Author: Chris Castellano

CW3E Publication Notice: Atmospheric River Reconnaissance: Mission Planning, Execution, and Incorporation of Operational and Science Objectives

CW3E Publication Notice

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

CW3E Publication Notice: The potential impacts of improved MJO prediction on the prediction of MJO teleconnections in the UFS global fully coupled model

CW3E Publication Notice

The potential impacts of improved MJO prediction on the prediction of MJO teleconnections in the UFS global fully coupled model

September 8, 2025

A new paper entitled “The potential impacts of improved MJO prediction on the prediction of MJO teleconnections in the UFS global fully coupled model” was recently published in Climate Dynamics by CW3E researcher Jiabao Wang, Daniela I. V. Domeisen (University of Lausanne and ETH Zurich, Switzerland), Chaim Garfinkel (Hebrew University of Jerusalem, Israel), Andrea Jenney (Oregon State University), Hyemi Kim (Ewha Womans University, Korea), Zheng Wu (University of Albany), Cheng Zheng (Stony Brook University), and Cristiana Stan (George Mason University). This research, supported by the California Department of Water Resources and NOAA/OAR Weather Program Office, investigates the value of Madden-Julian oscillation (MJO) prediction for extratropical subseasonal forecasts by examining the extent to which reliable MJO simulation translates to reliable simulation of its teleconnections. This work supports the Advanced Precipitation and Streamflow Prediction priority in CW3E’s 2025-2029 Strategic Plan by advancing understanding of “forecasts of opportunity” where sub-seasonal prediction skill can be improved.

This study examines the prediction of the MJO and its teleconnections in two NOAA Unified Forecast System (UFS) coupled model prototypes: Prototype 7 (UFS7) and Prototype 8 (UFS8). These are the prototypes for the development of a fully coupled atmosphere-ocean-sea ice-wave-aerosol model, which will be implemented in the next generation of NOAA operational forecast systems. They share some common standard features in terms of the dynamical core, resolution, and coupling. UFS8 has additional coupling with aerosols and upgraded model physics. Results show that the MJO is skillfully predicted at a lead time of 27 days in UFS8, which is a considerable improvement (~ one-week skill increase) compared to UFS7 (Fig. 1). The MJO eastward propagation is also better captured in UFS8 with the amplitude of convection and wind anomalies closer to reanalysis and more realistic easterly winds to the east of the active convection center. For its teleconnections, a more realistic prediction of the pattern and amplitude of the geopotential height response and its evolution following active MJO events is observed in UFS8 (Fig. 2). Both UFS7 and UFS8 reproduce the PNA pattern in Week 1 with a pattern correlation greater than 0.95. However, starting in Week 2, a large difference emerges between the two prototypes. While UFS7 has large biases such as a notably weaker response over North America, UFS8 is able to capture the evolution of the PNA such as a southeastward extension of the trough towards the North American West Coast. The Week 4 PNA pattern is difficult to predict for both prototypes, but it is generally better predicted in UFS8. Note that this systematic improvement in UFS8 is not observed in weak MJO cases, suggesting that the enhanced performance is more due to improved MJO predictions and subsequent excitation of poleward-propagating Rossby waves, rather than reduced growth of biases from the atmospheric initial conditions.

The effect of the enhanced MJO prediction skill on MJO teleconnection prediction via other tropospheric and stratospheric pathways is also examined. The results show that the dipole response in the storm tracks over the North Pacific, the upward wave propagation, and the subsequent weakening of the polar vortex are better simulated in UFS8. These prototypes, however, still struggle to predict the downstream impacts in the North Atlantic and Europe, and the precipitation and temperature responses.

This study suggests that the potential for increasing the MJO teleconnection prediction skill, although not in all variables, lies in improving MJO predictions in dynamical models with more coupled components and upgraded model physics. The results better address the importance of enhancing the understanding and prediction of MJO, the dominant source of subseasonal prediction, which has potential value to stakeholders, including water resource managers.

Figure 1. a–c Longitude-time composites of outgoing longwave radiation (OLR; shading; W m-2) and 850-hPa zonal wind (U850; contours; interval 0.3 m s-1) anomalies averaged over 15° S–15° N for active MJO events in reanalysis, UFS7, and UFS8, respectively. The results are for events initialized during MJO phases 2 and 3. The vertical lines indicate 120°E (approximately the center of the Maritime Continent). A 5-day moving average is applied. d MJO prediction skill for UFS7 and UFS8 reforecasts initialized with active MJO events. The prediction skill is evaluated based on ACC (solid lines) and RMSE (dashed lines) between the model and reanalysis RMM indices. The gray solid horizontal line indicates an ACC of 0.5. Figure 1 from Wang et al. (2025).

Figure 2. Weekly averaged phase composites of 500-hPa geopotential height anomalies (Z500a) after MJO phases 2 and 3 for the lead times from week 1 to week 4 in a–d reanalysis, e–h UFS7, and i–l UFS8. The dotted areas indicate the significance at the 0.05 level. The numbers in the upper right corner of each plot indicate the spatial correlation between the model and reanalysis over the region. Figure 3 from Wang et al. (2025).

