Virtual Lab

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Virtual Lab

The Virtual Lab grants access to the experiments performed in all scientific domains of the PrimeWater project: remote sensing, process-based modeling, and data-driven modeling. The Virtual Lab describes in detail the data, equipment, and experimental procedures followed in each experiment to enhance transparency and secure the reproducibility of results. Nonetheless, the Virtual Lab is not a mere repository of experiments; it welcomes and embraces collaborative research efforts across organizational boundaries. The Virtual Lab facilitates research groups to exchange knowledge in a collaborative framework that will ultimately advance remote sensing and hydro-ecological modeling.

Virtual Lab

Remote Sensing

Imaging spectrometry

Explore current imaging spectrometry data (e.g. PRISMA, DESIS) in order to describe in terms of pigments, size classes, and functional traits of primary producers.

Remote Sensing

Multiscale Optical Data

Explore concurrent measurements from in situ, drone, airborne and satellite sensors

Process-based modelling

Water Quality Forecasting

Can we combine EOs and 4dVAR data assimilation technique, to improve the predictive skill of hydro-ecological modelling in reservoirs?

Process-based modelling

Hydrology

Best practices for seasonal forecasting

Process-based modeling

Developing an error-correcting framework

Can we train data-driven algorithms to accurately detect systematic errors produced by process-based models?

Data-driven modeling

Predicting water quality

How much more accurate can data-driven algorithms be in predicting water quality compared to naïve predicting alternatives?

Data-driven modeling

Assessing predictor importance

Which external forcings are the most critical in predicting water quality accurately?

Data-driven modeling

Expanding the forecasting horizon

Which is the most efficient strategy to progress data-driven modeling towards short-to-medium range forecasts?

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The project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 870497.

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