Data assimilation in the field of predictive land surface modeling is generally limited to using observational data to estimate optimal model states or restrict model parameter ranges. To date, very little work has attempted to systematically define and quantify error resulting from a model's inherent inability to simulate the natural system. This paper introduces a data assimilation technique that moves toward this goal by accounting for those deficiencies in the model itself that lead to systematic errors in model output. This is done using a supervised artificial neural network to "learn" and simulate systematic trends in the model output error. These simulations in turn are used to correct the model's output each time step. The technique is applied in two case studies, using fluxes of latent heat flux at one site and net ecosystem exchange (NEE) of carbon dioxide at another. Root-mean-square error (rmse) in latent heat flux per time step was reduced from 27.5 to 18.6 W m-2 (32%) and monthly from 9.91 to 3.08 W m-2 (68%). For NEE, rmse per time step was reduced from 3.71 to 2.70 μmol m-2 s-1 (27%) and annually from 2.24 to 0.11 μmol m-2 s-1 (95%). In both cases the correction provided significantly greater gains than single criteria parameter estimation on the same flux.
ASJC Scopus subject areas
- Atmospheric Science