Collocation uncertainty in climate monitoring

Understanding collocation mismatch is particularly relevant for atmospheric profiles obtained by radiosondes, as the balloons containing the measuring instruments tend to drift uncontrollably from their initial launch position. We propose a heteroskedastic functional regression model capable of explaining the relationship between collocation uncertainty and a set of environmental factors, height and distance between imperfectly collocated trajectories. Along this line, a five-fold decomposition of the total collocation uncertainty is proposed, giving both a profile budget and an integrated column budget. Considering the profiles as three-dimensional trajectories, we extend the model to include a trivariate smooth function that accounts for time and space mismatch. Results from a case study where we model collocation error of relative humidity and atmospheric pressure show that model fitting is improved once heteroskedasticity is taken into account.