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