Remote Sensing of SWE in Canada

Transcription

1 Remote Sensing of SWE in Canada Anne Walker Climate Research Division, Environment Canada Polar Snowfall Hydrology Mission Workshop, June 26-28, 2007

2 Satellite Remote Sensing Snow Cover Optical -- Snow extent Passive Microwave Depth/SWE Reflection of visible light < 1 km spatial res. Impeded by cloud cover and lack of sunlight 40+ year record of satellite sensors (AVHRR, Landsat, MODIS) Microwave emission from earth s surface km spatial res. All-weather, independent of light conditions ~ 30 year record of satellite sensors (SMMR, SSM/I, AMSR-E)

3 Passive Microwave Remote Sensing of Snow Cover Properties Snow cover snow grains and air Microwave energy emitted by underlying ground (T Bg ) is scattered by grains T B snow surface < T Bg Amount of scattering is a function of snow depth and density SWE T B = SWE Volume scattering of emitted earth radiation by snow cover provides basis for retrieval of snow cover properties from passive microwave data Data available in near real time and as historical archive in gridded format (1978 SMMR, 1987 SSM/I, 2002 AMSR-E) Canadian focus on development of regional-based retrieval methods (algorithms) for dominant landscapes prairies, boreal forest, tundra

4 Environment Canada SWE Algorithms Prairie (open) algorithm SWE algorithm developed using airborne microwave radiometer data set (1982 experiment) weekly SWE maps produced using SSM/I data since 1989 SWE = a + b (T B 37V - T B 19V) 18 Boreal forest algorithms 3 forest SWE algorithms derived using BOREAS airborne microwave radiometer data, ground SWE data coniferous, deciduous, sparse forest 4 algorithms applied to gridded SSM/I data with addition of land cover classification data to yield an overall SWE value that takes into account effects of land cover variations SWE = F D SWE D + F C SWE C + F S SWE S + F O SWE O D - deciduous; C - conifer, S - sparse forest, O - open SWE i = A + B (37V - 19V) F i = Land cover fraction per grid point (i = D, C, S or O)

5 Regional SWE Products for Research and Operational Applications C Canadian Prairies -weekly maps produced and sent to users (federal, provincial agencies, private industry) who have a requirement for regular monitoring of snow cover in western Canada - available to public on (State of Canadian Cryosphere) Manitoba Red River watershed - specialized maps sent to provincial water resource agencies focussed on priority river basins for forecasting spring runoff and flood risk Mackenzie Basin - MAGS research on snow cover variations, RCM evaluation Snare River Basin NWT - maps for hydro companies (e.g. NWT Power Corp.) in support of planning hydroelectric power operations

6 Regional SWE for Weather Forecasting NWT/Nunavut Request from Arctic Weather Centre in Edmonton Investigation into potential contribution of SSM/I SWE maps for prediction of blowing snow events (severe weather forecasting) SWE maps provided 3X per week for use/evaluation by forecasters Change in SWE map areas of change over a week (Friday to Friday) Change in SWE over a week

7 Validation of Satellite Derived SWE Information 6.9 GHz GHz 85.5 GHz 37 GHz 1.4 GHz 1) Airborne/field validation campaigns Acquisition of airborne microwave radiometer data and ground-based measurements to support: - validation of satellite retrievals - algorithm refinement/new development Current MSC Snow Depth/SWE Network MSC microwave radiometers on NRC Twin Otter In-situ measurements 2) Regional snow surveys Targetted to specific landscape environments ground-based measurement transects over extensive areas 3) Comparison with snow depth/swe available from EC monitoring networks + other agencies

8 Improving Passive Microwave SWE Retrievals for Northern Canada (EC( and Wilfrid Laurier U.) Tundra Ecosystem Research Station (TERS) located at Daring Lake NWT Study Objectives: Conduct in-situ snow survey to assess variability and physical properties of snow cover. Integrate ground based and airborne radiometer data to investigate snow cover relationships for representative terrain units at a variety of scales (aircraft/field campaigns). Investigate the influence of lake ice on passive microwave brightness temperatures - existing algorithms do not consider lake covered area and cause SWE underestimations. Field sampling and aircraft remote sensing data collection during Derksen et al., Remote Sensing of Environment, 2005

9 Northern Boreal Forest Primary challenge: existing algorithms tend to underestimate SWE in the typically deep (up to 1 metre) snowpacks of the northern boreal forest. AMSR-E Estimated SWE (mm) r 2 = in situ SWE (mm) Use of AMSR-E 10 GHz Surface measurement sites, Stakeholders NWT Power, Manitoba Hydro have supported this research through in-kind support of field activities.. AMSR-E Estimated SWE (mm) Screened 180 r 2 = in situ SW E (mm)

10 Tundra Landscape Effects on SWE Retrieval Terrain Lakes Lakes present a major problem for SWE retrieval in tundra regions high fractional lake surface area

11 Ongoing Challenges for SWE Retrieval Areas of dense forest cover, deep snowpacks Complex landscapes (heteorogeneous land cover, terrain), esp. mountains Seasonal and interannual variations in snowpack properties (e.g. melt/re-freeze) Lack of available ground measurements for evaluating algorithm performance (esp. in northern regions of Canada) Coarse spatial resolution of current satellite instruments (10-25km) No single algorithm will provide representative SWE over Canada due to the wide range of landscapes and snow cover properties Regional algorithm development an ongoing activity

12 Variability and Change in the Canadian Cryosphere Can. contribution to the State and Fate of the Cryosphere IPY 105 Activities Cryospheric information contributing to the IPY snapshot Cryosphere-climate variability and feedbacks Improved representation of Arctic processes in CLASS Simulation of the cryosphere in climate models The human dimension Canadian cryospheric data portal for IPYDIS Planned IPY snow cover field campaigns in Canadian tundra regions: Photo: Vital Arctic Graphics, UNEP, GRID-Arendal, 2005 April-May 2007 NWT Jan-Feb 2008 Northern Quebec April-June 2008 NWT & Arctic Islands

13 IPY Field Activities in Support of Validated Satellite SWE Products (Maps, Data Sets) April 2007 Alaska-Canada Barrens Snowmobile Transect (SnowSTAR2007) and 2008 Field surveys and aircraft/field campaigns

14 IPY research to address snow cover retrieval in mountains Kelly (U. Waterloo) in collaboration with Hall (NASA) and Cline (NOHRSC) will apply SNODAS snow data assimilation system to the Yukon at ~1 km scale Will make use of MODIS, QuikSCAT, AMSR-E, surface observations and snow model (SNTHERM) simulations Sample SNODAS output for southern Rockies, April 1, 2006