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Sixth Annual Yampa Basin Rendezvous Brings Together Community to Address Water Resilience

July, 17 2023

The 6^th Annual Yampa Basin Rendezvous (YBR) conference was held June 1-2^nd, providing a platform for leading experts, policymakers, non-profit organizations, and community stakeholders to address the pressing water resource challenges faced by the Yampa Basin region. The conference, held at Colorado Mountain College’s Steamboat Springs campus, was organized by the Center for Western Weather and Water Extremes (CW3E), Colorado Mountain College, Yampa Valley Sustainability Council, Friends of the Yampa, the Upper Yampa Water Conservancy District, River Network, Yampatika, and Steamboat Ski and Resort Corporation.

The conference theme, “Snows, Flows, and Drought: Managing for Western Water Resilience” aimed to foster collaboration, share knowledge, and promote innovative solutions to ensure sustainable water management practices in the Yampa Basin. The basin, located in northwest Colorado, is known for its unique water supply dynamics as one of the wildest remaining major tributaries of the Colorado River and the importance of its resources for various sectors, including agriculture, energy, and recreation.

YBR began with an inspiring welcome address by newly-elected Congressman Joe Neguse, U.S. Representative for Colorado’s 2nd Congressional District, focusing on the urgent need to adapt to changing climate patterns and the potential impacts on water availability in the region. Congressman Neguse highlighted the important role that the Yampa River plays in the greater Colorado River system and several recent pieces of legislation designed to protect the river system and those who rely on it.

Over the course of the conference, a series of panel discussions, field trips, a community keynote, and a poster session focused on critical topics including future climate projections, community engagement, ecosystem health, and adaptation. Participants engaged in lively discussions and shared cutting-edge research findings, technological advancements, and best practices from their respective fields.

YBR also facilitated valuable networking opportunities, enabling stakeholders to connect with experts and forge collaborations. Participants included representatives from federal and state agencies, local governments, tribal nations, non-profit organizations, academia, and industry sectors dependent on the Yampa Basin’s water resources.

A significant highlight of the YBR was the Community Keynote event co-hosted by YBR and Friends of the Yampa. The event was headlined by Erica Gies, Author of Water Always Wins, who inspired attendees with her keynote address on water resiliency strategies utilized by communities around the world. The event also featured Friends of the Yampa’s (FOTY) annual State of the Yampa address delivered by Kent Vertrees, FOTY board member and owner of Steamboat Powdercats.

YBR demonstrated the power of collective action and knowledge sharing in addressing water resource challenges. By fostering interdisciplinary dialogue, the event aimed to lay the foundation for a resilient future in the Yampa Basin, ensuring water security for generations to come.

As the conference drew to a close, participants departed with a renewed commitment to collaborate, innovate, and prioritize the sustainable management of the Yampa Basin’s precious water resources. With the knowledge and connections gained from the YBR conference, stakeholders are now better equipped to navigate the complex water challenges ahead and develop effective strategies for a thriving future utilizing the Yampa Basin as a model for western water resources.

CW3E Publication Notice: Seasonal forecasting of precipitation, temperature, and snow mass over the western U.S. by combining ensemble post-processing with empirical ocean-atmosphere teleconnections

CW3E Publication Notice

Seasonal forecasting of precipitation, temperature, and snow mass over the western U.S. by combining ensemble post-processing with empirical ocean-atmosphere teleconnections

July 14, 2023

A new paper entitled “Seasonal forecasting of precipitation, temperature, and snow mass over the western U.S. by combining ensemble post-processing with empirical ocean-atmosphere teleconnections,” was recently published in the AMS Journal of Weather and Forecasting by William Scheftic, Xubin Zeng, and Michael Brunke from the University of Arizona. This paper tests our experimental real-time ensemble seasonal forecasts of snowpack through snow water equivalent (SWE), 2m temperature (T2m) and precipitation (P) across hydrologic subbasins of the western U.S. after post-processing two currently operational state-of-the-art climate models: NCEP CFSv2 and ECMWF SEAS5. The winter forecasts have been hosted internally at CW3E and contribute to DeFlorio et al. (2023; submitted to BAMS and under revision). This work contributes to CW3E’s 2019–2024 Strategic Plan to enable more effective reservoir management through improved weather and water forecasts by demonstrating successful experimental ensemble forecasts of snowpack at the seasonal scale.

