Report on the State of Operational Snow Data Provisioning in Canada EC25089 Unless otherwise specified, you may not reproduce materials in this publication, in whole or in part, for the purposes of commercial redistribution without prior written permission from Environment and Climate Change Canada’s copyright administrator. To obtain permission to reproduce Government of Canada materials for commercial purposes, apply for Crown Copyright Clearance by contacting: Environment and Climate Change Canada Public Information Centre Place Vincent Massey building 351 St-Joseph Boulevard Gatineau, Quebec K1A 0H3 Toll free: 1-800-668-6767 Email: enviroinfo@ec.gc.ca Cover photo (from top to bottom): © BC Hydro, © Gouvernement du Québec 2025, © Environment and Climate Change Canada © His Majesty the King in Right of Canada, as represented by the Minister of the Environment, Climate Change and Nature, 2026 Aussi disponible en français enviroinfo@ec.gc.ca Report on the State of Operational Snow Data Provisioning in Canada II | P a g e Editor Frank Weber, British Columbia Hydro Lead authors Frank Weber, British Columbia Hydro Chantale Cerny, Environment and Climate Change Canada Sean McLeod, Environment and Climate Change Canada Contributors Keith Abbott, Newfoundland and Labrador Department of Environment and Climate Change Anthony Bier, Yukon Environment Leah Burry, Newfoundland and Labrador Department of Environment and Climate Change Paul Chanel, Manitoba Hydro Kristine Confalone, Environment and Climate Change Canada Sarah Cusiter, Environment and Climate Change Canada Ryan Connon, Government of the Northwest Territories Katrina Euteneier, Saskatchewan Water Security Agency James Floyer, Avalanche Canada Curtis Hallborg, Saskatchewan Water Security Agency Jeffery Hoover, Environment and Climate Change Canada Jonathan Kolot, Environment Yukon Mélanie LeBlanc, New Brunswick Department of Environment and Local Government Peter Leibiuk, Environment and Climate Change Canada Tony Litke, British Columbia Ministry of Environment and Parks Rahgull Manoragavan, Environment and Climate Change Canada Peter Marshall, Metro Vancouver Alexandre Mischler, Yukon Environment Report on the State of Operational Snow Data Provisioning in Canada III | P a g e Kyle Nault, Yukon Environment Brandi Newton, Alberta Environment and Protected Areas Stephen Paton, Environment and Climate Change Canada Loki Piper, Environment and Climate Change Canada Emma Riley, Government of the Northwest Territories Alison Sass, Manitoba Agriculture Jared Scott, New Brunswick Power Craig Smith, Environment and Climate Change Canada Pierre-Yves St-Louis, Direction de la qualité de l’air et du climat, Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs Gary Tsim, Environment and Climate Change Canada William Weston, New Brunswick Power Report on the State of Operational Snow Data Provisioning in Canada IV | P a g e Executive Summary This report summarizes the methodologies used in Canada to measure snow variables and process snow data, the agencies involved, and the portals on which observations and modelled snow products are disseminated. The report highlights issues and provides recommendations for improving data quality, accessibility, and discoverability. An assessment is presented on how well available snow data currently aligns with user needs. The focus of this report is on operational, i.e., routine and ongoing, monitoring by countrywide and regionally operating agencies. Operational snow monitoring can be broadly grouped into four categories: detailed snowpack analysis conducted by avalanche safety programs, snow monitoring by surface-based atmospheric monitoring programs, snow survey programs that measure the water equivalent of snowpack for hydrologic modelling applications, and satellite remote sensing of snow by national and international agencies. Key variables, for which monitoring, modelling, and data production methods are described include the total depth of snow, depth of new snowfall, water equivalent of snow cover, water equivalent of new snowfall, snow density, snow temperature and total precipitation. The description of manual and automated methods, and instrumentation used by the various agencies reveals both agreement and differences between the methodologies. This information will prove useful for interpreting data recorded by different monitoring networks. The description of snow observations in this report is biased towards surface-based station networks. Operationally used, remotely sensed, and operationally used modelled snow products are only briefly described. A snapshot of surface-based monitoring networks in Canada comprising approximately 4600 stations from 73 network owners was compiled. The information is used to infer spatial network distributions and the level of automation in snow monitoring. Surface-based monitoring station network density is highest for total snow depth, followed by the network densities for total precipitation and water equivalent of snow cover. Canada-wide, approximately half of the monitoring stations are automated. Analyzing information separately for each individual variable shows automation ranging from 85 percent for total precipitation to 18 percent for water equivalent of snow. The report includes an inventory of agencies responsible for snow monitoring, data production, and data dissemination. The level of user satisfaction with their snow data needs was assessed. It varied according to the specific application, variable of interest, and region, and whether currently available snow data are deemed representative, reliable, and accurate, or not. Consensus points towards an appreciation of long-term records, a need for denser monitoring networks, a greater level of network automation, a need for readily available quality-controlled data, and an improved communication of the accuracy of remotely sensed data and modelled reanalysis products. The large size of the country, the inaccessibility of much of the area, and the ensuing high costs of operating surface monitoring networks contribute to significant gaps in spatial data distribution. Operational snow products generated with systems that integrate surface-based and remotely sensed observations with snow modelling systems could help fill those gaps and meet those users’ needs who require snow data of higher spatial resolution. Certain environmental conditions can create challenges for existing snow monitoring technologies and reduce data accuracy. For example, snow caps can develop on unattended precipitation gauges and reduce the collecting area to the point that no additional precipitation can enter the gauge; weighting technologies for measuring the water equivalent of snow cover can be inaccurate due to strong layers of Report on the State of Operational Snow Data Provisioning in Canada V | P a g e snow preventing the full transfer of weight to the weighting devices; snow pillow systems are frequently damaged by animal activity; and Federal Snow Samplers have been shown to produce positively biased results. Targeted and coordinated research and development can potentially overcome these challenges. The availability of snow data varies from region to region. Not all data are publicly available, and some data are published with a significant latency. To access data, users are typically required to refer to multiple sources. Overall, data and metadata accessibility and discoverability are deemed fair by survey respondents. However, the authors of this report encountered challenges in compiling current, accurate, and useful station metadata. Improved collaboration between the various science fields and regions would help develop a comprehensive and coordinated strategy for improving snow data provisioning in Canada. Such a strategy would provide a vital link between surface-based snow monitoring agencies, remote sensing centers, and snow modelling institutes, as well as between meteorological, avalanche, and snow survey monitoring programs, and between provincially, territorially, and countrywide operating agencies. Innovation could be accelerated by bringing together ideas, developing skills, sharing resources and workload, and could lead to snow products that better meet user needs. Report on the State of Operational Snow Data Provisioning in Canada VI | P a g e Key Organisations and Monitoring Networks AB - AG Alberta Agriculture and Irrigation AB - AGFC Alberta Grains - FarmCash AB - AGI Alberta Agricultural Growth International AB - EPA Alberta Environment and Protected Areas AB Lakewood Systems Alberta Lakewood Systems Avalanche Canada Avalanche Canada BC - ENV British Columbia Ministry of Environment and Parks (formerly: Ministry of Environment and Climate Change Strategy) BC - MoTI, Avalanche British Columbia - Ministry of Transportation and Transit (Avalanche Stations; formerly: Ministry of Transportation and Infrastructure) BC - MoTI, Road Weather British Columbia - Ministry of Transportation and Transit (Road Weather Stations; formerly: Ministry of Transportation and Infrastructure) BC Hydro - HMP British Columbia Hydro - Hydrometeorological Monitoring Program Centre d’étude nordiques Centre d’étude nordiques CF(L)Co HMP Churchill Falls (Labrador) Corporation Hydromet Monitoring Program City of Calgary City of Calgary CoCoRaHS Community Collaborative Rain, Hail, and Snow Network ECCC - AWS Environment and Climate Change Canada – Automatic Weather Station ECCC - CCN Environment and Climate Change Canada – Cooperative Climate Network ECCC – SSP Environment and Climate Change Canada – Snow Survey Program FADQ Financière agricole du Québec HQ Hydro-Québec HQ - CF(L)Co HMP Hydro-Québec - Churchill Falls (Labrador) Corporation Hydromet Monitoring Program InfoEx Canadian Avalanche Association Information Exchange KRG Kativik Regional Government MB - Ag Manitoba Agriculture - Ag. Weather Program MB - MTI Snow Survey Manitoba Transportation and Infrastructure Snow Survey Report on the State of Operational Snow Data Provisioning in Canada VII | P a g e MB - WS Manitoba Wildfire Service MB Hydro - SMP Manitoba Hydro - Snow Monitoring Program MELCCFP - RSC (Automated) Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs - Réseau de surveillance du climat (Automatique) MELCCFP - RSC (Snow) Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs - Réseau de surveillance du climat (Nivométrie) MELCCFP - RSC (Manual) Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs - Réseau de surveillance du climat (Observateur) MFCo HMP Muskrat Falls Corporation Hydromet Monitoring Program NAV CANADA NAV CANADA NB - DNRED New Brunswick Department of Natural Resources (formerly: New Brunswick Department of Natural Resources and Energy Development) NB - DTI New Brunswick Department of Transportation and Infrastructure NB - ELG New Brunswick Department of Environment and Local Government NB - WSM New Brunswick Winter Severity Monitoring NL - ECC, WRMD Newfoundland and Labrador Ministry of Environment and Climate Change, Water Resources Management Division NL - DLP Deer Lake Power - Kruger NL - Hydro Newfoundland and Labrador Hydro NL - TI Newfoundland and Labrador Department of Transportation & Infrastructure NT - ECC Northwest Territories Ministry of Environment and Climate Change NTGS Northwest Territories Geological Survey ON - MNR, AFFES Ontario Ministry of Natural Resources, Aviation, Forest Fire and Emergency Services ON – MNR, WLF Ontario Ministry of Natural Resources, Wildlife and Fish ON – MNR, SWMC Ontario Ministry of Natural Resources, Surface Water Monitoring Centre ON - OPG Ontario Power Generation PC Parks Canada Agency Report on the State of Operational Snow Data Provisioning in Canada VIII | P a g e PC - AUKMMNPRC Parks Canada Agency - Akami-UapishkU-KakKasuak-Mealy Mountains National Park PC - BPNP Parks Canada - Bruce Peninsula National Park PC - GM Parks Canada - Gros Morne National Park PC - GNP Parks Canada - Glacier National Park PC - GRNP Parks Canada - Grasslands National Park PC - OW Parks Canada - Ontario Waterways PC - TN Parks Canada - Terra Nova National Park PC - WAFU Parks Canada - Western Arctic Field Unit QC - MNRF Ministère des Ressources naturelles et des Forêts du Québec QueensU Queen's University/Nunatsiavut - Coastal Labrador Weather and Climate Monitoring Program Rio Tinto Rio Tinto RMCQ Réseau météorologique coopératif du Québec SCRD Sunshine Coast Regional District Sépaq and Avalanche Québec Sépaq and Avalanche Québec SK - Grasslands Saskatchewan Grasslands SmartICE SmartICE SOPFEU Société de protection des forêts contre le feu SPSA Saskatchewan Public Safety Agency SPSA/WSA Saskatchewan Public Safety Agency/Water Security Agency SRC Saskatchewan Research Council – Climate Reference Stations Tata Steel Minerals Canada Tata Steel Minerals Canada Town of Canmore Town of Canmore USDA, NRCS US Department of Agriculture, Natural Resources Conservation Service Vale Canada Vale Canada YT - WFM Yukon Territory Wildland Fire Management YT - WRB Yukon Territory Water Resources Branch YT - WRB McMasterU Yukon Territory Water Resources Branch/McMaster University Report on the State of Operational Snow Data Provisioning in Canada IX | P a g e Table of Contents 1 INTRODUCTION ................................................................................................................ 