Cloud cover | EPFL Graph Search

Cloud cover (also known as cloudiness, cloudage, or cloud amount) refers to the fraction of the sky obscured by clouds on average when observed from a particular location. Okta is the usual unit for measurement of the cloud cover. The cloud cover is correlated to the sunshine duration as the least cloudy locales are the sunniest ones while the cloudiest areas are the least sunny places, as clouds can block sunlight, especially on sunrise and sunset where sunlight is already limited. The global cloud cover averages around 0.68 when analyzing clouds with optical depth larger than 0.1. This value is lower (0.56) when considering clouds with an optical depth larger than 2, and higher when counting subvisible cirrus clouds. Particularly over the oceans cloud cover is persistent with an average 72% of cloud cover. Role in the climate system Clouds play multiple critical roles in the climate system and diurnal cycle. In particular, being bright objects in the visible pa

Reliable Averages and Risky Extremes

Annelen Kahl, Michael Lehning, Viet Anh Nguyen, Karine Maria Sarrasin

With the perspective of Switzerland’s phase-out from nuclear energy, solar energy potential may take a leading role for the country’s future in renewable energy. Unlike nuclear power stations, photovoltaic (PV) production is prone to intermittency as it depends on the immediate solar irradiance, which fluctuates in space and time. If a large percentage of Switzerland’s electricity was to be derived from solar radiation, stochastic fluctuations pose a risk to the robust supply and healthy function of the electricity network. For most efficient PV planning and siting, it is hence imperative to understand and quantify this variability in solar radiation, in order to anticipate average production as well as worst-case scenarios. Based on 12 years of satellite derived, spatially distributed data of daily average surface incoming shortwave radiation (SIS) this work analyses standard statistics, spatial correlation patterns and extreme conditions of cloud cover over Switzerland. Having compared different irradiance products, we decided to use the SIS product captured on the Meteosat Second Generation satellites, because it provides the most reliable snow/cloud discrimination, which is essential for spatial analysis over alpine terrain. Particularly in regions with high elevation differences, correlation between cloud cover and elevation undergo an annual cycle. In winter more clouds are found in the valleys, while in summer convective clouds dominate at higher elevations. The highest average irradiance values occur in the southern parts of the country, and make the cantons of Vallais, Tessin and Grison ideal candidate locations for PV installations. Simultaneously the Tessin shows a higher risk of periods with long lasting cloud cover, which would discourage from relying too much on solar power in that area. However looking at the question of suitability by studying spatial and temporal correlations of extremes, we see that the Tessin appears to be comparably decoupled from the rest of the country, and leads us to believe that this zone plays a valuable role in compensating temporary deficits in solar production from other regions of the country. This is just one of many example that highlight the complexity of spatio-temporal variability in solar irradiance and its implications for a reliable electricity supply in a future renewable Switzerland.

2016

Cloud Motion Identification Algorithms Based on All-Sky Images to Support Solar Irradiance Forecast

Lydie Catherine Magnone, Mario Paolone, Enrica Scolari, Fabrizio Sossan

Cloud motion is a cause of direct irradiance variations at ground level and determines significant fluctuations of PV generation. In this work, we investigate on how integrating information on clouds motion extracted from all-sky images into a time series-based forecasting tool for global horizontal irradiance (GHI) to enhance its prediction performance. We consider three different cloud motion algorithms: heuristic motion detection (HMD), particle image velocimetry (PIV), and a persistent model. The HMD method is originally proposed in this paper. It consists in choosing the cloud motion vector leading to the best cloud map prediction considering the most recent sky images. Results show that integrating the information of the predicted cloud coverage in the circumsolar area leads to a decrease of the width of the GHI prediction intervals up to 2% for prediction horizons in the range 1 to 10 minutes.

2017

LMDZ6A: The Atmospheric Component of the IPSL Climate Model With Improved and Better Tuned Physics

Etienne Gabriel Henri Vignon

This study presents the version of the LMDZ global atmospheric model used as the atmospheric component of the Institut Pierre Simon Laplace coupled model (IPSL-CM6A-LR) to contribute to the 6th phase of the international Coupled Model Intercomparison Project (CMIP6). This LMDZ6A version includes original convective parameterizations that define the LMDZ "New Physics": a mass flux parameterization of the organized structures of the convective boundary layer, the "thermal plume model," and a parameterization of the cold pools created by reevaporation of convective rainfall. The vertical velocity associated with thermal plumes and gust fronts of cold pools are used to control the triggering and intensity of deep convection. Because of several shortcomings, the early version 5B of this New Physics was worse than the previous "Standard Physics" version 5A regarding several classical climate metrics. To overcome these deficiencies, version 6A includes new developments: a stochastic triggering of deep convection, a modification of the thermal plume model that allows the representation of stratocumulus and cumulus clouds in a unified framework, an improved parameterization of very stable boundary layers, and the modification of the gravity waves scheme targeting the quasi-biennal oscillation in the stratosphere. These improvements to the physical content and a more well-defined tuning strategy led to major improvements in the LMDZ6A version model climatology. Beyond the presentation of this particular model version and documentation of its climatology, the present paper underlines possible methodological pathways toward model improvement that can be shared across modeling groups. Plain Language Summary The improvement of global numerical models is essential for the anticipation of future climate changes. We present significant advances in the physical content of a particular atmospheric model which contributes to the simulations of the Coupled Model Intercomparison project CMIP that feed reports from the IPCC. We document in particular the improvements of the representation through "parameterizations" of convective and cloudy processes. The article emphasizes the importance of strengthening the formalization of the methodology of development and tuning of models, so that new physical ideas can be translated into effective improvement of the climate representation.

2020