SnowCloudMetrics: Snow Information for Everyone

Snow is a critical component of the climate system, provides fresh water for millions of people globally, and affects forest and wildlife ecology. Snowy regions are typically data sparse, especially in mountain environments. Remotely-sensed snow cover data are available globally but are challenging to convert into accessible, actionable information. SnowCloudMetrics is a web portal for on-demand production and delivery of snow information including snow cover frequency (SCF) and snow disappearance date (SDD) using Google Earth Engine (GEE). SCF and SDD are computed using the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Binary 500 m (MOD10A1) product. The SCF and SDD metrics are assessed using 18 years of Snow Telemetry records at more than 750 stations across the Western U.S. SnowCloudMetrics provides users with the capacity to quickly and efficiently generate local-to-global scale snow information. It requires no user-side data storage or computing capacity, and needs little in the way of remote sensing expertise. SnowCloudMetrics allows users to subset by year, watershed, elevation range, political boundary, or user-defined region. Users can explore the snow information via a GEE map interface and, if desired, download scripts for access to tabular and image data in non-proprietary formats for additional analyses. We present global and hemispheric scale examples of SCF and SDD. We also provide a watershed example in the transboundary, snow-dominated Amu Darya Basin. Our approach represents a new, user-driven paradigm for access to snow information. SnowCloudMetrics benefits snow scientists, water resource managers, climate scientists, and snow related industries providing SCF and SDD information tailored to their needs, especially in data sparse regions.

How Do We Define Climate Change? Considering the Temporal Resolution of Niveo-Meteorological Data

Historically, snowpack trends have been assessed using one fixed date to represent peak snow accumulation prior to the onset of melt. Subsequent trend analyses have considered the peak snow water equivalent (SWE), but the date of peak SWE can vary by several months due to inter-annual variability in snow accumulation and melt patterns. A 2018 assessment evaluated monthly SWE trends. However, since the month is a societal construct, this current work examines daily trends in SWE, cumulative precipitation, and temperature. The method was applied to 13 snow telemetry stations in Northern Colorado, USA for the period from 1981 to 2018. Temperature trends were consistent among all the stations; warming trends occurred 63% of the time from 1 October through 24 May, with the trends oscillating from warming to cooling over about a 10-day period. From 25 May to 30 September, a similar oscillation was observed, but warming trends occurred 86% of the time. SWE and precipitation trends illustrate temporal patterns that are scaled based on location. Specifically, lower elevations stations are tending to record more snowfall while higher elevation stations are recording less. The largest SWE, cumulative precipitation, and temperature trends were +30 to −70 mm/decade, +30 to −30 mm/decade, and +4 to −2.8 °C/decade, respectively. Trends were statistically significance an average of 25.8, 4.5, and 29.4% of the days for SWE, cumulative precipitation, and temperature, respectively. The trend in precipitation as snow ranged from +/−2%/decade, but was not significant at any station.

GEFS Precipitation Forecasts and the Implications of Statistical Downscaling over the Western United States

Contemporary operational medium-range ensemble modeling systems produce quantitative precipitation forecasts (QPFs) that provide guidance for weather forecasters, yet lack sufficient resolution to adequately resolve orographic influences on precipitation. In this study, cool-season (October–March) Global Ensemble Forecast System (GEFS) QPFs are verified using daily (24 h) Snow Telemetry (SNOTEL) observations over the western United States, which tend to be located at upper elevations where the orographic enhancement of precipitation is pronounced. Results indicate widespread dry biases, which reflect the infrequent production of larger 24-h precipitation events (≳22.9 mm in Pacific ranges and ≳10.2 mm in the interior ranges) compared with observed. Performance metrics, such as equitable threat score (ETS), hit rate, and false alarm ratio, generally worsen from the coast toward the interior. Probabilistic QPFs exhibit low reliability, and the ensemble spread captures only ~30% of upper-quartile events at day 5. In an effort to improve QPFs without exacerbating computing demands, statistical downscaling is explored based on high-resolution climatological precipitation analyses from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM), an approach frequently used by operational forecasters. Such downscaling improves model biases, ETSs, and hit rates. However, 47% of downscaled QPFs for upper-quartile events are false alarms at day 1, and the ensemble spread captures only 56% of the upper-quartile events at day 5. These results should help forecasters and hydrologists understand the capabilities and limitations of GEFS forecasts and statistical downscaling over the western United States and other regions of complex terrain.

A Comparison of Snow Telemetry and Snow Course Measurements in the Colorado River Basin

Temporal and spatial differences in snow-water equivalent (SWE) at 240 snow telemetry (SNOTEL) and at 500 snow course sites and a subset of 93 collocated sites were evaluated by examining the correlation of site values over the snow season, interpolating point measurements to basin volumes using hypsometry and a maximum snow extent mask, and variogram analysis. The lowest correlation at a point (r = 0.79) and largest interpolated volume differences (as much as 150 mm of SWE over the Gunnison basin) occurred during wet years (e.g., 1993). Interpolation SWE values based on SNOTEL versus snow course sites were not consistently higher or lower relative to each other. Interpolation rmse was comparable for both datasets, increasing later in the snow season. Snow courses correlate over larger distances and have less short-scale variability than SNOTEL sites, making them more regionally representative. Using both datasets in hydrologic models will provide a range of predicted streamflow, which is potentially useful for water resources management.