Passive Microwave Systems & Products

1. Passive Microwave Systems& Products Chris Derksen Climate Research Division Environment Canada

2. The Satellite Passive Microwave Time Series • Scanning Microwave Multichannel Radiometer (NIMBUS-7) • October 1978-August 1987 • Relatively narrow swath; shut down every other day • Special Sensor Microwave Imager (DMSP F8, F10, F11, F12, F13, and F15) • June 1987-present (F15 - degraded) • Well calibrated inter-sensor • time series • Special Sensor Microwave • Imager/Sounder (DMSP F16, F17, F18) • November 2006-present • Includes sounding frequencies; • continuity with DMSP F15 • Advanced Microwave Scanning • Radiometer (AQUA) • June 2002-October 2011 • Improved spatial resolution; • addition of 6.9 and 10.7 GHz • Advanced Microwave Scanning Radiometer 2 (GCOM-W) • May 2013-present Sapiano et al, TGARSS, 2013

3. 300 0 Passive Microwave Derived Snow Products:‘Standalone’ Snow Water Equivalent • AMSR-E standard product (Kelly, 2008; Tedesco, Kim and others) • Shallow snow detector (89 GHz) • Considers forest fraction • Utilizes 10 GHz for deep snow • Dynamic coefficients for grain size • AMSR-2standard product (Kelly) • NSIDC (Armstrong and Brodzik, 2002) • Close to the original Chang approach • Correction for vegetation • Static coefficients • NOAA Office of Satellite and Product Operations • Snow depth and SWE available online • Poorly documented • Environment Canada regional products (Goodison; Goita, Derksen and others) • Empirical, static algorithms • Questionable transferability

4. Passive Microwave Derived Snow Products:Snow Cover Extent • AMSU snow extent (Kongoli et al., 2004) • Daily near real time products • NOAA IMS (Helfrich et al., 2007) • Supplementary data source for operational snow charting • Not utilized in a systematic fashion SSM/I vs IMS: 2006041 D. Robinson IMS> SSM/I> no SSM/I both snow

5. Snow by both sensors Snow by AMSR_E, MODIS cloud or no data Snow by MODIS, AMSR_E no snow or orbit gap No snow by MODIS or AMSR_E but cloud obscured No snow: no snow by MODIS in clear view but, AMSR_E detects snow Cloud by MODIS in AMSR_E orbit gap Snow free land by both MODIS and AMSR_E Passive Microwave Derived Snow Products:Combined • Microwave + Optical • ANSA (Hall, Foster, Kim and others) • MODIS + AMSR snow extent; QuikSCAT melt • NSIDC + Optical (Armstrong, Brodzik and other) • NOAA snow extent; SMMR + SSM/I SWE • MODIS snow extent; AMSR SWE E. Kim

6. Passive Microwave Derived Snow Products:Combined October February M-J Brodzik and R. Armstrong

7. Passive Microwave Derived Snow Products:Combined • Microwave + Conventional • GlobSnow (Takala et al., 2011) • Climate station snow depth observations used to generate first guess background field, and as input to forward snow emission model simulations for SWE retrieval • Alpine areas masked • Includes uncertainty field Mountain mask: >1500 m

8. Where We Stand as a Community: The Good 1. Significant progress through airborne measurements and field campaigns in the U.S., Canada and Europe. 2. Improved modeling capabilities: Physical snow models; distributed snow models; snow emission models; coupling these models NARR+SNOWPACK+MEMLS NARR+SNOWPACK • Requires successive corrections for grain size and density Langlois et al, WRR, 2012

9. Where We Stand as a Community: The Good • 3. Progress made with some ‘classic’ sources of uncertainty: • grain size and microstructure • -Grenoble workshopon grain size measurement, April 2013 • -New IACS working group • -Davos campaign, March 2014 • ice lenses (modeling and observing) • forest transmissivity (Langlois and others) • 4. Synergistic retrievals: conventional observations and forward snow emission modeling RMSE= 47 mm RMSE= 92 mm Takala et al, RSE, 2011

10. Where We Stand as a Community:Continuing Challenges • 1. Persistent ‘classic’ sources of uncertainty: • vegetation • deep snow • sub-grid heterogeneity SWE<150 mm All SWE RMSE = 32 mm Bias = +8.5 mm r = 0.68 RMSE = 43 mm Bias = +1.1 mm r = 0.67 Takala et al, RSE, 2011

11. Where We Stand as a Community: Continuing Challenges • 1. Persistent ‘classic’ sources of uncertainty: • vegetation • deep snow • sub-grid heterogeneity Sub-grid SWE PDF from intensive tundra measurements (n>5000)

12. Where We Stand as a Community: Continuing Challenges • 2. Utility of retrievals for operational land surface data assimilation, hydrological modeling etc. • Requires well characterized uncertainty, including minimal random error • Must improve first guess over currently utilized analysis • 3. What’s our baseline for coarse resolution SWE products? What performance benchmarks are we trying to reach? • 4. Data are readily available; information on validation/uncertainty is not • 5. Validation datasets required for a large range of snow conditions

13. Where We Stand as a Community: Continuing Challenges 6. SWE in alpine areas Tong et al, CJRS, 2010

14. Conclusions • The satellite passive microwave data record is long and robust. • Both standalone and synergistic SWE data sets are readily available. • Significant progress in recent years has been made from innovative field campaigns, improved modeling (physical; emission), and new retrieval approaches. • The nature of the brightness temperature versus SWE relationship, combined with the characteristics of current spaceborne passive microwave measurements, means retrieval challenges remain. • While valuable for some climate and hydrological applications, the current generation of satellite passive microwave measurements are not suitable to address user needs in many applications and locations.