On Monday, 11 July from 3:00 p.m. through Wednesday, 13 July until 5:00 p.m. (USA Mountain Time), NSIDC data distribution, services, and Web site will be unavailable to accommodate a major upgrade to our data center. We apologize for any inconvenience this may cause you. Need to talk to us? You can always contact our friendly User Services Office at firstname.lastname@example.org or + 1 303.492.6199.
This data set consists of a Northern Hemisphere subset of the Canadian Meteorological Centre (CMC) operational global daily snow depth analysis. Data include daily analysed snow depths from 1998 through 2015, snow depth and estimated snow water equivalent (SWE) monthly averages, plus monthly climatologies of snow depth and estimated SWE. Please note that this data set is NOT homogeneous. It is derived from operational data that is subject to frequent changes. See the Detailed Data Description and Warnings and Notices sections for details.
As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.
Brown, Ross D. and Bruce Brasnett. 2015, updated annually. Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data. © Environment Canada, 2010. Boulder, Colorado USA: National Snow and Ice Data Center.
Important: These data are provided by Environment Canada
for research purposes only. Refer to the Conditions
of Data Use section before working with these data.
|Data format||ASCII tab-delimited text
Audio-Visual Interleave (AVI animation)
Graphic Interchange Format (GIF image)
|Spatial coverage and resolution||Southernmost Latitude: 0° N
Northernmost Latitude: 90° N
Westernmost Longitude: 180° W
Easternmost Longitude: 180° E
Spatial resolution: 24 km
|Temporal coverage and resolution||01 August 1998 - 31 December 2015
Daily data collection
|Tools for accessing data||Any text editor or Web browser
Any video/media player software
|File naming convention||File names vary; refer to Table 3.|
|File size||1 - 900 MB|
Snow Water Equivalent (SWE)
|Error Sources||See Warnings and Notices.|
|Procedures for obtaining data||Data are available via FTP and HTTPS.|
Ross D. Brown
Climate Processes and Earth Observation Division
Environment Canada @ Ouranos
550 Sherbrooke St. West, 19th Floor
Montréal QC H3A 1B9
NSIDC User Services
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449 USA
phone: +1 303.492.6199
fax: +1 303.492.2468
form: Contact NSIDC User Services
The PI wishes to thank Bruce Brasnett, Meteorological Service of Canada, for his unfailing help and support in providing this dataset to the scientific community.
This data set contains daily analysed snow depths from 1998 through 2015, snow depth and estimated SWE monthly averages, plus monthly climatologies of snow depth and estimated SWE through 2012. The snow depth analysis is generated from real-time, in situ daily snow depth observations, using optimal interpolation with a first-guess field generated from a simple snow accumulation and melt model driven with analyzed temperatures and forecast precipitation from the Canadian forecast model (see Brasnett, 1999). In situ observations include snow depths from surface synoptic (synop) observations, meteorological aviation (metar) reports, and special aviation (SA) reports from the World Meteorological Organization (WMO) information system.
Note: this data set is NOT homogeneous. It is derived from operational data that is subject to frequent changes. See the Warnings and Notices section of this document for details.
Data files are provided in compressed (.zip) ASCII tab-delimited format. In addition, a comma-separated value data mask is available for continental-scale studies (see Warnings and Notices: #2).
A Northern Hemisphere subset of the CMC daily global analysis was interpolated to a standard 24 km polar stereographic grid that closely approximates the grid used by NOAA for the 24 km daily IMS snow product. At 706 pixels, the grid covers a slightly smaller area than the full 1024-pixel IMS grid. The land/sea mask should be used to remove snow over sea ice and over Greenland.
