# Presentation: 806424

## Development of RT models based on Optimal Spectral Sampling (OSS) Method 

**Jean-Luc Moncet, Gennadi Uymin and Sid Boukabara**
**AER, Inc.**

## Accomplishments

- Built OSS models for SSMIS, AMSU, GOES sounder/imager, HIRS and AIRS and delivered to NOAA
- Supported OSS/OPTRAN intercomparison
- OSS integration into CRTM (beta version delivered April 6)
- Additional OSS development:
- Multiple channel (generalized) training
- Application to cloudy atmospheres
- Goals: relax memory requirements and further increase model speed

## Topics

- CRTM integration (covered in Yong Han *et al.’*s presentation)
- OSS training
- About OSS Jacobians
- Cloudy validation (status)
- Generalized training

## Overview of the OSS approach

- OSS method (Moncet and Uymin, 2003; Moncet *et al.* 2001) models the channel radiance as
- Wavenumber ***i** *(nodes) and weights *w**i* are determined by fitting “exact” calculations (from line-by-line model) for globally representative set of atmospheres (training set)
- Monochromatic RT (using look-up tables of absorption coefficients for relevant species stored at the selected nodes)
- Maximum brightness temperature error with current LUT < 0.05K in infrared and <~0.01K in microwave

## OSS training

## 52 ECMWF vs. 49 UMBC:  more vertical variability in ECMWF profiles

- From Dr. Moncet, AER

## OSS training: ECMWF vs. UMBC  (only temperature is allowed to vary)

- OSS training: ECMWF vs. UMBC  (only temperature is allowed to vary)

- From Dr. Moncet, AER

- The ECMWF set is a better choice for training OSS

**Trained with UMBC set**

**Trained with ECMWF set**

## OSS training

- OSS training set built by adding noise to original ECMWF temperature and ozone profiles until *rms* error falls below 0.05K when validated against diverse profile sets (including UMBC, TIGR and NOAA-88)
- Training performed here in radiance:
- Weight levels according to their contribution to TOA radiances
- Provide simple mechanism for including Planck function variation across wider channels (HIRS, GOES, AMSU/SSMIS-183 GHz)

## About OSS Jacobians

- Fit all species together as opposed to fitting individual species
- Training algorithm stops adding nodes when prescribed accuracy is reached
- Weak constituents (impact on TB < e.g. 0.05K) may be ignored or not well represented
- Jacobians accuracy consistent with impact of constituent variability on radiances

## Impact of O3 variations on HIRS brightness temperatures

- Sensitivity obtained by multiplying LBLRTM and OSS Jacobians by the 48 UMBC O3 profiles

| Red | Tb< 0.05K |
| --- | --- |
| White | 0.05K< Tb< 0.5 K |
| Green | Tb> 0.5K |

## OSS cloudy validation (preliminary)

- Scatterers effect is to increase photon path lengths in the layers within and below the clouds (reflective surface)
- For narrow channels (*no spectral variation in cloud optical properties across channel*) clear sky (transmittance?) RT using representative distribution of path lengths may be used
- In microwave and thermal IR current clear-sky radiance training appears so far to work well
- Validation in scattering atmospheres (with LBLRTM/CHARTS) started

## OSS cloudy validation (no scattering)

- Instrument: AIRS
- Mean UMBC profile
- 2cloud layers:
- liquid (P=670 mb)
- spherical ice (P=220 mb)
- Blue:
- Clear
- Green:
- r=8m/CLW=~5g/m2
- r=10m/IWP=~5g/m2
- Red:
- r=8m/CLW=~50g/m2
- r=30m/IWP= ~50g/m2

## Generalized training

- OSS selection simultaneously operates on *N* (adjacent) channels, instead of one channel at a time
- Two selection methods considered:
**Method A:** Extension of current method to multiple channels, i.e. nodes are successively added until *rms* difference between exact and approximate calculation for all channels in domain considered falls below prescribed threshold (reference)
**Method B:** Clustering: start from set of pre-selected nodes encompassing domain of interest and coalesce pairs of nodes with similar information content
- Clustering (not optimized yet) is fast and applies to broad spectral domains (large number of channels) -  Method A is limited to ~100 channels

