CrIMSS Error Modeling with ATMS Proxy Data Bill Blackwell, Laura - - PowerPoint PPT Presentation

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CrIMSS Error Modeling with ATMS Proxy Data Bill Blackwell, Laura Jairam, Vince Leslie, Michael Pieper, Jenna Samra NASA Sounding Science Meeting May 4-7, 2009 This work was sponsored by the National Oceanic and Atmospheric Administration under

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SLIDE 1

AIRS Science Team: 1 WJB 6/4/2009

MIT Lincoln Laboratory

CrIMSS Error Modeling with ATMS Proxy Data

This work was sponsored by the National Oceanic and Atmospheric Administration under contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the United States Government.

Bill Blackwell, Laura Jairam, Vince Leslie, Michael Pieper, Jenna Samra NASA Sounding Science Meeting

May 4-7, 2009

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SLIDE 2

MIT Lincoln Laboratory

AIRS Science Team: 2 WJB 6/4/2009

Successful Postlaunch NPP Cal/Val: Intellectual Framework

  • Goals:

– Error characterization of radiances and derived products that is:

Extensive (global, seasonal, all channels, etc.) Comprehensive (wide assortment of meteorological conditions, ground truth, etc.)

– Error attribution to atmospheric, sensor, or algorithm mechanisms

  • Necessary Ingredients:

– Prelaunch sensor testing and calibration – Prelaunch algorithm evaluation – Error models and budgets (including ground truth) – Postlaunch radiance/product characterization – Refinement of error models/budgets based on observations

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SLIDE 3

MIT Lincoln Laboratory

AIRS Science Team: 3 WJB 6/4/2009

Major Components of ATMS Cal/Val

  • ATMS/CrIMSS System Error Model/Budget

– RDR TDR SDR EDR+IP – Derived and evaluated with three data sources:

Thermal Vac; Simulated data; Proxy data

  • Post-Launch Cal/Val Planning
  • Development of Cal/Val Tools

– Neural network EDR algorithm – Matchup/RadTran comparison tools (SDR) – Raw radiance assessment tools (RDR)

  • NAST-M Aircraft Comparisons
  • Improved Pre-Launch Characterization (C1, but maybe PFM)
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SLIDE 4

MIT Lincoln Laboratory

AIRS Science Team: 4 WJB 6/4/2009

ATMS/CrIMSS RDR/SDR/EDR Error Modeling

  • There is a need for simple, accurate error models with budgets

for accuracy and precision resulting from:

– Scan biases, nonlinearity, calibration biases, NEdT, pointing errors, polarization impurity, many others…

  • RDR error model based on radiometric math model and thermal

vacuum environmental testing

  • SDR error model (calibration, geolocation, resampling)

– Based on Phil Rosenkranz’s radiative transfer package – Backus-Gilbert footprint processing

  • EDR error modeling is much more difficult (highly nonlinear and

dependent on scene conditions)

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SLIDE 5

MIT Lincoln Laboratory

AIRS Science Team: 5 WJB 6/4/2009

ATMS Proxy Data Background

  • “Proxy” ATMS data is needed to test operational software

– Observed data from on-orbit microwave sensors AMSU-A and MHS are transformed spatially/spectrally to resemble ATMS data – Captures real-world atmospheric variations better than simulations based on imperfect/incomplete surface, atmospheric, and radiative transfer models – Caveats: Radiometric characteristics of original sensor are embedded in proxy data

  • MIT-LL roles:

– Generate ATMS proxy data and provide it to “NPOESS community” – Coordinate with other proxy data providers to ensure consistency – Solicit feedback from community to improve/extend data set

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SLIDE 6

MIT Lincoln Laboratory

AIRS Science Team: 6 WJB 6/4/2009

Generation of ATMS Proxy Data

  • AMSU-A/B observations can be transformed (spatially and

spectrally) to resemble ATMS observations

– 11 channels are identical – 5 channels are identical EXCEPT for polarization – 6 channels are new, but can be estimated [with some error] – Footprint sizes and spatial sampling are different for frequencies < 89 GHz – ATMS measures wider swath angles – Orbits altitudes are slightly different

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SLIDE 7

MIT Lincoln Laboratory

AIRS Science Team: 7 WJB 6/4/2009

ATMS Proxy Data Generation Flow Chart

AMSU Level 1B Observations Apply regression coefficients ATMS TDR Apply inverse ATMS transfer

