Articles | Volume 12, issue 9
https://doi.org/10.5194/gmd-12-4115-2019
https://doi.org/10.5194/gmd-12-4115-2019
Development and technical paper
 | 
23 Sep 2019
Development and technical paper |  | 23 Sep 2019

MELPF version 1: Modeling Error Learning based Post-Processor Framework for Hydrologic Models Accuracy Improvement

Rui Wu, Lei Yang, Chao Chen, Sajjad Ahmad, Sergiu M. Dascalu, and Frederick C. Harris Jr.

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Cited articles

Andreadis, K. and Lettenmaier, D.: Assimilating remotely sensed snow observations into a macroscale hydrology model, Adv. Water Resour., 29, 872–886, 2006. a
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The paper mainly has two contributions. First, a post-processor framework is proposed to improve hydrologic model accuracy. The key is to characterize possible connections between model inputs and errors. Based on results, it is also possible to replace the time-consuming model calibration step using our post-processor framework. Second, a window selection method is proposed to handle nonstationary data. A window size is chosen containing stable data using a measure named DS proposed by us.