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.

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.

Xu, B., Wang, N., Chen, T., & Li, M. (2015, November 27). Empirical Evaluation of Rectified Activations in Convolutional Network. arXiv. Retrieved from

Weather Balloon Launch Demonstration with SIO60 Students

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

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 ( -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:

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:

CW3E Scientists Attend Women in the Sciences Leadership Workshop

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 ( 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:

CW3E Welcomes Dr. Jorge Baño-Medina

CW3E Welcomes Dr. Jorge Baño-Medina

April 14, 2023

Jorge Baño-Medina joined CW3E’s subseasonal-to-seasonal (S2S) team as a Postdoctoral Scholar in April 2023. He graduated with a Ph.D. from the Climate and Data Science Department at the University of Santander in 2021. Prior to CW3E, his main research focused on exploring the benefits and limitations of deep neural networks to downscale global climate simulations, producing century-length high-resolution fields of precipitation and temperature for a variety of emission scenarios. Also, he evaluated the suitability of deep learning topologies to emulate regional climate models, and he aimed to produce high-resolution fields of fire-related indices.

At CW3E, Jorge’s research interests are centered on analyzing the potential of machine learning to improve sub-seasonal to seasonal forecasts of precipitation and temperature over the western United States as compared to current operational tools. Jorge is also keenly interested in exploring diverse research topics such as fire danger, climate change, and explainable artificial intelligence.

CW3E Publication Notice: Multi-Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated with Atmospheric Rivers over the Western U.S.

CW3E Publication Notice

Multi-Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated with Atmospheric Rivers over the Western U.S.

March 24, 2023

A new paper entitled “Multi-Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated with Atmospheric Rivers over the Western U.S.” was recently published in the Journal of Geophysical Research: Atmospheres and authored by CW3E researcher Zhenhai Zhang, Michael DeFlorio (CW3E), Luca Delle Monache (CW3E), Aneesh Subramanian (University of Colorado Boulder), Martin Ralph (CW3E), Duane Waliser (NASA JPL), Minghua Zheng (CW3E), Bin Guan (NASA JPL), Alexander Goodman (NASA JPL), Andrea Molod (NASA GMAO), Frederic Vitart (ECMWF), Arun Kumar (NCEP CPC), and Hai Lin (ECCC). As part of CW3E’s 2019-2024 Strategic Plan, CW3E seeks to improve atmospheric river (AR) prediction at subseasonal to seasonal (S2S; 2-week to 6-month) lead times. This work contributes to that specific goal and provides an assessment of subseasonal prediction skill of weekly water vapor transport associated with ARs over the Western U.S. in four dynamical model hindcast systems.

More skillful S2S forecasts of ARs are in high demand in the water supply management and flood control communities. This study aims to provide a multi-model S2S prediction skill assessment for the accumulated water vapor transport associated with ARs, which is closely related to wintertime extreme precipitation over the western U.S. The subseasonal prediction skill is evaluated at 1–4 weeks lead time in four dynamical model hindcast datasets from National Centers for Environmental Prediction (NCEP), European Centre for Medium‐Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), and Global Modeling and Assimilation Office (GMAO) at National Aeronautics and Space Administration (NASA). Three reanalysis datasets, including NCEP CFSR, ECMWF ERA5, and NASA MERRA2, are used to evaluate the sensitivity of the prediction skill to the choice of reference dataset.

This study shows that the AR-related water vapor transport is underestimated in ECMWF and ECCC over most of the investigated region, while its maximum has a southeastward shift in NCEP and GMAO at 3–4 weeks lead time. The root mean square error, anomaly correlation coefficient, and Brier skill score are calculated to quantify the prediction skill in both a deterministic and probabilistic sense. At week-3 lead time, the models have significant skill near the lower latitudes (< 40N) of the eastern North Pacific, extending northeastward to the California coastal area (Figure 1). Models have higher skills in forecasting no and strong AR-related water vapor transport cases than weak cases at weeks 1–4 lead time. The impacts of the Madden-Julian Oscillation (MJO) on the prediction skill are also explored. The results show that the MJO’s impact at week-3 lead time is larger than at week-1 and week-2 lead. At week-3 lead, the modulation of prediction skill by MJO mainly occurs over Central and Southern California. However, the impacts have large uncertainties across models, which might be caused by the different model performances in predicting the MJO and the relevant teleconnections.

This work is supported by the California Department of Water Resources Atmospheric River Program. It provides scientific support to the CW3E Subseasonal AR Experimental Forecasts, which are developed via a close collaboration between CW3E and NASA JPL.

Figure 1: (a)-(d): Anomaly correlation coefficients (ACCs) of the AR T-IVT for the four models with respect to ERA5 at week-1 lead time during the cool seasons of the hindcast periods. (e)-(h), (i)-(l), and (m)-(p) are the same as (a)-(d) but for week-2, week-3, and week-4 lead times, respectively. Only ACC values at 95% confidence level based on a 1000-resampling bootstrap statistical significance test are plotted.

Zhang, Z., DeFlorio, M. J., Delle Monache, L., Subramanian, A. C., Ralph, F. M., Waliser, D. E., … & Lin, H. Multi‐Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated with Atmospheric Rivers over the Western US. Journal of Geophysical Research: Atmospheres, e2022JD037608.

CW3E AR Update: 5 April 2023 Outlook

CW3E AR Update: 5 April 2023 Outlook

April 5, 2023

Click here for a pdf of this information.

Multiple Atmospheric Rivers Forecast to Impact Pacific Northwest Though the Weekend

  • A series of atmospheric rivers (ARs) are forecast to develop over the North Pacific Ocean and make landfall over the Pacific Northwest during the next several days
  • The first AR is forecast to make landfall tonight and bring AR2 conditions (based on the Ralph et al. 2019 AR Scale) to coastal Washington and Oregon
  • Another AR is forecast to make landfall this weekend, bringing AR2 conditions to coastal Washington and Oregon and AR1 conditions to coastal Northern California
  • There is still considerable uncertainty in the timing, duration, and intensity of the second landfalling AR
  • The NWS Weather Prediction Center (WPC) is forecasting at least 3–7 inches of precipitation in the Cascades and Coast Ranges in Washington and Oregon during the next 7 days, with more than 10 inches possible in the Olympic Mountains
  • The NWS WPC has issued a marginal risk of rainfall exceeding flash flood guidance along the coast from Northern California to the Olympic Peninsula tomorrow into Friday morning
  • Several rivers in Washington and Oregon are forecast to rise above monitor stage during the next 7 days
  • A majority of the precipitation is forecast to fall as rain in most watersheds due to higher freezing levels during these AR events

Click images to see loops of GFS IVT and IWV forecasts

Valid 1200 UTC 5 April – 1200 UTC 10 April 2023










Summary provided by C. Castellano, S. Bartlett, and S. Roj; 5 April 2023

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

*Outlook products are considered experimental

CW3E Welcomes Dr. Zhiqi Yang

CW3E Welcomes Dr. Zhiqi Yang

April 3, 2023

Zhiqi Yang joined CW3E’s subseasonal-to-seasonal (S2S) team as a Postdoctoral Scholar in April 2023. She graduated with a Ph.D. from the Civil and Environmental Engineering Department at the University of Iowa in 2021. Prior to CW3E, her research focused on extreme precipitation, climate change, numerical climate modeling (WRF-CHEM), developing and maintaining the Euro-Mediterranean Center on Climate Change (CMCC) seasonal forecast system, and evaluating seasonal forecasted TCs activities.

At CW3E, Zhiqi’s research interests are centered on enhancing the deterministic and probabilistic S2S prediction of weather parameters, particularly precipitation and atmospheric river (AR)-related parameters, over the western United States. She aims to develop an innovative experimental S2S forecast product that will greatly benefit water management in the region. Zhiqi is also keenly interested in exploring diverse research topics such as extreme weather, climate change, hydrometeorology, and Tropical cyclones (TCs)!

