Author: Sam Bartlett

CW3E Publication Notice: Heresy in ENSO teleconnections: Atmospheric Rivers as disruptors of canonical seasonal precipitation anomalies in the Southwestern US

CW3E Publication Notice

Heresy in ENSO teleconnections: Atmospheric Rivers as disruptors of canonical seasonal precipitation anomalies in the Southwestern US

January 31, 2025

This research was recently highlighted by the UC San Diego Today campus-wide newsletter in an article titled Atmospheric Rivers Explain Atypical El Niño and La Niña Years

The paper “Atmospheric Rivers as disruptors of canonical seasonal precipitation anomalies in the Southwestern US” has been recently accepted for publication in Climate Dynamics. The study was conducted by Rosy Luna-Niño (CW3E), Alexander Gershunov (CW3E), F. Martin Ralph (CW3E), Alexander Weyant (UCSD), Kristen Guirguis (CW3E), Michael J. DeFlorio (CW3E), Daniel R. Cayan (CW3E), and Park Williams (UCLA). This research was supported by the California Department of Water Resources Atmospheric River Program Phase III.

The motivation of this paper was the unexpected wetness observed in Southern California (SoCal) and, overall, the Southwestern US during the water year 2023 (WY2023: October 2022- April 2023). Considering the westwide domain, WY2023 precipitation anomalies were opposite to those expected and predicted in the majority of seasonal precipitation prediction systems based on the concurrent La Niña conditions (e.g. DeFlorio et al. 2024). We identified other WYs that didn’t behave as expected with respect to ENSO conditions and term them “heretical”, i.e. behave in a way opposite to ENSO canon. Two types of heretical WYs were defined (Figure 1a): unexpectedly wet or heretical La Niña years (e.g 2011, 2017, and 2023) and unexpectedly dry or heretical El Niño years (e.g. 1964, 1977, 1987, 2007, 2013, and 2015).

One of the main results from this research is that ARs were key wildcards, or agents of heresy, in producing the opposite precipitation anomalies during the heretical WYs: heretical La Niña/El Niño WYs were characterized by anomalously robust/deficient AR activity (Figure 1b). Three out of the five lowest-ranking seasons, with respect to AR landfall days, occurred during heretical El Niño years (WYs 1964, 1977, 1987). While, the record high frequency of AR days occurred during the weak La Niña WY2017 — one of the wettest years on record for California (Gershunov et al. 2017). In SoCal, precipitation totals in heretical Las Niña WYs were comparable to those of canonical El Niño WYs 1998 and 1983. In Northern California, the wettest heretical year was the weak La Niña 2017, with total precipitation comparable to El Niño WY1998, surpassed only by El Niño WY1983. These results emphasize the critical role of AR activity in shaping ENSO-related precipitation anomalies in the Southwestern US. The anomalous bounty or paucity of ARs, their intensity and orientation (Guirguis et al. 2019), can either amplify the expected anomalies based on ENSO conditions or negate them, disrupting seasonal predictions to the extent of generating entirely unexpected — heretical — precipitation anomalies.

Figure 1. a) Total precipitation (October-April; mm) and b) Number of days of landfalling ARs in Southern California. Solid horizontal lines in a) and b) indicate mean annual precipitation and average landfalling AR days, respectively; dashed lines in a) show thresholds based on the standard deviation (σ). Circles in b) show the October SST anomalies in the Niño3.4 region as indicator of ENSO conditions at the beginning of the WY. Green and brown colors highlight unexpected wet and dry water years, respectively, based on ENSO: unexpectedly wet for La Niña and unexpectedly dry for El Niño.

To understand the implication of ARs and their precipitation in seasonal prediction, we reexamined the relationship between ENSO and seasonal precipitation — separating precipitation into non-AR and AR components. In concordance with previous studies (e.g., Dettinger et al. 2011; DeFlorio et al. 2013), we found that the correlation between ENSO and total seasonal precipitation is positive and significant mostly in the southwestern US (Figure 2a). The correlation of ENSO with non-AR precipitation looks very similar to that with total precipitation (Figure 2b). On the other hand, the overall correlation pattern for AR precipitation is weaker (Figure 2c), with the highest correlations obtained over southern Arizona, and New Mexico (Sonoran) desert. The Desert Southwest may be the region of the Western US where AR landfalling activity produces a precipitation signal synchronized with contemporaneous ENSO activity, especially during late winter (January-March) and early spring (February-April). This topic needs further investigation, focusing on the landfalling ARs in Baja California and their inland penetration into the southwestern desert.

Figure 2. Spearman correlation between Oceanic Niño Index and seasonal a) total precipitation, b) nonAR precipitation, and c) AR precipitation during the WYs 1952-2023. Shaded colors indicate significant correlations at 95% confidence level. Black contour limits areas of positive and negative correlation.

Our results show that the relationship between ENSO and precipitation from ARs is complex and affects seasonal predictability in the Western US. While ENSO remains the main source of seasonal predictability, AR landfalls can disrupt expected ENSO precipitation patterns such as during the heretical wet La Niña years of 2011, 2017, and 2023. This nuance is now expressed as a disclaimer in CW3E CCA-forecast based on Pacific SST (https://cw3e.ucsd.edu/s_and_s_forecasts/), noting that CCA predicts mostly seasonal non-Atmospheric River precipitation.

