The Global Navigation Satellite System (GNSS) airborne radio occultation (ARO) technique is used to retrieve profiles of the atmosphere during reconnaissance missions for atmospheric rivers (ARs) on the west coast of the United States. The measurements are a horizontal integral of refractive index over long ray-paths extending between a spaceborne transmitter and a receiver onboard an aircraft. A specialized forward operator is required to allow assimilation of ARO observations into numerical weather prediction models to support forecasting of ARs. A two-dimensional (2D) bending angle operator is proposed to enable capturing key atmospheric features associated with strong ARs. Comparison to a one-dimensional (1D) forward model supports the evidence of large bending angle departures within 3-7 km impact heights for observations collected in a region characterized by the integrated water vapor transport (IVT) magnitude above 500 kg m-1 s-1. The assessment of the 2D forward model for ARO retrievals is based on a sequence of six flights leading up to a significant AR precipitation event in January 2021. Since the observations often sampled regions outside the AR where moisture is low, the significance of horizontal variations is obscured in the average statistics. However, examples from an individual flight preferentially sampling the cross-section of an AR further support the need for the 2D forward model for targeted ARO observations. Additional simulation experiments are performed to quantify forward modeling errors due to tangent point drift and horizontal gradients suggesting contributions on the order of 5 % and 20 %, respectively.

Capturing watershed-scale runoff response remains difficult, in part because of heterogeneous land surface characteristics in mountainous regions. This challenge has impacted our progress in understanding soil moisture role in modulating rainfall-runoff process. Situated in Northern California, the Russian River watershed is frequented by atmospheric rivers (ARs) that bring most of the significant rainfall events to the area and are associated with almost all of the floods. To observe the precipitation in this watershed, NOAA Hydrometeorology Testbed has installed 14 telemetered stations across the watershed since 2005, each with 2-minute soil moisture volumetric water content (VWC) sensors at 6 depths. The Center for Western Weather and Water Extremes at the University of California San Diego has installed 6 more stations since 2017. Understanding soil moisture variability is crucial for hydrologic modeling and operations, particularly flood prediction. This high resolution soil moisture observation network allows comprehensive analysis of soil moisture variability. For instance, correlation analysis of 2-minute VWC at 10-cm depth reveals a uniform shallow-layer soil moisture behavior with correlations of >0.8 at most locations and across different seasons, demonstrating the network’s utility in capturing spatial and temporal soil moisture variabilities. Following this result, we investigate how antecedent soil moisture condition modulates the rainfall-runoff process. We include precipitation and stream discharge records from the same stations and nearby USGS gauges. A series of AR events in February 2019 offers a prime example. The February 2nd and Valentine’s Day ARs saturated the soil in most parts of the watershed and resulted in minor flooding. Percentile rank analysis indicated the subsequent February 26th-27th ARs recorded the highest event total rainfalls since 2017 at most gauges. Consequently, the February 26th-27th ARs resulted in rapid runoff responses and widespread flooding. This example also reveals the spatial variation in antecedent soil moisture VWC “threshold” where runoff generation becomes efficient. Work is ongoing to profile this threshold variation within the watershed, and preliminary analysis suggests a range from <0.2 to >0.5 at 10-cm depth.

Observational networks enhance real-time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multi-tiered observational network in California provided insights into hydrometeorological processes and impacts during a three-day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows for: 1) the validation of record cool season precipitable water observations over southern California, 2) the identification of phenomena that produce natural hazards and present difficulties for short-term weather forecast models, such as extreme precipitation amounts and snow level variability, 3) the use of soil moisture data to improve hydrologic model forecast skill in northern California’s Russian River basin, and 4) the combination of meteorological data with seismic observations to “observe” a large avalanche on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of diverse observations to characterize climatological context and advance understanding of processes modulating extreme weather.