cris use or disclosure of data contained on this sheet is subject to npoess program restrictions....

CrIS Use or disclosure of data contained on this sheet is subject to NPOESS Program restrictions.

• ITT INDUSTRIES • AER • BOMEM • BALL • DRS

EDR Algorithms for the Cross-track Infrared Sounder

Xu LiuAtmospheric Environmental research, Inc.

131 HartwellAve, Lexington, Ma 02421

Contact: [email protected]

and

Ronald J. Glumb, Christopher E. Lietzke and Joseph P. PredinaITT Industries, ITT Aerospace/Communications,

1919 West Cook Road, P.O. Box 3700, Fort Wayne, IN 46801, USA

Contact: [email protected]



2

CrIMSS EDR Products

ATMS

SpacecraftNavigation

Space Segment(Generate RDRs)

Ground Calibration(Generate SDRs)

CrISIR S

cen

esM

icro

wav

e S

cen

es

ATMS

CrIS IR (9 FOVs)

First Guess Profile

CMIS

Mer

ge

SD

Rs

MicrowaveRetrieval

Produce ATMS SDRs

Produce CrIS SDRs

Produce CMIS SDRs

S/C

Sto

re a

nd

Fo

rwar

d

Calibrated and Geolocated Radiances

Ground Science Algorithms(Generate EDRs)

Produce CMIS EDR

High AltitudeTemperature

Mic

row

ave

Sce

nes

CrIS Hardware & Software

Other NPOESS ElementsCrIMSS

EDR Products

SceneClassification

Infrared and MicrowaveRetrieval

CrIS EDR Algorithm

ps (NWP)

Surface Map

Climateology(first guess)



3

AMSU/MHS Channels Used(ATMS to replace AMSU/MHS)

CrIS EDR Algorithm

Chan #

Central Freq.(GHz)

Side Band (GHz)

Bandwidth (MHz)

Channel Function

0 270 Water Vapor Burden 1 2

23.800 31.400 0 180 Surface Temperature

3 50.300 0 180 Surface Temperature 4 52.800 0 400 Surface Temperature 5 53.596 0.115 170 Tropospheric Temp. 6 54.400 0 400 Tropospheric Temp. 7 54.940 0 400 Tropospheric Temp. 8 55.500 0 330 Tropospheric Temp. 9 57.290 0 330 Stratospheric Temp.

10 57.290 0.217 78 Stratospheric Temp. 11 57.290 0.322 0.048 36 Stratospheric Temp. 12 57.290 0.322 0.022 16 Stratospheric Temp. 13 57.290 0.322 0.010 8 Stratospheric Temp. 14 57.290 0.322 0.0045 3 Stratospheric Temp. 15 89.000 0 6000 Cloud Top and Snow 16 89.000 0.9 1000 Precipitation 17 150.000 0.9 1000 Water Vapor 18 183.310 1 500 Water Vapor 19 183.310 3 1000 Water Vapor 20 183.310 7 2000 Water Vapor



4

CrIS Channels Used

• Channels Excluded During MW/IR Retrieval– Ozone Band from 950 cm-1 to 1095 cm-1– Trace gas channels– Low information content channels (channel selection)

– Good results using only 400, 300 and 150 channels

• Special High Information Content Channels Used– 709.5 cm-1 to 746 cm-1 for cloud parameter estimation and

Principle Component Analysis (PCA) for estimating number of cloud formations

– 2190 cm-1 to 2250 cm-1 for cloud parameter estimation

Band Frequency Range Resolution (1/2L) # Channels LWIR 650-1095 cm-1 0.625 cm-1 713 MWIR 1210-1750 cm-1 1.25 cm-1 433 SWIR 2155-2550 cm-1 2.5 cm-1 159

Total 1305

CrIS EDR Algorithm



5

Fast Forward Model

• Optimal Spectral Sampling (OSS)– Developed at Atmospheric Environmental Research (AER)– Fast and accurate (validated against Line-by-line models)– Can model non-localized Instrument Line Shape (ILS)– Computes Jacobian efficiently– Accurate treatment of reflective radiation from surface– Both MW and IR versions developed

CrIS EDR Algorithm

Benchmarked against AIRS fast forward model in 1999………OSS > 20 times fasterOSS > 40 times faster when including Jacobians

Only 20% Speed Penalty when Modeling sinc vs. Blackman-Harris Instrument ILS



6

Retrieval Methodology (Rogers, 1976)

)(1

1 aiiioyTixi

Tixai xxKyySKSKKSxx

aiiioyTixiy

Tiai xxKyySKSKSKxx

1111

1

CrIS EDR Algorithm

Observed Radiance

Calculated Radiance

Matrix of Partial Derivatives of yi With Respect to x

Retrieved Variable

Error Covariance Matrix of Background Error Covariance

Matrix of Measurement

Background Temp/moisture/

Emissivity/Reflectivity/

etc.