DeFlorio, M. J., D. W. Pierce, D. R. Cayan, and A. J. Miller. 2013., Western U.S. extreme precipitation events and their relation to ENSO and PDO in CCSM4. Journal of Climate, 26, 4231-4243. doi:10.1175/JCLI-D-12-00257.1. https://doi.org/10.1175/JCLI-D-12-00257.1

DeFlorio, MJ., Sengupta, A., Castellano, CM., Wang, J., Zhang, Z., Gershunov, A., Guirguis, K., Luna-Niño, R., Clemesha, RES., Pan, M., Xiao, M., Kawzenuk, B., Gibson, PB., Scheftic, W., Broxton, PD., Cornuelle, BD., Miller AJ., Kalansky, J., Delle Monache, L., Ralph, FM., Waliser, DE., Robertson, AW., Zeng, X., DeWitt, DG., Jones, J., Anderson, ML. 2024. From California’s extreme drought to major flooding: Evaluating and synthesizing experimental seasonal and subseasonal forecasts of landfalling atmospheric rivers and extreme precipitation during Winter 2022 – 2023. Bulletin of the American Meteorological Society. 105(1), E84-E104 https://doi.org/10.1175/BAMS-D-22-0208.1

Dettinger, M. D., Ralph, F. M., Das, T., Neiman, P. J., and Cayan, D. R. 2011. Atmospheric rivers, floods and the water resources of California. Water, 3(2), 445-478. https://doi.org/10.3390/w3020445

Gershunov, A., Shulgina, T., Ralph, F. M., Lavers, D. A., and Rutz, J. J. 2017. Assessing the climate‐scale variability of atmospheric rivers affecting western North America. Geophysical Research Letters, 44(15), 7900-7908. https://doi.org/10.1002/2017GL074175

Guirguis, K., Gershunov, A., Shulgina, T., Clemesha, R. E., and Ralph, F. M. 2019. Atmospheric rivers impacting Northern California and their modulation by a variable climate. Climate Dynamics, 52, 6569-6583. https://doi.org/10.1007/s00382-018-4532-5

Luna-Niño, R., Gershunov, A., Ralph, F. M., Weyant, A., Guirguis, K., DeFlorio, M. J., Cayan, D. R., and Williams, A. P. 2024. Heresy in ENSO teleconnections: Atmospheric Rivers as disruptors of canonical seasonal precipitation anomalies in the Southwestern US. Preprint Available: https://doi.org/10.21203/rs.3.rs-4583843/v1

CW3E Field Team Installs Telemetered Streamgages

CW3E Field Team Installs Telemetered Streamgages

December 30, 2024

Throughout 2023-2024, the CW3E Field Team installed telemetry at eight existing streamgages and two new streamgages in the Russian River and Yuba River watersheds.

In the Russian River watershed, the six upgraded sites are White Creek (WIC), Perry Creek (PEC), Mill Creek (MCR), Cold Creek (CDC), Boyes Creek (BYS), and Mewhinney Creek (MEW) near Lake Mendocino. The two newly installed sites are Galloway Creek (GAC) and Dry Creek (DAC), which both flow into Lake Sonoma.

In the Yuba River watershed, the two upgraded sites are Upper Dry Creek (UDC) and Little Dry Creek (LDM) on the Dry Creek tributary of the Yuba River.

Real-time stage and discharge data is now available on CDEC’s website for WIC, PEC, MCR, CDC, and stage data for BYS, MEW, GAC, DAC, UDC, and LDM. Discharge data for BYS, MEW, GAC, DAC, UDC, and LDM will be available once the rating curves to calculate discharge are developed for these sites.

The stage and discharge data supports the Russian River Forecast Informed Reservoir Operations (FIRO) project and the Yuba-Feather FIRO project, which research ways to improve the reservoir operations at Lake Mendocino, New Bullards Bar Reservoir, and Lake Oroville by updating the water control manuals to reflect improvements in weather and streamflow forecasts. A list of all of CW3E’s sites on CDEC with links to data, both streamgages and weather stations, can be found on CDEC’s website.

Fig. 1: Left: A map showing the locations of the telemetered streamgages at six sites flowing into Lake Mendocino and two sites flowing into Lake Sonoma in the Russian River Watershed . Right: A map showing the locations of the telemetered streamgages at two sites on the Dry Creek tributary of the Yuba River.

Fig. 2: (left) Garrett McGurk installs the new water level sensor at Cold Creek. (middle left) Ethan Morris installs a staff gauge at Dry Creek. (middle right) Sarah Burnett installs the new water level sensor at Little Dry Creek. (right) Adolfo Lopez-Miranda and Ava Cooper prepare to route the water level sensor cable at Upper Dry Creek.

CW3E AR Update: 23 December 2024 Outlook

CW3E AR Update: 23 December 2024 Outlook

December 23, 2024

Click here for a pdf of this information.

Unsettled Weather Pattern to Continue Over US West Coast Through This Weekend

  • A series of atmospheric rivers (ARs) will continue to propagate across the North Pacific and impact the Pacific Northwest and Northern California over the next 7 days.
  • The first AR is forecast to make landfall early late today and bring AR 2/AR 3 conditions (based on the Ralph et al. 2019 AR Scale) to coastal Southern Oregon.
  • The second and third ARs are forecast to make landfall on Wed 25 Dec and Thu 26 Dec.
  • The fourth and potentially most impactful AR is forecast to make landfall on Fri 27 Dec.
  • As the fourth AR moves onshore, a mesoscale frontal wave may prolong the AR duration and bring a second pulse of stronger moisture transport to the region. There is considerable uncertainty in the evolution of this AR.
  • The NWS Weather Prediction Center is forecasting 7–15 inches of total precipitation over portions of western Washington, western Oregon, and Northern California during the next 7 days.
  • Model differences in the forecast evolution of these ARs is driving differences in forecast precipitation. Overall, EPS is forecasting much higher precipitation totals across western Oregon and Northern California during the next 10 days compared to GEFS.
  • A marginal risk excessive rainfall outlook (ERO) has been issued for the Southern Oregon and Northern California Coast Ranges, as well as the Northern Sierra Nevada foothills today into early Wed 25 Dec.
  • Marginal risk EROs have also been issued for coastal Washington, Oregon, and Northern California for Wed 25 Dec into early Sat 28.
  • Numerous stream gages in western Washington, western Oregon, and Northern California are forecast to rise above action/bankfull stage over the next 10 days, with the greatest potential for flooding in southwestern Oregon.

Click images to see loops of GFS IVT and IWV forecasts

Valid 0000 UTC 23 December 2024 – 0600 UTC 30 December 2024

Summary provided by C. Castellano, S. Bartlett, and M. Steen; 23 December 2024

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.

