gifts clear sky fast model its adjoint the neglected reflected term n.
Skip this Video
Loading SlideShow in 5 Seconds..
GIFTS clear sky fast model, its adjoint, & the neglected reflected term PowerPoint Presentation
Download Presentation
GIFTS clear sky fast model, its adjoint, & the neglected reflected term

Loading in 2 Seconds...

play fullscreen
1 / 21

GIFTS clear sky fast model, its adjoint, & the neglected reflected term - PowerPoint PPT Presentation

Download Presentation
GIFTS clear sky fast model, its adjoint, & the neglected reflected term
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. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. GIFTS clear sky fast model, its adjoint,& the neglected reflected term MURI Hyperspectral Workshop Madison WI, 2005 June 7 bob knuteson, leslie moy, dave tobin, paul van delst, hal woolf

  2. Outline of Talk • Fast Model: Development & Status • Tangent Linear, Adjoint: Development & Status • Surface Reflected Term: Work in Progress

  3. Fixed Gas Amounts Spectral line parameters Lineshapes & Continua Reduce to sensor’s spectral resolution Fast Model Coefficients, ci Fast Model Regressions Profile Database Compute monochromatic layer-to-space transmittances Layering, l Convolved Layer-to-Space Transmittances, tz (l) Fast Model Predictors, Qi Effective Layer Optical Depths, keff Fast Model Production Flowchart:

  4. RMS Error: water linesRed=before, Blue=after Why the improvement? Mainly from SVD regression & Optical Depth Weighting.

  5. RMS Error: water continuumRed=before, Blue=after Why the improvement? Mainly from regressing nadir only Optical Depths and applying constant factors to off-nadir values.

  6. ------- GIFTS NeDT@296K ------- OSS RMS upper limit* Dependent Set Statistics: RMS(LBL-FM) Yr 2002 model MURI version MURI model w/ new regressions AIRS model c/o L. Strow, UMBC OSS model c/o Xu Liu, AER, Inc. OPTRAN, AIRS 281 channel set c/o PVD

  7. User Input: User Output: Profile of temperature, dry gases, water vapor at 101 levels Profileperturbation of temperature, ozone, water vapor at 101 levels Use to adjust initial profile Forward Model: Adjoint Model: Layer.m - convert 101 level values to 100 layer values Predictor.m - convert layer values to predictor values Calc_Trans.m - using predictors and coefficients calculate level to space transmittance Trans_to_Rad.m - calculate radiance Layer_AD.m - layer to level sensitivities Predictor_AD.m - level to predictor sensitivities Calc_Trans_AD.m - predictor to transmittance sensitivities Trans_to_Rad_AD.m - transmittance to radiance sensitivities User Output: User Input: Compare to observations Radiance Spectrum Radiance Spectrum perturbation

  8. Simple Example: One Line Forward Model • Forward (FWD) model. The FWD operator maps the input state vector, X, to the model prediction, Y, e.g. for predictor #11: • Tangent-linear (TL) model. Linearization of the forward model about Xb, the TL operator maps changes in the input state vector, X, to changes in the model prediction, Y, Or, in matrix form:

  9. TL testing for Dry Predictor #6 (T2) vs Temp at layer 44.* TL results must be linear.* TL must equal (FWD-To) at dT=0. TL results = blue, FWD-T0 results = red Difference between TL and FWD Input Temperature at Layer 44 were varied 25%.

