Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

Abstract

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

Improving Mountain Snowpack Estimation Using Machine Learning With Sentinel‐1, the Airborne Snow Observatory, and University of Arizona Snowpack Data

“…Furthermore, the inclusion of information about accumulation/ablation also provides a way to leverage other satellite‐based datasets of related variables like precipitation to improve snow monitoring. Likewise, the snow data produced here could also be directly compared to precipitation datasets over mountain areas, by leveraging the strong link between snowpack and precipitation during the snow accumulation season (Behrangi, 2021; Behrangi et al., 2018; Broxton, Dawson, & Zeng, 2016; Song et al., 2021).…”

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

“…Besides the three most frequently employed spaceborne data sources (PMW, multispectral (MS), and gravimetric), there are three that employ SAR data. Two of them used data collected by the Quick Scatterometer Mission (Seawinds) by NASA [153,160], and one study used data collected by the synthetic aperture radar (C-band), also known as SAR-C, by the European Space Agency (ESA) [216]. Looking at the utilized sensors overall, the majority of the employed sensors are PMW sensors.…”

A Novel Approach to Validate Satellite Snowfall Retrievals by Ground-Based Point Measurements

“…Severe underestimation against SNOTEL is found in both the radar and satellite products. In addition, Song et al [42] used SNOTEL data to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges in Alaska. They unraveled that most of the products can capture snowfall events; however, the reanalysis products tend to overestimate while the IMERG underestimates snowfall accumulation.…”