AR Recon WY2025 Operations Update

AR Recon WY2025 Operations Update

December 20, 2024

A pallet of three weather buoys parachutes to the Pacific Ocean after being released by the Air Force Reserve’s 53rd Weather Reconnaissance Squadron Hurricane Hunters Dec. 15, 2021. Credit: (U.S. Air Force courtesy photo)

In support of AR Recon, CW3E, in collaboration with the NOAA-funded Scripps Institution of Oceanography’s Global Drifter Program continues to enhance our understanding of atmospheric pressure at the ocean’s surface. This year, satellite-tracked drifters, equipped with barometers, are again being deployed in the North Pacific during the AR Recon season. We utilize two types of drifting buoys: Directional Wave Spectra-Barometer (DWS-B) and Surface Velocity Program – Barometer (SVP-B). The DWS-B buoys, which are un-drogued, provide valuable information on wave spectra, while the drogued SVP-B buoys measure near-surface ocean currents at a depth of 15 meters. Both types deliver critical data on sea-surface temperature and sea-level pressure, essential for numerical weather predictions and climate studies in this data-sparse region. These buoys are meant to increase sampling density in regions in the North Pacific that experience the highest number of Atmospheric River (AR) days annually and are frequently associated with high pressure gradients.

Already this year, the USAF 53rd WRS has conducted two drifter deployment flights, the first on December 7th and the second on December 13th, releasing a total of 30 new drifters. These new buoys join 345 drifters deployed as part of AR Recon since its founding. Additionally, during the second buoy deployment mission, the 53rd WRS was also able to deploy 14 dropsondes over an AR event that brought significant impacts to the Bay Area on Saturday December 14th.

These AR Recon observations and buoy deployments have been shown to improve forecasts, and targeted deployments are coordinated annually in collaboration between CW3E, NOAA, and the US Air Force. More information on AR Recon, our data collection efforts this season, as well as our plans for the rest of 2025 can be found in this recent press release.

(Deployments marked by cyan symbols at approximately 40N and 140W-160W): Drifting buoy locations in the northeast Pacific, with those released during AR Recon 2023 shown in purple. SLP: Sea Level Pressure. SVP-B: Surface Velocity Program – Barometer. DWS – B: Directional Wave Spectra Barometer. Graphic courtesy of B. Kawzenuk, CW3E.

CW3E Director of Research Presents at ADIA Lab Symposium

CW3E Director of Research Presents at ADIA Lab Symposium

December 10, 2024

Luca Delle Monache, CW3E Director of Research, presenting at the ADIA Lab Symposium on Abu Dhabi on 20 November 2024. (link to video recording)

CW3E Director of Research Luca Delle Monache recently attended the second edition of the Abu Dhabi Investment Authority (ADIA) Lab Symposium as an invited speaker. The symposium was held in Abu Dhabi, United Arab Emirates, from November 19 to 21, 2024.

The symposium speakers’ lineup included Nobel Prize winners and leading figures from academia, industry, and government, who discussed the recent developments in data science and artificial intelligence. This year’s Symposium’s critical themes included sustainability, innovation, and trustworthy AI.

During his presentation, titled “Deep Learning and Data-Driven Models for the Prediction of Extreme Weather Events”, Dr. Delle Monache provided an overview of Atmospheric Rivers (ARs) and the associated impacts, a description of the Forecast Informed Reservoir Operations Program, and CW3E’s strategy to improve our ability to predict extreme events associated with ARs based on a tailored physics-based model (West-WRF), a 200-member dynamical ensemble, machine learning postprocessing of the ensemble, and the recently developed artificial intelligence (AI) data-driven models.

The talk was well received with questions on the specific choice of the weather neural operator, how the new AI data-driven models may impact geoengineering, and what the impact of climate change may be on AR intensity and frequency.

CW3E Publication Notice: Seasonality and climate modes influence the temporal clustering of unique atmospheric rivers in the Western U.S.

CW3E Publication Notice

Seasonality and climate modes influence the temporal clustering of unique atmospheric rivers in the Western U.S.

November 27, 2024

A recent study has provided new insights into the temporal clustering of atmospheric rivers (ARs) in the Western United States, highlighting the significant role of climate modes and seasonality in shaping these impactful weather phenomena–ARs clustering and orientation. This research is detailed in the paper titled “Seasonality and Climate Modes Influence the Temporal Clustering of Unique Atmospheric Rivers in the Western U.S.”, recently published in Nature Communications Earth & Environment. The study was conducted by Zhiqi Yang (CW3E), Michael J. DeFlorio (CW3E), Agniv Sengupta (CW3E), Jiabao Wang (CW3E), Christopher M. Castellano (CW3E), Alexander Gershunov (CW3E), Kristen Guirguis (CW3E), Emily Slinskey (CW3E), Bin Guan (UCLA/JPL), Luca Delle Monache (CW3E), and F. Martin Ralph (CW3E). This research was supported by the California Department of Water Resources (CA DWR) Atmospheric River Program and aligns with CW3E’S 2019-2024 Strategic Plan by seeking to improve subseasonal and seasonal predictability of extreme hydroclimate variables over the western U.S. region.

The study was motivated by the occurrence of nine consecutive atmospheric rivers within a mere three-week span in California during winter 2022/2023 (DeFlorio et al. 2024), as well as the significant economic and hydrological impacts of AR temporal clustering, which can triple damages compared to isolated events (Bowers et al. 2024). Temporal clustering, coupled with the orientation of ARs, modulates their interaction with terrain and influences precipitation and flooding. Despite its importance, the drivers behind AR clustering remain unclear, as well as differences in location and magnitude of clustering between early-winter and late-winter.

In this new article, the authors identified unique ARs over the North Pacific and Western U.S. and utilized Cox regression and composite analyses to examine the influence of six major climate modes—the Arctic Oscillation (AO), Quasi-Biennial Oscillation (QBO), El Niño–Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), Pacific Decadal Oscillation (PDO), and Pacific-North American pattern (PNA) on temporal clustering and AR orientation during the extended boreal winter (November–March). This study also characterizes differences in location and magnitude of AR clustering in early and late winter periods.

The results reveal that climate modes significantly condition the temporal clustering of ARs. The PNA pattern strongly influences clustering from early to late winter, while the AO dominates early winter clustering. The QBO and PDO emerge as key modulators of late winter clustering (Figure 1). Furthermore, ENSO has a profound effect on AR orientation, particularly influencing the orientation of temporally clustered ARs (Figure 2).

