Vincent Fortin

Assimilation of Satellite-Based Rainfall Estimations in the Canadian Precipitation Analysis

AbstractThe Canadian Precipitation Analysis (CaPA) produces a gridded product by assimilating data from surface stations and radar, using a background field provided by the Global Environmental Mul...

Hydrological Evaluation of the Canadian Meteorological Ensemble Reforecast Product

ABSTRACT This paper evaluates the quality of the ensemble hydrological reforecasts obtained using the 18-year ensemble meteorological reforecast dataset available from the Canadian Centre for Meteorological and Environmental Prediction (CCMEP). This study focuses on four large watersheds in the province of Quebec. A distribution-based scaling (DBS) post-processing method is used to correct the 18-year ensemble precipitation reforecasts. An Ensemble Kalman Filter (EnKF) assimilation technique is also assessed to improve the initial conditions of the hydrologic model. There is a slight improvement in performance and reliability after applying the DBS approach to precipitation reforecasts, but this technique induces a reduction in the spread. The impact of the integration of the post-processed precipitation into the hydrologic model is also quite marginal. However, the addition of an EnKF provides better ensemble hydrological reforecasts with high performance, reliability, and skill, especially in the first reforecast horizons. The best results are, however, generally obtained when using DBS and an EnKF together. Combining DBS and an EnKF, hydrological forecasts for the next two weeks are obtained using the CCMEP reforecast and also the second generation Global Ensemble Forecast System (GEFS v2) reforecast, which is considered a reference. Forecasts of comparable skill and spread are obtained, with CCMEP-based forecasts showing better spread during the first week, and GEFS v2–based reforecasts showing better skill and spread during the second week. Finally, it is shown that the two meteorological reforecast products assessed in this study have similar economic value for hydrological forecasting applications based on the cost–loss model.