SUMMER SCHOOL
SATELLITE-BASED HYDROLOGICAL DATA ASSIMILATION
SUMMER SCHOOL
SATELLITE-BASED HYDROLOGICAL DATA ASSIMILATION
This Summer School offers early career researchers interested in large-scale hydrology research an opportunity to engage in dedicated lectures and practical sessions. The focus will be on advanced techniques for measuring and modeling the water cycle, as well as integrating Satellite Gravity, Satellite Altimetry, and Soil Remote Sensing into large-scale hydrological models. Participants will be introduced to ensemble-based sequential Data Assimilation (DA) techniques for merging satellite data with models. The school emphasizes hands-on learning, featuring practical exercises in Matlab/Octave and Python, along with various interactive activities.
INFORMATION
PROGRAMME
AIMS AND SCOPE
Understanding changes in freshwater resources and predicting their future availability are crucial for assessing the impacts of climate change and extreme weather events on agricultural activities, including local irrigation schemes, regional or large-scale water management, and food/drought security plans. Advances in hydrological modeling and satellite monitoring of hydrological variables have paved the way for integrating modeling and measurement techniques to enhance hydrological understanding and predictions. However, this integration is practically challenging and requires a deep understanding of models, the signal and noise content of observations, and advances in high-performance computing.
This Summer School offers early career researchers (M.Sc. and Ph.D. students, as well as junior scientists) interested in large-scale hydrology research an opportunity to engage in dedicated lectures and practical sessions. The focus will be on advanced techniques and open challenges in measuring and modeling the water cycle, as well as integrating satellite-based observations including Satellite Gravity (GRACE/GRACE-FO and future missions NGGM/MAGIC), Satellite Altimetry (Sentinel and SWOT), and Soil Remote Sensing into large-scale hydrological models. Participants will be introduced to ensemble-based sequential Data Assimilation techniques for merging satellite data with models. The school emphasizes hands-on learning, featuring practical exercises in Matlab and Python, along with various interactive activities.
Programme committee
- Ehsan Forootan (Aalborg University)
- Maike Schumacher (Aalborg University)