Atmospheric River Reconnaissance: Mission Planning, Execution, and Incorporation of Operational and Science Objectives

September 16, 2025

A paper titled “Atmospheric River Reconnaissance: Mission Planning, Execution, and Incorporation of Operational and Science Objectives” was recently published in the AMS’s flagship journal Bulletin of the American Meteorological Society. This study was led by Minghua Zheng (CW3E) with contributions from AR Recon PI F. Martin Ralph (CW3E), AR Recon Co-PI Vijay Tallapragada (NOAA/NWS/NCEP), and 30 other Atmospheric River Reconnaissance [AR Recon, Ralph et al. (2020)] collaborators from international, federal, and state agencies, universities, and industry. The study provides a comprehensive summary of AR Recon planning processes, targeted sampling strategies, forecast impacts, scientific advancements, and lessons learned, and highlights benefits for actionable decision support. This collaborative effort supports four priorities identified in the CW3E 2025-2029 Strategic Plan, including Novel Observations; Atmospheric Rivers and Extreme Precipitation Research; Prediction, and Applications; FIRO: Resilient Water Management; and Advanced Precipitation and Streamflow Prediction.

Since 2016, AR Recon has partnered with NOAA and U.S. Air Force Reserve Command to release dropsondes over the North Pacific via aircraft, collecting meteorological data for real-time operational use and research. Complementary observations include airborne radio occultation, radiosondes launched from the West Coast, and barometer-equipped drifting buoys alleviating oceanic data gaps (Fig. 1). Additionally, AR Recon collaborates with WindBorne Systems, which operates a constellation of long-duration, vertically controllable balloons that collects observations of meteorological data.

Figure 1. Flight tracks and geographical distribution of the data collected from AR Recon 2024 intensive observing period (hereafter, IOP) 24, centered at 0000 UTC 23 January 2024. Red stars indicate departing airports. Background meteorological fields are based on ERA-5 reanalysis data valid at 0000 UTC 23 January 2024. Shades with gray contours: IVT magnitude (kg m^-1 s^-1). Black dashed contours: mean sea level pressure (MSLP; hPa) with an interval of eight hPa. Cyan/green filled circles: locations of actual/planned dropsondes collected during this IOP mission. Dark green lines: ARO slanted profiles, with the highest point at the flight track and the lowest part at the end of the line furthest from the flight track. Figure 1 from Zheng et al. (2025).

From November to March, when an AR is expected to impact the West Coast within approximately a week, AR Recon initiates daily forecast meetings with a forecast briefing, quantitative tool synthesis, and flight track design. Flights are tailored to sample essential atmospheric structures (Fig. 2), which includes AR cores, edges, jets, troughs, vorticity anomalies, mesoscale frontal waves, and extratropical cyclones. Ensemble and adjoint sensitivity tools support flight planning. A Mission Director makes final decisions on whether to fly and where, emphasizing targeting of essential atmospheric structures and considering operational benefits and science objectives.

The campaign’s success relies on mission planning grounded in the Research and Operations Partnership (RAOP), such that the missions are informed by both operational needs and the latest scientific insights. This RAOP approach enhances forecast accuracy (Fig. 3) in the western U.S. and beyond (Table 5 in Zheng et al. 2025), and advances science. Via improved forecasts of ARs and associated precipitation, AR Recon data supports more effective decision-making for water management and flood risk reduction, and its datasets are publicly available as a valuable resource for advancing foundational, process-based research and cross-validating observations from other platforms, model reanalysis products, and novel machine-learning-based datasets.