North Polar Stereographic
ni (# rows) = 706, nj (# cols) = 706, bottom left grid point = (1,1)
Grid rotation -10°
Pole position = (353, 353)
Grid resolution at 60°N = 23812.5 m
Latitude and longitude coordinates corresponding to the center of each grid cell are stored in cmc_analysis_ps_lat_long.txt (compressed as cmc_analysis_ps_lat_long.zip on the FTP site). This file lists each cell and its geographic coordinates as a row, column (i, j) ordered pair starting with (1,1), the cell in the lower left corner of the polar stereographic grid. Cell locations then increment across each column of the first row, each column of the second row, and so on, until reaching the upper right corner at (706,706). Refer to Figure 1 to see the first 10 entries from cmc_analysis_ps_lat_long.txt:
I J Lat Long ----------------------------------------------- 1 1 1.665461E-01 -125.000000 1 2 2.479315E-01 -125.081500 1 3 3.293158E-01 -125.163200 1 4 4.106980E-01 -125.245200 1 5 4.920774E-01 -125.327400 1 6 5.734532E-01 -125.409800 1 7 6.548244E-01 -125.492500 1 8 7.361903E-01 -125.575400 1 9 8.175499E-01 -125.658500 1 10 8.989025E-01 -125.741900
Figure 1. Latitude and longitude coordinates for cells (1, 1) through (1, 10)
Each file contains one year of data with the following FORTRAN statements:
do 1001 iday=1,ndays ! 365 or 366 depending on # days in year write(60,*) idate ! date in format YYYYMMDDHH where HH is always 00 do 1002 i=1,NI write(60,100) (sdep(i,j),j=1,706) !cm 100 format(706(1x,f5.1)) !values exceeding 1000 set to 999.9 1002 continue 1001 continue
Note: In 1998, the data start at 01 August, listed as 1998080100 in the data file.
The CMC analysis includes snow on sea ice. A custom binary (0,1) land/sea mask is provided to mask out ocean areas and Greenland. Land (mask=1) was assigned for grid land cover fractions greater than or equal to fifty percent. The array should be read in as follows:
open(50,file='cmc_analysis_lsmask_binary_nogland.txt',status='old') do 5 i=1,706 read(50,21) (lsmask(i,j),j=1,706) 21 format(706(i1)) 5 continue
Monthly average snow depth data are included with one year of data per file with naming convention: cmc_analysis_mly_avg_YYYY.txt. An upper snow depth limit of 600 cm was applied to the recent analyses to maintain continuity. The land/sea mask should be used to remove snow over sea ice and over Greenland where the analysis is unreliable.
Each file contains one year of data with the following FORTRAN statements:
do 1000 imon=1,12 write(60,*) iyear,imon do 1001 i=1,ni write(60,100) (sd_avg(i,j),j=1,706) !cm 100 format(706(1x,f5.1)) 1001 continue 1000 continue
Note: In 1998, the data start in August.
The directory structure on the FTP site is described in Table 2.
|Top Level Directory||Contents|
|nsidc0447_CMC_snow_depth_v01||Along with the three subdirectories below, the top-level directory contains the
|Monthly_Climatologies¹||Contains the following files:
|Monthly_Snow_Depth_Files||Contains monthly average snow depth files for each calendar year from 1998–2015:
|Monthly_SWE_Estimates||Contains one zip file with monthly average SWE values for each calendar year from 1998–2015:
|¹Climatologies are not updated beyond 2012 to provide a fixed reference period for anomaly tracking.|
For a description of file names, refer to Table 2.
The data files employ a variety of naming
conventions. All file name variables are listed and described in Table 3.
|cmc||Canadian Meteorological Centre|
|ps||Polar Stereographic grid|
|ims||Interactive Multisensor Snow and Ice Mapping System|
|nogl||No Greenland (used in land/sea mask file that masks out Greenland)|
|swe||Snow Water Equivalent|
|.gz||Zipped file (Gnu Zipped Archive)|
|.txt||ASCII text file|
|.avi||Audio Video Interleaved animation file|
|.gif||GIF image file|
|¹yyyy in climatology files refers to the start of the snow season.|
The files included in this data set range from approximately 1 MB to 900 MB.
Southernmost Latitude: 0° N
Northernmost Latitude: 90° N
Westernmost Longitude: 180° W
Easternmost Longitude: 180° E
The analysis has a resolution of 1/3° Gaussian. The error correlations used by the analysis have an e-folding distance of approximately 120 km horizontally and 800 m vertically.
Data include daily observations from 01 August 1998 to 31
December 2015. Note: the daily analysis for 08 December 2014 was missing, so the investigators replaced it with the previous day.
The analysis is updated every six hours, but only the 00 hour analysis is archived in this data set.. For more information, refer to the Processing Steps section of this document.
