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Publication Date
29 January 2021

Precipitation Processes Workshop Takes on Challenges, Builds New Research Connections

Key to reducing impacts of extreme events is understanding the critical processes responsible for precipitation. Photo by Kelly Sikkema on Unsplash.

Each year in the United States, extreme events associated with precipitation—flooding and droughts—impact the nation with billions of dollars in damage and devastating consequences for people and communities.

A key to reducing these impacts is understanding the critical processes responsible for precipitation, the sources of predictability of precipitation, and the extent to which models represent the mean and extreme precipitation. This was the focus of an interagency workshop in late November 2020 that brought the observational and modeling research communities together with the operational weather prediction community for a series of virtual meetings and presentations.

Sponsored by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Department of Energy (DOE), the NOAA-DOE Precipitation Processes and Predictability Workshop focused on advancing the understanding of precipitation processes and predictability and improvement of modeling and prediction at a broad range of scales.   

RGMA Program Manager Renu Joseph, who served on the program organizing committee, reports that over three intensive days, participants discussed the following questions:

  • What are the sources of predictability that have the biggest influences on precipitation from subseasonal and seasonal (weather) to multidecadal timescales, including extremes?
  • What are the physical processes that have the strongest imprint on model biases and precipitation predictions and projections?
  • How can we most effectively take advantage of existing observations and data (satellite and in situ) to advance process-level understanding of the processes and predictability?
  • What are the gaps and needs for targeted observations and process studies to improve understanding and model representations of those key processes?
  • How do we benefit from national and international collaboration to make significant progress?

According to Angeline Pendergrass, a scientist with the National Center for Atmospheric Research (NCAR), assistant professor at Cornell University, and workshop session co-chair, an important objective was to get scientists talking about big challenges, recent advances they find compelling, and new approaches to integration that might be fruitful.

“At this workshop, there were some challenges to overcome—getting on the same page and talking across different subfields,” Pendergrass said. “We had a lot of folks who didn’t know each other and hadn’t worked together, even though we’re all interested in knowing more about precipitation and predicting it better.”

Another session co-chair, Samson Hagos—an earth scientist at Pacific Northwest National Laboratory—added that the virtual nature of the meeting created more in-depth side discussions in Slack, a team communications tool. “Participants shared detailed thoughts on Slack,” he said, noting that the technology preserves those ideas.

According to the workshop website, the challenges in forecasting precipitation and the ongoing user needs were acknowledged by the U.S. Congress and the Executive Office of the President with several established mandates, including the 2017 Weather Act, and the administration’s 2020 Earth System Predictability priority led by the Office of Science and Technology Policy (OSTP).

In response, NOAA launched the Precipitation Prediction Grand Challenge (PPGC) Initiative to help orient NOAA’s research efforts in the coming years. This is closely aligned with DOE’s Earth and Environmental Systems Sciences Division (EESSD), which has a broad interest in water cycle predictability. The 2018 EESSD Strategic Plan identifies the Integrated Water Cycle as one of its five grand challenges. 

  • EESSD Integrated Water Cycle Scientific Grand Challenge: Advance understanding of the integrated water cycle by studying relevant processes involving the atmospheric, terrestrial, oceanic, and human system components and their interactions and feedbacks across local, regional, and global scales, thereby improving the predictability of the water cycle and reducing associated uncertainties in response to short- and long-term perturbations.

According to Joseph, the workshop will have broader long-term benefits. “It has helped to set a precedent for future interagency collaborations on practicable predictability of precipitation,” she said. “Going forward, we anticipate a greater closeness between NOAA’s and DOE’s scientific communities.”

“I was impressed with how engaged all the participants were,” Pendergrass added. “People boldly asked questions and responding with ideas to help fill the gaps in language between subdisciplines. I thought seeing this interaction was great, especially knowing how challenging it is to work through the pandemic and being unable to meet in person.”

Pendergrass notes that a big challenge ahead is bridging boundaries between subdisciplines, which “are much more our construction than something that arises from precipitation processes and phenomena themselves,” she said. “I think this meeting showed that we can tackle new problems that lie at the intersection of these boundaries. We are ready.”

Hagos agreed, noting, “The key for us is to keep these conversations going among this new, larger community and explore opportunities to work together. We must make sure that advances and challenges in observational and modeling process studies, model development, and operational prediction are communicated broadly to accelerate scientific knowledge translation to concrete societal benefit. To this end, I think this workshop was an important step.”

A workshop report will be developed later in 2021 that summarizes discussions, findings, and research recommendations. The report will inform the scientific communities and modeling centers about research gaps and collaboration opportunities to improve understanding of precipitation processes and predictability.

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