Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences

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Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors (2017, methodology): “…and evaluation periods because it minimizes the dependence of results on the particular choice of periods, maximizes the length of data used for model evaluation, and corresponds closely to the verification protocols of many forecasting agencies [e.g., Wang and Robertson, 2011; Li et al., 2015].”
Merging Seasonal Rainfall Forecasts from Multiple Statistical Models through Bayesian Model Averaging (2012, methodology): “For forecast accuracy, we use the root-mean-squared error in probability (RMSEP) skill score to assess forecast medians, using the climatological median as a reference forecast (Wang and Robertson 2011).”
A log‐sinh transformation for data normalization and variance stabilization (2012, methodology): “…(8) where Jz!y is the Jacobian determinant of the transformation from z to y, Jz!y ¼ dz dy ¼ coth ðaþ byÞ: (9) [18] A Bayesian inference of the parameters a, b, and is made by assuming that the prior for the parameters is inversely proportional to [Gelman et al., 2004; Wang and Robertson, 2011].”