There is an increasing need for high spatial and temporal resolution information about past and future weather and climate. Such information is more and more important for assessing the variability and impact of weather and climate on various environmental social phenomena and is also indispensable as validation and calibration input for climate models.
This increasing demand requires new efficient methods and approaches for estimation the spatially distributed climate data and also new efficient applications for managing and analysing climatological and meteorological information at different temporal and spatial scales. An important aspect in this respect is the creation and further use of reference climatologies.

The aim of this session is to bring together experts, scientists and other interested people analysing in spatial interpolation and GIS modelling within meteorology, climatology and other related environmental sciences.

In this context the contributions on following topics are invited:
- Data processing and necessary method-development for establishing reference climatologies.
- Examples of user applications on climate sensitivity applying reference climatologies as basis.
- Progress, limitations and possibilities in spatial interpolation methods for meteorological variables:
- GIS - based tools and methods for atmospheric data modelling, processing, integration, and analysis
- GIS applications in climatology and meteorology, including contributions on weather observations and prediction as well as global climate modelling
- GIS based applications for generating local climate change scenarios (based on the local and global input data - downscaling)
- Applications in linking meteorological and climatological information to other Earth sciences (e.g. hydrology, oceanography, biology) and social sciences
- Gridded climatological databases: structures and methods for grids calculation
- Applications and examples of good practices in integration of spatial data from different sources and different measuring techniques (including remote sensing data)