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In the course of level 2 data processing for theGOCE (Gravity Field and Steady–State Ocean CirculationExplorer) satellite mission different streams of level 1b datawill be merged in a single solution providing the Earth’sgravity field, but also state-vector parameters and other quantities.A proper weighting of orbit tracking data, gravity gradiometrydata and certain a priori information, usually consideredas ‘solution constraints’, can be expected as crucialfor the solution quality. But the a priori stochastic models,based on pre–mission assessment of the expected instrumentbehaviour, may be over–optimistic or even too pessimisticsince they refer to an unprecedented mission with scientificpayload never tested in space. One way to derive an optimalweighting scheme on a statistically sound basis whilesimultaneously validating the stochastic noise levels of thedata is by including variance component estimation as a partof the level 1b to level 2 data analysis process. The idea isthat by applying Monte-Carlo techniques this method can beextended to a large-scale problem like GOCE data analysis,using software modules that already exist or are currentlyunder development. The proposed method has been testedusing simulated GOCE orbit trajectories as well as gravitygradiometry data corrupted by colored random noise.  <b>Key words. </b>GOCE, gravity field modelling, combinationsolutions, weight estimation, variance component estimatio

Noise variance estimation and optimal weight determination for GOCE gravity recovery­

Noise variance estimation and optimal weight determination for GOCE gravity recovery