Evaluating forecasting performance for data assimilation methods: The ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation

Advances in Water Resources

Volumn 60, Issue , 2013, Pages 47-63

  Dumedah, Gift ^a   Coulibaly, Paulin ^b  

^a MONASH UNIVERSITY   (Australia)

^b MCMASTER UNIVERSITY   (Canada)

Author keywords

Data assimilation; Evolutionary algorithms; Streamflow forecasting; The ensemble Kalman filter; The particle filter

Indexed keywords

COMPARATIVE EVALUATIONS; DATA ASSIMILATION; DATA ASSIMILATION METHODS; ENSEMBLE KALMAN FILTER; FORECASTING PERFORMANCE; HYDROLOGICAL FORECASTING; PARTICLE FILTER; STREAMFLOW FORECASTING;

CATCHMENTS; EVOLUTIONARY ALGORITHMS; KALMAN FILTERS; MONTE CARLO METHODS; SOIL MOISTURE; STREAM FLOW;

FORECASTING;

ALGORITHM; CATCHMENT; DATA ASSIMILATION; ENSEMBLE FORECASTING; HYDROLOGICAL RESPONSE; KALMAN FILTER; REAL TIME; STREAMFLOW;

CANADA; ONTARIO [CANADA]; SPENCER CREEK;

EID: 84882641938     PISSN: 03091708     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advwatres.2013.07.007     Document Type: Article

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