CW3E Welcomes Joseph Bursey

CW3E Welcomes Joseph Bursey

March 28, 2023

Joseph Bursey joined CW3E as a research project manager in March 2023. He received his BS in Biology from Winthrop University in Rock Hill, SC and then became manager for a plankton ecology research lab at NC State. Here, Joseph went on to also earn his Masters in Marine Science (2016) with research focusing on seasonal differences plankton in biodiversity, trophic cascades, and carbon energy transfer to upper trophic levels. Essentially who’s there seasonally, who’s eating who, and carbon energy movement through the food web in Bogue Sound, North Carolina.

After completing his masters degree, he became facility manager for an aquaculture research lab at NC State. Here, he worked on numerous larvae culture rearing projects with the scope to reduce overfishing by providing sustainable alternatives. He worked with PIs to meet their research needs at the facility and handled all facility maintenance and repairs. From here, he traveled across the country to become hatchery manager for a fish enhancement program at Hubbs Seaworld, San Diego where he managed a team to spawn and release thousands of fish.

Joseph is an avid wildlife photographer and musician and has a deep personal interest in mitigating human impacts on the environment. He has long aspired to join the Scripps Institution of Oceanographer to further his goal at a like-minded organization.

His role at CW3E will be serving as research project manager to help support Forecast Informed Reservoir Operations (FIRO). He looks forward to bringing his varied experience, managerial skills, and passion to CW3E research efforts to enhance CW3E’s ability to provide 21st Century water cycle science, technology and outreach to support effective policies and practices that address the impacts of extreme weather and water events on the environment, people and the economy of Western North America.

Undergraduates of AR Recon

Undergraduates of AR Recon

March 27, 2023

CW3E would like to highlight two UC San Diego undergraduate students who were instrumental in the success of the annual CW3E-led Atmospheric River Reconnaissance effort. Both served in the role of flight track coordinator under the leadership of CW3E scientist Shawn Roj.

Kyle Hurley, originally from Crofton, Maryland, enlisted into the United States Marine Corps in 2016 where he deployed two times within four years and was honorably discharged in 2020. Since then, Kyle has achieved an associate’s degree in both Biology and Chemistry with honors. In April of 2022, Kyle Hurley was then accepted into UC San Diego to pursue a bachelor’s degree in Oceanic and Atmospheric Sciences. Kyle has high hopes of becoming a meteorologist, working in the field of severe weather. Kyle joined the CW3E team back in November of 2022.

Jackson Ludtke, of Silver Spring, Maryland, enrolled in UC San Diego in the fall of 2021. He is majoring in Urban Studies and Planning with minors in Geosciences and Climate Change Studies. After earning his private pilot’s license in the summer of 2021, Jackson plans on pursuing a career in aviation and hopes of flying for NOAA or the Coast Guard one day. The late Professor Jane Teranes helped Jackson realize how much he enjoys helping out on the logistical side of science. He has been working at the Center since December of 2021.

Kyle and Jackson have quickly learned how to work with the flight planning tool and develop flight tracks during the morning briefings. They have both served as the lead flight planning coordinator. It took a large, dedicated team to cover CW3E’s longest and most active AR Recon season, and Kyle and Jackson are looking forward to contributing next season.

New CW3E Award Represented at the Colorado River District’s State of the River Event

New CW3E Award Represented at the Colorado River District’s State of the River Event

March 27, 2023

Colorado River District hosted the State of the River – Upper Yampa event on March 23, 2023, in Steamboat Springs, Colorado, with roughly 100 attendees. Madison Muxworthy, of Yampa Valley Sustainability Council, spoke on behalf of the project team working on Enhancing Soil Moisture Observations to Support Water Resource Management in the Upper Yampa River Basin. This project is led by PI Dr. Martin Ralph and includes staff from the Center for Western Weather and Water Extremes, Yampa Valley Sustainability Council, Colorado Mountain College, and Upper Yampa Water Conservancy District. The presentation focused on the future expansion of the Yampa Basin soil moisture monitoring network over the next three years, made possible by recent grant funding from Colorado River District’s Community Funding Partnership, Colorado Water Conservation District’s Water Plan Grant, and Upper Yampa Water Conservancy District. CW3E is really excited to be working with a group of stellar partners on such an impactful project to support robust monitoring in the region.