The discovery of relationships between factors with high subseasonal predictability and AR clustering/orientation would lay a foundation to evaluate the subseasonal (2–6 week lead) and seasonal (3–6 month lead) predictability of AR sequences occurring within a short time period. Water resource managers and other applied end users across the Western U.S. stand to reap numerous benefits, such as minimization of flood risks and enhanced planning and decision-making, from improved predictability of hydroclimate phenomena at these extended lead times.

Figure 1. Cox regression coefficients showing climate modes modulations on temporal clustering of unique ARs. Cox regression coefficients between AO, QBO, ENSO, MJO-RMM1, MJO-RMM2, PDO, PNA (removed ENSO signal), and the occurrence of unique ARs based on the ERA-5 AR dataset from 1940/1941 to 2017/2018 (for PDO analysis) and MERRA-2 AR dataset from 1982/1983 to 2020/2021 (for other climate modes analysis) during extended winter (NDJFM, left column), early winter (NDJ, middle column), and late winter (JFM, right column). Note that the coefficients from the regression of AO, QBO, ENSO, MJO, and PNA cannot directly be comparable to those from PDO (see Methods). The results show coefficients that are statistically significant at the 5% level. (Figure 2 in Yang et al., 2024)

Figure 2. Climate modes modulate IVT orientation of temporally clustered unique ARs. Composite analysis in anomalies of the life-cycle IVT orientation of temporally clustered unique ARs during early winter (NDJ) and late winter (JFM). The analysis based on positive (columns 1,3) and negative phases (columns 2,4) of AO, QBO, ENSO, MJO-RMM1, MJO-RMM2, PDO, PNA (removed ENSO signal), using the ERA-5 AR dataset from 1940/1941 to 2017/2018 (for PDO analysis) and MERRA-2 AR dataset from 1982/1983 to 2020/2021 (for other climate modes analysis). The anomalies represent a composite analysis of temporally clustered AR orientation based on the positive and negative phases of climate modes, minus the climatology of temporally clustered AR orientation. Dots indicate statistical significance at the 5% level. Unit: degree. (Figure 10 in Yang et al., 2024)

Bowers, C., Serafin, K. A. and Baker, J. W.(2024). Temporal compounding increases economic impacts of atmospheric rivers in California. Science Advances, 10(3), adi7905. https://doi.org/10.1126/sciadv.adi7905

DeFlorio, M. J., A. Sengupta, C. M. Castellano, J. Wang, Z. Zhang, A. Gershunov, K. Guirguis, R. Luna Niño, R. E. Clemesha, M. Pan, M. Xiao, B. Kawzenuk, P. B. Gibson, W. Scheftic, P. D. Broxton, M. B. Switanek, J. Yuan, M. D. Dettinger, C. W. Hecht, D. R. Cayan, B. D. Cornuelle, A. J. Miller, J. Kalansky, L. Delle Monache, F. M. Ralph, D. E. Waliser, A. W. Robertson, X. Zeng, D. G. DeWitt, J. Jones, and M. L. Anderson. (2024). From California’s extreme drought to major flooding: Evaluating and synthesizing experimental seasonal and subseasonal forecasts of landfalling atmospheric rivers and extreme precipitation during winter 2022/23. Bulletin of the American Meteorological Society, 105(1), E84-E104. https://doi.org/10.1175/BAMS-D-22-0208.1

Yang, Z., DeFlorio, M.J., Sengupta, A., Wang, J., Castellano, C.M., Gershunov, A., Guirguis, K., Slinskey, E., Guan, B., Delle Monache, L. and Ralph, F.M.,. (2024). Seasonality and climate modes influence the temporal clustering of unique atmospheric rivers in the Western US. Nature Communications Earth & Environment, 5(1), p.734. https://doi.org/10.1038/s43247-024-01890-x

CW3E at the American Geophysical Union (AGU) 2024 Annual Meeting

CW3E at the American Geophysical Union (AGU) 2024 Annual Meeting

CW3E will be participating in the upcoming 2024 American Geophysical Union (AGU) annual fall meeting this December. Several CW3E graduate students, post docs and researchers will travel to Washington, D.C. or connect remotely to present their work at this year AGU’s meeting, themed “What’s Next for Science.” More information about AGU 2024 annual meeting here.

Each year AGU’s annual meeting boasts the largest gathering of Earth and space scientists, convening over 25,000 attendees from over 100 countries to share research and connect with friends and colleagues. Joining scientists in attendance at AGU every year are educators, policymakers, journalists and communicators interested in better understanding and communicating earth science within their fields. CW3E will be represented by 16 presenters this year and has 36 authors represented in total across 39 submitted talks and poster presentations.

All CW3E submissions are linked below in alphabetical order of first author (last name). CW3E researchers, post-docs and graduate students names are bolded, CW3E funded research collaborators or alumni names are italicized.

Alipour R.S., P. Nikrou, A.M. Nemnem, S. Balachandran, E. Goharian, J. Imran, S.J. Burian, D. Weiss, J. Kastens, X. Li, S. Cohen. H53M-1264 Comparative Analysis of Low-Complexity Flood Inundation Models for Dam Failure Scenarios: OWP HAND-FIM vs FLDPLN.

Balachandran S., A.M. Nemnem, P. Nikrou, R.S. Alipour, E. Goharian, J. Imran, S.J. Burian, X. Li, S. Cohen. H53M-1281 Rapid Inundation Mapping for Catastrophic Floods Due to Dam Failures and Operations.

Battula S.B., J.M. Cordeira. A53G-2167 Analyses and Model Evaluation of Extreme Precipitation Caused by Atmospheric Rivers and Mesoscale Convective Systems over Southeast United States.

Brandt T., K. Haleakala, G. Lewis, R. Weihs, A. Cooper, A.M. Wilson, T. Dixon. H32C-07 Is it rain or snow—what observations are required for mountain precipitation phase determination?.

Chen X., T. Yang, K.M. Mahoney, M. Pan, N.L. Ciwro. A51L & A54B Advancing Precipitation Predictions with Physical Models and Artificial Intelligence.