This data set includes all available real-time snow depth observation measurements from synops, metars, and SAs.
Monthly average estimates of SWE (mm) are also included in this data set. Monthly average SWE for the October through June period of each year was estimated from monthly average snow depth files by applying a look-up table (see Table 3) of mean monthly snow density values. Mean monthly snow density values were derived from Canadian snow course observations corresponding to snow climate classes in the Sturm et al. (1995) classification. The July through September time period was not processed due to a lack of observed snow density information during the time period.
Table 4 lists the mean monthly snow density look-up table used
to estimate SWE based on Canadian snow course observations averaged over the
snow-climate classes defined by Sturm et al. (1995)
and Mote, 2009). This assumes the Canadian density
observations are representative of snow-climate classes in other regions of
the Northern Hemisphere.
The Sturm et al. (1995) snow climate classification was interpolated from the 0.5° by 0.5° gridded version (ARCSS045) at the University Corporation for Atmospheric Research (UCAR) (Liston and Sturm 1998) to the 24 km polar stereographic grid of the monthly averaged snow depth. This process resulted in land/sea confusion at a number of points which were excluded from the analysis. The number of points affected by this problem (nconf) is indicated in the header record for each month.
The SWE estimates are contained in a single file (cmc_mly_swe_1998to2015.txt) with -999.0 values at all grid points where SWE could not be calculated (such as ocean or Sturm snow-class undefined). Greenland was excluded from the analysis as the analysis is considered unreliable in this area. Values were written as tab-delimited with the following FORTRAN statements:
write(60,*)iyear,imonth,nconf do 1002 i=1,ni write(60,107) (swe(i,j),j=1,706) !mm 107 format(706(1x,f6.1)) 1002 continue
Note: In 1998, values start in October.
Monthly average snow depth for the 1998/99 to 2013/14 snow seasons are included along with an animation of monthly mean snow depth averaged over the 1999-2006 period (cmc_monthly_mean_sdep_animation_1999-2006.avi). The land/sea mask was applied to these outputs. A legend for the snow depth animation is included in a separate GIF image file (units are cm). SWE climatologies are provided for the 1998/99 to 2012/13 snow seasons.
The monthly snow depth climatology includes values from January to December while the SWE climatology includes values from October to June. Both files are written in comma-delimited latitude-longitude tagged format with a single header record containing the variable names. Climatologies are not updated beyond 2012 to provide a fixed reference period for anomaly tracking.
Note: Starting in 2013/2014, processing was migrated from a UNIX platform to a PC. As such, zero values in some files after this date are written as .0 instead of 0.0.
Figure 2 displays an example of the CMC analysis
data used for evaluating Global Climate Model (GCM) output from Brown
and Mote (2008).
|Figure 2. Sample
of CMC Analysis Data Used for Evaluating Model Output
Comparison of thirteen GCM mean SWEmax (panel 2) with estimated SWEmax from CMC daily snow depth analyses (panel 1) for 2001-2006. The mean model bias (model-CMC) is shown in panel 3. From top to bottom, Figure 1 displays panels 1, 2, and 3. Sample analysis image courtesy of Ross Brown.
A complete description of the analysis methodology and validation is published in Brasnett (1999). The methodology was applied to carry out a reanalysis of snow depths over North America for the Atmospheric Model Intercomparison Project - 2 (AMIP-2) project from 1979-1997 using a more elaborate snow model for the first guess field (Brown, et al. 2003). The scheme makes use of an analysis of screen-level temperature produced with vertical correlations taken into account.
Over most of the Arctic region there are no observations, so the analysis is based on estimated snow depths from the first guess field. In addition, snow depth observations over northern Canada tend to be biased to coastal locations with observing sites at open areas near airports. The snow at these sites tends to be shallower and to melt out earlier than snow in surrounding terrain.
