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Hyperspectral Cloud-Clearing

Hyperspectral Cloud-Clearing Allen Huang, Jun Li, Chian-Yi Liu, Kevin Baggett, Li Guan, & Xuebao Wu SSEC/CIMSS, University of Wisconsin-Madison. AIRS/AMSU V3.5 &V4.0 Cloud-Clearing Characteristic AIRS/AMSU Additional C.C. QC Using MODIS AIRS/MODIS Over Sampling Single-FOV N* C.C.

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Hyperspectral Cloud-Clearing

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  1. Hyperspectral Cloud-Clearing Allen Huang, Jun Li, Chian-Yi Liu, Kevin Baggett, Li Guan, & Xuebao Wu SSEC/CIMSS, University of Wisconsin-Madison • AIRS/AMSU V3.5 &V4.0 Cloud-Clearing Characteristic • AIRS/AMSU Additional C.C. QC Using MODIS • AIRS/MODIS Over Sampling Single-FOV N* C.C. • AIRS/MODIS 2-FOV Vs. AIRS/AMSU 9-FOV C.C. Comparison 5th Workshop on Hyperspectral Science of UW-Madison MURI, Airborne, LEO, and GEO Activities  The Pyle Center University of WisconsinMadison  7 June 2005

  2. Wisconsin Granule Australia Granule South Africa Granule Hurricane Isabel Granule Case Granule Dataset Used • 4 Granules of Collocated • AIRS & MODIS Data • MODIS 1-km Cloud Mask • AIRS C.M. (from MODIS) • No ancillary data used 2 Sep. 2002 AIRS Focus Day 17 Sep. 2003

  3. Why cloud_clearing? • AIRS clear footprints are less than 5% globally. Why use MODIS for AIRS cloud-clearing? • Many AIRS cloudy footprints contain clear MODIS pixels; it is effective for N* calculation and Quality Control for CC radiances • Cloud-clearing can be achieved on a single footprint basis (hence maintaining the spatial gradient information); • There is a direct relationship between MODIS and AIRS radiances because they see the same spectra region. AIRS All Observations AIRS Clear Only Obs.

  4. Case Global Dataset Used • 240 Granules of Collocated • AIRS & MODIS Data • MODIS 1-km Cloud Mask • AIRS C.M. (from MODIS) • No ancillary data used AIRS Global Window Channel Brightness Temp. Images Ascending Daytime Passes (Upper Left) Descending Nighttime Passes (Lower Right)

  5. AIRS/AMSU (3 by 3 AIRS FOV) V3.5 Vs V4.0 C.C. Comparison MODIS Band 22 3.95 microns Without Q.C. MODIS Band 22 3.95 microns X-axis: V3.5 C.C. Bt.- Blue circle V4.0 C.C. Bt. – Red cross Y-axis: Clear MODIS Bt.

  6. AIRS/AMSU (3 by 3 AIRS FOV) V3.5 Vs V4.0 C.C. Comparison MODIS Band 22 3.95 microns With Q.C. Q.C. filtered most of the unreliable data as well as some good data. X-axis: V3.5 C.C. Bt.- Blue circle V4.0 C.C. Bt. – Red Cross Y-axis: Clear MODIS Bt.

  7. AIRS/AMSU (3 by 3 AIRS FOV) V3.5 Vs V4.0 C.C. Comparison MODIS Band 28 7.3 microns Without Q.C. X-axis: V3.5 C.C. Bt.- Blue Circle V4.0 C.C. Bt. – Red Cross Y-axis: Clear MODIS Bt. MODIS Band 28 7.3 microns

  8. AIRS/AMSU (3 by 3 AIRS FOV) V3.5 Vs V4.0 C.C. Comparison MODIS Band 28 7.3 microns With Q.C. Q.C. filtered most of the unreliable data as well as some good data. X-axis: V3.5 C.C. Bt.- Blue Circle V4.0 C.C. Bt. – Red Cross Y-axis: Clear MODIS Bt.

  9. AIRS/AMSU (3 by 3 AIRS FOV) V3.5 Vs V4.0 C.C. Comparison MODIS Band 33 13.33 microns Without Q.C. MODIS Band 33 13.33 microns X-axis: V3.5 C.C. Bt.- Blue Circle V4.0 C.C. Bt. – Red Cross Y-axis: Clear MODIS Bt.

  10. AIRS/AMSU (3 by 3 AIRS FOV) V3.5 Vs V4.0 C.C. Comparison MODIS Band 33 13.33 microns With Q.C. Q.C. filtered most of the unreliable data as well as some good data. X-axis: V3.5 C.C. Bt.- Blue Circle V4.0 C.C. Bt. – Red Cross Y-axis: Clear MODIS Bt.

  11. Estimated AIRS/AMSU C.C. Bias and RMSE Without (Green) and With (Blue) MODIS as Q.C. Australia Granule Use MODIS data as AIRS/AMSU C.C. Q.C. can enhance yields & performance

  12. Estimated AIRS/AMSU C.C. Bias and RMSE Without (Green) and With (Blue) MODIS as Q.C. South Africa Granule Use MODIS data as AIRS/AMSU C.C. Q.C. can enhance yields & performance

  13. Estimated AIRS/AMSU C.C. Bias and RMSE Without (Green) and With (Blue) MODIS as Q.C. Wisconsin Granule Use MODIS data as AIRS/AMSU C.C. Q.C. can enhance yields & performance

  14. AIRS/MODIS Synergistic C.C. can Supplement AIRS/AMSU C.C. Especially over Desert Region AIRS/AMSU C.C. (3 by 3 AIRS FOV) V3.5 – Blue V4.0 – Green AIRS/MODIS C.C. (1 by 2 AIRS FOV) Red

  15. Aqua MODIS IR SRF Overlay on AIRS Spectrum Direct spectral relationship between IR MODIS and AIRS provides unique application of MODIS in AIRS cloud_clearing !

