Toward a Practical Verification Dataset for Cool Season Orographic Glaciogenic Cloud Seeding

While we now have unambiguous evidence that glaciogenic cloud seeding can in fact result in a more efficient precipitation process, and thus more precipitation at the ground (French et al. 2018), these increases often occur in the envelope of natural variability (Flossmann et al. 2019, Weather Modification, Inc 2005). While ground-based generators have a low cost overhead, the in-situ observation of favorable conditions and application of seeding material directly into supercooled liquid water regions during the cool season in complex terrain by aircraft is both more effective (Geerts and Rauber 2022) and more expensive. This high uncertainty, high-cost paradigm leads to a lot of due criticism to both the science and operational programs as well as strong pressure for the most efficient selection of targets and utilization of resources on operational projects. Such constraints demand a data-driven approach to operational cloud seeding decisions. A continuously updating dataset of potential targets/environments suitable for glaciogenic seeding would prove valuable to not only verify and tune human decision making, but can also be used to train forecasts, allowing the optimal use of resources. However, in-situ observations of supercooled liquid water in clouds suitable for glaciogenic seeding are often limited, come with high cost, and are not readily available for pre-decisional use on operational projects. While there is increasing utility in high-resolution multi-band geostationary products (GOES-R series RGB images), these products suffer from time-of-day and line-of-site issues (day vs night, multiple cloud layers obscuring the target layer from satellite view, etc.). Thus, there is a need for a practical verification/analysis dataset. “Practical” in this sense will mean low-cost, easily reproducible, and highly available. The High-Resolution Rapid Refresh (HRRR) model produced by NOAA (Dowell et al. 2020) is high-resolution, continuously cycled with observations, radar, and satellite ingest, is freely available, and is also highly available thanks to its archival in the NOAA Big Data Program in the public cloud. This makes the HRRR the ideal candidate to provide gridded analysis reasonably close to observed truth. In this project, the HRRR is tested to ensure it can analyze environments with supercooled liquid water (as shown with HRRRv2 in Tessendorf et al. 2021), then it is compared to a subset of events in the 2022-2023 operational program for Wyoming’s Wind River, Sierra Madre, and Medicine Bow ranges as an initial attempt at developing a “practical” verification dataset.