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| 2. |
- Gidhagen, Lars, et al.
(författare)
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Towards climate services for European cities : Lessons learnt from the Copernicus project Urban SIS
- 2020
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Ingår i: Urban Climate. - Amsterdam, Netherlands : Elsevier. - 2212-0955. ; 31
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Tidskriftsartikel (refereegranskat)abstract
- The growing share of Europe's population living in cities makes urban climate change impact assessment and adaptation a critical issue. The urban environment is characterized by its sensitivity to small-scale meteorological, hydrological and environmental processes. These are generally not fully described in climate models, largely because of the models' insufficient spatial resolution. The Urban SIS climate service offers historical and future simulated data downscaled to 1 km × 1 km resolution over selected European metropolitan areas. The downscaled data are subsequently used as input to air quality and hydrological impact models, all made available to users as Essential Climate Variables and Sectoral Impact Indicators through a web portal. This paper presents the Urban SIS climate service and demonstrates its functionality in a case study in Stockholm city, Sweden. Good model performance was attained for intra-city temperature gradients and small-scale precipitation extremes. Less positive results were obtained for large-scale precipitation and hydrology, mainly due to an insufficient domain size in the meteorological and climate modelling, in turn related to the substantial computational requirements. An uncertainty classification approach was developed to aid the interpretation and user application of the data. We hope our lessons learnt will support future efforts in this direction.
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| 3. |
- Bergström, Hans, et al.
(författare)
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Wind power in cold climates : Ice mapping methods
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In the Vindforsk V-313 project “Vindkraft i kallt klimat” the goal was to arrive at a methodology to construct a high resolution (1x1 km2) climatology of icing on wind power turbines. This is a very demanding task since observations of icing on instruments have only been routinely available in Sweden during three winter seasons and at a dozen locations. The term climatology in classical meteorology means statistics over 30 years of data, and usually in the form of direct or indirect measurements. Examples are mean temperatures (annual, monthly, maximum etc.), number of frost days, growing season, variability, number of days of precipitation, mean winds, cloudiness, solar radiation, to name a few.In the project researchers from Uppsala University, WeatherTech Scandinavia, and SMHI have been collaborating. Observations have been analysed and state-of-the-art numerical weather prediction models have been applied in case studies and tested in several sensitivity studies. Extensive model verification has been carried out. Modelled ice load and estimated production losses were also compared to measurements. The question of how to arrive at a method using only a few years to represent the long-term climatology was addressed and several methods were tested.The project has shed light on the uncertainties in modelling ice load and icing climate. The end results not only depend on which mesoscale model that is used but also on how the model is set up. In order to improve the models more accurate measurements of ice load is needed. Observations of liquid cloud water content and droplet size distributions could also be of significant value to better understand why the ice load models fail in capturing the observed ice load.
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| 4. |
- Molinder, Jennie, et al.
(författare)
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Probabilistic forecasting of wind power production losses in cold climates : a case study
- 2018
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Ingår i: Wind Energy Science. - : Copernicus GmbH. - 2366-7443 .- 2366-7451. ; 3, s. 667-680
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Tidskriftsartikel (refereegranskat)abstract
- The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next-day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a 2-week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climates can be valuable when planning next-day energy production, in the usage of de-icing systems and for site safety.
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| 5. |
- Molinder, Jennie, et al.
(författare)
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The Use of Uncertainty Quantification for the Empirical Modeling of Wind Turbine Icing
- 2019
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Ingår i: Journal of Applied Meteorology and Climatology. - : AMER METEOROLOGICAL SOC. - 1558-8424 .- 1558-8432. ; 58:9, s. 2019-2032
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Rydblom, Staffan, 1975-, et al.
(författare)
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Field Study of LWC and MVD Using the Droplet Imaging Instrument
- 2019
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - : IEEE. - 0018-9456 .- 1557-9662. ; 68:2, s. 614-622
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Tidskriftsartikel (refereegranskat)abstract
- The droplet imaging instrument (DII) is a new instrument for cost-effective in situ measurements of the size and concentration of water droplets. The droplet size distribution and the concentration of atmospheric liquid water are important for the prediction of icing on structures, such as wind turbines. To improve the predictions of icing, there is a need to explore cost-effective working solutions. Through imaging, a wide range of droplet sizes can be measured. This paper describes a study of the atmospheric liquid water content and the median volume diameter using the DII and a commercial reference instrument--the cloud droplet probe 2 from Droplet Measurement Technologies Inc. The measurement is done at a weather measurement station in mid-Sweden. For a second validation, the result is compared with predictions using a numerical weather prediction model. The size measurement of the DII is verified using polymer microspheres of four known size distributions. The study shows that the DII measurement is precise, but there is a systematic difference between the two compared instruments. It also shows that droplets larger than 50 μm in diameter are occasionally measured, which we believe is important for the prediction of icing.
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