edu.ucar.opensky::articles:26801 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T19:20:58.923666 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7h70kvf https://doi.org/10.5067/P4HZB9N27EKU https Related Dataset #1 NCEP/CPC L3 Half Hourly 4km Global (60S - 60N) Merged IR V1 The multi-scale interactions of atmospheric phenomenon in mean and extreme precipitation 2023-11-01T00:00:00Z publication https://n2t.org/ark:/85065/d7h70kvf https://doi.org/10.1029/2023EF003534 Prein, Andreas author Mooney, P. A. author Done, James M. author UCAR/NCAR - Library publisher Climate change increases the frequency and intensity of extreme precipitation, which in combination with rising population enhances exposure to major floods. An improved understanding of the atmospheric processes that cause extreme precipitation events would help to advance predictions and projections of such events. To date, such analyses have typically been performed rather unsystematically and over limited areas (e.g., the U.S.) which has resulted in contradictory findings. Here we present the Multi-Object Analysis of Atmospheric Phenomenon algorithm that uses a set of 12 common atmospheric variables to identify and track tropical and extra-tropical cyclones, cut-off lows, frontal zones, anticyclones, atmospheric rivers (ARs), jets, mesoscale convective systems (MCSs), and equatorial waves. We apply the algorithm to global historical data between 2001-2020 and associate phenomena with hourly and daily satellite-derived extreme precipitation estimates in major climate regions. We find that MCSs produce the vast majority of extreme precipitation in the tropics and some mid-latitude land regions, while extreme precipitation in mid and high-latitude ocean and coastal regions are dominated by cyclones and ARs. Importantly, most extreme precipitation events are associated with phenomena interacting across scales that intensify precipitation. These interactions are a function of the intensity (i.e., rarity) of extreme events. The presented methodology and results could have wide-ranging applications including training of machine learning methods, Lagrangian-based evaluation of climate models, and process-based understanding of extreme precipitation in a changing climate. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation