Poster

IAMAS

JMP03 - High-impact Weather and Climate Extremes

Reducing the Underestimation of Eastern North Pacific Atmospheric River Forecasts through Radio Occultation Data

This study explores the impact of satellite radio occultation (RO) data on atmospheric river (AR) forecasts in the eastern North Pacific and quantifies the variations in forecast errors. This study assesses the RO improvements in AR occurrence, intensity, and landfall forecasts and analyzes the key factors contributing to reduced underestimations. Eighteen AR cases are selected for numerical experiments and statistical analyses. Forecast experiments with no data assimilation, radiance data assimilation, and RO data assimilation are performed and compared. On average, three- to six-day lead time forecasts tend to underestimate AR characteristics compared to the analysis. Additional data assimilation can reduce the spatial and temporal forecast errors of the AR moisture flux by 21%. Compared to radiance data, the assimilation of RO data further reduces the moisture flux errors by 15%. Specifically, assimilating RO data increases the AR occurrence rate by 4%, increases the AR intensity by 28 kg/m/s, and reduces the centroid error by 201 km. The main contributors of RO data to reducing AR forecast underestimations are the wind improvement before AR landfall and the mid-level moisture improvement after AR landfall. In summary, this study highlights the potential impact of RO data on AR forecasts. Assimilating RO data can reduce AR forecast underestimations, mainly due to the RO contributions at mid-levels (700-500 hPa).