graduate course: advanced remote sensing data analysis and application
DESCRIPTION
Graduate Course: Advanced Remote Sensing Data Analysis and Application RETRIEVAL OF SURFACE AIR HUMIDITY FROM SSM/I Shu-Hsien Chou Dept. of Atmospheric Sciences National Taiwan University Objective : Retrieve surface air humidity from SSM/I over global oceans - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/1.jpg)
Graduate Course: Advanced Remote Sensing Data Analysis and ApplicationGraduate Course: Advanced Remote Sensing Data Analysis and Application
RETRIEVAL OF SURFACE AIR HUMIDITY FROM SSM/I
Shu-Hsien ChouDept. of Atmospheric Sciences
National Taiwan University
Objective :
Retrieve surface air humidity from SSM/I over global oceans
Chou, S.-H., R. M. Atlas, C.-L. Shie and J. Ardizzone, 1995: Estimates of surface humidity and latent heat fluxes over oceans from SSM/I data. Mon. Wea. Rev., 123, 2405-2425.
Chou, S.-H., C.-L. Shie, R. M. Atlas and J. Ardizzone, 1997: Air-sea fluxes retrieved from Special Sensor Microwave Imager data. J. Geophys. Res., 102, 12705-12726.
![Page 2: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/2.jpg)
Outlines:• Height Variation of Correlation of Surface Air
Humidity (Q) with Thick Layer Water Vapor Content
• EOF Method for Deriving Q (Chou et al. 1995)
• FGGE2b Stations Used for 6 W-based Climatic Regimes of Humidity Soundings
• Vertical Profiles of Mean, 1st and 2nd EOFs of 6 W-based Climate Regimes
• Comparison of EOF Method with SSG, and Liu86 Methods using FGGEF2b Humidity Soundings
• Comparison of SSM/I Retrieved-Q among EOF, SSG, and Liu86 Methods (Validated with Radiosonde Obs)
• Further Improvement of EOF Method for Retrieving Q from SSM/I (Chou et al. 1997)
• Validation of SSM/I Retrieved-Q based on Improved EOF Method Against 9 field experiments
![Page 3: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/3.jpg)
![Page 4: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/4.jpg)
Table 1. Characterics of SSM/I on board DMSP satellitesCenter of freq (GHZ) 19.35 22.24 37.00 85.50
Polarization V, H V V, H V, H
3 dB footprint (kmxkm) 69x43 50x40 37x29 15x13
Swath width (km) 1394 1394 1394 1394
Spatial sampling (km) 25 25 25 12.5__
SSM/I --- Special Sensor Microwave/Imager
DMSP --- Defense Meteorological Satellite Program
![Page 5: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/5.jpg)
Retrieval of surface air humidity:
Empirical orthogonal function (EOF) METHOD: (Chou et al. 1995, 1997)
profile q() = qm() + C1 F1() + C2 F2() (1)
surface Q10m = Mo + M1 W + M2 WB (2)
Q10m ---- surface air specific humidity
---- (p - 200 mb)/(ps - 200 mb)
q() ---- specific humidity vertical profile
qm, F1, F2 --- mean, 1st & 2nd EOFs humidity profiles of a climate regime (FGGE IIb q's, 6 W-based samples)
C1, C2 ---- principal components of F1 & F2
W --- SSM/I total column water vapor (Wentz 1994, 1997)
WB --- SSM/I 500m bottom layer water vapor (Schulz et al. 1993)
Mo (g kg-1): 0.09- 5.91 (Regimes 1- 6)
M1 (g kg-2 m2): (-0.04) - 0.02 (Regimes 1- 6)
M2 (g kg-2 m2): 1.94 - 1.23 (Regimes 1- 6)
![Page 6: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/6.jpg)
Retrieval of WB from SSM/I: (Schulz et al. 1993)
WB = b0 + b1 Tv19 + b2 Th
19 + b3 Tv22 + b4 Tv
37
b0 = 5.93390
b1 = 0.03697
b2 = 0.02390
b3 = 0.01559
b4 = 0.00497
*Criterion for valid WB (no rain): (Goodberlet et al. 199
0)
Th19 < 165 K
(Tv37 – Th
37) > 50 K
![Page 7: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/7.jpg)
![Page 8: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/8.jpg)
EOF156-86%
EOF1&271-92%
EOF26-15%
![Page 9: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/9.jpg)
Q = f (W, WB)
Q = f (WB)
Q = f (W)
![Page 10: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/10.jpg)
25 km cell<100km<1.5 hr
![Page 11: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/11.jpg)
![Page 12: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/12.jpg)
![Page 13: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/13.jpg)
25 km cell<100km<1.5 hr
![Page 14: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/14.jpg)
25 km cell<100km<1.5 hr
![Page 15: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/15.jpg)
![Page 16: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/16.jpg)
*Excluding CATCH, FETCH, and SCOPE for 9 collocated experiments
![Page 17: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/17.jpg)
*Excluding CATCH, FETCH, and SCOPE for 9 collocated experiments
![Page 18: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/18.jpg)
GSSTF2 surface air specific humidity vs those of nine field experiments conducted by NOAA ETL research ships. C: COARE, F: FASTEX, X: other experiments.
![Page 19: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/19.jpg)
10-m specific humidity averaged over 1992-93 for (a) GSSTF2 and differences of (b) HOAPS, (c) NCEP/NCAR reanalysis, and (d) da Silva et al. (1994) from GSSTF2.
![Page 20: Graduate Course: Advanced Remote Sensing Data Analysis and Application](https://reader036.vdocument.in/reader036/viewer/2022062409/56814dce550346895dbb28e9/html5/thumbnails/20.jpg)
CONCLUSIONS:
• GSSTF2 daily surface air humidity validated reasonably well with those measured by research ships of nine field experiments over tropical and northern midlatitude oceans during 1991-99.
• Comparisons with high quality research ship data, radiosonde measurements, and three humidity data sets over global oceans suggest that EOF method for retrieving surface air humidity improves upon those of Liu (1986), Schulz et al. (1993) and HOAPS.