Backus/Gilbert resampling

ATMS SDR ATMS RDR Bilinear interpolation

30-pixel swath TB 96-pixel swath TB 96-pixel swath Counts 30-pixel swath TB

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SLIDE 8

MIT Lincoln Laboratory

AIRS Science Team: 8 WJB 6/4/2009

Overview of Methodology

  • Generation of ATMS proxy data is non-trivial due to spectral

and spatial differences between AMSU/MHS and ATMS

  • A linear relationship (regression) is derived between ATMS

and AMSU channels that are not common to both sensors

  • Simulated data are used to derive the regressions
  • The simulated data are calculated using global AIRS Level2

profile data (Dec 2004 – Jan 2006), fastem 2.0 ocean surface model, and Phil Rosenkranz’s radiative transfer package

  • The relationships between ATMS and AMSU can vary as a

function of lat/lon, surface topography, and sensor scan

  • angle. Data stratification is used to improve the fit quality.
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SLIDE 9

MIT Lincoln Laboratory

AIRS Science Team: 9 WJB 6/4/2009

Spectral Differences: ATMS vs. AMSU/MHS

Ch GHz Pol Ch GHz Pol

1 23.8 QV 1 23.8 QV 2 31.399 QV 2 31.4 QV 3 50.299 QV 3 50.3 QH 4 51.76 QH 4 52.8 QV 5 52.8 QH 5 53.595 ± 0.115 QH 6 53.596 ± 0.115 QH 6 54.4 QH 7 54.4 QH 7 54.94 QV 8 54.94 QH 8 55.5 QH 9 55.5 QH 9 fo = 57.29 QH 10 fo = 57.29 QH 10 fo ± 0.217 QH 11 fo±0.3222±0.217 QH 11 fo±0.3222±0.048 QH 12 fo± 0.3222±0.048 QH 12 fo ±0.3222±0.022 QH 13 fo±0.3222±0.022 QH 13 fo± 0.3222±0.010 QH 14 fo±0.3222 ±0.010 QH 14 fo±0.3222±0.0045 QH 15 fo± 0.3222±0.0045 QH 15 89.0 QV 16 89.0 QV 16 88.2 QV 17 157.0 QV 17 165.5 QH 18 183.31 ± 1 QH 18 183.31 ± 7 QH 19 183.31 ± 3 QH 19 183.31 ± 4.5 QH 20 191.31 QV 20 183.31 ± 3 QH 21 183.31 ± 1.8 QH 22 183.31 ± 1 QH

Exact match to AMSU/MHS Only Polarization different Unique Passband Unique Passband, and Pol. different from closest AMSU/MHS channels

MHS AMSU-A

AMSU/MHS ATMS

  • ATMS has 22 channels and

AMSU/MHS have 20, with polarization differences between some channels

− QV = Quasi-vertical; polarization vector is parallel to the scan plane at nadir − QH = Quasi-horizontal; polarization vector is perpendicular to the scan place at nadir

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SLIDE 10

MIT Lincoln Laboratory

AIRS Science Team: 10 WJB 6/4/2009

Methodology Details

Three step procedure:

  • 1. Compile AIRS L2 profile ensembles for each stratification (~10,000)

Scan Angles used to do Linear Regression

1.65°- 47.85°, Δ = 3.3° 51.15°

(Slide 1 of 3)

Stratifications planned: Scan angle (16 angles total, from nadir out to 51.15˚) Ocean/Land Latitude (North, Tropical and mid-latitude, South) Surface pressure for Land (8 strats) Total: 432 transformation matrices AMSU and MHS Scan Angles

0.55° 47.85°

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SLIDE 11

MIT Lincoln Laboratory

AIRS Science Team: 11 WJB 6/4/2009

Methodology Details

  • 2. Simulate ATMS, AMSU/MHS radiances with Rosenkranz radiative

transfer model (RTM) software

− Account for beamwidth and polarization per channel − Surface emissivity models: For ocean, use fastem2* with wind speed based on ECMWF 2005 data For land, uniform distribution from [0.9 − 1] †

(Slide 2 of 3)

*See English & Hewison 1998, Deblonde 2000

†Hewison 2001

Mean Wind Speed Over Ocean, ECMWF 2005 ECMWF Horizontal Wind Speed at 10m

January 1st, 2005, 00hrs

m/s

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SLIDE 12

MIT Lincoln Laboratory

AIRS Science Team: 12 WJB 6/4/2009

  • 3. Generate 22x20 transformation matrix (“C”) via linear

regression for each stratification

( ) ( )

N X Y X C + = Cov , Cov

Y X v C v + − ⋅ = ) (

MHS AMSU, real ATMS proxy

X

Y

= simulated ensemble of AMSU and MHS radiances = simulated ensemble of ATMS radiances

