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Passive Microwave Systems & Products. Chris Derksen Climate Research Division Environment Canada. The Satellite Passive Microwave Time Series. Scanning Microwave Multichannel Radiometer (NIMBUS-7) October 1978-August 1987 Relatively narrow swath; shut down every other day - PowerPoint PPT Presentation
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Passive Microwave Systems& Products
Chris DerksenClimate Research DivisionEnvironment Canada
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 MicrowaveImager/Sounder (DMSP F16, F17, F18)November 2006-present
• Includes sounding frequencies;continuity with DMSP F15
Advanced Microwave ScanningRadiometer (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
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-2 standard 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
300
0
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
Passive Microwave Derived Snow Products:Snow Cover Extent
SSM/I vs IMS: 2006041
IMS> SSM/I> no SSM/I both snow
D. Robinson
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
Snow by both sensorsSnow by AMSR_E, MODIS cloud or no dataSnow by MODIS, AMSR_E no snow or orbit gapNo snow by MODIS or AMSR_E but cloud obscuredNo snow: no snow by MODIS in clear view but, AMSR_E detects snowCloud by MODIS in AMSR_E orbit gap
Snow free land by both MODIS and AMSR_E
E. Kim
Passive Microwave Derived Snow Products:Combined
October
February
M-J Brodzik and R. Armstrong
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
Passive Microwave Derived Snow Products:Combined
Where We Stand as a Community: The Good1. 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
• Requires successive corrections for grain size and density
NARR+SNOWPACK+MEMLS
Langlois et al, WRR, 2012
Where We Stand as a Community: The Good3. Progress made with some ‘classic’ sources of uncertainty:
• grain size and microstructure-Grenoble workshop on 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
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 mmBias = +8.5 mmr = 0.68
RMSE = 43 mmBias = +1.1 mmr = 0.67
Takala et al, RSE, 2011
Where We Stand as a Community: Continuing Challenges
1. Persistent ‘classic’ sources of uncertainty:• vegetation• deep snow• sub-grid heterogeneity
0.00
0.05
0.10
0.15
0.20
0.25
<20 40 60 80 100 120 140 160 180 200 >200
SWE (mm)
Rela
tive
Freq
uenc
y
Sub-grid SWE PDF from intensive tundra measurements (n>5000)
Where We Stand as a Community: Continuing Challenges2. 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
6. SWE in alpine areas
Where We Stand as a Community: Continuing Challenges
Tong et al, CJRS, 2010
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.