Author: Chris Castellano

CW3E AR Update: 30 October 2025 Outlook

CW3E AR Update: 30 October 2025 Outlook

October 30, 2025

Click here for a pdf of this information.

Multiple Atmospheric Rivers Forecast to Impact US West Coast Over Next 7 Days

  • A strong atmospheric river (AR) is forecast to make landfall over the Pacific Northwest early Fri 31 Oct and bring heavy precipitation to portions of western Washington.
  • A second, weaker AR is forecast to make landfall over Oregon and far northern California on Mon 3 Nov.
  • A third AR associated with a large closed low is forecast to approach the US West Coast on Tue 4 Nov, potentially bringing another round of heavy precipitation to the Pacific Northwest and northern California.
  • The first AR is expected to produce AR 3/AR 4 conditions (based on the Ralph et al. 2019 AR Scale) over coastal Washington and northern coastal Oregon.
  • There is considerable uncertainty in the timing and intensity of the third AR, with the ECMWF ensemble favoring an earlier AR landfall and higher peak IVT magnitudes compared to GEFS. About 70% of ECMWF members are forecasting an AR 4 or greater in southern coastal Oregon (versus only 35% of GEFS members).
  • The first AR is forecast to produce 3–5 inches of precipitation in the Olympic Peninsula and North Cascades.
  • A marginal risk excessive rainfall outlook (ERO) has been issued for much of western Washington Fri 31 Oct into early Sat 1 Nov due to potential for heavy precipitation from the first AR.
  • High freezing levels and heavy rainfall during the first AR are expected to result in significant increases in streamflow in western Washington, with 7 stream gages currently forecast to exceed action/monitor stage.
  • Both the deterministic ECMWF and EFS ensemble are forecasting higher precipitation totals in western Washington and western Oregon over the next 10 days compared to the deterministic GFS and GEFS ensemble.

Click images to see loops of GFS IVT and IWV forecasts

Valid 0000 UTC 30 October 2025 – 1200 UTC 6 November 2025

Summary provided by C. Castellano; 30 October 2025

To sign up for email alerts when CW3E post new AR updates click here.

*Outlook products are considered experimental

For any unfamiliar terms, please refer to the American Meteorological Society Glossary.

Ribbon-Cutting Ceremony for New Y-BASIN Station at Colorado Mountain College in Steamboat Springs, CO

Ribbon-Cutting Ceremony for New Y-BASIN Station at Colorado Mountain College in Steamboat Springs, CO

October 28, 2025

The Center for Western Weather and Water Extremes (CW3E) and Colorado partners and stakeholders gathered on October 6, 2025 to mark a significant step forward in regional climate and water resilience. The group celebrated the installation of a new hydrometeorological station on the Colorado Mountain College (CMC) Steamboat Springs campus with a ribbon cutting ceremony.

Ribbon-cutting ceremony at the new CMC Y-BASIN station. Pictured (left to right): Andy Rossi (UYWCD), Sonya Macys (Routt County Commissioner), Mike Camblin (CWCB), Michelle Stewart (Western Resilience Center), JC Norling (CMC), Marty Ralph (CW3E), John Lawrence (CMC), Meghan Lukens (CO State Representative), and Amy Moyer (CRD). Photo credit: Scott Kimmey (CMC).

This marks the 7th station in the Yampa Basin Atmosphere and Soil Moisture Integrated Network (Y-BASIN), a collaborative effort between UC San Diego’s CW3E, Western Resilience Center (formerly Yampa Valley Sustainability Council), Colorado Mountain College, Upper Yampa Water Conservancy District, Colorado River District, and the Colorado Water Conservation Board. By next year, 9 stations will be installed toward the goal of having 30 spanning the Yampa River Basin, from the headwaters near the Town of Yampa to Fortification Creek west of Craig.

CW3E staff and a local volunteer working on CMC Y-BASIN station install. Photo credit: Nathan Stewart (CMC).

As climate extremes intensify hydrologic variability, the Y-BASIN network will support water-management decision-making by integrating soil-moisture data with snowmelt and precipitation observations. With a growing period of record and expanding network, this data will help scientists and managers better understand how dry soils influence runoff and water yield from spring snow melt, which will in turn improve decision-making for natural resource management. The network will support adaptive planning and long-term resilience under increasingly variable climate conditions.

