1. 1 NCEP Radiative Transfer Model Status Paul van Delst

2. John Derber, NCEP/EMC Yoshihiko Tahara, JMA/NCEP/EMC Larry McMillin, NESDIS/ORA Tom Kleespies, NESDIS/ORA Hal Woolf, CIMSS/SSEC/UWisc Others involved

3. Introduction RT model “system” – Application – the RT model used in NCEP GDAS Offline tests of RT code Parallel runs of assimilation system using new RT code Issues with some coefficients – Transmittance regression coefficient generation New algorithm from Yoshihiko Tahara – Line-by-line transmittance generation Issues – Atmospheric profile inputs and manipulation – Regression coefficient quality control

4. RT application software

5. NCEP Radiative Transfer Model (RTM) All components completed: – Forward, tangent-linear, adjoint, K-matrix. – Parallel testing of updated code in GDAS ongoing. Memory usage and timing are same (even with 2-3x more calculations) for effectively unoptimised code. – Code supplied to NASA DAO, NOAA ETL and FSL. Code availablility – v1.3  Forward and K_matrix code available at http://airs2.ssec.wisc.edu/~paulv/#F90_RTM http://airs2.ssec.wisc.edu/~paulv/#F90_RTM – GOES, POES, and AIRS(*) coefficients available. Code comments – ANSI standard Fortran90; no vendor extensions – Platform testbeds: Linux (PGI compilers), IBM SP/RS6000, SGI Origin, Sun SPARC. – Code prototyped in IDL. Not the best choice but allows for simple in situ visualisation and easy detection/rectification of floating point errors.

6. TL and AD models used in tandem for testing Unit perturbations applied Floating point precision and underflow a concern with transmittance predictor formulation. – Some integrated predictors require the 3 rd and 4 th powers of absorber amount in the denominator. This is a problem for low absorber (e.g. water) amounts. – Current operational code will not run with floating point error handling enabled. Offline tests of RTM

7. TL N16 HIRS channel radiances wrt T(p)

8. AD N16 HIRS channel radiances wrt T(p)

9. |TL-AD| difference for N16 HIRS wrt T(p)

10. |TL-AD| difference for N16 AMSU wrt W(p)

11. Integrated absorber formulation Floating point underflow issues Integrated predictor formulation – X == Temperature or Pressure. – Denominator can get very small at high altitudes

14. Upgraded RTM improves bias in some channels, degrades it in others. Variability is better in some channels with upgraded RTM, but differences are quite small. Biggest improvements are in the solar affected channels and the microwave channels where cosmic background is significant. RTM Comparison in GDAS: Operational and Parallel Analysis Runs

15. Operational Run Mean  T b HIRS Mean Observed – Guess  T b ; no bias correction All: Gross quality controlled data. Used: RT-dependent quality controlled data. (e.g. clear sky data for lower peaking channels) NOTE: Ch. 1, 16-19 not assimilated. 3 7 9 10 12 15 18

16. HIRS Mean Observed – Guess  T b ; no bias correction Parallel Run Mean  T b 3 7 9 10 12 15 18 All: Gross quality controlled data. Used: RT-dependent quality controlled data. (e.g. clear sky data for lower peaking channels) NOTE: Ch. 1, 16-19 not assimilated.

17. Operational Run Std. Dev.  T b HIRS Std. Dev. Observed – Guess  T b ; no bias correction All: Gross quality controlled data. Used: RT-dependent quality controlled data. (e.g. clear sky data for lower peaking channels) NOTE: Ch. 1, 16-19 not assimilated.

18. Parallel Run Std.Dev.  T b HIRS Std. Dev. Observed – Guess  T b ; no bias correction All: Gross quality controlled data. Used: RT-dependent quality controlled data. (e.g. clear sky data for lower peaking channels) NOTE: Ch. 1, 16-19 not assimilated.

19. -10 –2 –0.5 0.2 1 5 -5 -1 -0.2 0.5 2 10 -10 –2 –0.5 0.2 1 5 -5 -1 -0.2 0.5 2 10 -5 –1 –0.2 0.1 0.5 2 -2 -0.5 -0.1 0.2 1 5 -5 –1 –0.2 0.1 0.5 2 -2 -0.5 -0.1 0.2 1 5 |  Tb(OP)| – |  Tb(NEW)| > 0  NEW is better|  Tb(OP)| – |  Tb(NEW)| < 0  NEW is worse  Tb(NEW) = Tb(NEW) – Tb(Obs)  Tb(OP) = Tb(OP) – Tb(Obs) HIRS Ch.18 comparison, no bias correction

20. -10 –2 –0.5 0.2 1 5 -5 -1 -0.2 0.5 2 10 -10 –2 –0.5 0.2 1 5 -5 -1 -0.2 0.5 2 10 -5 –1 –0.2 0.1 0.5 2 -2 -0.5 -0.1 0.2 1 5 -5 –1 –0.2 0.1 0.5 2 -2 -0.5 -0.1 0.2 1 5 |  Tb(OP)| – |  Tb(NEW)| > 0  NEW is better|  Tb(OP)| – |  Tb(NEW)| < 0  NEW is worse  Tb(NEW) = Tb(NEW) – Tb(Obs)  Tb(OP) = Tb(OP) – Tb(Obs) HIRS Ch.18 comparison, with bias correction

