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NOAA Climate Program Office’s Climate Observations and Monitoring Program awards $1.2 million for new precipitation data development and analysis

CPO’s Climate Observations and Monitoring (COM) Program is announcing seven new 3-year projects originally funded in Fiscal Year 2023 (FY23). The competitively selected projects total $1.2 million in FY23. These precipitation projects, motivated by NOAA’s Precipitation Prediction Grand Challenge (PPGC) strategic objectives, will help improve NOAA monitoring and modeling capabilities. NOAA plays an important role in providing timely and accurate precipitation predictions to protect lives and property.

The Fifth National Climate Assessment, just released by the U.S. Global Change Research Program, finds that climate change “will continue to cause profound changes in the water cycle, increasing the risk of flooding, drought, and degraded water supplies for both people and ecosystems. These impacts will disproportionately impact frontline communities.” In 2023 (as of November 8), the U.S. has experienced 25 weather/climate disaster events with losses exceeding $1 billion, including two flooding events, 19 severe storm events, one tropical cyclone event, and one winter storm event. 

To reduce the severity of these impacts, NOAA scientists need to build new databases for precipitation information, and these projects will facilitate that work. 

The seven new projects2 funded by the COM Program in FY23 are:

  • A Pacific North American blocking database: assessment of precipitation, synoptic, and tropical precursor properties
    • The obstruction of the normal West-to-East track of mid-latitude weather systems is known as atmospheric blocking. Even though this phenomenon is a key driver of precipitation extremes along western North America, it is currently not well-represented in forecast and climate models. This project will develop an all-season database of Pacific and North American blocking events and their impacts over North America.
    • PI: Stephanie Henderson, University of Wisconsin – Madison
    • Co-PIs: Claire Pettersen, University of Michigan. Collaborator: Pierre Kirstetter, University of Oklahoma
  • Providing forecasters a climate context for water vapor profiles
    • Atmospheric water vapor is the fuel for all kinds of precipitation. Although most precipitation data is available, there are still some gaps in how water vapor data is interpreted. This prevents forecasters from understanding and communicating the severity of a flood threat. This project will examine precipitable water data from several meteorological data sets and create multidecadal climate records.
    • PI: John Forsythe, Colorado State University 
    • Co-PIs: Daniel Bikos, Natalie Tourville, Andrew Orrison, Daniel Leins, Chris Gitro, Michael Jurewicz
  • Long-term changes in the shape of the precipitation distribution in GHCNd observations
    • The distribution of precipitation is shifting in response to the changing climate. Preparing and adapting to this shift will require a quantitative assessment of where and when precipitation falls. This project will study long-term changes in the shape of the distribution of precipitation in models by comparing them to data from the Global Historical Climatology Network Daily (GHCNd), an existing dataset of precipitation gauge observations.
    • PI: Angeline Pendergrass, Cornell University
  • A next-generation multi-decadal to real-time probabilistic gridded precipitation dataset and integrated precipitation event database for CONUS
    • Observation-based precipitation products in the form of a grid are used throughout NOAA, mainly to produce numerical weather prediction models. However, there is still a need for newer precipitation datasets that will be suitable for long-term climate monitoring, extreme event analysis, model validation and improvement projects. This project will develop a real-time updating, multi-decadal gridded precipitation dataset along with an extreme event quantification database.
    • PI: Andrew Wood, UCAR/NCAR 
    • Co-PIs: Andrew Newman, Guoqiang Tang (UCAR/NCAR); Pierre Kirstetter, University of Oklahoma
  • A global database of tropical intraseasonal oscillations based on life-cycle tracking of large-scale precipitation features
    • An important source of subseasonal-to-seasonal predictability is provided by tropical intraseasonal oscillations, represented by the well-known phenomena of the Madden-Julian Oscillation and the Boreal Summer Intraseasonal Oscillation. This project will develop a database of individual life cycles of tropical intraseasonal oscillations across the global tropics. This new database will be useful in evaluating the fidelity of reanalysis products in representing tropical intraseasonal oscillations, and understanding their extratropical teleconnections.
    • PI: Bin Guan, University of California, Los Angeles 
    • Co-PI: Xianan Jiang
  • Developing a High-Resolution Precipitation Climatology Data Record (HRPCDR) for studying hydroclimatologic variability and extremes
    • Recent research has established that the water cycle has intensified, leading to new implications for the frequency and intensity of precipitation extremes. A new approach that combines precipitation estimates with numerical models and analytical techniques is essential to provide a broader view of these extremes. This project will provide a high-resolution precipitation dataset that will contribute to the understanding of precipitation variability at both spatial and temporal scales, with an emphasis on extreme events.
    • PI: Phu Nguyen, University of California, Irvine
    • Co-PIs: Soroosh Sorooshian, Kuolin Hsu, Bita Analui (University of California, Irvine); Yi Ming, Boston College; Venkatachalam Ramaswamy, NOAA/GFDL
  • Long-term probabilistic precipitation records
    • Events of extreme precipitation (or lack of it) have devastating effects in our society. Long-term measurements of precipitation are key for understanding how climate change has already affected these events. This project will create a new extreme precipitation dataset based on historical gauge data and analyze it to refine historical trends.
    • PI: John W. Nielsen-Gammon, Texas A&M University

1The funding will be distributed over the life of the projects and future-year funding is conditional on appropriations.

2At the time of publication, all awards may not have been accepted by recipient institutions

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North Park Ashwin-Sunderraj and Raed Mansour.
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