Author: cw3e-web

Summary of CW3E Outlooks During Water Year 2024

Summary of CW3E Outlooks During Water Year 2024

April 22, 2024

CW3E summarizes and disseminates key forecast information about potentially hazardous weather over the Western US, with a strong emphasis on landfalling ARs, as part of the California Atmospheric River (AR) and Forecast Informed Reservoir Operations (FIRO) programs. These activities consist of written outlooks, “quick looks”, and post-event summaries of high-impact storms. The outlooks and quick looks provide valuable information and situational awareness guidance to stakeholders, such as the California Department of Water Resources and local water agencies, as well as the general public.

CW3E created and posted a total of 25 outlooks, 27 quick looks/Twitter threads, and 10 event summaries during October 2023 through March 2024. During a particularly active four-week period between 21 January 2024 and 17 February 2024, CW3E posted six outlooks and five quick looks. This period included the record-breaking precipitation and severe flooding event in San Diego County on 22 January 2024 and a strong AR in early February 2024 that produced >10 inches of rain in the Los Angeles metro area. Some locations in Southern California received more than a year’s worth of precipitation in the first 3 weeks of February 2024.

The CW3E outlooks and post-event summaries focus on storm events over the Western US; however, the team also prepared outlooks and event summaries for two exceptionally strong ARs that produced extreme precipitation and widespread flooding over the Eastern US in mid-December 2023 and early January 2024. These activities were sponsored by the nationwide expansion efforts of the FIRO program and funded projects with the NOAA Cooperative Institute for Research to Operations in Hydrology (CIROH).

A more recent initiative supported by the AR Program has involved the creation and posting of subseasonal-to-seasonal (S2S) forecast information via regularly scheduled Subseasonal and Seasonal Outlooks. While the AR outlooks and quick looks focus on weather time scales (forecast lead times of up to 10 days), the Subseasonal and Seasonal Outlooks focus on 2–6 week and 1–3 month lead times, respectively, and are posted between November and March. These longer time scales are a primary concern for the water resource management community, especially given the implications of cool-season precipitation and snowpack for drought and water supply in the Western US. CW3E posted its first S2S outlook in December 2021, and the S2S outlooks team has continuously worked to augment and improve the content in these updates based on an iterative process with direct input provided by practitioners.

In Water Year (WY) 2024, CW3E began posting subseasonal and seasonal forecast information in two unique outlooks; the Subseasonal Outlooks are typically posted weekly, and the Seasonal Outlooks are posted monthly. Both the Subseasonal and Seasonal Outlooks include information from experimental forecast products developed at CW3E and collaborating institutions such as NOAA and the International Research Institute for Climate and Society (IRI), as well as a single prediction based on a synthesis of the products. During WY 2024, CW3E posted 17 Subseasonal Outlooks and 5 Seasonal Outlooks. CW3E will solicit further feedback from users this year and aims to incorporate additional forecast products currently under review into its WY 2025 Subseasonal and Seasonal Outlooks.

Category

Oct

Nov

Dec

Jan

Feb

Mar

Total

AR/Precipitation Outlooks

4

2

5

9

5

0

25

Quick Looks/Twitter Threads

3

4

7

4

3

6

27

Event Summaries

0

0

3

3

3

1

10

Subseasonal Outlooks

0

2

3

5

3

4

17

Seasonal Outlooks

0

1

1

1

1

1

5

Reservoirs-plus-Snowpack Water Storage in the Sierra Nevada

Reservoirs-plus-Snowpack Water Storage in the Sierra Nevada

April 19, 2024

Current measures of water stored in Sierra Nevada reservoirs and snowpacks are now continually updated and available from CW3E. Mountain snowpacks provide an “extra” way that water gets stored in California and across the Western US, acting as natural reservoirs that hold winter precipitation (as snow) from the cold wet season until spring and summer when the water is released as snowmelt when water demands for human and environmental uses, including irrigation, are high. Thus the combination of water stored as snow and water stored in human-built reservoirs is a useful indicator of development, persistence, and termination of droughts in many western water-supply systems. As the climate warms in coming decades, the “typical” mix of water in snowpack versus reservoir is projected to change, with far less snow holding far less water in future winters. Whether the water no long stored in snow ends up residing instead in reservoirs for more of the year in the future will depend on a variety of tradeoffs associated with winter-weather and hydrological changes, infrastructure constraints, and flood- and resource-management responses that tracking of the combined storage amounts may help to highlight.