11 1.1 BACKGROUND ................................................................................................................................. 11 1.2 OBJECTIVES .................................................................................................................................... 12 1.3 APPROACH ...................................................................................................................................... 12 2 SNOW VARIABLES AND MEASUREMENT METHODS .................................................... 15 2.1 TOTAL SNOW DEPTH........................................................................................................................ 15 2.2 DEPTH OF NEW SNOWFALL .............................................................................................................. 19 2.3 WATER EQUIVALENT OF SNOW COVER ............................................................................................. 20 2.4 WATER EQUIVALENT OF NEW SNOWFALL .......................................................................................... 23 2.5 SNOW DENSITY ............................................................................................................................... 23 2.6 SNOW TEMPERATURE ...................................................................................................................... 26 2.7 TOTAL PRECIPITATION ...................................................................................................................... 26 2.8 LIQUID WATER CONTENT OF SNOW .................................................................................................. 30 2.9 SNOW ALBEDO ................................................................................................................................ 30 2.10 SNOW-COVERED AREA .................................................................................................................... 30 2.11 PRESENCE OF SNOW ON THE GROUND ............................................................................................ 30 2.12 SNOW HARDNESS ............................................................................................................................ 31 3 SNOW REMOTE SENSING AND MODELLED SNOW PRODUCTS .................................. 32 3.1 ENVIRONMENT AND CLIMATE CHANGE CANADA CANADIAN PRAIRIE SNOW WATER EQUIVALENT ......... 32 3.2 UNITED STATES NOAA NATIONAL WEATHER SERVICE NATIONAL OPERATIONAL HYDROLOGIC REMOTE SENSING CENTER AIRBORNE GAMMA RADIATION SNOW SURVEY PROGRAM ..................................... 32 3.3 UNITED STATES NOAA NATIONAL WEATHER SERVICE NATIONAL OPERATIONAL HYDROLOGIC REMOTE SENSING CENTER’S SNOW DATA ASSIMILATION SYSTEM (SNODAS) ................................................ 33 3.4 UNITED STATES NATIONAL ICE CENTER INTERACTIVE MULTISENSOR SNOW AND ICE MAPPING SYSTEM - DAILY NORTHERN HEMISPHERE SNOW AND ICE ANALYSIS................................................................. 33 3.5 NASA MODERATE RESOLUTION IMAGING SPECTRORADIOMETER (MODIS) SNOW COVER ................ 34 3.6 NASA ADVANCED MICROWAVE SOUNDING RADIOMETER 2 (AMSR2) ............................................... 34 3.7 EUROPEAN SPACE AGENCY COPERNICUS SENTINEL FRACTIONAL SNOW COVER .............................. 35 3.8 ENVIRONMENT AND CLIMATE CHANGE CANADA CANADIAN LAND DATA ASSIMILATION SYSTEM (CALDAS) ...................................................................................................................................... 35 3.9 EUROPEAN SPACE AGENCY GLOBAL SNOW MONITORING FOR CLIMATE RESEARCH (GLOBSNOW) ..... 36 3.10 NASA ARCTIC-BOREAL VULNERABILITY EXPERIMENT (ABOVE) SNOW MODEL ................................. 37 3.11 RESEARCH AND DEVELOPMENT ACTIVITIES ...................................................................................... 37 4 COUNTRYWIDE SNOW MONITORING AND LARGER REGIONAL NETWORKS ............ 38 4.1 ENVIRONMENT AND CLIMATE CHANGE CANADA – METEOROLOGICAL SERVICE OF CANADA, ATMOSPHERIC MONITORING SERVICE .............................................................................................. 38 4.2 NAV CANADA ................................................................................................................................ 44 4.3 COCORAHS ................................................................................................................................... 47 4.4 AVALANCHE CANADA ....................................................................................................................... 50 4.5 CANADIAN AVALANCHE ASSOCIATION ............................................................................................... 52 Report on the State of Operational Snow Data Provisioning in Canada X | P a g e 5 SNOW MONITORING NETWORKS IN PROVINCES AND TERRITORIES ........................ 55 5.1 YUKON ............................................................................................................................................ 55 5.2 NORTHWEST TERRITORIES .............................................................................................................. 64 5.3 NUNAVUT ........................................................................................................................................ 71 5.4 BRITISH COLUMBIA .......................................................................................................................... 76 5.5 ALBERTA ......................................................................................................................................... 89 5.6 SASKATCHEWAN .............................................................................................................................. 96 5.7 MANITOBA ..................................................................................................................................... 102 5.8 ONTARIO ....................................................................................................................................... 110 5.9 QUEBEC ........................................................................................................................................ 118 5.10 NEW BRUNSWICK .......................................................................................................................... 127 5.11 NOVA SCOTIA ................................................................................................................................ 134 5.12 PRINCE EDWARD ISLAND ............................................................................................................... 139 5.13 NEWFOUNDLAND AND LABRADOR ................................................................................................... 145 6 USER NEEDS ................................................................................................................... 153 7 ANALYSIS OF STRENGTHS, WEAKNESSES, OPPORTUNITIES, AND THREATS ....... 155 8 CONCLUSIONS AND RECOMMENDATIONS ................................................................. 159 9 REFERENCES ................................................................................................................. 164 APPENDIX A: DEFINITION OF KEY TERMS ............................................................................. 171 APPENDIX B: SNOW DATA USE ............................................................................................... 172 APPENDIX C: USER NEEDS ..................................................................................................... 178 APPENDIX D: DATA AND METADATA ACCESSIBILITY AND DISCOVERABILITY ................. 181 APPENDIX E: ANALYSIS OF STRENGTHS, WEAKNESSES, OPPORTUNITIES AND THREATS .................................................................................................................................................... 182 APPENDIX F: STATISTICS OF REGIONAL SNOW MONITORING STATIONS ......................... 185 APPENDIX G HYPSOMETRIC DISTRIBUTION OF SNOW MONITORING STATIONS ............. 190 APPENDIX H: SNOW MONITORING NETWORKS IN CANADA ............................................... 204 APPENDIX I: QUESTIONS OF THE SURVEY OF SNOW MONITORING NETWORK OPERATORS AND SNOW DATA USERS .................................................................................. 217 Report on the State of Operational Snow Data Provisioning in Canada 11 | P a g e 1 Introduction Lead author: F. Weber 1.1 Background Snow plays an important part in Canadian life, and Canadians have a deep cultural connection to snow. It is relied on as a life force by the Inuit population (Climate Atlas of Canada, 2022), for winter recreational activities such as downhill and cross-country skiing, snowshoeing and snowmobiling, and brings joy to many. Snow is a key determinant in wildlife migration, hibernation, and survival (Canada International Year of Glaciers’ Preservation National Committee, 2025). Snow is a good insulator as it holds in the heat from the ground below and prevents moisture from evaporating into the atmosphere. In areas that frequently receive snow, fauna and flora use snow as a protection against the cold (National Snow and Ice Data Center, 2025). On the other hand, snow can present risks to structures and Canadian livelihood that Canadians need to mitigate. Structures can collapse due to heavy snow build-up and roofs can collapse due to snow loads that exceed the design capacity (Engel et al., 2022). Winter road safety is compromised by snow. Avalanches are a serious hazard to communities, highways, and winter recreation. On average there are seven avalanche deaths each year in British Columbia alone (British Columbia Ministry of Public Safety and Solicitor General - Coroners Service, 2023). Flooding is the most common weather-related catastrophe in Canada (StatsCan, 2025), and flood risks associated with snowmelt in the spring and snowmelt during heavy rainstorms are a serious concern in many areas. Snow is a component of the water cycle and specifically of the cryosphere, which refers to the areas of Earth where water exists in any of its frozen states. Canada can be broadly categorized into three snow climates (Haegeli and McClung 2003, Avalanche Canada 2024). A maritime snow climate is found in areas closest to the ocean, such as the Coast Mountains and Cascades in western Canada, and coastal Quebec and Newfoundland in eastern Canada. Maritime snowpack regions are characterized by relatively heavy snowfall and