This research contributed to our understanding of, and efforts to improve, seasonal climate forecasting over the western U.S. in several ways, as highlighted in Figure 1. First, through our stage 1 adjustment which corrects for prior biases in the forecast distribution and adjusts for errors in spread, significant biases even in current state-of-the-art seasonal climate models were removed from all variables and improved skill over the original model. Second, through our stage 2 adjustments which further adjusts the forecast based on the linear relationship between stage 1 forecast errors and a set of land, oceanic and atmospheric predictor indices, we showed that significant SWE errors arising from poor model snowpack initialization could be further adjusted leading to improved forecasts. The impact of stage 2 adjustments on T2m and P forecasts was more limited, as the residual stage 1 error was not significantly related to the predictor indices. Finally, this study contributed to our understanding of the predictability of SWE relative to T2m and P. Here we saw enhanced skill in SWE, especially during the spring snowmelt period and in regions where snowpack is important but more challenging to initialize and forecast. Further analysis showed that the skill of SWE outperformed, but was strongly related to, the persistence of observed anomalies.

Figure 1: Adapted from Scheftic et al. (2023). The differences between original and Stage 1 forecasts (col 1) and the differences between Stage 1 and 2 forecasts (col 2) of the average of CFSv2 and SEAS5 median RPSS across all seasons for all subbasins in the western U.S. for T2m (top), P (middle), and SWE (bottom) at 1 month lead over all seasons. Subbasins outlined in bold black are 95% significant according to a bootstrap analysis. Skill for super-ensemble (SENS) Stage 2 forecasts for RPSS (right two columns). The 3rd column shows median skill across all seasons for all subbasins at 1 month lead. Subbasins outlined in thick gray show significant improvement (at 95% level) over one of the two models and those in bold black show significant improvement over both models (note: no subbasins were significant over both models). The 4th column shows median skill across all subbasins for each season and lead time. For instance, the value corresponding to “0-mon” and “OND” refers to the median RPSS for early October forecasts of October-December, while the value corresponding to “1-mon” and “NDJ” refers to the median SAC for early October forecasts of November-January. Note: RPSS is the ranked probability skill score which compares the performance of the full ensemble of the forecast to a reference ensemble that uses all historical observed data in the training period acting as a climatological distribution. RPSS of 1 represents perfect skill, RPSS of 0 has the same skill as the reference, and negative RPSS represents forecasts that performed worse than the reference.

DeFlorio, M. J., & Coauthors (2023). The transition from California’s extreme drought to major flooding: Evaluating experimental subseasonal and seasonal forecasts of the onslaught of landfalling atmospheric rivers and associated extreme precipitation during Winter 2022-2023, Bulletin of the American Meteorological Society, in review.

Scheftic, W. D., Zeng, X., & Brunke, M. A. (2023). Seasonal forecasting of precipitation, temperature, and snow mass over the western U.S. by combining ensemble post-processing with empirical ocean-atmosphere teleconnections, Weather and Forecasting (published online ahead of print 2023). https://doi.org/10.1175/WAF-D-22-0099.1

CW3E Members Attend 4th Annual Atmospheric River Reconnaissance Workshop at ECMWF in Reading, UK

CW3E Members Attend 4^th Annual Atmospheric River Reconnaissance Workshop at ECMWF in Reading, UK