Figure 2. A summary of key phenomena for ARs. (a) A plan-view representation of the AR and the surrounding meteorological features (essential atmospheric structures sampled by AR Recon, adapted from Wilson et al. 2022, © American Meteorological Society. Used with permission.); b) Typical vertical cross-section of key meteorological features in and near an AR over the Northeast Pacific Ocean (adapted from Zheng et al. 2021, © American Meteorological Society. Used with permission.). The cross-section is made from A (cold side) to A’ (warm side) on panel (a). c) A real case valid at 0000 UTC 10 January 2023: a plan-view of key phenomena, including IVT magnitude (shaded, kg m^-1 s^-1), the 2-PVU (blue contour), and 250-hPa wind speed (thick grey contour, m s^-1). The cyan dots are dropsonde locations deployed by one C-130 (offshore of California) and the G-IV (north of Hawaii). d) A real cross-section of AR-associated features, including PV, horizontal wind speed, layered IVT magnitude (integrated eq. 1 within each 50-hPa atmospheric layer), and cloud fraction. The cross-section is made from B (cold side) to B’ (warm side) on panel (c), passing through the western flight track sampled by the G-IV. Analyses for (c) and (d) are based on ERA-5 reanalysis data. PVU stands for potential vorticity units, where 1 PVU = 10-6 K m² kg^-1 s^-1 (Hoskins et al. 1985). Figure 2 from Zheng et al. (2025).

Figure 3. Percentage of RMSE differences (%) for 24-h accumulated precipitation forecasts over California, comparing GFS WithDROP and NoDROP experiments for forecast hours of 36–60. The analysis uses a precipitation threshold of 12.7 mm (0.5 inches), with Stage-IV precipitation products as ground truth. The average value is statistically significant at the 90% confidence level (Student’s t-test). The x-axis represents IOP dates for model initialization dates that targeted California. Dates without bars are IOPs that didn’t meet the precipitation threshold. This figure is modified from Figure 16 in Zheng et al. (2025).

Zheng, M., Ralph, F. M., Tallapragada, V., Wilson, A. M., Babbitt, S. H., Bartlett, S. M., Cao, B., Centurioni, L., Cordeira, J. M., Davis, C., Monache, L. D., Doyle, J. D., Elless, T. J., Feuer, S., Haase, J. S., Hathaway, N., Hutchinson, T., Iniguez, P., Kawzenuk, B., Knappe, E., Lavers, D. A., Lundry, A., Michaelis, A., Pappenberger, F., Reynolds, C. A., Rickert, R., Roj, S., Rutz, J. J., Subramanian, A. C., Torn, R. D., Wang, J., Wu, K., & Wu, X. (2025). Atmospheric River Reconnaissance: Mission Planning, Execution, and Incorporation of Operational and Science Objectives. Bulletin of the American Meteorological Society (published online ahead of print 2025), BAMS-D-24-0160.1. https://doi.org/10.1175/BAMS-D-24-0160.1

Ralph, F. M., Cannon, F., Tallapragada, V., Davis, C. A., Doyle, J. D., Pappenberger, F., Subramanian, A., Wilson, A. M., Lavers, D. A., Reynolds, C. A., Haase, J. S., Centurioni, L., Ingleby, B., Rutz, J. J., Cordeira, J. M., Zheng, M., Hecht, C., Kawzenuk, B., & Delle Monache, L. (2020). West Coast forecast challenges and development of atmospheric river reconnaissance. Bulletin of the American Meteorological Society, 101(8), E1357-E1377. https://doi.org/10.1175/BAMS-D-19-0183.1

Wilson, A. M., Cobb, A., Ralph, F. M., Tallapragada, V., Davis, C., Doyle, J., Delle Monache, L., Pappenberger, F., Reynolds, C., Subramanian, A., Cannon, F., Cordeira, J., Haase, J., Hecht, C., Lavers, D., Rutz, J. J., & Zheng, M. (2022). Atmospheric River Reconnaissance Workshop Promotes Research and Operations Partnership. Bulletin of the American Meteorological Society, 103(3), E810-E816. https://doi.org/10.1175/BAMS-D-21-0259.1

Zheng, M., Delle Monache, L., Wu, X., Ralph, F. M., Cornuelle, B., Tallapragada, V., Haase, J. S., Wilson, A. M., Mazloff, M., Subramanian, A., & Cannon, F. (2021). Data Gaps within Atmospheric Rivers over the Northeastern Pacific. Bulletin of the American Meteorological Society, 102(3), E492-E524. https://doi.org/10.1175/BAMS-D-19-0287.1