mild temperatures. Rain is possible anytime during the winter. A continental snow climate exists in areas that are a long distance from the ocean and is characterized by cold weather and thin snow cover from relatively infrequent storms. The snowpack in a transitional snow climate shares characteristics of both maritime and continental snowpacks. In Canada, it is found for example in the Columbia Mountains of the British Columbia Interior. Snow depth of a transitional snowpack can be similar to that of a coastal snowpack, but snow density is generally lower. The accurate, representative, and timely monitoring of snow is important for many applications such as the following: • Avalanche hazard analysis and forecasting • Forest fire hazard analysis and forecasting • Effective water resource management • Drought and flood management and forecasting • Municipal water supply management • Soil moisture and agricultural growing condition analysis and forecasting • Winter road maintenance • Engineering design • Wildlife survival and habitat assessments • Winter recreation Report on the State of Operational Snow Data Provisioning in Canada 12 | P a g e • Climate trend analysis Operational (i.e., routine and ongoing) snow monitoring can be broadly grouped into four categories: detailed snowpack analysis conducted by avalanche safety programs, snow monitoring by surface-based atmospheric monitoring programs, snow survey programs for obtaining water equivalent of snow cover data for hydrological forecasting, and satellite remote sensing of snow by national and international agencies. Further, international, national, commercial, and university groups produce modelled, gridded snow products that are the output from coupled reanalysis1 systems or from offline simulations of snow models driven by historical meteorological forcings, and all of which may also assimilate snow observations from in-situ surface networks or remotely sensed data (Mudryk et al., 2024). 1.2 Objectives The idea to summarize current operational snow provisioning in Canada was born at the March 2022 meeting of the Canadian Council for Weather and Climate Monitoring (CWAC). The Canadian Council for Weather and Climate Monitoring is a working group comprised of government agencies that operate surface-based, in-situ meteorological monitoring networks and that have the objective of improving communication, collaboration, and standardization between these agencies.2 It was speculated that snow measurement and data production methods may vary across the country. However, there is limited research quantifying the extent of these variations or evaluating the need for, or benefits of, standardization. For data users, understanding these differences is crucial, as they directly impact the interpretation and application of snow data. A compilation of snow measurement approaches should also foster the transfer of knowledge between agencies and inspire innovation. Additionally, it remained unclear how different organizations have adopted technological advancements and adjusted operations to financial pressures. Most importantly, there has been no comprehensive countrywide assessment of whether the snow data available to users meet current and future needs in terms of accuracy, reliability, frequency, timeliness, spatial coverage, accessibility, and discoverability. Similarly, the degree of collaboration and data sharing among snow monitoring agencies within provinces and territories is not well understood. A Snow Monitoring Working Group was formed under the Canadian Council for Weather and Climate Monitoring. It was tasked to address the questions described above. The group is comprised of representatives from snow monitoring network operators at government agencies across Canada, municipalities, and hydroelectric utilities. 1.3 Approach This report was modelled after the European Snow booklet (Haberkorn, 2019). The Summary of Ground- Based Snow Measurements for the Northeastern United States (Engel et al., 2022) and a report on Emerging Technologies in Snow Monitoring (Baerup, 2021) were other references used. 1 Reanalysis is a method in which historical observations are combined with numerical computer model predictions to create long-term records, typically, of past hydrometeorological conditions. 2 Quebec participates as an observer at the Canadian Council for Weather and Climate Monitoring. Although it supports the general objectives of the Canadian Council for Weather and Climate Monitoring, it is not bound by the decisions made therein. Report on the State of Operational Snow Data Provisioning in Canada 13 | P a g e This report provides an overview of operational snow monitoring in and of Canada, whereby snow refers to both snowfall and snow on the ground. Overviews of the major operational surface-based snow monitoring networks, remotely sensed snow observations of Canada, and commonly used modelled snow products are provided. The instrumentation and methods used by the various networks are described. Note that the mention of product names is not an endorsement. Where available, information about data production methods has been added. The report compiles online snow data and snow station metadata dissemination portals where data can be freely accessed. Information was provided by members of the Snow Monitoring Working Group and gathered through an online survey of Canadian snow monitoring network operators and snow data users. Survey questions are provided in Appendix I. Survey responses were collected from November to December 2023. 45 data producers and 39 data users were surveyed. Network operators were identified based on the regional knowledge of the Snow Monitoring Working Group members. It proved to be difficult to comprehensively identify snow monitoring agencies and snow data users in Canada, and to receive detailed information back from the identified agencies. In some cases, station counts reported by network representatives through the survey did not match this project’s station inventory. However, it is thought that the survey and report capture the majority of snow monitoring agencies. The report compiles a 2023-2024 snapshot of basic snow monitoring station and network metadata. This inventory includes operational monitoring programs only. The inventoried stations are active, surface- based snow monitoring stations in Canada for which the recorded data may or may not be publicly available. The annually-updated inventory of surface-based water equivalent of snow cover monitoring stations compiled by Vionnet et al. (2025) was used as an additional source of information. However, the two inventories could not be integrated due to a lack of common station identifiers. Network densities were calculated for each variable measured. Required network densities are use- specific and depend on local hydrometeorological gradients. World Meteorological Organization (2008) minimum densities are provided for general reference only. The hypsometric distribution of monitoring stations - representing both the percentage of Earth’s surface and the number of stations - was plotted as a function of elevation. Further, information about the level of automation in provincial/territorial networks could be gleaned from the data. It was determined that a gap analysis hinges on a sound understanding of user needs - such as the specific snow variables of interest, data reliability, data uncertainty, timeliness, and data representativeness. User perspectives were directly provided by data users and indirectly by data producers who conveyed the requirements expressed by their clients. The strengths, weaknesses, opportunities, and threats to snow data quality and availability were identified based on a combination of working group members’ expertise and survey responses. Recommendations for improving snow monitoring in Canda are provided. This report is organized in eight main sections: • Section 1 provides the rationale for this report, the scope, and the methods used • Section 2 defines the snow variables within the scope of this report and describes common operational technologies and methods that are used in Canada • Section 3 describes snow remote sensing and modelled snow products commonly used in Canada • Section 4 provides descriptions of countrywide and larger regional snow monitoring networks Report on the State of Operational Snow Data Provisioning in Canada 14 | P a g e • Section 5 describes snow monitoring networks in all 13 provinces and territories from west to east • Section 6 summarizes users’ snow data needs • Section 7 is a summary of identified strengths and weaknesses of, and opportunities and threats for snow monitoring in Canada • Section 8 summarizes the report and provides recommendations. Report on the State of Operational Snow Data Provisioning in Canada 15 | P a g e 2 Snow Variables and Measurement Methods Lead authors: C. Smith and F. Weber The definitions of common snow variables referred to in this document are based on World Meteorological Organization (2023), World Meteorological Organization (2018), World Meteorological Organization (2012), and Fierz et a. (2009). In accordance with World Meteorological Organization, this document refers to variables when describing the physical or meteorological quantities representing the state of the local atmosphere and cryosphere. Snow variables can be broadly divided into those that describe solid precipitation falling to the ground and those that describe snow cover that has accumulated on the ground. Snow cover is the accumulation of snow on the base surface and is sometimes also referred to as snowpack. Several variables describe the physical properties of the snow cover, and either for the entire snow column or for individual snow layers. Technologies and methods described here are operational methods commonly used in Canada and which are referenced in subsequent sections. This report does not provide a compilation of all technologies currently potentially available. In this report, manual measurements refer to methods that require a human observer to take the measurement. Locations where these measurements are taken are called manual stations. In contrast, automated measurements are collected by instruments without human intervention. The data are stored in a connected data logger and/or transmitted to a database. Sites where measurements are automatically recorded are referred to as automated stations. 