July, 13 2023

Members of CW3E’s AR Recon team attended the 4th Annual Atmospheric River Reconnaissance (AR Recon) Workshop at the European Centre for Medium-Range Weather Forecasts (ECMWF) held in Reading, UK and online during June 27–30, 2023. The goals of this workshop included highlighting the accomplishments of the exceptionally active 2023 AR Recon season and spotlighting the impact of data collected and assimilated during AR Recon into various operational weather models. The workshop also previewed research in progress using data collected during the season, discussed the upcoming collaboration between the North Atlantic Waveguide, Dry Intrusion, and Downstream Impact Campaign (NAWDIC) and AR Recon, and discussed future plans for the growth of AR Recon. Organization of the workshop was guided by the AR Recon Modeling and Data Assimilation Steering Committee.

The first day of the workshop began with opening remarks by Florian Pappenberger, Director of Forecasts & Deputy-Director General at ECMWF, and a presentation on AR Recon motivation and achievements by Marty Ralph, CW3E Director and PI of AR Recon. The remainder of the day focused on AR Recon during Water Year 2023 (and, at times, beyond). The morning sessions focused primarily on observational platforms and techniques associated with AR Recon, as well as analysis of the meteorological characteristics associated with landfalling ARs based on AR Recon observations. The afternoon sessions focused primarily on the use of sensitivity products, both the adjoint and ensemble methods, to highlight areas where targeted AR Recon observations, particularly dropsondes, would be most beneficial to the accuracy of numerical weather prediction model run solutions. The importance of AR Recon to water management operations was also highlighted by Cary Talbot, National Lead, Forecast-Informed Reservoir Operations Program (FIRO) at the US Army Engineer Research & Development Center. The day concluded with a reception and poster viewing session.

The second day of the workshop began with a presentation by Rear Admiral Nancy Hann, Director of the NOAA Office of Marine and Aviation Operations (OMAO), describing NOAA’s current and future aircraft support for AR Recon, followed by an overview of AR Recon observational strategies and impacts on NOAA/NCEP modeling by Vijay Tallapragada, Senior Scientist for EMC and Co-PI of AR Recon. The remainder of the morning continued to focus on modeling, data assimilation, and impact studies related to AR Recon observations. This general theme continued into the afternoon, which featured a celebration of ECMWF’s successful implementation of its newest version of the Integrated Forecasting System (Cycle 48r1). Chris Davis, NCAR Senior Scientist and Chair, WMO WWRP Science Steering Committee, highlighted activities of the World Weather Research Programme relevant to AR Recon. The day concluded with a workshop dinner at Bel and the Dragon in the Reading town centre.

The third day of the workshop was kicked off by Helen Dacre, Professor of Meteorology at the University of Reading, who presented on precipitation efficiencies in extratropical cyclones and the role of ARs. The remainder of the morning session continued with a focus on scientific advances in physical process understanding and how these advances could be considered and/or applied toward future AR Recon targeting operations. Topics included interactions between the WCB and the jet stream, dry intrusions, and even tropical cyclones in the western Pacific. The afternoon featured a panel discussion regarding new directions and the future of AR Recon. Each member of the audience was invited to provide one comment based on the workshop, and a wide variety of constructive ideas was discussed.

The fourth day of the meeting transitioned to the NAWDIC Workshop, with a presentation from Christian Grams, Professor of Meteorology at the Karlsruhe Institute of Technology outlining the primary field campaign and its various complementary projects. CW3E Director, Marty Ralph, gave a presentation on the potential collaborations between AR Recon and the NAWDIC campaign, discussing the potential for coordinating sampling flights off the US East Coast with flights over Europe. The morning session featured discussion of various projects that will occur under the NAWDIC umbrella, including the UK led CAPRI field campaign (Cyclones, coupled Atmosphere-surface, Processes and Risk of Impacts), French led contributions via SAFIRE aircraft and ground-based sensor deployment, and proposed US plans to sample upstream, antecedent atmospheric conditions associated with high-impact weather events. The day finished with breakout group sessions on Atmospheric Rivers and Dry Intrusions, the presentation of a case study representing the full collaborative efforts of the group, and reporting on the results of the breakout group discussions.

