Improving Precipitation Estimation Using Multilinear Model Selection Algorithms

TL;DR

This study enhances precipitation estimation accuracy by applying model selection techniques like LASSO and Bayesian Model Averaging to merge multiple rainfall data sources across the US.

Contribution

It introduces the use of LASSO and Bayesian Model Averaging for optimal merging of diverse rainfall estimates, improving precipitation modeling.

Findings

BMA and LASSO improve overall precipitation estimation accuracy.

OCK and CBPCK outperform other methods for rainfall >10 mm.

IDW estimates show small bias but poor overall accuracy.

Abstract

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.