Graduation Semester and Year
2021
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Civil Engineering
Department
Civil Engineering
First Advisor
Dong-Jun Seo
Abstract
Many multivariate analysis techniques involve minimizing mean square error (MSE) or error variance under unbiasedness. In the presence of observation error, variance minimization tends to introduce negative and positive biases, or conditional bias (CB), over the upper and lower tails of the predictands, respectively. This work describes and evaluates three multivariate merging techniques, 1) adaptive conditional bias-penalized cokriging (CBPCK), 2) conditional bias-penalized Multiple Linear Regression (CBP-MLR), and 3) conditional bias-penalized Bayesian Model Averaging (CBP-BMA) which implements CBP-MLR in place of MLR. CBPCK and CBP-MLR minimize a linearly weighted sum of errors squared and the sum of the Type-II error squared, thereby addressing the type-II CB explicitly. CBPCK is applied to improve multisensor precipitation estimation using rain gauge data and remotely-sensed quantitative precipitation estimates (QPE). The remotely-sensed QPEs used are radar-only and radar-satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for Sep 13-30, 2015, and Oct 7-9, 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar-satellite fusion, conditional bias-penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean-field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root mean square error (RMSE) of radar-only QPE by 9 to 16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for Sep 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4 and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite the higher computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE. CBP-MLR and CBP-BMA are described and evaluated for improved multi-model streamflow prediction using several operationally produced streamflow forecasts. For comparative evaluation, 10-fold cross-validation is carried out over the NWS Middle Atlantic River Forecast Center’s (MARFC) service area for the period of Jan 1, 2017, to Oct 29, 2018. The input streamflow forecasts used are the MARFC single-valued forecast, the Hydrologic Ensemble Forecast System (HEFS) ensemble forecast, the National Water Model (NWM) medium-range single-valued forecast, and the Meteorological Model-based Ensemble Forecast System (MMEFS) ensemble forecasts forced by the Global Ensemble Forecast System (GEFS), the North American Ensemble Forecast System (NAEFS), and the Short-Range Ensemble Forecast System (SREF). Whereas CBP-MLR improves prediction over tails, it degrades the performance near the median. To retain MLR-like performance near median while exploiting the ability of CBP-MLR to improve prediction over tails, composite MLR (CompMLR), which linearly weight-averages the MLR and CBP-MLR estimates, is also developed and evaluated. MLR and CBP-MLR do not consider uncertainty in the regression model. They typically choose a single model and fit it to the data. This approach disregards the uncertainty in the model selection which leads to overconfident inferences. To address the model uncertainty while improving performance for large flow, CBP-MLR is implemented in BMA to produce CBP-BMA. The proposed methods are applied for multi-model streamflow prediction using several operationally produced streamflow forecasts as predictors. The results for the MARFC’s service area show that the relative performance among different input forecasts varies most significantly with the range of the verifying observed streamflow, and both CompMLR and CBP-BMA are generally superior to the best performing forecasts in the mean squared error sense under widely varying conditions of predictability and predictive skill.
Keywords
QPE, Ordinary CoKriging, BMA, CBPCK, CBP-MLR, CompMLR, CBP-BMA
Disciplines
Civil and Environmental Engineering | Civil Engineering | Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Jozaghi, Ali, "IMPROVING OPERATIONAL HYDROLOGIC FORECASTING VIA CONDITIONAL BIAS-PENALIZED MULTI-SENSOR PRECIPITATION ESTIMATION AND MULTI-MODEL STREAMFLOW PREDICTION" (2021). Civil Engineering Dissertations. 444.
https://mavmatrix.uta.edu/civilengineering_dissertations/444
Comments
Degree granted by The University of Texas at Arlington