CURRENT STATE OF SNOW

REMOTE SENSING

OBSERVATIONS, FUTURE

DIRECTION AND REMAINING

CHALLENGES1

Thanks to: Nick Rutter, Ian Davenport, Debbie Clifford, Adam Winstral

Outline

• VIS / NIR observations

– Extent

– Grain size

– Snow mass (LIDAR)

• Microwave observations

– Historical algorithms

– Snow heterogeneity

– Physics-based modelling

• Summary

• Future mission?

2

VIS / NIR

3

Snow – cloud discrimination

4

VIS / NIR Remote sensing of snow

Nolin, A., J. Dozier (2000), A hyperspectral method for remotely sensing the grain size of snow, Rem. Sens. Env., 74 (2), 207–

216

Surface /

near

surface

Airborne Snow Observatory

http://www.jpl.nasa.gov/images/earth/california/20131209/AS

O_AGUPressRelease_9Dec2013_vF.pdf

http://www.jpl.nasa.gov/images/earth/california/20131209/ASO_AGUPressRelease_9

Dec2013_vF.pdf

$3.9m

Isnobal Density

underestimation

overestimation

Absolute

value is

important!

SNOW MASS FROM MICROWAVE

9

SWE = 4.77 * (18H - 37H)

February

climatology

The basis of the Chang Algorithm

Chang et. al. (1987) Ann. Glaciol. 9: 39-44

Snow Water Equivalent (cm)

Bri

ghtn

ess tem

pe

ratu

re (

K)

SWE = 4.77 * (18H - 37H)

Microwave emission (Tb) vs snow mass (SWE) is

derived using the Mie Scattering model

melt…

SWE (depth)

Sensitivity of snow mass algorithm to grain size

Relationship between Chang SWE and actual SWE

for different grain sizes

(snow density = 300 kg m^-3)

0

100

200

300

400

500

600

700

800

0 200 400 600 800 1000 1200

SWE in HUT forward model (mm)

Cha

ng-d

erived

SW

E (

mm

)

0.2mm 0.4mm

0.6mm 0.8mm

1.0mm 1.2mm

1.4mm 1.6mm

1.8mm 2.0mm

1:1 line

Other parameters must be known!

SWE = 4.77 * (18H - 37H)

Other

approaches

12

SD = b (∆TB)2 + c

∆TB

b, c: ƒ(deff, ρ)

SWE = 4.77 * (18H -

37H)

GlobSnow

13

GlobSnow

14

• Snow density 240 kg

m-3

• Single layer

Stratigraphy

Courtesy Nick

Rutter

Snow metamorphism

16

Electron and Confocal Microscopy Laboratory, Agricultural Research Service, U.

S. Department of Agriculture.

Growth is driven by density, temperature and

temperature gradient: snow models

Snow mass data assimilation system

Snow microwave emission model

Variables – temperature, density, liquid

water content, grain size, salinity.

TB d-,q( ) = TB 0+,q( ) e- ke-qks( )secq d

+kaTs

ke - qks1- e

- ke-qks( )secq d( )

Includes multiple scattering within the

snow layer, scattering and reflectivity via

Fresnel equations

ATMOSPHERE, mostly transparent

SNOW

GROUND

Accuracy of emission models

19

Bias: 36-

68K

A note on snow microstructure

20

Dmax

vs Dopt

vs Deff

A range of

length

scales!

ASMEx

21

More data

needed…

Summary

• Snowpack information is valuable

• Sensors have different benefits and assumptions

• Other information is required to give snow mass

estimates (stratigraphy, density, grain size….)

• Snowpack evolution models can give snow

parameters

• Microwave emission models need further development

• Know which direction to go in but….

22

Without a snow mission there will be minimal

funding for algorithm and model development

The future….CoReH20?

• Dual-band SAR

(9.65 / 17.25GHz)

• 6am / pm overpass

• Revisit: 3 / 15 days

• Resolution:

few 100m

• Launch in 2019?

What do we want?

past

Mission Requirements

• What depth of snow is important and accuracy (c.f. 4%

soil moisture)

• Spatial resolution

• Repeat cycle

• Melt state

• Regional or global

Email me: m.j.sandells@reading.ac.uk

24

Your opportunity – planning has started for next

ESA / NASA mission concepts

Airborne Snow Observatory

25

26

Correlation function

27

A(x) = exp(-x / pex)

Autocorrelation function may be a different shape

Get rid of

empirical

corrections!

Absorption and Scattering Within Snow

28

• Sensitive to the snow grain size (and density)

• Scattering mostly in the forward direction (96%)

• Wet snow highly absorptive, near blackbody

Capabilities

• Evaluate against time series of microstructure and

temperature profiles, and temporal TB

• Use other microwave models, and examine

microstructure metric relationships

• Can we go further?

29

JIM

• Contains all major snow parameterisations

• 1701 Unique model combinations

• 63 model subset:

– Compaction parameterisation (3)

– Thermal conductivity (3)

– Fresh snow density (3)

– Snow hydrology (3)

• This has now been coupled with 3 of 5 microstructure

evolution functions: MOSES, SNICAR, SNTHERM

30

JIM subset: Sodankylä

31

MOSES

SNICAR

SNTHERM

JIM subset: DMRTML

32

MOSES

SNICAR

SNTHERM

JIM subset: DMRTML-SNTHERM

33

Physical

Empirical

None