Simulations of the Bergen orographic wind shelter

Even though the coast of western Norway is very windy, the centre of Bergen is rather calm. To gain further understanding of this wind shelter, we study the flow in the complex topography of Bergen during two south-westerly windstorms using surface observations and high-resolution numerical simulations. The results reveal large spatial variability in the local wind field. In some areas, there are periods of sustained winds of more than 25m s−1, while at nearby locations the winds are typically less than 5m s−1. The centre of Bergen is among the calmest areas. To investigate the effect of the individual mountains upstream (Løvstakken) and downstream (Fløyen) on the wind field in the city centre of Bergen, they have been removed stepwise from the model topography. Areas with relatively large wind speed reductions are found immediately downstream of Løvstakken and immediately upstream of Fløyen. At Florida, situated close to the city centre, both a wake effect of Løvstakken and a blocking effect of Fløyen are evident, but the latter is more prominent. The total impact of both mountains on the winds in the city is close to the sum of each of them. A spillover effect of Løvstakken acts to substantially increase the local precipitation in the centre of Bergen. The spillover effect is presumably less pronounced for cases with weaker winds.publishedVersio

Satellite and Ground Observations of Snow Cover in Tibet during 2001–2015

The seasonal snow cover of the Tibetan Plateau exerts a profound environmental influence both regionally and globally. Daily observations of snow depth at 37 meteorological stations in Tibet and MODIS eight-day snow products (MOD10A2) during the period 2001–2015 are analyzed with respect to the frequency and spatial distribution of snow cover for each season and for various altitude ranges. The results show that the average snow cover percentage was 16%. Snow cover frequency was less than 21% for 70% of the Tibetan area, while it was more than 40% in eastern Tibet and in the Himalayas. We also estimated the variations in the starting times of snow accumulation and ablation. During the 15 years, both datasets revealed a significant trend of earlier onset of ablation, but no evident trend for the start of accumulation. The two datasets differed slightly with respect to the seasonal variation of snow cover. MODIS data showed more snow in winter than in other seasons, but the ground data showed most snow in early spring. For the station locations, the correlation between ground and MODIS snow cover percentage (number of snow-covered stations/number of cloud-free stations) is 0.77. Combining the advantages of remote sensing data and ground observation data is the best way to investigate snow in Tibet

Satellite and Ground Observations of Snow Cover in Tibet during 2001–2015

The seasonal snow cover of the Tibetan Plateau exerts a profound environmental influence both regionally and globally. Daily observations of snow depth at 37 meteorological stations in Tibet and MODIS eight-day snow products (MOD10A2) during the period 2001–2015 are analyzed with respect to the frequency and spatial distribution of snow cover for each season and for various altitude ranges. The results show that the average snow cover percentage was 16%. Snow cover frequency was less than 21% for 70% of the Tibetan area, while it was more than 40% in eastern Tibet and in the Himalayas. We also estimated the variations in the starting times of snow accumulation and ablation. During the 15 years, both datasets revealed a significant trend of earlier onset of ablation, but no evident trend for the start of accumulation. The two datasets differed slightly with respect to the seasonal variation of snow cover. MODIS data showed more snow in winter than in other seasons, but the ground data showed most snow in early spring. For the station locations, the correlation between ground and MODIS snow cover percentage (number of snow-covered stations/number of cloud-free stations) is 0.77. Combining the advantages of remote sensing data and ground observation data is the best way to investigate snow in Tibet

Simulations of the Bergen orographic wind shelter

Even though the coast of western Norway is very windy, the centre of Bergen is rather calm. To gain further understanding of this wind shelter, we study the flow in the complex topography of Bergen during two south-westerly windstorms using surface observations and high-resolution numerical simulations. The results reveal large spatial variability in the local wind field. In some areas, there are periods of sustained winds of more than 25m s−1, while at nearby locations the winds are typically less than 5m s−1. The centre of Bergen is among the calmest areas. To investigate the effect of the individual mountains upstream (Løvstakken) and downstream (Fløyen) on the wind field in the city centre of Bergen, they have been removed stepwise from the model topography. Areas with relatively large wind speed reductions are found immediately downstream of Løvstakken and immediately upstream of Fløyen. At Florida, situated close to the city centre, both a wake effect of Løvstakken and a blocking effect of Fløyen are evident, but the latter is more prominent. The total impact of both mountains on the winds in the city is close to the sum of each of them. A spillover effect of Løvstakken acts to substantially increase the local precipitation in the centre of Bergen. The spillover effect is presumably less pronounced for cases with weaker winds

Influence of synoptic weather patterns on solar irradiance variability in northern Europe

Observations have revealed strong variability of shortwave (SW) irradiance at Earth’s surface on decadal time scales, referred to as global dimming and brightening. Previous studies have attributed the dimming and brightening to changes in clouds and atmospheric aerosols. This study assesses the influence of atmospheric circulation on clouds and SW irradiance to separate the influence of ‘‘natural’’ SW variability from direct and, to some extent, indirect aerosol effects. The focus is on SW irradiance in northern Europe in summer and spring because there is little high-latitude SW irradiance during winter. As a measure of large-scale circulation the Grosswetterlagen (GWL) dataset, a daily classi- fication of synoptic weather patterns, is used. Empirical models of normalized SW irradiance are constructed based on the GWL, relating the synoptic weather patterns to the local radiative climate. In summer, a temporary SW peak in the 1970s and subsequent dimming is linked to variations in the synoptic patterns over Scandinavia, possibly related to a northward shift in the North Atlantic storm track. In spring, a decrease of anticyclonic and increase of cyclonic weather patterns over northern Europe contributes to the dimming from the 1960s to 1990. At many sites, there is also a residual SWirradiance trend not explained by the GWL model: a weak nonsignificant residual dimming from the 1950s or 1960s to around 1990, followed by a statistically significant residual brightening. It is concluded that factors other than the large-scale circulation (e.g., decreasing aerosol emissions) also play an important role in northern Europe

Solar resource assessment: a review

Solar Radiation Resource assessment is a broad term that refers to the various aspects of solar radiation relevant to solar energy and related applications. The terms resource characterization, resource measurement, resource information, and resource evaluation all fall under the comprehensive definition of solar radiation resource assessment. Solar resource information is a significant input for a wide range of applications: collector and device testing, building simulations (with either architectural or HVAC emphasis), solar system simulation for feasibility, design, siting or operational purposes. For each application however, resource assessment may mean something quite different. For some applications, the information and tools available today may be more than adequate but for others it may fall far short. It is useful to classify solar resource information in terms of a physical quantity or parameter with specific temporal and spatial characteristics. Examples of physical quantities are global irradiance, direct illuminance, or zenith luminance. The temporal characteristics include time step (e.g., one-minute, hourly, daily, etc.) and time specificity (e.g., time specific, typical, average, stochastic, forecasted, real time, etc.). Spatial characteristics refer to geographical determination of the resource information. This information may be point specific (e.g., based on a single station) or extended (e.g., a gridded map). The main concern with point specific data is the information's validity as distance increases. The concern for extended resource information is its spatial resolution