Wang, J., Domeisen, D. I. V., Garfinkel, C. I., Jenney, A. M., Kim, H., Wu, Z., Zheng, C., & Stan, C. (2025). The potential impacts of improved MJO prediction on the prediction of MJO teleconnections in the UFS global fully coupled model. Climate Dynamics, 63, 312. https://doi.org/10.1007/s00382-025-07783-9

CW3E Publication Notice: Impacts of atmospheric river reconnaissance dropsondes on ECMWF Integrated Forecasting System precipitation forecasts

CW3E Publication Notice

Impacts of atmospheric river reconnaissance dropsondes on ECMWF Integrated Forecasting System precipitation forecasts

September 8, 2025

A paper titled “Impacts of atmospheric river reconnaissance dropsondes on ECMWF Integrated Forecasting System precipitation forecasts” by Jia Wang (CW3E), David Lavers (ECMWF), Luca Delle Monache (CW3E), Bruce Ingleby (ECMWF), Minghua Zheng (CW3E), Xingren Wu (NCEP), F. Martin Ralph (CW3E), and Florian Pappenberger (ECMWF), was recently published in the Quarterly Journal of the Royal Meteorological Society. This study represents a collaborative effort among CW3E, ECMWF, and NCEP to continue monitoring the impacts of Atmospheric River Reconnaissance (AR Recon) observations in operational models, and supports multiple priorities identified in the CW3E 2025-2029 Strategic Plan, including Atmospheric Rivers and Extreme Precipitation Research, Prediction, and Applications; Novel Observations; and Advanced Precipitation and Streamflow Prediction.

This study focuses on the influence of AR Recon dropsonde observations on ECMWF Integrated Forecasting System (IFS) precipitation forecasts during the 2023 and 2024 field seasons. Dropsonde impacts are evaluated through Observing System Experiments (OSEs). Two sets of experiments are conducted: control runs, which assimilate all operationally available observations, and data denial runs, which assimilate all observations except dropsondes. Validation is performed using stage IV precipitation data across three domains: California (CA), the Pacific Northwest (PNW), and the western United States (WEST; defined as west of 107°W).

Results (Fig. 1) show that dropsondes provide forecast improvements over CA during two periods: 24–36 h and 72–96 h. Improvements during the latter period are larger, with more values passing the significance test. We hypothesize that these two periods of improvements are associated with aircraft operations from two bases: California and Hawaii. Over the PNW, dropsondes increase forecast skill mainly at 48–72 h, with secondary improvements at 96 h and 120 h. The difference in timing of improvements between PNW and CA in 2023 is associated with the southward propagation of error reductions in the large-scale flow (Fig. 2). Across the WEST domain, dropsonde assimilation is overall beneficial through 96 h, with the exception around 48 h.

Dropsonde impacts are also assessed in the NCEP Global Forecast System (GFS) for 2023 (Fig. 3). Notable consistency emerges between the two operational systems: (1) both exhibit two periods of improvement over CA, with greater benefits in the second period; (2) both show that the skill enhancement over the PNW peaks around 72 h. The consistency in the timing of improvements between the IFS and GFS, the alignment of impacts on both large-scale flow and precipitation forecasts, and the temporal coherence among three evaluation metrics collectively strengthen confidence that the dropsonde benefits are physically meaningful.

Figure 1. The percentage differences (%) between the control experiments (CTRL) and the denial experiments (noDROP) for (a–c) Gilbert Skill Score (GSS), (d–f) Spearman’s rank correlation coefficient (SCC), and (g–i) root-mean-squared error (RMSE) for the two seasons combined. Half-filled cells represent absolute percentage differences smaller than 0.5%. For the GSS and SCC, the difference is CTRL − noDROP, while for the RMSE, the difference is noDROP − CTRL. Thus, green represents improvement from dropsondes, and brown indicates degradation. The left, middle, and right columns correspond to the California (CA), Pacific Northwest (PNW), and WEST verification domains, respectively. The x-axis represents thresholds for 24-hour accumulated precipitation. Values with frames are those passing the significance test, using the 90% confidence interval. Figure credit: Figure 5 of Wang et al. (2025).

Figure 2. The differences [m] in mean absolute errors for 500-hPa geopotential height between the control experiments (CTRL) and the denial experiments (noDROP) (CTRL − noDROP), for (a–g) 2022/23, across lead times from 48 to 120 hours. Dotted areas indicate statistically significant differences at the 90% confidence level, in which uncertainty is estimated as ±1.645×(σ/√n), with σ representing the standard deviation and n the sample size. Contours are averaged 500-hPa geopotential height from the CTRL analyses. Contour interval is 80 m. Figure credit: Figure 8 of Wang et al. (2025).

Figure 3. As in Figure 1a–c, but are dropsonde impacts in Global Forecast System (GFS) 2022/23 experiments. Half-filled cells represent absolute percentage differences smaller than 0.5%. Figure credit: Figure 9 of Wang et al. (2025).

