NCEP Radiative Transfer Model Status
Paul van Delst
1
Others involved

John Derber, NCEP/EMC

Yoshihiko Tahara, JMA/NCEP/EMC

Larry McMillin, NESDIS/ORA

Tom Kleespies, NESDIS/ORA

Hal Woolf, CIMSS/SSEC/UWisc
Introduction

RT model “system”
–
Application – the RT model used in NCEP GDAS



–
Transmittance regression coefficient generation

–

Offline tests of RT code
Parallel runs of assimilation system using new RT code
Issues with some coefficients
New algorithm from Yoshihiko Tahara
Line-by-line transmittance generation
Issues
–
–
Atmospheric profile inputs and manipulation
Regression coefficient quality control
RT application software
NCEP Radiative Transfer Model (RTM)

All components completed:
–
–
–

Code availablility
–
–

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.
v1.3 Forward and K_matrix code available at
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.
Offline tests of RTM

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 3rd and 4th 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.
TL N16 HIRS channel radiances wrt T(p)
AD N16 HIRS channel radiances wrt T(p)
|TL-AD| difference for N16 HIRS wrt T(p)
|TL-AD| difference for N16 AMSU wrt W(p)
Floating point underflow issues

Integrated absorber formulation
p
sec
A p 
q p dp

g p
0

Integrated predictor formulation
–
–
X == Temperature or Pressure.
Denominator can get very small at high altitudes
A
X
n*
n 1


X
A
A
dA

 A  c  0
A
n 1
A
 dA
0
; n  1, 2, or 3
RTM Comparison in GDAS: Operational and
Parallel Analysis Runs

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.
Operational Run Mean Tb
HIRS Mean Observed – Guess Tb; no bias correction
12
7
15
3
18
9
10
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.
Parallel Run Mean Tb
HIRS Mean Observed – Guess Tb; no bias correction
12
7
15
3
18
9
10
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.
Operational Run Std. Dev. Tb
HIRS Std. Dev. Observed – Guess Tb; 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.
Parallel Run Std.Dev. Tb
HIRS Std. Dev. Observed – Guess Tb; 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.
HIRS Ch.18 comparison, no bias correction
Tb(OP) = Tb(OP) – Tb(Obs)
Tb(NEW) = Tb(NEW) – Tb(Obs)
–2
-10
-5
–0.5
-1
0.2
-0.2
1
0.5
–1
-2
–0.2
0.1
0.5
-0.5
-0.1
0.2
1
-5
10
|Tb(OP)| – |Tb(NEW)| > 0  NEW is better
-5
–2
-10
5
2
–0.5
-1
0.2
-0.2
1
0.5
5
2
10
|Tb(OP)| – |Tb(NEW)| < 0  NEW is worse
2
–1
-5
5
-2
–0.2
0.1
0.5
-0.5
-0.1
0.2
1
2
5
HIRS Ch.18 comparison, with bias correction
Tb(OP) = Tb(OP) – Tb(Obs)
Tb(NEW) = Tb(NEW) – Tb(Obs)
–2
-10
-5
–0.5
-1
0.2
-0.2
1
0.5
–1
-2
–0.2
0.1
0.5
-0.5
-0.1
0.2
1
-5
10
|Tb(OP)| – |Tb(NEW)| > 0  NEW is better
-5
–2
-10
5
2
–0.5
-1
0.2
-0.2
1
0.5
5
2
10
|Tb(OP)| – |Tb(NEW)| < 0  NEW is worse
2
–1
-5
5
-2
–0.2
0.1
0.5
-0.5
-0.1
0.2
1
2
5
RT transmittance coefficient generation
New Transmittance Algorithm

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.
Polynomial fit to absorption coefficient

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.
Absorption coefficient

New absorption coefficient k’
Dry gas
effective 


k’ has smoother profiles than k.
k’ can be negative.
k’
(new)
NOAA/HIRS
Ch.3
k
(org)
layer
layer
Weighting Regression Method



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
weight
sample
layer
How to select predictors
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 Tb.
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.
0.04
Tb RMSE

RMSE Variation for Predictor
Sets; NOAA/HIRS Ch.3
0.02
0
0
0.0005
Trans. RMSE
0.001
NOAA-14/HIRS Ch.9
New
S.D.
SD = 1.49k
Mean
Error
w/ No
Bias
Cor.
Error
Map
w/ No
Bias
Cor.
Mean Err. = -1.20k
SD = 1.64k
Mean Err. = -1.76k
Original
NOAA-14/HIRS Ch.4
New
S.D.
Mean
Error
w/ No
Bias
Cor.
Error
Map
w/ No
Bias
Cor.
SD = 1.54k
SD = 1.42k
Mean Err. = +0.22k
Mean Err. = +0.71k
Original
New
NOAA-14/HIRS Ch.17
S.D.
Mean
Error
w/ No
Bias
Cor.
New is Better
Error
Map
w/ No
Bias
Cor.
Original is Better
Original
RT line-by-line transmittance generation
LBL transmittances




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.
Impact of spectroscopic changes

Plot provided by Dave Tobin and Dave Turner at CIMSS/SSEC/UWisc.
HIRS ch.10 FWHM
Issues to be addressed/further work
Profile units conversion/interpolation (1)




Profile data supplied with AIRS dependent set transmittances were
layer column densities in kmol/cm2. 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 (2)
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.
N2 profile (same for all climatologies)
10-6
Level
10-4
Pressure (hPa)

Layer
10-2
100
102
104
7.50
7.60
7.70
7.80
N2 amount (ppmv x1.0e05)
7.90
Profile units conversion/interpolation (3)

Must ensure the column density calculation is consistent with the LBL
code.
Coefficient issues – GOES Sounder

GOES Sounder channel 2 dry coefficients are responsible for artifacts
for high absorber (== large zenith angle)

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-ofatmosphere. Coefficient
problems not seen below
2.4hPa.
Large view angle causes
transmittance anomaly to
“migrate” down.



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 boundary
T254 – higher TOA boundary
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 boundary
T254 – higher TOA boundary