Increasing the density of Global Navigation Satellite System radio occultation (RO) with commercial Smallsats and the next generation COSMIC-2 constellation is expected to improve analyses of the state of atmosphere, which is essential for numerical weather prediction. High vertical resolution RO profiles could be useful to observe atmospheric rivers (ARs) over the ocean, which transport water vapor in shallow, elongated corridors that frequently impact the west coasts of continents. The multi-year AR Reconnaissance campaign has extensively sampled ARs over the northeastern Pacific with dropsondes, providing an invaluable dataset to evaluate the reliability of RO retrievals. These dropsondes, and a reanalysis product that assimilates them, are compared to three RO datasets: (1) established operational missions, (2) COSMIC-2, and (3) the commercial Spire constellation. Each RO dataset has biases relative to reanalyses of less than 0.5% N in the upper troposphere and negative biases in the lower troposphere. Direct colocations with dropsondes indicate that vertical refractivity gradients present within ARs may be contributing to negative biases at higher altitudes inside than outside ARs, where the greatest variability and vertical gradients are at the well-defined boundary layer top. Observations from Spire are overly smooth, affecting the ability to resolve the low-level structure of an AR. Surprisingly, the depth of penetration into the lower troposphere is greater inside an AR than outside for all datasets. The results indicate that the observation errors used for assimilation of RO within ARs should consider the height dependent biases that are associated with the structure of the atmosphere.

Variations in the water vapor that atmospheric rivers (ARs) carry toward North America within Pacific storms strongly modulates the spatiotemporal distribution of west‐coast precipitation. The “AR Recon” program was established to improve forecasts of landfalling Pacific‐coast ARs and their associated precipitation. Dropsondes are deployed from weather reconnaissance aircraft and pressure sensors have been added to drifting ocean buoys to fill a major gap in standard weather observations, while research is being conducted on the potential for airborne Global Navigation Satellite System (GNSS) radio occultation (ARO) to also contribute to forecast improvement. ARO further expands the spatial coverage of the data collected during AR Recon flights. This study provides the first description of these data, which provide water vapor and temperature information typically as far as 300 km to the side of the aircraft. The first refractivity profiles from European Galileo satellites are provided and their accuracy is evaluated using the dropsondes. It is shown that spatial variations in the refractivity anomaly (difference from the climatological background) are modulated by AR features, including the low‐level jet and tropopause fold, illustrating the potential for RO measurements to represent key AR characteristics. It is demonstrated that assimilation of ARO refractivity profiles can influence the moisture used as initial conditions in a high‐resolution model. While the dropsonde measurements provide precise, in situ wind, temperature and water vapor vertical profiles beneath the aircraft, and the buoys provide surface pressure, ARO provides complementary thermodynamic information aloft in broad areas not otherwise sampled at no additional expendable cost.

Landfalling atmospheric rivers (ARs) over the western US are responsible for ∼30%–50% of the annual precipitation, and their accurate forecasts are essential for aiding water management decisions and reducing flood risks. Sparse coverage of conventional observations over the Pacific Ocean, which can cause inadequate upstream initial conditions for numerical weather prediction models, may limit the improvement of forecast skill for these events. A targeted field program called AR Reconnaissance (Recon) was initiated in 2016 to better understand and reduce forecast errors of landfalling ARs at 1–5 days lead times. During the winter seasons of 2016, 2018, and 2019, 15 Intensive Observation Periods (IOPs) sampled the upstream conditions for landfalling ARs. This study evaluates the impact on forecast accuracy of assimilating these dropsonde data. Data denial experiments with (WithDROP) and without (NoDROP) dropsonde data were conducted using the Weather Research and Forecasting model with the Gridpoint Statistical Interpolation four‐dimensional ensemble variational system. Comparisons between the 15 paired NoDROP and WithDROP experiments demonstrate that AR Recon dropsondes reduced the root‐mean‐square error in integrated vapor transport (IVT) and inland precipitation for more than 70% of the IOPs, averaged over all forecast lead times from 1 to 6 days. Dropsondes have improved the spatial pattern of forecasts of IVT and precipitation in all 15 IOPs. Significant improvements in skill are found beyond the short range (1–2 days). IOP sequences (i.e., back‐to‐back IOPs every other day) show the most improvement of inland precipitation forecast skill.

Accurate representation of cloud microphysical processes in numerical weather and climate models has proven challenging, in part because of the highly specialized instrumentation required for diagnosing errors in simulated distributions of hydrometeors. Global Navigation Satellite System (GNSS) polarimetric radio occultation (PRO) is a promising new technique that is sensitive to hydrometeors and has the potential to help address these challenges by providing microphysical observations that are relevant to larger spatial scales, especially if this type of observing system can be implemented on aircraft that can target heavy precipitation events. Two numerical experiments were run using a mesoscale model configured with two different microphysical parameterization schemes for a very intense atmospheric river (AR) event that was sampled by aircraft deploying dropsondes just before it made landfall in California, during the CalWater 2015 field campaign. The numerical experiments were used to simulate profiles of airborne polarimetric differential phase delay observations. The differential phase delay due to liquid water hydrometeors below the freezing level differed significantly in the two experiments, as well as the height of the maximum differential phase delay due to all hydrometeors combined. These results suggest that PRO observations from aircraft have the potential to contribute to validating and improving the representation of microphysical processes in numerical weather forecasts once these observations become available.