21. RT transmittance coefficient generation

22. Memory requirement for OPTRAN coefficients becomes prohibitive for high resolution IR sensors. – Currently, OPTRAN requires 5400 available coefficients for each channel; 6 coefficients (offset + 5 predictors) for 300 absorber layers for each absorber (wet, dry, ozone). – Assimilation of 431 channels would require ~20MB memory simply for coefficient data. – Problem exacerbated if an increase in the number of absorber layers or predictors is warranted, or more channels assimilated. Mr. Yoshihiko Tahara, visiting scientist from JMA, is investigating a different method – within the OPTRAN framework – to predict absorption coefficient and transmittance profiles. New method fits the vertical absorption coefficient profile and this reduces the need for a large number of coefficients. New Transmittance Algorithm

23. The number of regression coefficients is significantly decreased. – For a polynomial order of 10, the number of coefficients is ~200 per channel. No interpolation required in generating regression coefficients or predicting absorption coefficient. Harder to fit LBL absorption coefficients at all levels. Polynomial fit to absorption coefficient

24. layer k’ (new) layer k (org) New absorption coefficient k’ k’ has smoother profiles than k. k’ can be negative. NOAA/HIRS Ch.3 Dry gas effective  Absorption coefficient

25. Absorption coefficients should be predicted accurately over highly sensitive layers for accurate radiance calculation. The sensitivity is used as the weight of regression coefficients. The weighting method saves LBL information lost by introducing polynomial fitting. NOAA/HIRS Ch.6, Dry Gas layer sample weight Weighting Regression Method

26. Index for predictor selection – RMSE of predicted transmittances against LBL has been found to be a better index for selecting predictors rather than that of predicted T b. Stable calculation – Many regression coefficients sometimes cause unstable calculation. – Careful selection amongst highly correlated predictors is needed. – Not always 5 predictors are needed for wet and ozone gas. RMSE Variation for Predictor Sets; NOAA/HIRS Ch.3 How to select predictors

27. Mean Error w/ No Bias Cor. Error Map w/ No Bias Cor. NOAA-14/HIRS Ch.9 New Original S.D. SD = 1.49k Mean Err. = -1.20kMean Err. = -1.76k SD = 1.64k

28. NOAA-14/HIRS Ch.4 New S.D. Mean Error w/ No Bias Cor. Error Map w/ No Bias Cor. Original SD = 1.54k Mean Err. = +0.22k SD = 1.42k Mean Err. = +0.71k

29. NOAA-14/HIRS Ch.17 New S.D. Mean Error w/ No Bias Cor. Error Map w/ No Bias Cor. Original New is BetterOriginal is Better

30. RT line-by-line transmittance generation

31. Gearing up system for generating line-by-line transmittances routinely. (  W recommendations?) Profiles anyone….? (please) HITRAN changes, LBL algorithm changes, profile set changes, etc. occur frequently. Goal is to make the operation as simple as possible. Software exists; just needs to be assembled. – Profile units conversion code – LBL input file generation code – LBL convolution code – Data readers (native LBL and netCDF formats) Moving dependent data (e.g. atmospheric profiles, instrument SRFs, final LBL and convolved transmittances) into netCDF format. LBL transmittances

32. Impact of spectroscopic changes Plot provided by Dave Tobin and Dave Turner at CIMSS/SSEC/UWisc. HIRS ch.10 FWHM

33. Issues to be addressed/further work

34. Profile data supplied with AIRS dependent set transmittances were layer column densities in kmol/cm 2. Dependent profile set “made up” so level profiles do not exist. These values were converted to ppmv and then exponentially interpolated to the level pressures. This introduced small, subtle but still significant differences. These level profile sets were used in generating OPTRAN coefficients for AIRS. RT performed using both profile sets on “truth” transmittances: Profile units conversion/interpolation (1)

35. Profile units conversion/interpolation (2) N 2 amount (ppmv x1.0e05) 7.507.607.707.807.90 Pressure (hPa) 10 4 10 2 10 0 10 -2 10 -4 10 -6 N 2 profile (same for all climatologies) Layer Level Once level profile values in ppmv (or whatever units) are converted to column density (integrated layer quantity), the result should not be converted back to ppmv.

36. Profile units conversion/interpolation (3) Must ensure the column density calculation is consistent with the LBL code.

37. GOES Sounder channel 2 dry coefficients are responsible for artifacts for high absorber (== large zenith angle) Coefficient issues – GOES Sounder Effect only seen in the parallel GDAS runs using the updated RT code. Operational GDAS results o.k. Off-line tests show old RT code also exhibits problem. Problem is with top-of- atmosphere. Coefficient problems not seen below 2.4hPa. Large view angle causes transmittance anomaly to “migrate” down.

38. Coefficient issues – GOES Imager GOES Imager coefficients are not valid for large view angle (e.g. beyond 55 ° ) or high absorber amount. Known problem, but a lot of good data is getting thrown out. Plots provided by Xiujuan Su at NCEP/EMC. Zenith angle vs.  T for G10 IMGR ch4  T (obs-calc) T62 – lower TOA boundaryT254 – higher TOA boundary

39. Coefficient issues – GOES Imager GOES Imager channel 3: case where high absorber amount occurs at relatively small angles. Appears to have a TOA boundary component also. “Rings” appear in temperature residual images.  T images for G10 IMGR ch3 T62 – lower TOA boundaryT254 – higher TOA boundary