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A complex and underexplored relationship exists between atmospheric rivers (ARs) and mesoscale frontal waves (MFWs). The present study further explores and quantifies the importance of diabatic processes to MFW development and the AR–MFW interaction by simulating two ARs impacting Northern California’s flood-vulnerable Russian River watershed using the Model for Prediction Across Scales-Atmosphere (MPAS-A) with and without the effects of latent heating. Despite the storms’ contrasting characteristics, diabatic processes within the system were critical to the development of MFWs, the timing and magnitude of integrated vapor transport (IVT), and precipitation impacts over the Russian River watershed in both cases. Low-altitude circulations and lower-tropospheric moisture content in and around the MFWs are considerably reduced without latent heating, contributing to a decrease in moisture transport, moisture convergence, and IVT. Differences in IVT are not consistently dynamic (i.e., wind-driven) or thermodynamic (i.e., moisture-driven), but instead vary by case and by time throughout each event. For one event, AR conditions over the watershed persisted for 6 h less and the peak IVT occurred 6 h earlier and was reduced by ~17%; weaker orographic and dynamic precipitation forcings reduced precipitation totals by ~64%. Similarly, turning off latent heating shortened the second event by 24 h and reduced precipitation totals by ~49%; the maximum IVT over the watershed was weakened by ~42% and delayed by 18 h. Thus, sufficient representation of diabatic processes, and by inference, water vapor initial conditions, is critical for resolving MFWs, their feedbacks on AR evolution, and associated precipitation forecasts on watershed scales.

Despite numerous studies documenting the importance of atmospheric rivers (AR) to the global water cycle and regional precipitation, the evolution of their water vapor fluxes has been difficult to investigate given the challenges of observing and modeling precipitation processes within ARs over the ocean. This study uses satellite-based radar reflectivity profiles from the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR), combined with kinematic and thermodynamic conditions in the vicinity of the precipitation diagnosed from the Climate Forecast System Reanalysis, to evaluate the characteristics and dynamical origins of precipitation in ARs over the northeast Pacific Ocean. Transects of 192 ARs between 2014 and 2018 are examined. Both stratiform and convective precipitation were abundant in these GPM transects and the precipitation was most often generated by forced ascent in the vicinity of a cold front in frontogenetic environments. Conditioning composite vertical profiles of reflectivity and latent heating from GPM-DPR on frontogenesis near the moist-neutral low-level jet demonstrated the importance of frontally forced precipitation on atmospheric heating tendencies. A case study of a high-impact landfalling AR is analyzed using the Weather Research and Forecasting Model, which showed how the precipitation processes and subsequent latent heat release offshore strongly influenced AR evolution. Although these precipitation mechanisms are present in global-scale models, the difficulty that coarse-resolution models have in accurately representing resultant precipitation likely translates to uncertainty in forecasting heating tendencies, their feedbacks on AR evolution, and ultimately the impacts of ARs upon landfall in the western United States.

Avalanches occurred near Mount Shasta in Northern California and San Gorgonio in Southern California, evacuations near burn areas were ordered due to the risk of debris flows and flash flooding, and heavy snow in the Sierra Nevada and northern Central Valley resulted in disruptions to transportation and commerce. The dynamically favorable characteristics of this AR included an amplifying upper-level midlatitude trough, the presence of multiple surface low pressure systems over the North Pacific basin, and the intensification of a mesoscale frontal wave (MFW) into a secondary low pressure system that all contributed to the evolution of this AR. GFS analysis (F-0) of 500-hPa geopotential height (red; 550-dam contour), SLP (black; 1,000- and 996-hPa contours), 250-hPa wind speed (green; 130-kt contour), IVT magnitude (color coded according to scale), and IVT vectors (kg m−1 s−1; plotted according to the reference vector in the top right) valid at (a) 0000 UTC 12 Feb, (b) 1200 UTC 12 Feb, (c) 0000 UTC 13 Feb, and (d) 1200 UTC 13 Feb 2019. (e) Stage-IV precipitation (color coded according to scale) valid from 1200 UTC 12 Feb through 1200 UTC 15 Feb 2019 and gridpoint AR scale (circles, according to scale and corresponding to lateral coastal grid point). Alternatively, ensemble-mean forecasts verifying at 0000 UTC 13 February 2019 did not forecast SLP values < 996 hPa associated with the secondary cyclone and 250-hPa winds > 130 kt associated with the upper-tropospheric jet streak over the Pacific Northwest until initializations at 0000 UTC 10 and 11 February, respectively (Figs. 3a,b).

Short-duration, high-intensity rainfall in Southern California, often associated with narrow cold-frontal rainbands (NCFR), threaten life and property. While the mechanisms that drive NCFRs are relatively well understood, their regional characteristics, specific contribution to precipitation hazards, and their predictability in the western United States have received little research attention relative to their impact. This manuscript presents observations of NCFR physical processes made during the Atmospheric River Reconnaissance field campaign on 2 February 2019 and investigates the predictability of the observed NCFR across spatiotemporal scales and forecast lead time. Dropsonde data collected along transects of an atmospheric river (AR) and its attendant cyclone during rapid cyclogenesis, and radiosonde observations during landfall 24 h later, are used to demonstrate that a configuration of the Weather Research and Forecasting (WRF) Model skillfully reproduces the physical processes responsible for the development and maintenance of the impactful NCFR. Ensemble simulations provide quantitative uncertainty information on the representation of these features in numerical weather prediction and instill confidence in the utility of WRF as a forecast guidance tool for short- to medium-range prediction of mesoscale precipitation processes in landfalling ARs. This research incorporates novel data and methodologies to improve forecast guidance for NCFRs impacting Southern California. While this study focuses on a single event, the outlined approach to observing and predicting high-impact weather across a range of spatial and temporal scales will support regional water management and hazard mitigation, in general.

