Passive Microwave Physics & Basics. Edward Kim NASA/GSFC

Transcription

1 Passive Microwave Physics & Basics Edward Kim NASA/GSFC NASA Snow Remote Sensing Workshop, Boulder CO, Aug 14 16,

2 Contents How does passive microwave sensing of snow work? What are passive microwaves (PMW) sensitive to in snow? What are the limitations & strengths Some other PMW considerations NASA Snow Remote Sensing Workshop, Boulder CO, Aug 14 16,

3 Mary Jo & Richard Search for the 1 st Hidden Truth of Passive Microwaves and Snow The snow itself isn t really emitting, so where is the source? It s somewhere in here. We just have to keep digging. 3

4 PMW snow sources Microwave radiation can interact with matter via only 3 mechanisms (at least in our remote sensing regime) Absorption & Emission, which involve energy gain or loss Scattering, which is considered lossless Terminology: extinction = scattering + absorption snow ground NASA Snow Remote Sensing Workshop, Boulder CO, Aug 14 16,

5 Dielectric Properties of Snow are a Key At the most basic level, a PMW sensor observes brightness temperature TB= (physical temp) x emissivity TB ranges from say K Note TB can get colder due to either term on RHS Emissivity = f(intrinsic dielectric properties, shape/geometry dielectric properties) The dielectric properties of snow are a key NASA Snow Remote Sensing Workshop, Boulder CO, Aug 14 16,

6 Example TB Imagery You can probably figure out which image is from which MSA North Park Rabbit Ears Fraser CLPX 1 PMW airborne PSR/A imagery at ~100m resolution

7 Snow as a dielectric medium 1 Start with the basic case: a layer of dry snow with no ice layers The snow in this basic layer is not solid ice (i.e., there are air pores); it is a collection of ice grains; the grains are different sizes, shapes, & orientations There are 2 constituents: air and ice Real part of dielectric constants of air & ice are indep. of freq & temp in the mw region. Real part affects scattering, emission, & absorption 7

8 Snow as a dielectric medium 2 Imaginary part of dielectric constant is more complicated Different formulations exist (Stogryn, Hufford, Matzler) and values vs. freq vary significantly Extinction in dry snow has 2 components Dielectric losses due to the imaginary part of dielectric constant Scattering losses due to the geometric structure of snow For typical snow grain sizes & mw wavelengths, we have 2 regimes: For wavelengths > few cm, scattering losses << dielectric losses For cm and shorter wavelengths, scattering losses increase and become dominant at mm wavelengths Already we see that grain size is important 8

9 Snow as a dielectric medium 3 Thus, RT in (dry) snow becomes mainly a scattering problem But dry snow scattering is a very strong function of grain size & snow density Note how the plot of penetration depth for dry snow (attenuation due to scattering) at right varies by orders of magnitude with grain size for a given frequency (e.g., the red line at about 12 GHz) From Ulaby, Moore, & Fung, Vol 3. p

10 Density of Snow kg/m3 g/cm3 New snow typical range Wind crust Hard wind slab Solid ice Liquid water Note: snow values are approximate values only Heavy, strong, Like concrete Electon microscope photo from 10

11 Snowpack Property Knowledge Requirements to Achieve Modeled TB Accuracy of ±5K based on a sensitivity study using MEMLS and ~1m deep snowpack from CLPX 1 Ice layer density known to within ± 40 kg/m3 Ice layer correlation length to within ±0.17 mm Snow correlation length to within ± mm effective optical grain size to within ±0.045 mm Snow density within ±34 kg/m3 Snow depth within ±0.16 m M. Durand, E.J. Kim, and S.A. Margulis; Quantifying uncertainty in modeling snow microwave radiance for a mountain snowpack at the point scale, including stratigraphic effects; IEEE Trans. Geosci. Remote Sens., 46 (6), June,

12 Wet snow Wetness usually expressed as volume % Because the dielectric constant of liquid water >> ice; wetness has a strong effect on the MW signature of snow Measurements and models by Cumming (1952); Colbeck (1974), Ulaby & Stiles (1970 s), Hallikainen (1980 s), Matzler, and others 2 regimes depending on whether air is continuous (Pendular) or water is continuous (Funicular) in pore spaces Pendular; water is needle shaped throughout the wet snow volume Funicular; water is no longer needle shaped At 1 2% wetness, start seeing strong increase in Tb toward 273K Tb typically saturates at 273K in the 4 6% wetness range >7% wetness: snowpack becomes mechanically unstable (avalanche) So for all but very slightly wet snow (< 2% wetness), absorption dominates vs. scattering as long as the wet layer is thick vs. the penetration depth at that frequency Both passive & active MW sensors see large amplitude changes between wet vs. dry snow i.e., the onset of melt & refreezing can be detected relatively easily, especially through the use of time series data. 12

13 The original crystal shape eventually no longer matters Orientation (of grains, not layers) also no longer matters Eventually, only grain size (distribution), spacing, & density really matter Electon microscope photos from 13