This approach to tracking water-supply status was initially developed for drought early-warning and tracking purposes, but has also demonstrated value in wet years. Notably, last year, these figures attracted widespread attention when they showed that the 2023 snowpack in drainages above the ancient Tulare Lake bed contained as much as three times the total amount of space in the reservoirs in that drainage even if all the water currently in the reservoirs was released to make space. This highlighted that nearly all of the water that entered the reservoirs that year was going to have to be routed out of the Tulare Basin or else flooding would certainly occur. However, that much water could not be routed out of the Basin with existing infrastructures and so flooding did occur, and Tulare Lake refilled, covering large expanses of agricultural land and impacting communities. In the three preceding drought years (2020-2022), by contrast, snowpacks never grew to sizes that would refill Sierra Nevada reservoirs, and so water in storage declined each year.

Since 2015, scientists at Scripps have been developing and circulating simple graphics that showed how the combination of these two forms of storage evolved, in order to better communicate water resource status as the water years progressed. CW3E researcher Mike Dettinger demonstrated the value of tracking of water supply status by tracking these combined storages during the 2012-2015 California drought (Dettinger and Anderson, 2015), and has been producing and circulating them on a roughly monthly (or more frequent, depending on circumstances) basis since. The development and original updates were supported by NOAA’s National Integrated Drought Information System (NIDIS) via the California Nevada Adaptation Program (CNAP).

This spring the process of updating and generating these diagrams was automated so that now they will be updated on a daily basis, with support from the California Department of Water Resources via the AR Program. Updated storage diagrams will be available at:

https://cw3e.ucsd.edu/water_storage_tracking/

along with background information, explanations, and archives of the diagrams from previous years (to put the current year’s progress into historical contexts).

Dettinger, M.D., and Anderson, M.L., 2015, Storage in California’s reservoirs and snowpack in this time of drought: San Francisco Estuary and Watershed Science, 13(2), 5 p., http://dx.doi.org/10.15447/sfews.2015v13iss2art1.

CW3E Publication Notice: Subseasonal Potential Predictability of Horizontal Water Vapor Transport and Precipitation Extremes in the North Pacific

CW3E Publication Notice

Subseasonal Potential Predictability of Horizontal Water Vapor Transport and Precipitation Extremes in the North Pacific

April 17, 2024

A new paper entitled “Subseasonal Potential Predictability of Horizontal Water Vapor Transport and Precipitation Extremes in the North Pacific” was recently published in Weather and Forecasting by CU Boulder PhD Candidate Tim Higgins, Aneesh Subramanian (CU Boulder), Will Chapman (NCAR), David Lavers (ECMWF), and Andrew Winters (CU Boulder). This work This work contributes to the Subseasonal to Seasonal Prediction of Extreme Weather priority area in CW3E’s 2019-2024 Strategic Plan by exploring differences between the predictability of integrated vapor transport (IVT) and precipitation at lead times of 3 weeks and 4 weeks. The results demonstrated that extreme IVT events (exceeding the 90th percentile) have higher predictability in the subseasonal range than extreme precipitation events do. The connection between the differences in predictability to the predictability of the North Pacific Jet, which was shown to have some forecast skill out to a lead time of four weeks in Winters (2021), was also examined.

This work used the “potential predictability” approach, which has also been used to demonstrate differences between the predictability of IVT and precipitation at medium-range lead times (Lavers et al. 2016). When assessing potential predictability, a single member from an ensemble forecast functions as the “model-observation” and the remaining ensemble members function as the forecast. The process is repeated until every ensemble member functions as the “model-observation” one time. This method operates under the assumption of a perfect model, which allows the authors to assess predictability rather than prediction skill and eliminates any potential model bias.

The study primarily focused on predictability in a region off the US West Coast that was defined as the “jet exit region”. This region has some forecast skill out to week 4 of both IVT and precipitation, is relevant to anomalously high precipitation on the coast, and is greatly impacted by regimes of the North Pacific Jet. When both IVT and precipitation were skillfully forecasted, the strength of the jet extension regime was greater than it was when IVT and precipitation were not skillfully forecasted (Figure 1). The connection between the strength of the jet extension regime and 90th percentile IVT was also stronger than the correlation between the jet extension regime and 90th percentile precipitation.

