# Presentation: 947360

## Characterization of Aura-TES 

- (Tropospheric Emission Spectrometer)
- Nadir and Limb Retrievals

- Helen Worden, Reinhard Beer, Kevin Bowman, Annmarie Eldering, Mingzhao Luo, Gregory Osterman, Susan Sund Kulawik, John Worden
*Jet Propulsion Laboratory, California Institute of Technology*
- Shepard A. Clough, Mark Shephard, *AER*
*(Atmospheric and Environmental Research, Inc.)*
- Michael Lampel
*Raytheon Systems Co, ITSS*
- Clive Rodgers
*Oxford University*

## TES

## TES Data Processing Steps

**Level 1A:   Produces geolocated interferograms.**

**Notes:**
I wont be saying much more about level 1A – geolocation is the main algorithm and we are taking advantage of MISR experience.

## L1B Products 

- 0.07 cm-1
- resolution

- 0.0175 cm-1
- resolution

- L1B Products

## Level 2 Algorithms

-  Target Scene Selection
-  Logic for selecting which scenes to process and the order of processing (e.g. Nadir ocean first)
-  Retrieval
-  Algorithms for a single “target”, either limb or nadir where nadir is the average of 2 scans (same ground location) in the Global Survey.
-  Following slides will describe:
-  Retrieval Strategy & Suppliers
-  ELANOR (Earth Limb and Nadir Operational Retrieval)
-  Error Analysis
-  Product Generation
-  L2 standard products for each Global Survey
-  Temporal & spatial averaging

## Level 2

- Retrieval Strategy & Suppliers

-   Retrieval Suppliers
- (Inputs for a single target scene)
-   Appropriate pressure grid including surface for nadir targets
-   Atmospheric & surface Full State Vector (FSV) initial guess
-   T, H2O from meteorological data
-   Other trace gases from climatology data
-   land surface emissivity derived from surface type data
-   Microwindows (small spectral frequency ranges used for retrieval)
-   Mapping of FSV to retrieval parameters (and back to FSV)
-   *A priori* vectors and covariance matrices or other constraint types
-   L1B spectral averaging, apodization & microwindow extraction

## Level 2

- Retrieval Strategy & Suppliers

-   Retrieval Strategy
- (determine appropriate retrieval steps)
-   FSV input for cloud free target
-   Cloud determination step
-   Revision of input if cloud detected

## Global Land Use Database

- Operational Support Product (OSP)
- used by surface emissivity supplier

- Reference: _[http://edcdaac.usgs.gov/glcc/glcc.html](http://edcdaac.usgs.gov/glcc/glcc.html)_
- Land Processes Distributed Active Archive Center

## Level 2

- Nadir Cloud Detection

-  Cloud detection will use tests of the radiances in the “window” region (11m)
-  L1B will flag large variability of this radiance across detectors so that nadir scenes with broken clouds or variable surface are rejected for L2.
-  Retrieval strategy must make an initial estimate of the clear-sky radiance to identify uniform cloudy scenes (*i.e*., cloud-filled footprint) vs. clear.

## Limb Cloud Scenarios

- 1)

- cloud before or after T.P.

## Approach for Including Cloud Radiances in FOV Integration

- partly cloudy

- clear

- clear

- clear

- clear

- clear

- Detector 0

- Detector 15

- modeled rays

- Lray_0 = Ldet_0  (measured)

- Lray_1 = Ldet_1  (measured)

- Lray_2 = Ldet_2  (measured)

## Level 2 Algorithms

-  Retrieval step is for a subset of retrieved species using
-   spectral “microwindows” of the L1B data.
-  Performs non-linear least squares fit with iterations of:
-   Forward model (FM) spectrum & Jacobian generation
-    for estimate of atmospheric and surface FSV
-    (full state vector)
-  Ray Tracing
-  Optical depth calculation (tables or line-by-line)
-  Radiative Transfer
-  ILS (Instrument Line Shape) convolution
-  Limb only: FOV (Field of View) integration
-   Inversion (FM compared to L1B data) for retrieval
-    parameters and update of FSV.
-   Levenberg-Marquardt algorithm
-  Algorithm inheritance from LBLRTM (AER) and SEASCRAPE (JPL).

- ELANOR
**(Earth Limb And Nadir Operational Retrieval)**

## Level 2

- Error Analysis & Post-retrieval Processing

- For each retrieved parameter in a target scene, we
- estimate covariances for:
-   Retrieval noise (using measurement NESR)
-   Smoothing error (accuracy due to vertical smoothing)
-   Systematic errors from instrument and model

## Level 3 Algorithms

- Level 3 will mainly produce browse products with gridded,
- interpolated map images.