The full meeting agenda, recording of each talk, copies of presentation slides, and posters presented at the meeting can be found here, on the ECMWF’s website for the event.

AR Recon Workshop Session Topics

AR Recon in Water Year 2023

Modeling, Data Assimilation and Impact Studies

Scientific Advances in Physical Process Understanding

The Future of AR Recon

Attendees of the 4th Annual Atmospheric River Reconnaissance Workshop at the European Centre for Medium-Range Weather Forecasts offices in Reading, UK (27 June 2023, Photo: ECMWF)

CW3E Welcomes Nick Schneider

June 12, 2023

Nick Schneider joined CW3E on May 1, 2023, as a research engineer to support Forecast Informed Reservoir Operations projects. Nick previously worked for the San Francisco Public Utilities Commission where he assisted in the development of an automated flood predicition interface utilizing precipitation forecasts from the AQPI system. Nick earned a B.S. in civil engineering with a minor in mathematics from the University of California, Davis in 2021. While in Davis, he also interned at Resource Management Associates where he assisted in the development and application of numerical models for the analysis of current, forecasted, and proposed conditions for surface water systems ranging from tidal marshes to large scale river-reservoir systems. At CW3E, Nick will assist in FIRO engineering research and in the evaluation of other reservoir systems throughout the West beyond Lake Mendocino.

CW3E Publication Notice: Using Deep Learning for an Analysis of Atmospheric Rivers in a High-Resolution Large Ensemble Climate Dataset

CW3E Publication Notice

Using Deep Learning for an Analysis of Atmospheric Rivers in a High-Resolution Large Ensemble Climate Dataset

June 5, 2023

A new paper entitled “Using Deep Learning for an Analysis of Atmospheric Rivers in a High-Resolution Large Ensemble Climate Dataset” was recently published in Journal of Advances in Modeling Earth Systems by CU Boulder PhD Candidate Tim Higgins, Aneesh Subramanian (CU Boulder), Andre Graubner (ETH Zurich), Lukas Kapp-Schwoerer (ETH Zurich), Peter A. G. Watson (University of Bristol), Sarah Sparrow (University of Oxford), Karthik Kashinath (NVIDIA), Sol Kim (UC Berkeley), Luca Delle Monache (CW3E), and Will Chapman (NCAR). In this work, a light-weight convolutional neural network (CNN) that was first introduced in Wu et al. (2019) and named “CGNet”, was applied towards tracking ARs. The same CNN has previously been applied to other purposes including object identification in cityscapes (Figure 3a). This method, which took the name “CG-Climate”, was used to both track ARs in reanalysis data to show consistency with other common methods and to demonstrate the benefits of using it to track ARs in a large-ensemble regional climate dataset.

Human expert hand labels from 80 different weather and climate scientists at labeling campaigns taking place at CW3E, NCAR, UC Berkeley, Lawrence Berkeley National Laboratory, the 2019 ARTMIP workshop, and the 2019 Climate Informatics Workshop were used to create the training dataset, “ClimateNet” (Prabhat et al. 2021). Examples of human hand labels are shown in Figure 3b. The CNN was trained on AR masks as well as IWV, mean sea level pressure, 850 mb zonal wind, and 850 mb meridional wind. While there are already many existing AR tracking methods that work well, this method has some unique benefits in some contexts. It is able to run at an exceptionally fast speed and uses an exceptionally low amount of computational resources. It is also highly flexible with constraints on input data. It does not require IVT, which can be unavailable in some datasets, and it can be run on any regional domain and at any resolution.