14 Melting & re freezing Left: Little free water Right: lots of free water 14

15 PMW basics 1.0 mm Frequencies used for PMW and what you see Effect of polarization Vertical spatial resolution vs. vertical variability in a snowpack Horizontal spatial resolution (caveat w/gridded products) Effect of wet snow Effect of snow metamorphism Temporal resolution (it s ~instantaneous) Effect of surface roughness (more an issue for active mw) Effect of vegetation, trees (Fraser alpine example) Effect of soil (frozen or thawed) Effect of the atmosphere all SEM pictures are same scale (from USDA EMU) Electon microscope photos from NASA Snow Remote Sensing Workshop, Boulder CO, Aug 14 16, 2013 Soil 15

16 PMW limitations TB signal saturates at ~150 mm SWE Emissivity not necessarily monotonic vs. depth at high frequencies Connection w/radiative transfer theory is non trivial: Snow is a dense medium Strong sensitivity to grain size; errors if static grain size coefficient used Low sensitivity to thin/ephemeral snow High sensitivity to wet snow (can be a strength) Sensitive to ice layers & densified snow layers (can be a strength) Sensitive to land cover & vegetation, forests TB is an integrated quantity; multiple states can yield same TB at a single freq Low spatial resolution (vs. some other obs) from satellites Most retrievals assume unfrozen soil Not completely free of atmospheric effects NASA Snow Remote Sensing Workshop, Boulder CO, Aug 14 16,

17 PMW horizontal resolution: what does it look like from 500m 25km? 500m 1000m m 1500m 2500m February 23, 2003 North Park MSA TBs shown at various resolutions Grey scale corresponds to TB (black = low TB, white = high TB) 5000m 12500m

18 NSIDC blended product Fall Season Example (R.Armstrong) red= snow seen By VISIBLE but not by PMW Blended snow product prototypes, SWE (mm) from AMSR E with additional snow extent from MODIS in red (October 24 31, 2003, max). Lower image represents AMSR E snow extent in grey, with percent area of additional pixels that MODIS classifies as snow in blues. (AMSR 25 km, MODIS 5 km) 18

19 NSIDC blended product Winter Season Example (R.Armstrong) blue= snow seen By VISIBLE but not by PMW Blended snow product prototypes, SWE (mm) from AMSR E with additional snow extent from MODIS in red (Feb 26 Mar 5, 2003, max). Lower image represents AMSR E snow extent in grey, with percent area of additional pixels that MODIS classifies as snow in blues. (AMSR 25 km, MODIS 5 km) 19

20 PMW horizontal resolution: what does it look like from 100m 500m? Aerial photo (Fraser Alpine ISA) North is up Warm trees 100 m 200 m 300 m 500 m Increasing pixel size

21 1.0 Emissivity is not nec. monotonic vs. frequency at high frequencies Emissivity Emissivity Particle Radius = 0.25 mm Snow Depth = 10 cm f=0.4 f=0.3 f= Frequency (GHz) (1 RS )(1 ) S 1 R Exp( d / L) S 1 g 1 g S 2 2 f=0.4 f=0.3 From Norman Grody, Journal of Geophysical Research: Atmospheres Particle Radius = 0.5 mm f=0.2 Snow Depth = 10 cm Frequency (GHz) Snow emissivity as a function of frequency with fractional volume as a parameter. The snow depth is set at 10 cm and the particle radius is set to 0.25 mm (Top) and 0.5 mm (Bottom). 21

22 PMW strengths Sensitive to snow below the surface; some sensitivity to surface (roughness, ice, etc) Sensitive to SWE and/or depth as well as snow extent Sensitive to information encoded in snowpack internal structure (layers, grain size, wetness, etc) Multiple freqs & pols available & can help constrain snowpack state All weather, day/night, winter, & polar observing are no problem Many free satellite data sources, multi decade global data record Relatively good correspondence between TB obs and forward RTMs when detailed ground truth available High sensitivity to wet snow (can be a limitation) Connections to snow on sea ice & ice sheets, falling snow NASA Snow Remote Sensing Workshop, Boulder CO, Aug 14 16,

23 Example of Reading PMW time series North Slope of Alaska 1 year SSMI (Kim and England, JGR, 2003)

24 Better spatial resolution is a 2 edged sword Beware the simplistic approach: CLPX spread diagram Distributions can help Resolution defines possible observation types; every type comes with accuracy limitations; if the real goal is better accuracy, then what is the right mix of observation types & resolutions? NASA Snow Remote Sensing Workshop, Boulder CO, Aug 14 16,

25 One Effect of better Spatial Resolution North Park MSA: (retrieved measured) SCLP MIS AMSR Tb aggregated Before applying Chang s alg. 5 km 10 km 15 km 20 km 25 km

26 Brightness Temperature Distributions 37 NORTH PARK FRASER 19 SSM/I AMSR-E From M. Tedesco, E.J. Kim, A. Gasiewski, M. Klein, and B. Stankov,; Geophysical Research Letters, V32, L18501, Sept

27 Ed s list of PMW related issues (in order) 1. General lack of good PMW field obs together with good ground truth and synergistic sensors to help nail the PMW physics 2. General lack of good snow validation data with high spatial density to help with algorithms & retrievals 3. Better spatial resolution comes at a price; which price do we want to pay 4. Land modeling of snow (especially large scale DA) has too much faith in DA and not enough faith in physics and observational science 5. Radiometric accuracy & inter sensor calibration NASA Snow Remote Sensing Workshop, Boulder CO, Aug 14 16,