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Storm Peak Laboratory, located on the Steamboat Springs Ski Resort in Colorado on the west summit of Mount Werner at 10 532 ft (3220 m) MSL, is an internationally recognized high-elevation atmospheric research station that has been in use for over 40 years. This article provides a brief history of the Storm Peak Laboratory and the major research themes it has supported and discusses opportunities to leverage mountain observatory measurements to advance our understanding of the atmospheric processes. This facility provides long-term measurements of meteorology, clouds, aerosols, snow hydrology, and atmospheric gases, and it serves as a “proving ground” for instrument development and testing. Storm Peak Laboratory is part of multiple national and international observational networks. Due to the unique capabilities of Storm Peak Laboratory, there is a long history of targeted field campaigns primarily within the following research areas: mixed-phase cloud microphysics; atmospheric chemistry pertaining to the formation, characterization, and hygroscopicity of aerosols; and the transport and transformation of atmospheric mercury. Research training has been central to the mission of Storm Peak Laboratory (SPL) over the last 40 years. Currently, SPL hosts both undergraduate- and graduate-level courses in atmospheric science and snow hydrology organized by numerous institutions. Examples of these unique research training opportunities are provided. 

Abstract. This study presents the first full annual cycle (2019–2020) of ambient surface aerosol particle number concentration measurements (condensationnuclei > 20 nm, N20) collected at Summit Station (Summit), in the centre of the Greenland Ice Sheet (72.58∘ N, −38.45∘ E; 3250 ma.s.l.). The mean surface concentration in 2019 was 129 cm−3, with the 6 h mean ranging between 1 and 1441 cm−3. The highest monthly mean concentrations occurred during the late spring and summer, with the minimum concentrations occurring in February (mean: 18 cm−3). High-N20 events are linked to anomalous anticyclonic circulation over Greenland and the descent of free-tropospheric aerosol down to the surface, whereas low-N20 events are linked to anomalous cyclonic circulation over south-east Greenland that drives upslope flow and enhances precipitation en route to Summit. Fog strongly affects particle number concentrations, on average reducing N20 by 20 % during the first 3 h of fog formation. Extremely-low-N20 events (< 10 cm−3) occur in all seasons, and we suggest that fog, and potentially cloud formation, can be limited by low aerosol particle concentrations over central Greenland. 

Abstract. We use the CloudSat 2006–2016 data record to estimate snowfall over theGreenland Ice Sheet (GrIS). We first evaluate CloudSat snowfall retrievalswith respect to remaining ground-clutter issues. Comparing CloudSatobservations to the GrIS topography (obtained from airborne altimetrymeasurements during IceBridge) we find that at the edges of the GrISspurious high-snowfall retrievals caused by ground clutter occasionallyaffect the operational snowfall product. After correcting for this effect,the height of the lowest valid CloudSat observation is about 1200&thinsp;mabove the local topography as defined by IceBridge. We then use ground-based millimeter wavelength cloud radar (MMCR) observations obtained from the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland (ICECAPS) experiment to devise a simple,empirical correction to account for precipitation processes occurringbetween the height of the observed CloudSat reflectivities and the snowfallnear the surface. Using the height-corrected, clutter-cleared CloudSatreflectivities we next evaluate various Z–S relationships in terms ofsnowfall accumulation at Summit through comparison with weekly stake fieldobservations of snow accumulation available since 2007. Using a set of threeZ–S relationships that best agree with the observed accumulation at Summit,we then calculate the annual cycle snowfall over the entire GrIS as well asover different drainage areas and compare the derived mean values and annualcycles of snowfall to ERA-Interim reanalysis. We find the annual meansnowfall over the GrIS inferred from CloudSat to be 34±7.5&thinsp;cm&thinsp;yr⁻¹liquid equivalent (where the uncertainty is determined by the range invalues between the three different Z–S relationships used). In comparison,the ERA-Interim reanalysis product only yields 30&thinsp;cm&thinsp;yr⁻¹ liquid equivalentsnowfall, where the majority of the underestimation in the reanalysisappears to occur in the summer months over the higher GrIS and appears to berelated to shallow precipitation events. Comparing all available estimatesof snowfall accumulation at Summit Station, we find the annually averagedliquid equivalent snowfall from the stake field to be between 20 and 24&thinsp;cm&thinsp;yr⁻¹, depending on the assumed snowpack density and from CloudSat 23±4.5&thinsp;cm&thinsp;yr⁻¹. The annual cycle at Summit is generally similar betweenall data sources, with the exception of ERA-Interim reanalysis, which showsthe aforementioned underestimation during summer months. 

Abstract The Greenland Ice Sheet (GrIS) is losing mass at an increasing rate yet mass gain from snowfall still exceeds the loss attributed to surface melt processes on an annual basis. This work assesses the relationship between persistent atmospheric blocking across the Euro‐Atlantic region and enhanced precipitation processes over the central GrIS during June–August and September–November. Results show that the vast majority of snowfall events in the central GrIS coincide with Euro‐Atlantic blocking. During June–August, snowfall events are produced primarily by mixed‐phase clouds (88%) and are linked to a persistent blocking anticyclone over southern Greenland (84%). The blocking anticyclone slowly advects warm, moist air masses into western and southern Greenland, with positive temperature and water vapor anomalies that intensify over the central GrIS. A zonal integrated water vapor transport pattern south of Greenland indicates a southern shift of the North Atlantic storm track associated with the high‐latitude blocking. In contrast, snowfall events during September–November are largely produced by ice‐phase clouds (85%) and are associated with a blocking anticyclone over the Nordic Seas and blocked flow over northern Europe (78%). The blocking anticyclone deflects the westerly North Atlantic storm track poleward and enables the rapid transport of warm, moist air masses up the steep southeastern edge of the GrIS, with positive temperature and water vapor anomalies to the east and southeast of Greenland. These results emphasize the critical role of Euro‐Atlantic blocking in promoting snowfall processes over the central GrIS and the importance of accurate representation of blocking in climate model projections. 

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.