One key characteristic of this method is that ARs detected from it were typically bigger than those detected by more traditional heuristic-based methods. This resulted in inconsistencies between CG-Climate and other methods when evaluating every individual grid point (Figure 8a). This could largely be attributed to labels in the training set that were created by humans being larger than labels that were calculated from other methods. CG-Climate was consistent in finding the same AR events as a variety of other detection methods (Figure 8b).

CG-Climate was run on a large-ensemble and high-resolution climate dataset named “Weather@Home”. A case study was used to extrapolate the time and computational resources that would be required to run it on a common heuristic-based method. CG-Climate used several orders of magnitude less computational memory and almost one order of magnitude less time, which can be a critical constraint when applying an AR tracking method to a large amount of data. ARs that were tracked in Weather@Home under the early-21st century forcing scenario had a similar frequency to those tracked in reanalysis data over the same years.

Figure 1: Figure 3 from Higgins et al. (2023). An example of two different applications of semantic segmentation. (a) A feature-label pair of objects in a cityscape. (b) A feature label pair of ARs in an IWV field. AR contours are shown in purple, blue, green, and pink.

Figure 1: Figure 8 from Higgins et al. (2023). Precision and recall between each detection algorithm and all other ARTMIP algorithms (truth). The thresholds used are the number of algorithms that overlap for any given AR. Precision is defined as the number of true positives divided by the sum of true positives and false positives. Recall is defined as the number of true positives divided by the sum of true positives and false negatives. The area under the curve indicates the level of agreement with the truth. Precision/recall curves are shown for (a) AR events and (b) AR grid points.

Higgins, T. B., Subramanian, A. C., Graubner, A., Kapp‐Schwoerer, L., Watson, P. A. G., Sparrow, S., et al. (2023). Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set. Journal of Advances in Modeling Earth Systems, 15(4), e2022MS003495. https://doi.org/10.1029/2022MS003495

Prabhat, Kashinath, K., Mudigonda, M., Kim, S., Kapp-Schwoerer, L., Graubner, A., et al. (2021). ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather. Geoscientific Model Development, 14(1), 107–124. https://doi.org/10.5194/gmd-14-107-2021

Xu, B., Wang, N., Chen, T., & Li, M. (2015, November 27). Empirical Evaluation of Rectified Activations in Convolutional Network. arXiv. Retrieved from http://arxiv.org/abs/1505.00853

Weather Balloon Launch Demonstration with SIO60 Students

May 17, 2023

Students of SIO60 and instructors Kate Ricke and Drew Lucas joined CW3E Lead Engineer Douglas Alden and Lab Assistant Ali Wolman for a balloon launch demonstration on Wednesday morning April 5th at Scripps Pier. Built in 1988, the modern Ellen Browning Scripps Memorial Pier houses numerous environmental monitoring stations and enables small boat and scientific diving operations. CW3E has a permanent weather station installed to observe atmospheric conditions. Weather balloons, referred to in the weather community as radiosondes, are also launched from the pier during the rainy season to observe atmospheric rivers (ARs).

The class took a field trip to the pier on a clear, sunny day to participate in a weather balloon launch demonstration and learn about CW3E’s research. SIO 60: Experiences in Oceanic and Atmospheric Sciences is a class in which students gain exposure to the people & technology involved in conducting atmospheric & marine research. Douglas introduced the class to CW3E’s methods of data collection in studying ARs and applications of our research.

Students learned about AR Reconnaissance (AR Recon), a Research and Operations Partnership (RAOP) to study ARs led by CW3E with partners including the U.S. Air Force Reserve Command, NOAA, and others. Students also learned about Forecast Informed Reservoir Operations (FIRO), another RAOP CW3E is working on with water managers to support reservoir operations through improved weather and water forecasts.

Douglas and Ali introduced students to equipment & sensors, including the small, lightweight radiosondes which transmit atmospheric pressure, temperature, moisture & GPS data from which winds are derived. Students followed along as the radiosonde was prepared and the balloon was filled with helium. Student volunteers held onto the balloon, radiosonde & parachute & released them after a countdown by the class. During the demonstration students were actively engaged and asked thoughtful questions.