  10. TL testing for Dry Predictor #6 vs Temp at all layers.Similar plots made for each subroutine’s variables. D(dry.pred#6) Layer no. D(temp), %

  11. Adjoint (AD) model. The AD operator maps in the reverse direction where for a given perturbation in the model prediction, Y, the change in the state vector, X, can be determined. The AD operator is the transpose of the TL operator. Using the example for predictor #11 in matrix form, Expanding this into separate equations:

  12. Adjoint code testing for Dry Predictor #6 vs Temperature layer.AD - TLt residual must be zero.Similar plots are produced for every subroutine’s variables. 10 -18 x AD - TLt residual Output variable layer Input variable layer

  13. I atmos I surf reflect I surf emiss r, r I surf reflect = TtoaI(i,i) cos(i) sin(i) d(i) d(i)  BDRF(r,r: i,i) Clear Sky Top of Atmosphere Radiance I TOA = I atmos + I surf emiss + I surf reflect =I atmos + Ttoa B(tempsurf ) surf + TtoaFluxsurf Reflectivity current fast model we write the expression more explicitly below This Term is often ignored because Refl < 10%. IF the term is calculated accurately enough, it can be exploited to derive surf and hence Tsurf

  14. Approximations made & Their Associated Errors I surf reflect = TtoaI(i,i) cos(i) sin(i) d(i) d(i)  BDRF(r,r: i,i) Approx.1: Lambertian surface (reflection is independent of incident angle): BDRF = R(r,r) = 1-surf (r,r) Approx.2: Low Order Gaussian Quadrature technique for calculating flux # quadrature points needed? which table to use? (Abramowitz and Stegun, 1972) Approx.3: Resolution Reduction SRF  {Ttoa2  I()  d}  {SRF Ttoa } {SRF  2  I()  d} Approx.4: Calculating Downwelling Radiance from Upwelling Fast Model SRF  I() (usingLBLRTM)  (T GIFTSlayer to space convolved) B(templayer)

  15. Expanding on Approx. 2: 2 /2 Downwelling flux = 0 0I(,) cos() sin() d d = 2 0 I() cos() sin() d substituting  = cos (), = 2 0  I() d Diffusivity approximation, Low Order Gaussian Quadrature technique 0  I() d =  wi I(i ) p.921, Abramozwitz & Stegun, 1972 n=1,  0.5 I(=48) n=2,  0.2 I(1=69°) + 0.3 I(2 =32°) In contrast to the 2-stream model application: -1 I() d =  wi I(i ) p.916, Abramozwitz & Stegun, 1972 n=1, I(=54.73) /2 1 1 n i=1 1 n i=1

  16. Approx 2: Error in Gaussian Quadrature Approximations (difference from using 4 points) Using two points Difference, W/(cm2cm-1 ster) Using one point

  17. Approx. 3: Convolution Error Product of Convolution Minus Convolution of Products Difference, W/(cm2cm-1 ster)

  18. 1 point Gauss. Qaud. Errors from 1 Point Gaussian Quad & Convolution Approximations Convolution Error Both approx.

  19. 2 point Gauss. Qaud. Both approx. Errors from 2 Point Gaussian Quad & Convolution Approximations Convolution Error

  20. Approx. 4: Using Fast Model Upwelling Level-2-space Transmissivity to calculate Downwelling Radiance TOA radiance rad = rad + 0.5 (ba+bb) (1-Tb/ Ta) Ta layer trans level 2 space layer radiance emission Ta BOA radiance rad = rad + 0.5 (ba+bb) (1-Tb/ Ta ) (T1/ Tb) Tb T1 level 2 ground Close up of the window region Differences lblrtm - From Tran(lev2space) Comparison of Downwelling Radiance from Tran(lev2space) from lblrtm

  21. Accomplishments: Reproduce and Upgrade existing GIFTS/IOMI Fast Model • Coefficients promulgated 2003. • Greatly improved the dependent set statistics (esp. water vapor). • Water continuum regression made at nadir and applied to all angles. • SVD regression and optical depth weighting incorporated. • Written in flexible code with visualization capabilities. Under CVS control. Write the Corresponding Tangent Linear and Adjoint Code • Tested to machine precision accuracy. • User friendly “wrap-around” code complete. • Transferred code to FSU. Investigate Surface Reflected Radiance • Great improvement with two point Gaussian Quadrature (over 1 point). • Convolution order causes large errors – may be overcome with regression algorithm? • Depending on the application (micro-window or on/off line) using upwelling transmissivity for downwelling radiance may be reasonable.