Each Y-BASIN station is equipped with six soil moisture and temperature sensors buried at depths of 5, 10, 15, 20, 50, and 100 cm, allowing continuous profiling of subsurface moisture conditions. The stations also include surface meteorological sensors measuring wind speed and direction, atmospheric pressure and temperature, relative humidity, snow depth, and fuel moisture and temperature. The first Y-BASIN station was installed near Stagecoach Reservoir in 2022 with support from Upper Yampa Water Conservancy District. Between 2023-2024, the network added five additional stations, one in the Trout Creek Basin, one in the lower Elk River watershed, one along the Yampa River at Carpenter Ranch near Hayden, and one in the Elkhead Creek drainage. A sixth station, known as Red Creek, was installed south of Steamboat Lake in August 2025. The newest addition of the Y-BASIN station installed at CMC Steamboat Springs Campus.

Jacob Morgan (CW3E) and other attendees at the poster session following the ribbon-cutting ceremony at CMC Steamboat Springs campus. Photo credit: Aimee Kimmey (CMC).

The newest station at CMC is the first to be sited within Steamboat city limits. It fills an important data gap in the local watershed while providing rich educational and technical training opportunities for students. The ribbon-cutting ceremony featured remarks from speakers representing all partner organizations, and state and local governments. Speakers included CMC Vice President and Campus Dean JC Norling; CW3E Director Marty Ralph; Western Resilience Center Executive Director Michelle Stewart; Colorado State Representative Meghan Lukens; Routt County Commissioner Sonja Macys; UYWCD General Manager Andy Rossi; CWCB Board Yampa-White Basin Representative Mike Camblin; CRD Chief of Strategy Amy Moyer, and CMC Dean of the School of Natural and Applied Sciences John Lawrence. Following the formal program and ribbon-cutting, attendees had the opportunity to tour the station, engage with the representatives from partner organizations, and learn more about the data collection and analysis through a poster session. Additional coverage of the event and speakers was provided by Steamboat Radio and the Steamboat Pilot.

The Center looks forward to continuing its collaboration with Colorado partners to expand observational capacity, advance scientific understanding, and support data-informed water management across the state.

Attendees gather for the ribbon-cutting ceremony for the new CMC Y-BASIN station. Photo credit: Scott Kimmey (CMC).

CW3E AR Update: 20 October 2025 Outlook

CW3E AR Update: 20 October 2025 Outlook

October 20, 2025

Click here for a pdf of this information.

Multiple Atmospheric Rivers Forecast to Impact US West Coast Later This Week into This Weekend

  • A strong atmospheric river (AR) is forecast to make landfall over the Pacific Northwest late Thu 23 Oct and move southward into northern California on Fri 24 Oct.
  • A second, weaker AR is forecast to make landfall over southern Oregon and northern California this weekend, but there is considerable uncertainty in its timing and strength. The ECMWF model is favoring an earlier AR landfall and stronger IVT magnitudes compared to the GFS model.
  • The unsettled weather pattern may continue through the end of October, with additional landfalling AR activity possible over the region next week.
  • About 80% of GEFS and ECMWF ensemble members are forecasting at least an AR 3 (based on the Ralph et al. 2019 AR Scale) over northern coastal Oregon in association with the first AR.
  • The NWS Weather Prediction Center (WPC) is forecasting at least 2–6 inches of precipitation in much of western Washington, western Oregon, and northern California during the next 7 days, with higher amounts expected in the Olympic Peninsula and North Cascades.
  • The WPC has issued a marginal risk excessive rainfall outlook (ERO) for coastal Washington, Oregon, and northern California, and the Washington Cascades due to the potential for heavy precipitation from the first AR.
  • Most of the precipitation during the first AR is expected to fall as rain, but lower freezing levels during the second AR may support significant snowfall accumulations in the higher terrain of the Oregon Cascades.
  • Model differences in the forecasts of the second AR are driving differences in forecast precipitation over the next 10 days. Overall, the ECMWF ensemble is forecasting higher precipitation totals in western Washington, western Oregon, and northern California compared to the GEFS ensemble.

Click images to see loops of GFS IVT and IWV forecasts

Valid 0000 UTC 20 October 2025 – 1200 UTC 27 October 2025

Summary provided by C. Castellano, S. Bartlett, J. Kalansky, S. Roj, and M. Steen; 20 October 2025

To sign up for email alerts when CW3E post new AR updates click here.

*Outlook products are considered experimental

For any unfamiliar terms, please refer to the American Meteorological Society Glossary.