Clemesha, R.E.S., A. Gershunov, R.L. Niño, C. Poulsen, E. Fleishman, G.L. Rochelle, S. Shanahan, B.H. Udall. A13L-05 History and Future Projections of the Diverse Seasonalities of Precipitation within the Colorado River Basin and Southwestern United States.

DeMott C.A., A. Subramanian, C.A. Davis, J.D. Doyle, S.A. Henderson, H. Kim, D. Lavers, A. Levine, C.A. Reynolds, V. Tallapragada, A.M. Wilson. A53G-2274 Teleconnecting process studies: How can we enhance synergies across observing campaigns of teleconnected phenomena?.

Erfani M., J. Kalansky, L. Su, C. Delaney, J. Mendoza. H43T-04 Evaluating Forecast-Informed Reservoir Operations (FIRO) for Climate Change Adaptation: A Case Study on Lake Mendocino in Northern California.

Ge G., M. Zheng, A. Jensen, H. Li, W. Mayfield, A. Back. A42G Numerical Modeling, Data Assimilation, and Research to Operations (R2O) for Better Forecasting of High-Impact Weather Events.

Ge G., M. Zheng, A. Jensen, H. Li, W. Mayfield, J. Wang. A43M Numerical Modeling, Data Assimilation, and Research to Operations (R2O) for Better Forecasting of High-Impact Weather Events.

Ghazvinian M., T. Dixon, L. Delle Monache, A. Sengupta, M. Pan, P. Mulrooney, B. Whitin, P. Fickensher, F.M. Ralph. H31D-06 Deep Learning-Based Ensemble meteorological Forcings for Probabilistic Hydrologic Forecasting.

Gilmore T.E., F. Birgand, E. Goharian, C. Terry. H13C Cameras as Sensors: Application of Images, Videos, and Artificial Intelligence for Hydrological Monitoring Poster.

Goharian E., S.N. Young, J.S. Horsburgh, S. Khan, R. Isa, S. Neupane, M.H. Goloujeh. H13C-1039 Advancing Camera-Based Monitoring for Operational Hydrologic Applications.

Goharian, E., S. Steinschneider, C. Delaney, X. Cai, K.S. Raney. H41T & H43T Water and Society: Forecast-Informed Reservoir Operations (FIRO).

Gorooh V.A., Z. Zhang, D. Axisa, L. Delle Monache. A21G-1805 Development and Application of LSTM for Nowcasting Seedable Cloud Features Using Geostationary Satellite Information over the United Arab Emirates.

Higgins T., W. Chapman, A. Subramanian, C. Pedersen, L.Delle Monache. GC21C-06 Using Generative AI to Improve Data-driven Weather Model Forecasts.

Hsu T.Y., M.R. Mazloff, R. Sun, S.T. Gille, B.D. Cornuelle, M. Zheng, L. Delle Monache. A21D-1750 The Modification of Air-sea Fluxes in High-Wind Environments by Mesoscale Sea Surface Temperature Structure.

Karakan I., J.F. Jamal, E. Goharian, H. Chaudhry, J. Imran. H51T-0985A Diffusive Wave Routing Model Using Meshless Radial Basis Function Collocation Method for Operational Forecasting.

Kim E.Y., V. Chandrasekar, K.D. Waal, L. Delle Monache. A21G-1807 Dual-Polarization Radar Characterization of Extreme Rainfall Event Over the UAE Region.

Lewis G., T. Dixon, B. Whitin, P. Fickensher, A. Reising, D. Rizzardo, R. Hartman, M. Pan, M. Xiao. H06-118 Partnership Approach to Assessing and Improving Operational Water Supply Forecasting Across the Sierra Nevada.

Martens H.R., Q. Cao, A.A. Borsa, M. Pan, A.M. Wilson, E.Knappe, M. Ralph. G11A-04 Water-Storage Gains Driven by Atmospheric Rivers in the Western United States.

Nash D.. EP51C-1348 Differentiating between impactful and non-impactful Atmospheric River events in Southeast Alaska.

Nash D., J. Rutz, J.M. Cordeira, F.M. Ralph, K. Sanders, E. Walter. A52B-06 Inland- penetrating Atmospheric Rivers and Hydrometeorological Impacts in Colorado.

Nemnem A.M., A.H. Tanim, E. Goharian, S. Khan, J. Imran. H12C-03 Modeling Dam Break Flooding under Rainfall Uncertainty: A Case Study of Storm Daniel in Derna, Libya.

Pan M., M. Xiao, Y. Yang, G. Lewis, T. Dixon, C. Shen, Y. Song, D. Feng. H13R-04 Some Lessons Learned in Process-based and Data-driven Modeling for Applied Hydrology.

Patton A.I., L. Luna, A.B. Jacobs, E. Lawrence, B.B. Mirus, D. Nash, J.J. Roering. EP53G-02 Climate Controls on Rainfall Thresholds for Landslide Warning in Southeast Alaska.

Perez A., M. Erfani, E. Goharian. H41T-0795 Integrating Ensemble Hydrologic Forecasts and Interpretable ML Models for FIRO-Based Reservoir Management.

Poulsen C., R.E.S. Clemesha, A. Gershunov, M. D. Dettinger, Z. Zhang, I. Howard, D.W. Stahle, F.M. Ralph. A53J-2230 Influence of Past and Present Spring Precipitation on Western US Drought: A Story of the 1991 California “Miracle March”.

Ralph F.M.. 51D-01 Atmospheric Rivers: From Science to Solutions.

Ralph F.M. H43T-02 Putting the “F” in FIRO: The Challenges and Opportunities of Better Predicting Extreme Precipitation and Stream Flow and the Storms That Produce Them.

Raney K.S., M. Erfani, E. Goharian. H43T-06 Forecast Informed Reservoir Operations: Leveraging Ensemble Forecasts and Advanced Model Predictive Control Methods for Improved Water Storage and Flood Control.

Rush W., J.M. Lora, C.B. Skinner, S. Menemenlis, C.A. Shields, P. Ullrich, T.A. O’Brien, S. Brands, B. Guan, K.S. Mattingly, E.E. McClenny, B.D. Mundhenk, A.B. Nellikkattil, E. Shearer, K. Reid, A. Ramos, R. Tomé, R. Leung, J, Wille, F.M. Ralph, J. Rutz, M.F. Wehner. A34C Atmospheric Rivers: Processes, Impacts, Observations and Uncertainties.