2.1 Total Snow Depth Total snow depth, also referred to as height of snow, is defined as the vertical distance from the snow surface to a stated reference level and is reported in centimetres. In most cases, the reference level corresponds to the base surface such as the bare ground or surface target. Snow depth should not be confused with snow thickness, as snow thickness is defined as the perpendicular distance from the snow surface to the reference level (Figure 2.1-1). Manual Measurement Methods: Total snow depth can be measured with rulers (Figure 2.1-2), graduated avalanche probes (Figure 2.1-3), or graduated snow samplers (Figure 2.1-4), which are inserted into the snowpack. At a manual snow survey site, several samples are taken with a snow sampler at designated locations. Sample results are averaged for the reported measurement. Total snow depth can also be measured with permanently installed snow stakes (Figure 2.1-5). Automated Measurement Methods: Ultrasonic snow depth sensors measure snow depth by measuring the travel time of ultrasonic pulses reflected to the sensor from the ground or snow surface. Ancillary air temperature measurements are required to adjust the temperature dependant speed of sound. Campbell Scientific SR50 series (Figure 2.1-6, Figure 2.1-7,Figure 2.1-8), Campbell Scientific SnowVue10, and the Sommer Messtechnik USH-8 (Figure 2.1-9) and USH-9 (Figure 2.1-10) instruments are commonly used. Report on the State of Operational Snow Data Provisioning in Canada 16 | P a g e Figure 2.1-1 Relationship between total snow depth (HS) and snow thickness (DS) on a slope (source: World Meteorological Organization, 2023) Figure 2.1-2 An Environment and Climate Change Canada Type 4 snow ruler (photo: Environment and Climate Change Canada) Figure 2.1-3 An avalanche probe is used to measure total snow depth (photo: BC Hydro/Northwest Hydraulic Consultants) Report on the State of Operational Snow Data Provisioning in Canada 17 | P a g e Figure 2.1-4 Manual snow surveying of typical forested Deer Winter Severity Index site with the Geoscientific Federal Snow Sampler (photo: New Brunswick Department of Environment and Local Government) Figure 2.1-5 A permanently installed, graduated snow depth stake is used to measure total snow depth (photo: F. Weber) Figure 2.1-6 British Columbia Ministry of Environment and Parks station with Campbell Scientific SR50A ultrasonic snow depth sensor over top of snow pillow target (photo: British Columbia Ministry of Environment and Parks) Figure 2.1-7 Saskatchewan Research Council station with Campbell Scientific SR50A ultrasonic snow depth sensor (photo: Saskatchewan Research Council) Report on the State of Operational Snow Data Provisioning in Canada 18 | P a g e Figure 2.1-8 Environment and Climate Change Canada Automated Weather Station with Campbell Scientific SR50A snow depth sensor over top of plastic target (photo: Environment and Climate Change Canada) Figure 2.1-9 BC Hydro station with Sommer Messtechnik USH-8 ultrasonic snow depth sensor over top of gravel target (photo: BC Hydro/Northwest Hydraulic Consultants) Figure 2.1-10 BC Hydro station with Sommer Messtechnik USH-9 ultrasonic snow depth sensor (photo: BC Hydro/Montrose Environmental) Report on the State of Operational Snow Data Provisioning in Canada 19 | P a g e 2.2 Depth of New Snowfall The depth of snowfall is the vertical depth in centimetres of freshly fallen snow that has accumulated on a surface in a specific period. That period can be a 24-hour period, two periods between twice-a-day observations, or the period since the beginning of a storm. Manual Measurement Methods: Observers measure the depth of new snowfall with a ruler on a snow board or on bare ground if no board is available. The depth of new snowfall is measured in several places and the measurements are then averaged. After measurements are taken, the board is cleaned and put back onto a level and unobstructed location. An example of a snow board is the Environment and Climate Change Canada – Meteorological Service of Canada-approved Weaverboard shown in Figure 2.2-1. The length of a side of the square is 42.5 centimetres; the thickness of the board is two centimetres. The lip extends one centimetre above the actual wooden board to prevent snow that has fallen on the board from blowing off. The vertical stick is 38.5 centimetres high. Note that the snow ruler shown in Figure 2.2-1 is for scale only. Automated Measurement Methods: The depth of new snowfall can be measured with automated snow depth sensors as shown in Figure 2.2-2, but the board has to be manually cleared. The authors are not aware of a fully automated method for measuring the depth of new snowfall. It is therefore thought that the survey of network operators incorrectly suggests that the depth of new snowfall can be measured in a fully automated manner. Figure 2.2-1 The Weaverboard is one of the snow boards used (photo: Environment and Climate Change Canada) Figure 2.2-2 Snow board with automated snow depth sensor (photo: Parks Canada, 2024) Report on the State of Operational Snow Data Provisioning in Canada 20 | P a g e 2.3 Water Equivalent of Snow Cover Water equivalent of snow cover, also known as snow water equivalent, is the vertical depth of water that would be obtained if the snow cover melted completely. It equates to the snow cover mass per unit area and is expressed either in millimetres water equivalent or in kilograms per square metre. Water equivalent of snow cover is also the product of the snow height in metres and the vertically integrated density in kilograms per cubic metre. Manual Measurement Methods: At a manual snow survey site, usually five or 10 samples are taken at designated locations. Snow survey sampling stations are typically located along a straight line, but other layouts are possible. The sampling stations of a snow course are typically numbered and identified with two signs which face a sampling location (Figure 2.3-1). The distance of the sampling location from the signs is written on the signs. The direction the signs are facing in combination with the distance information allows the surveyor to triangulate the sampling location. Alternatively, stakes can mark the sampling locations. In Canada, commonly used snow samplers for manual snow surveying are the Federal Snow Sampler (Figure 2.3-2) and the Prairie Snow Sampler (Figure 2.3-3), which is also known as Eastern Snow Conference (ESC) 30 sampler. The 30 refers to the size of the cutter area in square centimetres. The McCall Snow Sampler is a less commonly used snow sampler. The inner diameter of the Federal and McCall Sampler cutters is 3.61 centimetres (1.485 inch). The inner diameter of the Prairie Snow Sampler cutter is 6.18 centimetres. The McCall sampling kit is designed for very deep and icy snowpack (Figure 2.3-4). Its sidewalls are twice as thick as those of the Federal Snow Sampler and its cutter is about three times as long as that of the Federal Snow Sampler. The McCall sampling kit is sold with a digital scale as its weighting capacity is higher than that of the spring scales used in the Federal sampling kit. The digital scale outputs weight and water equivalent has to be manually calculated by the surveyor. Farnes et al. (1983) reported that cutters with blunt teeth, such as the cutter of the standard Federal Snow Sampler, over-measure by 10 to 12 percent. Farnes et al. (1983) speculated that the shape of the teeth was partly responsible for the overmeasurement bias and specifically that the teeth on the flat cutter force snow from outside the cutter opening into the core. A snow sampler is pushed vertically through the snowpack to the ground surface (Figure 2.3-5). The sampler is weighed before and after extraction of the core (Figure 2.3-6), the difference in weight determined, and the water equivalent of snow cover calculated. For the standard procedure, sample results from each sampling station are averaged to obtain the reported measurement at a snow survey site. In shallow snowpack conditions of about 0.5 metres or less, the bulk sampling technique is typically used. The bulk sampling technique requires that the cores of all samples at a site are aggregated in a single container and are weighted once. Report on the State of Operational Snow Data Provisioning in Canada 21 | P a g e Figure 2.3-1 Manual snow survey sampling station sign (photo: BC Hydro) Figure 2.3-2 GeoScientific Federal Snow Sampler (Geo Scientific Ltd., 2017) Figure 2.3-3 GeoScientific Prairie Snow Sampler (Geo Scientific Ltd., 2017) Figure 2.3-4 GeoScientific McCall Snow Sampler (Geo Scientific Ltd., 2017) Report on the State of Operational Snow Data Provisioning in Canada 22 | P a g e Figure 2.3-5 Manual snow survey with the Federal Snow Sampler (photo: BC Hydro/Northwest Hydraulic Consultants) Figure 2.3-6 Manual snow survey with the Prairie Snow Sampler by Parks Canada staff (photo: Parks Canada Western Arctic Field Unit) Automated Measurement Methods: Commonly used technologies used for measuring water equivalent of snow cover at automated stations include the custom-manufactured, open hydraulic snow pillow system with shaft encoders or vented, submersible pressure transducers. Shaft encoders and submersible pressure transducers are installed in manometers. Snow pillows are custom-manufactured urethane coated nylon bladders filled with an antifreeze solution. Snow pillows measure the weight of the snowpack through the displacement of pillow fluid into the manometer. The displacement is then converted into the water equivalent of snow cover. Examples of snow pillows are shown in Figure 2.5-1, Figure 2.5-2, and Figure 2.5-3. Snow scales are electronic scales made from flat panels assembled on a rigid frame. Snow scales measure the weight of the snow cover. Snow scale models commonly used in Canada are Sommer Messtechnik SSG and SSG-2 scales (Figure 2.5-4), and Alpine Hydromet scales. The Campbell Scientific CS725 gamma ray sensor is a passive, noncontact spectrometer that measures the attenuation of the electromagnetic energy emitted by the snowpack. Electromagnetic energy is emitted during the radioactive decay of the naturally occurring radioactive isotopes Potassium 40 (40K) and Thallium 208 (208Tl). These isotopes are the most abundant radioactive elements found in soil (Engel et al., 2022). This instrument was developed by Hydro-Québec’s research institute (IREQ) under the name GMON, which stand for Gamma Monitoring, and is now commercially sold by Campbell Scientific as the CS725. Figure 2.5-5 shows a Campbell Scientific CS725 gamma ray sensor installation. The installation and operation of the Campbell Scientific CS725 is described in detail by English and Metcalf (2017). The cosmic ray technique relies on the attenuation of naturally occurring ‘background’ neutrons by the hydrogen contained in water. The sensor is placed on or just beneath the ground, where it is allowed to Report on the State of Operational Snow Data Provisioning in Canada 23 | P a g e be buried by falling snow (Figure 2.5-6). The sensor records the intensity of downward-directed secondary cosmic rays that penetrate the snowpack. The intensity of the recorded radiation is proportional to the water equivalent of snow cover traversed by cosmic rays (Hydroinnova, 2025). The Hydroinnova SnowFox Cosmic Ray Neutron sensor is used in Canada. 2.4 Water Equivalent of New Snowfall The water equivalent of new snowfall is the vertical depth of water that would be obtained if the new snowfall melted completely. It is measured in units of millimetres once every six hours, twice daily, or once daily. It may also be measured for the duration of a storm. This variable is generally not explicitly reported and is used as a component for calculating and reporting total precipitation. Manual Measurement Methods: The water equivalent of new snowfall can be estimated from the depth of new snowfall and assuming a snow density of 10 percent (Meteorological Service of Canada, 2012 and Environment and Climate Change Canada, 2015). If the site is equipped with a Nipher gauge, and only solid precipitation has occurred, the water equivalent of new snowfall can be measured by measuring the melted snow with a graduate cylinder. The water equivalent of the new snowfall can also be obtained by extracting a snow core from the reference surface and by reading the water equivalent off a calibrated scale or by converting the weight into water equivalent using the sampling area. Automated Measurement Methods: Automated instruments that measure total precipitation or the water equivalent of the snow cover, such as snow pillows, snow scales, gamma radiation sensors and neutron probes, can be used to measure the water equivalent of new snowfall if all of the precipitation is solid. However, liquid precipitation during the observation period makes it difficult to isolate solid from total precipitation amounts (Magnussen et al., 2025). Present weather sensors can distinguish between liquid and frozen precipitation, and record the associated water equivalent. However, they are not operationally used in Canada to record the water equivalent of new snowfall. 