Santander Meteorology Group

A multidisciplinary approach for weather & climate

The research group is formed by professors and researchers from the University of Cantabria (UC, Dept. of Applied Mathematics and Computer Science) and the National Research Council (CSIC, Instituto de Física de Cantabria IFCA), collaborating actively with the National Weather Service (AEMET, former INM), all working in Santander, Spain (see institutions for more details).

The group conducts research in topics related to weather forecast, climate research, data mining and high-performance computing, dealing also with the statistical and physical aspects of spatiotemporal chaotic systems (see research lines for more details). These activities are carried out in collaboration with different research groups and institutions.

The group is also involved in different pre- and post-graduate programs of Cantabria University: Masters of Mathematics and Computation, Physics and Technology, and Natural Risks Management, which obtained the quality certificate from the Spanish Education Ministry, and the official Ph.D. Program on Science, Technology and Computation. Thus, the group offers a good opportunity to undertake high-quality Ph.D. studies in different disciplines.

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Research topics: Numerical atmospheric models (regional models, WRF). Statistical downscaling techniques. Ensemble Prediction Systems (EPS). Seasonal forecast. Regional climate change scenarios. Machine learning and data mining methods. E-Science and grid computing.

Key Projects:

Key Publications:

• Error Growth in weather models [Phys Rev Lett] an characterization of the spatial and temporal growth components [Non Proc Geophys]. Application to multi-model characterization [Climate Dyn]
• Seasonal predictability in the tropics [TellusA] and extra-tropics (Spain) [J Geophys Res] [J Clim].
• Sensitivity to parameterizations in atmospheric regional models (MM5) [J Geophys Res].
• Statistical downscaling in medium-range prediction [Mon Weather Rev], seasonal forecast [TellusA], and climate change projection [Envir Mod Soft].
• Robustness of statistical downscaling methods [Geophys Res Lett].
• GRID computing for Earth Science (climate) applications [Earth Sci Infor].