Wang, J., Lavers, D. A., Delle Monache, L., Ingleby, B., Zheng, M., Wu, X., Ralph, F. M. & Pappenberger, F. (2025). Impacts of atmospheric river reconnaissance dropsondes on ECMWF Integrated Forecasting System precipitation forecasts. Quarterly Journal of the Royal Meteorological Society (published online ahead of print 2025), e70019. https://doi.org/10.1002/qj.70019

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

Zheng, M., Torn, R., Delle Monache, L., Doyle, J., Ralph, F. M., Tallapragada, V., Davis, C., Steinhoff, D., Wu, X., Wilson, A., Papadopoulos, C., & Mulrooney, P. (2024). An Assessment of Dropsonde Sampling Strategies for Atmospheric River Reconnaissance. Monthly Weather Review, 152(3), 811–835. https://doi.org/10.1175/MWR-D-23-0111.1

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

CW3E Publication Notice: A Trajectory-Based Method for Estimating the Contribution of Landfalling Atmospheric Rivers to Top-Decile Precipitation Across Colorado

CW3E Publication Notice

A Trajectory-Based Method for Estimating the Contribution of Landfalling Atmospheric Rivers to Top-Decile Precipitation Across Colorado

September 2, 2025

A new paper, “A Trajectory-Based Method for Estimating the Contribution of Landfalling Atmospheric Rivers to Top-Decile Precipitation Across Colorado” by Deanna Nash (CW3E), Jon Rutz (CW3E), Jason Cordeira (CW3E), Zhenhai Zhang (CW3E), F. Martin Ralph (CW3E), Kris Sanders (NWS Grand Junction), and Erin Walter (NWS Grand Junction), was recently published in Journal of Geophysical Research: Atmospheres. This research, supported by the Miramar Charitable Foundation on behalf of Eaton and Margaret Scripps, uses backward trajectories to quantify how much of Colorado’s most extreme precipitation (top 10% of daily totals within sub-basins) is linked to landfalling atmospheric rivers (ARs).

This work addresses one of CW3E’s Strategic Plan priorities: Atmospheric Rivers and Extreme Precipitation Research Prediction and Applications. It advances understanding of inland-penetrating ARs and their role in Colorado’s heaviest precipitation events, while strengthening Research and Operations Partnerships through collaboration with the National Weather Service (NWS) in Grand Junction, CO.

Results show that ARs contribute 21–78% of western Colorado’s cool-season top-decile precipitation, far exceeding earlier estimates of up to 30% (Fig. 1). This wide range reflects Colorado’s complex topography and the influence of storm track variations among inland-penetrating ARs. In western Colorado, most AR-related precipitation is tied to landfalling ARs near Southern California and the Baja Peninsula (Fig. 2). While fewer trajectories originated from the Pacific Northwest and Gulf of Mexico, these events also produced some of the state’s wettest days. Additionally, local factors such as slope orientation and moisture loss likely affect the magnitude of contributions. Because western Colorado snowpack is a critical source of water for the broader Southwestern United States, understanding the AR contribution to these extreme precipitation events is essential for regional water resource planning.

By demonstrating the significant role of landfalling ARs in Colorado’s cool-season extremes, this study underscores the importance of accurately representing ARs in forecast models to improve precipitation prediction. Building on these results, future work will focus on developing a historical outlook tool to provide forecasters with probabilistic guidance on sub-basin precipitation based on the location and strength of landfalling ARs.

Figure 1. The fraction of top-decile precipitation associated with landfalling atmospheric rivers (ARs) [shaded; % of total top-decile precipitation for the number of trajectories ran for the cool season (NDJFMA) between 2000 and 2023] for each sub-basin (contour, gray) in CO based on the Guan & Waliser (2024) AR detection tool.

Figure 2. A schematic showing the three most frequent pathways of cool-season landfalling AR trajectories. The bubbles on the coast show the relative number of landfalling ARs associated with top-decile precipitation days in Colorado sub-basins. Credit: Deanna Nash.

Nash, D., Rutz, J. J., Cordiera, J., Zhang, Z., Ralph, F. M., Sanders, K., & Walter, E. (2025). A Trajectory-Based Method for Estimating the Contribution of Landfalling Atmospheric Rivers to Top-Decile Precipitation Across Colorado. Journal of Geophysical Research: Atmospheres, 130(17), e2025JD043580. https://doi.org/10.1029/2025JD043580

CW3E Publication Notice: Characteristics and Predictability of Extreme Precipitation Related to Atmospheric Rivers, Mesoscale Convective Systems, and Tropical Cyclones in the U.S. Southeast

CW3E Publication Notice

Characteristics and Predictability of Extreme Precipitation Related to Atmospheric Rivers, Mesoscale Convective Systems, and Tropical Cyclones in the U.S. Southeast

August 12, 2025

Dr. Suma Battula, Dr. Jay Cordeira, and Dr. Marty Ralph from CW3E recently published an article titled “Characteristics and Predictability of Extreme Precipitation Related to Atmospheric Rivers, Mesoscale Convective Systems, and Tropical Cyclones in the U.S. Southeast” in the AGU Journal of Geophysical Research: Atmospheres. This research was sponsored by the NOAA Cooperative Institute for Research to Operations in Hydrology (CIROH) and USACE Forecast Informed Reservoir Operations (FIRO) projects. This work investigates the influence of storm types on predictability of synoptic patterns associated with extreme precipitation in the Southeastern United States (SEUS). These findings support the Atmospheric Rivers and Extreme Precipitation Research, Prediction, and Applications priority identified in CW3E’s 2025-2029 Strategic Plan.

Battula et al. (2025) identified six synoptic patterns associated with extreme precipitation in the SEUS (Figure 1). These patterns exhibited a distinct seasonality: three occurred in the cool season (CS), two in the warm season (WS), and one in the transition season (TS). While previous work by Moore et al. (2015) demonstrated extreme precipitation forecast skill associated with atmospheric rivers (ARs), Battula et al. (2025) expand on this by adding storm types such as mesoscale convective systems (MCS) and tropical cyclones (TCs, Figure 2). The MCS contribution is obtained using a climatology developed by the NOAA Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO, Squitieri et al., 2025). Approximately 35% of extreme precipitation events in the cool season, 24% in the transition season, and 29% in the warm season are associated with coincident ARs and MCSs.