2.5 Snow Density Snow density is the mass of snow per unit volume in kilograms per cubic metre or in percent. Snow density typically includes all the constituents of the sampled snow, i.e., ice, liquid water and air. Manual Measurement Methods: Bulk snow density can be calculated from collocated manual snow depth and water equivalent of snow cover measurements. The Federal and Prairie Snow samplers are typically used in Canada to manually measure these variables. Winter Engineering snow density kits are used in some avalanche snow science operations and directly output snow density (Figure 2.5-7). The cutter is pushed into the snow, typically perpendicular to the surface, until the top orifice is flush with the snow surface. The bottom of the cutter opening is covered Report on the State of Operational Snow Data Provisioning in Canada 24 | P a g e Figure 2.5-1 Automated Alberta Environment snow weather station with snow pillow (photo: Alberta Environment and Protected Areas) Figure 2.5-2 British Columbia Ministry of Environment and Parks station with snow pillow (photo: British Columbia Ministry of Environment and Parks) Figure 2.5-3 BC Hydro station with snow pillow (photo: BC Hydro/Montrose Environmental) Figure 2.5-4 Automated Alberta Environment snow weather station with snow scale (photo: Alberta Environment and Protected Areas) Report on the State of Operational Snow Data Provisioning in Canada 25 | P a g e Figure 2.5-5 Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs station with Campbell Scientific CS725 gamma ray sensor in the background (photo: © Gouvernement du Québec, 2025) Figure 2.5-6 Hydroinnova SnowFox Cosmic Ray Neutron sensor (photo: BC Hydro) Figure 2.5-7 Winter Engineering snow density cutter and scale (photo: F. Weber) Report on the State of Operational Snow Data Provisioning in Canada 26 | P a g e with an inserted piece of metal such as a spatula. The cutter is then removed from the snowpack with care to not lose any snow and placed on a custom scale to directly read snow density or it is placed on a digital scale and snow density is calculated. Automated Measurement Methods: Snow density can be obtained automatically by measuring the dielectric coefficient along a flat-band cable. The volume of ice, water and air is determined, and snow density calculated. Bulk snow density estimates can also be calculated from collocated automated snow depth and water equivalent of snow cover measurements. 2.6 Snow Temperature Snow temperature is measured in degrees Celsius. It can be a measurement of the snow temperature at a specified height above the ground, below the snow surface or at the snow surface. 2.7 Total Precipitation Precipitation is defined as the liquid and/or solid product of the condensation of water vapour falling from clouds or deposited from air onto the ground. It includes rain, hail, snow, dew, rime, hoar frost and fog precipitation. The total amount of precipitation which reaches the ground in a stated period is expressed in terms of the vertical depth of water (or water equivalent in the case of solid forms) in millimetres. Solid precipitation is defined as precipitation occurring at least partially in a solid state, such as snow, ice pellets, sleet, or hail. Manual Measurement Methods: A Type B rain gauge consists of a collecting funnel, a cylindrical outer container, which is 36.5 centimetres high and 12.5 centimetre in diameter, and a graduated inner container made of a high-strength plastic (Figure 2.7-1). The CoCoRaHS plastic precipitation gauge (Figure 2.7-2) is similar in design to the Type B rain gauge, but its orifice diameter measures four inches (10.2 centimetres). The Nipher shielded snow gauge (Figure 2.7-3) collects total precipitation including snowfall, if present, in a collector (Figure 2.7-4). At the end of the accumulation period the collector is removed from the gauge and replaced with an empty collector. The measurement is made after melting the snow caught in the container by adding a measured quantity of warm water or by applying low heat. The melted snow is then measured volumetrically in a special graduated cylinder. It is not recommended to use the Nipher shielded snow gauge for measuring high intensity rainfall, since rain can splash off the shield and fall into the collector. The water equivalent of the observation is normally expressed to the nearest 0.2 millimetre. Automated Measurement Methods: Automated total precipitation is commonly measured in Canada with accumulating weighing gauges such as the OTT Pluvio² L 200 and 400 (Figure 2.7-5), OTT Pluvio (Figure 2.7-6), Geonor T200B and FTS All Weather Precipitation Gauge. Lambrecht rain[e]H3 heated tipping bucket gauges (Figure 2.7-7) and custom-designed and -manufactured standpipe precipitation gauges (Figure 2.7-8) are also used. Report on the State of Operational Snow Data Provisioning in Canada 27 | P a g e Figure 2.7-1 Type B precipitation gauge (photo: F. Weber) Figure 2.7-2 CoCoRaHS plastic four inch diameter precipitation gauge (photo: R. Fleetwood) Figure 2.7-3 Nipher snow gauge (photo: F. Weber) Report on the State of Operational Snow Data Provisioning in Canada 28 | P a g e Figure 2.7-4 Nipher copper collector and glass graduated cylinder (photo: Environment and Climate Change Canada) Different manufacturers employ different measurement principles in their weighing precipitation gauges. The OTT Pluvio gauges use a sensitive load cell, while the Geonor T200B uses a vibrating wire transducer to measure the weight of the bucket. Weighting gauges require the use of antifreeze in the collecting bucket to enable operation during cold temperatures. The Lambrecht rain[e] gauge collects precipitation with a funnel that is temperature controlled. Solid precipitation, such as snow, is melted by the heated funnel surface. The collected precipitation passes the funnel through the drop former and ends up in a self-emptying collecting system, where the drop is weighed and recorded (Lambrecht meteo, 2025). Commercially available gauges do not meet the monitoring requirements in all situations. Due to the propensity of most commercially available gauges to snow cap when left unattended some operators opt to custom-design and -manufacture instruments. An example is the pressure level recording standpipe precipitation gauge that is popular in British Columbia and the Yukon. A standard standpipe precipitation gauge typically features a cylindrical tube, approximately 1200 to 1400 mm in height, sealed at the bottom, and equipped with a pressure transducer mounted through the base plate. To promote the melting of solid precipitation and avoid the freezing of liquid precipitation, antifreeze is added to the standpipe. To prevent evaporation and counter the hygroscopic properties of the antifreeze, a layer of mineral oil is applied to the contents of the standpipe. Some models use bilge pumps to mix precipitation with the antifreeze on a regular basis, as water and certain types of antifreeze would separate otherwise. Report on the State of Operational Snow Data Provisioning in Canada 29 | P a g e Figure 2.7-5 OTT Pluvio2 L with double Alter shield (photo: Environment and Climate Change Canada) Figure 2.7-6 OTT Pluvio precipitation gauge shown in the center of the photo (photo: F. Weber) Figure 2.7-7 NAV CANADA station with Lambrecht rain[e]H3 precipitation gauge and wind shield (NAV CANADA, 2025) Figure 2.7-8 Standpipe precipitation gauge at a high snowpack site (photo: BC Hydro/Northwest Hydraulic Consultants) Report on the State of Operational Snow Data Provisioning in Canada 30 | P a g e 2.8 Liquid Water Content of Snow Liquid water content is defined as the amount of water within the snow that is in the liquid phase. This variable is synonymous with the free-water content of a snow sample. Liquid water in snow originates from either melt, rain, or a combination of the two. Measurements of liquid water content or wetness are expressed as either a volume or mass fraction. Both can be reported as a percent, which usually requires a separate measurement of density. 2.9 Snow Albedo Snow albedo is the ratio of reflected to incoming radiation. It is usually reported in percent. The Apogee SN-500-SS net radiometer, shown in the centre of Figure 2.9-1, is used in some networks to measure incoming and reflected radiation over snow, and to calculate snow albedo. Figure 2.9-1 Apogee SN-500-SS net radiometer (photo: BC Hydro/Northwest Hydraulic Consultants) 2.10 Snow-Covered Area The snow-covered area is the areal extent of snow-covered ground. The base surface is the interface at the bottom of the snow cover or snowpack, which is the ground surface in case of soil, or the surface of another component of the cryosphere such as the surface of sea ice. On glaciers, it is either the ice surface within the ablation zone or the previous year’s hardened summer surface in the accumulation zone. The spatial extent of snow cover is measured in units of square kilometres. 2.11 Presence of Snow on the Ground Presence of snow on the ground is a binary observation of the presence of snow cover at the measurement location. If the field of view at the measurement location is more than 50 percent covered with snow of any depth, then the location is considered to have snow on the ground. The measurement area can be an instrument compound and is generally about 50 metre by 50 metre but not less than 10 metre by 10 metre in size. Report on the State of Operational Snow Data Provisioning in Canada 31 | P a g e 2.12 Snow Hardness Snow hardness is the resistance of an object to penetration into snow. It represents a relative index value related to both the instrument and the instrument operator and is therefore device dependent. Snow hardness can be measured in newtons. Report on the State of Operational Snow Data Provisioning in Canada 32 | P a g e 3 Snow Remote Sensing and Modelled Snow Products Lead authors: F. Weber and C. Derksen This section describes snow remote sensing and modelled snow products commonly used in Canada. The products described here are not a compilation of all products potentially available. An assessment of gridded water equivalent of snow cover products is provided by Mudryk et al. (2025). The authors found that many of the evaluated products can reasonably represent the climatology and variability of non- mountainous water equivalent of snow cover, but have substantially lower skill in mountainous regions. 