Analysis of extreme high-resolution precipitation based on gauge-corrected satellite observations in mountainous Southern California

Abstract

Mountainous Southern California experiences both wet and dry extremes in precipitation. During the wet extremes, shallow landslides and flash flooding are common consequences. These hazardous land surface impacts are typically triggered by short periods of extreme and local precipitation, oftentimes embedded within a larger storm. To characterize the hydrometeorological conditions that result in these impactful events, high-resolution precipitation information is required. In the topographically complex areas of Southern California, existing radars have insufficient coverage due to beam blockage and other issues, while sparse sub-daily rain gauge networks are not able to represent the high spatiotemporal precipitation variability. Coincidentally, this variability is often important in determining the locations where shallow landslides or flash floods are triggered. To this end, this work has developed a set of high-resolution quantitative precipitation estimates (QPEs) by blending information from rain gauges and bias corrected satellite precipitation estimates from U.S. operational precipitation products. The final product is a decadal (2014-2023) record of QPEs with high spatial (4km) and temporal (6-hourly) resolution, calibrated for the region and suitable for use in analyses of mountainous extreme precipitation events and associated hydrologic impacts. Validation of this final dataset is presented, including cross-validation to verify the bias correction efficacy. The final dataset is then used to examine the orographic precipitation variability and extremes. Both the climatological and event-scale orographic variability are examined for the Southern California mountainous regions. At the event-scale, emphasis is placed on understanding the variability for the most extreme precipitation events, which have the highest likelihood of resultant land surface impacts. A rigorous statistical analysis of the precipitation extremes is also presented, including an examination of the dominant patterns of extreme precipitation and several indices to characterize the nature of these extremes. Lastly, the influence of upstream atmospheric precursor conditions (namely, atmospheric instability and boundary layer moisture flux) on the distribution of the most significant extreme precipitation events is explored. As the spatial distribution of extreme precipitation events can impact the locations likely to experience hazardous land surface conditions during a particular storm, this has the potential to provide additional information for enhancement of predictability of these impactful events.