Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis

Abstract

Abstract. Several data assimilation (DA) approaches exist to generate consistent and continuous precipitation fields valuable for hydrometeorological applications and land data assimilation. Usually, DA is based on either static or dynamic approaches. Static methods rely on deterministic forecasts to estimate background error covariance matrices, whereas dynamic approaches use ensemble forecasts. Associating the two methods is known as hybrid DA, and it has proven beneficial for different applications as it combines the advantages of both approaches. The present study intends to explore hybrid DA for the 6 h Canadian Precipitation Analysis (CaPA). Based on optimal interpolation (OI), CaPA blends forecasts and observations from surface stations and ground-based radar datasets to provide precipitation fields over the North American domain. The application of hybrid DA to CaPA consisted of finding the optimal linear combination between (i) an OI based on the Regional Deterministic Prediction System (RDPS) and (ii) an ensemble Kalman filter (EnKF) based on the 20-member Regional Ensemble Prediction System (REPS). The results confirmed the known effectiveness of the hybrid approach when low-density observation networks are assimilated. Indeed, the experiments conducted for the summer without radar datasets and for the winter (characterized by very few observations in CaPA) showed that attributing a relatively high weight to the EnKF (50 % and 70 % for summer and winter, respectively) resulted in better analysis skill and a reduction in false alarms compared with the OI method. A deterioration in the moderate- to high-intensity precipitation bias was, however, observed during summer. Reducing the weight attributed to the EnKF to 30 % alleviated the bias deterioration while improving skill compared with the OI-based CaPA.