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• W3164020146 abstract "Abstract The spatiotemporal character of rainfall is particularly important for hydrologic modeling, as well as hydroclimatic risk estimation and impact assessment. Existing atmospheric reanalysis datasets offer extensive record lengths and global coverage, but usually their spatial resolution is coarse for distributed hydrologic simulations at small spatial scales. On the other hand, the temporal coverage of high‐resolution radar‐based rainfall estimates can be rather short for risk applications. To address these shortcomings, we simultaneously bias‐correct and downscale a state‐of‐the‐art atmospheric reanalysis (ERA5) rainfall dataset, using the radar‐based Stage IV precipitation product as fine resolution reference, to develop an hourly CONUS‐wide precipitation product over a 4‐km grid, which extends back to 1979. In this regard, we refine an existing parametric quantile mapping framework based on a two‐component theoretical distribution model, where we impose continuity of the parametric forms via optimal threshold selection to transition between higher and lower rain rates. An evaluation over the probability frequency and time domains, using NOAA’s raingauge measurements as benchmark, reveals that the developed product benefits from the strengths of the calibration datasets, demonstrating good performance and robust behavior over all studied time periods and Köppen climate classification zones, including snow‐prone regions or areas where mesoscale convective systems become dominant. The accuracy of the yielded high spatial‐resolution rain rates, especially in low probability events, shows that the developed product can be effectively used for hydroclimatic risk applications and frequency analysis, while its high temporal and spatial resolution makes it particularly useful for distributed hydrologic modeling." @default.
• W3164020146 created "2021-06-07" @default.
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• W3164020146 date "2021-06-01" @default.
• W3164020146 modified "2023-09-27" @default.
• W3164020146 title "An ERA‐5 Derived CONUS‐Wide High‐Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework" @default.
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• W3164020146 doi "https://doi.org/10.1029/2020wr029548" @default.
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