Rutz J., M. Steen, R. Vilela, V. Chandrasekar, B. Garcia. A43A-1944 Comparison of AQPI X-Band and NEXRAD S-Band Radar-Estimated Rain Rates during an Extreme Atmospheric River Event.

Rutz J., V. Chandrasekar, R. Vilela, M. Steen, D. Roberts, F.M. Ralph. A06-03 Updates on the Advanced Quantitative Precipitation Information (AQPI) Project: Improved Radar-based Observations and Forecasts for the San Francisco Bay Area.

Slinskey E.A., J. Rutz, F.M. Ralph. A53G-2164 The NCEI Climate Data Record for Atmospheric Rivers: Initial Results over the Western United States.

Tallapragada V., X. Wu, K. Wu, A.M. Wilson, F.M. Ralph. A51C-01 Advancing Aircraft Reconnaissance Observations, Modeling and Data Assimilation for Improved Atmospheric River Forecasts.

Wang J., M. Zheng, J. Rutz, L.Delle Monache, J. Kalansky, F.M. Ralph. A43M-2159 Assimilating NEXRAD into a Regional Model for Improved Precipitation Forecasts during Two Landfalling Atmospheric Rivers.

Wilson A.M., F.M. Ralph, L.Delle Monache, A. Sengupta, M. Pan, E. Goharian, J.M. Cordeira, V. Tallapragada, C. Talbot, J. Forbis. NH34B-02 Supporting Hazard Preparedness for Extreme Precipitation Associated with Atmospheric Rivers.

Webb M., C. Albano, D. Bozkurt, R. Garreaud, G. Yu, A.M. Wilson. A53J-2316 Drivers of Enhanced Flooding Risk During Atmospheric Rivers in the Western U.S. and Central Chile.

Yang Y., D. Feng, H. Beck, W. Hu, A. Sengupta, L. Delle Monache, R. Hartman, P. Lin, C. Shen, M. Pan. H54D-01 Global Daily Discharge Estimation Based on Grid-Scale Long Short-Term Memory (LSTM) Model and River Routing.

Yang Y., M. Pan, C. Shen, M. Xiao, L. Delle Monache, A. Sengupta, R. Hartman. H53Z-05 Augmentation for Western US Streamflow Prediction Using LSTM-powered Data Fusion Approach.

Yang Z., W. Hu, A. Sengupta, L. Delle Monache, M.J. Deflorio, M. Ghazvinian, M. Pan, J. Kalansky. A41K-1723 Enhancing Weeks 1-2 Forecasts of 2-m Temperature in the Sierra Nevada California through Analog Ensemble post-processing.

Zhang Z., F.M. Ralph, J. Rutz, D. Nash. A53G-2176 The Role of Atmospheric Rivers in the Snowpack over the Upper Colorado River Basin during Water Year 2023.

Zhang Z., F.M. Ralph, X. Zou, B. Kawzenuk, M. Zheng, I. Gorodetskaya, P.M. Rowe, D.H. Bromwich. A41J-1709 Extending the CW3E Atmospheric River Scale to the Polar Regions.

Zheng M., L. Delle Monache, X. Wu, B. Kawzenuk, F.M. Ralph, Y. Zhu, R. Tom, V. Tallapragada, Z. Zhang, K. Wu, J. Wang. A43M-2169 Impact of Atmospheric River Reconnaissance Dropsonde Data on the Assimilation of Satellite Radiance Data in GFS.

CW3E Publication Notice: Extending the CW3E Atmospheric River Scale to the Polar Regions

CW3E Publication Notice

Extending the CW3E Atmospheric River Scale to the Polar Regions

November 25, 2024

Atmospheric rivers (ARs) are the primary mechanism for transporting water vapor from low latitudes to polar regions. They play a critical role in regional climate and extreme weather events in polar regions (e.g., Figure 1), exerting an important influence on the polar cryosphere. With the rapidly growing interest in polar ARs during the past decade, it is imperative to establish an objective framework quantifying the strength and impact of these ARs for both scientific research and practical applications.

The AR scale developed by Ralph et al. (2019) has been widely used in scientific research, weather forecasts, and media reports. While the AR scale was developed based on the climatology of water vapor transport at middle latitudes, it is insufficient for polar regions where temperatures and moisture content are significantly lower. A new paper entitled “Extending the Center for Western Weather and Water Extremes (CW3E) Atmospheric River Scale to the Polar Regions” was recently published in The Cryosphere. It is led by CW3E researcher Zhenhai Zhang and co-authored by Martin Ralph (CW3E), Xun Zou (CW3E), Brian Kawzenuk (CW3E), Minghua Zheng (CW3E), Irina Gorodetskaya (University of Porto, Portugal), Penny Rowe (North-West Research Associates), and David H. Bromwich (The Ohio State University). This paper introduced an extended version of the AR scale tuned to polar regions (Figure 2) based on the climatology there. Three new ranks specific for polar regions (AR P1, AR P2, and AR P3) with the minimum integrated water vapor transport (IVT) thresholds of 100, 150, and 200 kg m-1 s-1 are added to the standard AR scale (AR1 – AR5) to capture low-IVT ARs in both the Arctic and Antarctic. The polar AR scale also uses the same approach as the AR scale to promote or demote by one rank based on the duration of the AR at that location being less than 24 hours or longer than 48 hours.

Using the polar AR scale, this paper examined AR frequency, seasonality, trends, and associated precipitation and surface melt over Antarctica and Greenland. ARs contribute about one-third of the total precipitation amount over the coastal regions of Antarctica and Greenland. Weak and moderate ARs (AR P1 – AR2) are responsible for the majority of the AR-related precipitation amount along the Antarctic and Greenland coasts due to their relatively high frequency. Meanwhile, the strong and extreme ARs (AR3 and AR4) are usually associated with extreme precipitation, although their frequency is relatively low. In addition, ARs trigger 23% and 26% surface melting on average in Antarctica and Greenland, respectively, and around one-third of the surface melting over the coastal regions due to the relatively higher frequency and strength of the ARs over the coasts. With these high impacts, the frequency of landfalling ARs has shown significant increasing trends over the Antarctic Peninsula and East Greenland coasts in the last few decades, indicating the even greater importance of ARs in the polar regions, which are acknowledged to be vulnerable to a changing climate.