Recent work by Cordeira et al. (2025) found lower critical success index (CSI) in the SEUS using the NOAA GEFS reforecast v12 QPF product from 2001-2019. Battula et al. (2025) expand on this by illustrating that annual QPF skill for extreme precipitation increases in years with higher frequencies of atmospheric rivers (ARs) over the SEUS, corresponding to higher CSI values (Figure 3). The cool season pattern (CS2), characterized by high IVT and frequency of ARs, has higher CSI and lower FAR (Figure 4). In contrast, the warm season pattern with high convective available potential energy and integrated water vapor has lower QPF skill across multiple lead times. In addition, patterns with higher frequency of ARs or coincident ARs and MCSs have better predictability than those with isolated MCSs. These findings provide insight into storm type dependence of predictability in the SEUS. This methodology could be extended to classify synoptic patterns across the entire CONUS, determine the pattern‐wise contributions of storm types, and assess extreme QPF skill at various spatial scales, including for individual watersheds.

Figure 1. Topography (m) of the study area. Locations of Jackson (KJAN), Nashville (KBNA), and Atlanta (KATL) are marked as reference. Black box marks the area where QPE is averaged to obtain days with precipitation above the 90th percentile. From Figure 1 in Battula et al. (2025).

Figure 2. Pattern‐wise composites of atmospheric river (AR) probability computed using the Tracking ARs Globally as Elongated Targets (tARget) algorithm, and SLP (contoured every 2 hPa). Purple dots represent tropical cyclone locations obtained from the National Hurricane Center’s Atlantic Hurricane Database (HURDAT). From Figure 7 in Battula et al. (2025).

Figure 3. Annual series of critical success index (bars) or threat score and the number of atmospheric rivers (black line) within the black box in Figure 1 for a precipitation threshold of 10 mm. From Figure 10 in Battula et al. (2025).

Figure 4. Pattern‐wise False Alarm Ratio and Critical Success Index for the 95th percentile precipitation threshold of 15 mm at lead times of one (red), two (yellow), and three (gray) days. From Figure 12 in Battula et al. (2025).

Battula, S. B., Cordeira, J. M., & Ralph, F. M. (2025). Characteristics and predictability of extreme precipitation related to atmospheric rivers, mesoscale convective systems, and tropical cyclones in the US Southeast. Journal of Geophysical Research: Atmospheres, 130(15), e2024JD042471. https://doi.org/10.1029/2024JD042471

Cordeira, J. M., Ralph, F. M., Talbot, C., Forbis, J., Novak, D. R., Nelson, J. A., Mahoney, K., Weihs, R., Slinskey, E., & Delle Monache, L. (2025) A Summary of US Watershed Precipitation Forecast Skill and the National Forecast Informed Reservoir Operations Expansion Pathfinder Effort. Weather and Forecasting, 40(8), 1529-1542. https://doi.org/10.1175/WAF-D-24-0188.1

Moore, B. J., Mahoney, K. M., Sukovich, E. M., Cifelli, R., & Hamill, T. M. (2015). Climatology and environmental characteristics of extreme precipitation events in the southeastern United States. Monthly Weather Review, 143(3), 718-741. https://doi.org/10.1175/MWR-D-14-00065.1

Squitieri, B. J., Wade, A. R., & Jirak, I. L. (2025). On a modified definition of a derecho. Part I: Construction of the definition and quantitative criteria for identifying future derechos over the contiguous United States. Bulletin of the American Meteorological Society, 106(1), E84-E110. https://doi.org/10.1175/BAMS-D-24-0015.1

CW3E Publication Notice: A Case Study of Forecast Uncertainty Prior to a High-Impact Landfalling Atmospheric River in California in January 2021

CW3E Publication Notice

A Case Study of Forecast Uncertainty Prior to a High-Impact Landfalling Atmospheric River in California in January 2021

August 11, 2025

An article titled “A Case Study of Forecast Uncertainty Prior to a High-Impact Landfalling Atmospheric River in California in January 2021,” co-authored by CW3E’s Jason Cordeira, Brian Kawzenuk, Samuel Bartlett, Chad Hecht, Christopher Castellano, Shawn Roj, and F. Martin Ralph, was recently published in the American Meteorological Society’s Weather and Forecasting.

This study analyzes the forecast uncertainty associated with a high-impact landfalling atmospheric river (AR) that struck California between 26–28 January 2021. The AR delivered extreme precipitation, including 300–400 mm of rain and over 250 cm of snow in the Sierra Nevada (Figure 1), and triggered a destructive debris flow near Big Sur on the burn scar of the 2020 Dolan Fire. The event was characterized by a strong and long-duration integrated vapor transport (IVT) maximum that stalled along the Central California coast, creating a challenging precipitation forecast scenario.

