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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

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Passive microwave systems products

Passive Microwave Systems& Products

Chris Derksen

Climate Research Division

Environment Canada


The satellite passive microwave time series
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


Passive microwave derived snow products standalone snow water equivalent

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


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


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


Passive Microwave Derived Snow Products:Combined

October

February

M-J Brodzik and R. Armstrong


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


Where we stand as a community the good
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


Where we stand as a community the good1
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


Where we stand as a community continuing challenges
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


Where we stand as a community continuing challenges1
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)


Where we stand as a community continuing challenges2
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


Where we stand as a community continuing challenges3
Where We Stand as a Community: Continuing Challenges

6. SWE in alpine areas

Tong et al, CJRS, 2010


Conclusions
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.


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