Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth

Abstract

It is critically important but challenging to estimate the amount of snow on the ground over large areas due to its strong spatial variability. Point snow data are used to generate or improve (i.e., blend with) gridded estimates of snow water equivalent (SWE) by using various forms of interpolation; however, the interpolation methodologies often overlook the physical mechanisms for the snow being there in the first place. Using data from the Snow Telemetry and Cooperative Observer networks in the western United States, we show that four methods for the spatial interpolation of peak of winter snow water equivalent (SWE) and snow depth based on distance and elevation can result in large errors. These errors are reduced substantially by our new method, i.e., the spatial interpolation of these quantities normalized by accumulated snowfall from the current or previous water years. Our method results in significant improvement in SWE estimates over interpolation techniques that do not consider snowfall, regardless of the number of stations used for the interpolation. Furthermore, it can be used along with gridded precipitation and temperature data to produce daily maps of SWE over the western United States that are comparable to existing estimates (which are based on the assimilation of much more data). Our results also show that not honoring the constraint between SWE and snowfall when blending in situ data with gridded data can lead to the development and propagation of unrealistic errors.

Why Do Global Reanalyses and Land Data Assimilation Products Underestimate Snow Water Equivalent?

“…In contrast, our method is based directly on daily PRISM precipitation and temperature data (and hence relies on more in situ precipitation and temperature data) and relies on station reports of SWE, snow depth, and precipitation that go back further than 30 years, enabling the creation of a dataset spanning 301 years. Broxton et al (2016) found that the two SWE datasets are comparable, and their differences can be interpreted as a rough estimate of our SWE data uncertainty.…”

“…However, the effect of this difference on the SWE assimilation is mediated somewhat in the UA data by the fact that a normalized quantity (SWE/S), instead of SWE itself, is interpolated between the point observations. The normalized quantity was shown in Broxton et al (2016) to be spatially more consistent than the unnormalized quantity. Furthermore, the dataset is strongly constrained by the gridded temperature and precipitation data, which makes it relatively consistent, regardless of what SWE data are included (and their uncertainty).…”

“…PRISM precipitation that occurs when the air temperature below this interpolated threshold is considered snow; otherwise, it is considered rain. The generation of SWE estimates follows the methodology of Broxton et al (2016). Namely, SWE at each station is divided by the difference between accumulated snowfall (from the time that no snow was on the ground to the time of the SWE estimate) measured at the station and an estimate of accumulated ablation (for the same period), which is given by a simple temperature index snow model.…”

Using CloudSat‐CPR Retrievals to Estimate Snow Accumulation in the Canadian Arctic

“…Data from the Blended‐4 SWE gridded product are regridded from 0.5° resolution to 1° resolution so that it aligns with the grid used for CloudSat overpass aggregation at each station. Since the Blended‐4 product provides daily estimates of SWE on ground, we estimate monthly snow accumulation as the sum of all positive differences in SWE from consecutive days: , where is the subscript for each day in a month, is the total number of days in a month, and is the difference in mm SWE computed as (Broxton et al, ). This method does not account for any melt or sublimation that may have occurred between two consecutive days but provides a conservative estimate of accumulation (only) that can be compared with snowfall estimates from CloudSat.…”

Snowpack Change From 1982 to 2016 Over Conterminous United States

“…It is developed by consistently assimilating in situ measurements of SWE and/or SD at thousands of sites (Broxton, Dawson, & Zeng, ) and 4‐km gridded PRISM precipitation and temperature data (Daly et al, ) over ConUS. The details of the methodology and the robustness and accuracy of the data set have been reported (Broxton, Dawson, & Zeng, ; Broxton, Zeng, & Dawson, ; Dawson et al, , ). Here we outline key steps in generating this data set: The ratio of observed SWE over estimated net snowfall (accumulated snowfall minus accumulated snow ablation), rather than SWE itself, is used for interpolation from point measurements to other points or pixels (Broxton, Dawson, & Zeng, ). The snowfall versus rainfall is separated using a daily 2‐m air temperature threshold based on station data, and the snow ablation is also estimated as a function of temperature based on station data (Broxton, Dawson, & Zeng, ). A new snow density parameterization (Dawson et al, ) is developed to combine the SWE and SD measurements from hundreds of SNOTEL sites with the SD measurements from thousands of COOP sites. These steps are combined with the PRISM gridded daily temperature and precipitation data (Daly et al, ) to produce the UA daily 4‐km SWE and SD (Broxton, Dawson, & Zeng, ). SCE is computed from UA SWE with a threshold value of 3 mm. …”