N = AMSU and MHS noise

Linear regression of X and Y: Transformation matrix is applied to real AMSU/MHS data:

Methodology Details

(Slide 3 of 3)

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SLIDE 13

MIT Lincoln Laboratory

AIRS Science Team: 13 WJB 6/4/2009

Results

Transformation matrix for nadir (1.65°)

Ch GHz Pol

1 23.8 QV 2 31.4 QV 3 50.3 QH 4 51.76 QH 5 52.8 QH 6 53.596 ± 0.115 QH 7 54.4 QH 8 54.94 QH 9 55.5 QH 10 fo = 57.29 QH 11 fo±0.3222±0.217 QH 12 fo± 0.3222±0.048 QH 13 fo±0.3222±0.022 QH 14 fo±0.3222 ±0.010 QH 15 fo± 0.3222±0.0045 QH 16 88.2 QV 17 165.5 QH 18 183.31 ± 7 QH 19 183.31 ± 4.5 QH 20 183.31 ± 3 QH 21 183.31 ± 1.8 QH 22 183.31 ± 1 QH

ATMS

Exact match to AMSU/MHS Only Polarization different Unique Passband Unique Passband, and Pol. different from closest AMSU/MHS channels

(ocean, mid-latitude)

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SLIDE 14

MIT Lincoln Laboratory

AIRS Science Team: 14 WJB 6/4/2009

Example of ATMS proxy data

ATMS Channel 4, ocean, mid-latitude, January 5th, 2008 (12hrs)

Brightness Temperature (TB) [Kelvin]

Note: The most extreme scan angles are not plotted here

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SLIDE 15

MIT Lincoln Laboratory

AIRS Science Team: 15 WJB 6/4/2009

Validation Plan

  • Use observed data to validate our proxy data, with two existing
  • perational sensors with similar (but not identical) spectral

characteristics (like ATMS relationship to AMSU/MHS)  AMSU-B and MHS  Use coincident data from NOAA-17 and METOP from 2008

Ch GHz Pol Ch GHz Pol

1 89.0± 0.9 QV 1 89.0 QV 2 150.0± 0.9 QV 2 157.0 QV 3 183.31 ± 1 QV 3 183.31 ± 1 QH 4 183.31 ± 3 QV 4 183.31 ± 3 QH 5 183.31 ± 7 QV 5 191.31 QV

AMSU-B MHS

Exact match to AMSU-B Only Polarization different Unique Passband Unique Passband, and Pol. different from closest AMSU-B channels

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SLIDE 16

MIT Lincoln Laboratory

AIRS Science Team: 16 WJB 6/4/2009

ATMS Proxy Data Deliverables and Schedule

  • We are using Oct. 19, 2007 MetOp-A observations for initial

validation

  • HDF format will be used (compatible with PEATE?)
  • We plan to deliver code (Fortran) and coefficients to PEATE
  • Focus days to be specified
  • Schedule: Initial delivery of “beta release” for testing

– Target date is June 1st

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SLIDE 17

MIT Lincoln Laboratory

AIRS Science Team: 17 WJB 6/4/2009

ATMS Proxy Data Development Status

  • General baseline code complete

– Pipeline of components for modeling ATMS, generating regression coefficients, applying coeffs to AMSU/MHS data, then co-locating to ECMWF (for sanity checking)

  • Generation of C matrices for all stratifications is underway

– “beta” release will include data at all scan angles and a subset of stratifications – Ocean “tropical + mid-latitude” matrices complete – Land “tropical + mid-latitude” matrices in progress

  • Validation underway using AMSU-B and MHS
  • Initial beta release soon: HDF output format planned
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SLIDE 18

MIT Lincoln Laboratory

AIRS Science Team: 18 WJB 6/4/2009

Summary

  • ATMS/CrIMSS error models/budgets are being developed to

predict on-orbit performance and sensitivities

  • Error models/budgets will be used during cal/val to characterize

performance and help attribute sources

  • ATMS proxy data is a critical component of prelaunch testing
  • ATMS proxy data generator will be delivered to Sounder PEATE

– Beta testing in progress, preliminary version ready in June – We’ll work with PEATE and sounding science team to maximize utility and compatibility

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SLIDE 19

MIT Lincoln Laboratory

AIRS Science Team: 19 WJB 6/4/2009

Backup Slides

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SLIDE 20

MIT Lincoln Laboratory

AIRS Science Team: 20 WJB 6/4/2009

Example of ATMS proxy data

ATMS Channel 4, ocean, mid-latitude, January 5th, 2008 (12hrs)