CW3E Publication Notice: Observing atmospheric rivers using multi-GNSS airborne radio occultation: system description and data evaluation

CW3E Publication Notice

Observing atmospheric rivers using multi-GNSS airborne radio occultation: system description and data evaluation

October 1, 2025

Airborne radio occultation (ARO) allows research aircraft to act as storm-targeted vertical profilers of atmospheric rivers, providing more than three times the number of atmospheric profiles. A recent paper, “Observing atmospheric rivers using multi-GNSS airborne radio occultation: system description and data evaluation”, published in Atmospheric Measurement Techniques, was led by Bing Cao (IGPP/SIO) and Jennifer Haase (SIO/CW3E), with contributions from Michael Murphy (SIO/NASA Goddard/UMBC) and Anna Wilson (CW3E). The study presents a four-year ARO dataset (2018–2021) from Atmospheric River Reconnaissance (AR Recon) missions, spanning the central Pacific, where atmospheric rivers gather tropical moisture, and the eastern Pacific, where they make landfall and affect the U.S. West Coast. This work supports the Atmospheric Rivers and Extreme Precipitation Research, Prediction, and Applications, and Novel Observations priorities in CW3E’s 2025-2029 Strategic Plan by advancing our capability to observe critical parameters in the atmosphere and use these observations in operational forecasts. The results show that ARO has become a mature and reliable technology, providing dense, storm-targeted observations that strengthen our ability to understand and forecast atmospheric rivers

In total, 1,734 multi-GNSS occultation profiles were collected over ~266 flight hours, typically 5–7 profiles per hour or 35-50 profiles per flight, with ~80% of profiles inside the ±3 h data-assimilation window around 00:00 UTC. The ARO retrieval combines GPS/GNSS precise positioning, carrier-phase residuals, and a tailored partial-bending/Abel inversion to produce profiles of bending angle, refractivity, and dry-air pressure/temperature suitable for research and NWP. Sampling resolves ~400 m vertically between 5–10 km altitude range. The horizontal along-path integration is typically ~200–300 km centered on the tangent points. Because tangent points drift, profiles extend sampling well beyond the flight track—typically ~400–600 km, and up to ~700 km in deep cases—capturing cross-AR structure that complements dropsondes. Figure 1 illustrates a representative data distribution of one AR Recon flight: the clockwise flight track, dropsonde release points, and the projected tangent-point paths for setting (solid) and rising (dashed) occultations are overlaid on IVT magnitude and vectors; symbols denote the lowest-point altitude (<3 km, 3–6 km, >6 km), short line segments highlight the ray-path portion contributing 50% of excess phase.

A detailed evaluation of ARO data confirms its accuracy for use in numerical weather prediction models. When compared with ERA5 reanalysis, refractivity differences are small—biases are less than 0.5% with standard deviations under 1.5% above 4 km (reaching a minimum of about 1% near 8 km), increasing to around 2.8% below 4 km. Relative to dropsondes and ERA5, the data show slightly positive biases at higher altitudes and near –2% close to the surface, consistent with known super-refraction effects. These statistics, summarized in Figure 2, include results for all profiles as well as broken down by GPS, GLONASS, and Galileo constellations. To maximize the value of ARO, a specialized assimilation operator has been developed that uses the more primitive refractive bending measurements while accounting for its unique asymmetric geometry (Hordyniec et al. 2025). Multiple assimilation experiments using this operator have demonstrated positive impacts on atmospheric analyses and short-range forecasts (Do et al. 2025; Haase et al. 2021). The full dataset, including maps and CDAAC-style atmPrf files, is openly available through the UCSD Library and the project’s website.

Figure 1. (Figure 5 from Cao et al. 2025). Overview of IOP04 (4 Feb 2020): flight track (thin black line), dropsonde releases (black circles), and setting/rising ARO tangent-point paths (solid/dashed blue lines) over IVT, with symbols marking lowest-altitude class and short segments indicating the ray-path portion contributing 50% of excess phase.

Figure 2. (Figure 12 from Cao et al. 2025). Summary of ARO–ERA5 refractivity differences—mean and standard deviation versus height (all profiles and by constellation)—showing <0.5% bias and <1.5% SD above 4 km and broadly consistent performance across GPS, GLONASS, and Galileo.

