Open AccessComparison of pre‐ and post‐processors for ensemble streamflow prediction
Author(s) -
Kang TaeHo,
Kim YoungOh,
Hong IlPyo
Publication year - 2010
Publication title -
atmospheric science letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.951
H-Index - 45
ISSN - 1530-261X
DOI - 10.1002/asl.276
Subject(s) - streamflow , categorical variable , climatology , meteorology , environmental science , computer science , ensemble forecasting , drainage basin , machine learning , geography , cartography , geology
This study conducted a broad review of the pre‐ and post‐processor methods for ensemble streamflow prediction using a Korean case study. Categorical forecasts offered by the Korea Meteorogical Administration and deterministic forecasts of a regional climate model called Seoul National University Regional Climate Model(SNURCM) were selected as climate forecast information for the pre‐processors and incorporated into Ensemble Streamflow Prediction(ESP) runs with the TANK hydrologic model. The post‐processors were then used to minimize a possible error propagated through the streamflow generation. The application results show that use of the post‐processor more effectively reduced the uncertainty of the no‐processor ESP than use of the pre‐processor, especially in dry season. Copyright © 2010 Royal Meteorological Society