Case studies of the effects of a total solar eclipse on weather with varying degrees of cloud cover using a local numerical weather prediction model

The importance of renewable energies for the European electricity market is growing rapidly. This presents transmission grids and the power market in general with new challenges which stem from the higher spatiotemporal variability of power generation. This uncertainty is due to the fact that renewable power production results from weather phenomena, thus making it difficult to plan and control. We present a sensitivity study of a total solar eclipse in central Europe in March. The weather in Germany and Europe was modeled using the German Weather Service's local area models COSMO-DE and COSMO-EU, respectively (http://www.cosmo-model.org/). The simulations were performed with and without considering a solar eclipse for the following 3 situations: 1. An idealized, clear-sky situation for the entire model area (Europe, COSMO-EU) 2. A real weather situation with mostly cloudy skies (Germany, COSMO-DE) 3. A real weather situation with mostly clear skies (Germany, COSMO-DE) The data should help to evaluate the effects of a total solar eclipse on the weather in the planetary boundary layer. The results show that a total solar eclipse has significant effects particularly on the main variables for renewable energy production, such as solar irradiation and temperature near the ground.

Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.

A probabilistic model estimating oil spill clean-up costs - a case study for the Gulf of Finland

Existing models estimating oil spill costs at sea are based on data from the past, and they usually lack a systematic approach. This make them passive, and limits their ability to forecast the effect of the changes in the oil combating fleet or location of a spill on the oil spill costs. In this paper we make an attempt towards the development of a probabilistic and systematic model estimating the costs of clean-up operations for the Gulf of Finland. For this purpose we utilize expert knowledge along with the available data and information from literature. Then, the obtained information is combined into a framework with the use of a Bayesian Belief Networks. Due to lack of data, we validate the model by comparing its results with existing models, with which we found good agreement. We anticipate that the presented model can contribute to the cost-effective oil-combating fleet optimization for the Gulf of Finland. It can also facilitate the accident consequences estimation in the framework of formal safety assessment (FSA).