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The project aimed to design an optical instrument based on Compressive Sensing (CS) for a space applications. Conventional sampling or compression methodologies follow the Shannon-Nyquist theorem, which states that if a signal frequency band or spectrum is limited to a frequency B [Hz], the signal is completely determined by a sample-rate larger than 2xB samples per second. Compressive Sensing (CS) provides a fundamentally new approach to data acquisition or signal sampling.

The main objectives of this project were to develop the tools and structure to enable routine EarthCARE radar and lidar observations of clouds to be assimilated in the ECMWF Numerical Weather Prediction model and to prepare for the real-time monitoring of the observations to detect instrument degradation or errors. This required adjustments of assimilation tools, such as the observation operators, observation error definition, quality control, data screening and bias correction.

BIOMASS is a polarimetric P-band (435 MHz) synthetic aperture radar (SAR) in a dawn-dusk low Earth orbit. Its principal objective is to measure biomass content and biomass change in all the Earth’s forests. The mission launch is envisaged around 2020, for five-year duration. The ionosphere introduces Faraday rotation and scintillations on every pulse emitted by low- frequency SAR, impacting the quality of the imaging. Some of these effects are due to Total Electron Content (TEC) and its gradients along the propagation path.

This study aims to investigate a feasible regional TEC temporal fluctuations product derived from ground-based monitoring stations Western Sub Saharan African region. To assess how the existing ionospheric monitoring capabilities in that region can satisfy related requirements, for a selected science application and for Biomass external calibration.