yan, J. (2018). Managing agricultural drought in Sweden using a novel spatially-explicit model from the perspective of water-food-energy nexus. Journal of Cleaner Production, 197, 1382-1393
Open this publication in new window or tab >>Managing agricultural drought in Sweden using a novel spatially-explicit model from the perspective of water-food-energy nexus
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2018 (English)In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 197, p. 1382-1393Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4956 (URN)10.1016/j.jclepro.2018.06.096 (DOI)000441998400126 ()
Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2018-09-05Bibliographically approved
Landelius, T., Andersson, S. & Abrahamsson, R. (2018). MODELLING AND FORECASTING PV PRODUCTION IN THE ABSENCE OF BEHIND-THE-METER MEASUREMENTS. In: : . Paper presented at 35th European Photovoltaic Solar Energy Conference and Exhibition (pp. 1684-1689).
Open this publication in new window or tab >>MODELLING AND FORECASTING PV PRODUCTION IN THE ABSENCE OF BEHIND-THE-METER MEASUREMENTS
2018 (English)Conference paper, Published paper (Refereed)
National Category
Remote Sensing
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-5145 (URN)0.4229/35thEUPVSEC20182018-6DO.11.2 (DOI)
Conference
35th European Photovoltaic Solar Energy Conference and Exhibition
Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-01-21Bibliographically approved
Landelius, T., Andersson, S. & Abrahamsson, R. (2018). System imbalance from solar energy trading. In: 8th Solar International Workshop on Integration of Solar into Power Systems: . Paper presented at 8th Solar International Workshop on Integration of Solar into Power Systems 16-17 October 2018 in Stockholm.
Open this publication in new window or tab >>System imbalance from solar energy trading
2018 (English)In: 8th Solar International Workshop on Integration of Solar into Power Systems, 2018Conference paper, Published paper (Refereed)
National Category
Meteorology and Atmospheric Sciences
Research subject
Meteorology
Identifiers
urn:nbn:se:smhi:diva-5186 (URN)
Conference
8th Solar International Workshop on Integration of Solar into Power Systems 16-17 October 2018 in Stockholm
Available from: 2019-04-16 Created: 2019-04-16 Last updated: 2019-04-16Bibliographically approved
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