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: 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 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 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

CW3E Publication Notice: Atmospheric Rivers in Southeast Alaska: Meteorological Conditions Associated With Extreme Precipitation

CW3E Publication Notice

Atmospheric Rivers in Southeast Alaska: Meteorological Conditions Associated With Extreme Precipitation

February 29, 2024

Figure 1. The Vortex Landslide near Hoonah, AK which occurred during the devastating December 2020 AR and continues to block roads to important hunting areas highlights the impacts of extreme precipitation events associated with ARs in Southeast Alaska, threatening the stability and safety of indigenous and rural Alaska communities. Credit: Deanna Nash, October 2022

A new paper titled, “Atmospheric Rivers in Southeast Alaska: Meteorological Conditions Associated With Extreme Precipitation” by Deanna Nash (CW3E), Jon Rutz (CW3E), and Aaron Jacobs (NWS Juneau) was recently published in Journal of Geophysical Research – Atmospheres. The research was supported by National Science Foundation’s Coastlines and Peoples Program (award 2052972) and presents a climatology (1980–2019) of atmospheric rivers (ARs) and heavy precipitation, as well as other relevant synoptic, mesoscale, and local meteorological characteristics for six rural and indigenous communities in Southeast Alaska.

This work is associated with two of five major priorities outlined in CW3E’s Strategic Plan: “Atmospheric Rivers Research and Applications” and “Monitoring and Projections of Climate Variability and Change”. These priorities are addressed by furthering our understanding of AR-related extreme precipitation events that trigger floods, landslides, and avalanche events across Southeast Alaska, establishing a historical baseline for comparison against future climate change scenarios, and developing operationally useful forecast tools in collaboration with the National Weather Service (NWS) in Juneau, Alaska. This study also highlights deep collaboration with local community partners to address the increased risk of impacts related to extreme weather disproportionately impacting coastal, rural and Indigenous communities. In developing this work, Deanna spent over a month in Southeast Alaska, learning about community priorities for extreme precipitation events, co-producing the research with NWS Juneau, and engaging with the community through education on AR events associated with extreme precipitation.

Results show that from 1980 to 2019, ARs occur on ∼120 days per year in Southeast Alaska, but ∼6 days produce 68%–91% of precipitation >95th percentile. The AR conditions are canonical – high-amplitude upper-level patterns across the northeastern Pacific Ocean favor southwesterly ARs reaching Southeast Alaska (Figure 2a), where moisture is orographically lifted, resulting in heavy precipitation, particularly on southwest facing slopes (Figure 3a). However, across the complex, post-glacial terrain of this region, variations in mesoscale and meteorological conditions can mean extreme precipitation for one region, but not another. For example, extreme precipitation days at Klukwan are most favored with a more cyclonic orientation of the anomalous IVT pattern associated with ARs, allowing moisture to flow up the inland channels (Figure 2b).

Figure 2. (a; Fig. 4c) Average daily composites of ERA5 IVT (shaded and vectors; kg m-1 s-1), 250 hPa geopotential height (gray contours; dam), and MSLP (black contours; hPa) for all AR days in the six communities that are >95th percentile precipitation (n=911). (b; Fig. 4g) Composite differences of ERA5 IVT (shaded and vectors; kg m-1 s-1) and 250 hPa geopotential height (contours; dam) for Klukwan during extreme Atmospheric River days and the average for all communities during extreme AR days (e.g., Community AR IVT – Average AR IVT). The red dot indicates the location of Klukwan. IVT vectors are only plotted where IVT and 250 hPa geopotential height values are statistically significant at the 95% confidence interval.

In areas like Klukwan and Skagway, 80%–90% of extreme AR days have south-southwesterly or south-southeasterly IVT (Figure 3b). In areas like Yakutat, although southeasterly IVT is more common, extreme precipitation events are most common with southwesterly IVT. These results are being broadly shared with these communities through non-technical takeaways and future work plans to incorporate general information on potential socio-economic impacts into the NWS warning process to improve their effectiveness.

Figure 3. (a; Fig. 6d) Average daily composites of WRF precipitation (shaded; mm day-1), ERA5 IVT (gray vectors, kg m-1 s-1), and WRF 1000 hPa winds (pink vectors, m s-1) for Klukwan during extreme AR days. The location of Klukwan is shown by the black point. (b; Fig. 5d) Topographical map of Hoonah using USGS GMT elevation data (shaded; m) where higher elevations are darker shades. Wind rose diagrams for IVT direction from ERA5 data for all days when an AR was present in Southeast Alaska is overlaid, centered on the grid cell nearest Klukwan. The total length of each bar indicates the frequency (%) of events with IVT in that particular direction. The length of colored areas within the bar indicates the frequency (%) of events with precipitation <2.5 mm day-1 (yellow), <95th percentile precipitation (blue), and >95th percentile precipitation (aqua) that also occurred in that direction.

Nash, D., Rutz, J.J., and Jacobs, A. Atmospheric Rivers in Southeast Alaska: Meteorological Conditions Associated With Extreme Precipitation JGR-Atmospheres (2024).https://doi.org/10.1029/2023JD039294