Using deterministic and ensemble forecasts from both the GFS and ECMWF models, the authors examined lead times from 1–7 days in advance of landfall. The GFS provided earlier (~2–3 day) signals of landfall (Figure 2), while the ECMWF better captured the subsequent stalling behavior and associated rainfall distribution. Both models exhibited a dry bias, especially over coastal topography. A key source of forecast uncertainty was traced to differences in how models initialized the evolution of synoptic-scale features over the North Pacific, especially the development and interaction of two Rossby wave trains (RWTs) and upstream cyclogenesis involving remnant tropical moisture. Forecasts initialized on 21–22 January differed significantly in how they resolved these features, with ECMWF forecasts after 22 January more accurately capturing downstream impacts (Figure 3).

The results emphasize the importance of accurate upstream observations, such as those from AR Reconnaissance missions, in improving forecast reliability for high-impact West Coast weather events. The study highlights key objectives in CW3E’s 2025-2029 Strategic Plan to better understand the science of ARs and extreme precipitation, including physical processes, forecasting, and impacts.

Figure 1. (Figure 1 from Cordeira et al. 2025) (a) ERA5 integrated water vapor transport (IVT; shaded and vectors; kg m–1 s–1) and mean sea level pressure (contours; hPa) valid at 0300 UTC 28 January 2021, (b) ERA5 integrated water vapor (IWV; shaded; mm) and mean sea level pressure (contours; hPa) valid at 0300 UTC 28 January 2021, (c) NCEP Stage-IV quantitative precipitation estimate (QPE; shaded; mm) valid for the 72-h period ending 1200 UTC 29 January 2021, and (d) NOHRSC snowfall analysis (shaded; cm) valid for the 72-h period ending 1200 UTC 29 January 2021.

Figure 2. (Figure 9 from Cordeira et al. 2025) Left panels illustrate lead-time–coastal latitude “dProg/dt” analysis of the (a) GFS ensemble and (b) ECMWF ensemble forecast probability of IVT magnitude ≥250 kg m–1 s–1 (shading as a fraction) and ensemble mean IVT magnitude (contoured every 100 kg m–1 s–1 starting at 100 kg m–1 s–1) for locations along the West Coast of North America for forecasts all verifying at 0000 UTC 28 January 2021. Right map panels illustrate IVT magnitude shaded yellow at 250 kg m–1 s–1 and orange at 500 kg m–1 s–1 from the corresponding model analyses (0-h control forecast). The analysis is derived from the coastal locations drawn in the right panels; the locations are drawn black for IVT magnitudes <250 kg m–1 s–1 and red for IVT magnitudes ≥250 kg m–1 s–1 at 0000 UTC 28 January 2021. The black box on left panels denotes the 6-to-10-day lead time discussed in the text.

Figure 3. (Figure 12 from Cordeira et al. 2025) Model forecasts of IVT magnitude (kg m–1 s–1; shaded according to scale) and direction (vectors) with sea-level pressure initialized by the deterministic (a) ECMWF and (b) GFS models at 0000 UTC 21 January 2021 (top row in each panel) and at 0000 UTC 22 January 2022 (bottom row in each panel). Model forecasts are valid at 0000 UTC on 24, 25, 26, and 27 January following the columns labeled in each panel. Red and black dashed lines refer to features discussed in the text. For consistency, the cyclone that develops from the remnants of the tropical depression is labeled as “TD” even though it is no longer tropical.

Cordeira, J. M., Kawzenuk, B. K., Bartlett, S. M., Hecht, C., Castellano, C., Roj, S., & Ralph, F. M. (2025). A Case Study of Forecast Uncertainty Prior to a High-Impact Landfalling Atmospheric River in California in January 2021. Weather and Forecasting, 40(8), 1543-1561. https://doi.org/10.1175/WAF-D-24-0088.1

AR Recon Program Named First “Anchor Project” by Global Precipitation EXperiment (GPEX)

AR Recon Program Named First “Anchor Project” by Global Precipitation EXperiment (GPEX)

July 24, 2025

Atmospheric River Reconnaissance (AR Recon), a CW3E-led program in partnership with NOAA (National Centers for Environmental Prediction and Aircraft Operations Center) and the U.S. Air Force, was recently named by the Global Precipitation EXperiment (GPEX) as an anchor project to coordinate global field campaigns. Called for in the 2023 ARROW Act, the bipartisan AR Forecasting Bill, and recognized as a World Weather Research Programme (WWRP) endorsed project, AR Recon serves as a critical program to improve western U.S. operational weather forecasts in direct support of emergency preparedness and resource management.

The Global Precipitation EXperiment (GPEX) is a cross-World Climate Research Programme (WCRP) initiative to improve global precipitation predictions, with an emphasis on storm types such ARs, mesoscale convective systems, monsoons, and tropical cyclones, including in polar and high-mountain regions. High impact events like floods and debris flows are often caused by extreme precipitation and projected to be exacerbated by a warmer climate. Accelerated improvements in precipitation products are essential to support emergency response, water management, and infrastructure planning amid changing precipitation patterns. GPEX provides a unique opportunity to close observational and process-level gaps and advance prediction capabilities for resilient, sustainable development.

