Creation and Validation of a Comprehensive 1° by 1° Daily Gridded North American Dataset for 1900–2009: Snowfall

Abstract

This study details the creation of a gridded snowfall dataset for North America, with focus on the quality of the interpolated product. Daily snowfall amounts from National Weather Service Cooperative Observer Program stations and Meteorological Service of Canada surface stations are interpolated to 1° by 1° grids from 1900 to 2009 and examined for data record length and quality. The interpolation is validated spatially and temporally through the use of stratified sampling and k-fold cross-validation analyses. Interpolation errors average around 0.5 cm and range from less than 0.01 to greater than 2.5 cm. For most locations, this is within the measurement sensitivity. Grid cells with large variations in elevation experience higher errors and should be used with caution. A new gridded snowfall climatology is presented based on in situ observations that capture seasonal and interannual variability in monthly snowfall over most of the North American land area from 1949 to 2009. The Community Collaborative Rain, Hail and Snow Network is used as an independent set of point data that is compared to the gridded product. Errors are mainly in the form of the gridded data underestimating snowfall compared to the point data. The spatial extent, temporal length, and resolution of the dataset are unprecedented with regard to observational snowfall products and will present new opportunities for examining snowfall across North America.

Synoptic climatology of lake‐effect snowfall conditions in the eastern Great Lakes region

“…This underestimation is partially attributed to the interpolation method, which smooth higher frequency signals through averaging of multiple station observations (Kluver et al . (). These differences are also likely due to the inconsistent locations comparing CoCoRaHS stations and the stations used in the interpolated data set.…”

Spatio‐temporal variability of Great Lakes basin snow cover ablation events

“…Snow depth data spanning 1960–2009 are obtained from a quality‐controlled daily North American dataset, interpolated onto a 1‐degree grid (Dyer & Mote, ; Kluver et al, ), archived at Rutgers University (http://climate.rutgers.edu/snowcover/noaamelt). This dataset is selected over other snow depth products such as the National Weather Services' National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System primarily due to its length of record and use in similar studies (Dyer & Mote, ).…”

“…Additionally, as there is substantial variability in the snowfall and snow cover climatologies within basin (Suriano & Leathers, 2017a, 2017b; this research investigates the spatial and temporal variability of ablation events and trends at a sub-basin scale seeking to identify particular regions within the basin with a higher susceptibility to changing climatic conditions. 2 | DATA AND METHODOLOGY 2.1 | Snow depth data Snow depth data spanning 1960-2009 are obtained from a quality-controlled daily North American dataset, interpolated onto a 1-degree grid (Dyer & Mote, 2006;Kluver et al, 2016), archived at Rutgers University (http://climate.rutgers.edu/snowcover/noaamelt/). This dataset is selected over other snow depth products such as the National Weather Services' National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System primarily due to its length of record and use in similar studies (Dyer & Mote, 2007).…”

The importance of choosing precipitation datasets

“…In addition to being a community effort, many organizations utilize the data that are generated from the CoCoRaHS network including, but not limited to, the National Oceanic and Atmospheric Administration and the National Hydrologic Warning Council. Furthermore, several peer‐reviewed publications utilize the data ranging from hydrologic analyses (e.g., Bunkers, Smith, Driscoll, & Hoogestraat, ; Grant et al, ; Smith, Smith, Baeck, & Miller, ), atmospheric aerosol analyses (e.g., Fang & Yongmei, ; Kelleners & Verma, ; Kelly et al, ), and verification of precipitation estimations from radar (e.g., Moon et al, ; Kluver et al, ; Martinaitis et al, ; Smith et al, ; Wolfe & Snider, ; Zhang, Qi, Langston, Kaney, & Howard, ). Therefore, the applications of the data generated from the CoCoRaHS network are very broad and impactful in several different fields of science.…”

“…Furthermore, several peer-reviewed publications utilize the data ranging from hydrologic analyses (e.g., Bunkers, Smith, Driscoll, & Hoogestraat, 2015;Grant et al, 2013;Smith, Smith, Baeck, & Miller, 2015), atmospheric aerosol analyses (e.g., Fang & Yongmei, 2012;Kelleners & Verma, 2012;Kelly et al, 2012), and verification of precipitation estimations from radar (e.g., Moon et al, 2009;Kluver et al, 2016;Martinaitis et al, 2014;Smith et al, 2015;Wolfe & Snider, 2012;Zhang, Qi, Langston, Kaney, & Howard, 2014). Therefore, the applications of the data generated from the CoCoRaHS network are very broad and impactful in several different fields of science.…”