Education is a CW3E core value and the Center appreciates the opportunity to connect with UCSD students and share our work. CW3E hopes continued demonstrations with students from UCSD and other programs will increase student access to and interest in STEM and environmental research.

Skew-T Log-P diagram from the launch displaying temperature (red line), dew point (blue line), winds, and water vapor flux (black line; right plot) throughout the atmosphere.

SIO60 students assisted with the weather balloon launch demonstration. Students shown here observing while 3 volunteers are holding the radiosonde, parachute & weather balloon before launching.

CW3E Welcomes Dr. Gabe Lewis

May 1, 2023

CW3E is pleased to welcome Gabe Lewis as an embedded hydrologist with the California-Nevada River Forecast Center and California Department of Water Resources. Gabe joins us from a postdoctoral position at the University of Nevada, Reno, where he studied flooding from rain-on-snow events and the interaction of forests, snow, and climate change. Gabe completed his PhD at Dartmouth College in 2019, where he investigated the changing climate in Greenland using ice cores and geophysical data from two snowmobile traverses across the ice sheet. At CW3E, Gabe will be assisting with CNRFC river forecasting and analyzing ensemble streamflow predictions on the UCSD supercomputer using the operational forecast software. He is also excited to assist with DWR snow surveys across the Sierra Nevada, where Gabe can often be found skiing and rock climbing in his free time. We’re delighted to welcome him to UCSD and hope he enjoys working with the wonderful CW3E hydrology team.

CW3E Publication Notice: Aerosol-heavy precipitation relationship within monsoonal regimes in the Western Himalayas

CW3E Publication Notice

Aerosol-heavy precipitation relationship within monsoonal regimes in the Western Himalayas

April 26, 2023

A new paper entitled Aerosol-heavy precipitation relationship within monsoonal regimes in the Western Himalayas was recently published in the Atmospheric Research authored by CW3E postdoc Suma Battula, Steven Siems (Monash University), Arpita Mondal (IIT Bombay) and Subimal Ghosh (IIT Bombay). We used independent dynamical regimes from a previous study (Battula et al. 2022) and derived correlations between aerosols and heavy precipitation within monsoonal regimes – M1 (westerly), M2 (westerly + easterly) and M3 (easterly) in Western Himalayas. Additionally, the influences from meteorological covariates on these correlations were eliminated using partial correlation analysis.

Figure 1: Figure 5 from Battula et al. (2023). Composites of a) Precipitation (mm.day^-1), b) AOD from MERRA2 reanalysis and winds at 850 hPa (m.s^-1) for heavy precipitation events (HPEs) in monsoonal regimes M1, M2 and M3. The black star represents an arbitrary location 30 N, 77E where it rained heavily in all the regimes.

The moisture convergence during strong monsoon circulation favors the buildup of aerosols through low-level westerlies for HPEs in M1 (Figure 1). On the other hand, moisture convergence in M2 under weak monsoon circulation increases the mixing of low-level polluted westerlies with relatively cleaner easterlies, decreasing the AOD and hence weakening the relationship between AOD and precipitation. Thus, aerosol-precipitation relations can either be underestimated or overestimated if the influence of covariates is not eliminated. The overall partial correlation coefficient between aerosols and heavy precipitation is 0.17 in M1 and M2 but insignificant in M3.

Figure 2: Figure 8 from Battula et al. (2023). Temporal evolution of a) cloud droplet effective radius of liquid and b) ice phase in (μm), c) Liquid water path (g.m^-2), d) Ice water path (g.m^-2), from day − 4 to day +3 of HPEs in M1, M2 and M3 regimes.

