A Multivariate Modeling Approach for Generating Ensemble Climatology Forcing for Hydrologic Applications
[report]
Sepideh Khajehei
2000
unpublished
Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment.
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... ue to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation ii Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach. iii Dedication To: My family for debts I can never repay iv Acknowledgments I would like to express the deepest appreciation to my advisor, Dr. Hamid Moradkhani, who has the attitude and the substance of a genius: he continually and convincingly conveyed a spirit of adventure in regards to research and an excitement in regards to teaching. Without his guidance and persistent help, this thesis would not have been possible. I also would like to thank my committee members, Dr. Heejun Chang and Dr. Arun Rana, for their support and for their timely review of my thesis. I greatly appreciate their willingness to serve in my thesis committee. For all the members of my research group, I thank you all for your hard work and positive attitude during all of our collaboration, and providing a great friendly atmosphere in the lab.
doi:10.15760/etd.2400
fatcat:miobtrmud5e7dmouzffzdhdrhe