Cao, B., Haase, J. S., Murphy Jr., M. J., & Wilson, A. M. (2025). Observing atmospheric rivers using multi-GNSS airborne radio occultation: system description and data evaluation. Atmospheric Measurement Techniques, 18(14), 3361–3392. https://doi.org/10.5194/amt-18-3361-2025

Hordyniec, P., Haase, J. S., Murphy, M. J., Jr., Cao, B., Wilson, A. M., & Banos, I. H. (2025). Forward modeling of bending angles with a two-dimensional operator for GNSS airborne radio occultations in atmospheric rivers. Journal of Advances in Modeling Earth Systems, 17(4), e2024MS004324. https://doi.org/10.1029/2024MS004324

Do, P.-N., Haase, J. S., Baños, I. H., Hordyniec, P., & Cao, B. (2025). Impact of airborne radio occultation observations on short term precipitation forecasts of an atmospheric river. Geophysical Research Letters, 52(13), e2025GL115639. https://doi.org/10.1029/2025GL115639

Haase, J. S., Murphy, M. J., Cao, B., Ralph, F. M., Zheng, M., & Delle Monache, L. (2021). Multi-GNSS airborne radio occultation observations as a complement to dropsondes in atmospheric river reconnaissance. Journal of Geophysical Research: Atmospheres, 126(21), e2021JD034865. https://doi.org/10.1029/2021JD034865

CW3E Publication Notice: Improving Weeks 1-2 Temperature Forecasts in the Sierra Nevada Region Using Analog Ensemble Post-Processing with Implications for Better Prediction of Snowmelt, Water Storage, and Streamflow

CW3E Publication Notice

Improving Weeks 1-2 Temperature Forecasts in the Sierra Nevada Region Using Analog Ensemble Post-Processing with Implications for Better Prediction of Snowmelt, Water Storage, and Streamflow

October 1, 2025

A new study demonstrates that the Analog Ensemble (AnEn) post-processing method substantially improves Week-1 and Week-2 temperature forecasts at high spatial resolution (4 km) over California’s Sierra Nevada during the spring snowmelt season (April–July). This research is detailed in the paper titled “Improving Weeks 1-2 Temperature Forecasts in the Sierra Nevada Region Using Analog Ensemble Post-Processing with Implications for Better Prediction of Snowmelt, Water Storage, and Streamflow”, recently published in the Journal of Hydrometeorology. The study was conducted by Zhiqi Yang (CW3E), Weiming Hu (UGA), Agniv Sengupta (CW3E), Luca Delle Monache (CW3E), Michael J. DeFlorio (CW3E), Mohammadvaghef Ghazvinian (Lynker), Mu Xiao (CW3E), Ming Pan (CW3E), Jacob Kollen (CA DWR), Andrew Reising (CA DWR), Angelique Fabbiani-Leon (CA DWR), David Rizzardo (CA DWR), and Julie Kalansky (CW3E). This research was supported by the California Department of Water Resources (CA DWR) Atmospheric River Program. This work supports the Advanced Precipitation and Streamflow Prediction priority in CW3E’s 2025-2029 Strategic Plan by advancing our understanding of potential postprocessing methods that could be implemented in near real-time over California to improve the skill of subseasonal temperature prediction in the vicinity of B120 watersheds.

California relies on Sierra Nevada spring snowmelt for 60% of its water, serving 23 million people. Forecasting this snowmelt is vital for water supply planning and is a key task of the California Department of Water Resources’ Bulletin-120 (Cuthbertson et al. 2014). Accurate predictions rely on subseasonal 2-m temperature (T2m) forecasts, especially at high elevations where snowpack and runoff contributions are greatest. Current systems like the California Nevada River Forecast Center’s (CNRFC) Hydrologic Ensemble Forecast Service (HEFS) have identified T2m forecasts as a key uncertainty source (https://www.cnrfc.noaa.gov/documentation/hefsAtCnrfc.pdf). Replacing the current T2m product with a higher-accuracy dataset offers a straightforward and effective pathway for enhancing snowmelt and flood risk predictions. Collaborating with CA DWR to facilitate the integration of such advancements into regional applications could provide significant value and offer a foundation for real-time operational forecasting.

This study uses the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset as ground truth and National Oceanic and Atmospheric Administration (NOAA) Global Ensemble Forecast System (GEFS) reforecasts to apply AnEn post-processing (Delle Monache et al. 2013), producing high-resolution (4-km) daily T2m forecasts for the Sierra Nevada. Since spring snowmelt and its streamflow contribution are elevation-dependent (Hunsaker et al. 2012; Musselman et al. 2017), we also evaluate AnEn performance across elevation bands from 0 to 3500 m in 500 m increments.