CW3E and Colorado Partners secure funding for soil moisture network expansion

CW3E and Colorado Partners secure funding for soil moisture network expansion

March 27, 2023

CW3E, Yampa Valley Sustainability Council (YVSC) and key partners Upper Yampa Water Conservancy District (UYWCD) and Colorado Mountain College (CMC) have secured funding for the project, “Enhancing Soil Moisture Observations to Support Water Resource Management in the Upper Yampa River Basin.” This funding enables the project team to expand upon the first soil moisture monitoring station that was installed in the Yampa River Basin in September 2022 and install eight more stations over the next two years.

The expanding Yampa Basin soil moisture network measures moisture concentrations in the soil as well as soil and air temperature, precipitation, snow depth, fuel moisture, and other key climatic variables. Soil moisture data is key to understanding how snowpack relates to spring runoff and river flows. Snow-to-flow dynamics – or how much water is delivered to our rivers from our snowpack – are mediated by soil moisture. Drier soils in upland areas function like dry sponges and absorb water, reducing the amount of water delivered to rivers and thus reducing flows. The Yampa Basin is already experiencing changes in snow-to-flow dynamics, where normal snowpack years are followed by low spring and summer flows.

“The timing for the launch of the network is key,” said Dr. Michelle Stewart, Executive Director of YVSC. “Our snow-to-flow patterns have been changing considerably in recent years and monitoring soil moisture data is an important step towards a better understanding of how water in our basin is changing due to changing climate.”

The Yampa Basin soil moisture network stands to benefit forecasters, water managers, and water users to better understand water supply by increasing the understanding of soils, which Dr. Marty Ralph, Principal Investigator on the project and Director of CW3E, calls the “fourth reservoir” in
water planning. The primary types of reservoirs water managers think about are snow, rivers/streams, and reservoirs, but soils have been a missing part of the puzzle. The project goals are to reduce uncertainty in seasonal snowmelt runoff predictions and work with stakeholders in the Yampa Valley to appropriately integrate these data into water management decision support, including at sub-seasonal to seasonal forecast lead times ranging from weeks, months, and seasons. The network will be critical to establishing a baseline for long-term monitoring of new trends in soil moisture expected due to greater evapotranspiration – the cumulative transfer of moisture from soils and plants to the atmosphere – related to warming as climate changes.
The funding is an exciting development for the Basin because it allows for investments in infrastructure that increase resilience to water changes in the basin. According to Andy Rossi, General Manager of the UYWCD, “the data will be invaluable to UYWCD in forecasting runoff and assisting with reservoir and water resource management decisions,” and the hope is that, “this network will help close a data gap in the Yampa River Basin and serve as a useful tool for water managers in our basin and beyond.” UYWCD’s initial funding has been instrumental to the successful installation of this first station and an important anchor for building out the network.

Colorado Mountain College, which will partner to provide student career training in climate monitoring and instrument maintenance, sees the expansion of the network as an important contribution to regional workforce development. Dr. Nathan Stewart, Professor of Ecosystem Science and Sustainability, identifies this funding as integral to student career pathways in water. “Expansion of our region’s Soil Moisture monitoring network will provide us with a state-of-the-art platform for technical training in meteorology, hydrology, and ecosystem science,” he said. “Student engagement with this network is essential to the recruitment and development of our future western water workforce.”

The Yampa Basin soil moisture network began in 2021 when extreme drought conditions led CW3E and YVSC to partner with the UYWCD to identify critical areas for soil moisture monitoring in the basin through a basin analysis. In 2022, UYWCD funded the installation of the first soil moisture and surface meteorological monitoring station near Stagecoach Reservoir and the development of an online data portal site for project partners and public use.

This three year project was funded through a joint grant from the Colorado Water Conservation Board (CWCB) Water Plan Grants, Colorado River District’s (CRD) Community Funding Partnership and support from UYWCD.

“It is an exciting opportunity for our Center and key partners YVSC and CMC to pull together and create this network,” says Ralph. “It is envisioned as a pathfinder for the future. We are excited to be working closely with UYWCD, CRD, and CWCB to develop this capability and apply it to the special water management needs in the Yampa, from local to state and regional scales.”

This collaborative effort between CW3E, YVSC, CMC, and UYWCD includes additional project partners at Aspen Global Change Institute and Natural Resources Conservation Service.