Climate on Earth is changing rapidly. In order to take the right measures it is important to know with which magnitude and rate the warming of the climate continues in the future. However, climate models currently predict different scenarios. The differing in response of shallow cumulus clouds to a warming climate explains the largest part of the spread of climate sensitivity in these models. It is known that clustered clouds have a different climate feedback than randomly distributed clouds and that we can expect more clustered cloud fields in a warmer climate. It is however unknown how these clouds respond and what the exact difference in climate feedback is. Future research must provide us with more insights and eventually a better understanding of the response of shallow cumulus clouds to warming. However, in order to investigate this an objective measure of the degree of cloud organization is necessary, something that is currently lacking. In this thesis a dimensionless combined organization measure is introduced that can be applied to a large range of cloud field organizations on different field sizes. This is a combined measure, as it makes use of the existing organization index I_org and the size of clouds and clear sky areas in the cloud field. A different approach on calculating I_org was introduced taking into account the sizes of the clouds, resulting in an useful and more realistic values from this parameter. The results of the combined organization measure were compared to a visual inspection of 557 cloud fields with dimensions of 10x10 degree, which showed promising results. Sub-fields of 5x5 degree and 2.5x2.5 degree were also analysed, which showed that the combined organization measure could often be applied on smaller scales as well but yielded some problems when areas contained either no or little clouds or some very large ones. It was concluded that the newly developed method is an improvement of the already existing method, providing scientists with a better and more reliable index to quantify the degree of cloud organization.

The region of Coquimbo, Chile, is located adjacent to the dry Andes. Water is scarce, and with very little precipitation during spring and summer, water supply in these seasons depends mostly on melt water from snow in the Andes mountains. A project funded by the local government was conducted by CEAZA, a research institute based in the cities of La Serena and Coquimbo. The aim of the project was to evaluate the effectiveness of snow fences in the Andes mountains. The hypothesis was that these fences would increase the snow volume, which would result in a longer period of water supply from melt water. Different methods to analyse the available data were used. The results were compared to evaluate what the most optimal method was. Two different sites (Tascadero and Llano de las Liebres) were evaluated and compared. On both sides, wooden snow fences were installed. Around these fences, a grid of bamboo snow stakes was placed. Every day at noon, photos were taken using a digital camera mounted on a mast in a fixed position. Three different ways to analyse the photos were evaluated. The most optimal method that was tested was based on measuring the length of the snow stakes before the first snow fall and again throughout the season. From the ratio between the two lengths, the snow depth could be derived. This proved to be a relatively fast and simple method. From the results it could be derived that the snow fences do have a positive influence on the snow depth. This was especially clear for Llano de las Liebres, where data was available for the full winter season.