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  • Author or Editor: Robert L. Mahoney x
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Janice L. Bytheway, Mimi Hughes, Kelly Mahoney, and Robert Cifelli


The Russian River in northern California is an important hydrological resource that typically depends on a few significant precipitation events per year, often associated with atmospheric rivers (ARs), to maintain its annual water supply. Because of the highly variable nature of annual precipitation in the region, accurate quantitative precipitation estimates (QPEs) are necessary to drive hydrologic models and inform water management decisions. The basin’s location and complex terrain present a unique challenge to QPEs, with sparse in situ observations and mountains that inhibit remote sensing by ground radars. Gridded multisensor QPE datasets can fill in the gaps but are susceptible to both the errors and uncertainties from the ingested datasets and uncertainties due to interpolation methods. In this study a dense network of independently operated rain gauges is used to evaluate gridded QPE from the Multi-Radar Multi-Sensor (MRMS) during 44 precipitation events occurring during the 2015/16 and 2016/17 wet seasons (October–March). The MRMS QPE products matched the gauge estimates of precipitation reasonably well in approximately half the cases but failed to capture the spatial distribution and intensity of the rainfall in the remaining cases. ERA-Interim reanalysis data suggest that the differences in performance are related to synoptic-scale patterns and AR landfall location. These synoptic-scale differences produce different rainfall distributions and influence basin-scale winds, potentially creating regions of small-scale precipitation enhancement or suppression. Data from four profiling radars indicated that a larger fraction of the precipitation in poorly captured events occurred as shallow stratiform rain unobserved by radar.

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