3.1 Environment and Climate Change Canada Canadian Prairie Snow Water Equivalent The Climate Research Division of the Science and Technology Branch of Environment and Climate Change Canada applies land cover sensitive water equivalent of snow cover retrieval algorithms to passive Scanning Multichannel Microwave Radiometer (Nimbus-7 SMMR) and Special Sensor Microwave/Imager (DMSP 5D2 SSM/I) brightness temperatures to estimate water equivalent of snow cover (Canadian Cryospheric Information Network, 2017). Data are of an approximately weekly resolution during the winter months. Online datasets are listed in Table 3.1-1. Table 3.1-1 Online datasets from Environment and Climate Change Canada’s Canadian Prairie Snow Water Equivalent Data-providing organization Web link Canadian Cryospheric Information Network https://ccin.ca/ccw/snow/current/swe.html 3.2 United States NOAA National Weather Service National Operational Hydrologic Remote Sensing Center Airborne Gamma Radiation Snow Survey Program The National Oceanic and Atmospheric Administration (NOAA) United States National Weather Service National Operational Hydrologic Remote Sensing Center (NOHRSC) maintains an operational Airborne Gamma Radiation Snow Survey Program to make airborne water equivalent of snow cover and soil moisture measurements. Low-flying aircraft conduct surveys in 31 U.S. states, including Alaska, as well as in 8 Canadian provinces and territories, i.e., areas of the Yukon, British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec and New Brunswick that border the United States. Airborne gamma radiation measurements record natural terrestrial gamma radiation emitted from the naturally occurring potassium, uranium, and thorium radioisotopes in the upper soil. Water mass in the snow cover attenuates the terrestrial radiation signal. The difference between airborne radiation measurements made over bare ground and snow-covered ground are used to measure the water equivalent of snow cover (Carroll, 2001). Online datasets are listed in Table 3.2-1. https://ccin.ca/ccw/snow/current/swe.html Report on the State of Operational Snow Data Provisioning in Canada 33 | P a g e Table 3.2-1 Online datasets from the United States NOAA National Weather Service’s National Operational Hydrologic Remote Sensing Center Airborne Gamma Radiation Snow Survey Program Data-providing organization Web link National Weather Service, National Operational Hydrologic Remote Sensing Center https://www.nohrsc.noaa.gov/snowsurvey/ 3.3 United States NOAA National Weather Service National Operational Hydrologic Remote Sensing Center’s Snow Data Assimilation System (SNODAS) These modelled data contain snowpack properties, such as snow depth and water equivalent of snow cover, from the NOAA United States National Weather Service National Operational Hydrologic Remote Sensing Center (NOHRSC) Snow Data Assimilation3 System (SNODAS). The temporal and spatial resolution are one day and one kilometre, respectively. Spatial Coverage in North America reaches to approximately 54° north. Online datasets are listed in Table 3.3-1. Table 3.3-1 Online datasets from the United States NOAA National Weather Service’s National Operational Hydrologic Remote Sensing Center’s Snow Data Assimilation System Data-providing organization Web link National Snow and Ice Data Center, University of Colorado Boulder https://nsidc.org/data/g02158/versions/1 National Weather Service, National Operational Hydrologic Remote Sensing Center https://www.nohrsc.noaa.gov/nsa/ 3.4 United States National Ice Center Interactive Multisensor Snow and Ice Mapping System - Daily Northern Hemisphere Snow and Ice Analysis United States National Ice Center produces daily binary snow extent at one-, four-, and 24-kilometre resolution for the Northern Hemisphere. Data are manually derived by analysts from a wide range of satellite remote sensing data and surface observations. The impact of cloud cover is reduced because of the manual analysis procedure. The Ice Mapping System provides instantaneous estimates of snow extent. Caution should be used if applying the data to climate analysis because of spurious trends in the 3 Data assimilation is the process of combining information from numerical computer models with observations to improve the accuracy of model predictions. https://www.nohrsc.noaa.gov/snowsurvey/ https://nsidc.org/data/g02158/versions/1 https://www.nohrsc.noaa.gov/nsa/ Report on the State of Operational Snow Data Provisioning in Canada 34 | P a g e dataset (particularly in October through December) introduced by changes to the input data and analysis methodology over the past two decades (e.g. Mudryk et al., 2017 and Brown and Derksen, 2013). Online datasets are listed in Table 3.4-1. Table 3.4-1 Online datasets from the United States National Ice Center’s Interactive Multisensor Snow and Ice Mapping System Data-providing organization Web link National Snow and Ice Data Center, University of Colorado Boulder https://nsidc.org/data/g02156/versions/1 National Snow and Ice Data Center, University of Colorado Boulder https://noaadata.apps.nsidc.org/NOAA/G02156/ U.S. National Ice Center https://usicecenter.gov/Products/ImsHome 3.5 NASA Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Daily snow cover and albedo are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua and Terra satellites. MODIS is a 36-channel visible to thermal-infrared sensor. Spatial resolution of the snow cover product is 500 metres. The biggest limitation to the use of the MODIS snow-cover products is cloud cover, which can prevent mapping. Online datasets are listed in Table 3.5-1. Table 3.5-1 Online datasets from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Data-providing organization Web link NASA Worldview https://worldview.earthdata.nasa.gov/?v=- 136.89206885355165,38.31152481855595,- 79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_D aily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t= 2025-01-15-T16%3A56%3A26Z National Snow and Ice Data Center, University of Colorado Boulder https://nsidc.org/data/myd10a1/versions/61 3.6 NASA Advanced Microwave Sounding Radiometer 2 (AMSR2) The Advanced Microwave Sounding Radiometer 2 (AMSR2) is installed on the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission 1st-Water, “SHIZUKU” (GCOM-W1) satellite. The snow products include binary snow cover (presence/absence of snow), snow depth, and 25 kilometre resolution water equivalent of snow cover. Note that multi-dataset intercomparisons have noted major uncertainty in the water equivalent of snow cover and snow depth datasets (Mortimer et al., 2020). https://nsidc.org/data/g02156/versions/1 https://noaadata.apps.nsidc.org/NOAA/G02156/ https://usicecenter.gov/Products/ImsHome https://worldview.earthdata.nasa.gov/?v=-136.89206885355165,38.31152481855595,-79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_Daily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2025-01-15-T16%3A56%3A26Z https://worldview.earthdata.nasa.gov/?v=-136.89206885355165,38.31152481855595,-79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_Daily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2025-01-15-T16%3A56%3A26Z https://worldview.earthdata.nasa.gov/?v=-136.89206885355165,38.31152481855595,-79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_Daily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2025-01-15-T16%3A56%3A26Z https://worldview.earthdata.nasa.gov/?v=-136.89206885355165,38.31152481855595,-79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_Daily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2025-01-15-T16%3A56%3A26Z https://worldview.earthdata.nasa.gov/?v=-136.89206885355165,38.31152481855595,-79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_Daily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2025-01-15-T16%3A56%3A26Z https://nsidc.org/data/myd10a1/versions/61 Report on the State of Operational Snow Data Provisioning in Canada 35 | P a g e Online datasets are listed in Table 3.6-1. Table 3.6-1 Online datasets from NASA’s Advanced Microwave Sounding Radiometer 2 Data-providing organization Web link NASA Worldview https://worldview.earthdata.nasa.gov/?v=- 136.89206885355165,38.31152481855595,- 79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_D aily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t= 2025-01-15-T16%3A56%3A26Z 3.7 European Space Agency Copernicus Sentinel Fractional Snow Cover The European Space Agency’s Sentinel satellites monitor earth with Radio Detection And Ranging (RADAR) and multi-spectral imaging instruments. Sentinel-1 is a polar-orbiting, all-weather, day-and-night RADAR imaging mission. Sentinel-2 is a polar-orbiting, multispectral high-resolution imaging mission made up of two identical satellites (Sentinel-2A and Sentinel-2B). They orbit Earth at an altitude of 786 kilometres and 180° apart. This configuration optimises coverage and revisit times. The measurement interval is every five days at the equator under cloud-free conditions and approximately two to three days at Canadian latitudes. The spatial resolution is 10 metres for visible and near-infrared bands, 20 metres for red edge, and 60 metres for shortwave infrared bands. Data are processed to Fractional Snow Cover (The European Space Agency, 2025 and Copernicus, 2025). Online datasets are listed in Table 3.7-1. Table 3.7-1 Online datasets from the European Space Agency’s Copernicus Sentinel Fractional Snow Cover Data-providing organization Web link Copernicus https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data Sentinelhub https://www.sentinel-hub.com/explore/eobrowser/ 3.8 Environment and Climate Change Canada Canadian Land Data Assimilation System (CaLDAS) The Canadian Land Data Assimilation System (CaLDAS) is a land-surface data assimilation system, which provides analyses of the land surface at a three-hour timestep over the domain of the High- Resolution Deterministic Prediction System (HRDPS) and at a 2.5-kilometre grid spacing. The National Surface and River Prediction System (NSRPS) assimilates satellite-based remote sensing observations into the High-Resolution Deterministic Prediction System to correct simulated initial conditions for model predictions, and to simulate snow depth and water equivalent of snow cover. Online datasets are listed in Table 3.8-1. https://worldview.earthdata.nasa.gov/?v=-136.89206885355165,38.31152481855595,-79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_Daily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2025-01-15-T16%3A56%3A26Z https://worldview.earthdata.nasa.gov/?v=-136.89206885355165,38.31152481855595,-79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_Daily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2025-01-15-T16%3A56%3A26Z https://worldview.earthdata.nasa.gov/?v=-136.89206885355165,38.31152481855595,-79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_Daily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2025-01-15-T16%3A56%3A26Z https://worldview.earthdata.nasa.gov/?v=-136.89206885355165,38.31152481855595,-79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_Daily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2025-01-15-T16%3A56%3A26Z https://worldview.earthdata.nasa.gov/?v=-136.89206885355165,38.31152481855595,-79.3171806739082,69.76780487087157&l=AMSRU2_Snow_Water_Equivalent_Daily,Coastlines_15m,MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2025-01-15-T16%3A56%3A26Z https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data https://www.sentinel-hub.com/explore/eobrowser/ Report on the State of Operational Snow Data Provisioning in Canada 36 | P a g e Table 3.8-1 Online datasets from Environment and Climate Change Canada’s Canadian Land Data Assimilation System Data-providing organization Web link Environment and Climate Change Canada https://dd.alpha.meteo.gc.ca/model_nsrps-caldas/ 3.9 European Space Agency Global Snow Monitoring for Climate Research (GlobSnow) The European Space Agency’s Global Snow Monitoring for Climate Research (GlobSnow) datasets contain satellite-retrieved information of snow extent and water equivalent of snow cover. They extend as far into the past as feasible using the selected sensor-families (Luojus, K., 2020). Data are intended for climate research and are updated annually. The current snow cover extent dataset is based on optical data from Envisat AATSR and ERS-2 ATSR-2 sensors covering the Northern Hemisphere. This global water equivalent of snow cover dataset from the European Space Agency Snow Climate Change Initiative were constructed by combining satellite-based passive microwave radiometer data (Nimbus-7 SMMR, DMSP 5D2 SSM/I and DMSP 5D3 SSMIS) with ground-based synoptic snow depth observations using Bayesian data assimilation. The Finnish Meteorological Institute is responsible for the generation of water equivalent of snow cover products. Water equivalent of snow cover product development is carried out in collaboration by the Finnish Meteorological Institute and Environment and Climate Change Canada. Data are currently provided for terrestrial, non-mountainous regions of the Northern Hemisphere, excluding glaciers and Greenland. Research is ongoing to expand European Space Agency Snow Climate Change Initiative water equivalent of snow cover products to mountainous areas (Sun et al., 2024). The GlobSnow program provides historical and near real-time snow extent and water equivalent of snow cover data using similar algorithms to the European Space Agency Snow Climate Change Initiative program. Online datasets are listed in Table 3.9-1. Table 3.9-1 