Multi-Model Combination techniques for Hydrological Forecasting: Application to Distributed Model Intercomparison Project Results

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This paper examines several multi-model combination techniques: the Simple Multi-model Average (SMA), the Multi-Model Super Ensemble (MMSE), Modified Multi-Model Super Ensemble (M3SE) and the Weighted Average Method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multi-model combination results were obtained using uncalibrated DMIP model outputs and were compared against the best uncalibrated as well as the best calibrated individual model results. The purpose of this study is to understand how different combination techniques ... continued below

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Ajami, N K; Duan, Q; Gao, X & Sorooshian, S April 11, 2005.

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This paper examines several multi-model combination techniques: the Simple Multi-model Average (SMA), the Multi-Model Super Ensemble (MMSE), Modified Multi-Model Super Ensemble (M3SE) and the Weighted Average Method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multi-model combination results were obtained using uncalibrated DMIP model outputs and were compared against the best uncalibrated as well as the best calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the skill levels of the multi-model predictions. This study revealed that the multi-model predictions obtained from uncalibrated single model predictions are generally better than any single member model predictions, even the best calibrated single model predictions. Furthermore, more sophisticated multi-model combination techniques that incorporated bias correction steps work better than simple multi-model average predictions or multi-model predictions without bias correction.

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PDF-file: 38 pages; size: 0.4 Mbytes

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  • Journal Name: Journal of Hydrometeorology, vol. 7, N/A, August 1, 2006, pp. 755-768; Journal Volume: 7

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  • Report No.: UCRL-JRNL-211311
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 936447
  • Archival Resource Key: ark:/67531/metadc901713

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  • April 11, 2005

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  • Sept. 27, 2016, 1:39 a.m.

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  • Dec. 6, 2016, 1:43 p.m.

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Ajami, N K; Duan, Q; Gao, X & Sorooshian, S. Multi-Model Combination techniques for Hydrological Forecasting: Application to Distributed Model Intercomparison Project Results, article, April 11, 2005; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc901713/: accessed October 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.