Satellite Remote Sensing and Applications in Hydrometeorology
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Transcript of Satellite Remote Sensing and Applications in Hydrometeorology
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Satellite Remote Sensing and Applications in Hydrometeorology
Xubin Zeng
Dept of Atmospheric Sciences University of Arizona Tucson, AZ 85721
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http://www.atmo.arizona.edu/~zeng/zeng.html
•Fractional cover (Zeng et al. 2000, 2003) and green vegetation cover (Miller et al. 2006)
•Albedo/BRDF (Wang et al. 2004, 2005, 206) and snow albedo (Barlage et al. 2005, 2006)
•Vegetation root (Zeng et al. 1998; Zeng 2001)
•Precip intensity and freq. (Kursinski and Zeng 2006)
•Precip, water vapor, and monsoon (Zeng and Lu 2004)
•Veget. pattern and growth (X.D. Zeng et al. 2006a,b)
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Tucson Landscape
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NCAR/CLM3: FVC(x,y), LAI(x,y,t)NCEP/Noah: GVF(x,y,t),LAI=Const
Validation:1-3m spy sat data,1-5m aircraft data,30m Landsat data,Surface survey data
Histogram of evergreenBroadleaf treeNDVIveg = 0.69
FVC vs LAI
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FVC (x,y)
LAI (x,y,t)
Versus
FVC (x,y,t)
LAI = 4
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Interannual variability and decadal trend ofglobal fractional vegetation cover from1982 to 2000
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Global FVC Data
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Data Impact
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NDSI and NDVI
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NLDAS GVF DataNoah 1/8 degree monthly
MODIS 2km 16-day
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Application of MODIS Maximum Snow Albedo to WRF-NMM/NOAH
• up to 0.5 C decreases in 2-m Tair in regions of significant albedo change
• > 0.5 C increase in 2-m Tair in several regions
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Land Surface albedo and its SZA dependence
ECMWF: no SZA dependenceNCEP: simple formulationNCAR (CLM3): two streamNASA (Catchment): simple fitting to two-stream
In satellite remote sensing retrieval of solar fluxes, including ISCCPFD, UMD (Pinker; ISCCP C1), CERES TRMM: surface albedo adjusted to match computed TOA solar flux with satellite measurements
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Tsvetsinskaya et al. (2002)
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The comparison of theMODIS blue-sky albedos with CERES/TRMM broadband albedos at 8 locations with differentvegetation types from July 11-26, 1998. The MODIS BSA at 60 SZA for the 16-day period starting fromJulian day 193 averaged from 2000 to 2004 are used.
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Maximum snow albedo• Maximum snow albedo is used as an end member of the interpolation from snow- to non-snow covered grids
• Current dataset is based on 1-year of DMSP observations from 1979
• Current resolution of 1°• Create new dataset using 4+ years of MODIS data with much higher resolution
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MODIS Albedo Data
(a) 1 km data in 10 deg tiles; global 0.05 deg
(vs. 1 deg in RK)
(b) seven narrow bands, VIS (0.4-0.7 microns),
NIR (0.7-5 microns), SW (0.4-5 microns)
(vs. SW from 0.4-1.1 microns in RK)
(c) Day 49 of 2000 - Day 177 of 2004
(vs. 75 images in 1979 and 5 images in 1978)
(d) Quality flags
(e) MODIS data from both Terra and Aqua
(f) Both albedo and BRDF
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NDSI and Snow Albedo
)64.1(6)55.0(4
)64.1(6)55.0(4
NDSI
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Current Logic Structure
NDSI > 0.4MODIS QC = good
Global Maximum Snow Albedo
Band 2 > 0.11
0.05o MODISAlbedo
Land Cover
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Final 0.05° Maximum Snow Albedo
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Application of MODIS Maximum Snow Albedo to NCEP Land Surface
ModelUp to 0.2 difference in high/mid latitudes can greatly affect surface energy balance, snow depth, and snow melt timing*Note: 0.05° maximum albedo dataset downscaled to 1° to compare with NOAH data
*
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Application of MODIS Maximum Snow Albedo to WRF-NMM/NOAH
• WRF-NMM Model: 10min(0.144°) input dataset converted from 0.05° by simple average; model run at 12km; initialized with Eta output;
• Winter simulation: 24hr simulation beginning 12Z 31 Jan 2006
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Vegetation type-dependent vegetation root distribution
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Offline simulation over the Amazon (deep roots maintain dry season ET)
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Precipitation intensity and frequency
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Gauge Radar
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Monsoon Onset/Retreat Indexes
Normalized precipitable water (PW) index:
NPWI = (PW – PWmin)/(PWmax – PWmin)
where PWmax and PWmin are the ten-year averages of the annual max and min daily PW at each grid cell.
Proposed objective criterion:
The monsoon onset (or retreat) date for grid cell G is defined as the first day (d) when NPWI is greater (or less) than the Golden Ratio (0.618) for 3 consecutive days in 7 of the 9cells centered at cell G in day d or d±1.
Explanations: `3 consecutive days’, `9 cells’, `Golden Ratio’
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Dynamic vegetation and spatial patterns
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Vegetation Pattern and DiversityVegetation Pattern and Diversity
(1) (2) (3) (4) (5) (6) (7)
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Annual Precipitation: 342 mm
Annual Precipitation: 297mm
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Annual Precipitation: 484mm
Annual Precipitation: 542mm
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