## One Day Test (ODT) Objectives

- Test L2 algorithm accuracy, robustness & performance for a large number of retrievals.
- 1168 Nadir, 3504 Limb targets
- Determine which retrieval diagnostics and visualization methods will be most useful for monitoring science data quality & instrument performance.
- Evaluate statistical accuracy of error estimates as compared to known errors (true-retrieved).
- Can’t do this with real data.
- Determine if target scene selection would significantly improve performance by reducing the number of iterations required.

## 1 Orbit of the One Day Test

- Mozart model for O3 at 422 mb with TES targets for a single orbit overplotted (**X**)

## Slide 17

## One Day Test Input and Assumptions

-    Simulated TES spectra were created using profiles sampled from MOZART3
-     (NCAR) model data. MOZART resolution is 2.8°x2.8° with 66 vertical levels
-     (0-140 km).
-   MOZART3 was driven by WACCM met. fields; data are for the Oct. 2 day of an
-     arbitrary year. (WACCM = Whole Atmosphere Community Climate Model,
-     from NCAR)
-   No clouds or aerosols. Results from this 1st test will be a baseline for later tests.
-   Retrieved surface T, but not surface emissivity.
-   Noise added to simulations is from instrument characterization.

## One Day Test Nadir Results

## Nadir O3 Error Comparison for the

- Fractional errors for O3 (log VMR) retrievals

**Nadir O****3**** Error Comparison for the**
**One-Day Test**

## TES Error Characterization

- In order to compare with the “true” profile, we have:

-  Where

- Constraint matrix

## Our smoothing error is somewhat underestimated:

-    Doesn’t include representation error
- (need to use full *K*)
-    *S**a**true** *has artificially small variance
- (computed from a single model day).
- Still need to characterize:
-    Forward model parameter error
*i.e*., errors from non-retrieved parameters:
*S**f **= GK**b**S**b**K**b**T**G**T*
*   *Model error
*i.e*., errors from model approximations

## Estimated Errors from Microwindow Selection

## Current / Future Work

-     Updating the ATBD (now at V1.2, V2.0 by 10/2003)
-     Preparation for post-launch mayhem
-          (a.k.a. commissioning)
-     Aura Intercomparisons
-     Limb “tomographic” retrieval and 2-D characterization
-     - uses 3 limb measurements to retrieve 4 profiles over
-               6o latitude.
-     Cloud top pressure retrieval
-                   - include lower bound pressure in retrieval vector.
-     Retrievals with transmissive clouds/aerosols using
-           proxy gray-body layers.

## Back-up Slides

## Slide 26

## 2B1 Magnitude Spectra, ST-2, part B

## Test of 7 different latitude cases to estimate effect of substituting measured detector radiances for modeled limb rays.

- Microwindow 6 is an optically thin case. Other optically thick cases have smaller radiance errors.

## Level 2

- Absorption Coefficient Tables

-  “ABSCO” tables allow
- better performance with
- only slightly worse accuracy (compared to
- line-by-line calculation)

-  Must be read efficiently and storage becomes an architecture design issue.

**Notes:**
This is one of the major algorithm decisions made early on.

## Inversion  

-       *Simultaneous fit to multiple atmospheric and surface parameters minimizes the cost function:*
*         **	**	** **[y - F(x)]**T **S**e**-1 **[y - F(x)]  + [x - x**a** ]**T **S**a**-1 **[x - x**a** ]*
*  *
*         **where y is the measured spectrum, S**e**  is the measurement covariance, x is the state vector of*
*         **atmospheric parameters, x**a  **is the a priori state vector and S**a  **is the a priori covariance.*
*    *
*            ** *_Cost function_   *algorithm uses either the* *first term only:* *maximum likelihood (****ML****) *
*	**                   **or both terms:  maximum a posteriori (****MAP****) *
*         *_Iteration step_   *x**i+1  **= x**i   **+ (K**T** S**e**-1 **K + S**a**-1 **+ ****)**-1 **(K**T** S**e**-1 **[y - F(x**i **)] - S**a**-1 **[x**i ** - x**a** ])*
*	**	** *
*	**	** **either ****Gauss-Newton**** (**** = 0; small residual case, i.e., good initial guess)  *
*                                     **or ****Levenberg-Marquardt**** (**** > 0; non-linear regimes - slower but more robust)*
*         *_Convergence_  *first term of* *cost function reaches estimated measurement noise variance.*

## Analysis Flow Legend

-  data

- Manual
- operation

- stored
- data

- procedure

- preparation

- decision

## TES L1B Post-launch Analysis Flow 

- (Nadir)