document: Data assimilation of local model error forecasts in a deterministic model One of the most popular data assimilation techniques in use today are of the Kalman filter type , which provide an improved estimate of the state of a system up to the current time level , based on actual measurements . From a forecasting viewpoint , this corresponds to an updating of the initial conditions . The standard forecasting procedure is to then run the model uncorrected into the future , driven by predicted boundary and forcing conditions . The problem with this methodology is that the updated initial conditions quickly ` wash-out ' , thus , after a certain forecast horizon the model predictions are no better than from an initially uncorrected model . This study demonstrates that through the assimilation of error forecasts -LRB- in the present case made using so-called local models -RRB- entire model domains can be corrected for extended forecast horizons -LRB- i.e. long after updated initial conditions have become washed-out -RRB- , thus demonstrating significant improvements over the conventional methodology . Some alternate uses of local models are also explored for the re-distribution of error forecasts over the entire model domain , which are then compared with more conventional Kalman filter type schemes
doc_bio_tags: B I O B I I I O O B I O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O
extractive_keyphrases: data assimilation local model error forecasts deterministic model kalman filter forcing conditions forecast horizon
abstractive_keyphrases: error prediction hydrodynamic modelling