  16. MODIS/AIRS Synergistic N* Cloud Clearing Threshold for AIRS Pair C.C. Retrieval: Each AIRS footprint within the C.C. pair (2 by 1 ) must have at least 15 MODIS confident clear (P=99%) pixel (partly cloudy)

  17. MODIS/AIRS Synergistic Single-Channel N* Cloud-Clearing General Principal Or Q.C. After Smith

  18. MODIS/AIRS Synergistic Multi-Channel N* Cloud Clearing General Principal Li et al, 2005, IEEE-GRS

  19. 3 5 8 7 2 1 6 4 Step 1: Get cloud-cleared AIRS radiances for Principal and supplementary footprints. Step 2: Find best Rcc by comparing with MODIS clear radiances observations in the principal footprint. MODIS/AIRS Synergistic N* Cloud Clearing AIRS Two-FOVs (Pair) Strategy • Principal footprint has to be partly cloudy, while the supplementary footprint can be either partly cloudy or full cloudy. • The 3 by 3 box moves by single AIRS footprint, therefore each partly cloudy footprint has chance to be cloud-cleared

  20. June issue of IEEE Trans. on Geoscience and Remote Sensing, 2005

  21. MODIS/AIRS Synergistic N* Cloud Clearing Over Sampling Strategy 8 possible AIRS pairs (2 FOVs) 1 2 3 8 4 1 7 6 5 1 Pseudo Single AIRS FOV

  22. MODIS/AIRS Synergistic N* Cloud Clearing Over Sampling Strategy 8 possible AIRS pairs (2 FOVs) 1 2 3 8 4 2 7 6 5 2 1 Pseudo Single AIRS FOV

  23. MODIS/AIRS Synergistic N* Cloud Clearing Over Sampling Strategy 8 possible AIRS pairs (2 FOVs) 1 2 3 8 4 3 7 6 5 2 1 3 Pseudo Single AIRS FOV

  24. Single-Channel Vs. Multi-Channel N* C.C. Error Comparison South Africa Granule

  25. Single-Channel Vs. Multi-Channel N* C.C. Error Comparison Wisconsin Granule

  26. Single-Channel Vs. Multi-Channel N* C.C. Error Comparison Hurricane Isabel Granules

  27. Single-Channel Vs. Multi-Channel N* C.C. Error Comparison 3.96 um 4.52 um 12.0 um 6.7 um

  28. Single-Channel N* Channel Selection (22 Vs. 31) C.C. Error Comparison Australia Granule

  29. Single-Channel N* Channel Selection (22 Vs. 31) C.C. Error Comparison Wisconsin Granule

  30. Multi-Channel N* Desert vs. Land C.C. Error Comparison

  31. Single-Channel N* Different Q.C. (0.5K vs. 1.0K) C.C. Yield & Error Comparison South Africa Granule

  32. Single-Channel N* Different Q.C. (0.5K vs. 1.0K) C.C. Yield & Error Comparison Wisconsin Granule

  33. Single-Channel N* Different Q.C. (0.5K vs. 1.0K) C.C. Yield & Error Comparison Hurricane Isabel Granules

  34. Wisconsin Granule AIRS/AMSU C.C. (3 by 3 AIRS FOV) V4.0 - Blue AIRS/MODIS C.C. (1 by 2 AIRS FOV) Multi-Ch. - Black Single-Ch.:Band 31 – Green; Band 22 - Red

  35. AIRS/MODIS Synergistic C.C. can Supplement AIRS/AMSU C.C. Especially over Desert Region AIRS/AMSU C.C. (3 by 3 AIRS FOV) V4.0 - Blue AIRS/MODIS C.C. (1 by 2 AIRS FOV) Multi-Ch. - Black Single-Ch.: Band 31 – Green Band 22 - Red Australia Granule

  36. AIRS/MODIS Synergistic C.C. can Supplement AIRS/AMSU C.C. Especially over Desert Region AIRS/AMSU C.C. (3 by 3 AIRS FOV) V4.0 - Blue AIRS/MODIS C.C. (1 by 2 AIRS FOV) Multi-Ch. - Black Single-Ch.: Band 31 – Green Band 22 - Red South Africa Granule

  37. AIRS BT (All) AIRS BT (Cloud-Cleared) AIRS BT (Clear Only)

  38. AIRS BT (Clear Only) AIRS BT (Cloud-Cleared) AIRS BT (All)

  39. Cloud-Cleared Vs. Cloud-Contaminated Retrieval Details see Wu/Li Retrieval presentation

  40. Synergistic AIRS/MODIS C.C.Summary • Synergistic AIRS/MODIS C.C. could provide cloud-cleared radiances over non-oceanic scenes with good yield and performance at high spatial resolution (pseudo single AIRS FOV) • AIRS/MODIS C.C. is one of the promising GOES-R risk reduction research for future HES cloudy sounding processing • Since no Geo-microwave sensor is been planned, synergistic HES/ABI C.C. might become one of the baseline processing approach that worthy of further study

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