IVT in the jet exit region maintained higher skill than precipitation did at all lead times from week 1 to week 4 (Figure 2). The differences were significant at all lead times when skill was assessed at each individual grid point. When conditions were averaged over the jet exit region before assessing skill to account for local variability, the skill gap decreased, with differences generally remaining significant at subseasonal lead times and no longer being significant at medium-range lead times. The relationship between IVT and a teleconnection (North Pacific Jet regimes) that has some forecast skill in the subseasonal range is a likely cause of differences in predictability.

Figure 1. (Fig. 5 from Higgins et al., 2024): Composites of Principal Component Analysis values of the model-observed NPJ during model-observed (a) 90th percentile precipitation at week 3, (b) 90th percentile IVT at week 3, (c) 90th percentile precipitation at week 4, and (d) 90th percentile IVT at week 4 during skillful (green) and unskillful (red) forecasts. Lighter colors represent earlier lead times and become darker through the progression of the forecasts. Forecasts of wet weeks within all individual points from 31.5°N – 40°N, 139.5°W – 152.5°W were used. Each data point represents a mean of lead times spanning 7 days starting at days 1-7. N (Above) and N (Below) represent the number of unique initialization times in which model-observed 90th percentile conditions existed during skillful and unskillful forecasts, respectively.

Figure 2. (Fig. 4 from Higgins et al., 2024): Mean ROC scores of precipitation and IVT in the jet exit region at all lead times up to 4 weeks. Spatial averaging was not applied in (a) and was applied in (b). A student t-test was used to assess statistical significance at the 95% level at all lead times. The shaded area represents one standard deviation above and below the mean.

Higgins, T. B., Subramanian, A. C., Chapman, W. E., Lavers, D. A., & Winters, A. C. (2024). Subseasonal Potential Predictability of Horizontal Water Vapor Transport and Precipitation Extremes in the North Pacific. Weather and Forecasting, https://doi.org/10.1175/WAF-D-23-0170.1.

Lavers, D.A., D.E. Waliser, F.M. Ralph and M.D. Dettinger, 2016: Predictability of horizontal water vapor transport relative to precipitation: Enhancing situational awareness for forecasting Western U.S. extreme precipitation and flooding. Geophysical Research Letters, 43, 2275-2282. https://doi.org/10.1002/2016GL067765.

Winters, A. C., 2021: Subseasonal Prediction of the State and Evolution of the North Pacific Jet Stream. Journal of Geophysical Research: Atmospheres, 126 (17), https://doi.org/10.1029/2021JD035094..

CW3E Publication Notice: Local and Object-based Perspectives on Atmospheric Rivers Making Landfall on the Western North American Coastline

CW3E Publication Notice

Local and Object-based Perspectives on Atmospheric Rivers Making Landfall on the Western North American Coastline

April 10, 2024

A new article titled, “Local and Object-based Perspectives on Atmospheric Rivers Making Landfall on the Western North American Coastline” was recently published in the American Meteorological Society’s Journal of Hydrometeorology by Wen-Shu Lin (CW3E), Joel Norris (CW3E), Michael DeFlorio (CW3E), and Marty Ralph (CW3E). This work contributes to the Atmospheric River Research and Applications priority area in CW3E’s 2019-2024 Strategic Plan by investigating characteristics of atmospheric rivers (ARs) affecting the western North American coastline, and was sponsored by the California Department of Water Resources Atmospheric River Program and the US Army Corp of Engineers.

This paper presents the climatology and variability of landfalling ARs over the western North American coastline, including AR intensity, duration, coastal extent, IVT temporal evolution, and synoptic conditions. ARs are defined by the Ralph et al. (2019) and Guan and Waliser (2019) AR detection algorithms for the local and object-based perspectives, respectively. The intensity of ARs is ranked following the classification scheme of Ralph et al. (2019). The local perspective shows higher AR frequency in Oregon and Washington and lower AR frequency in Southern California and southeastern Alaska, regardless of AR ranks (Fig. 1 left). Strong ARs are less frequent but with a greater seasonal cycle than weak ARs. However, the object-based perspective shows less geographical variation of AR frequency (Fig. 1 right). Although there is nearly no seasonal cycle of AR frequency in Alaska, ARs intensify in summer but weaken in winter.