EOF Form Used

GeneralForm Used

EOF Form Has Much Smaller Dimension & Is

Modified to Handle Nonlinear Case



7

Retrieved Parameters

Parameter # of elementsTemperature 20 EOFsWater vapor 10 EOFs

Ozone 1 (column correction)*Other trace gases Column correction onlySkin Temperature 1

MW Surface Emissivity 5 EOFsMW Cloud Liquid Water 1MW Cloud Top Pressure 1

IR Surface Emissivity 6 hinge pointsIR Surface Reflectivity 6 hinge points

CrIS EDR Algorithm



8

CrIMSS Retrieval Process (1 of 6)

• Initialization– Load instrument specifications (AMSU, MHS, CrIS

frequencies, Noise, etc.)– Load climatoligical atmospheric data base– Load surface background data base (Mean profiles, error

covariance matrices)– Load OSS parameters (Optical depth tables)– Load solar spectrum– Load topography & land/ocean mask

CrIS EDR Algorithm

Fast Forward Model Parameters Indexed Every 10 Degree of Temperature

GTOPO30 Digital Elevation Map, land/ocean mask

Used for First Guess



9


• Preprocessing– Test for precipitation– Compute surface pressure from NWP data– Test for surface type

• Land• Ocean• Ice• Snow• Coast: ocean/land• Coast: ice/snow

– Choose Background by Surface type & mix with emissivity information

CrIS EDR Algorithm

Land/ocean maps & MW Brightness Temperature tests @ 31, 50, 89 & 23 GHz

MW Land Emissivity per Grody model (Grody, 1988)

MW Ocean Emissivity per Wilheit model (Wilheit, 1979)

Interpolated in Time and Space



10


• Microwave Only Retrieval– Average radiances from 9 MHS

FOVs– Execute OSS fast forward model

based upon climatology first guess

– Perform inversion & update first guess profile

– Calculate new radiances using OSS

– Test for convergence– Continue iteration if convergence

criteria not met

CrIS EDR Algorithm



11

CrIMSS Retrieval Process (4 of 6)CrIS EDR Algorithm

• Scene Classification– Purpose

• Maximize # of good retrievals within a field of regard (FOR)

• Optimize an FOV clustering strategy for treatment of clouds and use of cloud clearing algorithm

– Method• Form matrix of 9 FOVs by 62 IR

channels in the spectral region from 709 to 748 cm-1

• Analyze for up to 9 principle components

• Determine number of cloud formations present from PCA analysis & 2 statistical tests

• Cluster minimum number of FOVs needed to perform cloud clearing

3 2 1

6 5 4

9 8 7

3 principle components indicating 2 cloud

formations

Example of PCA Analysis



12


C C flag

C a lcula te C C Radiance

Perform Phys ica l Invers ion

Update geophys ica l param etersC a lcula te new Radiance

A ll C lus tersProcessed?

Q ua li ty C ontrol

T rue

Yes

C a lcula te C lear Radiance es tim ate from M W Retr ieva l or NW P Profi le

C onvergence? M ax Ite ra tions?

A verage F O V 's w ith inc lus te r

Yes

F a lse

No

NextC lus ter

No

CrIS EDR Algorithm

• Combined MW/IR Retrieval– Uses MW retrieval as first

guess (profiles, surface, cloud parameters)

– Modified Maximum Likelihood Method for nonlinear retrieval

– Dynamically adjust channel weights to improve convergence and stability

– Cloud clearing parameter estimated each iteration

– FOVs averaged above 80 mbar– Test for convergence– Continue iterations if

convergence criteria not met



13


• Quality Control– If normalized 2 > 1.0 for

the retrieval, then retrieval is not reported

– If MW radiances after MW/IR retrieval differ by more than 3 K from observed radiances, then entire retrieval rejected

– If MW retrieval and MW/IR retrieval differ by more than 3 K, then the MW/IR retrieval is rejected

– If difference between cloud cleared CrIMSS radiances and VIIRS cloud free radiance is greater than 2 k, then retrieval is suspect

CrIS EDR Algorithm

nchanN

nchan

i i

obseri

retri

1

22 )(

R e t r i e v e d B r i g h t n e s s T e m p e r a t u r e i n

i t h C h a n n e l

N u m b e r o f S p e c t r a l C h a n n e l s ( o r s u p e r c h a n n e l s )

U s e d i n R e t r i e v a l

O b s e r v e d B r i g h t n e s s T e m p e r a t u r e i n

i t h C h a n n e l

S t o p I f• < 0 . 7• < 1 0 % c h a n g e b e t w e e n i t e r a t i o n s• # i t e r a t i o n s = 4

N o i s e V a r i a n c e f o r i t h C h a n n e l

nlev

IRMWTMWTnlev

iii

1

2))/()((

M i c r o w a v e R e t r i e v e d B r i g h t n e s s

T e m p e r a t u r e @ L e v e l i

C o m b i n e d M i c r o w a v e / I RR e t r i e v e d B r i g h t n e s s

T e m p e r a t u r e @ L e v e l i

N u m b e r o f R e t r i e v e d L e v e l s B e l o w 3 k m A l t i t u d e

I f > 3 K R e j e c t

M W / I R R e t r i e v a l



14

Performance for Various Levels of Channel SelectionCrIS EDR Algorithm



15

CrIMSS Projected Performance

0

5

10

15

20

25

30

0.0 0.5 1.0

Retrieved Pressure Accuracy (%)

Alt

itu

de

(k

m)

1

10

100

10000 0.5 1 1.5

Retrieved Temperature Error (K, rms)

Alt

itu

de

(m

ba

r)

100

1000

Retrieved Moisture Error (%, rms)

500Alt

itu

de

(m

ba

r)

0 5 10 15 20 25

“Threshold”Performance

CrIS EDR Algorithm

Based on Global Average



16

Expected Improvement Over HIRS(RMS Uncertainty, Global Average Basis)

CrIS EDR Algorithm

CrIS

HIRS

CrIS

HIRS

cris use or disclosure of data contained on this sheet is subject to npoess program restrictions....

Documents