Determination of atmospheric temperature, water vapor and heating rates from mid- andwater vapor, and heating rates from mid- and

far- infrared hyperspectral measurementsAGU Fall Meeting, Wednesday, December 12, 2007

GC34A‐02D.R. Feldman (Caltech); 

K.N. Liou (UCLA); Y.L. Yung (Caltech); D. G. Johnson (LaRC); M. L. Mlynczak (LaRC)

Presentation Outline

• Motivation for studying the far‐infraredMotivation for studying the far infrared

• FIRST instrument description

S i i i f id f bili i• Sensitivity tests of mid‐IR vs far‐IR capabilities– Clear‐sky

– Cloudy‐sky

• Multi‐instrument data comparison

• Climate model considerations

• Conclusions• Conclusions

OutlineOutline 2

The Far‐Infrared Frontier• Current EOS A-Train measure 3.4 to

15 μm, don’t measure 15-100 μm• IRIS-D measured to 25 μm in 1970

• Far-IR, through H2O rotational band, affects OLR, tropospheric cooling ratesF IR i f d f th• Far-IR processes inferred from other spectral regions

• Mid-IR, Microwave, Vis/NIR• Interaction between UT H O andInteraction between UT H2O and

cirrus clouds requires knowledge of both

• Currently inferred from measurements in other spectral regions

3

Figures derived from Mlynczak et al, SPIE, 2002MotivationMotivation

No spectral measurementsto the right of line

FIRST: Far Infrared Spectroscopy of the TroposphereTroposphere

• FTS w/ 0.6 cm‐1 unapodizedresolution, ±0.8 cm scan lengthM ltil b litt

FIRSTAIRS AIRS

• Multilayer beamsplitter– Germanium on polypropylene– Good performance over broad 

spectral ranges in the far‐infrared( 1)• 5‐200 μm (50 – 2000 cm‐1) 

spectral range• NeDT goal ~0.2 K (10‐60 μm), 

~0.5 K (60‐100 μm)( μ )• 10 km IFOV, 10 multiplexed 

detectors• Cooling

• Spectrometer LN cooled• Spectrometer LN2 cooled• Detectors liquid He cooled

• Scan time: 1.4‐8.5 sec• Balloon‐borne & ground‐based g

observations

FIRST instrumentFIRST instrument 4

Retrieval Sensitivity TestFlow ChartFlow Chart

T( )

Model Atmosphere A priori Atmospheric State)

Random Perturbations

T(z)H2O(z)O3(z)CWC(z)

S th ti M t

RTM + Noise

A i i t

RTMA prioriuncertainty

( )CER(z)

Synthetic Measurement A priori spectrum

R i l l i hRetrieval algorithm

Analyze retrieved state, spectra, 

5

y , p ,and associated statistics

Sensitivity testsSensitivity tests

Clear‐Sky Retrieval Test

• AIRS and FIRST T(z) retrievals comparable.• FIRST better than AIRS in H2O(z) retrievals 200-300 mbar.

6Sensitivity testsSensitivity tests

S bette t a S 2O( ) et eva s 00 300 ba .• Residual signal in far IR seen 100-200 cm-1 → low NeDT critical

Clear‐Sky Heating RatesTropical Conditions Sub-Artic Winter Conditions

• Spectra provide information about fluxes/heating rates

• Error propagation (Taylor et al, 1994; Feldman et al, In Review) can be used 

f h l d ll h h d l k f• Heating rate error for scenes with clouds generally higher due to lack of vertical cloud information

7Heating RatesHeating Rates

Extrapolating Far‐IR with Clouds• Retrieval of T(z), H2O(z), 

CWC(z), CER(z) difficult with AIRS spectra

• Use AIRS channels to extrapolate far‐IR channels?extrapolate far IR channels?– Depends on cloud conditions, 

T(Z), H2O(z)

Low BT channels from 6 3 μm– Low BT channels from 6.3 μm band ≈ low BT channels in far‐IR