Further, we found that M1, with a highly polluted environment, has the least cloud water path, droplet size with narrow size distribution than M2/M3 (Figure 2). Moreover, we did not find any significant correlation between AOD and cloud properties in the ice phase in any of the regimes. Therefore, the cold phase microphysical processes crucial for heavy precipitation are less sensitive to changes in the aerosols. Our findings imply that dynamical changes result in distinct aerosol-heavy precipitation relations and microphysical processes causing heavy precipitation in orographic regions such as Western Himalayas.

Battula, S. B., Siems, S., & Mondal, A. (2022). Dynamical and Thermodynamical Interactions in Daily Precipitation Regimes in the Western Himalayas. International Journal of Climatology ,42(9), 4909–4924. doi: https://doi.org/10.1002/joc.7511

Battula, S. B., Siems, S., Mondal, A., & Ghosh, S. (2023). Aerosol-Heavy Precipitation Relationship within Monsoonal Regimes in the Western Himalayas. Atmospheric Research, 288, 106728. doi: https://doi.org/10.1016/j.atmosres.2023.106728

CW3E Scientists Attend Women in the Sciences Leadership Workshop

April 25, 2023

Group photo of the participants of the workshop, with photo credit to Christina Olex who also gave the lectures during the event.

Two CW3E scientists, Dr. El Knappe and Dr. Minghua Zheng, recently attended the “Building Leadership Skills for Success in the Scientific Workforce” Workshop on 12-13 April. The event, which was co-hosted by NOAA and the College of Science-Geosciences at the University of Arizona, brought together 45 women in earth science disciplines from across the United States to train them in leadership and management skills. Attendees came from a variety of backgrounds, including graduate studies in atmospheric and marine sciences, research and support scientists, oceanographers, meteorologists, program managers, and other job titles.

The workshop kicked off with the Dominance, Influence, Steadiness and Conscientiousness (DISC) Personality Assessment, which showed the different personalities of the attendees. One takeaway was the importance of respecting each other’s personalities and working with coworkers in different ways accordingly. The first day also focused on emotional intelligence, with attendees sharing triggers that evoke emotional responses and how to deal with them strategically. They also discussed the traits of good and bad leaders and how to establish trustworthy relationships at the workplace.

On the second day, the focus shifted to unconscious bias, connection, and mentoring. Attendees shared the biases they have experienced, such as gender, race, culture, and education biases. The workshop also provided useful tips for making connections and mentorship.

Dr. Knappe and Dr. Zheng expressed their appreciation for the support they received from the Center to fund their attendance at the workshop, and highly recommend the event to any women scientists in leadership positions in the future. They found it to be a unique opportunity to gain knowledge, practice new understanding, and make networking connections. Overall, the workshop was a great success in training and empowering women in earth science disciplines to become successful leaders.

CW3E Publication Notice: Development of a Statistical Subseasonal Forecast Tool to Predict California Atmospheric Rivers and Precipitation Based on MJO and QBO Activity

CW3E Publication Notice

Development of a Statistical Subseasonal Forecast Tool to Predict California Atmospheric Rivers and Precipitation Based on MJO and QBO Activity

April 21, 2023

A new paper entitled “Development of a Subseasonal Statistical Forecast Tool to Predict California Atmospheric Rivers and Precipitation Based on MJO and QBO Activity” and authored by CW3E staff researcher Christopher Castellano, Michael DeFlorio, Peter Gibson (NIWA), Luca Delle Monache, Julie Kalansky, Jiabao Wang, Kristen Guirguis, Alexander Gershunov, F. Martin Ralph, Aneesh Subramanian (University of Colorado Boulder), and Michael Anderson (California DWR) was recently published in the Journal of Geophysical Research: Atmospheres. This research contributes to CW3E’s 2019–2024 Strategic Plan to improve atmospheric river (AR) prediction on subseasonal-to-seasonal (S2S) time scales by examining the relationship between the Madden–Julian oscillation (MJO), the quasi-biennial oscillation (QBO), and AR activity and precipitation in California. The authors also introduced an experimental forecast tool to predict the likelihood of above-normal and below-normal AR activity and precipitation at subseasonal lead times of 1–6 weeks based on the phase and amplitude of the MJO and QBO.

