Author: cw3e-web

Skill of rain-snow level forecasts for landfalling atmospheric rivers: A multi-model model assessment using California’s network of vertically profiling radars

January 30, 2020

Water resources managers must deal with substantial uncertainty in the forecasting of rain-snow levels during atmospheric river (AR) events in California, a new CW3E study shows. Major winter AR storms bring a highly variable mix of rain and snow to the Sierra Nevada; warmer events have higher rain-snow levels and thus produce more rain, greater flood risk, and contribute less to seasonal snowpack and water supply. The study showed that while operational weather models can forecast rain-snow levels well on average, they frequently had errors in rain-snow levels of several hundred meters, large enough to introduce major uncertainty in flood and water resources planning. This study is a part of CW3E’s ongoing effort to understand and improve the predictions of ARs and their impacts on public safety and water management, supporting local water agencies, California Department of Water Resources, the U.S. Army Corps of Engineers, and other agencies.

In the study by Brian Henn, Rachel Weihs, Andrew C Martin, F. Martin Ralph, and Tashiana Osborne of CW3E, forecasts of atmospheric rain-snow levels from December 2016 to March 2017, a period of active AR landfalls, were evaluated using 19 profiling radars in California. Three forecast model products were assessed: a global forecast model downscaled to 3 km grid spacing, 4 km river forecast center operational forecasts, and 50 km global ensemble reforecasts. Model forecasts of the rain-snow level were compared with observations of rain-snow melting level brightband heights. Models produced median bias magnitudes of less than 200 m across a range of forecast lead times. However, error magnitudes increased with lead time and were similar between models, averaging 342 m for lead times of 24 hr or less and growing to 700-800 m for lead times of greater than 144 hr. Significantly for flood forecasting, observed extremes in the rain-snow level were underestimated, particularly for warmer events, and the magnitude of errors increased with rain-snow level. Storms with high rain-snow levels were correlated with larger observed precipitation rates in Sierra Nevada watersheds. Flood risk increases with rain-snow levels, not only because a greater fraction of the watershed receives rain, but also because warmer storms carry greater water vapor and thus can produce heavier precipitation. The uncertainty of flood forecasts was shown to non-linearly with the rain-snow level for these reasons as well, highlighting the importance of improving forecast accuracy for flood risk management.

To aid water resources managers and the public, CW3E provides visualizations of current operational rain-snow level forecasts and their uncertainty for the U.S. West Coast.

The effect of rain-snow level forecast uncertainty on flood forecasting: a) schematic showing how the rain-snow level controls the fraction of precipitation received as rain in mountain watershed.. Bottom panels shows how rain-snow level forecast errors contributes most to flood risk uncertainty for high rain-snow level (warm) AR events, using the Merced River watershed above New ExchequerDam as an example: b) rain-snow level forecast error ranges as a function of observed rain-snow levels, c) rain-snow fraction ranges, d) precipitation rate ranges, and e) watershed rain rate ranges. Forecast uncertainty produces the largest watershed rain rate uncertainty for the warmest ARs.

Henn, B., R. Weihs, A.C. Martin, F.M. Ralph, and T. Osborne (2020): Skill of rain-snow level forecasts for landfalling atmospheric rivers: A multi-model model assessment using California’s network of vertically profiling radars. J. Hydrometeor., 124, 0 https://doi.org/10.1175/JHM-D-18-0212.1

Tracking Atmospheric Rivers Globally: Spatial Distributions and Temporal Evolution of Life Cycle Characteristics

January 22, 2020

CW3E collaborators Bin Guan (UCLA Project Scientist) and Duane Waliser (Chief Scientist of NASA/JPL Earth Science and Technology Directorate) recently published an article in the Journal of Geophysical Research: Atmospheres, titled “Tracking Atmospheric Rivers Globally: Spatial Distributions and Temporal Evolution of Life Cycle Characteristics”. The paper is included in the journal’s special issue on “Atmospheric Rivers: Intersection of Weather and Climate”. The research was supported by the California Department of Water Resources (DWR), the NASA Energy and Water cycle Study (NEWS) program, and the NASA GOES-16/17 special task.

The study documents Version 3, the latest iteration of the continual improvements in the Tracking Atmospheric Rivers Globally as Elongated Targets (tARget) algorithm, adding the capability to track AR life cycles globally along with other refinements. The algorithm was initially published in Guan and Waliser (2015) and later updated in Guan, Waliser, and Ralph (2018). As one of a few peer-reviewed and validated global AR detection algorithm currently available, the algorithm has been a vital tool used by scientists within the AR research and operations community, including many scientists at CW3E.

Using output from tARget Version 3, the study quantitatively characterized AR life cycles around the globe, including their spatial distributions, temporal evolution, and seasonal dependence, and examined the sensitivity of selected AR life cycle characteristics to various tracking considerations. It is expected that the consideration of AR life cycles will provide new insights into better understanding the fundamental processes of ARs – such as their moisture sources and pathways – and the representation of such processes in weather and climate models, and help improve our ability in predicting AR activity and impacts on subseasonal-to-seasonal (S2S) and longer time scales. S2S and seasonal prediction of ARs (and lack thereof, i.e., drought) is currently a key science and applications area of interest to DWR and with DWR-supported research and experimental forecast activities ongoing at CW3E, JPL, and UCLA.

Figure 1: Example output from the tARget algorithm, showing the track of one longer-lived AR (red) alongside other shorter-lived ARs. Only the 0600 time step every two days is shown for conciseness. The contour labels indicate the unique ID assigned to each track by the algorithm. For a given track, the ID number is populated to all grid cells within the boundary of all the AR shapes that belong to the given track. Key statistics for each track (lifetime, travel distance, mean travel speed, etc.; not shown) are also included in the output database. From Guan and Waliser 2019 (Figure 4).

Figure 2: Life cycle characteristics of ARs tracked by the tARget algorithm summarized from the results presented earlier. (upper) Spatial distributions of the long-term climatology. Smoothing was applied to highlight the most pronounced features. (lower) Temporal evolution of a typical/composite AR life cycle. Locations of the AR centroids (white dots) and track (light blue curve) are determined by propagating an AR centroid from an arbitrary starting point (here, 170°W, 36°N) forward in time based on the composite travel speed/direction at each stage of its life cycle as shown in Figure 15. Results are based on ~126,000 AR tracks in the NASA MERRA-2 reanalysis during 1980–2017. From Guan and Waliser 2019 (Figure 17).

Guan, B., & Waliser, D. E. (2019): Tracking Atmospheric Rivers Globally: Spatial Distributions and Temporal Evolution of Life Cycle Characteristics. Journal of Geophysical Research: Atmospheres, 124, 12523-12552 https://doi.org/10.1029/2019JD031205