Probabilistic Quantitative Precipitation Estimation in Complex Terrain

By M. Clark, A. Slater. 2005

205 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ad24fe10613ffe1afb34fa100c775ac1498cae09

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Citations in the corpus

Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, methodology): “In this sense we include within the realm of data assimilation any procedure that assimilates information from observations to reduce the uncertainty associated with one or more of the model components, be it the state, the parameters, or the system structure.”
Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation (2008, background): “Much progress has also been made in the description of forcing data error [Clark and Slater, 2006], development of a formal hierarchical framework to formulate, build and test conceptual watershed models [Clark et al.”
Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging (2007, methodology): “of X (see Clark and Slater [2006] for a viable method).”
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities (2012, methodology): “…observations of precipitation (e.g., from a station network; see Rakovec et al. (2012b, this issue) for an example on this) and/or other information (e.g., topography), have the potential to provide more reliable uncertainty estimates (e.g., Clark and Slater, 2006; G̈otzinger and B́ardossy, 2008).”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, background): “Much progress has also been made in the treatment of forcing data error (Clark and Slater, 2006; Kavetski et al., 2006a, b; Vrugt et al., 2008a); development of a formal hierarchical framework to formulate, build and test different watershed models (Clark et al., 2008); and algorithms for efﬁcient…”