Meteorological conditions affecting renewable energy

Synoptic situation and different meteorological phenomena can highly affect renewable energy production. Investigating different phenomena will give new information on the occurrence and characteristics of specific phenomena and their impacts on renewable energy applications. Different observational data sets and numerical models can be widely used in different phases of renewable energy projects; from planning of the project to help with the operation and the maintenance of the existing wind or solar field. In this thesis a meteorological phenomena, a low-level jet, is investigated. Thesis comprises analysis of the climatological occurrence of low-level jets, their characteristics and forcing mechanisms, as well as numerical model capability to capture the phenomena. In addition, solar radiation forecasts obtained from the operational numerical weather prediction model are evaluated and the role of cloud cover forecast skill in solar radiation forecast error is investigated. Long data sets of observational data: mainly Doppler wind lidar, ceilometer, and solar radiation observations, are used, in addition to reanalysis and operational numerical weather prediction model data. A low-level jet is a wind phenomenon that can affect wind energy production. Nighttime low-level jets are a commonly known boundary-layer phenomenon occurring during stably stratified conditions over flat terrain. In this thesis, new information on the occurrence, characteristics, and forcing mechanisms of a low-level jet was gained in different conditions: in Northern Hemisphere mid-latitude and polar regions based on reanalysis data and at two different sites in Finland and Germany based on long-term Doppler lidar observations. The low-level jet identification algorithms developed in these studies can be used to repeat the studies by using different models covering different areas or at any site operating a Doppler lidar. The low-level jet identification algorithm for Doppler lidar data can also be applied to operationally detect low-level jets, which is useful information for example from wind energy point-of-view. Solar radiation and cloud cover forecasts were evaluated at one site in Finland based on long time-series of solar radiation and ceilometer observations. The role of cloud cover forecast in solar radiation forecast error is investigated. The solar radiation and cloud cover forecasts were obtained from operational numerical weather prediction model that can be used to predict the expected power production at solar field day-ahead. It was found that there is a positive bias in the forecast incoming solar radiation even if the cloud cover forecast is correct. The study can guide model improvements as the bias is likely due to underestimation in the forecast cloud liquid water content or incorrect representation of cloud optical properties. The methods created in this study can be applied to hundreds of sites globally. In addition, the algorithms developed in this study can be further used in different applications in the field of renewable energy, for example to detect potential in-cloud icing conditions

Evaluating solar radiation forecast uncertainty

The presence of clouds and their characteristics have a strong impact on the radiative balance of the Earth and on the amount of solar radiation reaching the Earth's surface. Many applications require accurate forecasts of surface radiation on weather timescales, for example solar energy and UV radiation forecasts. Here we investigate how operational forecasts of low and mid-level clouds affect the accuracy of solar radiation forecasts. A total of 4 years of cloud and solar radiation observations from one site in Helsinki, Finland, are analysed. Cloud observations are obtained from a ceilometer and therefore we first develop algorithms to reliably detect cloud base, precipitation, and fog. These new algorithms are widely applicable for both operational use and research, such as in-cloud icing detection for the wind energy industry and for aviation. The cloud and radiation observations are compared to forecasts from the Integrated Forecast System (IFS) run operationally and developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We develop methods to evaluate the skill of the cloud and radiation forecasts. These methods can potentially be extended to hundreds of sites globally. Over Helsinki, the measured global horizontal irradiance (GHI) is strongly influenced by its northerly location and the annual variation in cloudiness. Solar radiation forecast error is therefore larger in summer than in winter, but the relative error in the solar radiation forecast is more or less constant throughout the year. The mean overall bias in the GHI forecast is positive (8 W m(-2)). The observed and forecast distributions in cloud cover, at the spatial scales we are considering, are strongly skewed towards clear-sky and overcast situations. Cloud cover forecasts show more skill in winter when the cloud cover is predominantly overcast; in summer there are more clear-sky and broken cloud situations. A negative bias was found in forecast GHI for correctly forecast clear-sky cases and a positive bias in correctly forecast overcast cases. Temporal averaging improved the cloud cover forecast and hence decreased the solar radiation forecast error. The positive bias seen in overcast situations occurs when the model cloud has low values of liquid water path (LWP). We attribute this bias to the model having LWP values that are too low or the model optical properties for clouds with low LWP being incorrect.Peer reviewe

