Latent Heat Nudging in the Canadian High-Resolution (2.5 km) Regional Deterministic Prediction System

Abstract

This study examines the application of latent heat nudging (LHN) for the assimilation of radar-derived precipitation rates in the 2.5-km High-Resolution Deterministic Prediction System (HRDPS) operated by Environment and Climate Change Canada. One goal of this study is to document the overall impact of applying LHN in the most recent operational implementation of the HRDPS. On average, for 110 forecasts conducted over a 2-month period in 2016, LHN is shown to improve a composite NWP index (measuring the average change in RMSE over many variables and observation types) by ∼1.5% compared to a control experiment with no LHN. For winds between 400 and 100 hPa, reductions in RMSE between 1% and 4% are found for lead times up to 48 h. For precipitation, improvements for lead times up to 6 h are shown. Another goal of this study is to quantify the relative contributions for certain components of the LHN implementation. The features being investigated are the inclusion of LHN within the assimilation cycle, the use of idealized heating profiles and the conservation of relative humidity. The 2-month forecasting experiments are conducted with these components deactivated in turn. By comparing these experiments, it is found that the inclusion of LHN in the assimilation cycle has the largest impact. It is also found that the use of idealized profiles in locations where precipitation is observed but not simulated is a critical aspect of this implementation of LHN. A case study is also provided to illustrate the impact of LHN on altitude winds.