Adjustments for Wind-Induced Undercatch in Snowfall Measurements Based on Precipitation Intensity - Colli, Matteo; Stagnaro, Mattia; Lanza, Luca G.; Rasmussen, Roy; Thériault, Julie M. (Journal of Hydrometeorology, 2020)
Abstract Adjustments for the wind-induced undercatch of snowfall measurements use transfer functions to account for the expected reduction of the collection efficiency with increasing the wind speed for a particular catching-type gauge. Based on field experiments or numerical simulation, collection efficiency curves as a function of wind speed also involve further explanatory variables such as surface air temperature and/or precipitation type. However, while the wind speed or wind speed and temperature approach is generally effective at reducing the measurement bias, it does not significantly reduce the root-mean-square error (RMSE) of the residuals, implying that part of the variance is still unexplained. In this study, we show that using precipitation intensity as the explanatory variable significantly reduces the scatter of the residuals. This is achieved by optimized curve fitting of field measurements from the Marshall Field Site (Colorado, United States), using a nongradient optimization algorithm to ensure optimal binning of experimental data. The analysis of a recent quality-controlled dataset from the Solid Precipitation Intercomparison Experiment (SPICE) campaign of the World Meteorological Organization confirms the scatter reduction, showing that this approach is suitable to a variety of locations and catching-type gauges. Using computational fluid dynamics simulations, we demonstrate that the physical basis of the reduction in RMSE is the correlation of precipitation intensity with the particle size distribution. Overall, these findings could be relevant in operational conditions since the proposed adjustment of precipitation measurements only requires wind sensor and precipitation gauge data.

Atmospheric and surface observations during the Saint John River Experiment on Cold Season Storms (SAJESS) - Thompson, Hadleigh D.; Thériault, Julie M.; Déry, Stephen J.; Stewart, Ronald E.; Boisvert, Dominique; Rickard, Lisa; Leroux, Nicolas R.; Colli, Matteo; Vionnet, Vincent (Earth System Science Data, 2023)
Abstract. The amount and the phase of cold-season precipitation accumulating in the upper Saint John River (SJR) basin are critical factors in determining spring runoff, ice jams, and flooding. To study the impact of winter and spring storms on the snowpack in the upper SJR basin, the Saint John River Experiment on Cold Season Storms (SAJESS) was conducted during winter–spring 2020–2021. Here, we provide an overview of the SAJESS study area, field campaign, and data collected. The upper SJR basin represents 41 % of the entire SJR watershed and encompasses parts of the US state of Maine and the Canadian provinces of Quebec and New Brunswick. In early December 2020, meteorological instruments were co-located with an Environment and Climate Change Canada station near Edmundston, New Brunswick. This included a separate weather station for measuring standard meteorological variables, an optical disdrometer, and a micro rain radar. This instrumentation was augmented during an intensive observation period that also included upper-air soundings, surface weather observations, a multi-angle snowflake camera, and macrophotography of solid hydrometeors throughout March and April 2021. During the study, the region experienced a lower-than-average snowpack that peaked at ∼ 65 cm, with a total of 287 mm of precipitation (liquid-equivalent) falling between December 2020 and April 2021, a 21 % lower amount of precipitation than the climatological normal. Observers were present for 13 storms during which they conducted 183 h of precipitation observations and took more than 4000 images of hydrometeors. The inclusion of local volunteers and schools provided an additional 1700 measurements of precipitation amounts across the area. The resulting datasets are publicly available from the Federated Research Data Repository at https://doi.org/10.20383/103.0591 (Thompson et al., 2023). We also include a synopsis of the data management plan and a brief assessment of the rewards and challenges of conducting the field campaign and utilizing community volunteers for citizen science.

Atmospheric and surface observation data collected during the Saint John River Experiment on Cold Season Storms - Thompson, Hadleigh D; Thériault, Julie M; Rickard, Lisa; Déry, Stephen J; Leroux, Nicolas R; Stewart, Ronald E; Vionnet, Vincent; Colli, Matteo; Boisvert, Dominique (Federated Research Data Repository / dépôt fédéré de données de recherche, 2023)
Meteorological data, manual observations, and photographic images of hydrometeors recorded during the Saint John River Experiment on Cold Season Storms. The dataset covers the period December 2020 to April 2021, with an intensive observation period from March 2021 to April 2021.
Keywords: Cold season; Disdrometre; Edmundston; Flux; Hydrology; Hydrometeorology; Meteorology; Meteorology and weather; Micro Rain Radar; Météorologie et temps; New Brunswick; Spring; Winter

How Well Are We Measuring Snow Post-SPICE? - Kochendorfer, John; Earle, Michael; Rasmussen, Roy; Smith, Craig; Yang, Daqing; Morin, Samuel; Mekis, Eva; Buisan, Samuel; Roulet, Yves-Alain; Landolt, Scott; Wolff, Mareile; Hoover, Jeffery; et al. (Bulletin of the American Meteorological Society, 2022)
Abstract Accurate snowfall measurements are necessary for meteorology, hydrology, and climate research. Typical uses include creating and calibrating gridded precipitation products, the verification of model simulations, driving hydrologic models, input into aircraft deicing processes, and estimating streamflow runoff in the spring. These applications are significantly impacted by errors in solid precipitation measurements. The recent WMO Solid Precipitation Intercomparison Experiment (SPICE) attempted to characterize and reduce some of the measurement uncertainties through an international effort involving 15 countries utilizing over 20 types and models of precipitation gauges from various manufacturers. Key results from WMO-SPICE are presented herein. Recent work and future research opportunities that build on the results of WMO-SPICE are also highlighted.