Low-Level Jets over Utö, Finland, Based on Doppler Lidar Observations

Over two years of meteorological observations from Utö, a small island in the Finnish outer archipelago in the Baltic Sea, were used to investigate the occurrence and characteristics of low-level jets (LLJs) and the diurnal and seasonal variations in these properties. An objective LLJ identification algorithm that is suitable for high-temporal-and-vertical-resolution Doppler lidar data was created and applied to wind profiles obtained from a combination of Doppler lidar data and two-dimensional sonic anemometer observations. This algorithm was designed to identify coherent LLJ structures and requires that they persist for at least 1 h. The long-term mean LLJ frequency of occurrence at Utö was 12%, the mean LLJ wind speed was 11.6 m s−1, and the vast majority of identified LLJs occurred below 150 m above ground level. The LLJ frequency of occurrence was much higher during summer (21%) and spring (18%) than in autumn (8%) and winter (3%). During winter and spring, the LLJ frequency of occurrence is evenly distributed throughout the day. In contrast, the LLJ frequency of occurrence peaks at night (1900–0100 UTC) during summer and during the evening hours (1700–1900 UTC) in autumn. The highest and strongest LLJs come from the southwest, which is also the predominant LLJ direction in all seasons. LLJs below 100 m are common in spring and summer, are weaker, and do not show a strong directional dependence.Peer reviewe

Long-Term Observations and High-Resolution Modeling of Midlatitude Nocturnal Boundary Layer Processes Connected to Low-Level Jets

Low-level-jet (LLJ) periods are investigated by exploiting a long-termrecord of ground-based remote sensing Doppler wind lidar measurements supported by tower observations and surface flux measurements at the Julich Observatory for Cloud Evolution (JOYCE), a midlatitude site in western Germany. LLJs were found 13% of the time during continuous observations over more than 4 yr. The climatological behavior of the LLJs shows a prevailing nighttime appearance of the jets, with a median height of 375 m and a median wind speed of 8.8 ms(-1) at the jet nose. Significant turbulence below the jet nose only occurs for high bulk wind shear, which is an important parameter for describing the turbulent characteristics of the jets. The numerous LLJs (16% of all jets) in the range of wind-turbine rotor heights below 200 m demonstrate the importance of LLJs and the associated intermittent turbulence for wind-energy applications. Also, a decrease in surface fluxes and an accumulation of carbon dioxide are observed if LLJs are present. A comprehensive analysis of an LLJ case shows the influence of the surrounding topography, dominated by an open pit mine and a 200-m-high hill, on the wind observed at JOYCE. High-resolution large-eddy simulations that complement the observations show that the spatial distribution of the wind field exhibits variations connected with the orographic flow depending on the wind direction, causing high variability in the long-term measurements of the vertical velocity.Peer reviewe

Characterizing Subsiding Shells in Shallow Cumulus Using Doppler Lidar and Large-Eddy Simulation

The existence of subsiding shells on the periphery of shallow cumulus clouds has major implications concerning the parameterization of shallow convection, with the mass exchange between the shell and cloudy air representing a significant deviation from the commonly used bulk-plume parameterization. We examine the structure and frequency of subsiding shells in shallow cumulus convection using Doppler lidars at the Atmospheric Radiation Measurement Southern Great Plains facility in the central United States and at the Julich ObservatorY for Cloud Evolution in western Germany. Doppler lidar indicates that the vertical subsiding shell extent is asymmetric, while shell width is typically similar to 100 m. Large-eddy simulation can reasonably simulate the observed shell structure using a grid spacing of 10 m and suggests that much of the observed asymmetry is not a result of transient cloud evolution