To serve as an anchor project supporting GPEX, AR Recon is collaborating with international field campaigns—such as NAWDIC and DOTSTAR—as it expands globally and advances AR science. A demonstration of an expanded version of AR Recon, known as the Global AR Recon Program 2026 Demonstration (GARRP-26 Demo), is planned for January and February 2026, in close coordination with following field campaigns sharing highly relevant scientific objectives:

  • SAFARI linking ocean and atmospheric observations (ONR-led)
  • NAWDIC in the Northeast Atlantic (European-led)
  • NURTURE at high latitudes based in Canada (NASA led)
  • TEPEX Tropical Pacific air-sea interaction (NOAA-led)
  • AR Recon – University Coordinated Radiosonde Program (CW3E-led)

In line with GPEX objectives, these campaigns focus on sampling precipitation across different regions and seasons, evaluating gridded datasets, identifying gaps in the global observing network, advancing kilometer-scale modeling, improving understanding of key precipitation processes through field observations and emerging tools like feature tracking and instrument simulators, and enhancing prediction of both extreme events and shifts in precipitation seasonality.

For more detailed information about the AR Recon program, please see the AR Recon webpage.

CW3E Publication Notice: Global Daily Discharge Estimation Based on Grid Long Short-Term Memory (LSTM) Model and River Routing

CW3E Publication Notice

Global Daily Discharge Estimation Based on Grid Long Short-Term Memory (LSTM) Model and River Routing

July 24, 2025

A paper titled “Global Daily Discharge Estimation Based on Grid Long Short-Term Memory (LSTM) Model and River Routing” was recently published in the AGU’s Water Resources Research. This study was led by Yuan Yang (CW3E), with contributions from a wide network of collaborating institutions. This work supports CW3E’s Advanced Precipitation and Streamflow Prediction priority described in the CW3E 2025-2029 Strategic Plan. This work was sponsored by NASA Energy and Water Cycle Study Program (NEWS) and NOAA Cooperative Institute for Research to Operations In Hydrology (CIROH) project.

Accurate global river discharge estimation is critical for applications in water resources, climate change, natural hazards, biodiversity, and energy production. Recent advances in machine learning, particularly Long Short-Term Memory (LSTM) networks, have shown strong potential in this domain. However, existing LSTM-based approaches typically treat each basin as a hydrological response unit (HRU) and rely on basin-averaged inputs to estimate discharge only at basin outlets. This approach has several limitations: 1) high computational cost when scaling to the global river network, 2) loss of spatial heterogeneity within large basins, and 3) lack of physical consistency across HRUs, such as upstream-downstream mass balance and temporal coherence, due to the absence of explicit river routing.

To address these challenges, we developed a new modeling scheme, Grid LSTM‐RAPID, to estimate discharge for every river reach worldwide. The framework consists of three steps (Figure 1):

  • Step 1, LSTM training over small basins: Train a single LSTM model over selected training basins.
  • Step 2, LSTM application over 0.25° grids: Apply the trained LSTM obtained in Step 1 over global 0.25° grids, using the gridded inputs, to generate global gridded runoff.
  • Step 3, RAPID routing: Implement the RAPID model to calculate the discharge for all reaches globally on the MERIT-Basins river network based on the gridded runoff from Step 2. RAPID uses a vector-matrix version of the Muskingum method and is well parallelized for large-scale applications. RAPID setup details can be found in Yang et al. (2021).

Figure 1. Grid LSTM‐RAPID daily discharge modeling framework. The basins, grids, and river networks depicted are simplified illustrations and do not represent their actual size, shape, or quantity. From Figure 2 in Yang et al. (2025).

Extensive evaluations against daily flow records from about 30,000 gauges globally demonstrate that our global LSTM implementation and supporting data work reasonably well. The model’s generalizability across time and space, including unseen regions and periods, has been thoroughly assessed. Compared to the traditional Basin LSTM, Grid LSTM‐RAPID model shows only a slight reduction in performance, while enabling global, reach-level discharge estimation without heavy computational cost. Despite this tradeoff, Grid LSTM-RAPID significantly outperforms a well‐calibrated process‐based benchmark.

Based on this framework, we developed a global reach‐level daily discharge dataset, named GRADES-hydroDL, covering 2.94 million river reaches globally from 1980 to near present. This dataset reproduces the global discharge every well (Figure 2) and offers valuable support for applications such as flood assessment, water resources management, and ecological studies. To facilitate broader use, we also developed an interactive interface (Figure 3), allowing users of all experience levels to explore any river, view local features and download data such as discharge, watershed boundaries and river networks.

The dataset is openly available at https://www.reachhydro.org/home/records/grades-hydrodl. The interactive Interface is accessible at: https://cw3e.ucsd.edu/hydro/grades_hydrodl.

Figure 2. Skill metrics over non-training basins for GRADES-hydroDL during the period of 1980-2020: (a) Kling-Gupta efficiency (KGE, for overall performance), (b) correlation coefficient (CC, for temporal coherence), (c) relative variability (RV, for bias in variability) and relative bias (RB, for bias in magnitude). From Figure 8 in Yang et al. (2025).

Figure 3. Selected functionalities of GRADES-hydroDL interactive interface: (a) Basic information for a selected river, (b) river networks and terrain visualization for a specific region, (c) interactive time series data of river discharge spanning the current two years and past 45 years, (d) delineation of water boundaries, upstream river network and downstream flow path associated with a selected gauge location.