Brightness Temperature (TB) [Kelvin]

Note: The most extreme scan angles are not plotted

coast USGS land mask (used to identify ocean pixels)

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SLIDE 21

AIRS Science Team: 21 WJB 6/4/2009

MIT Lincoln Laboratory

ATMS “Footprint Matching”

Jenna Samra and Bill Blackwell

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SLIDE 22

AIRS Science Team: 22 WJB 6/4/2009

MIT Lincoln Laboratory

Overview

  • Resampling algorithm

– Projection geometry – Scan geometry – Backus-Gilbert coefficients

  • Example of results

– ATMS channels 1-2 (5.2° BW) – ATMS channels 3-16 (2.2° BW) – ATMS channels 17-22 (1.1° BW)

  • Next steps
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SLIDE 23

AIRS Science Team: 23 WJB 6/4/2009

MIT Lincoln Laboratory

Projection to Earth’s Surface

xy cen 2 2 cen 2 xy 2 2 xy 1 1

R sin r sin cos r R 2 r R R y x r cos 90 R sin r sin x y tan α = ϕ α − + = + = λ θ + ° = α         α = ϕ = λ

− −

Transformation Equations

A l

  • n

g T r a c k A l

  • n

g S c a n Hsat Rcen Rxy r λ θ ψ φ (x,y) Satellite Beam Center Intercept α y x z

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SLIDE 24

AIRS Science Team: 24 WJB 6/4/2009

MIT Lincoln Laboratory

CrIS and ATMS Scan Parameters

  • Δ cross-track displacement = step angle
  • Δ along-track displacement = angular velocity x step time
  • Scan separation = angular velocity x scan time

Parameter CrIS ATMS

  • 3 dB beam width [deg]

3.3 1.1, 2.2, 5.2 Step time [ms] 200 18.018 Full scan period [s] 8 8/3 Angular velocity [deg/s] 0.458 0.458 Step angle [deg] 10/3 1.11 Number of earth views 30 96

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SLIDE 25

AIRS Science Team: 25 WJB 6/4/2009

MIT Lincoln Laboratory

Resampling Grid

FOR 1 FOR 15

FOR 1 FOR 15

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SLIDE 26

AIRS Science Team: 26 WJB 6/4/2009

MIT Lincoln Laboratory

Backus-Gilbert Methodology

  • Backus-Gilbert coefficients ai scale ATMS brightness temp

to approximate CrIS brightness temp ATMS Grid CrIS Target CrIS Estimate

a7 a8 a9 a1 a2 a3 a4a5 a6

= x ≈

BG Coeffs

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SLIDE 27

AIRS Science Team: 27 WJB 6/4/2009

MIT Lincoln Laboratory

Choosing BG Coefficients

Goal: find coefficient vector a to minimize cost function Q

– Minimizing Q0  highest accuracy – Minimizing e2  lowest noise amplification – γ chosen as tradeoff between best fit and lowest noise

Result: least-squares minimization yields where u, v, and V are specified in Stogryn, 1978

( ) ( ) ( ) ( )

γ γ + γ γ = sin a we cos a Q Q

2

( )

      γ − + γ =

− − −

u V u u v V u cos 1 v cos V a

1 T 1 T 1

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SLIDE 28

AIRS Science Team: 28 WJB 6/4/2009

MIT Lincoln Laboratory

Along-Track Transects, 1.1° Beam Width

FOR #15 FOR #1 3 x 3 grid 9 x 9 grid

Synthesized, γ = 0 Synthesized, γ = π/2 Target (CrIS) Observed (ATMS)

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SLIDE 29

AIRS Science Team: 29 WJB 6/4/2009

MIT Lincoln Laboratory

Along-Track Transects, 2.2° Beam Width

FOR #15 FOR #1 3 x 3 grid 9 x 9 grid

Synthesized, γ = 0 Synthesized, γ = π/2 Target (CrIS) Observed (ATMS)

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SLIDE 30

AIRS Science Team: 30 WJB 6/4/2009

MIT Lincoln Laboratory

Along-Track Transects, 5.2° Beam Width

FOR #15 FOR #1 3 x 3 grid 9 x 9 grid

Synthesized, γ = 0 Synthesized, γ = π/2 Target (CrIS) Observed (ATMS)

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SLIDE 31

AIRS Science Team: 31 WJB 6/4/2009

MIT Lincoln Laboratory

Next Steps

  • Evaluate AER coefficients

– Noise amplification – Matching error

  • Incorporate coefficients in TDR/SDR tools and error models