1 / 9

Spectral Aliassing I. “Bad Case Scenario” R. De Beek, M. Weber, R. Siddans (RAL), B. Latter (RAL)

Spectral Aliassing I. “Bad Case Scenario” R. De Beek, M. Weber, R. Siddans (RAL), B. Latter (RAL) all albedo sequences were analysed to identify worst case scenario from LANDSAT/artificial albedo perturbation provided by RAL

fiorenza-gorman
Télécharger la présentation

Spectral Aliassing I. “Bad Case Scenario” R. De Beek, M. Weber, R. Siddans (RAL), B. Latter (RAL)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Spectral Aliassing • I. “Bad Case Scenario” • R. De Beek, M. Weber, R. Siddans (RAL), B. Latter (RAL) • all albedo sequences were analysed to identify worst case scenario from LANDSAT/artificial albedo perturbation provided by RAL • worst case scenario was defined by maximum variance of differential albedo error spectra • definition of differential albedo error spectra: • I() – P() = Wa()(a()/a) – P() • I() relative intensity perturbation due to albedo variation • P() fitted quadratic (cubic) polynomial • Wa() albedo weighting function (SCIATRAN) • a() albedo sequence with mean albedo subtracted • a mean albedo of sequence

  2. Gas Position StDev StDev mean albedo sequence file • Scan (1m) (IFOV) albedo • O3 536 0 5.7e-043.6e-05 0.46 syn3/pix80_nd3_p1_10_1m_box_80_spec.sav NO2 509 0 3.0e-031.3e-04 0.51 syn10/pix80_nd10_p0_10_1m_box_80_spec.sav BrO 529 0 6.7e-046.3e-06 0.47 syn3/pix80_nd3_p1_10_1m_box_80_spec.sav • OClO 438 0 2.0e-038.3e-05 0.51 syn10/pix80_nd10_p0_10_1m_box_80_spec.sav Results: • worst case scenario from 1m box artifical albedo perturbation • IFOV convolution reduces StDev by a factor of 15-100 • albedo error spectra applied to biomass burning (No. 1) and ozone hole scenario (No. 2)

  3. Results from weighted intensities using weights determined from averaging a=0.05 and a=0.8 to obtain mean albedo Results from fits to I(a=0.05)

  4. Results from weighted intensities using weights determined from averaging a=0.05 and a=0.8 to obtain mean albedo Results from fits to I(a=0.05)

  5. Results from weighted intensities using weights determined from averaging a=0.05 and a=0.8 to obtain mean albedo Results from fits to I(a=0.05)

  6. Results from weighted intensities using weights determined from averaging a=0.05 and a=0.8 to obtain mean albedo Results from fits to I(a=0.05)

  7. Preliminary Conclusion for IT=0.1875ms and readout of 46ms: • errors on the order of less than 0.1% (O3), 80% (NO2), 11% (BrO), 5% (ozone hole OClO) have been observed for albedo variations of less than 0.04 (very small albedo variations!) • spatial aliassing equivalent noise isa dominant error source for current IT and readout time settings • larger albedo variations leads to variability of reference slant column and air mass factor (additional error source). DOAS assumes unique slant column within the window • fits will be repeated for intensities with proper mean albedo (a=0.46 and a=0.5) • additional case studies and error checking on current approach is planned.

More Related