Probabilistic Quantitative Precipitation Estimation

Abstract:

Progress in precipitation estimation is critical to advancing weather and water budget studies and to predicting natural hazards caused by extreme rainfall events from the local to the global scales. An interdisciplinary challenge in remote sensing, meteorology, and hydrology is the impact, representation, and use of uncertainty. Understanding hydrometeorological processes and applications requires more than just one deterministic “best estimate” to adequately cope with the intermittent, highly skewed distribution that characterizes precipitation. Yet the uncertainty structure of quantitative precipitation estimation (QPE) from ground-based radar networks like NEXRAD and satellite-based active and passive sensors of the Global Precipitation Measurement and the GOES-16 missions is largely unknown at fine spatiotemporal scales near the sensor measurement scale. We propose to advance uncertainty’s use as an integral part of QPE for groundbased and spaceborne sensors. Probability distributions of precipitation rates quantify the relationship between sensor measurement and the corresponding “true” precipitation. This approach preserves the sensor’s sampling properties and integrates sources of error in QPE. It provides a framework for diagnosing uncertainty when instruments sample raining scenes or processes challenging QPE algorithms’ assumptions. Precipitation probability maps compare favorably to deterministic QPE. Probabilistic QPE is shown to mitigate systematic biases from deterministic retrievals, quantify uncertainty, and advance the monitoring of precipitation extremes. It provides the basis precipitation ensembles needed for multisensor merging and precipitation assimilation, hydrometeorological hazard mitigation, decision making, and hydrological modeling. Perspectives on improved understanding and parameterizations of precipitation processes, estimation at multiple scales, hydrological prediction, and risk monitoring will be presented.