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

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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

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SWE (mm)

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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.