Based on the polar AR scale, we developed the CW3E Antarctica AR Scale Forecast tools using the NCEP Global Ensemble Forecast System (GEFS). These tools were extensively used in guiding radiosonde launches during the targeted observing periods (TOPs) in the Year of Polar Prediction in the Southern Hemisphere project (YOPP-SH), demonstrating reliability in guiding radiosonde launches from 24 stations during TOPs (Bromwich et al., 2024). Figure 3 shows an example of the CW3E Antarctica AR Scale Forecast tools. Our recently funded project by the National Science Foundation (NSF) will enhance the CW3E Antarctica AR Scale Forecast tools by incorporating real-time forecast data from the Antarctic Weather Research & Forecasting Model (WRF) Mesoscale Prediction System (AMPS) in the National Center for Atmospheric Research (NCAR), supporting both research and operational objectives. Additionally, we will also develop similar polar AR scale forecast products for Greenland in the future.

Overall, the CW3E polar AR scale provides an objective and concise description of the strength of AR events at the locations of interest based on their intensity and duration in the Antarctic and Arctic regions. It has the potential to enhance communication regarding ARs across observation, research, and forecast communities in polar regions.

Figure 1. An extreme landfalling AR over East Antarctica during 16–18 March 2022 based on ERA5 reanalysis. (a) Three-day time-integrated IVT (T-IVT) during 16–18 March 2022 as a percentage of normal (mean 3-day T-IVT during 1980-2021); the sub-panel at the top left shows the IVT (colors start from 200 kg m-1 s-1 with an increment of 100 kg m-1 s-1) at 00 UTC on 17 March. (b) Time series of averaged 3-day T-IVT within the blue box in panel (a) for 2022 (red), 1980-2021 (black), and climatological mean (1980-2021, green). (c) Temperature anomaly on 18 March 2022 based on ERA5 reanalysis. (d) Time series of 3 hourly observed temperatures at Dome C station (blue dot in panel a and c) for 2022 (red), 1996-2021 and 2023 (gray), and climatological mean (1996-2023 mean, blue). (Figure 1 in Zhang et al., 2024)

Figure 2. An extended AR scale for polar regions that categorizes AR events based on the duration of AR conditions (IVT ≥ 100 kg m-1 s-1) and the maximum IVT in the duration at a specific location. This scale includes ranks (AR P1, AR P2, and AR P3) designed specifically for ARs in polar regions. (Figure 5 in Zhang et al., 2024)

Figure 3. CW3E AR scale ensemble diagnostic forecast tool for 70°S, 5°E from the GEFS. initialized at 06Z 04/15/2022. Dots along the Antarctic coast indicate locations where information such as that shown in other panels can be provided; here other panels refer to the larger point at 70°S, 5°E).(a) Maximum Polar AR scale forecast over the next seven days along the Antarctic Coast (colored dots), enlarged dot represents the location shown in panels b-d. (b) Seven day forecast of IVT from each ensemble member (thin gray lines), the ensemble mean (green line), control member (black line) and +/- 1 standard deviation from the ensemble mean (red and blue lines and gray shading). Color shading represents the timing of the Polar AR scale from the control member. (c) Forecasted probability of each Polar AR scale ranking as a function of lead time based on the number of ensemble members predicting an AR. (d) Forecasted Polar AR scale timing and ranking from each ensemble member, text values represent the maximum IVT magnitude and timing during a forecasted AR. (Figure 14 in Zhang et al., 2024)

Zhang, Z., Ralph, F. M., Zou, X., Kawzenuk, B., Zheng, M., Gorodetskaya, I. V, … & Bromwich, D. H. (2024). Extending the Center for Western Weather and Water Extremes (CW3E) Atmospheric River Scale to the Polar Regions. The Cryosphere, 18(11), 5239–5258. https://doi.org/10.5194/tc-18-5239-2024

Ralph, F. M., Rutz, J. J., Cordeira, J. M., Dettinger, M., Anderson, M., Reynolds, D., … & Smallcomb, C. (2019). A scale to characterize the strength and impacts of atmospheric rivers. Bulletin of the American Meteorological Society, 100(2), 269-289. https://doi.org/10.1175/BAMS-D-18-0023.1

Bromwich, D. H., Gorodetskaya, I. V., Carpentier, S., Alexander, S., Bazile, E., Heinrich, V. J., … & Zou, X. (2024). Winter Targeted Observing Periods during the Year of Polar Prediction in the Southern Hemisphere (YOPP-SH). Bulletin of the American Meteorological Society, 105(9), E1662-E1684. https://doi.org/10.1175/BAMS-D-22-0249.1

CW3E Members Attend the 2024 Atmospheric River Reconnaissance Workshop at NOAA’s Center for Weather and Climate Prediction in College Park, MD

CW3E Members Attend the 2024 Atmospheric River Reconnaissance Workshop at NOAA’s Center for Weather and Climate Prediction in College Park, MD

November 18, 2024

The 2024 Atmospheric River Reconnaissance (AR Recon) Workshop took place at the NOAA Center for Weather and Climate Prediction in College Park, MD, from October 22-24, 2024. This event brought together AR Recon participants and experts to discuss recent advancements, coordination of future efforts, and methods to enhance collaborative research on atmospheric rivers.

The three-day event featured Ken Graham, NWS director, who delivered a keynote address highlighting the importance of atmospheric river research, observational data, and the collaborative efforts required to advance this field. The workshop included participation from various partners, including Lagrangian Drifter Laboratory, NAWDIC-AR Recon, and USACE FIRO partners, emphasizing the collaborative nature of the event, and showcased AR Recon’s recent endorsement from the World Weather Research Programme (WWRP)
.