Yang, Y., Feng, D., Beck, H.E., Hu, W., Ather, A., Sengupta, A., Delle Monache, L., Hartman, R., Lin, P., Shen, C. & Pan, M. (2025). Global Daily Discharge Estimation Based on Grid Long Short-Term Memory (LSTM) Model and River Routing. Water Resources Research, 61(6), e2024WR039764. https://doi.org/10.1029/2024WR039764

Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). Bulletin of the American Meteorological Society, 102(11), E2086-E2105. https://doi.org/10.1175/BAMS-D-20-0057.1

CW3E Hosts Collaborative Outreach and Research Event at Scripps Pier

CW3E Hosts Collaborative Outreach and Research Event at Scripps Pier

June 30, 2025

Figure 1. Bridget McNamara and the drone team demonstrate the instrumentation setup on the drone to the students prior to the flight.

On the 21st of May 2025, CW3E staff successfully hosted a dynamic outreach and research event at the Scripps Pier, bringing together university researchers, private-sector partners, and young students for a day of hands-on learning and scientific collaboration.

Participants included UCSD engineering students Julia Lee, Eric Limonadi, Bridget McNamara, and Cindy Tran, advised by SIO researcher Dr. Jooil Kim; SIO Professor Jennifer Haase and her research group; Windborne Systems engineers Jake Spisak and Nathan Kaplan; and approximately 70 fifth-grade students from Chollas Meed Elementary School. This outreach event was part of CW3E’s ongoing collaboration with Earthlab, a UCSD Community Station led by Groundwork San Diego and supported by the UCSD Center on Global Justice. The program aims to provide students hands-on exposure to the research conducted by CW3E and its partners. Windborne Systems has been a partner in CW3E’s Atmospheric River Reconnaissance Research and Operations Partnership for several years, and this marks the first time that we have conducted simultaneous data collection. CW3E has been a close collaborator with Dr. Haase and her research group, who conducted experimental atmospheric profiling measurements (humidity, temperature, and pressure) using a stationary GPS-based instrumentation at the nearby Scripps Munk Laboratory building to compare to the radiosonde measurements. The collaboration with Dr. Kim and the UCSD senior engineering students developed out of a previous radiosonde launch demonstration conducted for an SIO course, a previous outreach event. The presence of multiple research groups and cutting-edge technologies created a unique opportunity for elementary students to engage with real-world science. By interacting directly with scientists, engineers, and graduate students, the students gained a deeper appreciation for the collaborative nature of atmospheric research and hopefully got introduced to potential STEM career paths.

Figure 2. Ethan Morris, Nathan Kaplan, and Jake Spisak preparing for a dual radiosonde payload launch on the Windborne weather balloon.

In close collaboration with Windborne Systems and Dr. Kim’s team, CW3E executed a full schedule of radiosonde and drone launches to showcase various methods of atmospheric observation techniques (Fig. 1). The event featured four rounds of weather balloon launches: two led by CW3E, one led by Windborne, and the final coordinated launch by both teams (Fig. 2). Several of these launches included dual payloads of Windborne sensors and CW3E Vaisala radiosondes. Simultaneously, Dr. Kim’s team deployed a drone equipped with sensors to measure wind speed and direction to conduct intercomparison studies of near-surface wind measurements, demonstrating the integration of emerging technologies in meteorological research.

The elementary students participated in interactive tours highlighting the various teams’ instruments and related research, including a tour of the stationary weather station on the Scripps Pier. Student groups also enjoyed activities at the tide-pools on the beach adjacent to the Scripps campus. Many of the students were able to participate (hands-on) in the weather balloon launches, while all were able to view the data being transmitted in real-time from these launches.

Figure 3. CW3E and Windborne staff demonstrate CW3E Vaisala radiosonde launch to students.

The elementary students participated in interactive tours highlighting the various teams’ instruments and related research, including a tour of the stationary weather station on the Scripps Pier. Student groups also enjoyed activities at the tide-pools on the beach adjacent to the Scripps campus. Many of the students were able to participate (hands-on) in the weather balloon launches, while all were able to view the data being transmitted in real-time from these launches.

In total, seven Scripps staff participated in this outreach event, including Subin Yoon, Ethan Morris, Samuel Bartlett, Suma Bhanu Battula, and Yazmina Rojas Beltran from CW3E, and Valeria Gutierrez Carrillo and Carlene Burton from Scripps (Fig. 4).

The event displayed the collaborative nature of scientific research, bringing together multiple teams from across disciplines and institutions, all the while engaging the fifth-grade students.

Figure 4. Sam Bartlett, Yazmina Rojas Beltran, Subin Yoon, Nathan Kaplan, Jake Spisak, and Suma Battula photographed after the last launch of the event.

CW3E Publication Notice: Synthetic ensemble forecasts: operations-based evaluation and inter-model comparison for reservoir systems across California

CW3E Publication Notice

Synthetic ensemble forecasts: operations-based evaluation and inter-model comparison for reservoir systems across California

June 18, 2025

A new article, “Synthetic ensemble forecasts: operations-based evaluation and inter-model comparison for reservoir systems across California,” by Zach Brodeur (Cornell University), Will Taylor (University of California – Davis), Jon Herman (University of California – Davis), and Scott Steinschneider (Cornell University), has been published in Water Resources Research, a journal of the American Geophysical Union.

This work represents a key milestone in the ongoing collaboration between Cornell University, the University of California – Davis, CW3E, NOAA/NWS, USACE, and regional water agencies to integrate synthetic forecasting into FIRO. It directly supports the FIRO: Resilient Water Management priority outlined in CW3E’s 2025-2029 Strategic Plan (CW3E, 2025).

