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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Satellite Remote Sensing and Applications in Hydrometeorology. Xubin Zeng Dept of Atmospheric Sciences University of Arizona Tucson, AZ 85721. http://www.atmo.arizona.edu/~zeng/zeng.html Fractional cover (Zeng et al. 2000, 2003) and green - PowerPoint PPT Presentation

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Page 1: Satellite Remote Sensing and Applications               in Hydrometeorology

Satellite Remote Sensing and Applications in Hydrometeorology

Xubin Zeng

Dept of Atmospheric Sciences University of Arizona Tucson, AZ 85721

Page 2: Satellite Remote Sensing and Applications               in Hydrometeorology

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)

Page 3: Satellite Remote Sensing and Applications               in Hydrometeorology

Tucson Landscape

Page 4: Satellite Remote Sensing and Applications               in Hydrometeorology

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

Page 5: Satellite Remote Sensing and Applications               in Hydrometeorology

FVC (x,y)

LAI (x,y,t)

Versus

FVC (x,y,t)

LAI = 4

Page 6: Satellite Remote Sensing and Applications               in Hydrometeorology

Interannual variability and decadal trend ofglobal fractional vegetation cover from1982 to 2000

Page 7: Satellite Remote Sensing and Applications               in Hydrometeorology

Global FVC Data

Page 8: Satellite Remote Sensing and Applications               in Hydrometeorology

Data Impact

Page 9: Satellite Remote Sensing and Applications               in Hydrometeorology

NDSI and NDVI

Page 10: Satellite Remote Sensing and Applications               in Hydrometeorology

NLDAS GVF DataNoah 1/8 degree monthly

MODIS 2km 16-day

Page 11: Satellite Remote Sensing and Applications               in Hydrometeorology

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

Page 12: Satellite Remote Sensing and Applications               in Hydrometeorology

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

Page 13: Satellite Remote Sensing and Applications               in Hydrometeorology

Tsvetsinskaya et al. (2002)

Page 14: Satellite Remote Sensing and Applications               in Hydrometeorology
Page 15: Satellite Remote Sensing and Applications               in Hydrometeorology
Page 16: Satellite Remote Sensing and Applications               in Hydrometeorology

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.

Page 17: Satellite Remote Sensing and Applications               in Hydrometeorology

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

Page 18: Satellite Remote Sensing and Applications               in Hydrometeorology

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

Page 19: Satellite Remote Sensing and Applications               in Hydrometeorology

NDSI and Snow Albedo

)64.1(6)55.0(4

)64.1(6)55.0(4

NDSI

Page 20: Satellite Remote Sensing and Applications               in Hydrometeorology

Current Logic Structure

NDSI > 0.4MODIS QC = good

Global Maximum Snow Albedo

Band 2 > 0.11

0.05o MODISAlbedo

Land Cover

Page 21: Satellite Remote Sensing and Applications               in Hydrometeorology

Final 0.05° Maximum Snow Albedo

Page 22: Satellite Remote Sensing and Applications               in Hydrometeorology

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

*

Page 23: Satellite Remote Sensing and Applications               in Hydrometeorology

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

Page 24: Satellite Remote Sensing and Applications               in Hydrometeorology

Vegetation type-dependent vegetation root distribution

Page 25: Satellite Remote Sensing and Applications               in Hydrometeorology

Offline simulation over the Amazon (deep roots maintain dry season ET)

Page 26: Satellite Remote Sensing and Applications               in Hydrometeorology

Precipitation intensity and frequency

Page 27: Satellite Remote Sensing and Applications               in Hydrometeorology
Page 28: Satellite Remote Sensing and Applications               in Hydrometeorology

Gauge Radar

Page 29: Satellite Remote Sensing and Applications               in Hydrometeorology
Page 30: Satellite Remote Sensing and Applications               in Hydrometeorology

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’

Page 31: Satellite Remote Sensing and Applications               in Hydrometeorology
Page 32: Satellite Remote Sensing and Applications               in Hydrometeorology
Page 33: Satellite Remote Sensing and Applications               in Hydrometeorology

Dynamic vegetation and spatial patterns

Page 34: Satellite Remote Sensing and Applications               in Hydrometeorology

Vegetation Pattern and DiversityVegetation Pattern and Diversity

(1) (2) (3) (4) (5) (6) (7)

Page 35: Satellite Remote Sensing and Applications               in Hydrometeorology

Annual Precipitation: 342 mm

Annual Precipitation: 297mm

Page 36: Satellite Remote Sensing and Applications               in Hydrometeorology

Annual Precipitation: 484mm

Annual Precipitation: 542mm

Page 37: Satellite Remote Sensing and Applications               in Hydrometeorology