Key Highlights:

  • Advancements in Atmospheric River Research: The three-day workshop featured presentations, panel discussions, and poster sessions featuring the latest findings in atmospheric river studies, including new methodologies and technologies.
  • Data Collection Techniques: Participants discussed innovations in data collection, such as the use of Global Sounding Balloons (Windborne Systems) and Airborne Radio Occultation (ARO, Scripps PI Jennifer Haase) to improve atmospheric observations.
  • Collaborative Efforts: Emphasis was placed on the Research And Operations Partnership (RAOP) approach, which aims to improve predictions of land-falling atmospheric rivers in the U.S. through enhanced collaboration between research and operational communities.
  • Expansion of Operations: The workshop highlighted the geographic expansion of AR Recon operations, including new flight campaigns out of Guam in early 2024, and plans for Pacific (NE and NW) and Atlantic reconnaissance missions.
  • Panel Discussions: Each panel discussion focused on different aspects of atmospheric river research, data collection techniques, and collaborative efforts to improve weather forecasting and water management.

Workshop Goals:

  • Impact Assessment: Documenting the impacts of AR Recon data on weather forecasting and water management.
  • Operational Strategies: Reviewing and refining strategies for operational sampling, data collection, and dissemination.
  • Future Research Directions: Guiding future research efforts, including coordinated data impact studies and exploration of campaign expansion.

Agenda Highlights:

  • Day 1: Opening remarks, presentations on recent advancements in atmospheric river research, and sessions on data collection techniques.
  • Day 2: Workshops on data assimilation and metric development, followed by breakout sessions for RAOP working groups.
  • Day 3: Presentations on the impact assessment of AR Recon data, discussions on future research directions, and closing sessions focusing on operational strategies and collaborative efforts.

CW3E Publication Notice: Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA)

CW3E Publication Notice

Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA)

November 05, 2024

“Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA)” was recently published in Earth Interactions. This work was a collaboration among scientists from CW3E, the California Geological Survey, and the U.S. Geological Survey. The project was supported by the U.S. Geological Survey Landslide Hazards Program and the California Department of Water Resources Atmospheric River Program.

Landslides pose a threat to life, property, and infrastructure in mountainous areas of California. Previous work has focused on antecedent and within-storm rainfall triggering thresholds. However, site-specific rainfall information is not always available and there is uncertainty in these thresholds. This work explores whether there are common meteorological characteristics associated with landslide events that could aid in their prediction.

Previous research has noted the connection between atmospheric rivers and landslides. This analysis expands on previous work by exploring numerous atmospheric characteristics of landslide-producing storms in detail, including variables associated with ARs. The investigation evaluates 18 widespread shallow landslide events occurring in 13 unique storms where landslide timing could be constrained to a 6-hour window. Working with well-constrained landslide timing allows for evaluation of synoptic-to-mesoscale atmospheric characteristics associated with each event that would not be possible if longer time windows were used.

Figure 1. As in Figure 2 from Oakley et al. 2024; Location and year of landslide events in this analysis.

Results indicate that landslides occur under a broad range of synoptic patterns across the state. ARs were more prevalent in landslide producing storms in northern California than southern California, and the strength of AR conditions in landslide-producing storms was generally higher in northern California. Two-thirds of the 18 landslide events had Integrated Water Vapor Transport (IVT) exceeding the 99th percentile for the cool season and roughly 40% the events had Integrated Water Vapor (IWV) exceeding the 99th percentile. Nearly all events occurred with the upper-level jet stream overhead, and often associated with areas of the jet favorable for strong upward vertical motions. All 18 events were associated with Convective Available Potential Energy (CAPE) greater than 80th percentile for the season at the landslide location, with 12 greater than 95th percentile. High intensity rainfall features with radar reflectivity >50 dBZ were present in seven of 18 events. All but one landslide event had above normal Water Year (October 1) to-date precipitation, and that event featured a very intense, slow moving convective band, suggesting that the mesoscale characteristics of storms (e.g., embedded convection) may overcome below-average antecedent precipitation conditions and trigger landsliding.

Table 1. As in Table 2 from Oakley et al. 2024; Rainfall and moisture variable characteristics for landslide events. The “Event” column provides the event name (See Table 1 in Oakley et al. 2024). Superscripts in the “Event” column indicate landslide events that happened in different locations in the same storm. The “% Avg. WYTD Precip.” column provides the percent of water year to-date average precipitation prior to the landslide event. The “TECA-BARD AR” column provides the percent likelihood of detection of an AR object based on the TECA-BARD detection algorithm for a 6-hour period (24-hour period). Where no (24-hour) value is provided, the 24-hour value was equal to 6 hours. The “AR Scale (Coast)” column provides the AR Scale value at the coastal landfall location west of the landslide. A range is provided where the coastal scale is ambiguous. The “AR Scale (Loc.)” column provides the AR Scale value at the landslide location. For both AR Scale columns, “NA” indicates the location did not meet AR Scale criteria. The “IVT” and “IVT Pctile” columns provide the IVT value and percentile rank of the IVT value at the landslide location. The “IWV” and “IWV Pctile” columns provide the IWV value and the percentile rank of the IWV value at the landslide location.

Landslide producing storms occurred in all phases of the El Niño-Southern Oscillation; of the 13 storm events, six occurred during El Niño conditions, five during La Niña, and two during neutral conditions. However, five of seven storm events producing landslides in southern California occurred under El Niño condition, suggesting a stronger relationship in that region.

While some common characteristics were identified across landslide-producing storms, these storms share many characteristics of other hydrologically impactful storms that may or may not produce landslides. Further research is needed to discern between antecedent and in-storm characteristics associated with landslide activity or the lack thereof.

This work addresses the CW3E priority area of Atmospheric Rivers Research and Applications, as it explores atmospheric conditions associated with events including atmospheric rivers and their relation to landsliding to improve decision making associated with these hazards. It also addresses the priority area of Monitoring and Projections of Climate Variability and Change. This work provides new insights from research on historical extreme events to enhance conceptual understanding of these events. With an interdisciplinary team of geologists, engineers, and atmospheric scientists from state and federal agencies and university, this research displays the CW3E core value of Collaboration.

Oakley, N. S., Perkins, J. P., Bartlett, S. M., Collins, B. D., Comstock, K. H., Brien, D. L., Burgess, W. P., & Corbett, S. C. (2024). Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA). Earth Interactions (Early Online Release) https://doi.org/10.1175/EI-D-24-0003.1