Online datasets from European Space Agency’s Global Snow Monitoring for Climate Research (GlobSnow) Data-providing organization Web link GlobSnow v2.0 snow extent https://www.globsnow.info/se/ GlobSnow v3.0 snow water equivalent https://www.globsnow.info/swe/ PANGAEA https://doi.pangaea.de/10.1594/PANGAEA.911944 https://dd.alpha.meteo.gc.ca/model_nsrps-caldas/ https://www.globsnow.info/se/ https://www.globsnow.info/swe/ https://doi.pangaea.de/10.1594/PANGAEA.911944 Report on the State of Operational Snow Data Provisioning in Canada 37 | P a g e 3.10 NASA Arctic-Boreal Vulnerability Experiment (ABoVE) Snow Model The Arctic-Boreal Vulnerability Experiment (ABoVE) is a NASA Terrestrial Ecology Program field campaign in Alaska and western Canada. It started in 2015 (Liston, 2023). SnowModel simulation output is generated on a 3-kilometre grid and at a daily resolution. Model output includes snow depth, snow density, water equivalent of snow cover, snowfall, snow melt, snow sublimation and total precipitation. Online datasets are listed in Table 3.10-1. Table 3.10-1 Online datasets from NASA’s Arctic-Boreal Vulnerability Experiment (ABoVE) Snow Model Data-providing organization Web link NASA Oak Ridge National Laboratory Distributed Active Archive Center https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2105 3.11 Research and Development Activities The Terrestrial Snow Mass Mission is currently being developed by Environment and Climate Change Canada in collaboration with the Canadian Space Agency. If approved, the mission aims to derive water equivalent of snow cover from Ku-band RADAR measurements at a 500 metres horizontal resolution and at a 5 to 7-day time step. The earliest estimated launch of the mission is 2032. Limitations to the technology may be posed by very deep snowpack in western Canadian mountain regions. SnowCast is an experimental Canadian Hydrological Model data product that downscales the Global Environmental Multiscale model forecasts from Environment and Climate Change Canada to provide high resolution snowpack forecasts that take into account variable wind flow, solar radiation, precipitation, and temperature over complex terrain. Access to Canadian Hydrological Model simulations is via the SnowCast portal (http://www.snowcast.ca/). https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2105 http://www.snowcast.ca/ Report on the State of Operational Snow Data Provisioning in Canada 38 | P a g e 4 Countrywide Snow Monitoring and Larger Regional Networks This section summarizes snow monitoring and data production efforts by agencies that span several or all of Canada’s provinces and territories. 4.1 Environment and Climate Change Canada – Meteorological Service of Canada, Atmospheric Monitoring Service Lead authors: G. Tsim and C. Cerny 4.1.1 Network Overview The Meteorological Service of Canada is a department within Environment and Climate Change Canada, the federal department responsible for a wide range of environmental programs. It operates surface- based, hydrometeorological observation systems. The Meteorological Service of Canada‘s operational observation system consists of three in-situ monitoring networks, two of which are human observing networks and one which is a network of stations containing only automatic instruments (Mekis et al., 2018). The network operates in all provinces and territories of Canada. Most monitoring stations are in or near communities and urban centers. As of 2024, the Cooperative Climate Network consisted of approximately 200 manually operated stations, which are primarily run by volunteers, although some are contracted to provincial or municipal agencies. Manual observer stations are inspected once every three to five years depending on staff availability. The Snow Survey Program collects snow data at Upper Air Monitoring stations. During the winter months total snow depth and water equivalent of snow cover are measured. Snow survey sites are usually representative of the surrounding area, easily accessible and free of wind-drifting. Monitoring stations in the Automatic Weather Station Network typically record air temperature, relative humidity, accumulated precipitation, precipitation intensity, snow depth, air pressure, and wind speed and direction. Stations are inspected once or twice annually (Mekis et al., 2018). 4.1.2 Quality Assurance Meteorological technologists receive on-the-job training through a structured 1.5 to five year long Occupational Training Program. The standard for manual weather observations made in the Cooperative Climate Network is the Manual of Climatological Observations (MANCLIM, Meteorological Service of Canada, 2012). A collection of internally developed and public standards documents describes the configuration, installation and operation of the Automatic Weather Station network. Observation practices of the Snow Survey Program are described in the Aerological Observer’s Course Training Manual (Environment and Climate Change Canada, 2018). Report on the State of Operational Snow Data Provisioning in Canada 39 | P a g e 4.1.3 Monitoring of Total Snow Depth Manual measurements: Total snow depth is measured with a snow ruler on a bare surface by the Cooperative Climate Network. In the Snow Survey Program, it is measured using the metric Federal Snow Sampler. The Cooperative Climate Network observer takes a few measurements, particularly if the snowpack is uneven, and averages the values to obtain the reported measurement. At manual snow surveys, snow depth is recorded to the nearest 0.1 centimetre at each sample point. The mean of all samples is reported to the nearest 0.1 centimetre. Measurements are taken once or twice per day at Cooperative Climate Network sites. In the Snow Survey Program, five-point samples are taken weekly, and 10-point samples are taken twice a month. Automated measurements: The standard instrument has been the Campbell Scientific SR50A sensor. In 2024, the Campbell Scientific SR50A instruments started to get replaced with Campbell Scientific SnowVUE10 ultrasonic snow depth sensors. A standard Automatic Weather Station Network station has three snow depth sensors installed over individual plastic snow targets. Snow depth sensor targets improve data quality particularly at low snow depth. The top surface of artificial snow depth targets is level with the adjacent ground surface. Some stations may only have a single snow depth sensor installed. At some stations snow depth sensors use the natural ground as target. In 2024 there were 447 monitoring stations with at least one snow depth sensor, of which approximately 297 are operated with a triple redundant setup. Snow depth data are recorded every 15 minutes. Measurements are based on a five-minute sample average, which – in the case of Campbell Scientific SR50A data - is filtered for noise in the datalogger. Snow depth sensors are verified once a year or more as required if the data are suspect. The instruments are verified with a tape measure and by placing objects of known height on the snow target. 4.1.4 Monitoring of the Depth of New Snowfall Manual measurements: Cooperative Climate Network observers measure the depth of new snowfall on a snow board, or on bare ground if no board is available. Multiple measurements are taken and averaged. In the Snow Survey Program, the depth of new snowfall is not measured. Measurements are taken with a snow ruler once or twice a day. After each observation, the observer cleans the snow off the snowboard and places it horizontally on top of the snow surface. Automated measurements: Not measured. 4.1.5 Monitoring of Water Equivalent of Snow Cover Manual measurements: At snow survey sites, water equivalent of snow cover is measured with the Federal Snow Sampler. Snow survey stations consist of five or 10 fixed sampling locations. The bulk sampling method is used in most cases. Water equivalent data are recorded to the nearest 0.1 centimetre. Snow survey measurements for five-point samples are taken weekly, and 10-point samples are taken twice a month. Report on the State of Operational Snow Data Provisioning in Canada 40 | P a g e Automated measurements: Not measured with surface-based instrumentation. Section 3.1 provides details on Environment and Climate Change Canada’s operational product for water equivalent of snow cover, which is derived from remotely-sensed satellite data. 4.1.6 Monitoring of the Water Equivalent of New Snowfall Manual measurements: Cooperative Climate Network observers measure the water equivalent of new snowfall by measuring snow depth with a ruler and converting the data to water equivalent assuming a density of 10 percent, or by melting the snow accumulated in the Nipher shielded snow gauge. At Cooperative Climate Network stations equipped with a Nipher gauge, water equivalent of new snowfall is measured once or twice a day. Automated measurements: Not measured. 4.1.7 Monitoring of Snow Density Manual measurements: Snow density is not measured at Cooperative Climate Network stations. At snow survey sites, snow density is calculated from measured snow depth and water equivalent of the snow cover. Automated measurements: Neither measured nor calculated. 4.1.8 Monitoring of Snow Temperature Manual measurements: Not measured. Automated measurements: Not measured. 4.1.9 Monitoring of Total Precipitation Manual measurements: Precipitation is measured with the Type B rain gauge or the Nipher shielded snow gauge. At stations not equipped with a Nipher shielded snow gauge, it is necessary to estimate solid precipitation from the water equivalent of the new snow that has fallen. The depth of the freshly fallen snow is divided by 10 and converted to millimetre to obtain the water equivalent. Rainfall and snowfall measurements may have to be added together and are reported as total precipitation. Precipitation of less than 0.2 millimetres is called “trace” precipitation. Precipitation is measured once or twice per day. Automated measurements: Total precipitation is measured using all-weather precipitation gauges, including the Geonor T200B 600 millimetre capacity and the OTT Pluvio² L 200 1500 millimetre capacity gauges. Precipitation gauges are pre-charged with methanol-based Dimachem antifreeze and Isopar-M Oil. Geonor precipitation gauges are equipped with a single Alter shield that is attached to the sensor post. OTT Pluvio² L gauges are equipped with a double Alter shield that is supported by separate posts. The datalogger applies a filter correction to raw Geonor precipitation data to reduce noise. Raw OTT Pluvio² L data are corrected in the instrument using the manufacturer’s proprietary algorithm. Report on the State of Operational Snow Data Provisioning in Canada 41 | P a g e As of 2024 the Automatic Weather Station network was comprised of 170 Geonor gauges, 291 OTT Pluvio² L gauges and 20 OTT Pluvio 1 gauges. OTT Pluvio 1 gauges are slowly upgraded to OTT Pluvio² L gauges. Data are recorded every 15 minutes. The sensors are verified once a year or more as required if the data are suspect. Geonor precipitation gauges are verified by placing a known weight into the empty bucket. OTT Pluvio² L precipitation gauges are verified a proprietary weight set and sensor validation software. 4.1.10 Monitoring of Other Snow Variables In the Snow Survey Program, other snow parameters such as snow stratigraphy, and specifically the vertical position, thickness and hardness of hard, crusty and icy layers, and soil moisture and frost conditions may be reported. When measured, these parameters would be recorded during regular five- or 10-point samples. 