In cold, high precipitation environments, such as Greenland, the snow accumulation is capped at 600 cm to avoid excessive amounts of snow accumulating. The analysis is not considered reliable in these regions. This limit was changed to 1200 cm in October 2008 when a Limited Area Model (LAM) version of the analysis was implemented over British Columbia. Verification of that product showed it had insufficient residual snow during the summer months due to the 600 cm limit, so the limit was also changed in the global analysis. This limit is not unreasonable; mean annual snowfall in some locations in British Columbia exceeds 1000 cm, as illustrated by the mean annual snowfall levels for the 1951 through 1980 normal climate period listed in Table 5:
|Allison Pass||1431.5 cm|
|Rogers Pass||1106.7 cm|
|Pine Pass||1075.5 cm|
|Tahtsa Lake||1041.2 cm|
|Mt. Fidelity||1974.8 cm|
Values exceeding 1000 cm after 01 January 2007 were set to 999.9 to conform to the existing database format.
The analysis had been run essentially unchanged from 12 March 1998. An error was introduced into the operational implementation of the analysis in October 2006, but this has been corrected in offline runs and an offline version of the analysis is being run in parallel with the operational analysis to maintain data continuity.
NOTE: In October 2015, the output was switched from a frozen offline run of the analysis to the operational version of the analysis. A comparison of the two products for March 31, 2015 showed that differences were typically less than 5 cm between the two runs.
Figure 3. Land areas with mean annual maximum accumulations exceeding 300 mm. These areas need to be masked out with cmc_homog_mask_points.csv to maintain homogeneity in continental-scale studies in recent years.
Figure 4. Difference in snow depth (cm) between 01 January, 2010 and 31 December, 2009. Areas in dark blue are affected by the anomalous December 10–31 drop-out.
The volume of this data set is approximately four gigabytes.
Tools appropriate for viewing these data include any video or media software package and any text editor or Web browser.
Real-time snow depth data were originally acquired from the World Meteorological Organization (WMO) information system.
The analysis is updated every six hours using the method of optimum interpolation with an initial guess field provided by a simple snow accumulation and melt model using analyzed temperatures and forecast (six hour) precipitation from the CMC Global Environmental Multiscale (GEM) forecast model. The precipitation is assumed to be snow if the analyzed screen-level temperature is less than 0 degrees. A degree-day melting algorithm removes mass from the snowpack at the rate of 0.15 mm-h-1-K-1.
In regions where there are no observations of snow depth, the snow depth shown in the analysis corresponds to the initial guess field simplified assumptions regarding snowfall, melt and aging.
Note: When there is an ice cover, the analysis will accumulate snow on it so the snow field can extend over water bodies. This can be masked out if desired.
The first-guess snow model assumes the density of new snow
to be 100 kg/m3 and the snowpack gradually increases in density as it ages.
The increase in density with aging stops when the density reaches 300kg/m3
(except 210 kg/m3 if the vegetation is needleleaf forest, since in these
regions the canopy shelters the snowpack from wind and sunlight and densities
are less). New snow causes the density to decrease by an amount related
to the mass of the new snow and the mass of the existing snowpack. During
melting, the density is allowed to increase up to a maximum of 550 kg/m3.
Carrera, M. L., S. Bélair, and B. Bilodeau. 2015. The Canadian Land Data Assimilation System (CaLDAS): Description and Synthetic Evaluation Study. J. Hydrometeor 16: 1293–1314. DOI: http://dx.doi.org/10.1175/JHM-D-14-0089.1
Table 4 lists the acronyms and abbreviations used in this document.
|AMSR-E||Advanced Microwave Scanning Radiometer - Earth Observing System|
|AMIP-2||Atmospheric Model Intercomparison Project - 2|
|ASCII||American Standard Code for Information Interchange|
|AVI||Audio-Visual Interleave (video format)|
|CMC||Canadian Meteorological Centre|
|DAAC||Distributed Active Archive Center|
|FTP||File Transfer Protocol|
|GCM||Global Climate Model and/or General Circulation Model|
|GEM||Global Environmental Multiscale|
|GIF||Graphic Interchange Format|
|IMS||Interactive Multisensor Snow and Ice Mapping System|
|LAM||Limited Area Model|
|Metars||Meteorological aviation reports|
|NASA||National Aeronautics and Space Administration|
|NOAA||National Oceanic and Atmospheric Administration|
|NSIDC||National Snow and Ice Data Center|
|SA||Special Aviation report|
|SWE||Snow Water Equivalent|
|Synops||Surface synoptic observations|
|URL||Uniform Resource Locator|
|WMO||World Meteorological Organization|