  High quality Quantitative Precipitation Estimation at high spatiotemporal
resolution is crucial to many hydrologic/hydro-meteorological designs. Optimal
Quantitative Precipitation Estimation of rainfall improves the accuracy of
river and flash flood forecasts. In this study, we aim to merge multiple
rainfall estimates including rain gauge, radar, Inverse Distance Weighting,
Ordinary Co-Kriging, and Adaptive Conditional Bias Penalized Co-Kriging through
two most common model selection techniques known as Least Absolute Shrinkage
and Selection Operator and Bayesian Model Averaging. The methods were applied
to the entire United States for a certain period. Statistical measures such as
RMSE, ME, NSE, and Correlation Coefficient are used to investigate the accuracy
and reliability of the estimation models. It is shown that both BMA and LASSO
improve the precipitation estimation considering all ranges of rainfall
observation included. However, OCK and CBPCK technique outperforms other
methods in rainfall more than 10 mm. The IDW estimates show small bias, which
results in a poor estimation, which is due to the limitation in using secondary
variable radar. However, OCK and CBPCK address this problem by adding radar
rainfall estimates as the second variable.
