Impacts of Dropsonde Observations on Forecasts of Atmospheric Rivers and Associated Precipitation in the NCEP GFS and ECMWF IFS Models

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Abstract Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an intensive observation period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used. Significance Statement Atmospheric River Reconnaissance is a program that uses targeted aircraft flights over the northeast Pacific to take measurements of meteorological fields. These data are then ingested into global weather models with the intent of improving the initial conditions and resulting forecasts along the U.S. West Coast. The impacts of these observations on two global numerical weather models were investigated to determine their influence on the forecasts. The integrated vapor transport, a measure of both wind and humidity, saw significant improvements in both models with the additional observations. Precipitation forecasts were also improved, but with differing results between the two models.