Our results using object-based analysis additionally highlight that the strong ARs at lower latitudes are associated with stronger wind than weak ARs, while strong ARs at higher latitudes are associated with greater moisture than weak ARs (Fig. 2b,c). IVT at the AR core is largest for stronger ARs in Oregon and Washington and decreases poleward and equatorward (Fig. 2a). One common feature for ARs in object-based analysis is that both IVT in the AR core and cumulative IVT along the coastline usually increase from the first to second day for strong ARs but decrease after the first day of landfall for weak ARs, and hence the strength of an AR could not solely be determined by the IVT magnitudes upon ARs making landfall. These results are important to more comprehensively understand the relationship between AR characteristics and the resulting impacts on communities and the landscape.

Figure 1. Adapted from Figs. 2 and 5 from Lin et al. (2024). Left: The R19 AR frequency for (a) annual mean [hours month-1], (b) NDJFM departure from the annual mean [hours month-1], and (c) JJASO departure from the annual mean [hours month-1] for 250-km intervals along the western North American coastline. Right: AR frequency associated with the GW19 AR objects for (a) annual mean [hours month-1], (b) NDJFM departure from the annual mean [hours month-1], and (c) JJASO departure from the annual mean [hours month-1] for 250-km intervals along the western North American coastline.

Figure 2. Fig. 9 from Lin et al. (2024). (a) IVT [kg m-1 s-1] at the core location, (b) average IWV [kg m-2] at core location, (c) average ratio of IVT to IWV [m s-1] at core location, and (d) cumulative IVT [1014 kg] along the coastline and over the duration of the AR averaged over the GW19 AR objects in six coastal regions.

Lin, W., Norris, J. R., DeFlorio, M. J., & Ralph, F. M. (2024). Local and Object-based Perspectives on Atmospheric Rivers Making Landfall on the Western North American Coastline. Journal of Hydrometeorology (published online ahead of print 2024). https://doi.org/10.1175/JHM-D-22-0155.1

CW3E Publication Notice: Enhancing Regional Climate Downscaling Through Advances in Machine Learning

CW3E Publication Notice

Enhancing Regional Climate Downscaling Through Advances in Machine Learning

April 4, 2024

CW3E postdoctoral researcher Jorge Baño-Medina recently published a co-authored article titled “Enhancing regional climate downscaling through advances in machine learning” in Artificial Intelligence for the Earth Systems. This review article was led by researchers from the National Institute of Water and Atmospheric Research (New Zealand), along with researchers from the Santander Meteorology Group (Spain), the Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes (Australia), the Faculty of Geosciences and Environment (Switzerland), the National Center for Atmospheric Research (USA), and the Fraunhofer ITWM (Germany). The work aligns with CW3E’s 2019-2024 Strategic Plan to support Monitoring and Projections of Climate Variability and Change using Emerging Technologies.

Despite the sophistication of Global Climate Models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale. Statistical downscaling aims to bridge the gap between the global and the regional-to-local scales by learning an empirical function using large records of data. In the last few years, deep learning (DL) topologies have emerged as potential statistical downscaling tools, due to their ability to learn complex patterns from data. This study summarizes the existing literature about statistical downscaling of climate change scenarios by means of DL, offering a thorough exploration of the current applications, and training strategies that can circumvent existing limitations of statistical downscaling models (e.g., generalization to out-of-distribution spaces or extrapolation to climate change scenarios).

Figure 1. below illustrates the structure of the manuscript and the topics covered in the review. First, we provide an overview of empirical downscaling and we introduce computer-vision based climate downscaling algorithms. Then, we discuss recent machine learning (ML) advances in observational downscaling and Regional Climate Model (RCM) emulation, together with strategies to enhance the out-of-distribution performance of the models. Finally, we outline various future research directions and present an evaluation framework for ML-based empirical downscaling algorithms.

Figure 1: Adapted from Figure 2 in Rampal et al. (2024) An overview of the topics concerning climate downscaling that are discussed in this review article.