## Generalized training

- Large computational gains in clear sky (i.e. when cloud-clearing is used)
- Gain is mainly the result of the fact that _*eliminated nodes are reconstructed as linear combinations of the retained nodes*_
- Gain increases as size of spectral domain increases or spectral resolution increases
- In these examples, gain can be further increased by ~30-40%

| Interval (cm-1) | Interval width (cm-1) | # nodes
(current method) | Gain (multi-channel approach) |
| --- | --- | --- | --- |
| 645-675 | 30 | 286 | 9 |
| 780-820 | 40 | 141 | 6 |
| 645-745 | 100 | 1047 | 20 |
| 780-880 | 100 | 248 | 10 |
| 780-980 | 200 | 478 | 16 |

- Examples of gain achievable with multi-channel/clustering approaches
- (1cm-1 boxcars)

## Generalized training (cloudy atmospheres)

- Channel based RT
- Required number of nodes for any given channel actually increases compared to single channel training (i.e. current approach is optimal)
- In this example (gain ~ 15 in clear-sky), scattering calculations actually is ~3-4 times more time consuming than with current single channel approach

- Current

- Method A

- Method B

## Generalized cloudy training

- Must include slowly varying cloud/aerosol optical properties in training
- Over wide bands: training can be done by using a database of cloud/aerosol optical properties
- More general training obtained by breaking spectrum in intervals of the order of 10 cm-1 in width (impact of variations in cloud/aerosol properties on radiances is quasi-linear) and by performing independent training for each interval (lower computational gain but increased robustness)
- Direct cloudy radiance training not recommended !
- Clouds tend to mask molecular structure which makes training easier
- If “recipe” for mixture of clear and cloudy atmospheres in direct training not right: clear-sky performance degrades

## Alternate two-step training preserves clear-sky solution
First step: normal clear-sky (transmittance/radiance) training to model molecular absorption
Second step: duplicate/ spectrally redistribute nodes and recompute weights to incorporate slowly varying functions in the model

- First step: normal clear-sky (transmittance/radiance) training to model molecular absorption
- Second step: duplicate/ spectrally redistribute nodes and recompute weights to incorporate slowly varying functions in the model

- Generalized cloudy training

## Training method performance comparison (AIRS Channel 1-1262)

- Training conditions:
- ECMWF set
- 5 angles (minimize *rms* for each angle)
- Accuracy threshold = 0.05K
- Domain size (Method A) = ~20 cm-1
- Max absorption slope: 20%
- AIRS results (Method A)
- Clear-sky gain: ~75%
- Cloudy gain: ~40%

## Generalized training validation(no scattering)

- 48 UMBC profiles
- 3 cloud layers: 300 mb, 500 mb, 700 mb

## Summary

- Generalized training offers potential for high memory/time savings in the clear-sky
- Promising physical approach for inclusion of slowly varying functions into training
- only ~double the number of RT calculations and preserves clear-sky solution intact
- No impact on memory requirements if additional frequency mapping is introduced

## Development of RT models based on Optimal Spectral Sampling (OSS) Method

- Purpose:
- Integrate new OSS method for parameterizing molecular absorption into CRTM
- Progress so far:
- Clear-sky comparison (accuracy and timing) with OPTRAN
- Beta version of CRTM with OSS engine delivered
- Explored new approaches for speeding up (and reducing memory requirements) the method in clear and cloudy skies
- Preliminary cloudy validation
- Future plans:
- Work with NOAA to finalize the OSS integration into the CRTM
- Work with NOAA to complete OPTRAN comparison and extend to scattering atmospheres (other: complex surface emissivities / solar regime)
- Continue multi-channel selection development in parallel
- Export OSS generation S/W