Atmospheric River Reconnaissance (AR Recon) is a targeted campaign that complements other sources of observational data, forming part of a diverse observing system. AR Recon 2021 operated for ten weeks from January 13 to March 22, with 29.5 Intensive Observation Periods (IOPs), 45 flights and 1142 successful dropsondes deployed in the northeast Pacific. With the availability of two WC-130J aircraft operated by the 53 rd Weather Reconnaissance Squadron (53 WRS), Air Force Reserve Command (AFRC) and one National Oceanic and Atmospheric Administration (NOAA) Aircraft Operations Center (AOC) G-IVSP aircraft, six sequences were accomplished, in which the same synoptic system was sampled over several days. The principal aim was to gather observations to improve forecasts of landfalling atmospheric rivers on the U.S. West Coast. Sampling of other meteorological phenomena forecast to have downstream impacts over the U.S. was also considered. Alongside forecast improvement, observations were also gathered to address important scientific research questions, as part of a Research and Operations Partnership. Targeted dropsonde observations were focused on essential atmospheric structures, primarily atmospheric rivers. Adjoint and ensemble sensitivities, mainly focusing on predictions of U.S. West Coast precipitation, provided complementary information on locations where additional observations may help to reduce the forecast uncertainty. Additionally, Airborne Radio Occultation (ARO) and tail radar were active during some flights, 30 drifting buoys were distributed, and 111 radiosondes were launched from four locations in California. Dropsonde, radiosonde and buoy data were available for assimilation in real-time into operational forecast models. Future work is planned to examine the impact of AR Recon 2021 data on model forecasts.

Water management and flood control are major challenges in the western United States. They are heavily influenced by atmospheric river (AR) storms that produce both beneficial water supply and hazards; for example, 84% of all flood damages in the West (up to 99% in key areas) are associated with ARs. However, AR landfall forecast position errors can exceed 200 km at even 1-day lead time and yet many watersheds are <100 km across, which contributes to issues such as the 2017 Oroville Dam spillway incident and regularly to large flood forecast errors. Combined with the rise of wildfires and deadly post-wildfire debris flows, such as Montecito (2018), the need for better AR forecasts is urgent. Atmospheric River Reconnaissance (AR Recon) was developed as a research and operations partnership to address these needs. It combines new observations, modeling, data assimilation, and forecast verification methods to improve the science and predictions of landfalling ARs. ARs over the northeast Pacific are measured using dropsondes from up to three aircraft simultaneously. Additionally, airborne radio occultation is being tested, and drifting buoys with pressure sensors are deployed. AR targeting and data collection methods have been developed, assimilation and forecast impact experiments are ongoing, and better understanding of AR dynamics is emerging. AR Recon is led by the Center for Western Weather and Water Extremes and NWS/NCEP. The effort’s core partners include the U.S. Navy, U.S. Air Force, NCAR, ECMWF, and multiple academic institutions. AR Recon is included in the “National Winter Season Operations Plan” to support improved outcomes for emergency preparedness and water management in the West.

The goal of Atmospheric River Reconnaissance (AR Recon) is to support water management decisions and flood forecasting by using targeted airborne and buoy observations over the northeast Pacific to improve analysis and forecasts of landfalling ARs and their impacts on the U.S. West Coast at lead times of 0–5 days. Furthermore, AR Recon data have enabled advances in the understanding of physical processes that modulate AR characteristics such as intensity (Hatchett et al. 2020; Cannon et al. 2020; Norris et al. 2020; Demirdjian et al. 2020b; Cobb et al. 2021). [...]far, AR Recon has focused on improving forecast accuracy for the U.S. West Coast thanks to groundbreaking research programs like the U.S. Army Corps of Engineers’ Forecast Informed Reservoir Operations (FIRO; Delaney et al. 2020; Jasperse et al. 2020) and the California Department of Water Resources’ AR Program (Ralph et al. 2020). Workshop goals The AR Recon 2021 Workshop goals were defined as follows: * To share results from studies involving AR Recon data * To coordinate and inspire future work on data collection, data assimilation, metric development, and impact assessment * To strengthen the RAOP approach being developed in AR Recon The workshop program was designed by the Steering Committee to meet these goals and stimulate engaging and effective discussion among all participants. Presentations and discussion The AR Recon workshop featured collaborators from research and operational centers in the United States and around the globe, including the National Centers for Environmental Prediction (NCEP), National Weather Service (NWS), U.S. Naval Research Laboratory (NRL), National Center for Atmospheric Research (NCAR), European Centre for Medium-Range Weather Forecasts (ECMWF), Plymouth State University (PSU), University of Colorado Boulder (CU Boulder), Northern Illinois University, and University at Albany, State University of New York.