Building on Brodeur et al. (2024), this study further develops the emerging field of synthetic ensemble forecasting, a powerful approach that generates realistic ensemble forecasts that mimic the skill of existing hindcasts anywhere that streamflow data are available. This strategy addresses critical data availability and length limitations associated with existing hindcasts, and it has been highlighted as an important technology for improved robustness testing of FIRO control alternatives in recent viability assessments (Ralph et al. 2023, 2025).

The study makes three core contributions:

  1. A new, computationally efficient synthetic ensemble forecasting strategy
  2. A novel reservoir operations-based validation framework to evaluate whether a synthetic forecasting strategy is fit for purpose
  3. A demonstration of the generalizability of the newly developed synthetic forecasting strategy across a range of California reservoir systems with varying hydrologic and operational characteristics

A key innovation in the study is the fitness-for-purpose validation framework, illustrated in Figure 1. In this approach, a FIRO operations model is driven with many samples of synthetic ensemble streamflow forecasts, creating an ensemble of operational outcomes (e.g., reservoir storage and release series). These outcomes are then compared – using ensemble verification techniques – to a baseline trace generated by driving the operations model with the native hindcast. This validation enables a quantitative assessment of the suitability of a synthetic forecasting approach for operations-based risk analysis, focusing on outcomes that matter most to water managers and stakeholders.

Figure 1. The operations-based validation framework forwarded in this work. From Figure 1 in Brodeur et al. (2025).

The new synthetic forecasting strategy, referred to as ‘syn-M2’ in the figures below, significantly improves streamflow forecast verification performance over the earlier method (syn-M1) presented in Brodeur et al. (2024), while significantly reducing computation time (Figure 2). This advancement enables broader application in complex, multisite FIRO scenarios envisioned for Phase III of the FIRO initiative (CW3E, 2025).

Figure 2. Forecast verification comparisons at Oroville Dam between synthetic forecasting methods 1 and 2 (syn-M1, syn-M2). a-b) Cumulative rank histograms, c-d) binned spread-error (BSE) diagrams. From Figure 3 in Brodeur et al. (2025).

Moreover, the operations-based validation framework demonstrates that the new synthetic forecasting method (syn-M2) outperforms the original approach (syn-M1) in terms of reservoir simulation outcomes (Figure 3). For instance, not only does the ensemble of release series based on the newer method better encapsulate the release series based on the hindcasts (Figure 3a,b), but probability-integral-transform (PIT) diagrams (Figure 3c) and reliability diagrams (Figure 3d) both show the new method comes closer to ideal ensemble behavior (indicated by proximity to the 1:1 line). These results highlight that the new synthetic forecasting method exhibits a high degree of reliability when translated to FIRO-based operational outcomes, underscoring its potential for systems-based risk analysis.

Figure 3. Operations-based validation performance between synthetic forecasting methods at Oroville Dam. a-b) Reservoir release series around the 1997 flood, c) probability-integral-transform (PIT) for top 1% of inflows (n ~ 100 events) and reliability statistic (πrel), d) reliability diagram for non-zero releases based on the 99th percentile release threshold (R99) and reliability component of the Brier Score (BSrel). From Figure 4 in Brodeur et al. (2025).

The study shows that forecast-informed reservoir operations based on the new synthetic forecasting method perform well across a diverse set of California reservoirs (Prado Dam, Lake Mendocino, New Hogan Lake, New Bullards Bar, Oroville Dam), highlighting the method’s generalizability. These results underscore not only the value of continued refinement of synthetic ensemble forecasting technology, but also signal that these approaches have matured to a point where they can be integrated into ongoing and future FIRO viability assessments and water control manual updates.

Brodeur, Z. P., Delaney, C. , Whitin, B., & Steinschneider, S. (2024). Synthetic forecast ensembles for evaluating Forecast Informed Reservoir Operations. Water Resources Research, 60(2), e2023WR034898. https://doi.org/10.1029/2023WR034898

Brodeur, Z. P., Taylor, W., Herman, J. D., & Steinschneider, S. (2025). Synthetic ensemble forecasts: Operations‐based evaluation and inter‐model comparison for reservoir systems across California. Water Resources Research, 61(6), e2024WR039324. https://doi.org/10.1029/2024WR039324

CW3E. (2025). 5-year Strategic Plan: 2025-2029, Center for Western Weather and Water Extremes. Center for Western Weather and Water Extremes (CW3E) – Scripps Institute of Oceanography, University of California – San Diego. https://cw3e.ucsd.edu/wp-content/uploads/CW3E_Strategic_Plan_2025.pdf

Ralph, F. M., Hutchinson, A., Anderson, M., Fairbank, T., Forbis, J., Haynes, A., Sweeten, J., Talbot, C., Tyler, J., & White, R. (2023). Prado Dam Forecast Informed Reservoir Operations: Final Viability Assessment. University of California, San Diego. https://cw3e.ucsd.edu/FIRO_docs/Prado/FIRO_Prado_FVA.pdf

Ralph, F. M., James, J., Leahigh, J., White, M., Anderson, M., Talbot, C., Forbis, J., Fromm, J., & Haynes, A. (2025). Yuba-Feather Forecast Informed Reservoir Operations: Final Viability Assessment. University of California, San Diego. https://cw3e.ucsd.edu/FIRO_docs/Yuba-Feather_FVA/Yuba-Feather_FVA.pdf