Synthesis of ARM User Facility Surface Precipitation Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Abstract. Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM precipitation instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially at specific precipitation event properties such as maximum precipitation rates. A machine-learning (ML) analysis indicates that in some cases (e.g., certain instruments or deployments), atmospheric state variables influence the measured quantities and therefore the observed deviations between instruments. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from various ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1-minute maximum precipitation rate at a 1-year timeframe increasing up to 5 mm and 9 mm/hr (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/) cover multiple ARM deployments up until March 2025, PrecipBE will soon become an operational product with a several-day lag from real-time, and we invite the ARM user community to leverage this new product and welcome user feedback to enhance it further.