4.1.11 Snow Data Production Daily data are calculated based on the climate day definition and stored in the national archives. Each time a snow survey is attempted, a Snow Survey Monthly Report (Form 2333) needs to be completed and a Snow Survey Bulletin (CSCN10) must be coded and transmitted. Timeframes for CSCN10 issuance can vary. Some stations report data the same day that recordings are made, however some station reports have a delay. Parameters that are recorded at all stations are snow depth, water equivalent of snow cover, and snow density, however some may record additional parameters such as crust information, soil conditions, or ice layer information. Records from some ongoing and historical stations date back to 1928. Automatic Weather Station network stations report in real-time, which is typically once per hour and in some cases once per minute. Daily data summaries, hourly summary bulletins and other products are subsequently generated. 4.1.12 Snow Data Dissemination Online datasets are listed in Table 4.1-1. Table 4.1-1 Online datasets from Environment and Climate Change Canada – Meteorological Service of Canada’s Atmospheric Monitoring Service Data-providing organization Web link Environment and Climate Change Canada https://climate.weather.gc.ca/historical_data/search_historic_data_e.html Environment and Climate Change Canada e.g., https://dd.weather.gc.ca/today/observations/swob-ml/ Environment and Climate Change Canada e.g., https://dd.weather.gc.ca/today/bulletins/alphanumeric/ A national aggregate of daily water equivalent of snow cover data is produced by Vionnet et al. (2025). The Canadian historical Snow Water Equivalent dataset (CanSWE) contains data from 1928 until the end https://climate.weather.gc.ca/historical_data/search_historic_data_e.html https://dd.weather.gc.ca/observations/swob-ml/ https://dd.weather.gc.ca/today/observations/swob-ml/ https://dd.weather.gc.ca/bulletins/alphanumeric/ https://dd.weather.gc.ca/today/bulletins/alphanumeric/ Report on the State of Operational Snow Data Provisioning in Canada 42 | P a g e of July 2024 (at the time of report production). Environment and Climate Change Canada Snow Survey Program data are included. The archive is not updated in real-time. Updating and quality control occur on an annual basis. Water equivalent of snow cover data undergo some quality control, including range and outlier checks. An additional quality control measure was applied to continuous data from automated stations. The aim of the additional quality control step is to identify spurious water equivalent of snow cover – snow depth data pairs through the sample Mahalanobis distance. Data can be downloaded in NetCDF file format from the link provided in Table 4.1-2. Table 4.1-2 Online datasets from the Canadian historical Snow Water Equivalent dataset (CanSWE) Data-providing organization Web link Zedodo https://zenodo.org/records/14901399 4.1.13 Sensor Testing, and Research and Development With snow and solid precipitation being one of the most difficult meteorological variables to measure, there are continued efforts in research and development of new and improved automated techniques and best practices. The World Meteorological Organization Solid Precipitation Inter-Comparison Experiment (SPICE; Nitu et al., 2018) produced multiple results from assessments and intercomparisons of automated precipitation gauges, and snow depth and water equivalent of snow cover sensors. Work included the development of practical wind-bias adjustments for gauge measurements of solid precipitation (Kochendorfer et al., 2017a, 2017b, 2018). Since the end of SPICE, technology has developed further with new sensors for measuring snow depth and the water equivalent of snow cover. Global Navigation Satellite System (GNSS) dual receiver (L1- band microwave) phase delay sensors (Capelli et al., 2022) and cosmic ray (neutron and muon) attenuation sensors (Jitnikovitch et al., 2021; Gugerli et al., 2022) are becoming more mainstream for measuring the water equivalent of snow cover and have high potential for operational monitoring. GNSS multipath reflectometry techniques for the measurement of landscape scale snow depth have been improving (Li et al., 2021) and low cost and low power W-band RADAR ranging devices for snow depth measurement (Ayhan et al.; 2017) are now commercially available. Small scale (e.g. handheld) Light Detection And Raging (LiDAR) devices, as demonstrated by King et al. (2022) are also showing immense potential for development into operationally viable snow depth sensors. Non-catching instruments for measuring precipitation, such as optical or RADAR-based disdrometers and present weather detectors, underwent some limited assessment for the observation of solid precipitation during World Meteorological Organization-SPICE. Nitu et al. (2018) showed that event-scale solid precipitation accumulation measurements from non-catching instruments were problematic, but that the devices had immense potential for augmenting and improving conventional gauge measurements through event detection (especially at low precipitation intensities), phase determination, and hydrometeor fall velocity measurement to improve wind undercatch adjustments. Following the results of World Meteorological Organization-SPICE, the World Meteorological Organization Expert Team on Surface and Sub-surface Measurements is developing a strategy and an experiment plan for a robust intercomparison of non-catching instruments for measuring precipitation parameters including those https://zenodo.org/records/14901399 Report on the State of Operational Snow Data Provisioning in Canada 43 | P a g e associated with snow. This international collaborative intercomparison is expected to begin in 2026 and be completed by 2028. The Cooperative Climate Network needs to be reviewed to ensure collected data are useful. Environment and Climate Change Canada’s research and development effort in snow remote sensing is described in Section 3.11. 4.1.14 Use of Other Snow Products No information available. Report on the State of Operational Snow Data Provisioning in Canada 44 | P a g e 4.2 NAV CANADA Lead author: S. McLeod 4.2.1 Network Overview NAV CANADA is a private, not-for-profit company, established in 1996, that provides air traffic control, airport advisory services, weather briefings, and aeronautical information services across Canada. As of 2024 NAV CANADA operated 178 hydrometeorological monitoring stations across all provinces and territories in Canada. 4.2.2 Quality Assurance NAV CANADA stations are operated according to the Manual of Surface Weather Observations (MANOBS, Meteorological Service of Canada, 2023). All Automated Weather Observing Systems (AWOS) have the same site layout. Siting criteria are based on World Meteorological Organization (2018). Most of sites are on airport property and often close to the runway complex. Obstructions impacting precipitation measurements are normally not a concern. Preventative maintenance is annual. 4.2.3 Monitoring of Total Snow Depth Manual measurements: Observers measure total snow depth with a snow ruler. NAV CANADA measures total snow depth manually at approximately 80 sites across Canada. Automated measurements: Not measured. 4.2.4 Monitoring of the Depth of New Snowfall Manual measurements: Observers measure the depth of new snowfall with a snow ruler. NAV CANADA measures depth of new snowfall manually at approximately 70 sites across Canada. Automated measurements: Not measured. 4.2.5 Monitoring of Water Equivalent of Snow Cover Manual measurements: Not measured. Automated measurements: Not measured. 4.2.6 Monitoring of the Water Equivalent of New Snowfall Manual measurements: NAV CANADA measures water equivalent of new snowfall manually at approximately 40 sites across Canada. Automated measurements: NAV CANADA measures water equivalent of new snowfall automatically at approximately 30 sites across Canada. Report on the State of Operational Snow Data Provisioning in Canada 45 | P a g e 4.2.7 Monitoring of Snow Density Manual measurements: Neither measured nor calculated. Automated measurements: Neither measured nor calculated. 4.2.8 Monitoring of Snow Temperature Manual measurements: Not measured. Automated measurements: Not measured. 4.2.9 Monitoring of Total Precipitation Manual measurements: Precipitation is measured with the Type B rain gauge or the Nipher shielded snow gauge. NAV CANADA measures total precipitation manually at approximately 10 sites. Automated measurements: Total precipitation is measured with Lambrecht rain[e]H3 heated, self- emptying, weighing gauges and MetOne 375C heated tipping buckets. As of 2024, NAV CANADA measured total precipitation automatically at approximately 170 sites. Precipitation gauges are equipped with single Alter-type wind screen from Novalynx. 4.2.10 Monitoring of Other Snow Variables No information available. 4.2.11 Snow Data Production No information available. 4.2.12 Snow Data Dissemination NAV CANADA data are disseminated via the Environment and Climate Change Canada portal. Online datasets are listed in Table 4.1-2. Table 4.2-1 Online datasets from NAV CANADA Data-providing organization Web link Environment and Climate Change Canada https://climate.weather.gc.ca/historical_data/search_historic_data_e.html Environment and Climate Change Canada e.g., https://dd.weather.gc.ca/today/observations/swob-ml/ 4.2.13 Sensor Testing, and Research and Development No information available. https://climate.weather.gc.ca/historical_data/search_historic_data_e.html https://dd.weather.gc.ca/observations/swob-ml/ https://dd.weather.gc.ca/today/observations/swob-ml/ Report on the State of Operational Snow Data Provisioning in Canada 46 | P a g e 4.2.14 Use of Other Snow Products No information available. Report on the State of Operational Snow Data Provisioning in Canada 47 | P a g e 4.3 CoCoRaHS Lead author: C. Cerny 4.3.1 Network Overview The Community Collaborative Rain, Hail, and Snow Network (CoCoRaHS) is an American non-profit community-based network of volunteers that monitors precipitation across the USA, Canada, and the Bahamas. Its aim is to provide high quality data for natural resource, education, and research applications. CoCoRaHS currently operates approximately 880 monitoring stations across all provinces and territories in Canada. The province with the largest number of active stations is Ontario. The variables measured vary by station. Since the authors were not able to obtain a variable-specific network inventory, in this report the locations of CoCoRaHS stations are not shown on variable-specific network maps. Volunteers submit their data at the online CoCoRaHS portal or via mobile app. 4.3.2 Quality Assurance Volunteers are provided with training and low-cost equipment. CoCoRaHS has standard methods for measuring variables, which are described in training slide decks (Community Collaborative Rain, Hail and Snow Network, 2025). 4.3.3 Monitoring of Total Snow Depth Manual measurements: Observers measure total snow depth with a ruler or graduated stick. Several measurements are taken and averaged. Measurements are taken once per day typically at 7:00 am local time. Automated measurements: Not measured. 4.3.4 Monitoring of the Depth of New Snowfall Manual measurements: Observers measure the depth of new snowfall with a ruler on a snowboard. Several measurements are taken and averaged. In the CoCcoRaHS network, 24-hour snowfall is defined as the maximum accumulation of new snow and ice in the past 24 hours, prior to melting or settling. New snowfall is measured as soon as possible after it ends, and before settling and melting occur, or typically at 07:00 hours local time. Automated measurements: Not measured. 4.3.5 Monitoring of Water Equivalent of Snow Cover Manual measurements: The measurement is obtained by taking a snow core on the ground using the outer cylinder of the CoCoRaHS plastic four-inch diameter precipitation gauge, cutting the snow core off at the bottom with a thin spatula-like device, melting the snow, and measuring the water level in the precipitation gauge. Measurements are typically taken once per week on Mondays. Report on the State of Operational Snow Dat