Rampal, N., Hobeichi, S., Gibson, P. B., Baño-Medina, J., Abramowitz, G., Beucler, T., … & Gutiérrez, J. M. (2024). Enhancing Regional Climate Downscaling Through Advances in Machine Learning. Artificial Intelligence for the Earth Systems. https://doi.org/10.1175/AIES-D-23-0066.1

CW3E Subseasonal Outlook: 22 March 2024

CW3E Subseasonal Outlook: 22 March 2024

March 22, 2024

Click here for a pdf of this information.


 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Summary provided by C. Castellano, J. Wang, Z. Yang, M. DeFlorio, and J. Kalansky; 22 March 2024

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

*Outlook products are considered experimental

CW3E Hydrologists Ming Pan and Taylor Dixon Participate in an Exchange with DWR

CW3E Hydrologists Ming Pan and Taylor Dixon Participate in an Exchange with DWR

March 19, 2024

On Tuesday, March 5th, DWR’s Hydrology Section hosted CW3E employees, Ming Pan and Taylor Dixon, to get better acquainted with each other’s hydrologic forecasting work. Even though the two teams have already developed a strong partnership in improving hydrologic runoff models together, the in-person meeting proved to be extremely beneficial.

DWR’s staff shared with CW3E how the legacy system of forecasting water supply runoff volumes for the Bulletin 120 have been produced. This included the technical details of the various multi-linear equations and other methods that have historically been used to forecast April through July runoff volumes for nearly two dozen watersheds in California. DWR further discussed how the 80 percent confidence range is calculated and what innovations the DWR team has produced over the past few years to help improve the median forecasts and the confidence range. DWR staff highlighted which innovations have come from their work with CW3E which includes advancements in the West-WRF forecasts, S2S forecasts, and CW3E’s development of WRF-Hydro models. Through rich back and forth discussions, the CW3E representatives learned more about the long-standing but evolving statistical forecast procedures and gained a deep appreciation for the institutional dedication to and value of the DWR forecasting platform. The CW3E representatives asked many questions and helped identify potential future collaboration opportunities between the two groups.

The discussion later was focused on the WRF-Hydro model where CW3E gave an update on the model products produced the same week and a deeper dive into the modeling framework. This included discussions on fundamental concepts such as model pre-processing, spatial/temporal resolution on the hydrometeorological data used, modeling calibration methods, and post processing algorithms. The discussions were also expanded to include how to leverage the benefit of both physical (process-based) and statistical (data-driven) modeling, as well as the rich collection of observation records, in the context of Machine Learning and how that may apply to problems like data assimilation/fusion and impact of climate change and landcover change (e.g. wildfire). The teams went through the WRF-Hydro “sandbox” established by CW3E on DWR’s Linux workstation, including the JupyterHub/JupyterLab platform, WRF-Hydro model, and hydrometeorological forcing data in order to help DWR implement innovations made by the CW3E team into water supply forecasts. The meeting was extremely valuable, it brought the two teams’ relationship closer together, and both teams look forward to the next opportunity.

CW3E Subseasonal Outlook: 15 March 2024

CW3E Subseasonal Outlook: 15 March 2024

March 15, 2024

Click here for a pdf of this information.


 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Summary provided by Z. Yang, C. Castellano, J. Wang, M. DeFlorio, and J. Kalansky; 15 March 2024

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

*Outlook products are considered experimental

CW3E Publication Notice: Synthetic forecast ensembles for evaluating Forecast Informed Reservoir Operations

CW3E Publication Notice

Synthetic forecast ensembles for evaluating Forecast Informed Reservoir Operations

March 11, 2024

A new article titled “Synthetic forecast ensembles for evaluating Forecast Informed Reservoir Operations” by Zach Brodeur (Cornell University), Chris Delaney (Sonoma Water), Brett Whitin (NOAA/NWS), and Scott Steinschneider (Cornell University) was recently published in the American Geophysical Union’s Water Resources Research. The work supports an ongoing, combined effort between Cornell University, CW3E, NOAA/NWS, USACE, and local water agencies to integrate synthetic forecasts into the development of robust and resilient Forecast Informed Reservoir Operations (FIRO). Prior CW3E-led FIRO viability assessments have identified the development of synthetic forecasts as a key effort to enable the thorough design and testing of FIRO policies, ensuring they are safe and effective across the widest possible range of events.