Consistent with previous studies, this paper demonstrates that subseasonal AR activity and precipitation in California are strongly modulated by the MJO and QBO, particularly during winter and early spring [i.e., January–March (JFM)]. There is a tendency toward below-normal AR activity and precipitation following an active MJO and easterly QBO conditions, and above-normal AR activity and precipitation following an active MJO and westerly QBO conditions in JFM. The opposite patterns are generally observed during fall and early winter (OND), but the anomaly signals are weaker and less coherent, especially for AR activity. A composite analysis of outgoing longwave radiation (OLR), 500-hPa geopotential heights, and 250-hPa winds revealed that easterly (westerly) QBO periods in JFM are associated with increased (decreased) MJO activity in the tropical western Pacific, anomalous ridging (troughing) over the Northeast Pacific, and a zonal retraction (extension) of the North Pacific jet. Differences in midlatitude large-scale circulation patterns between easterly QBO and westerly QBO are even more pronounced when the enhanced MJO convection is located over the tropical western Pacific (Phase 7). These findings suggest that the QBO plays an important role in directly modulating MJO activity and modifying MJO-related teleconnections via changes to the background state over the North Pacific.

In order to assess the potential utility of the experimental forecast tool, the authors conducted a skill assessment of probabilistic AR activity and precipitation hindcasts in Northern, Central, and Southern California, as well as two sets of smaller geographical domains. The hindcast skill was quantified by pairing a rigorous cross-validation approach with the ranked probability skill score. On average, the MJO/QBO statistical forecasts showed little or no improvement over climatological reference forecasts. However, certain combinations of MJO/QBO phase, lag time, and season yielded notably higher skill scores, reinforcing the notion of “windows of opportunity” for skillful subseasonal predictions. These forecasts of opportunity were predominantly associated with easterly QBO in JFM and FMA. Given the strong tendency for decreased AR activity and precipitation following easterly QBO in JFM and FMA, this forecast tool may provide useful information for water resource managers and dam operators during the latter half of the wet season. Furthermore, a lack of degradation of forecast skill for the smaller domains suggests that a more targeted application of this forecasting approach at the watershed scale is feasible. CW3E plans to launch this new experimental subseasonal forecast tool on its public S2S website (/s2s_forecasts/) next winter.

Figure 1: Figure 2 from Castellano et al. (2023). Probability matrices illustrating the weeks 1–6 lagged probability of below-normal (brown shading) or above-normal (green shading) AR-TIVT for all MJO/QBO phase configurations during OND (left) and JFM (right) in Northern California (top), Central California (middle), and Southern California (bottom). White squares indicate that the near-normal category has the highest probability. The black dots denote statistically significant probabilities of below- or above-normal AR-TIVT (based on the bootstrapping analysis).

Figure 2: Figure 11 from Castellano et al. (2023). Experimental forecast product showing the probability of above-normal (green arrows) and below-normal (brown arrows) precipitation in Northern California during the 6 weeks following an MJO in Phase 7 and easterly QBO conditions on 3 February 2015. The gray bars indicate the climatological normal range of values (i.e., the intertercile range) for a given calendar period. The climatological mean and median values are denoted by the horizontal black line and black dot, respectively. Above-normal values are greater than the upper tercile of climatology, and below-normal values are less than the lower tercile of climatology.

Castellano, C. M., DeFlorio, M. J., Gibson, P. B., Delle Monache, L., Kalansky, J. F., Wang, J., et al. (2023). Development of a statistical subseasonal forecast tool to predict California atmospheric rivers and precipitation based on MJO and QBO activity. Journal of Geophysical Research: Atmospheres, 128, e2022JD037360. doi: https://doi.org/10.1029/2022JD037360