– High BT channels scale from mid‐to far IRto far‐IR

– For tropics, channels with BT 250‐270 K (emitting ~ 5‐8 km) are complicatedare complicated

8CloudsClouds

Test Flight on September 18, 2006:Ft, Sumner NM

AQUA MODIS L1B RGB Image

9

AIRS FootprintsFIRST Balloon

CloudSat/CALIPSO TrackTest flightTest flight

CloudSat/CALIPSO signals

• CloudSat and CALIPSO near collocation• No signal from CloudSat

10Test flightTest flight

• CALIPSO signal consistent with FIRST residual

FIRST and AIRS Cloud Signatures• Instrument collocation

• FIRST balloon-borne spectra• AIRS• MODIS

• Residuals are consistent with clouds ~ 5 km, De ~ 60 μm

CloudDetected !

11Test flightTest flight

Climate Model Considerations

• Climate models produce fields that specify mid- & far-IR spectra.

• Multi-moment statistical comparisons of measured spectra and modeled spectra avoid subtle biases from data processing.– Spectral and atmospheric state spaces should be considered jointly.Spectral and atmospheric state spaces should be considered jointly.

• Far-IR climate model analysis requires more far-IR data– Far-IR extrapolation should retain physical basis and be verified with

measurementsmeasurements.– Agreement with CERES is only partial verification and presents a non-unique

checksum

• Future work to assess how spectra impart information towards• Future work to assess how spectra impart information towards climate model processes.

12Model evaluationModel evaluation

Conclusions• AIRS measures mid‐IR, but far‐IR is not covered A‐Train 

spectrometers.

FIRST id th h d i ti f f IR b t li it d• FIRST provides thorough description of far‐IR but limited spectra are available.

• FIRST clear‐sky T retrievals comparable, improved UT H2O y p , p 2retrieval relative to AIRS

– Implied cooling rate information difference is small .

• Extrapolating far‐IR channels good for Tb ~ 220 K, ok for Tb ~ 300 K, difficult for Tb ~250‐270 K.

• Multi‐instrument analysis with A‐Train facilitates• Multi‐instrument analysis with A‐Train facilitates comprehensive understanding of FIRST test flight spectra.

• AIRS mid-IR spectra can validate climate models, but far-IR should not be neglected.

13ConclusionsConclusions

Acknowledgements

• NASA Earth Systems Science Fellowship, grant number y p, gNNG05GP90H.

• Yuk Yung Radiation Group: J k M li Vij N t j Ki F i Li & K i LJack Margolis, Vijay Natraj, King‐Fai Li, & Kuai Le

• George Aumann and Duane Waliser from JPL

• Xianglei Huang from U Michigan and Yi Huang from PrincetonXianglei Huang  from U. Michigan and Yi Huang from Princeton

• AIRS, CloudSat, and CALIPSO Data Processing Teams

14Thank you for your timeThank you for your time

Cloud Radiative Effect (CRE)

• CRE = TOA clear broadband flux TOA b db d fl– TOA broadband flux 

• CERES provides collocated measurements of CRE from broadband radiometers– Most CERES products contain 

multiple data‐streamsmultiple data streams

• AIRS L3 CRE lower than CERES CRE– Other A‐Train sets 

(CloudSat/CALIPSO) can arbitrate difference

15CloudsClouds

Towards CLARREO• NRC Decadal Survey recommended CLARREO for

– Radiance calibration– Climate monitoringC g

• CLARREO specified to cover 200 – 2000 cm-1 with < 2 cm-1 resolution– NIST traceability requirement

• Prototyped far IR instruments provide a science and engineering test bed• Prototyped far-IR instruments provide a science and engineering test-bed for next generation of satellite instruments

• Further orbital simulations required to test how mid-IR state space uncertainties appear as far-IR spectral residuals

• More integrated A-train analyses w.r.t. Far-IR warranted

• Larger Far-IR dataset analysis needed to demonstrate utility of long wavelength measurements for climate monitoring

• Don’t forget about 50-200 cm-1 (200-50 μm).

16Future directionsFuture directions