In the featured work, the authors develop a statistical methodology to generate synthetic ensemble forecasts that emulate the skill and behavior of ensemble forecasts from the NOAA/NWS Hydrologic Ensemble Forecasting System (HEFS). The synthetic forecast approach models the ensemble forecast uncertainty as a function of streamflow observations, enabling the generation of new sets of plausible ensemble forecast sequences for any series of streamflow observations in the historical record (see Figure 1). Since HEFS hindcasts are limited to the period from the early 1980s to present (because of limitations to the satellite record), this capability has the potential to expand the availability of forecast data to the 1950s or earlier in many locations. Moreover, the technique enables generation of plausible ensemble forecasts for any streamflow sequence (e.g. stochastic streamflow models, scaled design events, climate change simulations, etc.), allowing performance assessments of FIRO strategies under a wide range of potential scenarios. The methodology is demonstrated at Lake Mendocino, CA, the pilot site for the FIRO viability assessment process.

Figure 1: Adapted from Figure S3.1 in Brodeur et al. (2024) for inflows at Lake Mendocino, CA. a) HEFS forecast issued 5 days prior to the February 18, 1986 flood, b-d) 3x synthetic HEFS (sHEFS) ensembles for the same event and lead time.

The synthetic forecasting technique presented in this work overcomes earlier limitations in synthetic forecast methodologies that were too simplified to capture the complexities of forecast uncertainty across multiple lead times, sites, and ensemble members at a daily resolution. The evaluation of the synthetic forecast approach consists of two primary efforts: 1) ensemble forecast verification techniques to ensure parity between the synthetic forecasts (syn-HEFS) and actual forecasts (HEFS); and 2) operational validation of the synthetic forecasts using the Ensemble Forecast Operations (EFO) model of Delaney et al. (2020).

The operational validation results for the hindcast period (1986-present) are highlighted below in Figure 2, with particular emphasis on the February 18, 1986 flood. The synthetic HEFS forecasts emulate operational behavior of the single hindcast sequence of the actual HEFS, while also revealing operational vulnerabilities (i.e. potential emergency spillway usage) to alternative ensemble forecasting sequences during extreme events.

Figure 2: Adapted from Figure 9 in Brodeur et al. (2024). a) Lake Mendocino storage time series for HEFS, perfect forecast operations (PFO), and 100x samples of synthetic forecasts; b) as in (a), but zoomed in to the February 18, 1986 event; c) as in (b) but for the reservoir release time series.

Figure 3 demonstrates the capability to extend synthetic HEFS forecasts to the pre-hindcast period (prior to 1985) and assess operational policy performance outside the available hindcast record. This demonstration shows that the synthetic forecast approach is an effective risk analysis tool and underscores their potential for out-of-sample FIRO policy evaluation.

Figure 3: Adapted from Figure 10 in Brodeur et al. (2024). As in Figure 2, but for the pre-hindcast period (1948-1985) for (a) and zoomed in to the December 23, 1964 event for (b-c). Importantly, there are no available HEFS forecasts to compare in this period.

Overall, the study showcases an extremely important ‘first-of-its-kind’ effort towards the development of a synthetic ensemble forecasting methodology that operates at the requisite resolution needed for operationally relevant FIRO risk analysis. The synthetic forecast work is a continuing collaboration between Cornell University and CW3E and opens the door to a wide variety of potential future studies and exciting potential to integrate with ongoing FIRO activities, including forthcoming viability assessments.

Brodeur, Z. P., Delaney, C. , Whitin, B., & Steinschneider, S. (2024). Synthetic forecast ensembles for evaluating Forecast Informed Reservoir Operations. Water Resources Research, 60(2) e2023WR034898. https://doi.org/10.1029/2023WR034898

Delaney, C. J., Hartman, R. K., Mendoza, J., Dettinger, M., Delle Monache, L., Jasperse, J., et al. (2020). Forecast Informed Reservoir Operations using ensemble streamflow predictions for a multipurpose reservoir in Northern California. Water Resources Research, 56(9). https://doi.org/10.1029/2019WR026604