We find that during the spring snowmelt season (April–July), AnEn post-processing significantly improves T2m forecasts by reducing RMSE by up to 1°C (60% at 1-day leads and 20% at 15-day leads), lowering CRPS by 1.1°C across all lead times, increasing correlation by 11%, and extending predictive skill by up to one week beyond the dynamical benchmark GEFS (Figure 1). It also demonstrates clear advantages over a basic bias correction method (the Ensemble Model Output Statistics, EMOS), particularly during week 1, where EMOS performance is limited by growing spatial noise and is less spatially consistent. Moreover, The AnEn method demonstrates a substantial improvement in skill across all elevation bands, particularly at higher elevations (e.g., 3000–3500 m), where it yields even more notable enhancements (Figure 2). Specifically, RMSE decreases by approximately 4°C, CRPS reduces by approximately 4.5°C, the correlation increases from 0.1 to 0.9 over 3000–3500 m, and the skill period extends by two weeks. While EMOS also improves RMSE and CRPS, its performance is consistently weaker than AnEn’s, particularly during the first week. Since a 5°C shift can significantly influence snowmelt timing, by correcting the bias in temperature forecasts, AnEn could improve the accuracy of snowmelt timing predictions by aligning them more closely with observed snowmelt dates, which could potentially enhance the reliability of water resource management models that rely on snowmelt timing for streamflow and water storage predictions. Furthermore, from each station’s perspective, the AnEn method increases skill across much of the Sierra Nevada watershed domain, showing significantly reduced RMSE and CRPS, more than 2°C, in most stations (Figure 3).

Figure 1. (Figure 2 from Yang et al. 2025). Spatial correlations, RMSE (unit: °C), and CRPS (unit: °C) of GEFS ensemble mean (black lines), EMOS (orange lines), and AnEn (blue lines) forecasts compared to observation (PRISM) for 1-day to 15-day lead across the entire domain.

Figure 2. (Figure 3 from Yang et al. 2025). Spatial correlations (left column), RMSE (unit: °C, middle column), and CRPS (unit: °C, right column) of GEFS ensemble mean (black lines), EMOS (orange lines), and AnEn (blue lines) forecasts compared to observation (PRISM) for 1-day to 15-day lead, evaluated across different elevation ranges (0–500 m, 1000–1500 m, 2000–2500 m, and 3000–3500 m).

Figure 3. (Figure 6 from Yang et al. 2025). Temporal correlations, RMSE (unit: °C), and CRPS (unit: °C) of AnEn forecasts minus temporal correlations, RMSE (unit: °C), and CRPS (unit: °C) of GEFS in each station at 1-day to 5-day lead (left column), 6-day to 10-day lead (middle column), and 11-day to 15-day lead (right column).

This study presents a simple yet effective post-processing approach that improves daily weather-to-subseasonal forecasts, outperforming basic bias correction and extending skill beyond traditional weather timescales, an underexplored area, particularly at regional scales with fine spatial resolution. The approach provides a practical pathway for post-processing other key variables, such as precipitation, snow depth, and snow water equivalent, across broader regions and establishes a framework for extending AnEn to longer lead times (e.g., 14–35 days). Moreover, our findings contribute to advancing subseasonal-to-seasonal hydrological forecasts in the Sierra Nevada, aligning with California’s water management priorities and fostering the development of high-resolution dynamic models.

Cuthbertson, A., Lynn, E., Anderson, M., & Redmond, K. (2014). Estimating historical California precipitation phase trends using gridded precipitation, precipitation phase, and elevation data. California Department of Water Resources, Sacramento, California.

Delle Monache, L., Eckel, F. A., Rife, D. L., Nagarajan, B., & Searight, K. (2013). Probabilistic weather prediction with an analog ensemble. Monthly Weather Review, 141(10), 3498-3516. https://doi.org/10.1175/MWR-D-12-00281.1

Hunsaker, C. T., Whitaker, T. W., & Bales, R. C. (2012). Snowmelt runoff and water yield along elevation and temperature gradients in California’s Southern Sierra Nevada. JAWRA Journal of the American Water Resources Association, 48(4), 667-678. https://doi.org/10.1111/j.1752-1688.2012.00641.x

Musselman, K. N., Molotch, N. P., & Margulis, S. A. (2017). Snowmelt response to simulated warming across a large elevation gradient, southern Sierra Nevada, California. The Cryosphere, 11(6), 2847-2866. https://doi.org/10.5194/tc-11-2847-2017

Yang, Z., Hu, W., Sengupta, A., Monache, L. D., DeFlorio, M. J., Ghazvinian, M., Xiao, M., Pan, M., Kollen, J., Reising, A., Fabbiani-Leon, A., Rizzardo, D., & Kalansky, J. (2025). Improving Weeks 1-2 Temperature Forecasts in the Sierra Nevada Region Using Analog Ensemble Post-Processing with Implications for Better Prediction of Snowmelt, Water Storage, and Streamflow. Journal of Hydrometeorology (published online ahead of print 2025). https://doi.org/10.1175/JHM-D-25-0012.1

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