Joint-probability-model
Joint probability model for post-processing of daily precipitation forecasts. Similar to Wang, Q.J.'s BJP model, but I used maximum likelihood estimation instead of Bayesian inference for parameter inference here.
Some related references:
Li, W., Q. Duan, A. Ye, and C. Miao, 2019: An improved meta-Gaussian distribution model for post-processing of precipitation forecasts by censored maximum likelihood estimation. J. Hydrol. , 574, 801-810.
Li, W., Q. Duan, C. Miao, A. Ye, W. Gong, and Z. Di, 2017: A review on statistical postprocessing methods for hydrometeorological ensemble forecasting. Wiley Interdisciplinary Reviews Water, 4, e1246.
Wang, Q. J., and D. E. Robertson, 2011: Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences. Water Resour. Res. , 47, 1-19.
Wang, Q. J., D. E. Robertson, and F. H. S. Chiew, 2009: A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites. Water Resour. Res. , 45, 1-18.
Shrestha, D. L., D. E. Robertson, J. C. Bennett, and Q. J. Wang, 2015: Improving Precipitation Forecasts by Generating Ensembles through Postprocessing. Mon. Weather. Rev., 143, 3642-3663.