Atmospheric River Program
Team members: F. Martin Ralph (PI), Michael DeFlorio, Luca Delle Monache, Amato Evan, Jennifer Hasse, Chad Hecht, Julie Kalansky, Nora Mascioli, Anna Wilson
Collaborators: Shu-Hua Chen (UC Davis), Jason Cordeira (Plymouth State University), Dennis Lettenmaier (UCLA), Gudrun Magnusdottir (UC Irvine), Sorrosh Sorroshian (UC Irvine)
Partners: Bin Guan (UCLA), Duane Waliser (NASA JPL)
Atmospheric rivers (ARs), elongated transports of water vapor, are important for water supply in California, however, the most extreme ARs can lead to flooding. Better monitoring and prediction of ARs has the potential to enhance use of existing reservoirs as to increase water storage while maintaining flood control capabilities. Implementation of existing knowledge and research to extend that knowledge and develop tools could mitigate risks of too much or too little precipitation and could aid in adaptation to changes in climate. The AR Program aims to develop the science of atmospheric rivers to support planning, forecasts and warning elements of flood management and water management in California.
The California Atmospheric River (AR) Program was authorized by California Senate Bill SB-758 and signed by Governor Brown on October 9, 2015. This legislation contained an appropriation to support AR research. The AR Program is housed in the CA Department of Water Resources and is managed by California State Climatologist Michael Anderson.
- AR research is being used to demonstrate the viability of Forecast-Informed Reservoir Operations (FIRO)
- In collaboration with Sonoma Water and an 11-member steering committee, the preliminary FIRO assessment at Lake Mendocino showed improved water supply reliability, adding 20,000 acre feet, which is enough to supply 50,000 homes for one year
- A second FIRO assessment is underway for Prado Dam in partnership with Orange County Water District, the US Army Corps of Engineers, US Fish and Wildlife Service and others.
- AR forecast tools use global and national data to more precisely forecast AR landfall position and intensity
- CW3E AR Tools have been used to support DWR during extreme storms & have rapid response capabilities
- With partners in the National Weather Service, US Army Corps of Engineers, and others, CW3E developed the first scale for characterizing the strength and impacts of ARs; it is being tested in winter 2018/19
- Regional weather forecast model tailored to AR prediction in California
- CW3E’s West-WRF model was created to better forecast ARs and associated extreme precipitation
- West-WRF now has better skill than some national model
- Snow-level Forecasting Tools and Verification
- New forecast products have been created for the first time to show probabilistic snow level forecasts and how this impacts the amount of rain versus snow and its uncertainty
- Evaluated snow level forecasts for California using the snow level radars operated by NOAA and DWR.
- AR Subseasonal Outlooks
- AR outlooks 2-3 weeks into the future have been developed and are now being validated
- New methods have been developed to display forecasts out 2-3 weeks, in terms of AR conditions
- Targeted observations of ARs offshore in order to improve landfall and intensity predictions
- AR Recon airborne field campaigns were conducted in 2016, 2018, and will be held in 2019. Collaborative studies with the leading producers of global operational weather prediction models are ongoing to quantify the impact of AR Recon observations on forecasts.
- AR Recon leverages federal resources for maximum impact by using Air Force and NOAA aircraft for AR recon in the winter, which is their off-season from hurricane reconnaissance that occurs during summer and fall.
CW3E continues to explore new approaches to enhance the 2-3 week outlooks and AR forecast products. CW3E will also lead the AR reconnaissance efforts during the 2019 winter. AR recon is a multi-agency effort leveraging federal funding and bringing in experts from global leaders in weather forecasting and data assimilation. Additionally, CW3E is developing new forecast verification products for ARs and continuing research to improved our understanding and predictability of ARs.