ALBERT

Description: Although it is generally accepted that the level of neutral buoyancy (LNB) is only a coarse estimate of updraft depth, the LNB is still used to understand and predict storm structure in both observations and modeling. This study uses case studies to quantify the variability associated with using environmental soundings to predict detrainment levels. Nine dual-Doppler convective cases were used to determine the observed level of maximum detrainment (LMD) to compare with the LNB. The LNB for each case was calculated with a variety of methods and with a variety of sources (including both observed and simulated soundings). The most representative LNB was chosen as the proximity sounding from NARR using the most unstable parcel and including ice processes. The observed cases were a mix of storm morphologies, including both supercell and multicell storms. As expected, the LMD was generally below the LNB, the mean offset for all cases being 2.2 km. However, there was a marked difference between the supercell and non-supercell cases. The two supercell cases had LMDs of 0.3 km and 0.0 km below the LNB. The remaining cases had LMDs that ranged from 4.0 km below to 1.6 km below the LNB, with a mean offset of 2.8 km below. Observations also showed that evolution of the LMD over the lifetime of the storm can be significant (e.g., 〉2 km altitude change in 30 min), and this time evolution is lacking from models with coarse time steps, missing significant changes in detrainment levels that may strongly impact the amount of boundary layer mass transported to the upper troposphere and lower stratosphere.

Description: The Western United States is highly dependent on winter snowpack from the Mountain West. Coupled with increasing water and renewable electricity demands, the predictability and viability of snowpack resources in a changing climate is becoming increasingly important. In Idaho, specifically, up to 75% of the state’s electricity comes from hydropower, which is dependent on the timing and volume of Spring snowmelt. While we know that snowpack is declining from observations and is expected to continue to decline from global climate models predictions, our ability to understand the variability of snowfall accumulation and distribution at the regional level is less robust. In this presentation, we analyze snowfall events using hourly 900-m-resolution Weather and Research Forecast model simulations to understand the intra-seasonal variability of snowfall accumulation and distribution over the mountains of Idaho between 1 Oct 2016 – 31 April 2017. Self-organizing maps and statistical analyses of snow events are used to explore the organization of snow distribution and amounts to develop a linkage between snowfall, cloud microphysics, kinematics, and thermodynamics. Our findings suggest that efficient snowfall conditions with ice water content to supercooled liquid water ratios 〉 1 produce snowfall of 〉10 mm per event throughout the winter season but are more impactful when surface temperatures are near or below freezing. Inefficient snowfall events (〈 10 mm of snowfall) are common, exceeding 50% of the total snowfall events for the year, with some of those occurring in peak winter. For such events, could for instance be targeted for glaciogenic cloud-seeding could to enhance the snowpack in this region.

Description: Recent observational studies from the Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment (SNOWIE) project have collected unprecedented measurements that demonstrate seeding with silver iodide (AgI) produces ice crystals that grow and fall to the ground as snow. These measurements include in situ and remote sensing data as well as ground-based observations to document the microphysics and precipitation formation processes in both natural and seeded clouds. The measurements from SNOWIE provide a rich dataset for studying the impacts of cloud seeding on orographic precipitation, as well as for improving understanding of the natural physics in orographic clouds that influence precipitation formation.In parallel, new supercomputing and sophisticated modeling capabilities have recently advanced our ability to simulate orographic precipitation. This laid the foundation to develop the WRF-WxMod® model, which simulates the physical effects of AgI seeding. WRF-WxMod is an innovative capability to evaluate the impacts of cloud seeding in controlled numerical experiments. Furthermore, when combined with detailed observations, such as from SNOWIE, WRF-WxMod provides new opportunities to transform our understanding of cloud seeding impacts as well as to investigate the impacts of cloud seeding across a variety of scales. This presentation will provide highlights of the research from SNOWIE that has advanced our understanding of natural and seeded orographic clouds and demonstrate the utility of WRF-WxMod to quantify the impacts of cloud seeding.